Examining network dynamics after traumatic brain injury using the extended unified SEM approach
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The current study uses effective connectivity modeling to examine how individuals with traumatic brain injury (TBI) learn a new task. We make use of recent advancements in connectivity modeling (extended unified structural equation modeling, euSEM) and a novel iterative grouping procedure (Group Iterative Multiple Model Estimation, GIMME) in order to examine network flexibility after injury. The study enrolled 12 individuals sustaining moderate and severe TBI to examine the influence of task practice on connections between 8 network nodes (bilateral prefrontal cortex, anterior cingulate, inferior parietal lobule, and Crus I in the cerebellum). The data demonstrate alterations in networks from pre to post practice and differences in the models based upon distinct learning trajectories observed within the TBI sample. For example, better learning in the TBI sample was associated with diminished connectivity within frontal systems and increased frontal to parietal connectivity. These findings reveal the potential for using connectivity modeling and the euSEM to examine dynamic networks during task engagement and may ultimately be informative regarding when networks are moving in and out of periods of neural efficiency.
KeywordsfMRI TBI Brain injury Rehabilitation Working memory Cognitive control
We wish to thank Julia Slocomb and Jeffrey Vesek for their efforts in subject recruitment and MRI data acquisition. This work was funded in part by the New Jersey Comission for Brain Injury Research (09.001.BIR1).
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