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dc.contributor.authorDoyen, Stephane
dc.contributor.authorNicholas, Peter
dc.contributor.authorPoologaindran, Anujan
dc.contributor.authorCrawford, Lewis
dc.contributor.authorYoung, Isabella M
dc.contributor.authorRomero-Garcia, Rafeael
dc.contributor.authorSughrue, Michael E
dc.date.accessioned2021-12-15T10:08:00Z
dc.date.available2021-12-15T10:08:00Z
dc.date.issued2022-03
dc.date.submitted2021-07-18
dc.identifier.issn1065-9471
dc.identifier.otherhbm25728
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/331434
dc.description.abstractFor 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.
dc.languageen
dc.publisherWiley
dc.subjectRESEARCH ARTICLE
dc.subjectRESEARCH ARTICLES
dc.subjectconnectivity
dc.subjectDTI
dc.subjectglioma
dc.subjectmachine learning
dc.subjectparcellation
dc.subjecttractography
dc.titleConnectivity-based parcellation of normal and anatomically distorted human cerebral cortex.
dc.typeArticle
dc.date.updated2021-12-15T10:08:00Z
prism.publicationNameHum Brain Mapp
dc.identifier.doi10.17863/CAM.78888
dcterms.dateAccepted2021-11-13
rioxxterms.versionofrecord10.1002/hbm.25728
rioxxterms.versionAO
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.contributor.orcidCrawford, Lewis [0000-0003-2231-5196]
dc.contributor.orcidYoung, Isabella M [0000-0001-7639-6679]
dc.contributor.orcidRomero-Garcia, Rafeael [0000-0002-5199-4573]
dc.contributor.orcidSughrue, Michael E [0000-0001-5407-2585]
dc.identifier.eissn1097-0193
pubs.funder-project-idDepartment of Energy (FG02‐08ER64581)
pubs.funder-project-idNational Center for Research Resources (U24‐RR021992)
pubs.funder-project-idCenter of Biomedical Research Excellence (COBRE) (5P20RR021938/P20GM103472)
pubs.funder-project-idNational Institute of Mental Health (R01MH084898‐01A1)
cam.issuedOnline2021-11-26


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