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April 17, 2018Scientific Data575 citationsOpen Access

A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei

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WPWolfgang M. PauliANAmanda NiliJTJ. Michael Tyszka

Key Points

  • To construct a high-resolution probabilistic anatomical atlas of subcortical brain nuclei to aid cognitive and behavioral research.
  • Generated 3D templates using diffeomorphic registration of T1- and T2-weighted images from 168 adults aged 22-35.
  • Employed data acquisition and processing techniques to enhance image detail.
  • Developed software tools for community-driven atlas creation.
  • Created detailed three-dimensional high-resolution templates with visible tissue boundaries.
  • Provided an evolving resource that complements existing histology-based atlases.
  • Facilitated ongoing development of an in vivo probabilistic anatomical atlas.

Abstract

Recent advances in magnetic resonance imaging methods, including data acquisition, pre-processing and analysis, have benefited research on the contributions of subcortical brain nuclei to human cognition and behavior. At the same time, these developments have led to an increasing need for a high-resolution probabilistic in vivo anatomical atlas of subcortical nuclei. In order to address this need, we constructed high spatial resolution, three-dimensional templates, using high-accuracy diffeomorphic registration of T1- and T2- weighted structural images from 168 typical adults between 22 and 35 years old. In these templates, many tissue boundaries are clearly visible, which would otherwise be impossible to delineate in data from individual studies. The resulting delineations of subcortical nuclei complement current histology-based atlases. We further created a companion library of software tools for atlas development, to offer an open and evolving resource for the creation of a crowd-sourced in vivo probabilistic anatomical atlas of the human brain.

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Cite This Study

Pauli et al. (2018) studied this question.

synapsesocial.com/papers/69d970711ad561c67368431fhttps://doi.org/10.1038/sdata.2018.63
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