Focal and Connectomic Mapping of Transiently Disrupted Brain Function

TL;DR

This paper introduces a comprehensive framework for mapping brain function by combining focal disruption data with connectomic information, demonstrated through electrical stimulation in epilepsy patients, revealing distributed neural dependencies.

Contribution

It presents a novel statistical framework for analyzing sparse disruptive brain data within a connectomic context, enabling differentiation of local and distributed neural effects.

Findings

Discrepancies found between local and remote connectivity effects on behavior.

Framework successfully maps distributed brain functions with minimal assumptions.

Application reveals remote connectivity influences on motor and sensory behaviors.

Abstract

The distributed nature of the neural substrate, and the difficulty of establishing necessity from correlative data, combine to render the mapping of brain function a far harder task than it seems. Methods capable of combining connective anatomical information with focal disruption of function are needed to disambiguate local from global neural dependence, and critical from merely coincidental activity. Here we present a comprehensive framework for focal and connective spatial inference based on sparse disruptive data, and demonstrate its application in the context of transient direct electrical stimulation of the human medial frontal wall during the pre-surgical evaluation of patients with focal epilepsy. Our framework formalizes voxel-wise mass-univariate inference on sparsely sampled data within the statistical parametric mapping framework, encompassing the analysis of distributed maps defined by any criterion of connectivity. Applied to the medial frontal wall, this transient dysconnectome approach reveals marked discrepancies between local and distributed associations of major categories of motor and sensory behaviour, revealing differentiation by remote connectivity to which purely local analysis is blind. Our framework enables disruptive mapping of the human brain based on sparsely sampled data with minimal spatial assumptions, good statistical efficiency, flexible model formulation, and explicit comparison of local and distributed effects.