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1. 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., and Goya-Maldonado, Roberto

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. 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

4. Asymmetry, cytoarchitectonic morphology and genetics associated with Broca’s area in schizophrenia

Author

Zimmermann, Saskia, Sakreida, Katrin, Bludau, Sebastian, Camilleri, Julia A., Hoffstaedter, Felix, Pelzer, Dominique I., Aleman, André, Brückner, Torsten, Derntl, Birgit, Frank, Elmar, Frodl, Thomas, Fuentes-Claramonte, Paola, García-León, María Ángeles, Gruber, Oliver, Hajak, Göran, Heim, Stefan, Jardri, Renaud, Kogler, Lydia, Kreuzer, Peter M., Mirlach, Daniela, Landgrebe, Michael, Langguth, Berthold, Pomarol-Clotet, Edith, Prasser, Julia, Schecklmann, Martin, Sim, Kang, Soler-Vidal, Joan, Sommer, Iris E., Zhou, Juan H., Mühleisen, Thomas W., Eickhoff, Simon B., and Poeppl, Timm B.

Published
2024
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6. 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

9. The temporal dynamics of attention: Thinking about oneself comes at a cost in sub-clinical depression but not in healthy participants

Author

Wang, Jing, Hoekstra, Corné, Enriquez-Geppert, Stefanie, Luo, Yuejia, Aleman, André, and Martens, Sander

Subjects
Attention -- Health aspects, Depression, Mental -- Risk factors, Psychology and mental health
Abstract

Self-relevant stimuli seem to automatically draw attention, but it is unclear whether this comes at a cost for processing subsequent stimuli, and whether the effect is depending on one's mental state (i.e. depression). To address this question, we performed two experiments. In Experiment 1, 45 participants were to report two words (T1 and T2) in an attentional blink (AB) paradigm. T1 was a personality characteristic varying in self-rated self-relevance, whereas T2 was a neutral word. A generalized linear mixed model (GLMM) was applied to compare the T1 and T2 accuracies when T1 was high or low self-relevant. A positive effect of self-relevance was found on T1, without observable carry-over effects on T2 performance. However, in Experiment 2, a GLMM applied on 93 participants showed that T1 self-relevance can affect T2, showing opposite effects depending on sub-clinical depression score. Our findings imply that people with low depression scores process self-relevant stimuli more efficiently, which is reflected in a reduced AB. In contrast, individuals with higher scores in depression demonstrated a difficulty to withdraw attention from self-relevant information, reflected in an increased AB. Our findings thus reveal that a processing advantage for highly self-relevant stimuli comes at either a subsequent cost or benefit in temporal attention depending on one's mental disposition., Author(s): Jing Wang [sup.1] [sup.2] [sup.3] , Corné Hoekstra [sup.4] , Stefanie Enriquez-Geppert [sup.1] [sup.2] [sup.5] , Yuejia Luo [sup.3] [sup.6] [sup.7] , André Aleman [sup.1] [sup.2] [sup.3] [sup.5] , [...]

Published
2023
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11. Cortical and subcortical neuroanatomical signatures of schizotypy in 3004 individuals assessed in a worldwide ENIGMA study

Author

Kirschner, Matthias, Hodzic-Santor, Benazir, Antoniades, Mathilde, Nenadic, Igor, Kircher, Tilo, Krug, Axel, Meller, Tina, Grotegerd, Dominik, Fornito, Alex, Arnatkeviciute, Aurina, Bellgrove, Mark A, Tiego, Jeggan, Dannlowski, Udo, Koch, Katharina, Hülsmann, Carina, Kugel, Harald, Enneking, Verena, Klug, Melissa, Leehr, Elisabeth J, Böhnlein, Joscha, Gruber, Marius, Mehler, David, DeRosse, Pamela, Moyett, Ashley, Baune, Bernhard T, Green, Melissa, Quidé, Yann, Pantelis, Christos, Chan, Raymond, Wang, Yi, Ettinger, Ulrich, Debbané, Martin, Derome, Melodie, Gaser, Christian, Besteher, Bianca, Diederen, Kelly, Spencer, Tom J, Fletcher, Paul, Rössler, Wulf, Smigielski, Lukasz, Kumari, Veena, Premkumar, Preethi, Park, Haeme RP, Wiebels, Kristina, Lemmers-Jansen, Imke, Gilleen, James, Allen, Paul, Kozhuharova, Petya, Marsman, Jan-Bernard, Lebedeva, Irina, Tomyshev, Alexander, Mukhorina, Anna, Kaiser, Stefan, Fett, Anne-Kathrin, Sommer, Iris, Schuite-Koops, Sanne, Paquola, Casey, Larivière, Sara, Bernhardt, Boris, Dagher, Alain, Grant, Phillip, van Erp, Theo GM, Turner, Jessica A, Thompson, Paul M, Aleman, André, and Modinos, Gemma

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Neurosciences, Serious Mental Illness, Schizophrenia, Clinical Research, Mental Health, Mental health, Good Health and Well Being, Bipolar Disorder, Female, Humans, Magnetic Resonance Imaging, Male, Psychotic Disorders, Schizotypal Personality Disorder, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Neuroanatomical abnormalities have been reported along a continuum from at-risk stages, including high schizotypy, to early and chronic psychosis. However, a comprehensive neuroanatomical mapping of schizotypy remains to be established. The authors conducted the first large-scale meta-analyses of cortical and subcortical morphometric patterns of schizotypy in healthy individuals, and compared these patterns with neuroanatomical abnormalities observed in major psychiatric disorders. The sample comprised 3004 unmedicated healthy individuals (12-68 years, 46.5% male) from 29 cohorts of the worldwide ENIGMA Schizotypy working group. Cortical and subcortical effect size maps with schizotypy scores were generated using standardized methods. Pattern similarities were assessed between the schizotypy-related cortical and subcortical maps and effect size maps from comparisons of schizophrenia (SZ), bipolar disorder (BD) and major depression (MDD) patients with controls. Thicker right medial orbitofrontal/ventromedial prefrontal cortex (mOFC/vmPFC) was associated with higher schizotypy scores (r = 0.067, pFDR = 0.02). The cortical thickness profile in schizotypy was positively correlated with cortical abnormalities in SZ (r = 0.285, pspin = 0.024), but not BD (r = 0.166, pspin = 0.205) or MDD (r = -0.274, pspin = 0.073). The schizotypy-related subcortical volume pattern was negatively correlated with subcortical abnormalities in SZ (rho = -0.690, pspin = 0.006), BD (rho = -0.672, pspin = 0.009), and MDD (rho = -0.692, pspin = 0.004). Comprehensive mapping of schizotypy-related brain morphometry in the general population revealed a significant relationship between higher schizotypy and thicker mOFC/vmPFC, in the absence of confounding effects due to antipsychotic medication or disease chronicity. The cortical pattern similarity between schizotypy and schizophrenia yields new insights into a dimensional neurobiological continuity across the extended psychosis phenotype.

Published
2022

12. Local molecular and global connectomic contributions to cross-disorder cortical abnormalities

Author

Hansen, Justine Y, Shafiei, Golia, Vogel, Jacob W, Smart, Kelly, Bearden, Carrie E, Hoogman, Martine, Franke, Barbara, van Rooij, Daan, Buitelaar, Jan, McDonald, Carrie R, Sisodiya, Sanjay M, Schmaal, Lianne, Veltman, Dick J, van den Heuvel, Odile A, Stein, Dan J, van Erp, Theo GM, Ching, Christopher RK, Andreassen, Ole A, Hajek, Tomas, Opel, Nils, Modinos, Gemma, Aleman, André, van der Werf, Ysbrand, Jahanshad, Neda, Thomopoulos, Sophia I, Thompson, Paul M, Carson, Richard E, Dagher, Alain, and Misic, Bratislav

Subjects
Neurosciences, Pediatric, Brain Disorders, Mental Health, Aetiology, Underpinning research, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Mental health, Neurological, Brain, Brain Diseases, Connectome, Humans, Magnetic Resonance Imaging, Neural Pathways
Abstract

Numerous brain disorders demonstrate structural brain abnormalities, which are thought to arise from molecular perturbations or connectome miswiring. The unique and shared contributions of these molecular and connectomic vulnerabilities to brain disorders remain unknown, and has yet to be studied in a single multi-disorder framework. Using MRI morphometry from the ENIGMA consortium, we construct maps of cortical abnormalities for thirteen neurodevelopmental, neurological, and psychiatric disorders from N = 21,000 participants and N = 26,000 controls, collected using a harmonised processing protocol. We systematically compare cortical maps to multiple micro-architectural measures, including gene expression, neurotransmitter density, metabolism, and myelination (molecular vulnerability), as well as global connectomic measures including number of connections, centrality, and connection diversity (connectomic vulnerability). We find a relationship between molecular vulnerability and white-matter architecture that drives cortical disorder profiles. Local attributes, particularly neurotransmitter receptor profiles, constitute the best predictors of both disorder-specific cortical morphology and cross-disorder similarity. Finally, we find that cross-disorder abnormalities are consistently subtended by a small subset of network epicentres in bilateral sensory-motor, inferior temporal lobe, precuneus, and superior parietal cortex. Collectively, our results highlight how local molecular attributes and global connectivity jointly shape cross-disorder cortical abnormalities.

Published
2022

13. Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group

Author

Han, Laura KM, Dinga, Richard, Hahn, Tim, Ching, Christopher RK, Eyler, Lisa T, Aftanas, Lyubomir, Aghajani, Moji, Aleman, André, Baune, Bernhard T, Berger, Klaus, Brak, Ivan, Filho, Geraldo Busatto, Carballedo, Angela, Connolly, Colm G, Couvy-Duchesne, Baptiste, Cullen, Kathryn R, Dannlowski, Udo, Davey, Christopher G, Dima, Danai, Duran, Fabio LS, Enneking, Verena, Filimonova, Elena, Frenzel, Stefan, Frodl, Thomas, Fu, Cynthia HY, Godlewska, Beata R, Gotlib, Ian H, Grabe, Hans J, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Hall, Geoffrey B, Harrison, Ben J, Hatton, Sean N, Hermesdorf, Marco, Hickie, Ian B, Ho, Tiffany C, Hosten, Norbert, Jansen, Andreas, Kähler, Claas, Kircher, Tilo, Klimes-Dougan, Bonnie, Krämer, Bernd, Krug, Axel, Lagopoulos, Jim, Leenings, Ramona, MacMaster, Frank P, MacQueen, Glenda, McIntosh, Andrew, McLellan, Quinn, McMahon, Katie L, Medland, Sarah E, Mueller, Bryon A, Mwangi, Benson, Osipov, Evgeny, Portella, Maria J, Pozzi, Elena, Reneman, Liesbeth, Repple, Jonathan, Rosa, Pedro GP, Sacchet, Matthew D, Sämann, Philipp G, Schnell, Knut, Schrantee, Anouk, Simulionyte, Egle, Soares, Jair C, Sommer, Jens, Stein, Dan J, Steinsträter, Olaf, Strike, Lachlan T, Thomopoulos, Sophia I, van Tol, Marie-José, Veer, Ilya M, Vermeiren, Robert RJM, Walter, Henrik, van der Wee, Nic JA, van der Werff, Steven JA, Whalley, Heather, Winter, Nils R, Wittfeld, Katharina, Wright, Margaret J, Wu, Mon-Ju, Völzke, Henry, Yang, Tony T, Zannias, Vasileios, de Zubicaray, Greig I, Zunta-Soares, Giovana B, Abé, Christoph, Alda, Martin, Andreassen, Ole A, Bøen, Erlend, Bonnin, Caterina M, Canales-Rodriguez, Erick J, Cannon, Dara, Caseras, Xavier, Chaim-Avancini, Tiffany M, Elvsåshagen, Torbjørn, Favre, Pauline, Foley, Sonya F, and Fullerton, Janice M

Subjects
Depression, Aging, Brain Disorders, Biomedical Imaging, Serious Mental Illness, Mental Health, Major Depressive Disorder, Behavioral and Social Science, Clinical Research, Neurosciences, 2.3 Psychological, social and economic factors, Aetiology, Mental health, Adolescent, Adult, Aged, Brain, Depressive Disorder, Major, Female, Humans, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

Published
2021

14. Variability and magnitude of brain glutamate levels in schizophrenia: a meta and mega-analysis

Author

Merritt, Kate, McCutcheon, Robert A., Aleman, André, Ashley, Sarah, Beck, Katherine, Block, Wolfgang, Bloemen, Oswald J. N., Borgan, Faith, Boules, Christiana, Bustillo, Juan R., Capizzano, Aristides A., Coughlin, Jennifer M., David, Anthony, de la Fuente-Sandoval, Camilo, Demjaha, Arsime, Dempster, Kara, Do, Kim Q., Du, Fei, Falkai, Peter, Galińska-Skok, Beata, Gallinat, Jürgen, Gasparovic, Charles, Ginestet, Cedric E., Goto, Naoki, Graff-Guerrero, Ariel, Ho, Beng-Choon, Howes, Oliver, Jauhar, Sameer, Jeon, Peter, Kato, Tadafumi, Kaufmann, Charles A., Kegeles, Lawrence S., Keshavan, Matcheri S., Kim, Sang-Young, King, Bridget, Kunugi, Hiroshi, Lauriello, J., León-Ortiz, Pablo, Liemburg, Edith, Mcilwain, Meghan E., Modinos, Gemma, Mouchlianitis, Elias, Nakamura, Jun, Nenadic, Igor, Öngür, Dost, Ota, Miho, Palaniyappan, Lena, Pantelis, Christos, Patel, Tulsi, Plitman, Eric, Posporelis, Sotirios, Purdon, Scot E., Reichenbach, Jürgen R., Renshaw, Perry F., Reyes-Madrigal, Francisco, Russell, Bruce R., Sawa, Akira, Schaefer, Martin, Shungu, Dikoma C., Smesny, Stefan, Stanley, Jeffrey A., Stone, James, Szulc, Agata, Taylor, Reggie, Thakkar, Katharine N., Théberge, Jean, Tibbo, Philip G., van Amelsvoort, Thérèse, Walecki, Jerzy, Williamson, Peter C., Wood, Stephen J., Xin, Lijing, Yamasue, Hidenori, McGuire, Philip, and Egerton, Alice

Published
2023
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18. ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

Author

Thompson, Paul M, Jahanshad, Neda, Ching, Christopher RK, Salminen, Lauren E, Thomopoulos, Sophia I, Bright, Joanna, Baune, Bernhard T, Bertolín, Sara, Bralten, Janita, Bruin, Willem B, Bülow, Robin, Chen, Jian, Chye, Yann, Dannlowski, Udo, de Kovel, Carolien GF, Donohoe, Gary, Eyler, Lisa T, Faraone, Stephen V, Favre, Pauline, Filippi, Courtney A, Frodl, Thomas, Garijo, Daniel, Gil, Yolanda, Grabe, Hans J, Grasby, Katrina L, Hajek, Tomas, Han, Laura KM, Hatton, Sean N, Hilbert, Kevin, Ho, Tiffany C, Holleran, Laurena, Homuth, Georg, Hosten, Norbert, Houenou, Josselin, Ivanov, Iliyan, Jia, Tianye, Kelly, Sinead, Klein, Marieke, Kwon, Jun Soo, Laansma, Max A, Leerssen, Jeanne, Lueken, Ulrike, Nunes, Abraham, Neill, Joseph O', Opel, Nils, Piras, Fabrizio, Piras, Federica, Postema, Merel C, Pozzi, Elena, Shatokhina, Natalia, Soriano-Mas, Carles, Spalletta, Gianfranco, Sun, Daqiang, Teumer, Alexander, Tilot, Amanda K, Tozzi, Leonardo, van der Merwe, Celia, Van Someren, Eus JW, van Wingen, Guido A, Völzke, Henry, Walton, Esther, Wang, Lei, Winkler, Anderson M, Wittfeld, Katharina, Wright, Margaret J, Yun, Je-Yeon, Zhang, Guohao, Zhang-James, Yanli, Adhikari, Bhim M, Agartz, Ingrid, Aghajani, Moji, Aleman, André, Althoff, Robert R, Altmann, Andre, Andreassen, Ole A, Baron, David A, Bartnik-Olson, Brenda L, Marie Bas-Hoogendam, Janna, Baskin-Sommers, Arielle R, Bearden, Carrie E, Berner, Laura A, Boedhoe, Premika SW, Brouwer, Rachel M, Buitelaar, Jan K, Caeyenberghs, Karen, Cecil, Charlotte AM, Cohen, Ronald A, Cole, James H, Conrod, Patricia J, De Brito, Stephane A, de Zwarte, Sonja MC, Dennis, Emily L, Desrivieres, Sylvane, Dima, Danai, Ehrlich, Stefan, Esopenko, Carrie, Fairchild, Graeme, Fisher, Simon E, Fouche, Jean-Paul, and Francks, Clyde

Subjects
ENIGMA Consortium, Brain, Humans, Magnetic Resonance Imaging, Reproducibility of Results, Depressive Disorder, Major, Neuroimaging, Neurosciences, Clinical Research, Mental Health, Brain Disorders, Behavioral and Social Science, Genetics, Basic Behavioral and Social Science, Prevention, 2.1 Biological and endogenous factors, 2.3 Psychological, social and economic factors, Mental health, Neurological, Clinical Sciences, Public Health and Health Services, Psychology
Abstract

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

Published
2020

20. Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

Author

Petrov, Dmitry, Kuznetsov, Boris A. Gutman Egor, van Erp, Theo G. M., Turner, Jessica A., Schmaal, Lianne, Veltman, Dick, Wang, Lei, Alpert, Kathryn, Isaev, Dmitry, Zavaliangos-Petropulu, Artemis, Ching, Christopher R. K., Calhoun, Vince, Glahn, David, Satterthwaite, Theodore D., Andreassen, Ole Andreas, Borgwardt, Stefan, Howells, Fleur, Groenewold, Nynke, Voineskos, Aristotle, Radua, Joaquim, Potkin, Steven G., Crespo-Facorro, Benedicto, Tordesillas-Gutierrez, Diana, Shen, Li, Lebedeva, Irina, Spalletta, Gianfranco, Donohoe, Gary, Kochunov, Peter, Rosa, Pedro G. P., James, Anthony, Dannlowski, Udo, Baune, Bernhard T., Aleman, Andre, Gotlib, Ian H., Walter, Henrik, Walter, Martin, Soares, Jair C., Ehrlich, Stefan, Gur, Ruben C., Doan, N. Trung, Agartz, Ingrid, Westlye, Lars T., Harrisberger, Fabienne, Riecher-Rossler, Anita, Uhlmann, Anne, Stein, Dan J., Dickie, Erin W., Pomarol-Clotet, Edith, Fuentes-Claramonte, Paola, Canales-Rodriguez, Erick Jorge, Salvador, Raymond, Huang, Alexander J., Roiz-Santianez, Roberto, Cong, Shan, Tomyshev, Alexander, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Ciullo, Valentina, Hong, Elliot, Busatto, Geraldo, Zanetti, Marcus V., Serpa, Mauricio H., Cervenka, Simon, Kelly, Sinead, Grotegerd, Dominik, Sacchet, Matthew D., Veer, Ilya M., Li, Meng, Wu, Mon-Ju, Irungu, Benson, Walton, Esther, and Thompson, Paul M.

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

We present several deep learning models for assessing the morphometric fidelity of deep grey matter region models extracted from brain MRI. We test three different convolutional neural net architectures (VGGNet, ResNet and Inception) over 2D maps of geometric features. Further, we present a novel geometry feature augmentation technique based on a parametric spherical mapping. Finally, we present an approach for model decision visualization, allowing human raters to see the areas of subcortical shapes most likely to be deemed of failing quality by the machine. Our training data is comprised of 5200 subjects from the ENIGMA Schizophrenia MRI cohorts, and our test dataset contains 1500 subjects from the ENIGMA Major Depressive Disorder cohorts. Our final models reduce human rater time by 46-70%. ResNet outperforms VGGNet and Inception for all of our predictive tasks., Comment: Accepted to Shape in Medical Imaging (ShapeMI) workshop at MICCAI 2018. arXiv admin note: substantial text overlap with arXiv:1707.06353

Published
2018

21. The Association Between Familial Risk and Brain Abnormalities Is Disease Specific: An ENIGMA-Relatives Study of Schizophrenia and Bipolar Disorder

Author

de Zwarte, Sonja MC, Brouwer, Rachel M, Agartz, Ingrid, Alda, Martin, Aleman, André, Alpert, Kathryn I, Bearden, Carrie E, Bertolino, Alessandro, Bois, Catherine, Bonvino, Aurora, Bramon, Elvira, Buimer, Elizabeth EL, Cahn, Wiepke, Cannon, Dara M, Cannon, Tyrone D, Caseras, Xavier, Castro-Fornieles, Josefina, Chen, Qiang, Chung, Yoonho, De la Serna, Elena, Di Giorgio, Annabella, Doucet, Gaelle E, Eker, Mehmet Cagdas, Erk, Susanne, Fears, Scott C, Foley, Sonya F, Frangou, Sophia, Frankland, Andrew, Fullerton, Janice M, Glahn, David C, Goghari, Vina M, Goldman, Aaron L, Gonul, Ali Saffet, Gruber, Oliver, de Haan, Lieuwe, Hajek, Tomas, Hawkins, Emma L, Heinz, Andreas, Hillegers, Manon HJ, Pol, Hilleke E Hulshoff, Hultman, Christina M, Ingvar, Martin, Johansson, Viktoria, Jönsson, Erik G, Kane, Fergus, Kempton, Matthew J, Koenis, Marinka MG, Kopecek, Miloslav, Krabbendam, Lydia, Krämer, Bernd, Lawrie, Stephen M, Lenroot, Rhoshel K, Marcelis, Machteld, Marsman, Jan-Bernard C, Mattay, Venkata S, McDonald, Colm, Meyer-Lindenberg, Andreas, Michielse, Stijn, Mitchell, Philip B, Moreno, Dolores, Murray, Robin M, Mwangi, Benson, Najt, Pablo, Neilson, Emma, Newport, Jason, van Os, Jim, Overs, Bronwyn, Ozerdem, Aysegul, Picchioni, Marco M, Richter, Anja, Roberts, Gloria, Aydogan, Aybala Saricicek, Schofield, Peter R, Simsek, Fatma, Soares, Jair C, Sugranyes, Gisela, Toulopoulou, Timothea, Tronchin, Giulia, Walter, Henrik, Wang, Lei, Weinberger, Daniel R, Whalley, Heather C, Yalin, Nefize, Andreassen, Ole A, Ching, Christopher RK, van Erp, Theo GM, Turner, Jessica A, Jahanshad, Neda, Thompson, Paul M, Kahn, René S, and van Haren, Neeltje EM

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Mental Health, Brain Disorders, Serious Mental Illness, Schizophrenia, Genetics, Neurosciences, Clinical Research, Bipolar Disorder, 2.3 Psychological, social and economic factors, Aetiology, 2.1 Biological and endogenous factors, Mental health, Adult, Brain, Cohort Studies, Female, Genetic Predisposition to Disease, Humans, Male, Middle Aged, Young Adult, Bipolar disorder, Familial risk, Imaging, Meta-analysis, Neurodevelopment, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological sciences, Biomedical and clinical sciences
Abstract

BackgroundSchizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects.MethodsWe performed a meta-analysis of global and subcortical brain measures of 6008 individuals (1228 FDRs-SZ, 852 FDRs-BD, 2246 control subjects, 1016 patients with schizophrenia, 666 patients with bipolar disorder) from 34 schizophrenia and/or bipolar disorder family cohorts with standardized methods. Analyses were repeated with a correction for intracranial volume (ICV) and for the presence of any psychopathology in the relatives and control subjects.ResultsFDRs-BD had significantly larger ICV (d = +0.16, q < .05 corrected), whereas FDRs-SZ showed smaller thalamic volumes than control subjects (d = -0.12, q < .05 corrected). ICV explained the enlargements in the brain measures in FDRs-BD. In FDRs-SZ, after correction for ICV, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, and thalamus volumes were significantly smaller; the cortex was thinner (d

Published
2019

26. Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging

Author

Petrov, Dmitry, Gutman, Boris A., Shih-Hua, Yu, van Erp, Theo G. M., Turner, Jessica A., Schmaal, Lianne, Veltman, Dick, Wang, Lei, Alpert, Kathryn, Isaev, Dmitry, Zavaliangos-Petropulu, Artemis, Ching, Christopher R. K., Calhoun, Vince, Glahn, David, Satterthwaite, Theodore D., Andreasen, Ole Andreas, Borgwardt, Stefan, Howells, Fleur, Groenewold, Nynke, Voineskos, Aristotle, Radua, Joaquim, Potkin, Steven G., Crespo-Facorro, Benedicto, Tordesillas-Gutierrez, Diana, Shen, Li, Lebedeva, Irina, Spalletta, Gianfranco, Donohoe, Gary, Kochunov, Peter, Rosa, Pedro G. P., James, Anthony, Dannlowski, Udo, Baune, Bernhard T., Aleman, Andre, Gotlib, Ian H., Walter, Henrik, Walter, Martin, Soares, Jair C., Ehrlich, Stefan, Gur, Ruben C., Doan, N. Trung, Agartz, Ingrid, Westlye, Lars T., Harrisberger, Fabienne, Riecher-Rossler, Anita, Uhlmann, Anne, Stein, Dan J., Dickie, Erin W., Pomarol-Clotet, Edith, Fuentes-Claramonte, Paola, Canales-Rodriguez, Erick Jorge, Salvador, Raymond, Huang, Alexander J., Roiz-Santianez, Roberto, Cong, Shan, Tomyshev, Alexander, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Ciullo, Valentina, Hong, Elliot, Busatto, Geraldo, Zanetti, Marcus V., Serpa, Mauricio H., Cervenka, Simon, Kelly, Sinead, Grotegerd, Dominik, Sacchet, Matthew D., Veer, Ilya M., Li, Meng, Wu, Mon-Ju, Irungu, Benson, Walton, Esther, and Thompson, Paul M.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70\%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability., Comment: Arxiv version of the MICCAI 2017 Machine Learning in Medical Imaging workshop paper

Published
2017

27. Alterations in Gray Matter Structural Networks in Amnestic Mild Cognitive Impairment: A Source-Based Morphometry Study.

Author

Setiadi, Tania M., Marsman, Jan-Bernard C., Martens, Sander, Tumati, Shankar, Opmeer, Esther M., Reesink, Fransje E., De Deyn, Peter P., Atienza, Mercedes, Aleman, André, and Cantero, Jose L.

Subjects
AMNESTIC mild cognitive impairment, VOXEL-based morphometry, ALZHEIMER'S disease, MAGNETIC resonance imaging, GRAY matter (Nerve tissue)
Abstract

Background: Amnestic mild cognitive impairment (aMCI), considered as the prodromal stage of Alzheimer's disease, is characterized by isolated memory impairment and cerebral gray matter volume (GMV) alterations. Previous structural MRI studies in aMCI have been mainly based on univariate statistics using voxel-based morphometry. Objective: We investigated structural network differences between aMCI patients and cognitively normal older adults by using source-based morphometry, a multivariate approach that considers the relationship between voxels of various parts of the brain. Methods: Ninety-one aMCI patients and 80 cognitively normal controls underwent structural MRI and neuropsychological assessment. Spatially independent components (ICs) that covaried between participants were estimated and a multivariate analysis of covariance was performed with ICs as dependent variables, diagnosis as independent variable, and age, sex, education level, and site as covariates. Results: aMCI patients exhibited reduced GMV in the precentral, temporo-cerebellar, frontal, and temporal network, and increased GMV in the left superior parietal network compared to controls (pFWER < 0.05, Holm-Bonferroni correction). Moreover, we found that diagnosis, more specifically aMCI, moderated the positive relationship between occipital network and Mini-Mental State Examination scores (pFWER < 0.05, Holm-Bonferroni correction). Conclusions: Our results showed GMV alterations in temporo-fronto-parieto-cerebellar networks in aMCI, extending previous results obtained with univariate approaches. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Transcranial Magnetic Stimulation and Transcranial Direct Current Stimulation Across Mental Disorders

Author

Sabé, Michel, primary, Hyde, Joshua, additional, Cramer, Catharina, additional, Eberhard, Antonia, additional, Crippa, Alessio, additional, Brunoni, André Russowsky, additional, Aleman, André, additional, Kaiser, Stefan, additional, Baldwin, David S., additional, Garner, Matthew, additional, Sentissi, Othman, additional, Fiedorowicz, Jess G., additional, Brandt, Valerie, additional, Cortese, Samuele, additional, and Solmi, Marco, additional

Published
2024
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33. What's in a score:A longitudinal investigation of scores based on item response theory and classical test theory for the Amsterdam Instrumental Activities of Daily Living Questionnaire in cognitively normal and impaired older adults

Author

Dubbelman, Mark A., Postema, Merel C., Jutten, Roos J., Harrison, John E., Ritchie, Craig W., Aleman, André, de Jong, Frank Jan, Schalet, Benjamin D., Terwee, Caroline B., van der Flier, Wiesje M., Scheltens, Philip, Sikkes, Sietske A.M., Dubbelman, Mark A., Postema, Merel C., Jutten, Roos J., Harrison, John E., Ritchie, Craig W., Aleman, André, de Jong, Frank Jan, Schalet, Benjamin D., Terwee, Caroline B., van der Flier, Wiesje M., Scheltens, Philip, and Sikkes, Sietske A.M.

Abstract

Objective: We aimed to investigate whether item response theory (IRT)-based scoring allows for a more accurate, responsive, and less biased assessment of everyday functioning than traditional classical test theory (CTT)-based scoring, as measured with the Amsterdam Instrumental Activities of Daily Living Questionnaire. Method: In this longitudinal multicenter study including cognitively normal and impaired individuals, we examined IRT-based and CTT-based score distributions and differences between diagnostic groups using linear regressions, and investigated scale attenuation. We compared change over time between scoring methods using linear mixed models with random intercepts and slopes for time. Results: Two thousand two hundred ninety-four participants were included (66.6 ± 7.7 years, 54% female): n = 2,032 (89%) with normal cognition, n = 93 (4%) with subjective cognitive decline, n = 79 (3%) with mild cognitive impairment, and n = 91 (4%) with dementia. At baseline, IRT-based and CTT-based scores were highly correlated (r = −0.92). IRT-based scores showed less scale attenuation than CTT-based scores. In a subsample of n = 1,145 (62%) who were followed for a mean of 1.3 (SD = 0.6) years, IRT-based scores declined significantly among cognitively normal individuals (unstandardized coefficient [B] = −0.15, 95% confidence interval, 95% CI [−0.28, −0.03], effect size = −0.02), whereas CTT-based scores did not (B = 0.20, 95% CI [−0.02, 0.41], effect size = 0.02). In the other diagnostic groups, effect sizes of change over time were similar. Conclusions: IRT-based scores were less affected by scale attenuation than CTT-based scores. With regard to responsiveness, IRT-based scores showed more signal than CTT-based scores in early disease stages, highlighting the IRT-based scores’ superior suitability for use in preclinical populations.

Published
2024

37. Expressive deficits and amotivation as mediators of the associations between cognitive problems and functional outcomes: Results from two independent cohorts

Author

Castelein, Stynke, Knegtering, Henderikus, Veling, Wim, Alizadeh, Behrooz Z., van Amelsvoort, Therese, Bartels-Velthuis, Agna A., van Beveren, Nico J., Bruggeman, Richard, Cahn, Wiepke, de Haan, Lieuwe, Delespaul, Philippe, Luykx, Jurjen J., Myin-Germeys, Inez, Kahn, Rene S., Schirmbeck, Frederike, Simons, Claudia J.P., van Haren, Neeltje E., van Os, Jim, van Winkel, Ruud, Liemburg, Edith J., Enriquez-Geppert, Stefanie, Wardenaar, Klaas J., and Aleman, André

Published
2020
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39. Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): An update (2014–2018)

Author

Lefaucheur, Jean-Pascal, Aleman, André, Baeken, Chris, Benninger, David H., Brunelin, Jérôme, Di Lazzaro, Vincenzo, Filipović, Saša R., Grefkes, Christian, Hasan, Alkomiet, Hummel, Friedhelm C., Jääskeläinen, Satu K., Langguth, Berthold, Leocani, Letizia, Londero, Alain, Nardone, Raffaele, Nguyen, Jean-Paul, Nyffeler, Thomas, Oliveira-Maia, Albino J., Oliviero, Antonio, Padberg, Frank, Palm, Ulrich, Paulus, Walter, Poulet, Emmanuel, Quartarone, Angelo, Rachid, Fady, Rektorová, Irena, Rossi, Simone, Sahlsten, Hanna, Schecklmann, Martin, Szekely, David, and Ziemann, Ulf

Published
2020
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40. Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization: An International Machine Learning Study

Author

Bartels-Velthuis, Agna A., Bruggeman, Richard, Castelein, Stynke, Jörg, Frederike, Pijnenborg, Gerdina H.M., Knegtering, Henderikus, Visser, Ellen, Chen, Ji, Patil, Kaustubh R., Weis, Susanne, Sim, Kang, Nickl-Jockschat, Thomas, Zhou, Juan, Aleman, André, Sommer, Iris E., Liemburg, Edith J., Hoffstaedter, Felix, Habel, Ute, Derntl, Birgit, Liu, Xiaojin, Fischer, Jona M., Kogler, Lydia, Regenbogen, Christina, Diwadkar, Vaibhav A., Stanley, Jeffrey A., Riedl, Valentin, Jardri, Renaud, Gruber, Oliver, Sotiras, Aristeidis, Davatzikos, Christos, and Eickhoff, Simon B.

Published
2020
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42. Apathy

Author

Lanctot, Krista, primary and Aleman, André, additional

Published
2021
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43. Brain Stimulation

Author

Aleman, André, primary, Dlabac-De Lange, Jozarni J., additional, and Padala, Prasad, additional

Published
2021
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46. Cognitive functions as predictors of daily functioning among older adults

Author

Wardana, Nido Dipo, primary, Zhang, Zhenyu, additional, Lejko, Nena, additional, Opmeer, Esther, additional, De Deyn, Peter Paul, additional, Reesink, Fransje E, additional, Spikman, Jacoba M, additional, Herrmann, Christoph S, additional, Enriquez‐Geppert, Stefanie, additional, Aleman, André, additional, and Ćurčić‐Blake, Branislava, additional

Published
2023
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47. Late positive potential component and cognitive functioning in occipital area of Mild Cognitive Impairment

Author

Zhang, Zhenyu, primary, Aleman, André, additional, Opmeer, Esther, additional, De Deyn, Peter Paul, additional, Reesink, Fransje E, additional, Spikman, Jacoba M, additional, Herrmann, Christoph S, additional, Wardana, Nido Dipo, additional, Enriquez‐Geppert, Stefanie, additional, Lejko, Nena, additional, and Ćurčić‐Blake, Branislava, additional

Published
2023
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