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Abstract #2397

Preserving Maximal Spatial Specificity in Resting State Group Analysis at 7 Tesla

Anna-Thekla Schmidt1,2, Julia M Huntenburg1, Christine L Tardif3,4, Claudine J Gauthier5, Arno Villringer1, Christopher J Steele1,6, and Pierre-Louis Bazin1,7,8

1Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 2MaxNetAging, Max Planck Institute for Demographic Research, Rostock, Germany, 3Montreal Neurological Institute and Hospital, Montreal, QC, Canada, 4McGill University, Montreal, QC, Canada, 5Physics, Concordia University / PERFORM Centre, Montreal, QC, Canada, 6Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada, 7Netherlands Institute for Neuroscience, Amsterdam, Netherlands, 8Spinoza Centre for Neuroimaging, Amsterdam, Netherlands

Most studies use standard software pipelines for processing and analyzing fMRI data. These pipelines were designed to work with data from 3 Tesla scanners. With more widespread availability of ultra-high field MRI scanners, new processing techniques need to be applied to address the unique demands of high resolution data and to fully take advantage of the high spatial specificity. Here, we propose a novel approach for processing and analysing high resolution resting state fMRI data.

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