functional diffusion tensor imaging measuring task-related fractional anisotropy changes in the human brain along white matter tracts功能性扩散张量成像测量任务相关分数各向异性的变化人类大脑白质束.pdfVIP

functional diffusion tensor imaging measuring task-related fractional anisotropy changes in the human brain along white matter tracts功能性扩散张量成像测量任务相关分数各向异性的变化人类大脑白质束.pdf

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functional diffusion tensor imaging measuring task-related fractional anisotropy changes in the human brain along white matter tracts功能性扩散张量成像测量任务相关分数各向异性的变化人类大脑白质束

Functional Diffusion Tensor Imaging: Measuring Task- Related Fractional Anisotropy Changes in the Human Brain along White Matter Tracts ´ 1 1 2 1 ´ 1 Rene C. W. Mandl *, Hugo G. Schnack , Marcel P. Zwiers , Arjen van der Schaaf , Rene S. Kahn , Hilleke E. Hulshoff Pol1 1 Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands, 2 Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University Nijmegen, Nijmegen,The Netherlands Abstract Background: Functional neural networks in the human brain can be studied from correlations between activated gray matter regions measured with fMRI. However, while providing important information on gray matter activation, no information is gathered on the co-activity along white matter tracts in neural networks. Methodology/Principal Findings: We report on a functional diffusion tensor imaging (fDTI) method that measures task- related changes in fractional anisotropy (FA) along white matter tracts. We hypothesize that these fractional anisotropy changes relate to morphological changes of glial cells induced by axonal activity although the exact physiological underpinnings of the measured FA changes remain to be elucidated. As expected, these changes are very small as compared to the physiological noise and a reliable detection of the signal change would require a large number of measurements. However, a substantial increase in signal-to-noise ratio was achieved by pooling the signal over the complete fiber tract. Adopting such a tract-based statistics enabled us to measure

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