Cross‐Scanner Harmonization of Neuromelanin‐Sensitive MRI for Multisite Studies

Background Neuromelanin‐sensitive magnetic resonance imaging (NM‐MRI) is a validated measure of neuromelanin concentration in the substantia nigra–ventral tegmental area (SN–VTA) complex and is a proxy measure of dopaminergic function with potential as a noninvasive biomarker. The development of gen...

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Veröffentlicht in:Journal of magnetic resonance imaging 2021-10, Vol.54 (4), p.1189-1199
Hauptverfasser: Wengler, Kenneth, Cassidy, Clifford, Pluijm, Marieke, Weinstein, Jodi J., Abi‐Dargham, Anissa, Giessen, Elsmarieke, Horga, Guillermo
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container_end_page 1199
container_issue 4
container_start_page 1189
container_title Journal of magnetic resonance imaging
container_volume 54
creator Wengler, Kenneth
Cassidy, Clifford
Pluijm, Marieke
Weinstein, Jodi J.
Abi‐Dargham, Anissa
Giessen, Elsmarieke
Horga, Guillermo
description Background Neuromelanin‐sensitive magnetic resonance imaging (NM‐MRI) is a validated measure of neuromelanin concentration in the substantia nigra–ventral tegmental area (SN–VTA) complex and is a proxy measure of dopaminergic function with potential as a noninvasive biomarker. The development of generalizable biomarkers requires large‐scale samples necessitating harmonization approaches to combine data collected across sites. Purpose To develop a method to harmonize NM‐MRI across scanners and sites. Study Type Prospective. Population A total of 128 healthy subjects (18–73 years old; 45% female) from three sites and five MRI scanners. Field Strength/Sequence 3.0 T; NM‐MRI two‐dimensional gradient‐recalled echo with magnetization‐transfer pulse and three‐dimensional T1‐weighted images. Assessment NM‐MRI contrast (contrast‐to‐noise ratio [CNR]) maps were calculated and CNR values within the SN–VTA (defined previously by manual tracing on a standardized NM‐MRI template) were determined before harmonization (raw CNR) and after ComBat harmonization (harmonized CNR). Scanner differences were assessed by calculating the classification accuracy of a support vector machine (SVM). To assess the effect of harmonization on biological variability, support vector regression (SVR) was used to predict age and the difference in goodness‐of‐fit (Δr) was calculated as the correlation (between actual and predicted ages) for the harmonized CNR minus the correlation for the raw CNR. Statistical Tests Permutation tests were used to determine if SVM classification accuracy was above chance level and if SVR Δr was significant. A P‐value
doi_str_mv 10.1002/jmri.27679
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The development of generalizable biomarkers requires large‐scale samples necessitating harmonization approaches to combine data collected across sites. Purpose To develop a method to harmonize NM‐MRI across scanners and sites. Study Type Prospective. Population A total of 128 healthy subjects (18–73 years old; 45% female) from three sites and five MRI scanners. Field Strength/Sequence 3.0 T; NM‐MRI two‐dimensional gradient‐recalled echo with magnetization‐transfer pulse and three‐dimensional T1‐weighted images. Assessment NM‐MRI contrast (contrast‐to‐noise ratio [CNR]) maps were calculated and CNR values within the SN–VTA (defined previously by manual tracing on a standardized NM‐MRI template) were determined before harmonization (raw CNR) and after ComBat harmonization (harmonized CNR). Scanner differences were assessed by calculating the classification accuracy of a support vector machine (SVM). To assess the effect of harmonization on biological variability, support vector regression (SVR) was used to predict age and the difference in goodness‐of‐fit (Δr) was calculated as the correlation (between actual and predicted ages) for the harmonized CNR minus the correlation for the raw CNR. Statistical Tests Permutation tests were used to determine if SVM classification accuracy was above chance level and if SVR Δr was significant. A P‐value &lt;0.05 was considered significant. Results In the raw CNR, SVM MRI scanner classification was above chance level (accuracy = 86.5%). In the harmonized CNR, the accuracy of the SVM was at chance level (accuracy = 29.5%; P = 0.8542). There was no significant difference in age prediction using the raw or harmonized CNR (Δr = −0.06; P = 0.7304). Data Conclusion ComBat harmonization removes differences in SN–VTA CNR across scanners while preserving biologically meaningful variability associated with age. Level of Evidence 2 Technical Efficacy 1</description><identifier>ISSN: 1053-1807</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.27679</identifier><identifier>PMID: 33960063</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley &amp; Sons, Inc</publisher><subject>Accuracy ; Adolescent ; Adult ; Age ; Aged ; Biological effects ; Biomarkers ; Classification ; ComBat ; dopamine ; Dopamine receptors ; Female ; Field strength ; harmonization ; Humans ; Image contrast ; Magnetic Resonance Imaging ; Male ; Mathematical analysis ; Melanins ; Middle Aged ; neurodegeneration ; neuromelanin ; neuromelanin‐sensitive magnetic resonance imaging ; Permutations ; Population studies ; Prospective Studies ; Scanners ; Statistical analysis ; Statistical methods ; Statistical tests ; Substantia nigra ; Substantia Nigra - diagnostic imaging ; Support vector machines ; Ventral tegmentum ; Young Adult</subject><ispartof>Journal of magnetic resonance imaging, 2021-10, Vol.54 (4), p.1189-1199</ispartof><rights>2021 International Society for Magnetic Resonance in Medicine</rights><rights>2021 International Society for Magnetic Resonance in Medicine.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4489-f1e345ad68441b2b704170d90ab47e4c0f9aae0ff2cb2d802a91ac89cc1388f73</citedby><cites>FETCH-LOGICAL-c4489-f1e345ad68441b2b704170d90ab47e4c0f9aae0ff2cb2d802a91ac89cc1388f73</cites><orcidid>0000-0002-8153-5183</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjmri.27679$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmri.27679$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,777,781,882,1412,27905,27906,45555,45556</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33960063$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wengler, Kenneth</creatorcontrib><creatorcontrib>Cassidy, Clifford</creatorcontrib><creatorcontrib>Pluijm, Marieke</creatorcontrib><creatorcontrib>Weinstein, Jodi J.</creatorcontrib><creatorcontrib>Abi‐Dargham, Anissa</creatorcontrib><creatorcontrib>Giessen, Elsmarieke</creatorcontrib><creatorcontrib>Horga, Guillermo</creatorcontrib><title>Cross‐Scanner Harmonization of Neuromelanin‐Sensitive MRI for Multisite Studies</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Background Neuromelanin‐sensitive magnetic resonance imaging (NM‐MRI) is a validated measure of neuromelanin concentration in the substantia nigra–ventral tegmental area (SN–VTA) complex and is a proxy measure of dopaminergic function with potential as a noninvasive biomarker. The development of generalizable biomarkers requires large‐scale samples necessitating harmonization approaches to combine data collected across sites. Purpose To develop a method to harmonize NM‐MRI across scanners and sites. Study Type Prospective. Population A total of 128 healthy subjects (18–73 years old; 45% female) from three sites and five MRI scanners. Field Strength/Sequence 3.0 T; NM‐MRI two‐dimensional gradient‐recalled echo with magnetization‐transfer pulse and three‐dimensional T1‐weighted images. Assessment NM‐MRI contrast (contrast‐to‐noise ratio [CNR]) maps were calculated and CNR values within the SN–VTA (defined previously by manual tracing on a standardized NM‐MRI template) were determined before harmonization (raw CNR) and after ComBat harmonization (harmonized CNR). Scanner differences were assessed by calculating the classification accuracy of a support vector machine (SVM). To assess the effect of harmonization on biological variability, support vector regression (SVR) was used to predict age and the difference in goodness‐of‐fit (Δr) was calculated as the correlation (between actual and predicted ages) for the harmonized CNR minus the correlation for the raw CNR. Statistical Tests Permutation tests were used to determine if SVM classification accuracy was above chance level and if SVR Δr was significant. A P‐value &lt;0.05 was considered significant. Results In the raw CNR, SVM MRI scanner classification was above chance level (accuracy = 86.5%). In the harmonized CNR, the accuracy of the SVM was at chance level (accuracy = 29.5%; P = 0.8542). There was no significant difference in age prediction using the raw or harmonized CNR (Δr = −0.06; P = 0.7304). Data Conclusion ComBat harmonization removes differences in SN–VTA CNR across scanners while preserving biologically meaningful variability associated with age. Level of Evidence 2 Technical Efficacy 1</description><subject>Accuracy</subject><subject>Adolescent</subject><subject>Adult</subject><subject>Age</subject><subject>Aged</subject><subject>Biological effects</subject><subject>Biomarkers</subject><subject>Classification</subject><subject>ComBat</subject><subject>dopamine</subject><subject>Dopamine receptors</subject><subject>Female</subject><subject>Field strength</subject><subject>harmonization</subject><subject>Humans</subject><subject>Image contrast</subject><subject>Magnetic Resonance Imaging</subject><subject>Male</subject><subject>Mathematical analysis</subject><subject>Melanins</subject><subject>Middle Aged</subject><subject>neurodegeneration</subject><subject>neuromelanin</subject><subject>neuromelanin‐sensitive magnetic resonance imaging</subject><subject>Permutations</subject><subject>Population studies</subject><subject>Prospective Studies</subject><subject>Scanners</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Statistical tests</subject><subject>Substantia nigra</subject><subject>Substantia Nigra - diagnostic imaging</subject><subject>Support vector machines</subject><subject>Ventral tegmentum</subject><subject>Young Adult</subject><issn>1053-1807</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc9u1DAQhy0EoqVw4QFQJC6oUsr4T5z4goRWlBa1ILFwthxnDF4ldmsnReXEI_CMPAletlTAgdNY408_zcxHyGMKRxSAPd9MyR-xVrbqDtmnDWM1azp5t7yh4TXtoN0jD3LeAIBSorlP9jhXEkDyfbJepZjzj2_f19aEgKk6MWmKwX81s4-hiq56i0uKE44m-LDlMGQ_-yuszt-fVi6m6nwZZ196WK3nZfCYH5J7zowZH93UA_Lx-NWH1Ul99u716erlWW2F6FTtKHLRmEF2QtCe9S0I2sKgwPSiRWHBKWMQnGO2Z0MHzChqbKespbzrXMsPyItd7sXSTzhYDHMyo75IfjLpWkfj9d8_wX_Wn-KVVsCllE0JeHYTkOLlgnnWk88Wx7IrxiVr1jDBZbksFPTpP-gmLimU9QrVMsaV4rJQhzvKbq-a0N0OQ0FvXemtK_3LVYGf_Dn-LfpbTgHoDvjiR7z-T5R-U1zsQn8CcCyiYg</recordid><startdate>202110</startdate><enddate>202110</enddate><creator>Wengler, Kenneth</creator><creator>Cassidy, Clifford</creator><creator>Pluijm, Marieke</creator><creator>Weinstein, Jodi J.</creator><creator>Abi‐Dargham, Anissa</creator><creator>Giessen, Elsmarieke</creator><creator>Horga, Guillermo</creator><general>John Wiley &amp; Sons, Inc</general><general>Wiley Subscription Services, Inc</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>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-8153-5183</orcidid></search><sort><creationdate>202110</creationdate><title>Cross‐Scanner Harmonization of Neuromelanin‐Sensitive MRI for Multisite Studies</title><author>Wengler, Kenneth ; Cassidy, Clifford ; Pluijm, Marieke ; Weinstein, Jodi J. ; Abi‐Dargham, Anissa ; Giessen, Elsmarieke ; Horga, Guillermo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4489-f1e345ad68441b2b704170d90ab47e4c0f9aae0ff2cb2d802a91ac89cc1388f73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Adolescent</topic><topic>Adult</topic><topic>Age</topic><topic>Aged</topic><topic>Biological effects</topic><topic>Biomarkers</topic><topic>Classification</topic><topic>ComBat</topic><topic>dopamine</topic><topic>Dopamine receptors</topic><topic>Female</topic><topic>Field strength</topic><topic>harmonization</topic><topic>Humans</topic><topic>Image contrast</topic><topic>Magnetic Resonance Imaging</topic><topic>Male</topic><topic>Mathematical analysis</topic><topic>Melanins</topic><topic>Middle Aged</topic><topic>neurodegeneration</topic><topic>neuromelanin</topic><topic>neuromelanin‐sensitive magnetic resonance imaging</topic><topic>Permutations</topic><topic>Population studies</topic><topic>Prospective Studies</topic><topic>Scanners</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Statistical tests</topic><topic>Substantia nigra</topic><topic>Substantia Nigra - diagnostic imaging</topic><topic>Support vector machines</topic><topic>Ventral tegmentum</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wengler, Kenneth</creatorcontrib><creatorcontrib>Cassidy, Clifford</creatorcontrib><creatorcontrib>Pluijm, Marieke</creatorcontrib><creatorcontrib>Weinstein, Jodi J.</creatorcontrib><creatorcontrib>Abi‐Dargham, Anissa</creatorcontrib><creatorcontrib>Giessen, Elsmarieke</creatorcontrib><creatorcontrib>Horga, Guillermo</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wengler, Kenneth</au><au>Cassidy, Clifford</au><au>Pluijm, Marieke</au><au>Weinstein, Jodi J.</au><au>Abi‐Dargham, Anissa</au><au>Giessen, Elsmarieke</au><au>Horga, Guillermo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Cross‐Scanner Harmonization of Neuromelanin‐Sensitive MRI for Multisite Studies</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J Magn Reson Imaging</addtitle><date>2021-10</date><risdate>2021</risdate><volume>54</volume><issue>4</issue><spage>1189</spage><epage>1199</epage><pages>1189-1199</pages><issn>1053-1807</issn><eissn>1522-2586</eissn><abstract>Background Neuromelanin‐sensitive magnetic resonance imaging (NM‐MRI) is a validated measure of neuromelanin concentration in the substantia nigra–ventral tegmental area (SN–VTA) complex and is a proxy measure of dopaminergic function with potential as a noninvasive biomarker. The development of generalizable biomarkers requires large‐scale samples necessitating harmonization approaches to combine data collected across sites. Purpose To develop a method to harmonize NM‐MRI across scanners and sites. Study Type Prospective. Population A total of 128 healthy subjects (18–73 years old; 45% female) from three sites and five MRI scanners. Field Strength/Sequence 3.0 T; NM‐MRI two‐dimensional gradient‐recalled echo with magnetization‐transfer pulse and three‐dimensional T1‐weighted images. Assessment NM‐MRI contrast (contrast‐to‐noise ratio [CNR]) maps were calculated and CNR values within the SN–VTA (defined previously by manual tracing on a standardized NM‐MRI template) were determined before harmonization (raw CNR) and after ComBat harmonization (harmonized CNR). Scanner differences were assessed by calculating the classification accuracy of a support vector machine (SVM). To assess the effect of harmonization on biological variability, support vector regression (SVR) was used to predict age and the difference in goodness‐of‐fit (Δr) was calculated as the correlation (between actual and predicted ages) for the harmonized CNR minus the correlation for the raw CNR. Statistical Tests Permutation tests were used to determine if SVM classification accuracy was above chance level and if SVR Δr was significant. A P‐value &lt;0.05 was considered significant. Results In the raw CNR, SVM MRI scanner classification was above chance level (accuracy = 86.5%). In the harmonized CNR, the accuracy of the SVM was at chance level (accuracy = 29.5%; P = 0.8542). There was no significant difference in age prediction using the raw or harmonized CNR (Δr = −0.06; P = 0.7304). Data Conclusion ComBat harmonization removes differences in SN–VTA CNR across scanners while preserving biologically meaningful variability associated with age. Level of Evidence 2 Technical Efficacy 1</abstract><cop>Hoboken, USA</cop><pub>John Wiley &amp; Sons, Inc</pub><pmid>33960063</pmid><doi>10.1002/jmri.27679</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-8153-5183</orcidid><oa>free_for_read</oa></addata></record>
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subjects Accuracy
Adolescent
Adult
Age
Aged
Biological effects
Biomarkers
Classification
ComBat
dopamine
Dopamine receptors
Female
Field strength
harmonization
Humans
Image contrast
Magnetic Resonance Imaging
Male
Mathematical analysis
Melanins
Middle Aged
neurodegeneration
neuromelanin
neuromelanin‐sensitive magnetic resonance imaging
Permutations
Population studies
Prospective Studies
Scanners
Statistical analysis
Statistical methods
Statistical tests
Substantia nigra
Substantia Nigra - diagnostic imaging
Support vector machines
Ventral tegmentum
Young Adult
title Cross‐Scanner Harmonization of Neuromelanin‐Sensitive MRI for Multisite Studies
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