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dc.contributor.authorLeming, Matthew
dc.contributor.authorSu, Li
dc.contributor.authorChattopadhyay, Shayanti
dc.contributor.authorSuckling, John
dc.date.accessioned2018-11-21T00:30:56Z
dc.date.available2018-11-21T00:30:56Z
dc.date.issued2019-01-01
dc.identifier.issn1053-8119
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/285521
dc.description.abstractFunctional connectivity is frequently derived from fMRI data to reduce a complex image of the brain to a graph, or "functional connectome". Often shortest-path algorithms are used to characterize and compare functional connectomes. Previous work on the identification and measurement of semi-metric (shortest circuitous) pathways in the functional connectome has discovered cross-sectional differences in major depressive disorder (MDD), autism spectrum disorder (ASD), and Alzheimer's disease. However, while measurements of shortest path length have been analyzed in functional connectomes, less work has been done to investigate the composition of the pathways themselves, or whether the edges composing pathways differ between individuals. Developments in this area would help us understand how pathways might be organized in mental disorders, and if a consistent pattern can be found. Furthermore, studies in structural brain connectivity and other real-world graphs suggest that shortest pathways may not be as important in functional connectivity studies as previously assumed. In light of this, we present a novel measurement of the consistency of pathways across functional connectomes, and an algorithm for improvement by selecting the most frequently occurring "normative pathways" from the k shortest paths, instead of just the shortest path. We also look at this algorithm's effect on various graph measurements, using randomized matrix simulations to support the efficacy of this method and demonstrate our algorithm on the resting-state fMRI (rs-fMRI) of a group of 34 adolescent control participants. Additionally, a comparison of normative pathways is made with a group of 82 age-matched participants, diagnosed with MDD, and in doing so we find the normative pathways that are most disrupted. Our results, which are carried out with estimates of connectivity derived from correlation, partial correlation, and normalized mutual information connectomes, suggest disruption to the default mode, affective, and ventral attention networks. Normative pathways, especially with partial correlation, make greater use of critical anatomical pathways through the striatum, cingulum, and the cerebellum. In summary, MDD is characterized by a disruption of normative pathways of the ventral attention network, increases in alternative pathways in the frontoparietal network in MDD, and a mixture of both in the default mode network. Additionally, within- and between-groups findings depend on the estimate of connectivity.
dc.description.sponsorshipUK Medical Research Council (grant: G0802226) National Institute for Health Research (NIHR) (grant: 06-05-01) Alzheimer’s Research UK (ARUK- SRF2017B-1) Gates Cambridge Scholarship
dc.format.mediumPrint-Electronic
dc.languageeng
dc.publisherElsevier BV
dc.subjectBrain
dc.subjectNerve Net
dc.subjectHumans
dc.subjectMagnetic Resonance Imaging
dc.subjectDepressive Disorder, Major
dc.subjectAlgorithms
dc.subjectModels, Neurological
dc.subjectAdolescent
dc.subjectAdult
dc.subjectChild
dc.subjectFemale
dc.subjectMale
dc.subjectConnectome
dc.titleNormative pathways in the functional connectome.
dc.typeArticle
prism.endingPage334
prism.publicationDate2019
prism.publicationNameNeuroimage
prism.startingPage317
prism.volume184
dc.identifier.doi10.17863/CAM.32878
dcterms.dateAccepted2018-09-10
rioxxterms.versionofrecord10.1016/j.neuroimage.2018.09.028
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-01
dc.contributor.orcidLeming, Matthew [0000-0001-9631-2567]
dc.contributor.orcidSuckling, John [0000-0002-5098-1527]
dc.identifier.eissn1095-9572
rioxxterms.typeJournal Article/Review
pubs.funder-project-idMedical Research Council (G0802226)
pubs.funder-project-idMedical Research Council (MR/J012084/1)
pubs.funder-project-idMedical Research Council (G1000183)
pubs.funder-project-idNETSCC (None)
pubs.funder-project-idMedical Research Council (G0001354)
cam.issuedOnline2018-09-21
rioxxterms.freetoread.startdate2019-09-14


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