Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.11046 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913662884642816 |
|---|---|
| author | Goya-Maldonado, Roberto Erwin-Grabner, Tracy Zeng, Ling-Li Ching, Christopher R. K. Aleman, Andre Amod, Alyssa R. Basgoze, Zeynep Benedetti, Francesco Besteher, Bianca Brosch, Katharina Bülow, Robin Colle, Romain Connolly, Colm G. Corruble, Emmanuelle Couvy-Duchesne, Baptiste Cullen, Kathryn Dannlowski, Udo Davey, Christopher G. Dols, Annemiek Ernsting, Jan Evans, Jennifer W. Fisch, Lukas Fuentes-Claramonte, Paola Gonul, Ali Saffet Gotlib, Ian H. Grabe, Hans J. Groenewold, Nynke A. Grotegerd, Dominik Hahn, Tim Hamilton, J. Paul Han, Laura K. M. Harrison, Ben J. Ho, Tiffany C. Jahanshad, Neda Jamieson, Alec J. Karuk, Andriana Kircher, Tilo Klimes-Dougan, Bonnie Koopowitz, Sheri-Michelle Lancaster, Thomas Leenings, Ramona Li, Meng Linden, David E. J. MacMaster, Frank P. Mehler, David M. A. Meinert, Susanne Melloni, Elisa Mueller, Bryon A. Mwangi, Benson Nenadić, Igor Ojha, Amar Okamoto, Yasumasa Oudega, Mardien L. Penninx, Brenda W. J. H. Poletti, Sara Pomarol-Clotet, Edith Portella, Maria J. Pozzi, Elena Radua, Joaquim Rodríguez-Cano, Elena Sacchet, Matthew D. Salvador, Raymond Schrantee, Anouk Sim, Kang Soares, Jair C. Solanes, Aleix Stein, Dan J. Stein, Frederike Stolicyn, Aleks Thomopoulos, Sophia I. Toenders, Yara J. Uyar-Demir, Aslihan Vieta, Eduard Vives-Gilabert, Yolanda Völzke, Henry Walter, Martin Whalley, Heather C. Whittle, Sarah Winter, Nils Wittfeld, Katharina Wright, Margaret J. Wu, Mon-Ju Yang, Tony T. Zarate, Carlos Veltman, Dick J. Schmaal, Lianne Thompson, Paul M. |
| author_facet | Goya-Maldonado, Roberto Erwin-Grabner, Tracy Zeng, Ling-Li Ching, Christopher R. K. Aleman, Andre Amod, Alyssa R. Basgoze, Zeynep Benedetti, Francesco Besteher, Bianca Brosch, Katharina Bülow, Robin Colle, Romain Connolly, Colm G. Corruble, Emmanuelle Couvy-Duchesne, Baptiste Cullen, Kathryn Dannlowski, Udo Davey, Christopher G. Dols, Annemiek Ernsting, Jan Evans, Jennifer W. Fisch, Lukas Fuentes-Claramonte, Paola Gonul, Ali Saffet Gotlib, Ian H. Grabe, Hans J. Groenewold, Nynke A. Grotegerd, Dominik Hahn, Tim Hamilton, J. Paul Han, Laura K. M. Harrison, Ben J. Ho, Tiffany C. Jahanshad, Neda Jamieson, Alec J. Karuk, Andriana Kircher, Tilo Klimes-Dougan, Bonnie Koopowitz, Sheri-Michelle Lancaster, Thomas Leenings, Ramona Li, Meng Linden, David E. J. MacMaster, Frank P. Mehler, David M. A. Meinert, Susanne Melloni, Elisa Mueller, Bryon A. Mwangi, Benson Nenadić, Igor Ojha, Amar Okamoto, Yasumasa Oudega, Mardien L. Penninx, Brenda W. J. H. Poletti, Sara Pomarol-Clotet, Edith Portella, Maria J. Pozzi, Elena Radua, Joaquim Rodríguez-Cano, Elena Sacchet, Matthew D. Salvador, Raymond Schrantee, Anouk Sim, Kang Soares, Jair C. Solanes, Aleix Stein, Dan J. Stein, Frederike Stolicyn, Aleks Thomopoulos, Sophia I. Toenders, Yara J. Uyar-Demir, Aslihan Vieta, Eduard Vives-Gilabert, Yolanda Völzke, Henry Walter, Martin Whalley, Heather C. Whittle, Sarah Winter, Nils Wittfeld, Katharina Wright, Margaret J. Wu, Mon-Ju Yang, Tony T. Zarate, Carlos Veltman, Dick J. Schmaal, Lianne Thompson, Paul M. |
| contents | Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. Here, we used globally representative data from the ENIGMA-MDD working group containing 7,012 participants from 30 sites (N=2,772 MDD and N=4,240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of such features and classifiers is unfeasible. Perhaps more sophisticated integration of multimodal information may lead to a higher performance in this diagnostic task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_11046 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model Goya-Maldonado, Roberto Erwin-Grabner, Tracy Zeng, Ling-Li Ching, Christopher R. K. Aleman, Andre Amod, Alyssa R. Basgoze, Zeynep Benedetti, Francesco Besteher, Bianca Brosch, Katharina Bülow, Robin Colle, Romain Connolly, Colm G. Corruble, Emmanuelle Couvy-Duchesne, Baptiste Cullen, Kathryn Dannlowski, Udo Davey, Christopher G. Dols, Annemiek Ernsting, Jan Evans, Jennifer W. Fisch, Lukas Fuentes-Claramonte, Paola Gonul, Ali Saffet Gotlib, Ian H. Grabe, Hans J. Groenewold, Nynke A. Grotegerd, Dominik Hahn, Tim Hamilton, J. Paul Han, Laura K. M. Harrison, Ben J. Ho, Tiffany C. Jahanshad, Neda Jamieson, Alec J. Karuk, Andriana Kircher, Tilo Klimes-Dougan, Bonnie Koopowitz, Sheri-Michelle Lancaster, Thomas Leenings, Ramona Li, Meng Linden, David E. J. MacMaster, Frank P. Mehler, David M. A. Meinert, Susanne Melloni, Elisa Mueller, Bryon A. Mwangi, Benson Nenadić, Igor Ojha, Amar Okamoto, Yasumasa Oudega, Mardien L. Penninx, Brenda W. J. H. Poletti, Sara Pomarol-Clotet, Edith Portella, Maria J. Pozzi, Elena Radua, Joaquim Rodríguez-Cano, Elena Sacchet, Matthew D. Salvador, Raymond Schrantee, Anouk Sim, Kang Soares, Jair C. Solanes, Aleix Stein, Dan J. Stein, Frederike Stolicyn, Aleks Thomopoulos, Sophia I. Toenders, Yara J. Uyar-Demir, Aslihan Vieta, Eduard Vives-Gilabert, Yolanda Völzke, Henry Walter, Martin Whalley, Heather C. Whittle, Sarah Winter, Nils Wittfeld, Katharina Wright, Margaret J. Wu, Mon-Ju Yang, Tony T. Zarate, Carlos Veltman, Dick J. Schmaal, Lianne Thompson, Paul M. Quantitative Methods Machine Learning Neurons and Cognition Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. Here, we used globally representative data from the ENIGMA-MDD working group containing 7,012 participants from 30 sites (N=2,772 MDD and N=4,240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of such features and classifiers is unfeasible. Perhaps more sophisticated integration of multimodal information may lead to a higher performance in this diagnostic task. |
| title | Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model |
| topic | Quantitative Methods Machine Learning Neurons and Cognition |
| url | https://arxiv.org/abs/2311.11046 |