A Study of Resting State FMRI Dynamic Functional Network Analysis of MTBI

Thumbnail Image


Publication or External Link





Mild Traumatic Brain Injury (MTBI) is one of the most common

neurological disorders. A subset of patients develop persistent

cognitive deficits. A number of the brain studies have been conducted

to discover the abnormalities and disruptions in the brain functional

networks using similar methods to those employed in more severe brain

disorders such as Alzheimer's or Schizophrenia. Static functional

network analysis using resting state brain fMRI images has shown some

promising results in identifying characteristics of MTBI. However,

recent development in the dynamics of functional networks have been

able to reveal insightful information about anomalies in brain

activities that have not been observed when using traditional static

analysis. Our work focuses on both static and dynamic functional

analysis of the brain. Our overall analysis pipeline is data-driven

using a dataset of 47 MTBI subjects and a demographically matching

healthy control group size of 30. The data-driven approach

proactively removes noise, focuses on the entire brain functional

networks and performs advanced independent component analysis,

followed by statistical tests to characterize the functional networks

of MTBI patients. A key distinction of our research is the finer

labeling of MTBI subject according to their long term 6 months

recovery status. Our results suggest that those MTBI subject who

suffer prolonged recovery exhibit disturbed functional networks, and

slowed dynamism in functional connectivity than those of the healthy

control or those MTBI participants who recovered quickly. A number of

useful network measurements have been found to capture the states and

changes of the brain functional networks for healthy and different

types of MTBI subjects in their resting state. We believe that our

findings can shed more light into the impact of MTBI on the

effectiveness of several functional networks and can contribute to

helping clinicians make more informed decisions to aid in the recovery

of MTBI patients.