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Abstract #1433

Estimating microscopy-informed fibre orientations from dMRI data in the UK Biobank

Silei Zhu1, Nicola K. Dinsdale2, Saad Jbabdi1, Karla L. Miller1, and Amy F.D. Howard3
1Nuffield department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 2Oxford Machine Learning in NeuroImaging Lab (OMNI), Department of Computer Science, University of Oxford, Oxford, United Kingdom, 3Department of Bioengineering, Imperial College, London, United Kingdom

Synopsis

Keywords: Tractography, Brain Connectivity, fMRI (resting state), White matter

Motivation: There is an unmet need for non-invasive methods to map complex neuroanatomy at the meso-scale. Diffusion MRI (dMRI) tractography is effective but limited by inaccuracies in fibre orientation estimates.

Goal(s): To leverage co-aligned microscopy and dMRI from a mostly postmortem dataset to develop a method for high-resolution, hybridised tractography in vivo.

Approach: We adapted a network to predict microscopy-informed fibre orientations from dMRI for in vivo data. Trained on a macaque dataset with MRI and microscopy, the network was applied to UK BioBank data.

Results: Microscopy-informed fibre orientations aligned with neuroanatomical expectations, producing tractography that captured individual connectivity differences.

Impact: We present a deep-learning model that reconstructs microscopy-informed fibre orientations from in vivo human dMRI data, enabling tractography that may better capture individual brain connectivity differences compared to conventional methods.

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Keywords