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Quantifying the timecourse of recovery from mild traumatic brain injury using diffusion tensor imaging

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Jing, Min, McGinnity, T.Martin, Coleman, SA, Fuchs, Armin, Steinberg , Fred and Kelso, J.A.Scott (2012) Quantifying the timecourse of recovery from mild traumatic brain injury using diffusion tensor imaging. In: The 18th Annual Meeting of the Organization for Human Brain Mapping (OHBM), Beijing, China. Organization for Human Brain Mapping . 1 pp. [Conference contribution]

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Abstract

Recent studies on mild traumatic brain injury (mTBI) by diffusion tensor imaging (DTI) rely on quantitative comparison of diffusion scalar maps such as fractional anisotropy (FA) and mean diffusivity (MD) between groups, quantitative tractography and tract-based spatial statistics (TBSS). However there is a lack of longitudinal DTI studies of mTBI and an effective approach to quantify temporal changes during recovery from mTBI. Furthermore, existing methods require large data samples which are not suitable for small sample case studies.In this preliminary study, we propose a group based independent component analysis (GICA) to study the temporal change of diffusion patterns during recovery from mTBI. By applying group based ICA, the common spatial pattern within the grouped maps can be separated from noise and artifact, and the temporal information during recovery can be revealed by the corresponding timecourse. The results from quantitative comparison within a mask based on the mean FA skeleton further reveal the trend of recovery. The proposed method not only provides an effective solution to quantify temporal changes in longitudinal studies, but also has the potential to be applied to individual case studies in clinical applications.

Item Type:Conference contribution (Poster)
Keywords:diffusion tensor imaging, mild traumatic brain injury, group independent component analysis
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Intelligent Systems Research Centre
ID Code:20386
Deposited By:Dr Min Jing
Deposited On:13 Aug 2012 10:41
Last Modified:13 Aug 2012 10:41

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