Abstract #2633

Linked Independent Component Analysis for Denoising multi-centre 7T MRI data

Catarina Rua^1,2, Alberto Llera^3,4, Olivier Mougin⁵, Mauro Costagli⁶, Renat Yakupov⁷, Richard Bowtell⁵, James B Rowe^1,8, and Christopher T Rodgers^9,10

¹Department of Clinical Neurosciences and University of Cambridge Centre for Parkinson-plus, University of Cambridge, Cambridge, United Kingdom, ²Wolfson Brain Imaging Centre, University of Cambridge, cambridge, United Kingdom, ³Cognitive Neuroscience, Radboud University Medical Centre, Nijemen, Netherlands, ⁴Donders Institute, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, Netherlands, ⁵Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom, ⁶IMAGO7 Foundation, Pisa, Italy, ⁷German Centre for Neurodegenerative Diseases (DZNE), Magdeburg, Germany, ⁸Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom, ⁹Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom, ¹⁰Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom

Multi-site studies are an attractive option at ultra-high field (7T) as it is possible to pool larger number of datasets of healthy and patients increasing the statistical power of neuroimaging studies. However, differences on scanner hardware and software increase variability in the measurements obtained on across imaging sites.In this study we have piloted the application of a multimodal ICA approach for denoising scanner effects across two different 7T MRI scanner platforms.

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