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2. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Aghajani, Moji, Aleman, Andre, Amod, Alyssa R., Basgoze, Zeynep, Benedetti, Francesco, Besteher, Bianca, Bülow, Robin, Ching, Christopher R. K., Connolly, Colm G., Cullen, Kathryn, Davey, Christopher G., Dima, Danai, Dols, Annemiek, Evans, Jennifer W., Fu, Cynthia H. Y., Gonul, Ali Saffet, Gotlib, Ian H., Grabe, Hans J., Groenewold, Nynke, Hamilton, J Paul, Harrison, Ben J., Ho, Tiffany C., Mwangi, Benson, Jaworska, Natalia, Jahanshad, Neda, Klimes-Dougan, Bonnie, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Li, Meng, Linden, David E. J., MacMaster, Frank P., Mehler, David M. A., Melloni, Elisa, Mueller, Bryon A., Ojha, Amar, Oudega, Mardien L., Penninx, Brenda W. J. H., Poletti, Sara, Pomarol-Clotet, Edith, Portella, Maria J., Pozzi, Elena, Reneman, Liesbeth, Sacchet, Matthew D., Sämann, Philipp G., Schrantee, Anouk, Sim, Kang, Soares, Jair C., Stein, Dan J., Thomopoulos, Sophia I., Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J. A., Völzke, Henry, Whittle, Sarah, Wittfeld, Katharina, Wright, Margaret J., Wu, Mon-Ju, Yang, Tony T., Zarate, Carlos, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Published
2024
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3. Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model

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, and Thompson, Paul M.

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

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., Comment: arXiv admin note: text overlap with arXiv:2206.08122

Published
2023

4. Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Author

Belov, Vladimir, Erwin-Grabner, Tracy, Gonul, Ali Saffet, Amod, Alyssa R., Ojha, Amar, Aleman, Andre, Dols, Annemiek, Scharntee, Anouk, Uyar-Demir, Aslihan, Harrison, Ben J, Irungu, Benson M., Besteher, Bianca, Klimes-Dougan, Bonnie, Penninx, Brenda W. J. H., Mueller, Bryon A., Zarate, Carlos, Davey, Christopher G., Ching, Christopher R. K., Connolly, Colm G., Fu, Cynthia H. Y., Stein, Dan J., Dima, Danai, Linden, David E. J., Mehler, David M. A., Pomarol-Clotet, Edith, Pozzi, Elena, Melloni, Elisa, Benedetti, Francesco, MacMaster, Frank P., Grabe, Hans J., Völzke, Henry, Gotlib, Ian H., Soares, Jair C., Evans, Jennifer W., Sim, Kang, Wittfeld, Katharina, Cullen, Kathryn, Reneman, Liesbeth, Oudega, Mardien L., Wright, Margaret J., Portella, Maria J., Sacchet, Matthew D., Li, Meng, Aghajani, Moji, Wu, Mon-Ju, Jaworska, Natalia, Jahanshad, Neda, van der Wee, Nic J. A., Groenewold, Nynke, Hamilton, Paul J., Saemann, Philipp, Bülow, Robin, Poletti, Sara, Whittle, Sarah, Thomopoulos, Sophia I., van, Steven J. A., Werff, der, Koopowitz, Sheri-Michelle, Lancaster, Thomas, Ho, Tiffany C., Yang, Tony T., Basgoze, Zeynep, Veltman, Dick J., Schmaal, Lianne, Thompson, Paul M., and Goya-Maldonado, Roberto

Subjects
Quantitative Biology - Quantitative Methods
Abstract

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (n=5,356) to provide a generalizable ML classification benchmark of major depressive disorder (MDD). Using brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD vs healthy controls (HC) with around 62% balanced accuracy, but when harmonizing the data using ComBat balanced accuracy dropped to approximately 52%. Similar results were observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may achieve more encouraging prospects., Comment: main document 37 pages; supplementary material 24 pages

Published
2022

7. Brain Correlates of Suicide Attempt in 18,925 Participants Across 18 International Cohorts

Author

Campos, Adrian I., Thompson, Paul M., Veltman, Dick J., Pozzi, Elena, van Veltzen, Laura S., Jahanshad, Neda, Adams, Mark J., Baune, Bernhard T., Berger, Klaus, Brosch, Katharina, Bülow, Robin, Connolly, Colm G., Dannlowski, Udo, Davey, Christopher G., de Zubicaray, Greig I., Dima, Danai, Erwin-Grabner, Tracy, Evans, Jennifer W., Fu, Cynthia H.Y., Gotlib, Ian H., Goya-Maldonado, Roberto, Grabe, Hans J., Grotegerd, Dominik, Harris, Matthew A., Harrison, Ben J., Hatton, Sean N., Hermesdorf, Marco, Hickie, Ian B., Ho, Tiffany C., Kircher, Tilo, Krug, Axel, Lagopoulos, Jim, Lemke, Hannah, McMahon, Katie, MacMaster, Frank P., Martin, Nicholas G., McIntosh, Andrew M., Medland, Sarah E., Meinert, Susanne, Meller, Tina, Nenadic, Igor, Opel, Nils, Redlich, Ronny, Reneman, Liesbeth, Repple, Jonathan, Sacchet, Matthew D., Schmitt, Simon, Schrantee, Anouk, Sim, Kang, Singh, Aditya, Stein, Frederike, Strike, Lachlan T., van der Wee, Nic J.A., van der Werff, Steven J.A., Völzke, Henry, Waltemate, Lena, Whalley, Heather C., Wittfeld, Katharina, Wright, Margaret J., Yang, Tony T., Zarate, Carlos A., Schmaal, Lianne, and Rentería, Miguel E.

Published
2021
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14. An exploration of actigraphy in the context of ketamine and treatment‐resistant depression.

Author

Punturieri, Claire, Duncan, Wallace C., Greenstein, Dede, Shandler, Gavi, Zarate, Carlos A., and Evans, Jennifer W.

Subjects
ACTIGRAPHY, KETAMINE, KETAMINE abuse, MENTAL depression, TIME series analysis
Abstract

Objectives: This study explored the potential of non‐parametric and complexity analysis metrics to detect changes in activity post‐ketamine and their association with depressive symptomatology. Methods: Individuals with treatment‐resistant depression (TRD: n = 27, 16F, 35.9 ± 10.8 years) and healthy volunteers (HVs: n = 9, 4F, 36.4 ± 9.59 years) had their activity monitored during an inpatient, double‐blind, crossover study where they received an infusion of ketamine or saline placebo. All participants were 18–65 years old, medication‐free, and had a MADRS score ≥20. Non‐parametric metrics averaged over each study day, metrics derived from complexity analysis, and traditionally calculated non‐parametric metrics averaged over two weeks were calculated from the actigraphy time series. A separate analysis was conducted for a subsample (n = 17) to assess the utility of these metrics in a hospital setting. Results: In HVs, lower intradaily variability was observed within daily rest/activity patterns post‐ketamine versus post‐placebo (F = 5.16(1,15), p = 0.04). No other significant effects of drug or drug‐by‐time or correlations between depressive symptomatology and activity were detected. Conclusions: Weak associations between non‐parametric variables and ketamine were found but were not consistent across actigraphy measures. Clinical Trial registration: ClinicalTrials.gov, NCT00088699. [ABSTRACT FROM AUTHOR]

Published
2024
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17. DenseNet and Support Vector Machine classifications of major depressive disorder using vertex-wise cortical features

Author

Belov, Vladimir, 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., Goya-Maldonado, Roberto, Belov, Vladimir, 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., and Goya-Maldonado, Roberto

Abstract

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. In this study, we used globally representative data from the ENIGMA-MDD working group containing an extensive sample of people with MDD (N=2,772) and HC (N=4,240), 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. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. 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 features and classifiers is unfeasible.

Published
2023

26. Brain Correlates of Suicide Attempt in 18,925 Participants Across 18 International Cohorts

Author

Campos, Adrian I, Thompson, Paul M., Veltman, Dick J, Pozzi, Elena, van Veltzen, Laura S, Jahanshad, Neda, Adams, Mark J, Baune, Bernhard T, Berger, Klaus, Brosch, Katharina, Bülow, Robin, Connolly, Colm G, Dannlowski, Udo, Davey, Christopher G, de Zubicaray, Greig I, Dima, Danai, Erwin-Grabner, Tracy, Evans, Jennifer W, Fu, Cynthia H Y, Gotlib, Ian H, Goya-Maldonado, Roberto, Grabe, Hans J, Grotegerd, Dominik, Harris, Matthew A, Harrison, Ben J, Hatton, Sean N, Hermesdorf, Marco, Hickie, Ian B, Ho, Tiffany C, Kircher, Tilo, Krug, Axel, Lagopoulos, Jim, Lemke, Hannah, McMahon, Katie, MacMaster, Frank P, Martin, Nicholas G, McIntosh, Andrew M, Medland, Sarah E, Meinert, Susanne, Meller, Tina, Nenadic, Igor, Opel, Nils, Redlich, Ronny, Reneman, Liesbeth, Repple, Jonathan, Sacchet, Matthew D, Schmitt, Simon, Schrantee, Anouk, Sim, Kang, Singh, Aditya, Stein, Frederike, Strike, Lachlan T, van der Wee, Nic J A, van der Werff, Steven J A, Völzke, Henry, Waltemate, Lena, Whalley, Heather C, Wittfeld, Katharina, Wright, Margaret J., Yang, Tony T, Zarate, Carlos A, Schmaal, Lianne, Rentería, Miguel E, other, and, Campos, Adrian I, Thompson, Paul M., Veltman, Dick J, Pozzi, Elena, van Veltzen, Laura S, Jahanshad, Neda, Adams, Mark J, Baune, Bernhard T, Berger, Klaus, Brosch, Katharina, Bülow, Robin, Connolly, Colm G, Dannlowski, Udo, Davey, Christopher G, de Zubicaray, Greig I, Dima, Danai, Erwin-Grabner, Tracy, Evans, Jennifer W, Fu, Cynthia H Y, Gotlib, Ian H, Goya-Maldonado, Roberto, Grabe, Hans J, Grotegerd, Dominik, Harris, Matthew A, Harrison, Ben J, Hatton, Sean N, Hermesdorf, Marco, Hickie, Ian B, Ho, Tiffany C, Kircher, Tilo, Krug, Axel, Lagopoulos, Jim, Lemke, Hannah, McMahon, Katie, MacMaster, Frank P, Martin, Nicholas G, McIntosh, Andrew M, Medland, Sarah E, Meinert, Susanne, Meller, Tina, Nenadic, Igor, Opel, Nils, Redlich, Ronny, Reneman, Liesbeth, Repple, Jonathan, Sacchet, Matthew D, Schmitt, Simon, Schrantee, Anouk, Sim, Kang, Singh, Aditya, Stein, Frederike, Strike, Lachlan T, van der Wee, Nic J A, van der Werff, Steven J A, Völzke, Henry, Waltemate, Lena, Whalley, Heather C, Wittfeld, Katharina, Wright, Margaret J., Yang, Tony T, Zarate, Carlos A, Schmaal, Lianne, Rentería, Miguel E, and other, and

Abstract

Background: Neuroimaging studies of suicidal behavior have so far been conducted in small samples, prone to biases and false-positive associations, yielding inconsistent results. The ENIGMA-MDD Working Group aims to address the issues of poor replicability and comparability by coordinating harmonized analyses across neuroimaging studies of major depressive disorder and related phenotypes, including suicidal behavior. Methods: Here, we pooled data from 18 international cohorts with neuroimaging and clinical measurements in 18,925 participants (12,477 healthy control subjects and 6448 people with depression, of whom 694 had attempted suicide). We compared regional cortical thickness and surface area and measures of subcortical, lateral ventricular, and intracranial volumes between suicide attempters, clinical control subjects (nonattempters with depression), and healthy control subjects. Results: We identified 25 regions of interest with statistically significant (false discovery rate < .05) differences between groups. Post hoc examinations identified neuroimaging markers associated with suicide attempt including smaller volumes of the left and right thalamus and the right pallidum and lower surface area of the left inferior parietal lobe. Conclusions: This study addresses the lack of replicability and consistency in several previously published neuroimaging studies of suicide attempt and further demonstrates the need for well-powered samples and collaborative efforts. Our results highlight the potential involvement of the thalamus, a structure viewed historically as a passive gateway in the brain, and the pallidum, a region linked to reward response and positive affect. Future functional and connectivity studies of suicidal behaviors may focus on understanding how these regions relate to the neurobiological mechanisms of suicide attempt risk.

Published
2021

29. A Randomized Trial of the N-Methyl-d-Aspartate Receptor Glycine Site Antagonist Prodrug 4-Chlorokynurenine in Treatment-Resistant Depression

Author

Park, Lawrence T, primary, Kadriu, Bashkim, additional, Gould, Todd D, additional, Zanos, Panos, additional, Greenstein, Deanna, additional, Evans, Jennifer W, additional, Yuan, Peixiong, additional, Farmer, Cristan A, additional, Oppenheimer, Mark, additional, George, Jomy M, additional, Adeojo, Lilian W, additional, Snodgrass, H Ralph, additional, Smith, Mark A, additional, Henter, Ioline D, additional, Machado-Vieira, Rodrigo, additional, Mannes, Andrew J, additional, and Zarate, Carlos A, additional

Published
2020
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39. Transdiagnostic alterations in white matter microstructure associated with suicidal thoughts and behaviours in the ENIGMA Suicidal Thoughts and Behaviours consortium.

Author

van Velzen LS, Colic L, Ceja Z, Dauvermann MR, Villa LM, Savage HS, Toenders YJ, Dehestani N, Zhu AH, Campos AI, Salminen LE, Agartz I, Alexander N, Ayesa-Arriola R, Ballard ED, Banaj N, Barkhau C, Başgöze Z, Bauer J, Benedetti F, Berger K, Besteher B, Brosch K, Canal-Rivero M, Cervenka S, Colle R, Connolly CG, Corruble E, Courtet P, Couvy-Duchesne B, Crespo-Facorro B, Cullen KR, Dannlowski U, Deverdun J, Diaz-Zuluaga AM, Dietze LMF, Evans JW, Fani N, Flinkenflügel K, Friedman NP, Gotlib IH, Groenewold NA, Grotegerd D, Hajek T, Hatoum AS, Hermesdorf M, Hickie IB, Hirano Y, Ho TC, Ikemizu Y, Iorfino F, Ipser JC, Isobe Y, Jackowski AP, Jollant F, Kircher T, Klug M, Koopowitz SM, Kraus A, Krug A, Le Bars E, Leehr EJ, Li M, Lippard ETC, Lopez-Jaramillo C, Maximov II, McIntosh AM, McLaughlin KA, McWhinney SR, Meinert S, Melloni E, Mitchell PB, Mwangi B, Nenadić I, Nerland S, Olie E, Ortiz-García de la Foz V, Pan PM, Pereira F, Piras F, Piras F, Poletti S, Reineberg AE, Roberts G, Romero-García R, Sacchet MD, Salum GA, Sandu AL, Sellgren CM, Shimizu E, Smolker HR, Soares JC, Spalletta G, Douglas Steele J, Stein F, Stein DJ, Straube B, Teutenberg L, Thomas-Odenthal F, Usemann P, Valabregue R, Valencia-Echeverry J, Wagner G, Waiter G, Walter M, Whalley HC, Wu MJ, Yang TT, Zarate CA, Zugman A, Zunta-Soares GB, van Heeringen K, van Rooij SJH, van der Wee N, van der Werff S, Thompson PM, Blumberg HP, van Harmelen AL, Rentería ME, Jahanshad N, and Schmaal L

Abstract

Previous studies have suggested that alterations in white matter (WM) microstructure are implicated in suicidal thoughts and behaviours (STBs). However, findings of diffusion tensor imaging (DTI) studies have been inconsistent. In this large-scale mega-analysis conducted by the ENIGMA Suicidal Thoughts and Behaviours (ENIGMA-STB) consortium, we examined WM alterations associated with STBs. Data processing was standardised across sites, and resulting WM microstructure measures (fractional anisotropy, axial diffusivity, mean diffusivity and radial diffusivity) for 25 WM tracts were pooled across 40 cohorts. We compared these measures among individuals with a psychiatric diagnosis and lifetime history of suicide attempt ( n =652; mean age=35.4±14.7; female=71.8%), individuals with a psychiatric diagnosis but no STB (i.e., clinical controls; n =1871; mean age=34±14.8; female=59.8%), and individuals with no mental disorder diagnosis and no STB (i.e., healthy controls; n =642; mean age=29.6±13.1; female=62.9%). We also compared these measures among individuals with recent suicidal ideation ( n =714; mean age=36.3±15.3; female=66.1%), clinical controls ( n =1184; mean age=36.8±15.6; female=63.1%), and healthy controls ( n =1240; mean age= 31.6±15.5; female=61.0%). We found subtle but statistically significant effects, such as lower fractional anisotropy associated with a history of suicide attempt, over and above the effect of psychiatric diagnoses. These effects were strongest in the corona radiata, thalamic radiation, fornix/stria terminalis, corpus callosum and superior longitudinal fasciculus. Effect sizes were small (Cohen's d < 0.25). Recent suicidal ideation was not associated with alterations in WM microstructure. This large-scale coordinated mega-analysis revealed subtle regional and global alterations in WM microstructure in individuals with a history of suicide attempt. Longitudinal studies are needed to confirm whether these alterations are a risk factor for suicidal behaviour., Competing Interests: Conflicts of interest ALvH receives consultancy fees from the Augeo foundation. CAZ is a full-time US government employee. He is listed as a co-inventor on a patent for the use of ketamine and its metabolites in major depression and suicidal ideation. Dr. Zarate has assigned his patent rights to the U.S. government but will share a percentage of any royalties that may be received by the government. HPB has consulted to the Milken Institute. MW is a member of the following advisory boards and gave presentations to the following companies: Bayer AG, Germany; Boehringer Ingelheim, Germany; and Biologische Heilmittel Heel GmbH, Germany. MW has further conducted studies with institutional research support from HEEL and Janssen Pharmaceutical Research for a clinical trial (IIT) on ketamine in patients with MDD, unrelated to this investigation. MW did not receive any financial compensation from the companies mentioned above. IA received speakers honorarium from Lundbeck. IBH is the Co-Director, Health and Policy at the Brain and Mind Centre (BMC) University of Sydney. The BMC operates an early-intervention youth services at Camperdown under contract to headspace. He is the Chief Scientific Advisor to, and a 3.2% equity shareholder in, InnoWell Pty Ltd which aims to transform mental health services through the use of innovative technologies. AC reports being currently an employee of the Regeneron Genetics Center, and may own Regeneron stock or stock options. JCS acknowledge to be related with the next companies ALKERMES (Advisory Board), BOEHRINGER Ingelheim (Consultant), COMPASS Pathways (Research Grant), JOHNSON & JOHNSON (Consultant), LIVANOVA (Consultant), RELMADA (Research Grant), SUNOVION (Research Grant),Mind Med (Research Grant). PMP received payment or honoraria for lectures and presentations in educational events for Sandoz, Daiichi Sankyo, Eurofarma, Abbot, Libbs, Instituto Israelita de Pesquisa e Ensino Albert Einstein, Instituto D’Or de Pesquisa e Ensino. All other authors report no biomedical financial interests, disclosures or potential conflicts of interest.

Published
2024
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