Paper Info
Title
Detecting Mild Traumatic Brain Injury Using Resting State Magnetoencephalographic Connectivity.
Abstract
Accurate means to detect mild traumatic brain injury (mTBI) using objective and quantitative measures remain elusive. Conventional imaging typically detects no abnormalities despite post-concussive symptoms. In the present study, we recorded resting state magnetoencephalograms (MEG) from adults with mTBI and controls. Atlas-guided reconstruction of resting state activity was performed for 90 cortical and subcortical regions, and calculation of inter-regional oscillatory phase synchrony at various frequencies was performed. We demonstrate that mTBI is associated with reduced network connectivity in the delta and gamma frequency range (>30 Hz), together with increased connectivity in the slower alpha band (8-12 Hz). A similar temporal pattern was associated with correlations between network connectivity and the length of time between the injury and the MEG scan. Using such resting state MEG network synchrony we were able to detect mTBI with 88% accuracy. Classification confidence was also correlated with clinical symptom severity scores. These results provide the first evidence that imaging of MEG network connectivity, in combination with machine learning, has the potential to accurately detect and determine the severity of mTBI.
Year
DOI
Venue
2016
10.1371/journal.pcbi.1004914
PLOS COMPUTATIONAL BIOLOGY
Field
DocType
Volume
Network connectivity,Biology,Brain damage,Resting state fMRI,Bioinformatics,Neuroimaging,Audiology,Traumatic brain injury,Magnetoencephalography,Electroencephalography,Magnetic resonance imaging
Journal
12
Issue
ISSN
Citations 
12
1553-7358
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Vasily A. Vakorin1555.75
Sam M. Doesburg2122.18
Leodante da Costa300.34
Rakesh Jetly400.34
Elizabeth W. Pang511.02
Margot J Taylor611027.22