4.7 Article

Connectivity-based parcellation of normal and anatomically distorted human cerebral cortex

期刊

HUMAN BRAIN MAPPING
卷 43, 期 4, 页码 1358-1369

出版社

WILEY
DOI: 10.1002/hbm.25728

关键词

connectivity; DTI; glioma; machine learning; parcellation; tractography

资金

  1. Department of Energy [FG02-08ER64581]
  2. National Center for Research Resources [U24-RR021992]
  3. Center of Biomedical Research Excellence (COBRE) [5P20RR021938/P20GM103472]
  4. National Institute of Mental Health [R01MH084898-01A1]

向作者/读者索取更多资源

By developing a novel connectivity-based parcellation approach, researchers created a personalized neurosurgical application method that can be applied at the single-subject level, overcoming the impact of pathology and resection on brain structure.
For over a century, neuroscientists have been working toward parcellating the human cortex into distinct neurobiological regions. Modern technologies offer many parcellation methods for healthy cortices acquired through magnetic resonance imaging. However, these methods are suboptimal for personalized neurosurgical application given that pathology and resection distort the cerebrum. We sought to overcome this problem by developing a novel connectivity-based parcellation approach that can be applied at the single-subject level. Utilizing normative diffusion data, we first developed a machine-learning (ML) classifier to learn the typical structural connectivity patterns of healthy subjects. Specifically, the Glasser HCP atlas was utilized as a prior to calculate the streamline connectivity between each voxel and each parcel of the atlas. Using the resultant feature vector, we determined the parcel identity of each voxel in neurosurgical patients (n = 40) and thereby iteratively adjusted the prior. This approach enabled us to create patient-specific maps independent of brain shape and pathological distortion. The supervised ML classifier re-parcellated an average of 2.65% of cortical voxels across a healthy dataset (n = 178) and an average of 5.5% in neurosurgical patients. Our patient dataset consisted of subjects with supratentorial infiltrating gliomas operated on by the senior author who then assessed the validity and practical utility of the re-parcellated diffusion data. We demonstrate a rapid and effective ML parcellation approach to parcellation of the human cortex during anatomical distortion. Our approach overcomes limitations of indiscriminately applying atlas-based registration from healthy subjects by employing a voxel-wise connectivity approach based on individual data.

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