Combination of nonlinear registration methods with high resolution fMRI for a fine exploration of human primary motor hand area

Functional investigation of human hand representation in the motor area M1 requires high resolution functional imaging, to finely separate activation in M1, and a perfect alignment of individual central sulci to improve functional areas overlap and significance of statistical parametric maps obtained from different hand movements. Based on anatomical measures, we show how recent global diffeomorphic registration techniques impact positively on the alignment of sulcal folds in M1 area. With functional measures, we evaluate their effect on the robust detection and localization of group brain activation for flexion/extension of right and left thumbs/fingers and wrists. The methodology we propose opens the way to a non invasive functional exploration of the human hand motor cortex at the group level under different normal, pathological or after rehabilitative conditions.

[1]  D. Hoffman,et al.  Muscle and movement representations in the primary motor cortex. , 1999, Science.

[2]  A. Schleicher,et al.  Two different areas within the primary motor cortex of man , 1996, Nature.

[3]  Satrajit S. Ghosh,et al.  Evaluation of volume-based and surface-based brain image registration methods , 2010, NeuroImage.

[4]  Lawrence L. Wald,et al.  Comparison of physiological noise at 1.5 T, 3 T and 7 T and optimization of fMRI acquisition parameters , 2005, NeuroImage.

[5]  H. Alkadhi,et al.  Localization of the motor hand area to a knob on the precentral gyrus. A new landmark. , 1997, Brain : a journal of neurology.

[6]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.

[7]  Y. Hsu,et al.  Study-specific EPI template improves group analysis in functional MRI of young and older adults , 2010, Journal of Neuroscience Methods.

[8]  Alain Trouvé,et al.  Diffeomorphic Brain Registration Under Exhaustive Sulcal Constraints , 2011, IEEE Transactions on Medical Imaging.

[9]  Katrin Amunts,et al.  Cortical Folding Patterns and Predicting Cytoarchitecture , 2007, Cerebral cortex.

[10]  Jean-Philippe Thiran,et al.  Local landmark-based registration for fMRI group studies of nonprimary auditory cortex , 2009, NeuroImage.

[11]  D. Louis Collins,et al.  Retrospective evaluation of intersubject brain registration , 2003, IEEE Transactions on Medical Imaging.

[12]  Michael A Yassa,et al.  A quantitative evaluation of cross-participant registration techniques for MRI studies of the medial temporal lobe , 2009, NeuroImage.

[13]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[14]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[15]  R. Herndon,et al.  National Multiple Sclerosis Society Working Group on Neuroimaging for the Medical Advisory Board , 1987, Neuroradiology.

[16]  R. Wiest,et al.  Mapping of direction and muscle representation in the human primary motor cortex controlling thumb movements , 2009, The Journal of physiology.