Normative pathways in the functional connectome

Functional 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 sem...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:NeuroImage (Orlando, Fla.) Fla.), 2019-01, Vol.184, p.317-334
Hauptverfasser: Leming, Matthew, Su, Li, Chattopadhyay, Shayanti, Suckling, John
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 334
container_issue
container_start_page 317
container_title NeuroImage (Orlando, Fla.)
container_volume 184
creator Leming, Matthew
Su, Li
Chattopadhyay, Shayanti
Suckling, John
description Functional 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
doi_str_mv 10.1016/j.neuroimage.2018.09.028
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2109336131</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1053811918308139</els_id><sourcerecordid>2109336131</sourcerecordid><originalsourceid>FETCH-LOGICAL-c452t-34521809a48751342b4b4403658f44baf3daacf8c4db1713aacb84839680f4ce3</originalsourceid><addsrcrecordid>eNqFkE1PwzAMQCMEYmPwF1AlLlzaJU3aJkeY-JImuMA5SlOXpVqbkbRD-_ek2gCJCxfbkZ7t-CEUEZwQTPJ5k3QwOGta9Q5JiglPsEhwyo_QlGCRxSIr0uOxzmjMCRETdOZ9gzEWhPFTNKE4TSnOyRTNn61rVW-2EG1Uv_pUOx-ZLupXENVDp3tjO7WOtO060L1t4Ryd1Grt4eKQZ-jt_u518RgvXx6eFjfLWLMs7WMaIuFYKMaLjFCWlqxkDNM84zVjpapppZSuuWZVSQpCw6PkjFORc1wzDXSGrvdzN85-DOB72RqvYb1WHdjByzTcSWlOKAno1R-0sYML3x6pcGlRUMEDxfeUdtZ7B7XcuODP7STBcpQqG_krVY5SJRYySA2tl4cFQ9lC9dP4bTEAt3sAgpGtASe9NtBpqIwL2mRlzf9bvgBNu4ui</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2130277398</pqid></control><display><type>article</type><title>Normative pathways in the functional connectome</title><source>MEDLINE</source><source>Access via ScienceDirect (Elsevier)</source><source>ProQuest Central UK/Ireland</source><creator>Leming, Matthew ; Su, Li ; Chattopadhyay, Shayanti ; Suckling, John</creator><creatorcontrib>Leming, Matthew ; Su, Li ; Chattopadhyay, Shayanti ; Suckling, John</creatorcontrib><description>Functional 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.</description><identifier>ISSN: 1053-8119</identifier><identifier>EISSN: 1095-9572</identifier><identifier>DOI: 10.1016/j.neuroimage.2018.09.028</identifier><identifier>PMID: 30223061</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adolescent ; Adolescent depression ; Adult ; Algorithms ; Alzheimer's disease ; Autism ; Brain - physiopathology ; Brain mapping ; Cerebellum ; Child ; Cingulum ; Connectome - methods ; Depressive Disorder, Major - physiopathology ; Efficiency ; Female ; Functional connectivity ; Functional magnetic resonance imaging ; Graph theory ; Graphs ; Humans ; Magnetic Resonance Imaging - methods ; Major depressive disorder ; Male ; Mental depression ; Mental disorders ; Models, Neurological ; Neostriatum ; Nerve Net - physiopathology ; Neural networks ; Neurodegenerative diseases ; Neuroimaging ; Pathways ; Resting-state fMRI</subject><ispartof>NeuroImage (Orlando, Fla.), 2019-01, Vol.184, p.317-334</ispartof><rights>2018</rights><rights>Copyright © 2018. Published by Elsevier Inc.</rights><rights>Copyright Elsevier Limited Jan 1, 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c452t-34521809a48751342b4b4403658f44baf3daacf8c4db1713aacb84839680f4ce3</citedby><cites>FETCH-LOGICAL-c452t-34521809a48751342b4b4403658f44baf3daacf8c4db1713aacb84839680f4ce3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2130277398?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30223061$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Leming, Matthew</creatorcontrib><creatorcontrib>Su, Li</creatorcontrib><creatorcontrib>Chattopadhyay, Shayanti</creatorcontrib><creatorcontrib>Suckling, John</creatorcontrib><title>Normative pathways in the functional connectome</title><title>NeuroImage (Orlando, Fla.)</title><addtitle>Neuroimage</addtitle><description>Functional 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.</description><subject>Adolescent</subject><subject>Adolescent depression</subject><subject>Adult</subject><subject>Algorithms</subject><subject>Alzheimer's disease</subject><subject>Autism</subject><subject>Brain - physiopathology</subject><subject>Brain mapping</subject><subject>Cerebellum</subject><subject>Child</subject><subject>Cingulum</subject><subject>Connectome - methods</subject><subject>Depressive Disorder, Major - physiopathology</subject><subject>Efficiency</subject><subject>Female</subject><subject>Functional connectivity</subject><subject>Functional magnetic resonance imaging</subject><subject>Graph theory</subject><subject>Graphs</subject><subject>Humans</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Major depressive disorder</subject><subject>Male</subject><subject>Mental depression</subject><subject>Mental disorders</subject><subject>Models, Neurological</subject><subject>Neostriatum</subject><subject>Nerve Net - physiopathology</subject><subject>Neural networks</subject><subject>Neurodegenerative diseases</subject><subject>Neuroimaging</subject><subject>Pathways</subject><subject>Resting-state fMRI</subject><issn>1053-8119</issn><issn>1095-9572</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqFkE1PwzAMQCMEYmPwF1AlLlzaJU3aJkeY-JImuMA5SlOXpVqbkbRD-_ek2gCJCxfbkZ7t-CEUEZwQTPJ5k3QwOGta9Q5JiglPsEhwyo_QlGCRxSIr0uOxzmjMCRETdOZ9gzEWhPFTNKE4TSnOyRTNn61rVW-2EG1Uv_pUOx-ZLupXENVDp3tjO7WOtO060L1t4Ryd1Grt4eKQZ-jt_u518RgvXx6eFjfLWLMs7WMaIuFYKMaLjFCWlqxkDNM84zVjpapppZSuuWZVSQpCw6PkjFORc1wzDXSGrvdzN85-DOB72RqvYb1WHdjByzTcSWlOKAno1R-0sYML3x6pcGlRUMEDxfeUdtZ7B7XcuODP7STBcpQqG_krVY5SJRYySA2tl4cFQ9lC9dP4bTEAt3sAgpGtASe9NtBpqIwL2mRlzf9bvgBNu4ui</recordid><startdate>20190101</startdate><enddate>20190101</enddate><creator>Leming, Matthew</creator><creator>Su, Li</creator><creator>Chattopadhyay, Shayanti</creator><creator>Suckling, John</creator><general>Elsevier Inc</general><general>Elsevier Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TK</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>88G</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2M</scope><scope>M7P</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PSYQQ</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>20190101</creationdate><title>Normative pathways in the functional connectome</title><author>Leming, Matthew ; Su, Li ; Chattopadhyay, Shayanti ; Suckling, John</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c452t-34521809a48751342b4b4403658f44baf3daacf8c4db1713aacb84839680f4ce3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adolescent</topic><topic>Adolescent depression</topic><topic>Adult</topic><topic>Algorithms</topic><topic>Alzheimer's disease</topic><topic>Autism</topic><topic>Brain - physiopathology</topic><topic>Brain mapping</topic><topic>Cerebellum</topic><topic>Child</topic><topic>Cingulum</topic><topic>Connectome - methods</topic><topic>Depressive Disorder, Major - physiopathology</topic><topic>Efficiency</topic><topic>Female</topic><topic>Functional connectivity</topic><topic>Functional magnetic resonance imaging</topic><topic>Graph theory</topic><topic>Graphs</topic><topic>Humans</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Major depressive disorder</topic><topic>Male</topic><topic>Mental depression</topic><topic>Mental disorders</topic><topic>Models, Neurological</topic><topic>Neostriatum</topic><topic>Nerve Net - physiopathology</topic><topic>Neural networks</topic><topic>Neurodegenerative diseases</topic><topic>Neuroimaging</topic><topic>Pathways</topic><topic>Resting-state fMRI</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Leming, Matthew</creatorcontrib><creatorcontrib>Su, Li</creatorcontrib><creatorcontrib>Chattopadhyay, Shayanti</creatorcontrib><creatorcontrib>Suckling, John</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Neurosciences Abstracts</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Psychology Database</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>NeuroImage (Orlando, Fla.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Leming, Matthew</au><au>Su, Li</au><au>Chattopadhyay, Shayanti</au><au>Suckling, John</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Normative pathways in the functional connectome</atitle><jtitle>NeuroImage (Orlando, Fla.)</jtitle><addtitle>Neuroimage</addtitle><date>2019-01-01</date><risdate>2019</risdate><volume>184</volume><spage>317</spage><epage>334</epage><pages>317-334</pages><issn>1053-8119</issn><eissn>1095-9572</eissn><abstract>Functional 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.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>30223061</pmid><doi>10.1016/j.neuroimage.2018.09.028</doi><tpages>18</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1053-8119
ispartof NeuroImage (Orlando, Fla.), 2019-01, Vol.184, p.317-334
issn 1053-8119
1095-9572
language eng
recordid cdi_proquest_miscellaneous_2109336131
source MEDLINE; Access via ScienceDirect (Elsevier); ProQuest Central UK/Ireland
subjects Adolescent
Adolescent depression
Adult
Algorithms
Alzheimer's disease
Autism
Brain - physiopathology
Brain mapping
Cerebellum
Child
Cingulum
Connectome - methods
Depressive Disorder, Major - physiopathology
Efficiency
Female
Functional connectivity
Functional magnetic resonance imaging
Graph theory
Graphs
Humans
Magnetic Resonance Imaging - methods
Major depressive disorder
Male
Mental depression
Mental disorders
Models, Neurological
Neostriatum
Nerve Net - physiopathology
Neural networks
Neurodegenerative diseases
Neuroimaging
Pathways
Resting-state fMRI
title Normative pathways in the functional connectome
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-01T20%3A02%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Normative%20pathways%20in%20the%20functional%20connectome&rft.jtitle=NeuroImage%20(Orlando,%20Fla.)&rft.au=Leming,%20Matthew&rft.date=2019-01-01&rft.volume=184&rft.spage=317&rft.epage=334&rft.pages=317-334&rft.issn=1053-8119&rft.eissn=1095-9572&rft_id=info:doi/10.1016/j.neuroimage.2018.09.028&rft_dat=%3Cproquest_cross%3E2109336131%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2130277398&rft_id=info:pmid/30223061&rft_els_id=S1053811918308139&rfr_iscdi=true