An Evolutionary Computation Approach to Examine Functional Brain Plasticity.

Arnab Roy,Rachel A Bernier,Frank G Hillary,Colin Campbell

doi:10.3389/fnins.2016.00146

Abstract

One common research goal in systems neurosciences is to understand how the functional relationship between a pair of regions of interest (ROIs) evolves over time. Examining neural connectivity in this way is well-suited for the study of developmental processes, learning, and even in recovery or treatment designs in response to injury. For most fMRI based studies, the strength of the functional relationship between two ROIs is defined as the correlation between the average signal representing each region. The drawback to this approach is that much information is lost due to averaging heterogeneous voxels, and therefore, the functional relationship between a ROI-pair that evolve at a spatial scale much finer than the ROIs remain undetected. To address this shortcoming, we introduce a novel evolutionary computation (EC) based voxel-level procedure to examine functional plasticity between an investigator defined ROI-pair by simultaneously using subject-specific BOLD-fMRI data collected from two sessions seperated by finite duration of time. This data-driven procedure detects a sub-region composed of spatially connected voxels from each ROI (a so-called sub-regional-pair) such that the pair shows a significant gain/loss of functional relationship strength across the two time points. The procedure is recursive and iteratively finds all statistically significant sub-regional-pairs within the ROIs. Using this approach, we examine functional plasticity between the default mode network (DMN) and the executive control network (ECN) during recovery from traumatic brain injury (TBI); the study includes 14 TBI and 12 healthy control subjects. We demonstrate that the EC based procedure is able to detect functional plasticity where a traditional averaging based approach fails. The subject-specific plasticity estimates obtained using the EC-procedure are highly consistent across multiple runs. Group-level analyses using these plasticity estimates showed an increase in the strength of functional relationship between DMN and ECN for TBI subjects, which is consistent with prior findings in the TBI-literature. The EC-approach also allowed us to separate sub-regional-pairs contributing to positive and negative plasticity; the detected sub-regional-pairs significantly overlap across runs thus highlighting the reliability of the EC-approach. These sub-regional-pairs may be useful in performing nuanced analyses of brain-behavior relationships during recovery from TBI.

Highlights

The human brain is an ever-changing network of complex interactions between billions of neurons operating at multiple spatio-temporal scales (Hebb, 1947; Diamond et al, 1981; Greenough and Chang, 1988)
We introduce a novel voxel-wise evolutionary computation (EC) based procedure to document plasticity between an investigator defined regions of interest (ROIs)-pair across two functional magnetic resonance imaging (fMRI)-scan sessions that are separated by a finite duration of time; the procedure can be run for each subject separately, and it simultaneously utilizes the blood oxygen level dependent (BOLD)-fMRI data from both of the sessions
We examine the functional plasticity between four ROI-pairs in traumatic brain injury (TBI) and healthy control (HC) subjects with focus on large-scale networks that are central to the cognitive neurosciences (e.g., default mode network (DMN), executive control network (ECN))

Summary

Introduction

The human brain is an ever-changing network of complex interactions between billions of neurons operating at multiple spatio-temporal scales (Hebb, 1947; Diamond et al, 1981; Greenough and Chang, 1988). In traumatic brain injury (TBI), there has been increasing interest in documenting the change in pairwise functional relationships among the regions central to large scale networks, such as the default mode network (DMN), salience network, and the executive control network (ECN; Hillary et al, 2011a, 2014; Sharp et al, 2011; Bonnelle et al, 2012; Arenivas et al, 2014; Venkatesan et al, 2014) It is the primary aim in these literatures to understand how the functional relationship between predefined regions in the brain evolves over time

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