XTRACT - Standardised protocols for automated tractography in the human and macaque brain.

Neuroimage

Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK. Electronic address:

Published: August 2020

AI Article Synopsis

  • * The protocols are robust, adaptable across species, and accurately represent known brain anatomy while preserving individual differences and similarities in tracts among subjects.
  • * The software and atlases are publicly available through FSL, promoting open science and enabling users to create their own tractography protocols.

Article Abstract

We present a new software package with a library of standardised tractography protocols devised for the robust automated extraction of white matter tracts both in the human and the macaque brain. Using in vivo data from the Human Connectome Project (HCP) and the UK Biobank and ex vivo data for the macaque brain datasets, we obtain white matter atlases, as well as atlases for tract endpoints on the white-grey matter boundary, for both species. We illustrate that our protocols are robust against data quality, generalisable across two species and reflect the known anatomy. We further demonstrate that they capture inter-subject variability by preserving tract lateralisation in humans and tract similarities stemming from twinship in the HCP cohort. Our results demonstrate that the presented toolbox will be useful for generating imaging-derived features in large cohorts, and in facilitating comparative neuroanatomy studies. The software, tractography protocols, and atlases are publicly released through FSL, allowing users to define their own tractography protocols in a standardised manner, further contributing to open science.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7260058PMC
http://dx.doi.org/10.1016/j.neuroimage.2020.116923DOI Listing

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