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 |
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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 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_9036665</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2524362760</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4489-f1e345ad68441b2b704170d90ab47e4c0f9aae0ff2cb2d802a91ac89cc1388f73</originalsourceid><addsrcrecordid>eNp9kc9u1DAQhy0EoqVw4QFQJC6oUsr4T5z4goRWlBa1ILFwthxnDF4ldmsnReXEI_CMPAletlTAgdNY408_zcxHyGMKRxSAPd9MyR-xVrbqDtmnDWM1azp5t7yh4TXtoN0jD3LeAIBSorlP9jhXEkDyfbJepZjzj2_f19aEgKk6MWmKwX81s4-hiq56i0uKE44m-LDlMGQ_-yuszt-fVi6m6nwZZ196WK3nZfCYH5J7zowZH93UA_Lx-NWH1Ul99u716erlWW2F6FTtKHLRmEF2QtCe9S0I2sKgwPSiRWHBKWMQnGO2Z0MHzChqbKespbzrXMsPyItd7sXSTzhYDHMyo75IfjLpWkfj9d8_wX_Wn-KVVsCllE0JeHYTkOLlgnnWk88Wx7IrxiVr1jDBZbksFPTpP-gmLimU9QrVMsaV4rJQhzvKbq-a0N0OQ0FvXemtK_3LVYGf_Dn-LfpbTgHoDvjiR7z-T5R-U1zsQn8CcCyiYg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2572239936</pqid></control><display><type>article</type><title>Cross‐Scanner Harmonization of Neuromelanin‐Sensitive MRI for Multisite Studies</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Wengler, Kenneth ; Cassidy, Clifford ; Pluijm, Marieke ; Weinstein, Jodi J. ; Abi‐Dargham, Anissa ; Giessen, Elsmarieke ; Horga, Guillermo</creator><creatorcontrib>Wengler, Kenneth ; Cassidy, Clifford ; Pluijm, Marieke ; Weinstein, Jodi J. ; Abi‐Dargham, Anissa ; Giessen, Elsmarieke ; Horga, Guillermo</creatorcontrib><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 <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 & 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 <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 & 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 & 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 <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 & 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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