NEUROIMAGING AND REHABILITATION SPECIAL ISSUE Examining network dynamics after traumatic brain injury using the extended unified SEM approach F. G. Hillary & J. D. Medaglia & K. M. Gates & P. C. Molenaar & D. C. Good # Springer Science+Business Media New York 2012 Abstract 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 Esti- mation, 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 alter- ations in networks from pre to post practice and differences in the models based upon distinct learning trajectories ob- served within the TBI sample. For example, better learning in the TBI sample was associated with diminished connec- tivity 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. Keywords fMRI . TBI . Brain injury . Rehabilitation . Working memory . Cognitive control Introduction Traumatic brain injury (TBI) is the most common neurolog- ical disorder in young adults, with an annual incidence of 1.7 million and societal costs including lost productivity estimated at $60 billion (Centers for Disease Control and Prevention, N.C.f.I.P.a.C.; Available from: http:// www.cdc.gov/traumaticbraininjury/). Unfortunately, there remains much unknown about how neural systems adjust to TBI and the factors associated with recovery. Over the past decade there has been a dramatic increase in the use of functional neuroimaging techniques such as blood oxygen level dependent functional magnetic resonance imaging (BOLD fMRI) to examine cognitive, sensory, and motor dysfunction in neurologically impaired samples. To date, fMRI has provided new avenues to study brain functioning and unparalleled opportunities to examine brain disorders. In the clinical neurosciences, fMRI methods have tradi- tionally been used to document between-group differences with focus on mean BOLD signal change between healthy and clinical samples. While informative, these between- group comparisons are often insensitive to individual differ- ences in response to injury. This problem is particularly important in neurological disorders that are notoriously het- erogeneous, such as TBI; findings observable at the group level likely reflect only gross brain responses and are un- likely to be sensitive to injury-specific adjustments in neural systems, making any inferences about the individual unfea- sible. The goal of the current study is to demonstrate the use of a novel connectivity method for documenting changes in neural networks over time in the individual after TBI. We aim to elicit short-term plasticity via repeated exposure to a F. G. Hillary (*) : J. D. Medaglia Department of Psychology, The Pennsylvania State University, 347 Moore Building, University Park, PA 16802, USA e-mail: fhillary@psu.edu K. M. Gates : P. C. Molenaar Human Development and Family Studies, University Park, PA 16802, USA F. G. Hillary : D. C. Good Department of Neurology, Hershey Medical Center, Hershey, PA, USA Brain Imaging and Behavior DOI 10.1007/s11682-012-9205-0