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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