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Directed functional and structural connectivity in a large-scale model for the mouse cortex.

ABSTRACT: Inferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the generalized partial directed coherence (GPDC), provide estimates of the causal influence between areas. However, the relation between causality estimates and structural connectivity is still not clear. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale network model of the mouse cortex. The model contains 19 cortical areas composed of spiking neurons, with areas connected by long-range projections with weights obtained from a tract-tracing cortical connectome. We show that GPDC values provide a reasonable estimate of structural connectivity, with an average Pearson correlation over simulations of 0.74. Moreover, even in a typical electrophysiological recording scenario containing five areas, the mean correlation was above 0.6. These results suggest that it may be possible to empirically estimate structural connectivity from functional connectivity even when detailed whole-brain recordings are not achievable.

SUBMITTER: Nunes RV

PROVIDER: S-EPMC8746117 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Directed functional and structural connectivity in a large-scale model for the mouse cortex.

Nunes Ronaldo V RV Reyes Marcelo B MB Mejias Jorge F JF de Camargo Raphael Y RY

Network neuroscience (Cambridge, Mass.) 20211130 4

Inferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the generalized partial directed coherence (GPDC), provide estimates of the causal influence between areas. However, the relation between causality estimates and structural connectivity is still not clear. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, d ...[more]

PMID: 35024534

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Project description:Altered functional connectivity (FC) between the medial prefrontal cortex (mPFC) and amygdala is widely implicated as a neural mechanism explaining risk for psychopathology among those exposed to early life trauma. Nonetheless, contemporary neuroimaging research has shifted toward large-scale network models of brain function, and it is not clear how this common bi-nodal finding fits into larger-scale network models. Here, using dynamic functional connectivity (DFC) approaches combined with large-scale network analyses, the larger role of bi-nodal FC between mPFC and amygdala among a sample of adolescent girls is investigated. The sample was comprised of 30 healthy control girls and 26 girls exposed to either physical or sexual assault who underwent a resting-state scan during 3T MRI. DFC using a sliding window approach was used to create weighted, undirected, graphs from the resting-state data following parcellation with a 215 regions-of-interest (ROI) atlas. Using a priori ROI, the predicted finding of lessor FC between mPFC and amygdala as a function of early life trauma was replicated in this sample. By contrast, early life trauma was associated with greater large-scale network modularity. Using a dynamic FC approach, it is also demonstrated that within-subject variability in this bi-nodal FC closely tracks within-subject fluctuations in large-scale network patterns, including connectivity between a limbic and default mode network (in which the amygdala and mPFC nodes belong, respectively) as well as overall modular organization. These results suggest that bi-nodal FC, such as amygdala-mPFC FC, may generally reflect larger-scale network patterns. Future research is necessary to understand whether these associations between nodal FC and large-scale network organization better reflect top-down processes (larger-scale network organization drives bi-nodal FC) or bottom-up processes (bi-nodal FC drives larger-scale network organization) and the related impact of early life trauma.

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Dataset Information

Directed functional and structural connectivity in a large-scale model for the mouse cortex.

Publications

Directed functional and structural connectivity in a large-scale model for the mouse cortex.

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