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Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for t...

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Autores principales: Nunes, Abraham, Schnack, Hugo G., Ching, Christopher R. K., Agartz, Ingrid, Akudjedu, Theophilus N., Alda, Martin, Alnæs, Dag, Alonso-Lana, Silvia, Bauer, Jochen, Baune, Bernhard T., Bøen, Erlend, Bonnin, Caterina del Mar, Busatto, Geraldo F., Canales-Rodríguez, Erick J., Cannon, Dara M., Caseras, Xavier, Chaim-Avancini, Tiffany M., Dannlowski, Udo, Díaz-Zuluaga, Ana M., Dietsche, Bruno, Doan, Nhat Trung, Duchesnay, Edouard, Elvsåshagen, Torbjørn, Emden, Daniel, Eyler, Lisa T., Fatjó-Vilas, Mar, Favre, Pauline, Foley, Sonya F., Fullerton, Janice M., Glahn, David C., Goikolea, Jose M., Grotegerd, Dominik, Hahn, Tim, Henry, Chantal, Hibar, Derrek P., Houenou, Josselin, Howells, Fleur M., Jahanshad, Neda, Kaufmann, Tobias, Kenney, Joanne, Kircher, Tilo T. J., Krug, Axel, Lagerberg, Trine V., Lenroot, Rhoshel K., López-Jaramillo, Carlos, Machado-Vieira, Rodrigo, Malt, Ulrik F., McDonald, Colm, Mitchell, Philip B., Mwangi, Benson, Nabulsi, Leila, Opel, Nils, Overs, Bronwyn J., Pineda-Zapata, Julian A., Pomarol-Clotet, Edith, Redlich, Ronny, Roberts, Gloria, Rosa, Pedro G., Salvador, Raymond, Satterthwaite, Theodore D., Soares, Jair C., Stein, Dan J., Temmingh, Henk S., Trappenberg, Thomas, Uhlmann, Anne, van Haren, Neeltje E. M., Vieta, Eduard, Westlye, Lars T., Wolf, Daniel H., Yüksel, Dilara, Zanetti, Marcus V., Andreassen, Ole A., Thompson, Paul M., Hajek, Tomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473838/
https://www.ncbi.nlm.nih.gov/pubmed/30171211
http://dx.doi.org/10.1038/s41380-018-0228-9
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author Nunes, Abraham
Schnack, Hugo G.
Ching, Christopher R. K.
Agartz, Ingrid
Akudjedu, Theophilus N.
Alda, Martin
Alnæs, Dag
Alonso-Lana, Silvia
Bauer, Jochen
Baune, Bernhard T.
Bøen, Erlend
Bonnin, Caterina del Mar
Busatto, Geraldo F.
Canales-Rodríguez, Erick J.
Cannon, Dara M.
Caseras, Xavier
Chaim-Avancini, Tiffany M.
Dannlowski, Udo
Díaz-Zuluaga, Ana M.
Dietsche, Bruno
Doan, Nhat Trung
Duchesnay, Edouard
Elvsåshagen, Torbjørn
Emden, Daniel
Eyler, Lisa T.
Fatjó-Vilas, Mar
Favre, Pauline
Foley, Sonya F.
Fullerton, Janice M.
Glahn, David C.
Goikolea, Jose M.
Grotegerd, Dominik
Hahn, Tim
Henry, Chantal
Hibar, Derrek P.
Houenou, Josselin
Howells, Fleur M.
Jahanshad, Neda
Kaufmann, Tobias
Kenney, Joanne
Kircher, Tilo T. J.
Krug, Axel
Lagerberg, Trine V.
Lenroot, Rhoshel K.
López-Jaramillo, Carlos
Machado-Vieira, Rodrigo
Malt, Ulrik F.
McDonald, Colm
Mitchell, Philip B.
Mwangi, Benson
Nabulsi, Leila
Opel, Nils
Overs, Bronwyn J.
Pineda-Zapata, Julian A.
Pomarol-Clotet, Edith
Redlich, Ronny
Roberts, Gloria
Rosa, Pedro G.
Salvador, Raymond
Satterthwaite, Theodore D.
Soares, Jair C.
Stein, Dan J.
Temmingh, Henk S.
Trappenberg, Thomas
Uhlmann, Anne
van Haren, Neeltje E. M.
Vieta, Eduard
Westlye, Lars T.
Wolf, Daniel H.
Yüksel, Dilara
Zanetti, Marcus V.
Andreassen, Ole A.
Thompson, Paul M.
Hajek, Tomas
author_facet Nunes, Abraham
Schnack, Hugo G.
Ching, Christopher R. K.
Agartz, Ingrid
Akudjedu, Theophilus N.
Alda, Martin
Alnæs, Dag
Alonso-Lana, Silvia
Bauer, Jochen
Baune, Bernhard T.
Bøen, Erlend
Bonnin, Caterina del Mar
Busatto, Geraldo F.
Canales-Rodríguez, Erick J.
Cannon, Dara M.
Caseras, Xavier
Chaim-Avancini, Tiffany M.
Dannlowski, Udo
Díaz-Zuluaga, Ana M.
Dietsche, Bruno
Doan, Nhat Trung
Duchesnay, Edouard
Elvsåshagen, Torbjørn
Emden, Daniel
Eyler, Lisa T.
Fatjó-Vilas, Mar
Favre, Pauline
Foley, Sonya F.
Fullerton, Janice M.
Glahn, David C.
Goikolea, Jose M.
Grotegerd, Dominik
Hahn, Tim
Henry, Chantal
Hibar, Derrek P.
Houenou, Josselin
Howells, Fleur M.
Jahanshad, Neda
Kaufmann, Tobias
Kenney, Joanne
Kircher, Tilo T. J.
Krug, Axel
Lagerberg, Trine V.
Lenroot, Rhoshel K.
López-Jaramillo, Carlos
Machado-Vieira, Rodrigo
Malt, Ulrik F.
McDonald, Colm
Mitchell, Philip B.
Mwangi, Benson
Nabulsi, Leila
Opel, Nils
Overs, Bronwyn J.
Pineda-Zapata, Julian A.
Pomarol-Clotet, Edith
Redlich, Ronny
Roberts, Gloria
Rosa, Pedro G.
Salvador, Raymond
Satterthwaite, Theodore D.
Soares, Jair C.
Stein, Dan J.
Temmingh, Henk S.
Trappenberg, Thomas
Uhlmann, Anne
van Haren, Neeltje E. M.
Vieta, Eduard
Westlye, Lars T.
Wolf, Daniel H.
Yüksel, Dilara
Zanetti, Marcus V.
Andreassen, Ole A.
Thompson, Paul M.
Hajek, Tomas
author_sort Nunes, Abraham
collection PubMed
description Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.
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spelling pubmed-74738382020-09-16 Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group Nunes, Abraham Schnack, Hugo G. Ching, Christopher R. K. Agartz, Ingrid Akudjedu, Theophilus N. Alda, Martin Alnæs, Dag Alonso-Lana, Silvia Bauer, Jochen Baune, Bernhard T. Bøen, Erlend Bonnin, Caterina del Mar Busatto, Geraldo F. Canales-Rodríguez, Erick J. Cannon, Dara M. Caseras, Xavier Chaim-Avancini, Tiffany M. Dannlowski, Udo Díaz-Zuluaga, Ana M. Dietsche, Bruno Doan, Nhat Trung Duchesnay, Edouard Elvsåshagen, Torbjørn Emden, Daniel Eyler, Lisa T. Fatjó-Vilas, Mar Favre, Pauline Foley, Sonya F. Fullerton, Janice M. Glahn, David C. Goikolea, Jose M. Grotegerd, Dominik Hahn, Tim Henry, Chantal Hibar, Derrek P. Houenou, Josselin Howells, Fleur M. Jahanshad, Neda Kaufmann, Tobias Kenney, Joanne Kircher, Tilo T. J. Krug, Axel Lagerberg, Trine V. Lenroot, Rhoshel K. López-Jaramillo, Carlos Machado-Vieira, Rodrigo Malt, Ulrik F. McDonald, Colm Mitchell, Philip B. Mwangi, Benson Nabulsi, Leila Opel, Nils Overs, Bronwyn J. Pineda-Zapata, Julian A. Pomarol-Clotet, Edith Redlich, Ronny Roberts, Gloria Rosa, Pedro G. Salvador, Raymond Satterthwaite, Theodore D. Soares, Jair C. Stein, Dan J. Temmingh, Henk S. Trappenberg, Thomas Uhlmann, Anne van Haren, Neeltje E. M. Vieta, Eduard Westlye, Lars T. Wolf, Daniel H. Yüksel, Dilara Zanetti, Marcus V. Andreassen, Ole A. Thompson, Paul M. Hajek, Tomas Mol Psychiatry Article Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data. Nature Publishing Group UK 2018-08-31 2020 /pmc/articles/PMC7473838/ /pubmed/30171211 http://dx.doi.org/10.1038/s41380-018-0228-9 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Nunes, Abraham
Schnack, Hugo G.
Ching, Christopher R. K.
Agartz, Ingrid
Akudjedu, Theophilus N.
Alda, Martin
Alnæs, Dag
Alonso-Lana, Silvia
Bauer, Jochen
Baune, Bernhard T.
Bøen, Erlend
Bonnin, Caterina del Mar
Busatto, Geraldo F.
Canales-Rodríguez, Erick J.
Cannon, Dara M.
Caseras, Xavier
Chaim-Avancini, Tiffany M.
Dannlowski, Udo
Díaz-Zuluaga, Ana M.
Dietsche, Bruno
Doan, Nhat Trung
Duchesnay, Edouard
Elvsåshagen, Torbjørn
Emden, Daniel
Eyler, Lisa T.
Fatjó-Vilas, Mar
Favre, Pauline
Foley, Sonya F.
Fullerton, Janice M.
Glahn, David C.
Goikolea, Jose M.
Grotegerd, Dominik
Hahn, Tim
Henry, Chantal
Hibar, Derrek P.
Houenou, Josselin
Howells, Fleur M.
Jahanshad, Neda
Kaufmann, Tobias
Kenney, Joanne
Kircher, Tilo T. J.
Krug, Axel
Lagerberg, Trine V.
Lenroot, Rhoshel K.
López-Jaramillo, Carlos
Machado-Vieira, Rodrigo
Malt, Ulrik F.
McDonald, Colm
Mitchell, Philip B.
Mwangi, Benson
Nabulsi, Leila
Opel, Nils
Overs, Bronwyn J.
Pineda-Zapata, Julian A.
Pomarol-Clotet, Edith
Redlich, Ronny
Roberts, Gloria
Rosa, Pedro G.
Salvador, Raymond
Satterthwaite, Theodore D.
Soares, Jair C.
Stein, Dan J.
Temmingh, Henk S.
Trappenberg, Thomas
Uhlmann, Anne
van Haren, Neeltje E. M.
Vieta, Eduard
Westlye, Lars T.
Wolf, Daniel H.
Yüksel, Dilara
Zanetti, Marcus V.
Andreassen, Ole A.
Thompson, Paul M.
Hajek, Tomas
Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
title Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
title_full Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
title_fullStr Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
title_full_unstemmed Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
title_short Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
title_sort using structural mri to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the enigma bipolar disorders working group
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473838/
https://www.ncbi.nlm.nih.gov/pubmed/30171211
http://dx.doi.org/10.1038/s41380-018-0228-9
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