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. 2017 Jan 25;37(4):839-853.
doi: 10.1523/JNEUROSCI.1672-16.2016.

Dynamic Reconfiguration of Visuomotor-Related Functional Connectivity Networks

Affiliations

Dynamic Reconfiguration of Visuomotor-Related Functional Connectivity Networks

Andrea Brovelli et al. J Neurosci. .

Abstract

Cognitive functions arise from the coordination of large-scale brain networks. However, the principles governing interareal functional connectivity dynamics (FCD) remain elusive. Here, we tested the hypothesis that human executive functions arise from the dynamic interplay of multiple networks. To do so, we investigated FCD mediating a key executing function, known as arbitrary visuomotor mapping, using brain connectivity analyses of high-gamma activity recorded using MEG and intracranial EEG. Visuomotor mapping was found to arise from the dynamic interplay of three partly overlapping cortico-cortical and cortico-subcortical functional connectivity (FC) networks. First, visual and parietal regions coordinated with sensorimotor and premotor areas. Second, the dorsal frontoparietal circuit together with the sensorimotor and associative frontostriatal networks took the lead. Finally, cortico-cortical interhemispheric coordination among bilateral sensorimotor regions coupled with the left frontoparietal network and visual areas. We suggest that these networks reflect the processing of visual information, the emergence of visuomotor plans, and the processing of somatosensory reafference or action's outcomes, respectively. We thus demonstrated that visuomotor integration resides in the dynamic reconfiguration of multiple cortico-cortical and cortico-subcortical FC networks. More generally, we showed that visuomotor-related FC is nonstationary and displays switching dynamics and areal flexibility over timescales relevant for task performance. In addition, visuomotor-related FC is characterized by sparse connectivity with density <10%. To conclude, our results elucidate the relation between dynamic network reconfiguration and executive functions over short timescales and provide a candidate entry point toward a better understanding of cognitive architectures.

Significance statement: Executive functions are supported by the dynamic coordination of neural activity over large-scale networks. The properties of large-scale brain coordination processes, however, remain unclear. Using tools combining MEG and intracranial EEG with brain connectivity analyses, we provide evidence that visuomotor behaviors, a hallmark of executive functions, are mediated by the interplay of multiple and spatially overlapping subnetworks. These subnetworks span visuomotor-related areas, the cortico-cortical and cortico-subcortical interactions of which evolve rapidly and reconfigure over timescales relevant for behavior. Visuomotor-related functional connectivity dynamics are characterized by sparse connections, nonstationarity, switching dynamics, and areal flexibility. We suggest that these properties represent key aspects of large-scale functional networks and cognitive architectures.

Keywords: MEG; SEEG; dynamic reconfiguration; executive functions; functional connectivity dynamics; high-gamma activity.

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Figures

Figure 1.
Figure 1.
A, Arbitrary visuomotor mapping task. B, MarsAtlas: cortical parcellation displaying the anatomical gradients both in the rostrocaudal and dorsoventral directions; for a detailed description, see Auzias et al. (2016). C, MarsAtlas: single-subject exemplar volumetric representation displaying subcortical regions included in the atlas: nucleus accumbens, amygdala, hippocampus, globus pallidus, putamen, caudate nucleus, and thalamus.
Figure 2.
Figure 2.
Statistical map displaying the brain areas associated with a significant increases in HGA with respect to baseline (time point and cluster-level threshold were set to q < 0.001 FDR corrected). The anatomical labels of subcortical areas are NAc (nucleus accumbens), Amyg (amygdala), Hipp (hippocampus), GP (globus pallidus), Put (putamen), Cd (caudate nucleus), and Thal (thalamus).
Figure 3.
Figure 3.
SFL. A, Mean SFL connectivity matrix averaged over time. B, SFL time course averaged over pairs of areas. The threshold for significant SFL was equal to 2.43. Error bars indicate the 95% confidence interval.
Figure 4.
Figure 4.
Graph theoretical measures. A, Dependence between threshold values (q-values) and FC density. B, Temporal evolution of density D at q = 0.05.
Figure 5.
Figure 5.
FCD. A, Time course of the average SFL for the three identified LCs. Error bars indicate the 95% confidence interval. Spatial patterns for the three LCs are displayed in BD. The thickness of the green links is proportional to the time-averaged SFL between areas and the color and size of nodes are proportional to the mean SFL between each area and the rest of the network (i.e., the weight).
Figure 6.
Figure 6.
Exemplar evolution for the dorsolateral motor (A, C) and premotor (B, D) areas. A and B show the involvement of Mdl and PMdl in the three LCs, respectively. C and D depict the involvement in percentage value.
Figure 7.
Figure 7.
Flexibility analysis. A, Mean evolution of network flexibility (shaded area represents the SD). B, Areal flexibility for the five most representative brain regions.
Figure 8.
Figure 8.
Relation between mean FC and distance for the thalamus (A) and caudate nucleus (B). Each dot corresponds to a brain area displaying significant FC with the seed regions.
Figure 9.
Figure 9.
Distribution of normalized inner products between the average dipole orientations of the left thalamus and caudate nucleus across participants (A). The boxplot depicts extreme values (whiskers), first and third quartile (box) and median (red line). B, Time course of mean correlation coefficient.
Figure 10.
Figure 10.
SEEG validation of HGA modulations. Comparison between SEEG single-subjects t-value for HGA (blue) and group-level MEG results (red curves). Subject number, brain areas, and the correlation coefficient between the curves are indicated on the top of each panel.

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References

    1. Ahn YY, Bagrow JP, Lehmann S. Link communities reveal multiscale complexity in networks. Nature. 2010;466:761–764. doi: 10.1038/nature09182. - DOI - PubMed
    1. Alegre M, Alonso-Frech F, Rodríguez-Oroz MC, Guridi J, Zamarbide I, Valencia M, Manrique M, Obeso JA, Artieda J. Movement-related changes in oscillatory activity in the human subthalamic nucleus: ipsilateral vs. contralateral movements. Eur J Neurosci. 2005;22:2315–2324. - PubMed
    1. Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD. Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex. 2014;24:663–676. doi: 10.1093/cercor/bhs352. - DOI - PMC - PubMed
    1. Amiez C, Petrides M. Neuroimaging evidence of the anatomo-functional organization of the human cingulate motor areas. Cereb Cortex. 2014;24:563–578. doi: 10.1093/cercor/bhs329. - DOI - PubMed
    1. Amirnovin R, Williams ZM, Cosgrove GR, Eskandar EN. Visually guided movements suppress subthalamic oscillations in Parkinson's disease patients. J Neurosci. 2004;24:11302–11306. doi: 10.1523/JNEUROSCI.3242-04.2004. - DOI - PMC - PubMed

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