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Supplementary Motor Area or Juxtapositional Lobule Cortex

Supplementary Motor Area or Juxtapositional Lobule Cortex



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Harvard-Oxford Cortical Structural Atlas now calls the 'Supplementary Motor Area' the 'Juxtapositional Lobule Cortex (formerly Supplementary Motor Cortex)'. I've looked for papers that explain the name change but haven't managed to locate any. All the papers I've found either continue to use SMA or give both ('JLC formerly SMC') without explanation. Where did this change originate? Is JLC the accepted term now?


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Research output : Contribution to journal › Review article › peer-review

N1 - Publisher Copyright: © 2016, Springer Science+Business Media New York. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

N2 - Alien hand syndrome (AHS) is a rare disorder of involuntary limb movement together with a sense of loss of limb ownership. It most commonly affects the hand, but can occur in the leg. The anterior (frontal, callosal) and posterior variants are recognized, with distinguishing clinical features and anatomical lesions. Initial descriptions were attributed to stroke and neurosurgical operations, but neurodegenerative causes are now recognized as most common. Structural and functional imaging and clinical studies have implicated the supplementary motor area, pre-supplementary motor area, and their network connections in the frontal variant of AHS, and the inferior parietal lobule and connections in the posterior variant. Several theories are proposed to explain the pathophysiology. Herein, we review the literature to update advances in the understanding of the classification, pathophysiology, etiology, and treatment of AHS.

AB - Alien hand syndrome (AHS) is a rare disorder of involuntary limb movement together with a sense of loss of limb ownership. It most commonly affects the hand, but can occur in the leg. The anterior (frontal, callosal) and posterior variants are recognized, with distinguishing clinical features and anatomical lesions. Initial descriptions were attributed to stroke and neurosurgical operations, but neurodegenerative causes are now recognized as most common. Structural and functional imaging and clinical studies have implicated the supplementary motor area, pre-supplementary motor area, and their network connections in the frontal variant of AHS, and the inferior parietal lobule and connections in the posterior variant. Several theories are proposed to explain the pathophysiology. Herein, we review the literature to update advances in the understanding of the classification, pathophysiology, etiology, and treatment of AHS.


Barbas H, Pandya DN (1987) Architecture and frontal cortical connections of the premotor cortex (area 6) in the rhesus monkey. J Comp Neurol 256:211–228

Braak H (1976) A primitive gigantopyramidal field buried in the depth of the cingulate sulcus of the human brain. Brain Res 109:219–233

Brodmann K (1925) Vergleichende Lokalisationslehre der Gross hirnrinde, 2nd edn. Barth, Leipzig

Colebatch JM, Cunningham VJ, Deiber M-P, Frackowiak RSJ, Passingham RE (1990) Regional cerebral blood flow during unilateral arm and hand movements in human volunteers. Abstr Physiological Soc, 9P

Crammond DJ, Kalaska JF (1989) Neuronal activity in primate parietal cortex area 5 varies with intended movement direction during an instructed-delay period. Exp Brain Res 76:458–462

Damasio Ar, van Hoesen GW (1983) Emotional disturbances associated with focal lesion of the limbic frontal lobe. In: Heilman K, Satz P (eds) Neuropsychology of human emotion. Guildford Press, New York, pp 85–110

Deecke L (1987) Bereitschaftspotential as an indicator of movement preparation in supplementary motor area and motor cortex. In: Porter R (ed) Motor areas of the cerebral cortex. Wiley, Chichester, pp 231–245

Eidelberg D, Galaburda AM (1984) Inferior parietal lobule: divergent architectonic asymmetries in the human brain. Arch Neurol 41:843–852

Fox PT, Pox JM, Raichle ME, Burde RM (1985) The role of cerebral cortex in the generation of voluntary saccades: a positron emission tomographic study. J Neurophysiol 54:348–369

Fox PT, Pardo JV, Petersen SE, Raichle ME (1987) Supplementary motor and premotor responses to actual and imagined hand movements with Positron Emission Tomography. Soc Neurosci Abstr 398:10

Friston KJ, Passingham RE, Nutt JG, Heather JD, Sawle GV, Frackowiak RSJ (1989) Localization in PET images: direct fitting of the intercommissural (AC-PC) line. J Cereb Blood Flow Metabol 9:690–695

Friston KJ, Frith CD, Liddle PF, Dolan RJ, Lammertsma AA, Frackowiak RSJ (1990) The relationship between global and local changes in PET scans. J Cereb Blood Flow Metabol 10:458–466

Galyon DD, Strick PL (1985) Multiple and differential projections from the parietal lobe to the premotor areas of the primate. Soc Neurosci Abstr 373.10

Godschalk M, Lemon RN, der Steen J van (1985) The involvement of monkey premotor cortex neurones in preparation of visually cued arm movements. Behav Brain Res 18:143–157

Godschalk M, Lemon RN (1989) Preparation of visually cued arm movements in monkey. Brain Behav Evol 33:122–126

Goldberg G (1985) Supplementary motor area structure and function: review and hypotheses. Behav Brain Sci 8:567–588

Goldman-Rakic PS (1987) Circuitry of primate prefrontal cortex and regulation of behavior by representational memory. In: Plum F (ed) The nervous system: higher functions of the brain. Am Physiol Soc, Bethesda, pp 373–417

Halsband U (1987) Higher disturbances of movement in monkeys (Macaca fascicularis). In: Gantchev GN, Dimitrov B, Galev PC (eds) Motor control. Plenum, New York, pp 79–85

Hutchins KD, Martino AM, Strick PL (1988) Corticospinal projections from the medial wall of the hemisphere. Exp Brain Res 71:667–672

Lammertsma AA, Cunningham VJ, Deiber MP, Heather JD, Bloomfield PM, Nutt J, Frackowiak RSJ, Jones T (1990) Combination of dynamic and integral methods for generating reproducible functional CBF images. J Cereb Blood Flow Metabol 10:675–686

Laplane D, Talairach J, Meininger V, Bancaud J, Orgogozo JM (1977) Clinical consequencies of corticectomies involving the supplementary motor area in man. J Neurol Sci 34:301–314

Martino AM, Strick PL (1987) Corticospinal projections originate from the arcuate premotor area. Brain Res 404:307–312

Matelli W, Luppino G, Rizzolatti G (1985) Patterns of cytochrome oxidase activity in the frontal agranular cortex of the macaque monkey. Behav Brain Res 18:125–136

Mushiake H, Inase M, Tanji J (1990) Selective coding of motor sequence in the supplementary motor area of the monkey cerebral cortex. Exp Brain Res 82:208–210

Okano K, Tanji J (1987) Neuronal activity in the primate motor fields of the agranular frontal cortex preceding visually triggered and self-paced movements. Exp Brain Res 66:155–166

Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychol 9:97–113

Passingham RE (1985) Premotor cortex: sensory cues and movement. Behav Brain Res 18:175–186

Passingham RE (1987) Two cortical systems for directing movement. In: Porter R (ed) Motor areas of the cerebral cortex. Wiley, Chichester, pp 151–164

Passingham RE (1988) Premotor cortex and preparation for movement. Exp Brain Res 70:590–596

Passingham RE, Thaler DE, Chen Y (1989) Supplementary motor cortex and self-initiated movement. In: Ito M (ed) Neural programming. Karger, Basel, pp 13–24

Pearson RCA, Powell TPS (1985) The projection of the primary somatic sensory cortex upon area 5 in the monkey. Brain Res Rev 9:89–107

Petrides M (1982) Motor conditional associative-learning after selective prefrontal lesions in the monkey. Behav Brain Res 5:407–413

Petrides M, Pandya DN (1984) Projections to the frontal lobes from the posterior parietal region in the rhesus monkey. J Comp Neurol 228:105–116

Raichle ME (1987) Circulatory and metabolic correlates of brain function in normal humans. In: Plum F (ed) The nervous system: higher functions of the brain. Am Physiol Soc, Bethesda, pp 643–674

Robinson CJ, Burton H (1980) Organization of somatosensory receptive fields in cortical areas 7b, retroinsula, postauditory and granular insular of Macaca fascicularis. J Comp Neurol 192:69–92

Roland PE, Seitz RJ (1989) Mapping of learning and memory functions in the human brain. In: Ottoson D (ed) Visualization of brain functions. Stockton Press, London, pp 141–151

Roland PE, Larsen B, Lassen NA, Skinhoj E (1980a) Supplementary motor area and other cortical areas in organization of voluntary movements in man. J Neurophysiol 43:118–136

Roland PE, Skinhoj E, Lassen NA, Larsen B (1980b) Different cortical areas in man in organization of voluntary movements in extrapersonal space. J Neurophysiol 43:137–150

Roland PE, Meyer E, Shibasaki T, Yamamoto YL (1982) Regional cerebral blood flow changes in cortex and basal ganglia during voluntary movements in normal human volunteers. J Neurophysiol 48:467–480

Romo R, Schultz W (1987) Neuronal activity preceeding selfinitiated or externally timed arm movements in area 6 of monkey cortex. Exp Brain Res 67:656–662

Seal J, Gross C, Bioulac B (1982) Activity of neurones in area 5 during a simple arm movement in monkeys before and after deafferentation of the trained limb. Brain Res 250:229–243

Spinks TJ, Jones T, Gilardi MC, Heather JD (1988) Physical performance of the latest generation of commercial positron scanner. IEEE Trans Nucl Sci 35:721–725

Stern CE (1987) Functions of the ventral striatum. PhD thesis. University of Oxford

Straub A, Siegel K (1988) Parkinsonian syndrome caused by a tumour of the left supplementary motor area. J Neurol Neurosurg Psychiatr 51:730–731

Talairach J, Szikla G (1967) Atlas d'anatomie stereotaxique du telencephale. Masson, Paris

Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain. Thieme, Stuttgart

Tanji J, Tanaguchi K, Saga T (1980) The supplementary motor area: neuronal responses to motor instructions. J Neurophysiol 43:60–68

von Economo C, Koskinas (1928) The cytoarchitectonics of the human cerebral cortex. Oxford University Press, London

Weinrich M, Wise SP, Mauritz K-H (1984) A neurophysiological study of the premotor cortex in the rhesus monkey. Brain 107:385–414

Wise SP (1989) Frontal cortex activity and motor set. In: Ito M (ed) Neural programming. Karger, Basel, pp 25–38


Cognitive aspects of human motor activity: Contribution of right hemisphere and cerebellum

Background. Concepts of movement and action are not completely synonymous, but what distinguishes one from the other? Movement may be defined as stimulus- driven motor acts, while action implies realization of a specific motor goal, essential for cognitively driven behavior. Although recent clinical and neuroimaging studies have revealed some areas of the brain that mediate cognitive aspects of human motor behavior, the identification of the basic neural circuit underlying the interaction between cognitive and motor functions remains a challenge for neurophysiology and psychology.

Objective. In the current study, we used functional magnetic resonance imaging (fMRI) to investigate elementary cognitive aspects of human motor behavior.

Design. Twenty healthy right-handed volunteers were asked to perform stimulus-driven and goal-directed movements by clenching the right hand into a fist (7 times). The cognitive component lay in anticipation of simple stimuli signals. In order to disentangle the purely motor component of stimulus-driven movements, we used the event-related (ER) paradigm. FMRI was performed on a 3 Tesla Siemens Magnetom Verio MR-scanner with 32-channel head coil.

Results. We have shown differences in the localization of brain activity depending on the involvement of cognitive functions. These differences testify to the role of the cerebellum and the right hemisphere in motor cognition. In particular, our results suggest that right associative cortical areas, together with the right posterolateral cerebellum (Crus I and lobule VI) and basal ganglia, de ne cognitive control of motor activity, promoting a shift from a stimulus-driven to a goal-directed mode.

Conclusion. These results, along with recent data from research on cerebro-cerebellar circuitry, redefine the scope of tasks for exploring the contribution of the cerebellum to diverse aspects of human motor behavior and cognition.

Sedov, A.S.
Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow, Russia
Popov, V.A.
Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow, Russia
Filyushkina, V.I.
Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow, Russia
Semenova, U.N.
Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow, Russia
Orlov, V. A.
National Research Center “Kurchatov Institute”, Moscow, Russia
Velichkovsky, Boris M.
National Research Center “Kurchatov Institute”, Moscow, Russia
Technische Universitaet, Dresden, Germany
Ushakov, V.L.
National Research Center “Kurchatov Institute”, Moscow, Russia

Keywords: action, movement, fMRI, lateralization, motor behavior, voluntary movement, cognition, cortex, cerebellum, basal ganglia


Anticorrelations between Active Brain Regions: An Agent-Based Model Simulation Study.

The not-well-defined nature of negative correlations stimulated several authors to study the persistence of significant negative correlations by means of fMRI-specific correction methods and to propose a possible physiological role for them [1-4]. In this regard, however, a clear mechanism about how negative interactions are related to the positive ones is not available as yet. A rewarding approach to the problem would be the simulation of brain activity, which opens the door to mechanistic models amenable to validation by empirical data.

Different models have been proposed [5] to approximate the collective activity of neurons such as the conductance-based biophysical model [6-8] or the FitzHugh-Nagumo model [9, 10], by the mean-field [11] or mass action [12] formalisms. fMRI produces data at a mesoscopic level while brain activities are inspected at a much larger scale than that of single neurons. This implies that we have to imagine how the behavior of single functional units, of major importance for the current understanding of brain's activities, may influence the observations at a higher hierarchical level [13].

In order to reproduce the brain resting state from fMRI acquisitions, the long-range myelinated fiber connections by diffusion imaging, or the folded cortical surface by high resolution imaging [14-17], have been used as a background for the interactions between brain areas. Such interactions have been simulated using the Kuramoto model [18], the Ising model [19], and some discrete-time dynamical models [20, 21]. In the last case [20, 21], a stochastic cellular automaton approach was used by two well-established brain computational models, the susceptible-excited-refractory (SER) [22] model and the FitzHugh-Nagumo model [9].

An alternative approach to the large-scale brain modeling is to simulate the brain activity using the functional connectivity map itself as a background. In such a context, Joyce et al. [23] realized an agent-based brain-inspired model (ABBM) using both positive and negative values of functional connectivity. In general, an agent-based model (ABM) includes a set of agents whose reciprocal interactions are defined by a set of rules depending upon the system at hand. These models can exhibit emergent behavior as described by Wolfram [24].

Here we develop a model using an ABM model and a biologically plausible SER model, which should account for both positive and negative interactions between large-scale brain areas. Different levels of functional connectivity in the background modulate the goodness-of-fit of simulations, and we focus, in particular, on the fraction of negative links to test their role in the organization of structured networks.

2.1. Data Collection. The sample is composed of 30 selected functional images of healthy controls from the Beijing Zang dataset (180 subject) in the 1000 Functional Connectomes Classic collection (http://fcon_1000.projects.nitrc.org/indi/ retro/BeijingEnhanced.html). Resting data were obtained using a 3.0 T Siemens scanner at the Imaging Center for Brain Research, Beijing Normal University. For each subject, a total of 240 volumes of EPI images were obtained axially (repetition time, 2000 ms echo time, 30 ms slices, 33 thickness, 3 mm gap, 0.6 mm field of view, 200 x 200 [mm.sup.2] resolution, 64 x 64 flip angle, 90[degrees]). For the anatomical images, a T1-weighted sagittal three-dimensional magnetization prepared rapid gradient echo (MPRAGE) sequence was acquired, covering the entire brain: 128 slices, TR= 2530 ms, TE = 3.39 ms, slice thickness = 1.33 mm, flip angle = 7[degrees], inversion time = 1100 ms, FOV = 256 x 256 mm, and in-plane resolution = 256 x 192.

2.2. Data Preprocessing. The first 10 scans of each subject were removed, and the remaining functional images were analyzed according to the procedures fully described elsewhere [25]. The SPM8 (Statistical Parametric Mapping) (Wellcome Department of Cognitive Neurology, London, UK) toolbox and the Functional Connectivity (CONN) toolbox were used in the preprocessing of data on a MATLAB R2010b platform.

The images from each subject were divided into 105 ROIs without brainstem and cerebellum (see Figure 1) through the MRI Atlas of the Human Brain, Harvard Medical School [26], and from each ROI, the time series was extracted. An average correlation matrix for each subject was calculated for all possible couples of the 105 ROIs considering both correlation signs and was used as an (individual) connectivity matrix. Thus, the global, mean matrix to be used as a background for the brain simulation was reckoned according to the following overall procedure:

(1) For each subject, the activation time series of 105 ROIs extracted from 240 functional images (see Data Collection) were coupled and correlated in all possible combinations, producing an individual connectivity matrix. Then, a global average concerning the whole group of subjects is obtained by averaging the 30 individual matrices, as schematized in Figure 2(a).

(2) For both positive and negative interactions, in the above average matrix, a series of 20 binary and thresholded matrices are constructed, taking fractions of the highest absolute correlation values in the range from 0% to 100% at 5% steps: this represents the network density (cost). Thus, 20 binary matrices of increasing cost are derived, having an unbalanced amount of total positive and negative links (total positive correlations 70%, total negative correlations 30%). We call this type of threshold absolute-values-proportional-threshold. A graphical overview of the procedure is reported in Figure 2(b).

(3) A further set of binary and thresholded matrices is calculated in order to distinguish the most significant correlation value for each sign: 15 matrices from the 0%-70% cost (maximum fraction of positive links), containing only positive values, and 7 matrices from the 0%-30% cost (maximum fraction of negative links), containing only negative values. Thus, we have different amounts of positive and negative correlations for the same fraction of total links. We call this type of threshold signed-values-proportional-threshold.

(4) Finally, all the combinations of positive and negative matrices for different thresholds are joined, producing 7 * 15 = 105 matrices having different amounts of positive and negative correlations.

2.3. Simulations by an ABBM Model. An agent-based approach was used in a large-scale brain network simulation able to account for the independent behavior of each brain region as well as for the interactions between different regions. Each node in the network represents, according to the susceptible-excited-refractory (SER) formalism [20, 21], a stylized biological neuron cycling in discrete time steps through the following three states: (S), a susceptible state in which the node can be excited with a transition probability called sop (E), an excited state after which the node enters in a refractory state and (R), a refractory state from which the node can be regenerated (S) stochastically with a recovery probability called nep.

The interactions among the nodes (agents) characterized by the (SER) states are defined through positive and negative links in a binary and thresholded matrix derived from empirical data and simulated through an agent-based braininspired model (ABBM) of the type suggested by Joyce [23].

In particular, each node is characterized by three variables ([[sigma].sub.s], [[sigma].sub.p], and [[sigma].sub.n) two parameters ([[pi].sub.p] and [[pi].sub.n]) (see Figure 3), which are defined as follows.

(i) [[sigma].sub.s] = 1 if the node is in the S (susceptible) state, namely, prone to change (otherwise, [[sigma].sub.s] = 0).

(ii) [[sigma].sub.p] and [[sigma].sub.n] are calculated from the average contribution of positive and negative neighbors, respectively each neighbor contributes to the average if in the active (on) state.

(iii) [[pi].sub.n] and [[pi].sub.p] are threshold parameters above which the average of negative and positive neighbors ([[phi].sub.p] and [[phi].sub.n]) are set to 1 (otherwise, are set to 0).

Taking into account the previous variables, we characterized an agent by three binary variables ([[phi].sub.s], [[phi].sub.p], and [[phi].sub.n]), namely, by one of [2.sup.3] possible combinations (111, 110, 101, 011, 100, 001, 010, 000). Simulations were carried out concurrently for all agents and for each step, and in contrast with Morris and Lecar [6], we designed some a priori rules to decide whether or not a brain region could become active at a given simulation step (Table 1).

Various combinations of the sop, nep (connectivity independent) and [[pi].sub.p], [[pi].sub.n] (connectivity dependent) couples of parameters have been checked in the above-described model in order to simulate at best the whole empirical, positive connectivity matrix by a given fraction of positive and negative links. In particular, if negative links are associated with noise, the simulation quality should decrease when their fractional amount increases and, inversely, increase in the opposite, symmetrical condition.

Simulations were repeated 100 times for each different combination of parameters, assigning to nodes a random series of 0 and 1 and a random SER state. Notice that in the case of the [[pi].sub.p], [[pi].sub.n] couple, the same value for each member of the couple was used. Each simulation included 200 time steps and produced a matrix of 105 columns (brain regions) and 200 rows (total time steps) see Figure 4. The Pearson correlation (r) carried out on the columns of such a matrix produced a 105 x 105 simulated connectivity matrix. The Pearson correlation between each of the 100 simulated matrices and the one derived from experimental data produced 100 correlations values for each combination of parameters which were averaged and the average value assigned to that parameter combination. It is worthy to underline that the Pearson correlation (r) was used throughout this work as an index of the agreement (goodness-of-fit) between simulations and empirical data.

The whole procedure included three series of simulations: The first two series aimed to optimize the parameter values in the third series, the importance of different fractions of negative and positive connectivities in the reproduction of the positive connectivity itself was estimated. In particular, the following should be noted:

(i) In the first series of simulations, each of the 20 matrices characterized by an absolute-values-proportional-threshold (from 0% to 100% of absolute value threshold with 5% steps) was used as a background, as well as large variations of the other parameters (sop and nep = 0.25-0.50-0.75 [[pi].sub.p]/[[pi].sub.n] from 0.1 to 1, step 0.1).

(ii) The second series of simulations aimed to improve the parameter precision within the range identified in the previous set of simulations.

(iii) Finally, the third series of simulations was carried out upon considering, within the 105 matrices characterized by any possible combination of 15 positive and 7 negative signed-values-proportional-thresholds, the one showing the best simulation performance, namely, the best reproduction of the original connectivity pattern.

The significance of the fitting performance was assessed as follows: in order to check the effect of positive and negative connectivities, 15 and 7 different fractions of positive and negative links, respectively, were used and subjected to a Friedman test. Then, a post hoc analysis using the ranks of the goodness-of-fit was performed by the Tukey-Kramer test.

3.1. Exploring the Parameters' Space of the Brain Model. In the first exploratory phase of the model validation, the goodness-of-fit between empirical data and simulations, as monitored by the Pearson (r), was studied over a wide range of connectivity-independent (sop, nep) and connectivity-dependent ([[pi].sub.p], [[pi].sub.n]) parameters, namely, 0.25-0.50-0.75 and from 0.1 to 1 at 0.1 steps, respectively.

In Figure 5(a), the [[pi].sub.p] and [[pi].sub.n] values associated with the goodness-of-fit peaks show a trend increasing with both sop and nep values. Since high sop and nep values point to an excitable system, endowed with high probability of spontaneous activation and low probability of resting in the refractory state, the fitting appears improved by a relatively conservative threshold for [[pi].sub.p] and [[pi].sub.n], namely, [[pi].sub.p] and [[pi].sub.n] = 0.1, under the condition of low excitability (sop and nep being equal to 0.25).

The above considerations suggest to focus on the lower range of parameters, namely, sop and nep from 0.025 to 0.25 (step = 0.025) and [[pi].sub.p] and [[pi].sub.n] from 0.025 to 0.1 (step = 0.025). Thus, the matching between simulation and empirical data could be improved by reaching the maximum value of 0.50 at the following connectivity-independent parameter values: sop = 0.025 nep = 0.175, 0.20, 0.225.

As shown in Figure 5(b), the highest goodness-of-fit is reached at [[pi].sub.p] = [[pi].sub.n] = 0.1 and using a small connectivity density (15%). At increasing [[pi].sub.p] and [[pi].sub.n] values, the trend changes gradually until at [[pi].sub.p] = [[pi].sub.n] = 0.1 an absolute minimum in the lower range of connectivity density can be observed, as well as a maximum in the higher range of connectivity density. Notice that sop and nep values are locked, respectively, at 0.025 and 0.225, and that changing the nep parameter does not alter the observed trends.

This behavior can be ascribed to the different amounts of positive and negative links using the absolute-values-proportional-threshold: The number of negative links is lower (almost nonsignificant for the lower level of general connectivity cost), and a more conservative threshold [[pi].sub.n] would further decrease the associated information. Thus, with a more labile threshold of [[pi].sub.n], more information from the negative connectivities can be extracted, which increases their modulation role. Due to the unbalanced distribution of positive and negative links, however, the simulation reaches a maximum value of goodness-of-fit only in the higher range of connectivity density (where a significant amount of negative connectivity is also increasing). At the same time, a lower threshold [[pi].sub.p] can introduce random positive connections, decreasing the goodness-of-fit in the lower range of the connectivity density.

3.2. Modeling Positive and Negative Links. In this phase, the task is to define the dependence of the fitting procedure on the relative amounts of positive and negative links, using the parameter values identified in the previous steps, namely, sop = 0.025, nep = 0.225, and [[pi].sub.p] = [[pi].sub.n] = 0.1. In Figure 6, the trend of correlation values at increasing positive connectivity fractions is characterized by a peak within the middle values of positive cost. Moreover, adding negative links at this stage further improves the fitting up to a maximum (0.57) at the higher values of negative network density.

A nonparametric statistical analysis (Friedman test) reported in Figure 7 confirms a significant effect (p < 0.0001, [chi square] = 97.3, df= 1) of positive links on the fitting performance of the model. The effect of negative links, however, is not significant (p = 0.55, [chi square] = 4.9, df = 6). The significant post hoc difference in the positive links is apparent in the range from 5% to 30% of positive network density (Figure 7(a)). The same nonparametric test for negative links in the range of higher values of goodness-of-fit is reported in Figure 7(c) where 6 different levels of positive cost (from 5% to 30%) are considered, while the levels of negative links remain 7. In contrast with previous results, under these conditions, a significant effect for the negative links (see Figure 7(c) p < 0.0001, [chi square] = 37.1, df=6) emerges. This indicates a possible interaction between different amounts of positive and negative links, so that only in the range of 5%-30% positive cost is there an increasing trend of goodness-of-fit upon addition of negative links (25%-30%). Under other conditions, only random fluctuations occur, probably caused by increasing variability levels.

3.3. Modeling Individual Variability. Given the noticeable level of individual variability in brain functional connectivity, the model has been individually applied on a small sample of subjects. For each of eight randomly chosen subjects, the simulations were repeated in the positive cost range indicated as significant by our previous work (positive cost: 5%-30%), and keeping the same values of the sop, nep and [[pi].sub.p]/[[pi].sub.n] parameters. The results, shown in Figure 8, are in line with the previous observation of a small effect of anticorrelation variability in the model.

4.1. General Issues about Our Brain Model. In this work, we propose a simple agent-based model able to simulate brain functional connectivity. Our results stress once again on how a set of simple rules between interacting agents can show a complex dynamics [24]. A peculiar feature of our work is the input used for the simulation: instead of the structural connectivity [14-17], we used the functional connectivity itself as a background and did that to underpin the role of a given amount of signed connectivity. In particular, we focused on the relative fraction of positive and negative links, to characterize the whole brain functions.

Our simulations exploit the appealing features of an ABBM-based strategy already used for the same purpose among several possible alternatives [23]. This approach showed different patterns of dynamics, but only some particular combinations of parameters produced nontrivial results [23] and, in addition, often lack coherent biological interpretation. We initially used some parameter values directly inspired to a biological system, and the results were unsatisfactory. Thus, we shifted to a SER model with the agents' dynamics defined by the sop and nep parameters. In this way, the brain regions show a stochastic oscillation in line with more realistic models [14, 15], and the connectivity represents a modulation among brain oscillating dynamics. As the first result of the adopted modeling strategy, the characterization of the system at hand was significantly improved.

4.2. Modeling Brain Activity Using Different Amounts of Positive and Negative Links. Different trends were found by our simulations depending upon the relative amount of positive and negative connectivities: In the former case (positive connectivities), the goodness-of-fit shows a peak at lower cost values, and a decreasing trend follows in the latter (negative connectivities), the goodness-of-fit shows an increasing trend with a maximum at the maximal fraction of negative links.

As for positive connectivities, the statistical analysis showed clear differences between the random model (no connections among nodes, and all brain regions showing random oscillations) in the range between 5% and 30%. This result is in line with previous findings pointing to a small-world topology in that range [27]: In the same range, the brain positive networks show an efficient balance between the segregation-integration properties, and brain regions can be clustered in different subnetworks without losing the possible information transfer among each other [28]. As for negative links, the goodness-of-fit shows a trend different from that of the random model only if the positive links are in the range 5%-30%: otherwise, the trend is lost. In this frame, negative links showed importance in order to improve the fitting and prove their nonartifactual nature, while a higher density of positive links may indicate a significant noise source.

The results gathered by our model on single subjects are in agreement with those on the average matrix, indicating a good reproduction of individual variability. As a more general validation of our study, the same analysis carried out over another set of 30 randomly chosen individuals from the same database (Beijing Zang dataset, the 1000 Functional Connectomes Classic collection) produced pretty similar results (not shown).

An objective interpretation of our observations should take into account several factors: (1) More positive than negative modulations could be favoured by our model (2) the anticorrelations have a more variable dynamics, more dependent on experimental conditions. From this point of view, such interactions are characteristic of the resting state itself and have a more local than global meaning (3) our preprocessing method (aCompCorr [29]) used for the fMRI analysis could be not good enough to characterize negative networks. The first issue can be tested using different types of simulations in order to work out models for negative connections. In this regard, we would need a more accurate large-scale brain modeling able to account for this type of brain interaction. As for the second issue, different evidence is prone to assess the local versus global nature of anticorrelations. As a matter of fact, two evidence pointed out these different hypotheses: Gopinath et al. [30] found intracluster anticorrelations in several task-positive networks (TPNs) during a resting state, indicating a possible state-dependent activity. However, more recently [4], we found a low-connection probability between the most connected nodes using anticorrelated functional networks (the highly connected nodes tend to avoid connections among each other, indicating a global network organization).

About the last issue, however, there is no univocal consensus, and alternative methods have been proposed [2], among which the aCompCorr appeared as a most reliable one [1].

A direct comparison of aCompCorr with GSR [31], however, did not allow us to provide a final answer to the general problem, which remains, then, still open to further exploration.

All in all, the target of the present work was not to develop an alternative to the already used large-scale brain models but to underpin the importance of different connectivity types for the brain system. To this aim, we introduced a simple model able to fit empirical data, provided a method to identify the random (or noisy) functional connections, and found some evidence about the importance of anticorrelations for the optimal characterization of connectivity patterns.

It seems fair to conclude that anticorrelations (1) should be distinguished from noise and (2) may improve the characterization of positive connectivity and contribute to the refinement of the global brain functional system in fMRI acquisitions.

Anatomical Labels of Brain Regions

(1) FP r (frontal pole right)

(3) IC r (insular cortex right)

(4) IC l (insular cortex left)

(5) SFG r (superior frontal gyrus right)

(6) SFG l (superior frontal gyrus left)

(7) MidFG r (middle frontal gyrus right)

(8) MidFG l (middle frontal gyrus left)

(9) IFG tri r (inferior frontal gyrus, pars triangularis right)

(10) IFG tri l (inferior frontal gyrus, pars triangularis left)

(11) IFG oper r (inferior frontal gyrus, pars opercularis right)

(12) IFG oper l (inferior frontal gyrus, pars opercularis left)

(13) PreCG r (precentral gyrus right)

(14) PreCG l (precentral gyrus left)

(15) TP r (temporal pole right)

(16) TP l (temporal pole left)

(17) aSTG r (superior temporal gyrus, anterior division right)

(18) aSTG l (superior temporal gyrus, anterior division left)

(19) pSTG r (superior temporal gyrus, posterior division right)

(20) pSTG l (superior temporal gyrus, posterior division left)

(21) aMTG r (middle temporal gyrus, anterior division right)

(22) aMTG l (middle temporal gyrus, anterior division left)

(23) pMTG r (middle temporal gyrus, posterior division right)

(24) pMTG l (middle temporal gyrus, posterior division left)

(25) toMTG r (middle temporal gyrus, temporooccipital part right)

(26) toMTG l (middle temporal gyrus, temporooccipital part left)

(27) aITG r (inferior temporal gyrus, anterior division right)

(28) aITG l (inferior temporal gyrus, anterior division left)

(29) pITG r (inferior temporal gyrus, posterior division right)

(30) pITG l (inferior temporal gyrus, posterior division left)

(31) toITG r (inferior temporal gyrus, temporooccipital part right)

(32) toITG l (inferior temporal gyrus, temporooccipital part left)

(33) PostCG r (postcentral gyrus right)

(34) PostCG l (postcentral gyrus left)

(35) SPL r (superior parietal lobule right)

(36) SPL l (superior parietal lobule left)

(37) aSMG r (supramarginal gyrus, anterior division right)

(38) aSMG l (supramarginal gyrus, anterior division left)

(39) pSMG r (supramarginal gyrus, posterior division right)

(40) pSMG l (supramarginal gyrus, posterior division left)

(41) AG r (angular gyrus right)

(42) AG l (angular gyrus left)

(43) sLOC r (lateral occipital cortex, superior division right)

(44) sLOC l (lateral occipital cortex, superior division left)

(45) iLOC r (lateral occipital cortex, inferior division right)

(46) iLOC l (lateral occipital cortex, inferior division left)

(47) ICC r (intracalcarine cortex right)

(48) ICC l (intracalcarine cortex left)

(49) MedFC (frontal medial cortex)

(50) SMA r (juxtapositional lobule cortex--formerly supplementary motor cortex right)

(51) SMA L (juxtapositional lobule cortex--formerly supplementary motor cortex left)

(52) SubCalC (subcallosal cortex)

(53) PaCiG r (paracingulate gyrus right)

(54) PaCiG l (paracingulate gyrus left)

(55) AC (cingulate gyrus, anterior division)

(56) PC (cingulate gyrus, posterior division)

(57) Precuneus (precuneus cortex)

(58) Cuneal r (cuneal cortex right)

(59) Cuneal l (cuneal cortex left)

(60) FOrb r (frontal orbital cortex right)

(61) FOrb l (frontal orbital cortex left)

(62) aPaHC r (parahippocampal gyrus, anterior division right)

(63) aPaHC l (parahippocampal gyrus, anterior division left)

(64) pPaHC r (parahippocampal gyrus, posterior division right)

(65) pPaHC l (parahippocampal gyrus, posterior division left)

(66) LG r (lingual gyrus right)

(67) LG l (lingual gyrus left)

(68) aTFusC r (temporal fusiform cortex, anterior division right)

(69) aTFusC l (temporal fusiform cortex, anterior division left)

(70) pTFusC r (temporal fusiform cortex, posterior division right)

(71) pTFusC l (temporal fusiform cortex, posterior division left)

(72) TOFusC r (temporal occipital fusiform cortex right)

(73) TOFusC l (temporal occipital fusiform cortex left)

(74) OFusG r (occipital fusiform gyrus right)

(75) OFusG l (occipital fusiform gyrus left)

(76) FO r (frontal operculum cortex right)

(77) FO l (frontal operculum cortex left)

(78) CO r (central opercular cortex right)

(79) CO l (central opercular cortex left)

(80) PO r (parietal operculum cortex right)

(81) PO l (parietal operculum cortex left)

(82) PP r (planum polare right)

(83) PP l (planum polare left)

(84) HG r (Heschl's gyrus right)

(85) HG l (Heschl's gyrus left)

(86) PT r (planum temporale right)

(87) PT l (planum temporale left)

(88) SCC r (supracalcarine cortex right)

(89) SCC l (supracalcarine cortex left)

(90) OP r (occipital pole right)

(91) OP l (occipital pole left)

The authors declare that there is no conflict of interest regarding the publication of this paper.

[1] X. J. Chai, A. N. Castanon, D. Ongur, and S. Whitfield-Gabrieli, "Anticorrelations in resting state networks without global signal regression," NeuroImage, vol. 59, no. 2, pp. 1420-1428, 2012.

[2] C. Chang and G. H. Glover, "Effects of model-based physiological noise correction on default mode network anticorrelations and correlations," NeuroImage, vol. 47, no. 4, pp. 1448-1459, 2009.

[3] M. D. Fox, D. Zhang, A. Z. Snyder, and M. E. Raichle, "The global signal and observed anticorrelated resting state brain networks," Journal of Neurophysiology, vol. 101, no. 6, pp. 3270-3283, 2009.

[4] F. Parente, M. Frascarelli, A. Mirigliani, F. Di Fabio, M. Biondi, and A. Colosimo, "Negative functional brain networks," Brain Imaging and Behavior, pp. 1-10, 2017.

[5] P. Sanz-Leon, S. A. Knock, A. Spiegler, and V. K. Jirsa, "Mathematical framework for large-scale brain network modeling in the virtual brain," NeuroImage, vol. 111, pp. 385-430, 2015.

[6] C. Morris and H. Lecar, "Voltage oscillations in the barnacle giant muscle fiber," Biophysical Journal, vol. 35, no. 1, pp. 193-213, 1981.

[7] R. Larter, B. Speelman, and R. M. Worth, "A coupled ordinary differential equation lattice model for the simulation of epileptic seizures," Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 9, no. 3, pp. 795-804, 1999.

[8] M. Breakspear, ""Dynamic" connectivity in neural systems: theoretical and empirical considerations," Neuroinformatics, vol. 2, no. 2, pp. 205-224, 2004.

[9] R. Fitzhugh, "Impulses and physiological states in theoretical models of nerve membrane," Biophysical Journal, vol. 1, no. 6, pp. 445-466, 1961.

[10] J. Nagumo, S. Arimoto, and S. Yoshizawa, "An active pulse transmission line simulating nerve axon," Proceedings of IRE, vol. 50, no. 10, pp. 2061-2070, 1962.

[11] H. Wilson and J. Cowan, "Excitatory and inhibitory interactions in localized populations of model neurons," Biophysical Journal, vol. 12, no. 1, pp. 1-24, 1972.

[12] W. J. Freeman, Mass Action in the Nervous System, Academic Press, New York San Francisco, London, 1975.

[13] G. Deco, V. K. Jirsa, and A. R. McIntosh, "Emerging concepts for the dynamical organization of resting-state activity in the brain," Nature Reviews Neuroscience, vol. 12, no. 1, pp. 43-56, 2011.

[14] J. Cabral, E. Hugues, O. Sporns, and G. Deco, "Role of local network oscillations in resting-state functional connectivity," NeuroImage, vol. 57, no. 1, pp. 130-139, 2011.

[15] G. Deco and V. K. Jirsa, "Ongoing cortical activity at rest: criticality, multistability, and ghost attractors," Journal of Neuroscience, vol. 32, no. 10, pp. 3366-3375, 2012.

[16] A. Ghosh, Y. Rho, A. R. McIntosh, R. Kotter, and V. K. Jirsa, "Cortical network dynamics with time delays reveals functional connectivity in the resting brain," Cognitive Neurodynamics, vol. 2, no. 2, pp. 115-120, 2008.

[17] C. J. Honey, O. Sporns, L. Cammoun et al., "Predicting human resting-state functional connectivity from structural connectivity," Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 6, pp. 2035-2040, 2009.

[18] J. A. Acebron, L. L. Bonilla, C. J. Perez Vicente, F. Ritort, and R. Spigler, "The Kuramoto model: a simple paradigm for synchronization phenomena," Reviews of Modern Physics, vol. 77, no. 1, pp. 137-185, 2005.

[19] T. K. Das, P. M. Abeyasinghe, J. S. Crone et al., "Highlighting the structure-function relationship of the brain with the Ising model and graph theory," BioMed Research International, vol. 2014, Article ID 237898, 14 pages, 2014.

[20] G. C. Garcia, A. Lesne, M. T. Hutt, and C. C. Hilgetag, "Building blocks of self-sustained activity in a simple deterministic model of excitable neural networks," Frontiers in Computational Neuroscience, vol. 6, p. 50, 2012.

[21] A. Messe, M. T. Hutt, P. Konig, and C. C. Hilgetag, "A closer look at the apparent correlation of structural and functional connectivity in excitable neural networks," Scientific Reports, vol. 5, no. 1, article 7870, 2015.

[22] A. R. Carvunis, M. Latapy, A. Lesne, C. Magnien, and L. Pezard, "Dynamics of three-state excitable units on poisson vs. power-law random networks," Physica A: Statistical Mechanics and its Applications, vol. 367, pp. 595-612, 2006.

[23] K. E. Joyce, P. J. Laurienti, and S. Hayasaka, "Complexity in a brain-inspired agent-based model," Neural Networks, vol. 33, pp. 275-290, 2012.

[24] S. Wolfram, "Universality and complexity in cellular automata," Physica D: Nonlinear Phenomena, vol. 10, no. 1-2, pp. 1-35, 1984.

[25] F. Parente and A. Colosimo, "The role of negative links in brain networks," Biophysics and Bioengineering Letters, vol. 9, no. 1, pp. 1-13, 2016.

[26] V. Caviness, J. Meyer, N. Makris, and D. Kennedy, "MRI-based topographic parcellation of human neocortex: an anatomically specified method with estimate of reliability," Journal of Cognitive Neuroscience, vol. 8, no. 6, pp. 566-587, 1996.

[27] S. Achard, R. Salvador, B. Whitcher, J. Suckling, and E. Bullmore, "A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs," The Journal of Neuroscience, vol. 26, no. 1, pp. 63-72, 2006.

[28] E. Bullmore and O. Sporns, "The economy of brain network organization," Nature Reviews Neuroscience, vol. 13, no. 5, pp. 336-349, 2012.

[29] Y. Behzadi, K. Restom, J. Liau, and T. T. Liu, "A component based noise correction method (CompCor) for bold and perfusion based fMRI," NeuroImage, vol. 37, no. 1, pp. 90-101, 2007.

[30] K. Gopinath, V. Krishnamurthy, R. Cabanban, and B. A. Crosson, "Hubs of anticorrelation in high-resolution resting-state functional connectivity network architecture," Brain Connectivity, vol. 5, no. 5, pp. 267-275, 2015.

[31] R. Murphy, R. M. Birn, D. A. Handwerker, T. B. Jones, and P. A. Bandettini, "The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?," NeuroImage, vol. 44, no. 3, pp. 893-905, 2009.

Fabrizio Parente (iD) and Alfredo Colosimo (iD)

Deparment of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy

Correspondence should be addressed to Fabrizio Parente [email protected]

Received 5 July 2017 Revised 19 December 2017 Accepted 9 January 2018 Published 19 March 2018

Academic Editor: Stuart C. Mangel

Caption: Figure 1: Brain parcellation. Location of the brain regions considered in the extraction of the BOLD signal and visible in a sagittal brain representation. For the complete list of the 105 regions considered in this work, taken from FSL Harvard-Oxford maximum likelihood cortical and subcortical atlas, see the Appendix.

Caption: Figure 2: Working out the connectivity matrices. (a) Refers to point (1) of the procedure detailed in the text. The fractions in (b) concern the highest absolute correlation values of the threshold in the corresponding matrices (see point (2) in the text for details).

Caption: Figure 3: State balance of an agent (A) surrounded by six neighbors. (a) Activity levels of an agent in the SER (susceptible-excited-refractory) states: top and bottom pictures refer to a cycling scheme and to the classical action potential scheme, respectively. In parentheses are the 0/1 activity level of the state. sop and nep indicate the probability of getting the S [right arrow] E and R [right arrow] S state change, respectively (see the text for further details). (b) The state of the central node (A) in the next time step depends upon local (endogenous) and global (exogenous) factors. Three out of the four positively linked neighbors are active (1), so the average activity (3/4) exceeds the [[[phi].sub.p] = 0.5 threshold. This is also the case for the both active (1) and negatively linked neighbors, since [[phi].sub.n] = 0.5 also.

Caption: Figure 4: Example of a simulated time series. The time series corresponds to the condition included in Figure 5(b) (blue curve), namely, to the following parameter values: sop = 0.225, nep = 0.025, [[pi].sub.p] = [[pi].sub.n] = 0.1, and absolute-values-proportional-threshold = 100%. The spots indicate an excited state (E) for each of the 105 brain regions in each step of the simulation.

Caption: Figure 5: Fitting empirical data by the ABM model: dependence upon model's parameters. (a) Connectivity-dependent parameters ([[pi].sub.p] and [[pi].sub.p]) on the x-axis. Blue, green, and red lines indicate, respectively, nep values of 0.25, 0.50, and 0.75. (b) Cost (network density) parameter on the x-axis sop and nep fixed at 0.025 and 0.225, respectively. Blue, green, red, and light-blue lines indicate, respectively, 0.1, 0.075, 0.05, and 0.025 values of [[pi].sub.p] and [[pi].sub.n]. Notice that a peak of the goodness-of-fit appears at [[pi].sub.p], [[pi].sub.n] = 0.1, in the lower range only of the network density. In all cases, the Pearson correlation (r) is used as a goodness-of-fit index.

Caption: Figure 6: Fitting empirical data by combinations of positive and negative cost. The false-color scale visualizes the Pearson correlation between experiments and simulations obtained using the fractions of negative and positive links indicated in the horizontal and vertical axes, respectively.

Caption: Figure 7: Post hoc analysis. Mean differences of the goodness-of-fit using an increasing amount of positive and negative links. (a) Goodnessof-fit as a function of positive links. (b) Goodness-of-fit as a function of negative links. (c) Goodness-of-fit as a function of negative links in the range of 5%-30% positive cost a significant difference between the first mean value in blue (no negative links) is reached for the highest value (in red) of negative cost: 25%-30%.

Caption: Figure 8: Modeling individual patterns. The goodness-of-fit values as a function of increasing amount of negative links (average of the fraction of positive links between 5% and 30%) concern 8 randomly chosen subjects. For the average values of the whole group of subjects, see Figure 7(c).


Cortical layers/BA

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2 Anatomy of Eloquent Cortical Brain Regions

We review the anatomy of eloquent cortical brain regions. Eloquent cortical areas are areas of the cortex that if removed may result in loss of linguistic ability, motor function, or sensory perception. These areas commonly include the precentral gyrus (primary motor cortex), postcentral gyrus (primary sensory cortex), supplementary motor area (speech and motor function), the perisylvian area (language), medial occipital lobe (primary visual cortex), and medial temporal lobe (memory). The localization of function in certain anatomical cortical regions, such as Broca area, is variable among individuals and the surgeon depends upon cortical stimulation and cortical mapping to correlate function and anatomy with certainty. However, knowledge of the anatomy of the sulci and gyri of the brain is helpful in planning stimulation, tumor resection, understanding tumor extensions, and correlating the findings of the magnetic resonance imaging with the operative field. We review the anatomy of the sulci and gyri of the cerebrum and divide it into seven lobes: frontal, central (precentral, postcentral, and paracentral gyri), parietal, occipital, temporal, insular, and limbic.

2.1 Introduction

Eloquent cortical areas are areas of the cortex that if removed may result in loss of linguistic ability, motor function, or sensory perception. These areas commonly include the precentral gyrus (primary motor cortex), postcentral gyrus (primary sensory cortex), supplementary motor area (speech and motor function), perisylvian area (language), medial occipital lobe (primary visual cortex), and medial temporal lobe (memory). Eloquent cortical area will depend also on whether the area is in the dominant hemisphere, as in the case of speech areas. Although the whole cortex may be regarded as eloquent if we consider function, we use the term eloquent to distinguish specific areas of the brain that carry a higher risk of morbidity and disability in the postoperative period.

The localization of function and certain anatomical cortical regions, such as Broca area, is variable among individuals and the surgeon depends upon cortical stimulation and cortical mapping to correlate function and anatomy with certainty. 1 Localization of function cannot depend only on anatomical landmarks. However, knowledge of the anatomy of the sulci and gyri of the brain provides the surgeon with several key elements to plan procedures. 2 ,​ 3 ,​ 4 ,​ 5 First, understanding the relation of the tumors with the sulci and gyri is helpful in planning the craniotomy for tumor resection. 2 Second, tumor location and extensions are often correlated with the anatomy of the gyri, as tumors are often located in a specific gyrus or lobe, and tumors are known to extend depending on the cytoarchitecture of the area where they originated. 4 Examples are tumors extending in the limbic lobe and tumors commonly spreading from the opercula to the insula. Third, there is a relationship between brain structure and brain function that allows the surgeon to plan in advance which intraoperative monitoring may be necessary for specific brain regions. 1 ,​ 6 ,​ 7 ,​ 8

The cerebrum is commonly divided into five lobes: frontal, temporal, parietal, occipital, and insula. Yasargil 4 proposed a division into seven cerebral lobes: frontal, central (precentral, postcentral, and paracentral gyri), parietal, occipital, temporal, insular, and limbic. Yasargil’s division was a surgical conception of the cerebrum, taking in consideration function and the embryological aspects of cortical organization. We follow Yasargil’s division, since this separates the central lobe in a distinct lobe, highlighting its importance as the primary sensory–motor area. We discuss the anatomy of the opercula and the insula as we review the anatomy of the Sylvian fissure. We review the anatomy of the cortical arteries as they relate to the sulci and gyri.

Although there is great variation in the anatomy of the sulci and gyri among individuals, there is a common pattern in the organization of the sulci and gyri of the cerebrum that can be recognized and studied. 3 ,​ 4 ,​ 5 Only four sulci are consistently uninterrupted: the Sylvian fissure, the collateral sulcus, the callosal sulcus, and the parieto-occipital sulcus. The central sulcus and the calcarine sulcus are uninterrupted in 92% of the cases. 4 ,​ 5 Because most of the sulci are interrupted, the anatomical boundaries of the gyri are not always clearly demarcated. Often, we consider a gyrus as areas of the brain consisting of several gyri, as in the case of the paracentral and medial frontal gyri. One gyrus may be continuous in another surface of the hemisphere: the inferior temporal gyrus (both lateral and basal surface of the temporal lobe) with the parahippocampal gyrus (both medial and basal surface of the temporal lobe).

2.2 Central Lobe

The central lobe is formed by the precentral and postcentral gyri on the lateral surface and by the paracentral lobule on the medial surface of the hemisphere. 3 ,​ 4 ,​ 8

2.2.1 Lateral Surface

The central lobe on the lateral surface of the hemisphere includes the precentral and postcentral gyri, divided by the central sulcus (Fig. 2‑1). The central lobe is one of the most important eloquent area of the brain, as it corresponds to the primary motor (precentral gyrus) and sensory (postcentral gyrus) area of the cortex. The anterior and posterior limits of the central lobule are the precentral and postcentral sulci, respectively. The central sulcus originates at the medial hemisphere and runs on the lateral surface from a posterior to an anterior direction toward the Sylvian fissure (Fig. 2‑1). The central sulcus usually does not reach the Sylvian fissure and it is separated from the fissure by a continuation of the precentral gyrus with the postcentral gyrus, called subcentral gyrus. Parallel to the central sulcus there are two interrupted sulci, one anterior (the precentral sulcus) and another posterior (the postcentral sulcus). The central sulcus is usually continuous and has a sinusoidal shape with three curves (Fig. 2‑2). The first curve is near the midline and here the sulcus has its convexity facing anteriorly. Then it curves again, making the middle genu, with its convexity facing posteriorly. Finally, the third curve has its convexity facing anteriorly. The precentral gyrus has the shape of an inverted Greek letter omega ( ʊ ) at the level of the second curve of the central sulcus, where the convexity of the sulcus is facing posteriorly (Fig. 2‑2). The omega on the precentral gyrus is where the motor representation of the hand is located. 6 The omega is easily seen on the CT or magnetic resonance imaging (MRI) scans because deeply inside the central sulcus there are two parallel sulci that run toward the base of the central sulcus on the superior and inferior aspects of the omega, giving its shape even in deeper cuts 6 (Fig. 2‑2a, b). The omega is also called the central knob. Another important anatomical relationship in this area is that the posterior part of the superior frontal sulcus ends at the level of the omega. After its third curve, the central sulcus continues inferiorly toward the Sylvian fissure in a sinusoidal line. 9 ,​ 10 ,​ 11 The part of the precentral gyrus in front of the last segment of the central sulcus is where the motor representation of the tongue is usually located. 9 Also, characteristic is the bifurcation of the superior end of the postcentral sulcus with the marginal ramus of the cingulate gyrus located between this bifurcation 10 (Fig. 2‑2).

Fig. 2.1 Lateral surface of the cerebrum. (a) 1, Superior frontal sulcus. 2, Inferior frontal sulcus. 3a, Superior part of the precentral sulcus. 3b, Inferior part of the precentral sulcus. 4a, Superior curve of the central sulcus. 4b, Middle loop of the central sulcus. 4c, Inferior curve of the central sulcus. 4d, Inferior part of the central sulcus. 5a, Superior part of the postcentral sulcus. 5b, Inferior part of the postcentral sulcus. 6, Intraparietal sulcus. 7, Sylvian fissure. 8, Superior temporal sulcus. (b) 1, Superior frontal gyrus. 2, Middle frontal gyrus. 3, Inferior frontal gyrus. 4, Connection of the middle frontal gyrus with the precentral gyrus. 5, Precentral gyrus. 6, Postcentral gyrus. 7, Superior parietal lobule. 8, Supramarginal gyrus. 9, Angular gyrus. 10, Superior temporal gyrus. 11, Middle temporal gyrus. 12, Occipital lobe. (c) Inferior part of the postcentral sulcus. 1b, Superior part of the postcentral sulcus. 2, Superior end of the marginal ramus. 3a, Intraparietal sulcus. 3b, Intraoccipital sulcus. 4, Parieto-occipital sulcus. 5, Supramarginal gyrus around the posterior end of the Sylvian fissure. 6, Angular gyrus around the posterior end of the superior temporal sulcus. 7, Preoccipital notch. (d) 1, Pars orbitalis. 2, Horizontal ramus. 3, Pars triangularis. 4, Ascending ramus. 5, Pars opercularis. 6, Precentral sulcus. 7, Precentral gyrus. 8, Central sulcus. 9, Postcentral gyrus. 10, Postcentral sulcus. 11, Posterior ascending ramus of the Sylvian fissure. 12, Supramarginal gyrus. 13, Inferior descending ramus of the Sylvian fissure. 14, Superior temporal gyrus. Asp, anterior Sylvian point psp, posterior Sylvian point. Fig. 2.2 Closer view of the area around the knob of the central sulcus. (a) 1, Precentral sulcus. 2, Posterior end of the superior frontal sulcus. 3, Knob of the precentral gyrus. 4, Superior curve of the central sulcus. 5, Longitudinal sulci forming the omega inside the second curve of the central sulcus. 6, Second loop of the central sulcus. 7, Third curve of the central sulcus. 8, Postcentral gyrus. 9, Superior parietal lobule. 10, Intraparietal sulcus. (b) 1, Omega (Ω) of the precentral gyrus. (c) 1, Superior frontal sulcus. 2, Knob of the precentral gyrus. 3, Superior end of the postcentral sulcus bifurcating around the marginal ramus. 4, Marginal ramus of the cingulate sulcus. (d) 1, Knob of the precentral gyrus. 2, Superior loop of the central sulcus. 3, Superior part of the postcentral sulcus. 4, Marginal ramus.

2.2.2 Medial Surface

On the medial surface of the hemisphere the central lobule has a quadrangular shape and its gyri are called the paracentral gyrus or lobule (Fig. 2‑3). This quadrangular shape is given by the limits of the paracentral gyrus: the cingulate sulcus inferiorly, the paracentral sulcus or ramus anteriorly, and the marginal ramus posteriorly. The paracentral sulcus has an upward direction and it is a sulcus that originates from the cingulate sulcus at the level of the middle of the corpus callosum. The marginal ramus is the posterior part of the cingulate sulcus as it curves upward at the level of the splenium of the corpus callosum. The most posterior part of the marginal ramus near the lateral surface is located at the level of the postcentral gyrus. The marginal ramus can be identified in the MRI in the middle of the bifurcation of the postcentral sulcus. The paracentral gyrus includes the continuation of the precentral and postcentral gyri on the medial surface. The supplementary motor area is an area that does not have clear boundaries, but it includes the paracentral gyrus anterior to the precentral gyrus and the posterior part of the superior frontal gyrus. 12 Stimulation in this area may cause complex postural movement, arrest of movement, or speech arrest. The supplementary area syndrome consists of reversible contralateral weakness and mutism following resection of the dominant supplementary motor area. 12

Fig. 2.3 Medial surface of the cerebrum. (a) 1, Cingulate sulcus. 2, Cingulate gyrus. 3, Medial frontal gyrus. 4, Paracentral sulcus. 5, Paracentral lobule. 6, Central sulcus. 7, Marginal ramus of the cingulate sulcus. 8, Precuneus. 9, Body of the corpus callosum. 10, Anterior limiting sulcus of the insula. 11, Heschl gyrus at the posterior part of the insula near the posterior limb of the internal capsule. (b) 1, Knob of the precentral gyrus. 2, Postcentral gyrus. 3, Intraparietal sulcus. 4, Parieto-occipital sulcus. 5, Supramarginal gyrus. 6, Heschl gyrus. 7, Temporal plane. (c) 1, Rostrum of the corpus callosum. 2, Genu of the corpus callosum. 3, Cingulate gyrus. 4, Callosal sulcus. 5, Body of the corpus callosum. 6, Splenium. 7, Septum pellucidum. 8, Fornix. (d) 1, Cuneus. 2, Parieto-occipital sulcus. 3, Calcarine sulcus. 4, Lingual gyrus. 5, Isthmus of the cingulate gyrus. 6, P3 segment of the PCA. 7, Inferior temporal branches of the PCA. 8, P2P segment. 9, P2A segment at the level of the uncal sulcus.

2.3 Frontal Lobe

The frontal lobe includes the superior, middle, and inferior frontal gyri on the lateral surface the orbital and rectus gyrus on the inferior surface and the medial frontal gyrus on the medial surface of the hemisphere.

2.3.1 Lateral Surface

The frontal lobe on the lateral surface of the hemisphere is limited posteriorly by the precentral sulcus and inferiorly by the Sylvian fissure (Fig. 2‑1, Fig. 2‑2, Fig. 2‑3). The frontal lobe is divided by two longitudinal sulci, the superior and inferior frontal sulci, into three gyri, the superior, middle, and inferior frontal gyri. The superior and inferior sulci have an anterior to posterior direction and end at the precentral sulcus. The precentral sulcus is anterior and parallel to the central sulcus. The superior frontal sulcus has its posterior portion near the omega of the precentral gyrus. The superior frontal gyrus runs parallel to the midline, between the interhemispheric fissure and the superior frontal sulcus. The middle frontal gyrus is the most prominent of the frontal gyri, located between the superior frontal sulcus and the inferior frontal sulcus. There may be an intermediary sulcus inside the middle frontal gyrus that separates the middle frontal gyrus in two middle frontal gyri. The middle frontal gyrus is continuous with the precentral gyrus. This continuation interrupts the precentral sulcus in two portions, superior and inferior. The continuation of the middle frontal gyrus with the precentral gyrus is used as a landmark for reference in the MRI. 11 The inferior frontal gyrus is located between the inferior frontal sulcus and the Sylvian fissure. The horizontal and ascending rami of the Sylvian fissure give a characteristic shape to the inferior frontal gyrus, dividing it into three portions: pars orbitalis, pars triangularis, and pars opercularis. There may be a sulcus along the pars opercularis, the diagonal sulcus. When it is present, the diagonal sulcus is posterior and parallel to the ascending ramus. Broca speech area consists of pars triangularis and pars opercularis on the dominant hemisphere. 7

2.3.2 Medial Surface

The frontal lobe in the medial aspect of the hemisphere extends from the paracentral sulcus posteriorly until the cingulate sulcus inferiorly, forming the anterior surface of the hemisphere until the anterior cranial base. The frontal lobe on the medial aspect is called the medial frontal gyrus and it is a continuation of the superior frontal gyrus on the medial aspect of the hemisphere. Below and in front of the genu of the corpus callosum, the medial frontal gyrus has two small sulci on its surface: the superior and inferior rostral sulci.


References

Heitzeg MM, Cope LM, Martz ME, Hardee JE. Neuroimaging risk markers for substance abuse: recent findings on inhibitory control and reward system functioning. Curr Addict Rep. 20152:91–103.

Rubio G, Jiménez M, Rodríguez‐Jiménez R, Martínez I, Ávila C, Ferre F, et al. The role of behavioral impulsivity in the development of alcohol dependence: a 4‐year follow‐up study. Alcohol Clin Exp Res. 200832:1681–7.

Fernie G, Peeters M, Gullo MJ, Christiansen P, Cole JC, Sumnall H, et al. Multiple behavioural impulsivity tasks predict prospective alcohol involvement in adolescents. Addiction. 2013108:1916–23.

Whelan R, Watts R, Orr CA, Althoff RR, Artiges E, Banaschewski T, et al. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature. 2014512:185–9.

King AC, de Wit H, McNamara PJ, Cao D. Rewarding, stimulant, and sedative alcohol responses and relationship to future binge drinking. Arch Gen Psychiatry. 201168:389–99.

King AC, McNamara PJ, Hasin DS, Cao D. Alcohol challenge responses predict future alcohol use disorder symptoms: a 6-year prospective study. Biol Psychiatry. 201475:798–806.

King AC, Hasin D, O’Connor SJ, McNamara PJ, Cao D. A prospective 5-year re-examination of alcohol response in heavy drinkers progressing in alcohol use disorder. Biol Psychiatry. 201679:489–98.

King AC, Vena A, Hasin D, de Wit H, O’Connor SJ, Cao D Subjective responses to alcohol in the development and maintenance of AUD. Am J Psychiatry. In press.

Chutuape MA, De Wit H. Relationship between subjective effects and drug preferences: ethanol and diazepam. Drug Alcohol Depend. 199434:243–51.

Beckwith SW, Czachowski CL. Alcohol‐preferring P rats exhibit elevated motor impulsivity concomitant with operant responding and self‐administration of alcohol. Alcohol Clin Exp Res. 201640:1100–10.

Bowers BJ, Wehner JM. Ethanol consumption and behavioral impulsivity are increased in protein kinase Cγ null mutant mice. J Neurosci. 200121:RC180.

Logue SF, Swartz RJ, Wehner JM. Genetic correlation between performance on an appetitive‐signaled nosepoke task and voluntary ethanol consumption. Alcohol Clin Exp Res. 199822:1912–20.

Wilhelm CJ, Reeves JM, Phillips TJ, Mitchell SH. Mouse lines selected for alcohol consumption differ on certain measures of impulsivity. Alcohol Clin Exp Res. 200731:1839–45.

Weafer J, Phan KL, De Wit H. Poor inhibitory control is associated with greater stimulation and less sedation following alcohol. Psychopharmacology. 2020237:825–32.

Berey BL, Leeman RF, Pittman B, O’Malley SS. Relationships of impulsivity and subjective response to alcohol use and related problems. J Stud Alcohol Drugs. 201778:835–43.

Berey BL, Leeman RF, Chavarria J, King AC. Relationships between generalized impulsivity and subjective stimulant and sedative responses following alcohol administration. Psychol Addict Behav. 201933:616.

Leeman RF, Ralevski E, Limoncelli D, Pittman B, O’Malley SS, Petrakis IL. Relationships between impulsivity and subjective response in an IV ethanol paradigm. Psychopharmacology. 2014231:2867–76.

Boileau I, Assaad JM, Pihl RO, Benkelfat C, Leyton M, Diksic M, et al. Alcohol promotes dopamine release in the human nucleus accumbens. Synapse. 200349:226–31.

Brunelle C, Assaad JM, Barrett SP, Ávila C, Conrod PJ, Tremblay RE, et al. Heightened heart rate response to alcohol intoxication is associated with a reward‐seeking personality profile. Alcohol: Clin Exp Res. 200428:394–401.

Weafer J, de Wit H. Inattention, impulsive action, and subjective response to d-amphetamine. Drug Alcohol Depend. 2013133:127–33.

Weafer J, Gorka SM, Hedeker D, Dzemidzic M, Kareken DA, Phan KL, et al. Associations between behavioral and neural correlates of inhibitory control and amphetamine reward sensitivity. Neuropsychopharmacology 201742:1905–13.

Aron AR, Robbins TW, Poldrack RA. Inhibition and the right inferior frontal cortex. Trends Cogn Sci. 20048:170–7.

Bari A, Robbins TW. Inhibition and impulsivity: behavioral and neural basis of response control. Prog Neurobiol. 2013108:44–79.

Congdon E, Mumford JA, Cohen JR, Galvan A, Aron AR, Xue G, et al. Engagement of large-scale networks is related to individual differences in inhibitory control. Neuroimage. 201053:653–63.

Boehler CN, Appelbaum LG, Krebs RM, Hopf JM, Woldorff MG. Pinning down response inhibition in the brain—conjunction analyses of the stop-signal task. Neuroimage. 201052:1621–32.

Duann JR, Ide JS, Luo X, Li CS. Functional connectivity delineates distinct roles of the inferior frontal cortex and presupplementary motor area in stop signal inhibition. J Neurosci. 200929:10171–9.

Ghahremani DG, Lee B, Robertson CL, Tabibnia G, Morgan AT, De Shetler N, et al. Striatal dopamine D2/D3 receptors mediate response inhibition and related activity in frontostriatal neural circuitry in humans. J Neurosci. 201232:7316–24.

Gilman JM, Ramchandani VA, Crouss T, Hommer DW. Subjective and neural responses to intravenous alcohol in young adults with light and heavy drinking patterns. Neuropsychopharmacology. 201237:467–77.

Gilman JM, Ramchandani VA, Davis MB, Bjork JM, Hommer DW. Why we like to drink: a functional magnetic resonance imaging study of the rewarding and anxiolytic effects of alcohol. J Neurosci. 200828:4583–91.

Völlm BA, De Araujo IE, Cowen PJ, Rolls ET, Kringelbach ML, Smith KA, et al. Methamphetamine activates reward circuitry in drug naive human subjects. Neuropsychopharmacology. 200429:1715–22.

Weafer J, Ross TJ, O’Connor S, Stein EA, de Wit H, Childs E. Striatal activity correlates with stimulant-like effects of alcohol in healthy volunteers. Neuropsychopharmacology. 201843:2532–8.

Weafer J, Crane NA, Gorka SM, Phan KL, de Wit H. Neural correlates of inhibition and reward are negatively associated. Neuroimage. 2019196:188–94.

Kareken DA, Dzemidzic M, Wetherill L, Eiler W, Oberlin BG, Harezlak J, et al. Family history of alcoholism interacts with alcohol to affect brain regions involved in behavioral inhibition. Psychopharmacology. 2013228:335–45.

Rubia K, Smith AB, Brammer MJ, Taylor E. Right inferior prefrontal cortex mediates response inhibition while mesial prefrontal cortex is responsible for error detection. Neuroimage. 200320:351–8.

Martin CS, Earleywine M, Musty RE, Perrine MW, Swift RM. Development and validation of the biphasic alcohol effects scale. Alcohol Clin Exp Res. 199317:140–6.

Fillmore MT. Cognitive preoccupation with alcohol and binge drinking in college students: alcohol-induced priming of the motivation to drink. Psychol Addict Behav. 200115:325.

Mulvihill LE, Skilling TA, Vogel-Sprott M. Alcohol and the ability to inhibit behavior in men and women. J Stud Alcohol. 199758:600–5.

Weafer J, Gallo DA, de Wit H. Effect of alcohol on encoding and consolidation of memory for alcohol‐related images. Alcohol Clin Exp Res. 201640:1540–7.

Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 200217:825–41.

Smith SM. Fast robust automated brain extraction. Hum brain Mapp. 200217:143–55.

Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med image Anal. 20015:143–56.

Andersson JL, Jenkinson M, Smith S Non-linear registration, aka spatial normalisation. FMRIB Technical Report TR07JA2. FMRIB Analysis Group of the University of Oxford. 2007.

Beckman I, Richard D Rutgers University-Mason Gross School of the Arts. 2014

Pruim RH, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage. 2015112:267–77.

Logan GD, Schachar RJ, Tannock R. Impulsivity and inhibitory control. Psychological Sci. 19978:60–64.

Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 200215:273–89.

Oswald LM, Wong DF, McCaul M, Zhou Y, Kuwabara H, Choi L, et al. Relationships among ventral striatal dopamine release, cortisol secretion, and subjective responses to amphetamine. Neuropsychopharmacology. 200530:821–32.

Courtney KE, Ghahremani DG, Ray LA. Fronto‐striatal functional connectivity during response inhibition in alcohol dependence. Addict Biol. 201318:593–604.

Hedeker D, Gibbons RD Longitudinal data analysis. John Wiley & Sons, Hoboken, NJ 2006.

Radoman M, Crane NA, Gorka SM, Weafer J, Langenecker SA, de Wit H, et al. Striatal activation to monetary reward is associated with alcohol reward sensitivity. Neuropsychopharmacology. 202146:343–50.

Gu R, Huang W, Camilleri J, Xu P, Wei P, Eickhoff SB, et al. Love is analogous to money in human brain: coordinate-based and functional connectivity meta-analyses of social and monetary reward anticipation. Neurosci Biobehav Rev. 2019100:108–28.

Plichta MM, Wolf I, Hohmann S, Baumeister S, Boecker R, Schwarz AJ, et al. Simultaneous EEG and fMRI reveals a causally connected subcortical-cortical network during reward anticipation. J Neurosci. 201333:14526–33.

Koob GF, Volkow ND. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiat. 20163:760–73.

Paulus MP, Stewart JL. Interoception and drug addiction. Neuropharmacology. 201476:342–50.

Peters SK, Dunlop K, Downar J. Cortico-striatal-thalamic loop circuits of the salience network: a central pathway in psychiatric disease and treatment. Front Syst Neurosci. 201627:104. 10

Droutman V, Read SJ, Bechara A. Revisiting the role of the insula in addiction. Trends Cogn Sci. 201519:414–20.

Chang LJ, Yarkoni T, Khaw MW, Sanfey AG. Decoding the role of the insula in human cognition: functional parcellation and large-scale reverse inference. Cereb Cortex. 201323:739–49.

Craig AD. Significance of the insula for the evolution of human awareness of feelings from the body. Ann NY Acad Sci. 20111225:72–82.

Craig AD. How do you feel–now? The anterior insula and human awareness. Nat Rev Neurosci. 200910:59–70.

Critchley HD, Wiens S, Rotshtein P, Öhman A, Dolan RJ. Neural systems supporting interoceptive awareness. Nat Neurosci. 20047:189–95.

Naqvi NH, Bechara A. The insula and drug addiction: an interoceptive view of pleasure, urges, and decision-making. Brain Struct Funct. 2010214:435–50.

Zhu W, Volkow ND, Ma Y, Fowler JS, Wang GJ. Relationship between ethanol-induced changes in brain regional metabolism and its motor, behavioural and cognitive effects. Alcohol Alcohol. 200439:53–58.

Smith CT, Dang LC, Cowan RL, Kessler RM, Zald DH. Variability in paralimbic dopamine signaling correlates with subjective responses to d-amphetamine. Neuropharmacology. 2016108:394–402.

Scuppa G, Tambalo S, Pfarr S, Sommer WH, Bifone A. Aberrant insular cortex connectivity in abstinent alcohol‐dependent rats is reversed by dopamine D3 receptor blockade. Addiction Biol. 202025:e12744.

Haaranen M, Scuppa G, Tambalo S, Järvi V, Bertozzi SM, Armirotti A, et al. Anterior insula stimulation suppresses appetitive behavior while inducing forebrain activation in alcohol-preferring rats. Transl Psychiatry. 202010:1–11.

Casey KF, Cherkasova MV, Larcher K, Evans AC, Baker GB, Dagher A, et al. Individual differences in frontal cortical thickness correlate with the d-amphetamine-induced striatal dopamine response in humans. J Neurosci. 201333:15285–94.

Watanabe T, Hanajima R, Shirota Y, Tsutsumi R, Shimizu T, Hayashi T, et al. Effects of rTMS of pre-supplementary motor area on fronto basal ganglia network activity during stop-signal task. J Neurosci. 201535:4813–23.

Xu B, Sandrini M, Wang WT, Smith JF, Sarlls JE, Awosika O, et al. PreSMA stimulation changes task‐free functional connectivity in the fronto‐basal‐ganglia that correlates with response inhibition efficiency. Hum Brain Mapp. 201637:3236–49.

Schmaal L, Joos L, Koeleman M, Veltman DJ, van den Brink W, Goudriaan AE. Effects of modafinil on neural correlates of response inhibition in alcohol-dependent patients. Biol Psychiatry. 201373:211–8.

Murray CH, Weafer J, de Wit H. Stability of acute responses to drugs in humans across repeated testing: Findings with alcohol and amphetamine. Drug Alcohol Depend. 2020212:107989.

Gan G, Guevara A, Marxen M, Neumann M, Jünger E, Kobiella A, et al. Alcohol-induced impairment of inhibitory control is linked to attenuated brain responses in right fronto-temporal cortex. Biol Psychiatry. 201476:698–707.


Disrupting the experience of control in the human brain: pre-supplementary motor area contributes to the sense of agency

The feeling of controlling events through one's actions is fundamental to human experience, but its neural basis remains unclear. This ‘sense of agency’ (SoA) can be measured quantitatively as a temporal linkage between voluntary actions and their external effects. We investigated the brain areas underlying this aspect of action awareness by using theta-burst stimulation to locally and reversibly disrupt human brain function. Disruption of the pre-supplementary motor area (pre-SMA), a key structure for preparation and initiation of a voluntary action, was shown to reduce the temporal linkage between a voluntary key-press action and a subsequent electrocutaneous stimulus. In contrast, disruption of the sensorimotor cortex, which processes signals more directly related to action execution and sensory feedback, had no significant effect. Our results provide the first direct evidence of a pre-SMA contribution to SoA.

References

Akkal D., Dum R. P.& Strick P. L.

. 2007 Supplementary motor area and presupplementary motor area: targets of basal ganglia and cerebellar output . J. Neurosci. 27, 10 659–10 673. (doi:10.1523/JNEUROSCI.3134-07.2007). Crossref, ISI, Google Scholar

Bates J. F.& Goldman-Rakic P. S.

. 1993 Prefrontal connections of medial motor areas in the rhesus monkey . J. Comp. Neurol. 336, 211–228. (doi:10.1002/cne.903360205). Crossref, PubMed, ISI, Google Scholar

Bestmann S., Baudewig J., Siebner H. R., Rothwell J. C.& Frahm J.

. 2004 Functional MRI of the immediate impact of transcranial magnetic stimulation on cortical and subcortical motor circuits . Eur. J. Neurosci. 19, 1950–1962. (doi:10.1111/j.1460-9568.2004.03277.x). Crossref, PubMed, Google Scholar

Blakemore S. J., Wolpert D.& Frith C.

. 2000 Why can't you tickle yourself? Neuroreport 11, R11–R16. (doi:10.1097/00001756-200008030-00002). Crossref, PubMed, Google Scholar

Blakemore S., Wolpert D.& Frith C.

. 2002 Abnormalities in the awareness of action . Trends Cogn. Sci. 6, 237–242. (doi:10.1016/S1364-6613(02)01907-1). Crossref, PubMed, ISI, Google Scholar

. 2002 Leader or follower? Involvement of the inferior parietal lobule in agency . Neuroreport 13, 1975–1978. (doi:10.1097/00001756-200210280-00029). Crossref, PubMed, ISI, Google Scholar

Deiber M. P., Passingham R. E., Colebatch J. G., Friston K. J., Nixon P. D.& Frackowiak R. S.

. 1991 Cortical areas and the selection of movement: a study with positron emission tomography . Exp. Brain Res. 84, 393–402. Crossref, PubMed, Google Scholar

. 2010 Time warp: authorship shapes the perceived timing of actions and events . Conscious. Cogn. 19, 481–489. (doi:10.1016/j.concog.2009.10.002). Crossref, PubMed, Google Scholar

Engbert K., Wohlschläger A., Thomas R.& Haggard P.

. 2007 Agency, subjective time, and other minds . J. Exp. Psychol. Hum. Percept. Perform. 33, 1261–1268. (doi:10.1037/0096-1523.33.6.1261). Crossref, PubMed, Google Scholar

Engbert K., Wohlschläger A.& Haggard P.

. 2008 Who is causing what? The sense of agency is relational and efferent-triggered . Cognition 107, 693–704. (doi:10.1016/j.cognition.2007.07.021). Crossref, PubMed, Google Scholar

. 2002 Experiencing oneself vs another person as being the cause of an action: the neural correlates of the experience of agency . NeuroImage 15, 596–603. (doi:10.1006/nimg.2001.1009). Crossref, PubMed, ISI, Google Scholar

Farrer C., Frey S. H., Van Horn J. D., Tunik E., Turk D., Inati S.& Grafton S. T.

. 2008 The angular gyrus computes action awareness representations . Cereb. Cortex 18, 254–261. (doi:10.1093/cercor/bhm050). Crossref, PubMed, Google Scholar

Fried I., Katz A., McCarthy G., Sass K. J., Williamson P., Spencer S. S.& Spencer D. D.

. 1991 Functional organization of human supplementary motor cortex studied by electrical stimulation . J. Neurosci. 11, 3656–3666. Crossref, PubMed, Google Scholar

Frith C. D., Friston K., Liddle P. F.& Frackowiak R. S. J.

. 1991 Willed action and the prefrontal cortex in man: a study with PET . Proc. R. Soc. Lond. B 244, 241–246. (doi:10.1098/rspb.1991.0077). Link, ISI, Google Scholar

. 2004 Supplementary motor area provides an efferent signal for sensory suppression . Brain Res. 19, 52–58. (doi:10.1016/j.cogbrainres.2003.10.018). Google Scholar

Haggard P., Aschersleben G., Gehrke J.& Prinz W.

. 2002a Action, binding and awareness . Common mechanisms in perception and action . Attention and Performance XIX (eds

). Oxford, UK : Oxford University Press . Google Scholar

Haggard P., Clark S.& Kalogeras J.

. 2002b Voluntary action and conscious awareness . Nat. Neurosci. 5, 382–385. (doi:10.1038/nn827). Crossref, PubMed, ISI, Google Scholar

Haggard P., Cartledge P., Dafydd M.& Oakley D. A.

. 2004 Anomalous control: when ‘free-will' is not conscious . Conscious. Cogn. 13, 646–654. (doi:10.1016/j.concog.2004.06.001). Crossref, PubMed, Google Scholar

Hikosaka O., Sakai K., Miyauchi S., Takino R., Sasaki Y.& Pütz B.

. 1996 Activation of human presupplementary motor area in learning of sequential procedures: a functional MRI study . J. Neurophysiol. 76, 617–621. Crossref, PubMed, Google Scholar

. 2004 The effect of short-duration bursts of high-frequency, low-intensity transcranial magnetic stimulation on the human motor cortex . Clin. Neurophysiol. 115, 1069–1075. (doi:10.1016/j.clinph.2003.12.026). Crossref, PubMed, Google Scholar

Huang Y., Edwards M. J., Rounis E., Bhatia K. P.& Rothwell J. C.

. 2005 Theta burst stimulation of the human motor cortex . Neuron 45, 201–206. (doi:10.1016/j.neuron.2004.12.033). Crossref, PubMed, ISI, Google Scholar

Huang Y., Chen R., Rothwell J. C.& Wen H.

. 2007 The after-effect of human theta burst stimulation is NMDA receptor dependent . Clin. Neurophysiol. 118, 1028–1032. (doi:10.1016/j.clinph.2007.01.021). Crossref, PubMed, Google Scholar

. 1739 A treatise of human nature . Oxford, UK : Oxford University Press . Google Scholar

1999 Cognitive motor control in human pre-supplementary motor area studied by subdural recording of discrimination/selection-related potentials . Brain 122, 915–931. Crossref, PubMed, Google Scholar

Inase M., Tokuno H., Nambu A., Akazawa T.& Takada M.

. 1999 Corticostriatal and corticosubthalamic input zones from the presupplementary motor area in the macaque monkey: comparison with the input zones from the supplementary motor area . Brain Res. 833, 191–201. (doi:10.1016/S0006-8993(99)01531-0). Crossref, PubMed, Google Scholar

. 1890 The principles of psychology . New York, NY : Henry Holt . Google Scholar

Johansen-Berg H., Behrens T. E. J., Robson M. D., Drobnjak I., Rushworth M. F. S., Brady J. M., Smith S. M., Higham D. J.& Matthews P. M.

. 2004 Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex . Proc. Natl Acad. Sci. USA 101, 13 335–13 340. (doi:10.1073/pnas.0403743101). Crossref, Google Scholar

Lau H. C., Rogers R. D., Haggard P.& Passingham R. E.

. 2004 Attention to intention . Science 303, 1208–1210. (doi:10.1126/science.1090973). Crossref, PubMed, Google Scholar

Lehéricy S., Ducros M., Krainik A., Francois C., Van de Moortele P., Ugurbil K.& Kim D.-S.

. 2004 3-D diffusion tensor axonal tracking shows distinct SMA and pre-SMA projections to the human striatum . Cereb. Cortex 14, 1302–1309. (doi:10.1093/cercor/bhh091). Crossref, PubMed, Google Scholar

. 1971 Transformed up-down methods in psychoacoustics . J. Acoust. Soc. Am. 49((Suppl. 2)), 467–477. (doi:10.1121/1.1912375). Crossref, Google Scholar

Luppino G., Matelli M., Camarda R.& Rizzolatti G.

. 1993 Corticocortical connections of area F3 (SMA-proper) and area F6 (pre-SMA) in the macaque monkey . J. Comp. Neurol. 338, 114–140. (doi:10.1002/cne.903380109). Crossref, PubMed, ISI, Google Scholar

Mars R. B., Klein M. C., Neubert F., Olivier E., Buch E. R., Boorman E. D.& Rushworth M. F. S.

. 2009 Short-latency influence of medial frontal cortex on primary motor cortex during action selection under conflict . J. Neurosci. 29, 6926–6931. (doi:10.1523/JNEUROSCI.1396-09.2009). Crossref, PubMed, Google Scholar

. 2008 Awareness of action: inference and prediction . Conscious. Cogn. 17, 136–144. (doi:10.1016/j.concog.2006.12.004). Crossref, PubMed, Google Scholar

Moore J. W., Lagnado D., Deal D. C.& Haggard P.

. 2009a Feelings of control: contingency determines experience of action . Cognition 110, 279–283. (doi:10.1016/j.cognition.2008.11.006). Crossref, PubMed, Google Scholar

Moore J. W., Wegner D. M.& Haggard P.

. 2009b Modulating the sense of agency with external cues . Conscious. Cogn. 18, 1056–1064. (doi:10.1016/j.concog.2009.05.004). Crossref, PubMed, Google Scholar

Moore J. W., Schneider S. A., Schwingenschuh P., Moretto G., Bhatia K. P.& Haggard P.

. 2010 Dopaminergic medication boosts action-effect binding in Parkinson's disease . Neuropsychologia 48, 1125–1132. (doi:10.1016/j.neuropsychologia.2009.12.014). Crossref, PubMed, Google Scholar

Nachev P., Wydell H., O'neill K., Husain M.& Kennard C.

. 2007 The role of the pre-supplementary motor area in the control of action . NeuroImage 36((Suppl. 2)), T155–T163. (doi:10.1016/j.neuroimage.2007.03.034). Crossref, PubMed, Google Scholar

. 1996 Motor areas of the medial wall: a review of their location and functional activation . Cereb. Cortex 6, 342–353. Crossref, PubMed, ISI, Google Scholar

. 2001 Imaging the premotor areas . Curr. Opin. Neurobiol. 11, 663–672. (doi:10.1016/S0959-4388(01)00266-5). Crossref, PubMed, ISI, Google Scholar

1994 Non-invasive electrical and magnetic stimulation of the brain, spinal cord and roots: basic principles and procedures for routine clinical application. Report of an IFCN committee . Eletroencephalogr. Clin. Neurophysiol. 91, 79–92. (doi:10.1016/0013-4694(94)90029-9). Crossref, PubMed, Google Scholar

Rowe J. B., Toni I., Josephs O., Frackowiak R. S.& Passingham R. E.

. 2000 The prefrontal cortex: response selection or maintenance within working memory? Science 288, 1656–1660. (doi:10.1126/science.288.5471.1656). Crossref, PubMed, Google Scholar

Rushworth M., Hadland K. A., Paus T.& Sipila P. K.

. 2002 Role of the human medial frontal cortex in task switching: a combined fMRI and TMS study . J. Neurophysiol. 87, 2577–2592. Crossref, PubMed, Google Scholar

Sirigu A., Daprati E., Pradat-Diehl P., Franck N.& Jeannerod M.

. 1999 Perception of self-generated movement following left parietal lesion . Brain 122, 1867–1874. Crossref, PubMed, Google Scholar

Stefan K., Wycislo M., Gentner R., Schramm A., Naumann M., Reiners K.& Classen J.

. 2006 Temporary occlusion of associative motor cortical plasticity by prior dynamic motor training . Cereb. Cortex 16, 376–385. (doi:10.1093/cercor/bhi116). Crossref, PubMed, Google Scholar

Stetson C., Cui X., Montague P. R.& Eagleman D. M.

. 2006 Motor-sensory recalibration leads to an illusory reversal of action and sensation . Neuron 51, 651–659. (doi:10.1016/j.neuron.2006.08.006). Crossref, PubMed, Google Scholar

Synofzik M., Vosgerau G.& Newen A.

. 2008 Beyond the comparator model: a multifactorial two-step account of agency . Conscious. Cogn. 17, 219–239. (doi:10.1016/j.concog.2007.03.010). Crossref, PubMed, Google Scholar

. 2003 Awareness of somatic events associated with a voluntary action . Exp. Brain Res. 149, 439–446. (doi:10.1007/s00221-003-1386-8). Crossref, PubMed, Google Scholar

. 2002 The illusion of conscious will (illustrated edition). Cambridge, MA : MIT Press . Google Scholar

Weiller C., Jüptner M., Fellows S., Rijntjes M., Leonhardt G., Kiebel S., Müller S., Diener H. C.& Thilmann A. F.

. 1996 Brain representation of active and passive movements . NeuroImage 4, 105–110. (doi:10.1006/nimg.1996.0034). Crossref, PubMed, Google Scholar

Wolters A., Sandbrink F., Schlottmann A., Kunesch E., Stefan K., Cohen L. G., Benecke R.& Classen J.

. 2003 A temporally asymmetric Hebbian rule governing plasticity in the human motor cortex . J. Neurophysiol. 89, 2339–2345. (doi:10.1152/jn.00900.2002). Crossref, PubMed, Google Scholar


1. Introduction

The ability to learn from performance feedback is crucial to flexibly adapt to a changing environment. Behavioral performance during feedback learning shows a protracted development which continues into adolescence (Huizinga et al., 2006). Several studies have investigated the neural underpinnings of feedback processing. Studies in adults have shown that learning from feedback is associated with activity in a frontoparietal network, including dorsolateral prefrontal cortex (DLPFC), supplementary motor area (SMA), anterior cingulate cortex (ACC) and superior parietal cortex (SPC) (Carter and van Veen, 2007, Mars et al., 2005, Zanolie et al., 2008). Intriguingly, developmental neuroimaging studies have reported age-related activity changes in this network during feedback processing, suggesting an important link between feedback learning and neural maturation of the frontoparietal network (Crone et al., 2008, Peters et al., 2014a, Van Duijvenvoorde et al., 2008, Velanova et al., 2008). Despite these findings, little is known about developmental trajectories in the frontoparietal network and there is surprising little consistency in the direction of change, with some studies reporting increased neural activation with age and others decreased neural activation with age (Crone and Dahl, 2012).

An important question in cognitive development concerns the shape of developmental trajectories. One possible hypothesis would be that activity in the frontoparietal network during feedback learning follows a linear trajectory, based on dual-systems models predicting steadily increasing frontoparietal recruitment from childhood to adulthood combined with an adolescent peak in socio-emotional sensitivity in subcortical systems (Ernst et al., 2006, Somerville and Casey, 2010, Steinberg, 2008). On the other hand, prior cross-sectional studies provided preliminary evidence for non-linear developmental patterns of frontoparietal activity during feedback learning (Peters et al., 2014a, Van den Bos et al., 2009, Van Duijvenvoorde et al., 2008). These findings indicated that young adolescents are capable of recruiting frontoparietal regions but in different situations than adults, arguing against a simple frontoparietal immaturity model with linear development in cognitive control regions.

Several recent neuroimaging studies have used longitudinal measurements of neural activity to test for neurocognitive changes over development (Ordaz et al., 2013, Paulsen et al., 2015). Longitudinal designs have critical advantages over cross-sectional designs. For instance, previous studies demonstrated important individual differences in developmental trajectories that can be overlooked in cross-sectional designs (Koolschijn et al., 2011, Ordaz et al., 2013, Shaw et al., 2013). Furthermore, longitudinal designs have increased power to detect developmental change, because testing within-individual changes reduces error related to cohort differences (Fjell et al., 2010, Koolschijn et al., 2011). In the current study, neural changes in frontoparietal cortex activity were examined by testing whether frontoparietal activity during feedback learning follows a linear pattern (i.e. monotonic development over time, no adolescent-specific changes), a quadratic pattern (i.e., adolescent-specific effects) or a cubic pattern (adolescent-emergent e.g. stable levels during childhood, steep changes in adolescence and stabilization in adulthood) (Braams et al., 2015, Somerville et al., 2013). Our longitudinal approach allows for a more specific test of the different hypotheses concerning the pattern of developmental change in frontoparietal areas.

Besides investigating age-related patterns of neural activity, a second goal of this study was to investigate other factors influencing time-related changes in frontoparietal activity in addition to age. There are multiple processes closely related to advancing age that may drive changes in neural activity. That is, an increase in age could be the sole factor explaining time-related increases or decreases in activity, but other factors might also play a role. The factors investigated in this study were task performance, working memory and structural brain development. Task performance has been shown to influence neural activity, and there is evidence that a portion of developmental changes attributed to advancing age are related more to changes in performance (Church et al., 2010, Dumontheil et al., 2010, Koolschijn et al., 2011). Here we tested whether performance on a feedback learning task partly explained changes in neural activation over time. Working memory has previously been argued to be a core prerequisite for cognitive development (Case, 1992) and cognitive control functions (Huizinga et al., 2006), and as such was investigated as an important contributor to changes over time in neural activity during feedback learning. That is, we aimed to study whether a portion of changes in neural activity during feedback learning was explained by individual differences in working memory. A final factor that was investigated is cortical thickness. Several cross-sectional studies have suggested a link between functional activity and structural gray matter in adults (Harms et al., 2013, Hegarty et al., 2012) and children (Dumontheil et al., 2010, Lu et al., 2009, Wendelken et al., 2011). It is likely that developmental changes in neural activity are at least partly influenced by structural development of these brain regions, although the longitudinal relation between structural maturation and development of brain function is not well understood.

Taken together, in this study, we tested developmental trajectories of activation in the frontoparietal network during feedback learning in a large longitudinal fMRI sample across a wide age range (N =򠈈, 8� years) with a two year interval between the first and second time point (see Peters et al., 2014a, Peters et al., 2014b). Our aims were (1) to examine growth trajectories of core areas in the frontoparietal network (DLPFC, SMA, ACC and SPC) and to define the shape of age-related changes, (2) to test the additional contributions of task performance, working memory and structural development to changes over time in neural activity for feedback learning.


References

Heitzeg MM, Cope LM, Martz ME, Hardee JE. Neuroimaging risk markers for substance abuse: recent findings on inhibitory control and reward system functioning. Curr Addict Rep. 20152:91–103.

Rubio G, Jiménez M, Rodríguez‐Jiménez R, Martínez I, Ávila C, Ferre F, et al. The role of behavioral impulsivity in the development of alcohol dependence: a 4‐year follow‐up study. Alcohol Clin Exp Res. 200832:1681–7.

Fernie G, Peeters M, Gullo MJ, Christiansen P, Cole JC, Sumnall H, et al. Multiple behavioural impulsivity tasks predict prospective alcohol involvement in adolescents. Addiction. 2013108:1916–23.

Whelan R, Watts R, Orr CA, Althoff RR, Artiges E, Banaschewski T, et al. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature. 2014512:185–9.

King AC, de Wit H, McNamara PJ, Cao D. Rewarding, stimulant, and sedative alcohol responses and relationship to future binge drinking. Arch Gen Psychiatry. 201168:389–99.

King AC, McNamara PJ, Hasin DS, Cao D. Alcohol challenge responses predict future alcohol use disorder symptoms: a 6-year prospective study. Biol Psychiatry. 201475:798–806.

King AC, Hasin D, O’Connor SJ, McNamara PJ, Cao D. A prospective 5-year re-examination of alcohol response in heavy drinkers progressing in alcohol use disorder. Biol Psychiatry. 201679:489–98.

King AC, Vena A, Hasin D, de Wit H, O’Connor SJ, Cao D Subjective responses to alcohol in the development and maintenance of AUD. Am J Psychiatry. In press.

Chutuape MA, De Wit H. Relationship between subjective effects and drug preferences: ethanol and diazepam. Drug Alcohol Depend. 199434:243–51.

Beckwith SW, Czachowski CL. Alcohol‐preferring P rats exhibit elevated motor impulsivity concomitant with operant responding and self‐administration of alcohol. Alcohol Clin Exp Res. 201640:1100–10.

Bowers BJ, Wehner JM. Ethanol consumption and behavioral impulsivity are increased in protein kinase Cγ null mutant mice. J Neurosci. 200121:RC180.

Logue SF, Swartz RJ, Wehner JM. Genetic correlation between performance on an appetitive‐signaled nosepoke task and voluntary ethanol consumption. Alcohol Clin Exp Res. 199822:1912–20.

Wilhelm CJ, Reeves JM, Phillips TJ, Mitchell SH. Mouse lines selected for alcohol consumption differ on certain measures of impulsivity. Alcohol Clin Exp Res. 200731:1839–45.

Weafer J, Phan KL, De Wit H. Poor inhibitory control is associated with greater stimulation and less sedation following alcohol. Psychopharmacology. 2020237:825–32.

Berey BL, Leeman RF, Pittman B, O’Malley SS. Relationships of impulsivity and subjective response to alcohol use and related problems. J Stud Alcohol Drugs. 201778:835–43.

Berey BL, Leeman RF, Chavarria J, King AC. Relationships between generalized impulsivity and subjective stimulant and sedative responses following alcohol administration. Psychol Addict Behav. 201933:616.

Leeman RF, Ralevski E, Limoncelli D, Pittman B, O’Malley SS, Petrakis IL. Relationships between impulsivity and subjective response in an IV ethanol paradigm. Psychopharmacology. 2014231:2867–76.

Boileau I, Assaad JM, Pihl RO, Benkelfat C, Leyton M, Diksic M, et al. Alcohol promotes dopamine release in the human nucleus accumbens. Synapse. 200349:226–31.

Brunelle C, Assaad JM, Barrett SP, Ávila C, Conrod PJ, Tremblay RE, et al. Heightened heart rate response to alcohol intoxication is associated with a reward‐seeking personality profile. Alcohol: Clin Exp Res. 200428:394–401.

Weafer J, de Wit H. Inattention, impulsive action, and subjective response to d-amphetamine. Drug Alcohol Depend. 2013133:127–33.

Weafer J, Gorka SM, Hedeker D, Dzemidzic M, Kareken DA, Phan KL, et al. Associations between behavioral and neural correlates of inhibitory control and amphetamine reward sensitivity. Neuropsychopharmacology 201742:1905–13.

Aron AR, Robbins TW, Poldrack RA. Inhibition and the right inferior frontal cortex. Trends Cogn Sci. 20048:170–7.

Bari A, Robbins TW. Inhibition and impulsivity: behavioral and neural basis of response control. Prog Neurobiol. 2013108:44–79.

Congdon E, Mumford JA, Cohen JR, Galvan A, Aron AR, Xue G, et al. Engagement of large-scale networks is related to individual differences in inhibitory control. Neuroimage. 201053:653–63.

Boehler CN, Appelbaum LG, Krebs RM, Hopf JM, Woldorff MG. Pinning down response inhibition in the brain—conjunction analyses of the stop-signal task. Neuroimage. 201052:1621–32.

Duann JR, Ide JS, Luo X, Li CS. Functional connectivity delineates distinct roles of the inferior frontal cortex and presupplementary motor area in stop signal inhibition. J Neurosci. 200929:10171–9.

Ghahremani DG, Lee B, Robertson CL, Tabibnia G, Morgan AT, De Shetler N, et al. Striatal dopamine D2/D3 receptors mediate response inhibition and related activity in frontostriatal neural circuitry in humans. J Neurosci. 201232:7316–24.

Gilman JM, Ramchandani VA, Crouss T, Hommer DW. Subjective and neural responses to intravenous alcohol in young adults with light and heavy drinking patterns. Neuropsychopharmacology. 201237:467–77.

Gilman JM, Ramchandani VA, Davis MB, Bjork JM, Hommer DW. Why we like to drink: a functional magnetic resonance imaging study of the rewarding and anxiolytic effects of alcohol. J Neurosci. 200828:4583–91.

Völlm BA, De Araujo IE, Cowen PJ, Rolls ET, Kringelbach ML, Smith KA, et al. Methamphetamine activates reward circuitry in drug naive human subjects. Neuropsychopharmacology. 200429:1715–22.

Weafer J, Ross TJ, O’Connor S, Stein EA, de Wit H, Childs E. Striatal activity correlates with stimulant-like effects of alcohol in healthy volunteers. Neuropsychopharmacology. 201843:2532–8.

Weafer J, Crane NA, Gorka SM, Phan KL, de Wit H. Neural correlates of inhibition and reward are negatively associated. Neuroimage. 2019196:188–94.

Kareken DA, Dzemidzic M, Wetherill L, Eiler W, Oberlin BG, Harezlak J, et al. Family history of alcoholism interacts with alcohol to affect brain regions involved in behavioral inhibition. Psychopharmacology. 2013228:335–45.

Rubia K, Smith AB, Brammer MJ, Taylor E. Right inferior prefrontal cortex mediates response inhibition while mesial prefrontal cortex is responsible for error detection. Neuroimage. 200320:351–8.

Martin CS, Earleywine M, Musty RE, Perrine MW, Swift RM. Development and validation of the biphasic alcohol effects scale. Alcohol Clin Exp Res. 199317:140–6.

Fillmore MT. Cognitive preoccupation with alcohol and binge drinking in college students: alcohol-induced priming of the motivation to drink. Psychol Addict Behav. 200115:325.

Mulvihill LE, Skilling TA, Vogel-Sprott M. Alcohol and the ability to inhibit behavior in men and women. J Stud Alcohol. 199758:600–5.

Weafer J, Gallo DA, de Wit H. Effect of alcohol on encoding and consolidation of memory for alcohol‐related images. Alcohol Clin Exp Res. 201640:1540–7.

Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 200217:825–41.

Smith SM. Fast robust automated brain extraction. Hum brain Mapp. 200217:143–55.

Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med image Anal. 20015:143–56.

Andersson JL, Jenkinson M, Smith S Non-linear registration, aka spatial normalisation. FMRIB Technical Report TR07JA2. FMRIB Analysis Group of the University of Oxford. 2007.

Beckman I, Richard D Rutgers University-Mason Gross School of the Arts. 2014

Pruim RH, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage. 2015112:267–77.

Logan GD, Schachar RJ, Tannock R. Impulsivity and inhibitory control. Psychological Sci. 19978:60–64.

Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 200215:273–89.

Oswald LM, Wong DF, McCaul M, Zhou Y, Kuwabara H, Choi L, et al. Relationships among ventral striatal dopamine release, cortisol secretion, and subjective responses to amphetamine. Neuropsychopharmacology. 200530:821–32.

Courtney KE, Ghahremani DG, Ray LA. Fronto‐striatal functional connectivity during response inhibition in alcohol dependence. Addict Biol. 201318:593–604.

Hedeker D, Gibbons RD Longitudinal data analysis. John Wiley & Sons, Hoboken, NJ 2006.

Radoman M, Crane NA, Gorka SM, Weafer J, Langenecker SA, de Wit H, et al. Striatal activation to monetary reward is associated with alcohol reward sensitivity. Neuropsychopharmacology. 202146:343–50.

Gu R, Huang W, Camilleri J, Xu P, Wei P, Eickhoff SB, et al. Love is analogous to money in human brain: coordinate-based and functional connectivity meta-analyses of social and monetary reward anticipation. Neurosci Biobehav Rev. 2019100:108–28.

Plichta MM, Wolf I, Hohmann S, Baumeister S, Boecker R, Schwarz AJ, et al. Simultaneous EEG and fMRI reveals a causally connected subcortical-cortical network during reward anticipation. J Neurosci. 201333:14526–33.

Koob GF, Volkow ND. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiat. 20163:760–73.

Paulus MP, Stewart JL. Interoception and drug addiction. Neuropharmacology. 201476:342–50.

Peters SK, Dunlop K, Downar J. Cortico-striatal-thalamic loop circuits of the salience network: a central pathway in psychiatric disease and treatment. Front Syst Neurosci. 201627:104. 10

Droutman V, Read SJ, Bechara A. Revisiting the role of the insula in addiction. Trends Cogn Sci. 201519:414–20.

Chang LJ, Yarkoni T, Khaw MW, Sanfey AG. Decoding the role of the insula in human cognition: functional parcellation and large-scale reverse inference. Cereb Cortex. 201323:739–49.

Craig AD. Significance of the insula for the evolution of human awareness of feelings from the body. Ann NY Acad Sci. 20111225:72–82.

Craig AD. How do you feel–now? The anterior insula and human awareness. Nat Rev Neurosci. 200910:59–70.

Critchley HD, Wiens S, Rotshtein P, Öhman A, Dolan RJ. Neural systems supporting interoceptive awareness. Nat Neurosci. 20047:189–95.

Naqvi NH, Bechara A. The insula and drug addiction: an interoceptive view of pleasure, urges, and decision-making. Brain Struct Funct. 2010214:435–50.

Zhu W, Volkow ND, Ma Y, Fowler JS, Wang GJ. Relationship between ethanol-induced changes in brain regional metabolism and its motor, behavioural and cognitive effects. Alcohol Alcohol. 200439:53–58.

Smith CT, Dang LC, Cowan RL, Kessler RM, Zald DH. Variability in paralimbic dopamine signaling correlates with subjective responses to d-amphetamine. Neuropharmacology. 2016108:394–402.

Scuppa G, Tambalo S, Pfarr S, Sommer WH, Bifone A. Aberrant insular cortex connectivity in abstinent alcohol‐dependent rats is reversed by dopamine D3 receptor blockade. Addiction Biol. 202025:e12744.

Haaranen M, Scuppa G, Tambalo S, Järvi V, Bertozzi SM, Armirotti A, et al. Anterior insula stimulation suppresses appetitive behavior while inducing forebrain activation in alcohol-preferring rats. Transl Psychiatry. 202010:1–11.

Casey KF, Cherkasova MV, Larcher K, Evans AC, Baker GB, Dagher A, et al. Individual differences in frontal cortical thickness correlate with the d-amphetamine-induced striatal dopamine response in humans. J Neurosci. 201333:15285–94.

Watanabe T, Hanajima R, Shirota Y, Tsutsumi R, Shimizu T, Hayashi T, et al. Effects of rTMS of pre-supplementary motor area on fronto basal ganglia network activity during stop-signal task. J Neurosci. 201535:4813–23.

Xu B, Sandrini M, Wang WT, Smith JF, Sarlls JE, Awosika O, et al. PreSMA stimulation changes task‐free functional connectivity in the fronto‐basal‐ganglia that correlates with response inhibition efficiency. Hum Brain Mapp. 201637:3236–49.

Schmaal L, Joos L, Koeleman M, Veltman DJ, van den Brink W, Goudriaan AE. Effects of modafinil on neural correlates of response inhibition in alcohol-dependent patients. Biol Psychiatry. 201373:211–8.

Murray CH, Weafer J, de Wit H. Stability of acute responses to drugs in humans across repeated testing: Findings with alcohol and amphetamine. Drug Alcohol Depend. 2020212:107989.

Gan G, Guevara A, Marxen M, Neumann M, Jünger E, Kobiella A, et al. Alcohol-induced impairment of inhibitory control is linked to attenuated brain responses in right fronto-temporal cortex. Biol Psychiatry. 201476:698–707.


2 Anatomy of Eloquent Cortical Brain Regions

We review the anatomy of eloquent cortical brain regions. Eloquent cortical areas are areas of the cortex that if removed may result in loss of linguistic ability, motor function, or sensory perception. These areas commonly include the precentral gyrus (primary motor cortex), postcentral gyrus (primary sensory cortex), supplementary motor area (speech and motor function), the perisylvian area (language), medial occipital lobe (primary visual cortex), and medial temporal lobe (memory). The localization of function in certain anatomical cortical regions, such as Broca area, is variable among individuals and the surgeon depends upon cortical stimulation and cortical mapping to correlate function and anatomy with certainty. However, knowledge of the anatomy of the sulci and gyri of the brain is helpful in planning stimulation, tumor resection, understanding tumor extensions, and correlating the findings of the magnetic resonance imaging with the operative field. We review the anatomy of the sulci and gyri of the cerebrum and divide it into seven lobes: frontal, central (precentral, postcentral, and paracentral gyri), parietal, occipital, temporal, insular, and limbic.

2.1 Introduction

Eloquent cortical areas are areas of the cortex that if removed may result in loss of linguistic ability, motor function, or sensory perception. These areas commonly include the precentral gyrus (primary motor cortex), postcentral gyrus (primary sensory cortex), supplementary motor area (speech and motor function), perisylvian area (language), medial occipital lobe (primary visual cortex), and medial temporal lobe (memory). Eloquent cortical area will depend also on whether the area is in the dominant hemisphere, as in the case of speech areas. Although the whole cortex may be regarded as eloquent if we consider function, we use the term eloquent to distinguish specific areas of the brain that carry a higher risk of morbidity and disability in the postoperative period.

The localization of function and certain anatomical cortical regions, such as Broca area, is variable among individuals and the surgeon depends upon cortical stimulation and cortical mapping to correlate function and anatomy with certainty. 1 Localization of function cannot depend only on anatomical landmarks. However, knowledge of the anatomy of the sulci and gyri of the brain provides the surgeon with several key elements to plan procedures. 2 ,​ 3 ,​ 4 ,​ 5 First, understanding the relation of the tumors with the sulci and gyri is helpful in planning the craniotomy for tumor resection. 2 Second, tumor location and extensions are often correlated with the anatomy of the gyri, as tumors are often located in a specific gyrus or lobe, and tumors are known to extend depending on the cytoarchitecture of the area where they originated. 4 Examples are tumors extending in the limbic lobe and tumors commonly spreading from the opercula to the insula. Third, there is a relationship between brain structure and brain function that allows the surgeon to plan in advance which intraoperative monitoring may be necessary for specific brain regions. 1 ,​ 6 ,​ 7 ,​ 8

The cerebrum is commonly divided into five lobes: frontal, temporal, parietal, occipital, and insula. Yasargil 4 proposed a division into seven cerebral lobes: frontal, central (precentral, postcentral, and paracentral gyri), parietal, occipital, temporal, insular, and limbic. Yasargil’s division was a surgical conception of the cerebrum, taking in consideration function and the embryological aspects of cortical organization. We follow Yasargil’s division, since this separates the central lobe in a distinct lobe, highlighting its importance as the primary sensory–motor area. We discuss the anatomy of the opercula and the insula as we review the anatomy of the Sylvian fissure. We review the anatomy of the cortical arteries as they relate to the sulci and gyri.

Although there is great variation in the anatomy of the sulci and gyri among individuals, there is a common pattern in the organization of the sulci and gyri of the cerebrum that can be recognized and studied. 3 ,​ 4 ,​ 5 Only four sulci are consistently uninterrupted: the Sylvian fissure, the collateral sulcus, the callosal sulcus, and the parieto-occipital sulcus. The central sulcus and the calcarine sulcus are uninterrupted in 92% of the cases. 4 ,​ 5 Because most of the sulci are interrupted, the anatomical boundaries of the gyri are not always clearly demarcated. Often, we consider a gyrus as areas of the brain consisting of several gyri, as in the case of the paracentral and medial frontal gyri. One gyrus may be continuous in another surface of the hemisphere: the inferior temporal gyrus (both lateral and basal surface of the temporal lobe) with the parahippocampal gyrus (both medial and basal surface of the temporal lobe).

2.2 Central Lobe

The central lobe is formed by the precentral and postcentral gyri on the lateral surface and by the paracentral lobule on the medial surface of the hemisphere. 3 ,​ 4 ,​ 8

2.2.1 Lateral Surface

The central lobe on the lateral surface of the hemisphere includes the precentral and postcentral gyri, divided by the central sulcus (Fig. 2‑1). The central lobe is one of the most important eloquent area of the brain, as it corresponds to the primary motor (precentral gyrus) and sensory (postcentral gyrus) area of the cortex. The anterior and posterior limits of the central lobule are the precentral and postcentral sulci, respectively. The central sulcus originates at the medial hemisphere and runs on the lateral surface from a posterior to an anterior direction toward the Sylvian fissure (Fig. 2‑1). The central sulcus usually does not reach the Sylvian fissure and it is separated from the fissure by a continuation of the precentral gyrus with the postcentral gyrus, called subcentral gyrus. Parallel to the central sulcus there are two interrupted sulci, one anterior (the precentral sulcus) and another posterior (the postcentral sulcus). The central sulcus is usually continuous and has a sinusoidal shape with three curves (Fig. 2‑2). The first curve is near the midline and here the sulcus has its convexity facing anteriorly. Then it curves again, making the middle genu, with its convexity facing posteriorly. Finally, the third curve has its convexity facing anteriorly. The precentral gyrus has the shape of an inverted Greek letter omega ( ʊ ) at the level of the second curve of the central sulcus, where the convexity of the sulcus is facing posteriorly (Fig. 2‑2). The omega on the precentral gyrus is where the motor representation of the hand is located. 6 The omega is easily seen on the CT or magnetic resonance imaging (MRI) scans because deeply inside the central sulcus there are two parallel sulci that run toward the base of the central sulcus on the superior and inferior aspects of the omega, giving its shape even in deeper cuts 6 (Fig. 2‑2a, b). The omega is also called the central knob. Another important anatomical relationship in this area is that the posterior part of the superior frontal sulcus ends at the level of the omega. After its third curve, the central sulcus continues inferiorly toward the Sylvian fissure in a sinusoidal line. 9 ,​ 10 ,​ 11 The part of the precentral gyrus in front of the last segment of the central sulcus is where the motor representation of the tongue is usually located. 9 Also, characteristic is the bifurcation of the superior end of the postcentral sulcus with the marginal ramus of the cingulate gyrus located between this bifurcation 10 (Fig. 2‑2).

Fig. 2.1 Lateral surface of the cerebrum. (a) 1, Superior frontal sulcus. 2, Inferior frontal sulcus. 3a, Superior part of the precentral sulcus. 3b, Inferior part of the precentral sulcus. 4a, Superior curve of the central sulcus. 4b, Middle loop of the central sulcus. 4c, Inferior curve of the central sulcus. 4d, Inferior part of the central sulcus. 5a, Superior part of the postcentral sulcus. 5b, Inferior part of the postcentral sulcus. 6, Intraparietal sulcus. 7, Sylvian fissure. 8, Superior temporal sulcus. (b) 1, Superior frontal gyrus. 2, Middle frontal gyrus. 3, Inferior frontal gyrus. 4, Connection of the middle frontal gyrus with the precentral gyrus. 5, Precentral gyrus. 6, Postcentral gyrus. 7, Superior parietal lobule. 8, Supramarginal gyrus. 9, Angular gyrus. 10, Superior temporal gyrus. 11, Middle temporal gyrus. 12, Occipital lobe. (c) Inferior part of the postcentral sulcus. 1b, Superior part of the postcentral sulcus. 2, Superior end of the marginal ramus. 3a, Intraparietal sulcus. 3b, Intraoccipital sulcus. 4, Parieto-occipital sulcus. 5, Supramarginal gyrus around the posterior end of the Sylvian fissure. 6, Angular gyrus around the posterior end of the superior temporal sulcus. 7, Preoccipital notch. (d) 1, Pars orbitalis. 2, Horizontal ramus. 3, Pars triangularis. 4, Ascending ramus. 5, Pars opercularis. 6, Precentral sulcus. 7, Precentral gyrus. 8, Central sulcus. 9, Postcentral gyrus. 10, Postcentral sulcus. 11, Posterior ascending ramus of the Sylvian fissure. 12, Supramarginal gyrus. 13, Inferior descending ramus of the Sylvian fissure. 14, Superior temporal gyrus. Asp, anterior Sylvian point psp, posterior Sylvian point. Fig. 2.2 Closer view of the area around the knob of the central sulcus. (a) 1, Precentral sulcus. 2, Posterior end of the superior frontal sulcus. 3, Knob of the precentral gyrus. 4, Superior curve of the central sulcus. 5, Longitudinal sulci forming the omega inside the second curve of the central sulcus. 6, Second loop of the central sulcus. 7, Third curve of the central sulcus. 8, Postcentral gyrus. 9, Superior parietal lobule. 10, Intraparietal sulcus. (b) 1, Omega (Ω) of the precentral gyrus. (c) 1, Superior frontal sulcus. 2, Knob of the precentral gyrus. 3, Superior end of the postcentral sulcus bifurcating around the marginal ramus. 4, Marginal ramus of the cingulate sulcus. (d) 1, Knob of the precentral gyrus. 2, Superior loop of the central sulcus. 3, Superior part of the postcentral sulcus. 4, Marginal ramus.

2.2.2 Medial Surface

On the medial surface of the hemisphere the central lobule has a quadrangular shape and its gyri are called the paracentral gyrus or lobule (Fig. 2‑3). This quadrangular shape is given by the limits of the paracentral gyrus: the cingulate sulcus inferiorly, the paracentral sulcus or ramus anteriorly, and the marginal ramus posteriorly. The paracentral sulcus has an upward direction and it is a sulcus that originates from the cingulate sulcus at the level of the middle of the corpus callosum. The marginal ramus is the posterior part of the cingulate sulcus as it curves upward at the level of the splenium of the corpus callosum. The most posterior part of the marginal ramus near the lateral surface is located at the level of the postcentral gyrus. The marginal ramus can be identified in the MRI in the middle of the bifurcation of the postcentral sulcus. The paracentral gyrus includes the continuation of the precentral and postcentral gyri on the medial surface. The supplementary motor area is an area that does not have clear boundaries, but it includes the paracentral gyrus anterior to the precentral gyrus and the posterior part of the superior frontal gyrus. 12 Stimulation in this area may cause complex postural movement, arrest of movement, or speech arrest. The supplementary area syndrome consists of reversible contralateral weakness and mutism following resection of the dominant supplementary motor area. 12

Fig. 2.3 Medial surface of the cerebrum. (a) 1, Cingulate sulcus. 2, Cingulate gyrus. 3, Medial frontal gyrus. 4, Paracentral sulcus. 5, Paracentral lobule. 6, Central sulcus. 7, Marginal ramus of the cingulate sulcus. 8, Precuneus. 9, Body of the corpus callosum. 10, Anterior limiting sulcus of the insula. 11, Heschl gyrus at the posterior part of the insula near the posterior limb of the internal capsule. (b) 1, Knob of the precentral gyrus. 2, Postcentral gyrus. 3, Intraparietal sulcus. 4, Parieto-occipital sulcus. 5, Supramarginal gyrus. 6, Heschl gyrus. 7, Temporal plane. (c) 1, Rostrum of the corpus callosum. 2, Genu of the corpus callosum. 3, Cingulate gyrus. 4, Callosal sulcus. 5, Body of the corpus callosum. 6, Splenium. 7, Septum pellucidum. 8, Fornix. (d) 1, Cuneus. 2, Parieto-occipital sulcus. 3, Calcarine sulcus. 4, Lingual gyrus. 5, Isthmus of the cingulate gyrus. 6, P3 segment of the PCA. 7, Inferior temporal branches of the PCA. 8, P2P segment. 9, P2A segment at the level of the uncal sulcus.

2.3 Frontal Lobe

The frontal lobe includes the superior, middle, and inferior frontal gyri on the lateral surface the orbital and rectus gyrus on the inferior surface and the medial frontal gyrus on the medial surface of the hemisphere.

2.3.1 Lateral Surface

The frontal lobe on the lateral surface of the hemisphere is limited posteriorly by the precentral sulcus and inferiorly by the Sylvian fissure (Fig. 2‑1, Fig. 2‑2, Fig. 2‑3). The frontal lobe is divided by two longitudinal sulci, the superior and inferior frontal sulci, into three gyri, the superior, middle, and inferior frontal gyri. The superior and inferior sulci have an anterior to posterior direction and end at the precentral sulcus. The precentral sulcus is anterior and parallel to the central sulcus. The superior frontal sulcus has its posterior portion near the omega of the precentral gyrus. The superior frontal gyrus runs parallel to the midline, between the interhemispheric fissure and the superior frontal sulcus. The middle frontal gyrus is the most prominent of the frontal gyri, located between the superior frontal sulcus and the inferior frontal sulcus. There may be an intermediary sulcus inside the middle frontal gyrus that separates the middle frontal gyrus in two middle frontal gyri. The middle frontal gyrus is continuous with the precentral gyrus. This continuation interrupts the precentral sulcus in two portions, superior and inferior. The continuation of the middle frontal gyrus with the precentral gyrus is used as a landmark for reference in the MRI. 11 The inferior frontal gyrus is located between the inferior frontal sulcus and the Sylvian fissure. The horizontal and ascending rami of the Sylvian fissure give a characteristic shape to the inferior frontal gyrus, dividing it into three portions: pars orbitalis, pars triangularis, and pars opercularis. There may be a sulcus along the pars opercularis, the diagonal sulcus. When it is present, the diagonal sulcus is posterior and parallel to the ascending ramus. Broca speech area consists of pars triangularis and pars opercularis on the dominant hemisphere. 7

2.3.2 Medial Surface

The frontal lobe in the medial aspect of the hemisphere extends from the paracentral sulcus posteriorly until the cingulate sulcus inferiorly, forming the anterior surface of the hemisphere until the anterior cranial base. The frontal lobe on the medial aspect is called the medial frontal gyrus and it is a continuation of the superior frontal gyrus on the medial aspect of the hemisphere. Below and in front of the genu of the corpus callosum, the medial frontal gyrus has two small sulci on its surface: the superior and inferior rostral sulci.


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Research output : Contribution to journal › Review article › peer-review

N1 - Publisher Copyright: © 2016, Springer Science+Business Media New York. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

N2 - Alien hand syndrome (AHS) is a rare disorder of involuntary limb movement together with a sense of loss of limb ownership. It most commonly affects the hand, but can occur in the leg. The anterior (frontal, callosal) and posterior variants are recognized, with distinguishing clinical features and anatomical lesions. Initial descriptions were attributed to stroke and neurosurgical operations, but neurodegenerative causes are now recognized as most common. Structural and functional imaging and clinical studies have implicated the supplementary motor area, pre-supplementary motor area, and their network connections in the frontal variant of AHS, and the inferior parietal lobule and connections in the posterior variant. Several theories are proposed to explain the pathophysiology. Herein, we review the literature to update advances in the understanding of the classification, pathophysiology, etiology, and treatment of AHS.

AB - Alien hand syndrome (AHS) is a rare disorder of involuntary limb movement together with a sense of loss of limb ownership. It most commonly affects the hand, but can occur in the leg. The anterior (frontal, callosal) and posterior variants are recognized, with distinguishing clinical features and anatomical lesions. Initial descriptions were attributed to stroke and neurosurgical operations, but neurodegenerative causes are now recognized as most common. Structural and functional imaging and clinical studies have implicated the supplementary motor area, pre-supplementary motor area, and their network connections in the frontal variant of AHS, and the inferior parietal lobule and connections in the posterior variant. Several theories are proposed to explain the pathophysiology. Herein, we review the literature to update advances in the understanding of the classification, pathophysiology, etiology, and treatment of AHS.


Cognitive aspects of human motor activity: Contribution of right hemisphere and cerebellum

Background. Concepts of movement and action are not completely synonymous, but what distinguishes one from the other? Movement may be defined as stimulus- driven motor acts, while action implies realization of a specific motor goal, essential for cognitively driven behavior. Although recent clinical and neuroimaging studies have revealed some areas of the brain that mediate cognitive aspects of human motor behavior, the identification of the basic neural circuit underlying the interaction between cognitive and motor functions remains a challenge for neurophysiology and psychology.

Objective. In the current study, we used functional magnetic resonance imaging (fMRI) to investigate elementary cognitive aspects of human motor behavior.

Design. Twenty healthy right-handed volunteers were asked to perform stimulus-driven and goal-directed movements by clenching the right hand into a fist (7 times). The cognitive component lay in anticipation of simple stimuli signals. In order to disentangle the purely motor component of stimulus-driven movements, we used the event-related (ER) paradigm. FMRI was performed on a 3 Tesla Siemens Magnetom Verio MR-scanner with 32-channel head coil.

Results. We have shown differences in the localization of brain activity depending on the involvement of cognitive functions. These differences testify to the role of the cerebellum and the right hemisphere in motor cognition. In particular, our results suggest that right associative cortical areas, together with the right posterolateral cerebellum (Crus I and lobule VI) and basal ganglia, de ne cognitive control of motor activity, promoting a shift from a stimulus-driven to a goal-directed mode.

Conclusion. These results, along with recent data from research on cerebro-cerebellar circuitry, redefine the scope of tasks for exploring the contribution of the cerebellum to diverse aspects of human motor behavior and cognition.

Sedov, A.S.
Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow, Russia
Popov, V.A.
Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow, Russia
Filyushkina, V.I.
Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow, Russia
Semenova, U.N.
Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow, Russia
Orlov, V. A.
National Research Center “Kurchatov Institute”, Moscow, Russia
Velichkovsky, Boris M.
National Research Center “Kurchatov Institute”, Moscow, Russia
Technische Universitaet, Dresden, Germany
Ushakov, V.L.
National Research Center “Kurchatov Institute”, Moscow, Russia

Keywords: action, movement, fMRI, lateralization, motor behavior, voluntary movement, cognition, cortex, cerebellum, basal ganglia


Barbas H, Pandya DN (1987) Architecture and frontal cortical connections of the premotor cortex (area 6) in the rhesus monkey. J Comp Neurol 256:211–228

Braak H (1976) A primitive gigantopyramidal field buried in the depth of the cingulate sulcus of the human brain. Brain Res 109:219–233

Brodmann K (1925) Vergleichende Lokalisationslehre der Gross hirnrinde, 2nd edn. Barth, Leipzig

Colebatch JM, Cunningham VJ, Deiber M-P, Frackowiak RSJ, Passingham RE (1990) Regional cerebral blood flow during unilateral arm and hand movements in human volunteers. Abstr Physiological Soc, 9P

Crammond DJ, Kalaska JF (1989) Neuronal activity in primate parietal cortex area 5 varies with intended movement direction during an instructed-delay period. Exp Brain Res 76:458–462

Damasio Ar, van Hoesen GW (1983) Emotional disturbances associated with focal lesion of the limbic frontal lobe. In: Heilman K, Satz P (eds) Neuropsychology of human emotion. Guildford Press, New York, pp 85–110

Deecke L (1987) Bereitschaftspotential as an indicator of movement preparation in supplementary motor area and motor cortex. In: Porter R (ed) Motor areas of the cerebral cortex. Wiley, Chichester, pp 231–245

Eidelberg D, Galaburda AM (1984) Inferior parietal lobule: divergent architectonic asymmetries in the human brain. Arch Neurol 41:843–852

Fox PT, Pox JM, Raichle ME, Burde RM (1985) The role of cerebral cortex in the generation of voluntary saccades: a positron emission tomographic study. J Neurophysiol 54:348–369

Fox PT, Pardo JV, Petersen SE, Raichle ME (1987) Supplementary motor and premotor responses to actual and imagined hand movements with Positron Emission Tomography. Soc Neurosci Abstr 398:10

Friston KJ, Passingham RE, Nutt JG, Heather JD, Sawle GV, Frackowiak RSJ (1989) Localization in PET images: direct fitting of the intercommissural (AC-PC) line. J Cereb Blood Flow Metabol 9:690–695

Friston KJ, Frith CD, Liddle PF, Dolan RJ, Lammertsma AA, Frackowiak RSJ (1990) The relationship between global and local changes in PET scans. J Cereb Blood Flow Metabol 10:458–466

Galyon DD, Strick PL (1985) Multiple and differential projections from the parietal lobe to the premotor areas of the primate. Soc Neurosci Abstr 373.10

Godschalk M, Lemon RN, der Steen J van (1985) The involvement of monkey premotor cortex neurones in preparation of visually cued arm movements. Behav Brain Res 18:143–157

Godschalk M, Lemon RN (1989) Preparation of visually cued arm movements in monkey. Brain Behav Evol 33:122–126

Goldberg G (1985) Supplementary motor area structure and function: review and hypotheses. Behav Brain Sci 8:567–588

Goldman-Rakic PS (1987) Circuitry of primate prefrontal cortex and regulation of behavior by representational memory. In: Plum F (ed) The nervous system: higher functions of the brain. Am Physiol Soc, Bethesda, pp 373–417

Halsband U (1987) Higher disturbances of movement in monkeys (Macaca fascicularis). In: Gantchev GN, Dimitrov B, Galev PC (eds) Motor control. Plenum, New York, pp 79–85

Hutchins KD, Martino AM, Strick PL (1988) Corticospinal projections from the medial wall of the hemisphere. Exp Brain Res 71:667–672

Lammertsma AA, Cunningham VJ, Deiber MP, Heather JD, Bloomfield PM, Nutt J, Frackowiak RSJ, Jones T (1990) Combination of dynamic and integral methods for generating reproducible functional CBF images. J Cereb Blood Flow Metabol 10:675–686

Laplane D, Talairach J, Meininger V, Bancaud J, Orgogozo JM (1977) Clinical consequencies of corticectomies involving the supplementary motor area in man. J Neurol Sci 34:301–314

Martino AM, Strick PL (1987) Corticospinal projections originate from the arcuate premotor area. Brain Res 404:307–312

Matelli W, Luppino G, Rizzolatti G (1985) Patterns of cytochrome oxidase activity in the frontal agranular cortex of the macaque monkey. Behav Brain Res 18:125–136

Mushiake H, Inase M, Tanji J (1990) Selective coding of motor sequence in the supplementary motor area of the monkey cerebral cortex. Exp Brain Res 82:208–210

Okano K, Tanji J (1987) Neuronal activity in the primate motor fields of the agranular frontal cortex preceding visually triggered and self-paced movements. Exp Brain Res 66:155–166

Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychol 9:97–113

Passingham RE (1985) Premotor cortex: sensory cues and movement. Behav Brain Res 18:175–186

Passingham RE (1987) Two cortical systems for directing movement. In: Porter R (ed) Motor areas of the cerebral cortex. Wiley, Chichester, pp 151–164

Passingham RE (1988) Premotor cortex and preparation for movement. Exp Brain Res 70:590–596

Passingham RE, Thaler DE, Chen Y (1989) Supplementary motor cortex and self-initiated movement. In: Ito M (ed) Neural programming. Karger, Basel, pp 13–24

Pearson RCA, Powell TPS (1985) The projection of the primary somatic sensory cortex upon area 5 in the monkey. Brain Res Rev 9:89–107

Petrides M (1982) Motor conditional associative-learning after selective prefrontal lesions in the monkey. Behav Brain Res 5:407–413

Petrides M, Pandya DN (1984) Projections to the frontal lobes from the posterior parietal region in the rhesus monkey. J Comp Neurol 228:105–116

Raichle ME (1987) Circulatory and metabolic correlates of brain function in normal humans. In: Plum F (ed) The nervous system: higher functions of the brain. Am Physiol Soc, Bethesda, pp 643–674

Robinson CJ, Burton H (1980) Organization of somatosensory receptive fields in cortical areas 7b, retroinsula, postauditory and granular insular of Macaca fascicularis. J Comp Neurol 192:69–92

Roland PE, Seitz RJ (1989) Mapping of learning and memory functions in the human brain. In: Ottoson D (ed) Visualization of brain functions. Stockton Press, London, pp 141–151

Roland PE, Larsen B, Lassen NA, Skinhoj E (1980a) Supplementary motor area and other cortical areas in organization of voluntary movements in man. J Neurophysiol 43:118–136

Roland PE, Skinhoj E, Lassen NA, Larsen B (1980b) Different cortical areas in man in organization of voluntary movements in extrapersonal space. J Neurophysiol 43:137–150

Roland PE, Meyer E, Shibasaki T, Yamamoto YL (1982) Regional cerebral blood flow changes in cortex and basal ganglia during voluntary movements in normal human volunteers. J Neurophysiol 48:467–480

Romo R, Schultz W (1987) Neuronal activity preceeding selfinitiated or externally timed arm movements in area 6 of monkey cortex. Exp Brain Res 67:656–662

Seal J, Gross C, Bioulac B (1982) Activity of neurones in area 5 during a simple arm movement in monkeys before and after deafferentation of the trained limb. Brain Res 250:229–243

Spinks TJ, Jones T, Gilardi MC, Heather JD (1988) Physical performance of the latest generation of commercial positron scanner. IEEE Trans Nucl Sci 35:721–725

Stern CE (1987) Functions of the ventral striatum. PhD thesis. University of Oxford

Straub A, Siegel K (1988) Parkinsonian syndrome caused by a tumour of the left supplementary motor area. J Neurol Neurosurg Psychiatr 51:730–731

Talairach J, Szikla G (1967) Atlas d'anatomie stereotaxique du telencephale. Masson, Paris

Talairach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain. Thieme, Stuttgart

Tanji J, Tanaguchi K, Saga T (1980) The supplementary motor area: neuronal responses to motor instructions. J Neurophysiol 43:60–68

von Economo C, Koskinas (1928) The cytoarchitectonics of the human cerebral cortex. Oxford University Press, London

Weinrich M, Wise SP, Mauritz K-H (1984) A neurophysiological study of the premotor cortex in the rhesus monkey. Brain 107:385–414

Wise SP (1989) Frontal cortex activity and motor set. In: Ito M (ed) Neural programming. Karger, Basel, pp 25–38


Disrupting the experience of control in the human brain: pre-supplementary motor area contributes to the sense of agency

The feeling of controlling events through one's actions is fundamental to human experience, but its neural basis remains unclear. This ‘sense of agency’ (SoA) can be measured quantitatively as a temporal linkage between voluntary actions and their external effects. We investigated the brain areas underlying this aspect of action awareness by using theta-burst stimulation to locally and reversibly disrupt human brain function. Disruption of the pre-supplementary motor area (pre-SMA), a key structure for preparation and initiation of a voluntary action, was shown to reduce the temporal linkage between a voluntary key-press action and a subsequent electrocutaneous stimulus. In contrast, disruption of the sensorimotor cortex, which processes signals more directly related to action execution and sensory feedback, had no significant effect. Our results provide the first direct evidence of a pre-SMA contribution to SoA.

References

Akkal D., Dum R. P.& Strick P. L.

. 2007 Supplementary motor area and presupplementary motor area: targets of basal ganglia and cerebellar output . J. Neurosci. 27, 10 659–10 673. (doi:10.1523/JNEUROSCI.3134-07.2007). Crossref, ISI, Google Scholar

Bates J. F.& Goldman-Rakic P. S.

. 1993 Prefrontal connections of medial motor areas in the rhesus monkey . J. Comp. Neurol. 336, 211–228. (doi:10.1002/cne.903360205). Crossref, PubMed, ISI, Google Scholar

Bestmann S., Baudewig J., Siebner H. R., Rothwell J. C.& Frahm J.

. 2004 Functional MRI of the immediate impact of transcranial magnetic stimulation on cortical and subcortical motor circuits . Eur. J. Neurosci. 19, 1950–1962. (doi:10.1111/j.1460-9568.2004.03277.x). Crossref, PubMed, Google Scholar

Blakemore S. J., Wolpert D.& Frith C.

. 2000 Why can't you tickle yourself? Neuroreport 11, R11–R16. (doi:10.1097/00001756-200008030-00002). Crossref, PubMed, Google Scholar

Blakemore S., Wolpert D.& Frith C.

. 2002 Abnormalities in the awareness of action . Trends Cogn. Sci. 6, 237–242. (doi:10.1016/S1364-6613(02)01907-1). Crossref, PubMed, ISI, Google Scholar

. 2002 Leader or follower? Involvement of the inferior parietal lobule in agency . Neuroreport 13, 1975–1978. (doi:10.1097/00001756-200210280-00029). Crossref, PubMed, ISI, Google Scholar

Deiber M. P., Passingham R. E., Colebatch J. G., Friston K. J., Nixon P. D.& Frackowiak R. S.

. 1991 Cortical areas and the selection of movement: a study with positron emission tomography . Exp. Brain Res. 84, 393–402. Crossref, PubMed, Google Scholar

. 2010 Time warp: authorship shapes the perceived timing of actions and events . Conscious. Cogn. 19, 481–489. (doi:10.1016/j.concog.2009.10.002). Crossref, PubMed, Google Scholar

Engbert K., Wohlschläger A., Thomas R.& Haggard P.

. 2007 Agency, subjective time, and other minds . J. Exp. Psychol. Hum. Percept. Perform. 33, 1261–1268. (doi:10.1037/0096-1523.33.6.1261). Crossref, PubMed, Google Scholar

Engbert K., Wohlschläger A.& Haggard P.

. 2008 Who is causing what? The sense of agency is relational and efferent-triggered . Cognition 107, 693–704. (doi:10.1016/j.cognition.2007.07.021). Crossref, PubMed, Google Scholar

. 2002 Experiencing oneself vs another person as being the cause of an action: the neural correlates of the experience of agency . NeuroImage 15, 596–603. (doi:10.1006/nimg.2001.1009). Crossref, PubMed, ISI, Google Scholar

Farrer C., Frey S. H., Van Horn J. D., Tunik E., Turk D., Inati S.& Grafton S. T.

. 2008 The angular gyrus computes action awareness representations . Cereb. Cortex 18, 254–261. (doi:10.1093/cercor/bhm050). Crossref, PubMed, Google Scholar

Fried I., Katz A., McCarthy G., Sass K. J., Williamson P., Spencer S. S.& Spencer D. D.

. 1991 Functional organization of human supplementary motor cortex studied by electrical stimulation . J. Neurosci. 11, 3656–3666. Crossref, PubMed, Google Scholar

Frith C. D., Friston K., Liddle P. F.& Frackowiak R. S. J.

. 1991 Willed action and the prefrontal cortex in man: a study with PET . Proc. R. Soc. Lond. B 244, 241–246. (doi:10.1098/rspb.1991.0077). Link, ISI, Google Scholar

. 2004 Supplementary motor area provides an efferent signal for sensory suppression . Brain Res. 19, 52–58. (doi:10.1016/j.cogbrainres.2003.10.018). Google Scholar

Haggard P., Aschersleben G., Gehrke J.& Prinz W.

. 2002a Action, binding and awareness . Common mechanisms in perception and action . Attention and Performance XIX (eds

). Oxford, UK : Oxford University Press . Google Scholar

Haggard P., Clark S.& Kalogeras J.

. 2002b Voluntary action and conscious awareness . Nat. Neurosci. 5, 382–385. (doi:10.1038/nn827). Crossref, PubMed, ISI, Google Scholar

Haggard P., Cartledge P., Dafydd M.& Oakley D. A.

. 2004 Anomalous control: when ‘free-will' is not conscious . Conscious. Cogn. 13, 646–654. (doi:10.1016/j.concog.2004.06.001). Crossref, PubMed, Google Scholar

Hikosaka O., Sakai K., Miyauchi S., Takino R., Sasaki Y.& Pütz B.

. 1996 Activation of human presupplementary motor area in learning of sequential procedures: a functional MRI study . J. Neurophysiol. 76, 617–621. Crossref, PubMed, Google Scholar

. 2004 The effect of short-duration bursts of high-frequency, low-intensity transcranial magnetic stimulation on the human motor cortex . Clin. Neurophysiol. 115, 1069–1075. (doi:10.1016/j.clinph.2003.12.026). Crossref, PubMed, Google Scholar

Huang Y., Edwards M. J., Rounis E., Bhatia K. P.& Rothwell J. C.

. 2005 Theta burst stimulation of the human motor cortex . Neuron 45, 201–206. (doi:10.1016/j.neuron.2004.12.033). Crossref, PubMed, ISI, Google Scholar

Huang Y., Chen R., Rothwell J. C.& Wen H.

. 2007 The after-effect of human theta burst stimulation is NMDA receptor dependent . Clin. Neurophysiol. 118, 1028–1032. (doi:10.1016/j.clinph.2007.01.021). Crossref, PubMed, Google Scholar

. 1739 A treatise of human nature . Oxford, UK : Oxford University Press . Google Scholar

1999 Cognitive motor control in human pre-supplementary motor area studied by subdural recording of discrimination/selection-related potentials . Brain 122, 915–931. Crossref, PubMed, Google Scholar

Inase M., Tokuno H., Nambu A., Akazawa T.& Takada M.

. 1999 Corticostriatal and corticosubthalamic input zones from the presupplementary motor area in the macaque monkey: comparison with the input zones from the supplementary motor area . Brain Res. 833, 191–201. (doi:10.1016/S0006-8993(99)01531-0). Crossref, PubMed, Google Scholar

. 1890 The principles of psychology . New York, NY : Henry Holt . Google Scholar

Johansen-Berg H., Behrens T. E. J., Robson M. D., Drobnjak I., Rushworth M. F. S., Brady J. M., Smith S. M., Higham D. J.& Matthews P. M.

. 2004 Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex . Proc. Natl Acad. Sci. USA 101, 13 335–13 340. (doi:10.1073/pnas.0403743101). Crossref, Google Scholar

Lau H. C., Rogers R. D., Haggard P.& Passingham R. E.

. 2004 Attention to intention . Science 303, 1208–1210. (doi:10.1126/science.1090973). Crossref, PubMed, Google Scholar

Lehéricy S., Ducros M., Krainik A., Francois C., Van de Moortele P., Ugurbil K.& Kim D.-S.

. 2004 3-D diffusion tensor axonal tracking shows distinct SMA and pre-SMA projections to the human striatum . Cereb. Cortex 14, 1302–1309. (doi:10.1093/cercor/bhh091). Crossref, PubMed, Google Scholar

. 1971 Transformed up-down methods in psychoacoustics . J. Acoust. Soc. Am. 49((Suppl. 2)), 467–477. (doi:10.1121/1.1912375). Crossref, Google Scholar

Luppino G., Matelli M., Camarda R.& Rizzolatti G.

. 1993 Corticocortical connections of area F3 (SMA-proper) and area F6 (pre-SMA) in the macaque monkey . J. Comp. Neurol. 338, 114–140. (doi:10.1002/cne.903380109). Crossref, PubMed, ISI, Google Scholar

Mars R. B., Klein M. C., Neubert F., Olivier E., Buch E. R., Boorman E. D.& Rushworth M. F. S.

. 2009 Short-latency influence of medial frontal cortex on primary motor cortex during action selection under conflict . J. Neurosci. 29, 6926–6931. (doi:10.1523/JNEUROSCI.1396-09.2009). Crossref, PubMed, Google Scholar

. 2008 Awareness of action: inference and prediction . Conscious. Cogn. 17, 136–144. (doi:10.1016/j.concog.2006.12.004). Crossref, PubMed, Google Scholar

Moore J. W., Lagnado D., Deal D. C.& Haggard P.

. 2009a Feelings of control: contingency determines experience of action . Cognition 110, 279–283. (doi:10.1016/j.cognition.2008.11.006). Crossref, PubMed, Google Scholar

Moore J. W., Wegner D. M.& Haggard P.

. 2009b Modulating the sense of agency with external cues . Conscious. Cogn. 18, 1056–1064. (doi:10.1016/j.concog.2009.05.004). Crossref, PubMed, Google Scholar

Moore J. W., Schneider S. A., Schwingenschuh P., Moretto G., Bhatia K. P.& Haggard P.

. 2010 Dopaminergic medication boosts action-effect binding in Parkinson's disease . Neuropsychologia 48, 1125–1132. (doi:10.1016/j.neuropsychologia.2009.12.014). Crossref, PubMed, Google Scholar

Nachev P., Wydell H., O'neill K., Husain M.& Kennard C.

. 2007 The role of the pre-supplementary motor area in the control of action . NeuroImage 36((Suppl. 2)), T155–T163. (doi:10.1016/j.neuroimage.2007.03.034). Crossref, PubMed, Google Scholar

. 1996 Motor areas of the medial wall: a review of their location and functional activation . Cereb. Cortex 6, 342–353. Crossref, PubMed, ISI, Google Scholar

. 2001 Imaging the premotor areas . Curr. Opin. Neurobiol. 11, 663–672. (doi:10.1016/S0959-4388(01)00266-5). Crossref, PubMed, ISI, Google Scholar

1994 Non-invasive electrical and magnetic stimulation of the brain, spinal cord and roots: basic principles and procedures for routine clinical application. Report of an IFCN committee . Eletroencephalogr. Clin. Neurophysiol. 91, 79–92. (doi:10.1016/0013-4694(94)90029-9). Crossref, PubMed, Google Scholar

Rowe J. B., Toni I., Josephs O., Frackowiak R. S.& Passingham R. E.

. 2000 The prefrontal cortex: response selection or maintenance within working memory? Science 288, 1656–1660. (doi:10.1126/science.288.5471.1656). Crossref, PubMed, Google Scholar

Rushworth M., Hadland K. A., Paus T.& Sipila P. K.

. 2002 Role of the human medial frontal cortex in task switching: a combined fMRI and TMS study . J. Neurophysiol. 87, 2577–2592. Crossref, PubMed, Google Scholar

Sirigu A., Daprati E., Pradat-Diehl P., Franck N.& Jeannerod M.

. 1999 Perception of self-generated movement following left parietal lesion . Brain 122, 1867–1874. Crossref, PubMed, Google Scholar

Stefan K., Wycislo M., Gentner R., Schramm A., Naumann M., Reiners K.& Classen J.

. 2006 Temporary occlusion of associative motor cortical plasticity by prior dynamic motor training . Cereb. Cortex 16, 376–385. (doi:10.1093/cercor/bhi116). Crossref, PubMed, Google Scholar

Stetson C., Cui X., Montague P. R.& Eagleman D. M.

. 2006 Motor-sensory recalibration leads to an illusory reversal of action and sensation . Neuron 51, 651–659. (doi:10.1016/j.neuron.2006.08.006). Crossref, PubMed, Google Scholar

Synofzik M., Vosgerau G.& Newen A.

. 2008 Beyond the comparator model: a multifactorial two-step account of agency . Conscious. Cogn. 17, 219–239. (doi:10.1016/j.concog.2007.03.010). Crossref, PubMed, Google Scholar

. 2003 Awareness of somatic events associated with a voluntary action . Exp. Brain Res. 149, 439–446. (doi:10.1007/s00221-003-1386-8). Crossref, PubMed, Google Scholar

. 2002 The illusion of conscious will (illustrated edition). Cambridge, MA : MIT Press . Google Scholar

Weiller C., Jüptner M., Fellows S., Rijntjes M., Leonhardt G., Kiebel S., Müller S., Diener H. C.& Thilmann A. F.

. 1996 Brain representation of active and passive movements . NeuroImage 4, 105–110. (doi:10.1006/nimg.1996.0034). Crossref, PubMed, Google Scholar

Wolters A., Sandbrink F., Schlottmann A., Kunesch E., Stefan K., Cohen L. G., Benecke R.& Classen J.

. 2003 A temporally asymmetric Hebbian rule governing plasticity in the human motor cortex . J. Neurophysiol. 89, 2339–2345. (doi:10.1152/jn.00900.2002). Crossref, PubMed, Google Scholar


Cortical layers/BA

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Anticorrelations between Active Brain Regions: An Agent-Based Model Simulation Study.

The not-well-defined nature of negative correlations stimulated several authors to study the persistence of significant negative correlations by means of fMRI-specific correction methods and to propose a possible physiological role for them [1-4]. In this regard, however, a clear mechanism about how negative interactions are related to the positive ones is not available as yet. A rewarding approach to the problem would be the simulation of brain activity, which opens the door to mechanistic models amenable to validation by empirical data.

Different models have been proposed [5] to approximate the collective activity of neurons such as the conductance-based biophysical model [6-8] or the FitzHugh-Nagumo model [9, 10], by the mean-field [11] or mass action [12] formalisms. fMRI produces data at a mesoscopic level while brain activities are inspected at a much larger scale than that of single neurons. This implies that we have to imagine how the behavior of single functional units, of major importance for the current understanding of brain's activities, may influence the observations at a higher hierarchical level [13].

In order to reproduce the brain resting state from fMRI acquisitions, the long-range myelinated fiber connections by diffusion imaging, or the folded cortical surface by high resolution imaging [14-17], have been used as a background for the interactions between brain areas. Such interactions have been simulated using the Kuramoto model [18], the Ising model [19], and some discrete-time dynamical models [20, 21]. In the last case [20, 21], a stochastic cellular automaton approach was used by two well-established brain computational models, the susceptible-excited-refractory (SER) [22] model and the FitzHugh-Nagumo model [9].

An alternative approach to the large-scale brain modeling is to simulate the brain activity using the functional connectivity map itself as a background. In such a context, Joyce et al. [23] realized an agent-based brain-inspired model (ABBM) using both positive and negative values of functional connectivity. In general, an agent-based model (ABM) includes a set of agents whose reciprocal interactions are defined by a set of rules depending upon the system at hand. These models can exhibit emergent behavior as described by Wolfram [24].

Here we develop a model using an ABM model and a biologically plausible SER model, which should account for both positive and negative interactions between large-scale brain areas. Different levels of functional connectivity in the background modulate the goodness-of-fit of simulations, and we focus, in particular, on the fraction of negative links to test their role in the organization of structured networks.

2.1. Data Collection. The sample is composed of 30 selected functional images of healthy controls from the Beijing Zang dataset (180 subject) in the 1000 Functional Connectomes Classic collection (http://fcon_1000.projects.nitrc.org/indi/ retro/BeijingEnhanced.html). Resting data were obtained using a 3.0 T Siemens scanner at the Imaging Center for Brain Research, Beijing Normal University. For each subject, a total of 240 volumes of EPI images were obtained axially (repetition time, 2000 ms echo time, 30 ms slices, 33 thickness, 3 mm gap, 0.6 mm field of view, 200 x 200 [mm.sup.2] resolution, 64 x 64 flip angle, 90[degrees]). For the anatomical images, a T1-weighted sagittal three-dimensional magnetization prepared rapid gradient echo (MPRAGE) sequence was acquired, covering the entire brain: 128 slices, TR= 2530 ms, TE = 3.39 ms, slice thickness = 1.33 mm, flip angle = 7[degrees], inversion time = 1100 ms, FOV = 256 x 256 mm, and in-plane resolution = 256 x 192.

2.2. Data Preprocessing. The first 10 scans of each subject were removed, and the remaining functional images were analyzed according to the procedures fully described elsewhere [25]. The SPM8 (Statistical Parametric Mapping) (Wellcome Department of Cognitive Neurology, London, UK) toolbox and the Functional Connectivity (CONN) toolbox were used in the preprocessing of data on a MATLAB R2010b platform.

The images from each subject were divided into 105 ROIs without brainstem and cerebellum (see Figure 1) through the MRI Atlas of the Human Brain, Harvard Medical School [26], and from each ROI, the time series was extracted. An average correlation matrix for each subject was calculated for all possible couples of the 105 ROIs considering both correlation signs and was used as an (individual) connectivity matrix. Thus, the global, mean matrix to be used as a background for the brain simulation was reckoned according to the following overall procedure:

(1) For each subject, the activation time series of 105 ROIs extracted from 240 functional images (see Data Collection) were coupled and correlated in all possible combinations, producing an individual connectivity matrix. Then, a global average concerning the whole group of subjects is obtained by averaging the 30 individual matrices, as schematized in Figure 2(a).

(2) For both positive and negative interactions, in the above average matrix, a series of 20 binary and thresholded matrices are constructed, taking fractions of the highest absolute correlation values in the range from 0% to 100% at 5% steps: this represents the network density (cost). Thus, 20 binary matrices of increasing cost are derived, having an unbalanced amount of total positive and negative links (total positive correlations 70%, total negative correlations 30%). We call this type of threshold absolute-values-proportional-threshold. A graphical overview of the procedure is reported in Figure 2(b).

(3) A further set of binary and thresholded matrices is calculated in order to distinguish the most significant correlation value for each sign: 15 matrices from the 0%-70% cost (maximum fraction of positive links), containing only positive values, and 7 matrices from the 0%-30% cost (maximum fraction of negative links), containing only negative values. Thus, we have different amounts of positive and negative correlations for the same fraction of total links. We call this type of threshold signed-values-proportional-threshold.

(4) Finally, all the combinations of positive and negative matrices for different thresholds are joined, producing 7 * 15 = 105 matrices having different amounts of positive and negative correlations.

2.3. Simulations by an ABBM Model. An agent-based approach was used in a large-scale brain network simulation able to account for the independent behavior of each brain region as well as for the interactions between different regions. Each node in the network represents, according to the susceptible-excited-refractory (SER) formalism [20, 21], a stylized biological neuron cycling in discrete time steps through the following three states: (S), a susceptible state in which the node can be excited with a transition probability called sop (E), an excited state after which the node enters in a refractory state and (R), a refractory state from which the node can be regenerated (S) stochastically with a recovery probability called nep.

The interactions among the nodes (agents) characterized by the (SER) states are defined through positive and negative links in a binary and thresholded matrix derived from empirical data and simulated through an agent-based braininspired model (ABBM) of the type suggested by Joyce [23].

In particular, each node is characterized by three variables ([[sigma].sub.s], [[sigma].sub.p], and [[sigma].sub.n) two parameters ([[pi].sub.p] and [[pi].sub.n]) (see Figure 3), which are defined as follows.

(i) [[sigma].sub.s] = 1 if the node is in the S (susceptible) state, namely, prone to change (otherwise, [[sigma].sub.s] = 0).

(ii) [[sigma].sub.p] and [[sigma].sub.n] are calculated from the average contribution of positive and negative neighbors, respectively each neighbor contributes to the average if in the active (on) state.

(iii) [[pi].sub.n] and [[pi].sub.p] are threshold parameters above which the average of negative and positive neighbors ([[phi].sub.p] and [[phi].sub.n]) are set to 1 (otherwise, are set to 0).

Taking into account the previous variables, we characterized an agent by three binary variables ([[phi].sub.s], [[phi].sub.p], and [[phi].sub.n]), namely, by one of [2.sup.3] possible combinations (111, 110, 101, 011, 100, 001, 010, 000). Simulations were carried out concurrently for all agents and for each step, and in contrast with Morris and Lecar [6], we designed some a priori rules to decide whether or not a brain region could become active at a given simulation step (Table 1).

Various combinations of the sop, nep (connectivity independent) and [[pi].sub.p], [[pi].sub.n] (connectivity dependent) couples of parameters have been checked in the above-described model in order to simulate at best the whole empirical, positive connectivity matrix by a given fraction of positive and negative links. In particular, if negative links are associated with noise, the simulation quality should decrease when their fractional amount increases and, inversely, increase in the opposite, symmetrical condition.

Simulations were repeated 100 times for each different combination of parameters, assigning to nodes a random series of 0 and 1 and a random SER state. Notice that in the case of the [[pi].sub.p], [[pi].sub.n] couple, the same value for each member of the couple was used. Each simulation included 200 time steps and produced a matrix of 105 columns (brain regions) and 200 rows (total time steps) see Figure 4. The Pearson correlation (r) carried out on the columns of such a matrix produced a 105 x 105 simulated connectivity matrix. The Pearson correlation between each of the 100 simulated matrices and the one derived from experimental data produced 100 correlations values for each combination of parameters which were averaged and the average value assigned to that parameter combination. It is worthy to underline that the Pearson correlation (r) was used throughout this work as an index of the agreement (goodness-of-fit) between simulations and empirical data.

The whole procedure included three series of simulations: The first two series aimed to optimize the parameter values in the third series, the importance of different fractions of negative and positive connectivities in the reproduction of the positive connectivity itself was estimated. In particular, the following should be noted:

(i) In the first series of simulations, each of the 20 matrices characterized by an absolute-values-proportional-threshold (from 0% to 100% of absolute value threshold with 5% steps) was used as a background, as well as large variations of the other parameters (sop and nep = 0.25-0.50-0.75 [[pi].sub.p]/[[pi].sub.n] from 0.1 to 1, step 0.1).

(ii) The second series of simulations aimed to improve the parameter precision within the range identified in the previous set of simulations.

(iii) Finally, the third series of simulations was carried out upon considering, within the 105 matrices characterized by any possible combination of 15 positive and 7 negative signed-values-proportional-thresholds, the one showing the best simulation performance, namely, the best reproduction of the original connectivity pattern.

The significance of the fitting performance was assessed as follows: in order to check the effect of positive and negative connectivities, 15 and 7 different fractions of positive and negative links, respectively, were used and subjected to a Friedman test. Then, a post hoc analysis using the ranks of the goodness-of-fit was performed by the Tukey-Kramer test.

3.1. Exploring the Parameters' Space of the Brain Model. In the first exploratory phase of the model validation, the goodness-of-fit between empirical data and simulations, as monitored by the Pearson (r), was studied over a wide range of connectivity-independent (sop, nep) and connectivity-dependent ([[pi].sub.p], [[pi].sub.n]) parameters, namely, 0.25-0.50-0.75 and from 0.1 to 1 at 0.1 steps, respectively.

In Figure 5(a), the [[pi].sub.p] and [[pi].sub.n] values associated with the goodness-of-fit peaks show a trend increasing with both sop and nep values. Since high sop and nep values point to an excitable system, endowed with high probability of spontaneous activation and low probability of resting in the refractory state, the fitting appears improved by a relatively conservative threshold for [[pi].sub.p] and [[pi].sub.n], namely, [[pi].sub.p] and [[pi].sub.n] = 0.1, under the condition of low excitability (sop and nep being equal to 0.25).

The above considerations suggest to focus on the lower range of parameters, namely, sop and nep from 0.025 to 0.25 (step = 0.025) and [[pi].sub.p] and [[pi].sub.n] from 0.025 to 0.1 (step = 0.025). Thus, the matching between simulation and empirical data could be improved by reaching the maximum value of 0.50 at the following connectivity-independent parameter values: sop = 0.025 nep = 0.175, 0.20, 0.225.

As shown in Figure 5(b), the highest goodness-of-fit is reached at [[pi].sub.p] = [[pi].sub.n] = 0.1 and using a small connectivity density (15%). At increasing [[pi].sub.p] and [[pi].sub.n] values, the trend changes gradually until at [[pi].sub.p] = [[pi].sub.n] = 0.1 an absolute minimum in the lower range of connectivity density can be observed, as well as a maximum in the higher range of connectivity density. Notice that sop and nep values are locked, respectively, at 0.025 and 0.225, and that changing the nep parameter does not alter the observed trends.

This behavior can be ascribed to the different amounts of positive and negative links using the absolute-values-proportional-threshold: The number of negative links is lower (almost nonsignificant for the lower level of general connectivity cost), and a more conservative threshold [[pi].sub.n] would further decrease the associated information. Thus, with a more labile threshold of [[pi].sub.n], more information from the negative connectivities can be extracted, which increases their modulation role. Due to the unbalanced distribution of positive and negative links, however, the simulation reaches a maximum value of goodness-of-fit only in the higher range of connectivity density (where a significant amount of negative connectivity is also increasing). At the same time, a lower threshold [[pi].sub.p] can introduce random positive connections, decreasing the goodness-of-fit in the lower range of the connectivity density.

3.2. Modeling Positive and Negative Links. In this phase, the task is to define the dependence of the fitting procedure on the relative amounts of positive and negative links, using the parameter values identified in the previous steps, namely, sop = 0.025, nep = 0.225, and [[pi].sub.p] = [[pi].sub.n] = 0.1. In Figure 6, the trend of correlation values at increasing positive connectivity fractions is characterized by a peak within the middle values of positive cost. Moreover, adding negative links at this stage further improves the fitting up to a maximum (0.57) at the higher values of negative network density.

A nonparametric statistical analysis (Friedman test) reported in Figure 7 confirms a significant effect (p < 0.0001, [chi square] = 97.3, df= 1) of positive links on the fitting performance of the model. The effect of negative links, however, is not significant (p = 0.55, [chi square] = 4.9, df = 6). The significant post hoc difference in the positive links is apparent in the range from 5% to 30% of positive network density (Figure 7(a)). The same nonparametric test for negative links in the range of higher values of goodness-of-fit is reported in Figure 7(c) where 6 different levels of positive cost (from 5% to 30%) are considered, while the levels of negative links remain 7. In contrast with previous results, under these conditions, a significant effect for the negative links (see Figure 7(c) p < 0.0001, [chi square] = 37.1, df=6) emerges. This indicates a possible interaction between different amounts of positive and negative links, so that only in the range of 5%-30% positive cost is there an increasing trend of goodness-of-fit upon addition of negative links (25%-30%). Under other conditions, only random fluctuations occur, probably caused by increasing variability levels.

3.3. Modeling Individual Variability. Given the noticeable level of individual variability in brain functional connectivity, the model has been individually applied on a small sample of subjects. For each of eight randomly chosen subjects, the simulations were repeated in the positive cost range indicated as significant by our previous work (positive cost: 5%-30%), and keeping the same values of the sop, nep and [[pi].sub.p]/[[pi].sub.n] parameters. The results, shown in Figure 8, are in line with the previous observation of a small effect of anticorrelation variability in the model.

4.1. General Issues about Our Brain Model. In this work, we propose a simple agent-based model able to simulate brain functional connectivity. Our results stress once again on how a set of simple rules between interacting agents can show a complex dynamics [24]. A peculiar feature of our work is the input used for the simulation: instead of the structural connectivity [14-17], we used the functional connectivity itself as a background and did that to underpin the role of a given amount of signed connectivity. In particular, we focused on the relative fraction of positive and negative links, to characterize the whole brain functions.

Our simulations exploit the appealing features of an ABBM-based strategy already used for the same purpose among several possible alternatives [23]. This approach showed different patterns of dynamics, but only some particular combinations of parameters produced nontrivial results [23] and, in addition, often lack coherent biological interpretation. We initially used some parameter values directly inspired to a biological system, and the results were unsatisfactory. Thus, we shifted to a SER model with the agents' dynamics defined by the sop and nep parameters. In this way, the brain regions show a stochastic oscillation in line with more realistic models [14, 15], and the connectivity represents a modulation among brain oscillating dynamics. As the first result of the adopted modeling strategy, the characterization of the system at hand was significantly improved.

4.2. Modeling Brain Activity Using Different Amounts of Positive and Negative Links. Different trends were found by our simulations depending upon the relative amount of positive and negative connectivities: In the former case (positive connectivities), the goodness-of-fit shows a peak at lower cost values, and a decreasing trend follows in the latter (negative connectivities), the goodness-of-fit shows an increasing trend with a maximum at the maximal fraction of negative links.

As for positive connectivities, the statistical analysis showed clear differences between the random model (no connections among nodes, and all brain regions showing random oscillations) in the range between 5% and 30%. This result is in line with previous findings pointing to a small-world topology in that range [27]: In the same range, the brain positive networks show an efficient balance between the segregation-integration properties, and brain regions can be clustered in different subnetworks without losing the possible information transfer among each other [28]. As for negative links, the goodness-of-fit shows a trend different from that of the random model only if the positive links are in the range 5%-30%: otherwise, the trend is lost. In this frame, negative links showed importance in order to improve the fitting and prove their nonartifactual nature, while a higher density of positive links may indicate a significant noise source.

The results gathered by our model on single subjects are in agreement with those on the average matrix, indicating a good reproduction of individual variability. As a more general validation of our study, the same analysis carried out over another set of 30 randomly chosen individuals from the same database (Beijing Zang dataset, the 1000 Functional Connectomes Classic collection) produced pretty similar results (not shown).

An objective interpretation of our observations should take into account several factors: (1) More positive than negative modulations could be favoured by our model (2) the anticorrelations have a more variable dynamics, more dependent on experimental conditions. From this point of view, such interactions are characteristic of the resting state itself and have a more local than global meaning (3) our preprocessing method (aCompCorr [29]) used for the fMRI analysis could be not good enough to characterize negative networks. The first issue can be tested using different types of simulations in order to work out models for negative connections. In this regard, we would need a more accurate large-scale brain modeling able to account for this type of brain interaction. As for the second issue, different evidence is prone to assess the local versus global nature of anticorrelations. As a matter of fact, two evidence pointed out these different hypotheses: Gopinath et al. [30] found intracluster anticorrelations in several task-positive networks (TPNs) during a resting state, indicating a possible state-dependent activity. However, more recently [4], we found a low-connection probability between the most connected nodes using anticorrelated functional networks (the highly connected nodes tend to avoid connections among each other, indicating a global network organization).

About the last issue, however, there is no univocal consensus, and alternative methods have been proposed [2], among which the aCompCorr appeared as a most reliable one [1].

A direct comparison of aCompCorr with GSR [31], however, did not allow us to provide a final answer to the general problem, which remains, then, still open to further exploration.

All in all, the target of the present work was not to develop an alternative to the already used large-scale brain models but to underpin the importance of different connectivity types for the brain system. To this aim, we introduced a simple model able to fit empirical data, provided a method to identify the random (or noisy) functional connections, and found some evidence about the importance of anticorrelations for the optimal characterization of connectivity patterns.

It seems fair to conclude that anticorrelations (1) should be distinguished from noise and (2) may improve the characterization of positive connectivity and contribute to the refinement of the global brain functional system in fMRI acquisitions.

Anatomical Labels of Brain Regions

(1) FP r (frontal pole right)

(3) IC r (insular cortex right)

(4) IC l (insular cortex left)

(5) SFG r (superior frontal gyrus right)

(6) SFG l (superior frontal gyrus left)

(7) MidFG r (middle frontal gyrus right)

(8) MidFG l (middle frontal gyrus left)

(9) IFG tri r (inferior frontal gyrus, pars triangularis right)

(10) IFG tri l (inferior frontal gyrus, pars triangularis left)

(11) IFG oper r (inferior frontal gyrus, pars opercularis right)

(12) IFG oper l (inferior frontal gyrus, pars opercularis left)

(13) PreCG r (precentral gyrus right)

(14) PreCG l (precentral gyrus left)

(15) TP r (temporal pole right)

(16) TP l (temporal pole left)

(17) aSTG r (superior temporal gyrus, anterior division right)

(18) aSTG l (superior temporal gyrus, anterior division left)

(19) pSTG r (superior temporal gyrus, posterior division right)

(20) pSTG l (superior temporal gyrus, posterior division left)

(21) aMTG r (middle temporal gyrus, anterior division right)

(22) aMTG l (middle temporal gyrus, anterior division left)

(23) pMTG r (middle temporal gyrus, posterior division right)

(24) pMTG l (middle temporal gyrus, posterior division left)

(25) toMTG r (middle temporal gyrus, temporooccipital part right)

(26) toMTG l (middle temporal gyrus, temporooccipital part left)

(27) aITG r (inferior temporal gyrus, anterior division right)

(28) aITG l (inferior temporal gyrus, anterior division left)

(29) pITG r (inferior temporal gyrus, posterior division right)

(30) pITG l (inferior temporal gyrus, posterior division left)

(31) toITG r (inferior temporal gyrus, temporooccipital part right)

(32) toITG l (inferior temporal gyrus, temporooccipital part left)

(33) PostCG r (postcentral gyrus right)

(34) PostCG l (postcentral gyrus left)

(35) SPL r (superior parietal lobule right)

(36) SPL l (superior parietal lobule left)

(37) aSMG r (supramarginal gyrus, anterior division right)

(38) aSMG l (supramarginal gyrus, anterior division left)

(39) pSMG r (supramarginal gyrus, posterior division right)

(40) pSMG l (supramarginal gyrus, posterior division left)

(41) AG r (angular gyrus right)

(42) AG l (angular gyrus left)

(43) sLOC r (lateral occipital cortex, superior division right)

(44) sLOC l (lateral occipital cortex, superior division left)

(45) iLOC r (lateral occipital cortex, inferior division right)

(46) iLOC l (lateral occipital cortex, inferior division left)

(47) ICC r (intracalcarine cortex right)

(48) ICC l (intracalcarine cortex left)

(49) MedFC (frontal medial cortex)

(50) SMA r (juxtapositional lobule cortex--formerly supplementary motor cortex right)

(51) SMA L (juxtapositional lobule cortex--formerly supplementary motor cortex left)

(52) SubCalC (subcallosal cortex)

(53) PaCiG r (paracingulate gyrus right)

(54) PaCiG l (paracingulate gyrus left)

(55) AC (cingulate gyrus, anterior division)

(56) PC (cingulate gyrus, posterior division)

(57) Precuneus (precuneus cortex)

(58) Cuneal r (cuneal cortex right)

(59) Cuneal l (cuneal cortex left)

(60) FOrb r (frontal orbital cortex right)

(61) FOrb l (frontal orbital cortex left)

(62) aPaHC r (parahippocampal gyrus, anterior division right)

(63) aPaHC l (parahippocampal gyrus, anterior division left)

(64) pPaHC r (parahippocampal gyrus, posterior division right)

(65) pPaHC l (parahippocampal gyrus, posterior division left)

(66) LG r (lingual gyrus right)

(67) LG l (lingual gyrus left)

(68) aTFusC r (temporal fusiform cortex, anterior division right)

(69) aTFusC l (temporal fusiform cortex, anterior division left)

(70) pTFusC r (temporal fusiform cortex, posterior division right)

(71) pTFusC l (temporal fusiform cortex, posterior division left)

(72) TOFusC r (temporal occipital fusiform cortex right)

(73) TOFusC l (temporal occipital fusiform cortex left)

(74) OFusG r (occipital fusiform gyrus right)

(75) OFusG l (occipital fusiform gyrus left)

(76) FO r (frontal operculum cortex right)

(77) FO l (frontal operculum cortex left)

(78) CO r (central opercular cortex right)

(79) CO l (central opercular cortex left)

(80) PO r (parietal operculum cortex right)

(81) PO l (parietal operculum cortex left)

(82) PP r (planum polare right)

(83) PP l (planum polare left)

(84) HG r (Heschl's gyrus right)

(85) HG l (Heschl's gyrus left)

(86) PT r (planum temporale right)

(87) PT l (planum temporale left)

(88) SCC r (supracalcarine cortex right)

(89) SCC l (supracalcarine cortex left)

(90) OP r (occipital pole right)

(91) OP l (occipital pole left)

The authors declare that there is no conflict of interest regarding the publication of this paper.

[1] X. J. Chai, A. N. Castanon, D. Ongur, and S. Whitfield-Gabrieli, "Anticorrelations in resting state networks without global signal regression," NeuroImage, vol. 59, no. 2, pp. 1420-1428, 2012.

[2] C. Chang and G. H. Glover, "Effects of model-based physiological noise correction on default mode network anticorrelations and correlations," NeuroImage, vol. 47, no. 4, pp. 1448-1459, 2009.

[3] M. D. Fox, D. Zhang, A. Z. Snyder, and M. E. Raichle, "The global signal and observed anticorrelated resting state brain networks," Journal of Neurophysiology, vol. 101, no. 6, pp. 3270-3283, 2009.

[4] F. Parente, M. Frascarelli, A. Mirigliani, F. Di Fabio, M. Biondi, and A. Colosimo, "Negative functional brain networks," Brain Imaging and Behavior, pp. 1-10, 2017.

[5] P. Sanz-Leon, S. A. Knock, A. Spiegler, and V. K. Jirsa, "Mathematical framework for large-scale brain network modeling in the virtual brain," NeuroImage, vol. 111, pp. 385-430, 2015.

[6] C. Morris and H. Lecar, "Voltage oscillations in the barnacle giant muscle fiber," Biophysical Journal, vol. 35, no. 1, pp. 193-213, 1981.

[7] R. Larter, B. Speelman, and R. M. Worth, "A coupled ordinary differential equation lattice model for the simulation of epileptic seizures," Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 9, no. 3, pp. 795-804, 1999.

[8] M. Breakspear, ""Dynamic" connectivity in neural systems: theoretical and empirical considerations," Neuroinformatics, vol. 2, no. 2, pp. 205-224, 2004.

[9] R. Fitzhugh, "Impulses and physiological states in theoretical models of nerve membrane," Biophysical Journal, vol. 1, no. 6, pp. 445-466, 1961.

[10] J. Nagumo, S. Arimoto, and S. Yoshizawa, "An active pulse transmission line simulating nerve axon," Proceedings of IRE, vol. 50, no. 10, pp. 2061-2070, 1962.

[11] H. Wilson and J. Cowan, "Excitatory and inhibitory interactions in localized populations of model neurons," Biophysical Journal, vol. 12, no. 1, pp. 1-24, 1972.

[12] W. J. Freeman, Mass Action in the Nervous System, Academic Press, New York San Francisco, London, 1975.

[13] G. Deco, V. K. Jirsa, and A. R. McIntosh, "Emerging concepts for the dynamical organization of resting-state activity in the brain," Nature Reviews Neuroscience, vol. 12, no. 1, pp. 43-56, 2011.

[14] J. Cabral, E. Hugues, O. Sporns, and G. Deco, "Role of local network oscillations in resting-state functional connectivity," NeuroImage, vol. 57, no. 1, pp. 130-139, 2011.

[15] G. Deco and V. K. Jirsa, "Ongoing cortical activity at rest: criticality, multistability, and ghost attractors," Journal of Neuroscience, vol. 32, no. 10, pp. 3366-3375, 2012.

[16] A. Ghosh, Y. Rho, A. R. McIntosh, R. Kotter, and V. K. Jirsa, "Cortical network dynamics with time delays reveals functional connectivity in the resting brain," Cognitive Neurodynamics, vol. 2, no. 2, pp. 115-120, 2008.

[17] C. J. Honey, O. Sporns, L. Cammoun et al., "Predicting human resting-state functional connectivity from structural connectivity," Proceedings of the National Academy of Sciences of the United States of America, vol. 106, no. 6, pp. 2035-2040, 2009.

[18] J. A. Acebron, L. L. Bonilla, C. J. Perez Vicente, F. Ritort, and R. Spigler, "The Kuramoto model: a simple paradigm for synchronization phenomena," Reviews of Modern Physics, vol. 77, no. 1, pp. 137-185, 2005.

[19] T. K. Das, P. M. Abeyasinghe, J. S. Crone et al., "Highlighting the structure-function relationship of the brain with the Ising model and graph theory," BioMed Research International, vol. 2014, Article ID 237898, 14 pages, 2014.

[20] G. C. Garcia, A. Lesne, M. T. Hutt, and C. C. Hilgetag, "Building blocks of self-sustained activity in a simple deterministic model of excitable neural networks," Frontiers in Computational Neuroscience, vol. 6, p. 50, 2012.

[21] A. Messe, M. T. Hutt, P. Konig, and C. C. Hilgetag, "A closer look at the apparent correlation of structural and functional connectivity in excitable neural networks," Scientific Reports, vol. 5, no. 1, article 7870, 2015.

[22] A. R. Carvunis, M. Latapy, A. Lesne, C. Magnien, and L. Pezard, "Dynamics of three-state excitable units on poisson vs. power-law random networks," Physica A: Statistical Mechanics and its Applications, vol. 367, pp. 595-612, 2006.

[23] K. E. Joyce, P. J. Laurienti, and S. Hayasaka, "Complexity in a brain-inspired agent-based model," Neural Networks, vol. 33, pp. 275-290, 2012.

[24] S. Wolfram, "Universality and complexity in cellular automata," Physica D: Nonlinear Phenomena, vol. 10, no. 1-2, pp. 1-35, 1984.

[25] F. Parente and A. Colosimo, "The role of negative links in brain networks," Biophysics and Bioengineering Letters, vol. 9, no. 1, pp. 1-13, 2016.

[26] V. Caviness, J. Meyer, N. Makris, and D. Kennedy, "MRI-based topographic parcellation of human neocortex: an anatomically specified method with estimate of reliability," Journal of Cognitive Neuroscience, vol. 8, no. 6, pp. 566-587, 1996.

[27] S. Achard, R. Salvador, B. Whitcher, J. Suckling, and E. Bullmore, "A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs," The Journal of Neuroscience, vol. 26, no. 1, pp. 63-72, 2006.

[28] E. Bullmore and O. Sporns, "The economy of brain network organization," Nature Reviews Neuroscience, vol. 13, no. 5, pp. 336-349, 2012.

[29] Y. Behzadi, K. Restom, J. Liau, and T. T. Liu, "A component based noise correction method (CompCor) for bold and perfusion based fMRI," NeuroImage, vol. 37, no. 1, pp. 90-101, 2007.

[30] K. Gopinath, V. Krishnamurthy, R. Cabanban, and B. A. Crosson, "Hubs of anticorrelation in high-resolution resting-state functional connectivity network architecture," Brain Connectivity, vol. 5, no. 5, pp. 267-275, 2015.

[31] R. Murphy, R. M. Birn, D. A. Handwerker, T. B. Jones, and P. A. Bandettini, "The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?," NeuroImage, vol. 44, no. 3, pp. 893-905, 2009.

Fabrizio Parente (iD) and Alfredo Colosimo (iD)

Deparment of Anatomy, Histology, Forensic Medicine and Orthopedics, Sapienza University of Rome, Rome, Italy

Correspondence should be addressed to Fabrizio Parente [email protected]

Received 5 July 2017 Revised 19 December 2017 Accepted 9 January 2018 Published 19 March 2018

Academic Editor: Stuart C. Mangel

Caption: Figure 1: Brain parcellation. Location of the brain regions considered in the extraction of the BOLD signal and visible in a sagittal brain representation. For the complete list of the 105 regions considered in this work, taken from FSL Harvard-Oxford maximum likelihood cortical and subcortical atlas, see the Appendix.

Caption: Figure 2: Working out the connectivity matrices. (a) Refers to point (1) of the procedure detailed in the text. The fractions in (b) concern the highest absolute correlation values of the threshold in the corresponding matrices (see point (2) in the text for details).

Caption: Figure 3: State balance of an agent (A) surrounded by six neighbors. (a) Activity levels of an agent in the SER (susceptible-excited-refractory) states: top and bottom pictures refer to a cycling scheme and to the classical action potential scheme, respectively. In parentheses are the 0/1 activity level of the state. sop and nep indicate the probability of getting the S [right arrow] E and R [right arrow] S state change, respectively (see the text for further details). (b) The state of the central node (A) in the next time step depends upon local (endogenous) and global (exogenous) factors. Three out of the four positively linked neighbors are active (1), so the average activity (3/4) exceeds the [[[phi].sub.p] = 0.5 threshold. This is also the case for the both active (1) and negatively linked neighbors, since [[phi].sub.n] = 0.5 also.

Caption: Figure 4: Example of a simulated time series. The time series corresponds to the condition included in Figure 5(b) (blue curve), namely, to the following parameter values: sop = 0.225, nep = 0.025, [[pi].sub.p] = [[pi].sub.n] = 0.1, and absolute-values-proportional-threshold = 100%. The spots indicate an excited state (E) for each of the 105 brain regions in each step of the simulation.

Caption: Figure 5: Fitting empirical data by the ABM model: dependence upon model's parameters. (a) Connectivity-dependent parameters ([[pi].sub.p] and [[pi].sub.p]) on the x-axis. Blue, green, and red lines indicate, respectively, nep values of 0.25, 0.50, and 0.75. (b) Cost (network density) parameter on the x-axis sop and nep fixed at 0.025 and 0.225, respectively. Blue, green, red, and light-blue lines indicate, respectively, 0.1, 0.075, 0.05, and 0.025 values of [[pi].sub.p] and [[pi].sub.n]. Notice that a peak of the goodness-of-fit appears at [[pi].sub.p], [[pi].sub.n] = 0.1, in the lower range only of the network density. In all cases, the Pearson correlation (r) is used as a goodness-of-fit index.

Caption: Figure 6: Fitting empirical data by combinations of positive and negative cost. The false-color scale visualizes the Pearson correlation between experiments and simulations obtained using the fractions of negative and positive links indicated in the horizontal and vertical axes, respectively.

Caption: Figure 7: Post hoc analysis. Mean differences of the goodness-of-fit using an increasing amount of positive and negative links. (a) Goodnessof-fit as a function of positive links. (b) Goodness-of-fit as a function of negative links. (c) Goodness-of-fit as a function of negative links in the range of 5%-30% positive cost a significant difference between the first mean value in blue (no negative links) is reached for the highest value (in red) of negative cost: 25%-30%.

Caption: Figure 8: Modeling individual patterns. The goodness-of-fit values as a function of increasing amount of negative links (average of the fraction of positive links between 5% and 30%) concern 8 randomly chosen subjects. For the average values of the whole group of subjects, see Figure 7(c).


1. Introduction

The ability to learn from performance feedback is crucial to flexibly adapt to a changing environment. Behavioral performance during feedback learning shows a protracted development which continues into adolescence (Huizinga et al., 2006). Several studies have investigated the neural underpinnings of feedback processing. Studies in adults have shown that learning from feedback is associated with activity in a frontoparietal network, including dorsolateral prefrontal cortex (DLPFC), supplementary motor area (SMA), anterior cingulate cortex (ACC) and superior parietal cortex (SPC) (Carter and van Veen, 2007, Mars et al., 2005, Zanolie et al., 2008). Intriguingly, developmental neuroimaging studies have reported age-related activity changes in this network during feedback processing, suggesting an important link between feedback learning and neural maturation of the frontoparietal network (Crone et al., 2008, Peters et al., 2014a, Van Duijvenvoorde et al., 2008, Velanova et al., 2008). Despite these findings, little is known about developmental trajectories in the frontoparietal network and there is surprising little consistency in the direction of change, with some studies reporting increased neural activation with age and others decreased neural activation with age (Crone and Dahl, 2012).

An important question in cognitive development concerns the shape of developmental trajectories. One possible hypothesis would be that activity in the frontoparietal network during feedback learning follows a linear trajectory, based on dual-systems models predicting steadily increasing frontoparietal recruitment from childhood to adulthood combined with an adolescent peak in socio-emotional sensitivity in subcortical systems (Ernst et al., 2006, Somerville and Casey, 2010, Steinberg, 2008). On the other hand, prior cross-sectional studies provided preliminary evidence for non-linear developmental patterns of frontoparietal activity during feedback learning (Peters et al., 2014a, Van den Bos et al., 2009, Van Duijvenvoorde et al., 2008). These findings indicated that young adolescents are capable of recruiting frontoparietal regions but in different situations than adults, arguing against a simple frontoparietal immaturity model with linear development in cognitive control regions.

Several recent neuroimaging studies have used longitudinal measurements of neural activity to test for neurocognitive changes over development (Ordaz et al., 2013, Paulsen et al., 2015). Longitudinal designs have critical advantages over cross-sectional designs. For instance, previous studies demonstrated important individual differences in developmental trajectories that can be overlooked in cross-sectional designs (Koolschijn et al., 2011, Ordaz et al., 2013, Shaw et al., 2013). Furthermore, longitudinal designs have increased power to detect developmental change, because testing within-individual changes reduces error related to cohort differences (Fjell et al., 2010, Koolschijn et al., 2011). In the current study, neural changes in frontoparietal cortex activity were examined by testing whether frontoparietal activity during feedback learning follows a linear pattern (i.e. monotonic development over time, no adolescent-specific changes), a quadratic pattern (i.e., adolescent-specific effects) or a cubic pattern (adolescent-emergent e.g. stable levels during childhood, steep changes in adolescence and stabilization in adulthood) (Braams et al., 2015, Somerville et al., 2013). Our longitudinal approach allows for a more specific test of the different hypotheses concerning the pattern of developmental change in frontoparietal areas.

Besides investigating age-related patterns of neural activity, a second goal of this study was to investigate other factors influencing time-related changes in frontoparietal activity in addition to age. There are multiple processes closely related to advancing age that may drive changes in neural activity. That is, an increase in age could be the sole factor explaining time-related increases or decreases in activity, but other factors might also play a role. The factors investigated in this study were task performance, working memory and structural brain development. Task performance has been shown to influence neural activity, and there is evidence that a portion of developmental changes attributed to advancing age are related more to changes in performance (Church et al., 2010, Dumontheil et al., 2010, Koolschijn et al., 2011). Here we tested whether performance on a feedback learning task partly explained changes in neural activation over time. Working memory has previously been argued to be a core prerequisite for cognitive development (Case, 1992) and cognitive control functions (Huizinga et al., 2006), and as such was investigated as an important contributor to changes over time in neural activity during feedback learning. That is, we aimed to study whether a portion of changes in neural activity during feedback learning was explained by individual differences in working memory. A final factor that was investigated is cortical thickness. Several cross-sectional studies have suggested a link between functional activity and structural gray matter in adults (Harms et al., 2013, Hegarty et al., 2012) and children (Dumontheil et al., 2010, Lu et al., 2009, Wendelken et al., 2011). It is likely that developmental changes in neural activity are at least partly influenced by structural development of these brain regions, although the longitudinal relation between structural maturation and development of brain function is not well understood.

Taken together, in this study, we tested developmental trajectories of activation in the frontoparietal network during feedback learning in a large longitudinal fMRI sample across a wide age range (N =򠈈, 8� years) with a two year interval between the first and second time point (see Peters et al., 2014a, Peters et al., 2014b). Our aims were (1) to examine growth trajectories of core areas in the frontoparietal network (DLPFC, SMA, ACC and SPC) and to define the shape of age-related changes, (2) to test the additional contributions of task performance, working memory and structural development to changes over time in neural activity for feedback learning.


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