Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders
The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences...
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| Veröffentlicht in: | Human brain mapping 2021-04, Vol.42 (6), p.1714-1726 |
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| creator | Rokicki, Jaroslav Wolfers, Thomas Nordhøy, Wibeke Tesli, Natalia Quintana, Daniel S. Alnæs, Dag Richard, Genevieve Lange, Ann‐Marie G. Lund, Martina J. Norbom, Linn Agartz, Ingrid Melle, Ingrid Nærland, Terje Selbæk, Geir Persson, Karin Nordvik, Jan Egil Schwarz, Emanuel Andreassen, Ole A. Kaufmann, Tobias Westlye, Lars T. |
| description | The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age‐matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two‐group case–control classifications revealed highest accuracy for AD using global T1‐weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF‐based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain‐based mapping of overlapping and distinct pathophysiology in common disorders.
The deviation between chronological age and age predicted using brain MRI is a marker of overall brain health. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF). We found that, combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. |
| doi_str_mv | 10.1002/hbm.25323 |
| format | Article |
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The deviation between chronological age and age predicted using brain MRI is a marker of overall brain health. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF). We found that, combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders.</description><identifier>ISSN: 1065-9471</identifier><identifier>ISSN: 1097-0193</identifier><identifier>EISSN: 1097-0193</identifier><identifier>DOI: 10.1002/hbm.25323</identifier><identifier>PMID: 33340180</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Abnormalities ; Accuracy ; Adolescent ; Adult ; Age ; Age Factors ; Aged ; Aged, 80 and over ; Aging ; Alzheimer Disease - diagnostic imaging ; Alzheimer Disease - pathology ; Alzheimer's disease ; arterial spin labeling ; Biological activity ; Bipolar disorder ; Bipolar Disorder - diagnostic imaging ; Bipolar Disorder - pathology ; Blood flow ; Brain ; Brain - blood supply ; Brain - diagnostic imaging ; Brain - pathology ; brain age ; brain disorders ; Brain mapping ; Case-Control Studies ; Cerebral blood flow ; Cerebrovascular Circulation - physiology ; Cognitive ability ; Cognitive Dysfunction - diagnostic imaging ; Cognitive Dysfunction - pathology ; Deviation ; Female ; Humans ; machine learning ; Magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Male ; Medical imaging ; Medicin och hälsovetenskap ; Mental disorders ; Middle Aged ; MRI ; Multimodal Imaging ; Neurodegenerative diseases ; Neuroimaging ; Neuroimaging - methods ; Neurological diseases ; Neurological disorders ; Predictions ; Schizophrenia ; Schizophrenia - diagnostic imaging ; Schizophrenia - pathology ; Spin labeling ; Spin Labels ; T1w/T2w ratio ; Young Adult</subject><ispartof>Human brain mapping, 2021-04, Vol.42 (6), p.1714-1726</ispartof><rights>2020 The Authors. published by Wiley Periodicals LLC.</rights><rights>2020 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.</rights><rights>2020. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>info:eu-repo/semantics/openAccess</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5553-e79820dc8994b52b9301b55bb98e2bc71038fe220076907b4a01e82ead28d08d3</citedby><cites>FETCH-LOGICAL-c5553-e79820dc8994b52b9301b55bb98e2bc71038fe220076907b4a01e82ead28d08d3</cites><orcidid>0000-0003-2876-0004 ; 0000-0001-6475-2576 ; 0000-0002-2679-9469 ; 0000-0001-8644-956X ; 0000-0003-3258-1674</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7978139/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7978139/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,552,727,780,784,864,885,1417,11562,26567,27924,27925,45574,45575,46052,46476,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33340180$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttp://kipublications.ki.se/Default.aspx?queryparsed=id:145358933$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Rokicki, Jaroslav</creatorcontrib><creatorcontrib>Wolfers, Thomas</creatorcontrib><creatorcontrib>Nordhøy, Wibeke</creatorcontrib><creatorcontrib>Tesli, Natalia</creatorcontrib><creatorcontrib>Quintana, Daniel S.</creatorcontrib><creatorcontrib>Alnæs, Dag</creatorcontrib><creatorcontrib>Richard, Genevieve</creatorcontrib><creatorcontrib>Lange, Ann‐Marie G.</creatorcontrib><creatorcontrib>Lund, Martina J.</creatorcontrib><creatorcontrib>Norbom, Linn</creatorcontrib><creatorcontrib>Agartz, Ingrid</creatorcontrib><creatorcontrib>Melle, Ingrid</creatorcontrib><creatorcontrib>Nærland, Terje</creatorcontrib><creatorcontrib>Selbæk, Geir</creatorcontrib><creatorcontrib>Persson, Karin</creatorcontrib><creatorcontrib>Nordvik, Jan Egil</creatorcontrib><creatorcontrib>Schwarz, Emanuel</creatorcontrib><creatorcontrib>Andreassen, Ole A.</creatorcontrib><creatorcontrib>Kaufmann, Tobias</creatorcontrib><creatorcontrib>Westlye, Lars T.</creatorcontrib><title>Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders</title><title>Human brain mapping</title><addtitle>Hum Brain Mapp</addtitle><description>The deviation between chronological age and age predicted using brain MRI is a putative marker of overall brain health. Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age‐matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two‐group case–control classifications revealed highest accuracy for AD using global T1‐weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF‐based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain‐based mapping of overlapping and distinct pathophysiology in common disorders.
The deviation between chronological age and age predicted using brain MRI is a marker of overall brain health. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF). We found that, combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders.</description><subject>Abnormalities</subject><subject>Accuracy</subject><subject>Adolescent</subject><subject>Adult</subject><subject>Age</subject><subject>Age Factors</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Aging</subject><subject>Alzheimer Disease - diagnostic imaging</subject><subject>Alzheimer Disease - pathology</subject><subject>Alzheimer's disease</subject><subject>arterial spin labeling</subject><subject>Biological activity</subject><subject>Bipolar disorder</subject><subject>Bipolar Disorder - diagnostic imaging</subject><subject>Bipolar Disorder - pathology</subject><subject>Blood flow</subject><subject>Brain</subject><subject>Brain - blood supply</subject><subject>Brain - diagnostic imaging</subject><subject>Brain - pathology</subject><subject>brain age</subject><subject>brain disorders</subject><subject>Brain mapping</subject><subject>Case-Control Studies</subject><subject>Cerebral blood flow</subject><subject>Cerebrovascular Circulation - physiology</subject><subject>Cognitive ability</subject><subject>Cognitive Dysfunction - diagnostic imaging</subject><subject>Cognitive Dysfunction - pathology</subject><subject>Deviation</subject><subject>Female</subject><subject>Humans</subject><subject>machine learning</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Medicin och hälsovetenskap</subject><subject>Mental disorders</subject><subject>Middle Aged</subject><subject>MRI</subject><subject>Multimodal Imaging</subject><subject>Neurodegenerative diseases</subject><subject>Neuroimaging</subject><subject>Neuroimaging - methods</subject><subject>Neurological diseases</subject><subject>Neurological disorders</subject><subject>Predictions</subject><subject>Schizophrenia</subject><subject>Schizophrenia - diagnostic imaging</subject><subject>Schizophrenia - pathology</subject><subject>Spin labeling</subject><subject>Spin Labels</subject><subject>T1w/T2w ratio</subject><subject>Young Adult</subject><issn>1065-9471</issn><issn>1097-0193</issn><issn>1097-0193</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><sourceid>EIF</sourceid><sourceid>3HK</sourceid><sourceid>D8T</sourceid><recordid>eNp1ks1u1DAUhSMEomVgwQtAJDawSHttxxN7gwQVUKRWbGBt-a8zLkkc7GRG8wy8NHf-CkXqyjfJd86Nj05RvCRwRgDo-dJ0Z5Qzyh4VpwRkUwGR7PF2nvNK1g05KZ7lfAtACAfytDhhjNVABJwWv6-ndgxddLotQ6cXoV_gOaS48rk0SYe-1AtfDsm7YMcQ8bF3ZfIrr9tcupDH0Nux1KaPqdNtGAPqUDRonPoxl-swLsshb-wy6DEFu9P3fkqxjYtgcS2axOR8ys-LJzfo6l8czlnx4_On7xeX1dW3L18vPlxVlnPOKt9IQcFZIWVtODWSATGcGyOFp8Y2BJi48ZQCNHMJjak1EC-o144KB8KxWVHtffPaD5NRQ8Kbp42KOqjDq584eVXPecM48vJBHpNyf0VHIak540JizLPi_V6LQOedxUySbu9b3PvSh6VaxJVqZCMIk2jwem9g0y5shUFrRUBwqgSRzZZ4e1iR4q_J51F1IVvftrr3ccqKYgHqOaU1RfTNf-htnFKPYSvKQVKsEJ8j9e64Muac_M3d7xJQ28IpLJzaFQ7ZV__e7448NgyB8z2wDq3fPOykLj9e7y3_APFZ4t8</recordid><startdate>20210415</startdate><enddate>20210415</enddate><creator>Rokicki, Jaroslav</creator><creator>Wolfers, Thomas</creator><creator>Nordhøy, Wibeke</creator><creator>Tesli, Natalia</creator><creator>Quintana, Daniel S.</creator><creator>Alnæs, Dag</creator><creator>Richard, Genevieve</creator><creator>Lange, Ann‐Marie G.</creator><creator>Lund, Martina J.</creator><creator>Norbom, Linn</creator><creator>Agartz, Ingrid</creator><creator>Melle, Ingrid</creator><creator>Nærland, Terje</creator><creator>Selbæk, Geir</creator><creator>Persson, Karin</creator><creator>Nordvik, Jan Egil</creator><creator>Schwarz, Emanuel</creator><creator>Andreassen, Ole A.</creator><creator>Kaufmann, Tobias</creator><creator>Westlye, Lars T.</creator><general>John Wiley & Sons, Inc</general><scope>24P</scope><scope>WIN</scope><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>7QR</scope><scope>7TK</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><scope>3HK</scope><scope>5PM</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D8T</scope><scope>ZZAVC</scope><orcidid>https://orcid.org/0000-0003-2876-0004</orcidid><orcidid>https://orcid.org/0000-0001-6475-2576</orcidid><orcidid>https://orcid.org/0000-0002-2679-9469</orcidid><orcidid>https://orcid.org/0000-0001-8644-956X</orcidid><orcidid>https://orcid.org/0000-0003-3258-1674</orcidid></search><sort><creationdate>20210415</creationdate><title>Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders</title><author>Rokicki, Jaroslav ; Wolfers, Thomas ; Nordhøy, Wibeke ; Tesli, Natalia ; Quintana, Daniel S. ; Alnæs, Dag ; Richard, Genevieve ; Lange, Ann‐Marie G. ; Lund, Martina J. ; Norbom, Linn ; Agartz, Ingrid ; Melle, Ingrid ; Nærland, Terje ; Selbæk, Geir ; Persson, Karin ; Nordvik, Jan Egil ; Schwarz, Emanuel ; Andreassen, Ole A. ; Kaufmann, Tobias ; Westlye, Lars T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5553-e79820dc8994b52b9301b55bb98e2bc71038fe220076907b4a01e82ead28d08d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Abnormalities</topic><topic>Accuracy</topic><topic>Adolescent</topic><topic>Adult</topic><topic>Age</topic><topic>Age Factors</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Aging</topic><topic>Alzheimer Disease - 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methods</topic><topic>Neurological diseases</topic><topic>Neurological disorders</topic><topic>Predictions</topic><topic>Schizophrenia</topic><topic>Schizophrenia - diagnostic imaging</topic><topic>Schizophrenia - pathology</topic><topic>Spin labeling</topic><topic>Spin Labels</topic><topic>T1w/T2w ratio</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Rokicki, Jaroslav</creatorcontrib><creatorcontrib>Wolfers, Thomas</creatorcontrib><creatorcontrib>Nordhøy, Wibeke</creatorcontrib><creatorcontrib>Tesli, Natalia</creatorcontrib><creatorcontrib>Quintana, Daniel S.</creatorcontrib><creatorcontrib>Alnæs, Dag</creatorcontrib><creatorcontrib>Richard, Genevieve</creatorcontrib><creatorcontrib>Lange, Ann‐Marie G.</creatorcontrib><creatorcontrib>Lund, Martina J.</creatorcontrib><creatorcontrib>Norbom, Linn</creatorcontrib><creatorcontrib>Agartz, Ingrid</creatorcontrib><creatorcontrib>Melle, Ingrid</creatorcontrib><creatorcontrib>Nærland, Terje</creatorcontrib><creatorcontrib>Selbæk, Geir</creatorcontrib><creatorcontrib>Persson, Karin</creatorcontrib><creatorcontrib>Nordvik, Jan Egil</creatorcontrib><creatorcontrib>Schwarz, Emanuel</creatorcontrib><creatorcontrib>Andreassen, Ole A.</creatorcontrib><creatorcontrib>Kaufmann, Tobias</creatorcontrib><creatorcontrib>Westlye, Lars T.</creatorcontrib><collection>Wiley-Blackwell Open Access Titles</collection><collection>Wiley Free Content</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Chemoreception Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - 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Age prediction based on structural MRI data shows high accuracy in common brain disorders. However, brain aging is complex and heterogenous, both in terms of individual differences and the underlying biological processes. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF) based on arterial spin labeling. For each of the 11 models we assessed the age prediction accuracy in healthy controls (HC, n = 750) and compared the obtained brain age gaps (BAGs) between age‐matched subsets of HC and patients with Alzheimer's disease (AD, n = 54), mild (MCI, n = 90) and subjective (SCI, n = 56) cognitive impairment, schizophrenia spectrum (SZ, n = 159) and bipolar disorder (BD, n = 135). We found highest age prediction accuracy in HC when integrating all modalities. Furthermore, two‐group case–control classifications revealed highest accuracy for AD using global T1‐weighted BAG, while MCI, SCI, BD and SZ showed strongest effects in CBF‐based BAGs. Combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders. The multimodal BAG was most accurate in predicting age in HC, while group differences between patients and HC were often larger for BAGs based on single modalities. These findings indicate that multidimensional neuroimaging of patients may provide a brain‐based mapping of overlapping and distinct pathophysiology in common disorders.
The deviation between chronological age and age predicted using brain MRI is a marker of overall brain health. Here, we implemented a multimodal model to estimate brain age using different combinations of cortical area, thickness and sub‐cortical volumes, cortical and subcortical T1/T2‐weighted ratios, and cerebral blood flow (CBF). We found that, combining multiple MRI modalities improves brain age prediction and reveals distinct deviations in patients with psychiatric and neurological disorders.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>33340180</pmid><doi>10.1002/hbm.25323</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-2876-0004</orcidid><orcidid>https://orcid.org/0000-0001-6475-2576</orcidid><orcidid>https://orcid.org/0000-0002-2679-9469</orcidid><orcidid>https://orcid.org/0000-0001-8644-956X</orcidid><orcidid>https://orcid.org/0000-0003-3258-1674</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1065-9471 |
| ispartof | Human brain mapping, 2021-04, Vol.42 (6), p.1714-1726 |
| issn | 1065-9471 1097-0193 1097-0193 |
| language | eng |
| recordid | cdi_swepub_primary_oai_swepub_ki_se_465735 |
| source | MEDLINE; NORA - Norwegian Open Research Archives; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; SWEPUB Freely available online; Wiley-Blackwell Open Access Titles; Wiley Online Library All Journals; PubMed Central |
| subjects | Abnormalities Accuracy Adolescent Adult Age Age Factors Aged Aged, 80 and over Aging Alzheimer Disease - diagnostic imaging Alzheimer Disease - pathology Alzheimer's disease arterial spin labeling Biological activity Bipolar disorder Bipolar Disorder - diagnostic imaging Bipolar Disorder - pathology Blood flow Brain Brain - blood supply Brain - diagnostic imaging Brain - pathology brain age brain disorders Brain mapping Case-Control Studies Cerebral blood flow Cerebrovascular Circulation - physiology Cognitive ability Cognitive Dysfunction - diagnostic imaging Cognitive Dysfunction - pathology Deviation Female Humans machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Male Medical imaging Medicin och hälsovetenskap Mental disorders Middle Aged MRI Multimodal Imaging Neurodegenerative diseases Neuroimaging Neuroimaging - methods Neurological diseases Neurological disorders Predictions Schizophrenia Schizophrenia - diagnostic imaging Schizophrenia - pathology Spin labeling Spin Labels T1w/T2w ratio Young Adult |
| title | Multimodal imaging improves brain age prediction and reveals distinct abnormalities in patients with psychiatric and neurological disorders |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-08T05%3A41%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_swepu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multimodal%20imaging%20improves%20brain%20age%20prediction%20and%20reveals%20distinct%20abnormalities%20in%20patients%20with%20psychiatric%20and%20neurological%20disorders&rft.jtitle=Human%20brain%20mapping&rft.au=Rokicki,%20Jaroslav&rft.date=2021-04-15&rft.volume=42&rft.issue=6&rft.spage=1714&rft.epage=1726&rft.pages=1714-1726&rft.issn=1065-9471&rft.eissn=1097-0193&rft_id=info:doi/10.1002/hbm.25323&rft_dat=%3Cproquest_swepu%3E2471462242%3C/proquest_swepu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2509232356&rft_id=info:pmid/33340180&rfr_iscdi=true |