Abstract #2397

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

Anna-Thekla Schmidt^1,2, Julia M Huntenburg¹, Christine L Tardif^3,4, Claudine J Gauthier⁵, Arno Villringer¹, Christopher J Steele^1,6, and Pierre-Louis Bazin^1,7,8

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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