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Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis
Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw u...
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Published in: | Schizophrenia bulletin 2016-01, Vol.42 (1), p.152-160 |
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description | Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects. |
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Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects.</description><identifier>ISSN: 0586-7614</identifier><identifier>EISSN: 1745-1701</identifier><identifier>DOI: 10.1093/schbul/sbv085</identifier><identifier>PMID: 26106217</identifier><language>eng</language><publisher>United States: Oxford University Press</publisher><subject>Adolescent ; Adult ; Aged ; Algorithms ; Auditory Cortex - physiopathology ; Basal Ganglia - physiopathology ; Brain - physiopathology ; Brain Mapping ; Case-Control Studies ; Female ; Functional Neuroimaging ; Humans ; Image Processing, Computer-Assisted ; Magnetic Resonance Imaging ; Male ; Middle Aged ; Neural Pathways - physiopathology ; Psychotic Disorders - physiopathology ; Regular ; Schizophrenia - physiopathology ; Sensorimotor Cortex - physiopathology ; Visual Cortex - physiopathology ; Young Adult</subject><ispartof>Schizophrenia bulletin, 2016-01, Vol.42 (1), p.152-160</ispartof><rights>The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.</rights><rights>The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com 2015</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c387t-e53aefa69cf42b5ac2bfd5e4ff5b5c3d66a9ffa98580aba53f3ece5094cc49133</citedby><cites>FETCH-LOGICAL-c387t-e53aefa69cf42b5ac2bfd5e4ff5b5c3d66a9ffa98580aba53f3ece5094cc49133</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4681547/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4681547/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26106217$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gopal, Shruti</creatorcontrib><creatorcontrib>Miller, Robyn L</creatorcontrib><creatorcontrib>Michael, Andrew</creatorcontrib><creatorcontrib>Adali, Tulay</creatorcontrib><creatorcontrib>Cetin, Mustafa</creatorcontrib><creatorcontrib>Rachakonda, Srinivas</creatorcontrib><creatorcontrib>Bustillo, Juan R</creatorcontrib><creatorcontrib>Cahill, Nathan</creatorcontrib><creatorcontrib>Baum, Stefi A</creatorcontrib><creatorcontrib>Calhoun, Vince D</creatorcontrib><title>Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis</title><title>Schizophrenia bulletin</title><addtitle>Schizophr Bull</addtitle><description>Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Auditory Cortex - physiopathology</subject><subject>Basal Ganglia - physiopathology</subject><subject>Brain - physiopathology</subject><subject>Brain Mapping</subject><subject>Case-Control Studies</subject><subject>Female</subject><subject>Functional Neuroimaging</subject><subject>Humans</subject><subject>Image Processing, Computer-Assisted</subject><subject>Magnetic Resonance Imaging</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Neural Pathways - physiopathology</subject><subject>Psychotic Disorders - physiopathology</subject><subject>Regular</subject><subject>Schizophrenia - physiopathology</subject><subject>Sensorimotor Cortex - physiopathology</subject><subject>Visual Cortex - physiopathology</subject><subject>Young Adult</subject><issn>0586-7614</issn><issn>1745-1701</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNpVkU1PHDEMhqMKVJZtj72iHLkMm0w-ZqYHJLRqYSUoFbRcI0_WYUNnkyGZpaK_nkFLEVxsyX782vJLyBfOjjhrxCzbVbvpZrl9YLX6QCa8kqrgFeM7ZMJUrYtKc7lH9nO-Y4zLRpcfyV6pOdMlrybEXvcweOjoDSQPwSL1gV5hHny4pe7iakF_4PA3pj-ZRkev7cr_i_0qYfBAf46TGIb8lZ4EughL7HEMYaA3aIeYxip0j9nnT2TXQZfx80uekt_fv_2anxXnl6eL-cl5YUVdDQUqAehAN9bJslVgy9YtFUrnVKusWGoNjXPQ1Kpm0IISTqBFxRpprWy4EFNyvNXtN-0al3Y8JUFn-uTXkB5NBG_ed4Jfmdv4YKSuuZLVKHD4IpDi_WZ8gln7bLHrIGDcZMMrxWTZyFqNaLFFbYo5J3Svazgzz8aYrTFma8zIH7y97ZX-74R4AgkEjxg</recordid><startdate>20160101</startdate><enddate>20160101</enddate><creator>Gopal, Shruti</creator><creator>Miller, Robyn L</creator><creator>Michael, Andrew</creator><creator>Adali, Tulay</creator><creator>Cetin, Mustafa</creator><creator>Rachakonda, Srinivas</creator><creator>Bustillo, Juan R</creator><creator>Cahill, Nathan</creator><creator>Baum, Stefi A</creator><creator>Calhoun, Vince D</creator><general>Oxford University Press</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>7X8</scope><scope>5PM</scope></search><sort><creationdate>20160101</creationdate><title>Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis</title><author>Gopal, Shruti ; Miller, Robyn L ; Michael, Andrew ; Adali, Tulay ; Cetin, Mustafa ; Rachakonda, Srinivas ; Bustillo, Juan R ; Cahill, Nathan ; Baum, Stefi A ; Calhoun, Vince D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c387t-e53aefa69cf42b5ac2bfd5e4ff5b5c3d66a9ffa98580aba53f3ece5094cc49133</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Auditory Cortex - physiopathology</topic><topic>Basal Ganglia - physiopathology</topic><topic>Brain - physiopathology</topic><topic>Brain Mapping</topic><topic>Case-Control Studies</topic><topic>Female</topic><topic>Functional Neuroimaging</topic><topic>Humans</topic><topic>Image Processing, Computer-Assisted</topic><topic>Magnetic Resonance Imaging</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Neural Pathways - physiopathology</topic><topic>Psychotic Disorders - physiopathology</topic><topic>Regular</topic><topic>Schizophrenia - physiopathology</topic><topic>Sensorimotor Cortex - physiopathology</topic><topic>Visual Cortex - physiopathology</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gopal, Shruti</creatorcontrib><creatorcontrib>Miller, Robyn L</creatorcontrib><creatorcontrib>Michael, Andrew</creatorcontrib><creatorcontrib>Adali, Tulay</creatorcontrib><creatorcontrib>Cetin, Mustafa</creatorcontrib><creatorcontrib>Rachakonda, Srinivas</creatorcontrib><creatorcontrib>Bustillo, Juan R</creatorcontrib><creatorcontrib>Cahill, Nathan</creatorcontrib><creatorcontrib>Baum, Stefi A</creatorcontrib><creatorcontrib>Calhoun, Vince D</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Schizophrenia bulletin</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gopal, Shruti</au><au>Miller, Robyn L</au><au>Michael, Andrew</au><au>Adali, Tulay</au><au>Cetin, Mustafa</au><au>Rachakonda, Srinivas</au><au>Bustillo, Juan R</au><au>Cahill, Nathan</au><au>Baum, Stefi A</au><au>Calhoun, Vince D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis</atitle><jtitle>Schizophrenia bulletin</jtitle><addtitle>Schizophr Bull</addtitle><date>2016-01-01</date><risdate>2016</risdate><volume>42</volume><issue>1</issue><spage>152</spage><epage>160</epage><pages>152-160</pages><issn>0586-7614</issn><eissn>1745-1701</eissn><abstract>Spatial variability in resting functional MRI (fMRI) brain networks has not been well studied in schizophrenia, a disease known for both neurodevelopmental and widespread anatomic changes. Motivated by abundant evidence of neuroanatomical variability from previous studies of schizophrenia, we draw upon a relatively new approach called independent vector analysis (IVA) to assess this variability in resting fMRI networks. IVA is a blind-source separation algorithm, which segregates fMRI data into temporally coherent but spatially independent networks and has been shown to be especially good at capturing spatial variability among subjects in the extracted networks. We introduce several new ways to quantify differences in variability of IVA-derived networks between schizophrenia patients (SZs = 82) and healthy controls (HCs = 89). Voxelwise amplitude analyses showed significant group differences in the spatial maps of auditory cortex, the basal ganglia, the sensorimotor network, and visual cortex. Tests for differences (HC-SZ) in the spatial variability maps suggest, that at rest, SZs exhibit more activity within externally focused sensory and integrative network and less activity in the default mode network thought to be related to internal reflection. Additionally, tests for difference of variance between groups further emphasize that SZs exhibit greater network variability. These results, consistent with our prediction of increased spatial variability within SZs, enhance our understanding of the disease and suggest that it is not just the amplitude of connectivity that is different in schizophrenia, but also the consistency in spatial connectivity patterns across subjects.</abstract><cop>United States</cop><pub>Oxford University Press</pub><pmid>26106217</pmid><doi>10.1093/schbul/sbv085</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adolescent Adult Aged Algorithms Auditory Cortex - physiopathology Basal Ganglia - physiopathology Brain - physiopathology Brain Mapping Case-Control Studies Female Functional Neuroimaging Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Male Middle Aged Neural Pathways - physiopathology Psychotic Disorders - physiopathology Regular Schizophrenia - physiopathology Sensorimotor Cortex - physiopathology Visual Cortex - physiopathology Young Adult |
title | Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis |
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