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Cerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis.

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cam.issuedOnline2022-07
dc.contributor.authorZhang, Jie
dc.contributor.authorSun, Zhe
dc.contributor.authorDuan, Feng
dc.contributor.authorShi, Liang
dc.contributor.authorZhang, Yu
dc.contributor.authorSolé-Casals, Jordi
dc.contributor.authorCaiafa, Cesar F
dc.contributor.orcidZhang, Jie [0000-0001-9205-2470]
dc.contributor.orcidSun, Zhe [0000-0002-6531-0769]
dc.contributor.orcidDuan, Feng [0000-0002-2179-2460]
dc.contributor.orcidShi, Liang [0000-0003-1979-4659]
dc.contributor.orcidZhang, Yu [0000-0003-4087-6544]
dc.contributor.orcidSolé-Casals, Jordi [0000-0002-6534-1979]
dc.contributor.orcidCaiafa, Cesar F [0000-0001-5437-6095]
dc.date.accessioned2022-07-02T08:00:13Z
dc.date.available2022-07-02T08:00:13Z
dc.date.issued2022-12-01
dc.date.submitted2022-03-27
dc.date.updated2022-07-02T08:00:12Z
dc.description.abstractUnderstanding the laminar brain structure is of great help in further developing our knowledge of the functions of the brain. However, since most layer segmentation methods are invasive, it is difficult to apply them to the human brain in vivo. To systematically explore the human brain's laminar structure noninvasively, the K-means clustering algorithm was used to automatically segment the left hemisphere into two layers, the superficial and deep layers, using a 7 Tesla (T) diffusion magnetic resonance imaging (dMRI)open dataset. The obtained layer thickness was then compared with the layer thickness of the BigBrain reference dataset, which segmented the neocortex into six layers based on the von Economo atlas. The results show a significant correlation not only between our automatically segmented superficial layer thickness and the thickness of layers 1-3 from the reference histological data, but also between our automatically segmented deep layer thickness and the thickness of layers 4-6 from the reference histological data. Second, we constructed the laminar connections between two pairs of unidirectional connected regions, which is consistent with prior research. Finally, we conducted the laminar analysis of the working memory, which was challenging to do in the past, and explained the conclusions of the functional analysis. Our work successfully demonstrates that it is possible to segment the human cortex noninvasively into layers using dMRI data and further explores the mechanisms of the human brain.
dc.identifier.doi10.17863/CAM.86117
dc.identifier.eissn1097-0193
dc.identifier.issn1065-9471
dc.identifier.otherhbm25998
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338704
dc.languageen
dc.language.isoeng
dc.publisherWiley
dc.publisher.urlhttp://dx.doi.org/10.1002/hbm.25998
dc.subjectcortical layers
dc.subjectdiffusion magnetic resonance imaging
dc.subjectin vivo
dc.subjectlaminar connections
dc.subjectnoninvasive
dc.subjectworking memory
dc.subjectHumans
dc.subjectMemory, Short-Term
dc.subjectMagnetic Resonance Imaging
dc.subjectCerebral Cortex
dc.subjectDiffusion Magnetic Resonance Imaging
dc.subjectBrain
dc.titleCerebral cortex layer segmentation using diffusion magnetic resonance imaging in vivo with applications to laminar connections and working memory analysis.
dc.typeArticle
dcterms.dateAccepted2022-06-15
prism.publicationNameHum Brain Mapp
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
rioxxterms.versionVoR
rioxxterms.versionofrecord10.1002/hbm.25998

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