Research Article: The evolution of cost-efficiency in neural networks during recovery from traumatic brain injury

Date Published: April 19, 2017

Publisher: Public Library of Science

Author(s): Arnab Roy, Rachel A. Bernier, Jianli Wang, Monica Benson, Jerry J. French, David C. Good, Frank G. Hillary, Emmanuel Andreas Stamatakis.


A somewhat perplexing finding in the systems neuroscience has been the observation that physical injury to neural systems may result in enhanced functional connectivity (i.e., hyperconnectivity) relative to the typical network response. The consequences of local or global enhancement of functional connectivity remain uncertain and this is particularly true for the overall metabolic cost of the network. We examine the hyperconnectivity hypothesis in a sample of 14 individuals with TBI with data collected at approximately 3, 6, and 12 months following moderate and severe TBI. As anticipated, individuals with TBI showed increased network strength and cost early after injury, but by one-year post injury hyperconnectivity was more circumscribed to frontal DMN and temporal-parietal attentional control regions. Cost in these subregions was a significant predictor of cognitive performance. Cost-efficiency analysis in the Power 264 data parcellation suggested that at 6 months post injury the network requires higher cost connections to achieve high efficiency as compared to the network 12 months post injury. These results demonstrate that networks self-organize to re-establish connectivity while balancing cost-efficiency trade-offs.

Partial Text

Each year in the United States there are approximately 1.5 million to 2 million cases of traumatic brain injury (TBI) resulting from an external source and causing significant physical, cognitive, and psychosocial impairment [1]. To aid in understanding the behavioral deficits associated with TBI, over the last decade, blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI, or fMRI) has been widely used to study functional connectivity changes associated with injury. In this literature functional connectivity strength has commonly been defined as the degree of temporal-correlation in the fMRI signals recorded from two identified regions in the brain [2]. Brain connectivity studies have provided novel insights into the influence of TBI on neural networks during motor learning (see [3]), working memory and attentional control [4–6] and the relationship between distinct subnetworks and cognitive performance (e.g., default mode network, DMN and salience network, SN) [4,7–10]. Here our goal is to use BOLD-fMRI to advance this literature in three important ways. First we examine the phenomenon of functional hyperconnectivity (discussed below) with focus on the timing when it occurs during the first year of recovery after TBI, and the regions where it is most likely to be expressed. The second goal was to examine the inter-relationship between network cost and its achieved efficiency (or cost-efficiency relationship) during the recovery period. Finally, we examine hyperconnectivity in the context of cognitive performance. In doing so this study represents an opportunity to examine global/local neural network characteristics at three time points during the first year following moderate and severe TBI.

The current study used a graph theoretical analysis of whole-brain resting fMRI data and structural MRI to examine the changes in the brain connectivity during the first year following moderate and severe TBI. In doing so, this represents the first study to examine the evolution of network cost-efficiency trade-offs during recovery from neurological insult. Using a network of 264 functionally defined regions, hyperconnectivity was observable across distinct regions (e.g., hubs, peripheral nodes) and connection types (long-distance, short-distance connections) but, in the current data, increased connectivity peaked at about 6-months post injury (Time-2) before equilibrating in most subjects resulting in residual hyperconnectivity in only a few subnetworks by Time-3.




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