Regionally Specific Atrophy Following Traumatic Brain Injury DG MCLAREN, BB BENDLIN, and SC JOHNSON University of Wisconsin—Madison & GRECC, Madison VA.

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Presentation transcript:

Regionally Specific Atrophy Following Traumatic Brain Injury DG MCLAREN, BB BENDLIN, and SC JOHNSON University of Wisconsin—Madison & GRECC, Madison VA Hospital

Background Identifying longitudinal changes are essential in understanding plasticity and predicting future outcomes Surface-based approaches have been used in functional and structural analyses

Using the Cortical Surface McLaren et al. 2007, Van Essen et al & 2006

Background Identifying longitudinal changes are essential in understanding plasticity and predicting future outcomes Surface-based approaches have been used in functional and structural analyses Can Surface-based approaches be used in longitudinal analyses?Can Surface-based approaches be used in longitudinal analyses?

Study Parameters T1-weighted SPGRs were collected at ~79 days and ~409 days post injury Standard Axial SPGR sequence with.9375x.9375x1.2mm voxel dimensions

Processing Steps 2 x Original T1 Align Brains via Skull; Compute Flow SIENA

Global Results Using SIENA N=30N=36 P<.0001 Trivedi et al. 2006

Processing Steps 2 x Original T1 Align Brains via Skull; Compute Flow Bias Corrected T1 (Brain Only) Normalize & Surface Analysis SIENA

Surface Creation and Registration Van Essen 2005

Within Subject Surface Co-registration Registration to PALS of the same surface produces identical results Registration of an image to itself doesn ’ t produce the same results

Within Subject Surface Co-registration T-statistic of time 2 minus time 1 scans Percent Brain Volume Change: P<.0001 corrected

Within Subject Surface Co-registration

TBI Patient Cross-section (time 1) 1028 P<.0001 corrected

TBI Patient Cross-section (time 2) P<.0001 corrected

TBI Patient Longitudinal Percent Brain Volume Change: P<.0001 corrected

Conclusions Surface registration is stable Normal Controls show little or no changes consistent with SIENA Method is sensitive to changes over time (and errors in segmentation) One of the first illustrations of longitudinal changes using the cortical surface

Future Directions Improve segmentation to increase accuracy More automated and stable procedure Create study specific averages Compare against SIENAr Other patient populations

Acknowledgements UW Institute of Aging Collaborators on the project Trivedi MA, Ward MA, Hess TM, Gale SD, Dempsey RJ, Rowley HA Funding Support: –NIH: T32 AG20013 –NIH: RO1 MH65723 –NIH: T32 GM –Merit Review Grant from the Department of Veterans Affairs