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1. Brain‐age prediction: Systematic evaluation of site effects, and sample age range and size

Author

Yu, Yuetong, Cui, Hao‐Qi, Haas, Shalaila S, New, Faye, Sanford, Nicole, Yu, Kevin, Zhan, Denghuang, Yang, Guoyuan, Gao, Jia‐Hong, Wei, Dongtao, Qiu, Jiang, Banaj, Nerisa, Boomsma, Dorret I, Breier, Alan, Brodaty, Henry, Buckner, Randy L, Buitelaar, Jan K, Cannon, Dara M, Caseras, Xavier, Clark, Vincent P, Conrod, Patricia J, Crivello, Fabrice, Crone, Eveline A, Dannlowski, Udo, Davey, Christopher G, de Haan, Lieuwe, de Zubicaray, Greig I, Di Giorgio, Annabella, Fisch, Lukas, Fisher, Simon E, Franke, Barbara, Glahn, David C, Grotegerd, Dominik, Gruber, Oliver, Gur, Raquel E, Gur, Ruben C, Hahn, Tim, Harrison, Ben J, Hatton, Sean, Hickie, Ian B, Pol, Hilleke E Hulshoff, Jamieson, Alec J, Jernigan, Terry L, Jiang, Jiyang, Kalnin, Andrew J, Kang, Sim, Kochan, Nicole A, Kraus, Anna, Lagopoulos, Jim, Lazaro, Luisa, McDonald, Brenna C, McDonald, Colm, McMahon, Katie L, Mwangi, Benson, Piras, Fabrizio, Rodriguez‐Cruces, Raul, Royer, Jessica, Sachdev, Perminder S, Satterthwaite, Theodore D, Saykin, Andrew J, Schumann, Gunter, Sevaggi, Pierluigi, Smoller, Jordan W, Soares, Jair C, Spalletta, Gianfranco, Tamnes, Christian K, Trollor, Julian N, Ent, Dennis Van't, Vecchio, Daniela, Walter, Henrik, Wang, Yang, Weber, Bernd, Wen, Wei, Wierenga, Lara M, Williams, Steven CR, Wu, Mon‐Ju, Zunta‐Soares, Giovana B, Bernhardt, Boris, Thompson, Paul, Frangou, Sophia, Ge, Ruiyang, and Group, ENIGMA‐Lifespan Working

Subjects
Biological Psychology, Psychology, Neurosciences, Clinical Research, Aging, Mental health, Neurological, Humans, Adolescent, Female, Aged, Adult, Child, Young Adult, Male, Brain, Aged, 80 and over, Child, Preschool, Middle Aged, Magnetic Resonance Imaging, Neuroimaging, Sample Size, benchmarking, brain aging, brainAGE, ENIGMA‐Lifespan Working Group, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Published
2024

2. A mega-analysis of functional connectivity and network abnormalities in youth depression

Author

Tse, Nga Yan, Ratheesh, Aswin, Tian, Ye Ella, Connolly, Colm G., Davey, Christopher G., Ganesan, Saampras, Gotlib, Ian H., Harrison, Ben J., Han, Laura K. M., Ho, Tiffany C., Jamieson, Alec J., Kirshenbaum, Jaclyn S., Liu, Yong, Ma, Xiaohong, Ojha, Amar, Qiu, Jiang, Sacchet, Matthew D., Schmaal, Lianne, Simmons, Alan N., Suckling, John, Wei, Dongtao, Yang, Xiao, Yang, Tony T., Cash, Robin F. H., and Zalesky, Andrew

Published
2024
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3. 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. 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

5. Regional, circuit and network heterogeneity of brain abnormalities in psychiatric disorders

Author

Segal, Ashlea, Parkes, Linden, Aquino, Kevin, Kia, Seyed Mostafa, Wolfers, Thomas, Franke, Barbara, Hoogman, Martine, Beckmann, Christian F., Westlye, Lars T., Andreassen, Ole A., Zalesky, Andrew, Harrison, Ben J., Davey, Christopher G., Soriano-Mas, Carles, Cardoner, Narcís, Tiego, Jeggan, Yücel, Murat, Braganza, Leah, Suo, Chao, Berk, Michael, Cotton, Sue, Bellgrove, Mark A., Marquand, Andre F., and Fornito, Alex

Published
2023
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6. Virtual Ontogeny of Cortical Growth Preceding Mental Illness

Author

Patel, Yash, Shin, Jean, Abé, Christoph, Agartz, Ingrid, Alloza, Clara, Alnæs, Dag, Ambrogi, Sonia, Antonucci, Linda A, Arango, Celso, Arolt, Volker, Auzias, Guillaume, Ayesa-Arriola, Rosa, Banaj, Nerisa, Banaschewski, Tobias, Bandeira, Cibele, Başgöze, Zeynep, Cupertino, Renata Basso, Bau, Claiton HD, Bauer, Jochen, Baumeister, Sarah, Bernardoni, Fabio, Bertolino, Alessandro, Bonnin, Caterina Del Mar, Brandeis, Daniel, Brem, Silvia, Bruggemann, Jason, Bülow, Robin, Bustillo, Juan R, Calderoni, Sara, Calvo, Rosa, Canales-Rodríguez, Erick J, Cannon, Dara M, Carmona, Susanna, Carr, Vaughan J, Catts, Stanley V, Chenji, Sneha, Chew, Qian Hui, Coghill, David, Connolly, Colm G, Conzelmann, Annette, Craven, Alexander R, Crespo-Facorro, Benedicto, Cullen, Kathryn, Dahl, Andreas, Dannlowski, Udo, Davey, Christopher G, Deruelle, Christine, Díaz-Caneja, Covadonga M, Dohm, Katharina, Ehrlich, Stefan, Epstein, Jeffery, Erwin-Grabner, Tracy, Eyler, Lisa T, Fedor, Jennifer, Fitzgerald, Jacqueline, Foran, William, Ford, Judith M, Fortea, Lydia, Fuentes-Claramonte, Paola, Fullerton, Janice, Furlong, Lisa, Gallagher, Louise, Gao, Bingchen, Gao, Si, Goikolea, Jose M, Gotlib, Ian, Goya-Maldonado, Roberto, Grabe, Hans J, Green, Melissa, Grevet, Eugenio H, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Haavik, Jan, Hahn, Tim, Harrison, Ben J, Heindel, Walter, Henskens, Frans, Heslenfeld, Dirk J, Hilland, Eva, Hoekstra, Pieter J, Hohmann, Sarah, Holz, Nathalie, Howells, Fleur M, Ipser, Jonathan C, Jahanshad, Neda, Jakobi, Babette, Jansen, Andreas, Janssen, Joost, Jonassen, Rune, Kaiser, Anna, Kaleda, Vasiliy, Karantonis, James, King, Joseph A, Kircher, Tilo, Kochunov, Peter, Koopowitz, Sheri-Michelle, Landén, Mikael, Landrø, Nils Inge, and Lawrie, Stephen

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Neurosciences, Genetics, Mental Health, Serious Mental Illness, Mental Illness, Women's Health, Brain Disorders, Preterm, Low Birth Weight and Health of the Newborn, Pregnancy, Pediatric, Perinatal Period - Conditions Originating in Perinatal Period, Behavioral and Social Science, Prevention, Schizophrenia, 2.1 Biological and endogenous factors, 2.3 Psychological, social and economic factors, Neurological, Reproductive health and childbirth, Mental health, Good Health and Well Being, Attention Deficit Disorder with Hyperactivity, Autism Spectrum Disorder, Bipolar Disorder, Cerebral Cortex, Child, Depressive Disorder, Major, Female, Humans, Infant, Newborn, Magnetic Resonance Imaging, Premature Birth, Cortical growth, Cortical surface area, Mental illness, Neurodevelopment, Neurogenesis, Psychiatric disorders, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological sciences, Biomedical and clinical sciences
Abstract

BackgroundMorphology of the human cerebral cortex differs across psychiatric disorders, with neurobiology and developmental origins mostly undetermined. Deviations in the tangential growth of the cerebral cortex during pre/perinatal periods may be reflected in individual variations in cortical surface area later in life.MethodsInterregional profiles of group differences in surface area between cases and controls were generated using T1-weighted magnetic resonance imaging from 27,359 individuals including those with attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia, and high general psychopathology (through the Child Behavior Checklist). Similarity of interregional profiles of group differences in surface area and prenatal cell-specific gene expression was assessed.ResultsAcross the 11 cortical regions, group differences in cortical area for attention-deficit/hyperactivity disorder, schizophrenia, and Child Behavior Checklist were dominant in multimodal association cortices. The same interregional profiles were also associated with interregional profiles of (prenatal) gene expression specific to proliferative cells, namely radial glia and intermediate progenitor cells (greater expression, larger difference), as well as differentiated cells, namely excitatory neurons and endothelial and mural cells (greater expression, smaller difference). Finally, these cell types were implicated in known pre/perinatal risk factors for psychosis. Genes coexpressed with radial glia were enriched with genes implicated in congenital abnormalities, birth weight, hypoxia, and starvation. Genes coexpressed with endothelial and mural genes were enriched with genes associated with maternal hypertension and preterm birth.ConclusionsOur findings support a neurodevelopmental model of vulnerability to mental illness whereby prenatal risk factors acting through cell-specific processes lead to deviations from typical brain development during pregnancy.

Published
2022

8. The Study of Ketamine for Youth Depression (SKY-D): study protocol for a randomised controlled trial of low-dose ketamine for young people with major depressive disorder

Author

Schwartz, Orli S., Amminger, Paul, Baune, Bernard T., Bedi, Gillinder, Berk, Michael, Cotton, Sue M., Daglas-Georgiou, Rothanthi, Glozier, Nick, Harrison, Ben, Hermens, Daniel F., Jennings, Emma, Lagopoulos, Jim, Loo, Colleen, Mallawaarachchi, Sumudu, Martin, Donel, Phelan, Bethany, Read, Nikki, Rodgers, Anthony, Schmaal, Lianne, Somogyi, Andrew A., Thurston, Lily, Weller, Amber, and Davey, Christopher G.

Published
2023
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9. Towards a youth mental health paradigm: a perspective and roadmap

Author

Uhlhaas, Peter J., Davey, Christopher G., Mehta, Urvakhsh Meherwan, Shah, Jai, Torous, John, Allen, Nicholas B., Avenevoli, Shelli, Bella-Awusah, Tolulope, Chanen, Andrew, Chen, Eric Y. H., Correll, Christoph U., Do, Kim Q., Fisher, Helen L., Frangou, Sophia, Hickie, Ian B., Keshavan, Matcheri S., Konrad, Kerstin, Lee, Francis S., Liu, Cindy H., Luna, Beatriz, McGorry, Patrick D., Meyer-Lindenberg, Andreas, Nordentoft, Merete, Öngür, Dost, Patton, George C., Paus, Tomáš, Reininghaus, Ulrich, Sawa, Akira, Schoenbaum, Michael, Schumann, Gunter, Srihari, Vinod H., Susser, Ezra, Verma, Swapna K., Woo, T. Wilson, Yang, Lawrence H., Yung, Alison R., and Wood, Stephen J.

Published
2023
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10. The Addition of Fish Oil to Cognitive Behavioral Case Management for Youth Depression: A Randomized, Double-Blind, Placebo-Controlled, Multicenter Clinical Trial

Author

Amminger, G. Paul, Rice, Simon, Davey, Christopher G., Quinn, Amelia L., Hermens, Daniel F., Zmicerevska, Natalia, Nichles, Alissa, Hickie, Ian, Incerti, Lisa, Weller, Amber, Joseph, Sarah, Hilton, Zarah, Pugh, Charlotte, Rayner, Madeline, Reid, Nate, Ratheesh, Aswin, Yung, Alison R., Yuen, Hok Pan, Mackinnon, Andrew, Hetrick, Sarah, Parker, Alexandra, Street, Rebekah, Berger, Maximus, Berk, Michael, McGorry, Patrick D., and Lin, Ashleigh

Published
2024
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11. 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

16. Structural brain alterations associated with suicidal thoughts and behaviors in young people: results from 21 international studies from the ENIGMA Suicidal Thoughts and Behaviours consortium

Author

van Velzen, Laura S., Dauvermann, Maria R., Colic, Lejla, Villa, Luca M., Savage, Hannah S., Toenders, Yara J., Zhu, Alyssa H., Bright, Joanna K., Campos, Adrián I., Salminen, Lauren E., Ambrogi, Sonia, Ayesa-Arriola, Rosa, Banaj, Nerisa, Başgöze, Zeynep, Bauer, Jochen, Blair, Karina, Blair, Robert James, Brosch, Katharina, Cheng, Yuqi, Colle, Romain, Connolly, Colm G., Corruble, Emmanuelle, Couvy-Duchesne, Baptiste, Crespo-Facorro, Benedicto, Cullen, Kathryn R., Dannlowski, Udo, Davey, Christopher G., Dohm, Katharina, Fullerton, Janice M., Gonul, Ali Saffet, Gotlib, Ian H., Grotegerd, Dominik, Hahn, Tim, Harrison, Ben J., He, Mengxin, Hickie, Ian B., Ho, Tiffany C., Iorfino, Frank, Jansen, Andreas, Jollant, Fabrice, Kircher, Tilo, Klimes-Dougan, Bonnie, Klug, Melissa, Leehr, Elisabeth J., Lippard, Elizabeth T. C., McLaughlin, Katie A., Meinert, Susanne, Miller, Adam Bryant, Mitchell, Philip B., Mwangi, Benson, Nenadić, Igor, Ojha, Amar, Overs, Bronwyn J., Pfarr, Julia-Katharina, Piras, Fabrizio, Ringwald, Kai G., Roberts, Gloria, Romer, Georg, Sanches, Marsal, Sheridan, Margaret A., Soares, Jair C., Spalletta, Gianfranco, Stein, Frederike, Teresi, Giana I., Tordesillas-Gutiérrez, Diana, Uyar-Demir, Aslihan, van der Wee, Nic J. A., van der Werff, Steven J., Vermeiren, Robert R. J. M., Winter, Alexandra, Wu, Mon-Ju, Yang, Tony T., Thompson, Paul M., Rentería, Miguel E., Jahanshad, Neda, Blumberg, Hilary P., van Harmelen, Anne-Laura, and Schmaal, Lianne

Published
2022
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23. Predicting Depression Onset in Young People Based on Clinical, Cognitive, Environmental, and Neurobiological Data

Author

Banaschewski, Tobias, Bokde, Arun L.W., Desrivières, Sylvane, Flor, Herta, Grigis, Antoine, Garavan, Hugh, Gowland, Penny, Heinz, Andreas, Brühl, Rüdiger, Martinot, Jean-Luc, Paillère Martinot, Marie-Laure, Nees, Frauke, Orfanos, Dimitri Papadopoulos, Lemaitre, Herve, Paus, Tomáš, Poustka, Luise, Hohmann, Sarah, Fröhner, Juliane H., Smolka, Michael N., Walter, Henrik, Whelan, Robert, Schumann, Gunter, Toenders, Yara J., Kottaram, Akhil, Dinga, Richard, Davey, Christopher G., Quinlan, Erin Burke, Stringaris, Argyris, van Noort, Betteke, Penttilä, Jani, Grimmer, Yvonne, Insensee, Corinna, Becker, Andreas, and Schmaal, Lianne

Published
2022
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24. Active Inference in Psychology and Psychiatry: Progress to Date?

Author

Badcock, Paul B. and Davey, Christopher G.

Abstract

The free energy principle is a formal theory of adaptive self-organising systems that emerged from statistical thermodynamics, machine learning and theoretical neuroscience and has since been translated into biologically plausible 'process theories' of cognition and behaviour, which fall under the banner of 'active inference'. Despite the promise this theory holds for theorising, research and practical applications in psychology and psychiatry, its impact on these disciplines has only now begun to bear fruit. The aim of this treatment is to consider the extent to which active inference has informed theoretical progress in psychology, before exploring its contributions to our understanding and treatment of psychopathology. Despite facing persistent translational obstacles, progress suggests that active inference has the potential to become a new paradigm that promises to unite psychology's subdisciplines, while readily incorporating the traditionally competing paradigms of evolutionary and developmental psychology. To date, however, progress towards this end has been slow. Meanwhile, the main outstanding question is whether this theory will make a positive difference through applications in clinical psychology, and its sister discipline of psychiatry. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Sex differences in physiological correlates of affectively driven decision-making behavior in adult ADHD.

Author

Halbe, Eva, Heger, Alina Sophie, Kolf, Fabian, Hüpen, Philippa, Bergmann, Moritz, Harrison, Ben J., Davey, Christopher G., Philipsen, Alexandra, and Lux, Silke

Subjects
GALVANIC skin response, PSYCHOPHYSIOLOGY, AUTONOMIC nervous system, EMOTIONAL competence, RISK perception
Abstract

Background: Sex differences in the symptomatology of adults with attention-deficit/hyperactivity disorder (ADHD) have often been overlooked when studying behavioral abnormalities. However, it is known that women exhibit considerably more stronger symptoms related to emotional competence than men. Since affective functions significantly influence the processing of risky decision-making and risk-engagement, we assume that risky behavior in ADHD is affected by sex differences. Therefore, we specifically investigated sex-specific effects on the interaction between emotionally induced changes in physiology and behavioral performance on a decision-making task. Methods: Skin conductance responses of twenty-nine adults with ADHD (n = 16 male; n = 13 female) and thirty-three adults in the control group (n = 14 male; n = 19 female) were recorded during the performance in a modified version of the Balloon Analogue Risk Task (BART). Additional questionnaires were used to reveal insights in the self-assessment of emotional competence, risk perception, and feedback sensitivity. Emotional arousal and decision-making behavior were analyzed using linear mixed-effects models. Results: Results showed different effects of sex on risk behaviors in controls and ADHD. In contrast to healthy controls, female adults with ADHD showed a significantly greater risk engagement in the BART compared to males with ADHD. This contrary sex relation was not observed in skin conductance responses and revealed a significantly different sex-dependent correlation of body response and behavioral task performance in ADHD. Comparisons with results from self-assessments furthermore indicate a reduced behavioral self-perception in women with ADHD, but not in men. Conclusion: In summary, we found an altered interaction between physiological activity and risky behavior in women with ADHD. Thus, the present study indicates a reduced sensitivity towards the own bodily responses in women with ADHD, which could consequently cause increased risky DM behavior in daily life. The current results suggest that more consideration needs to be given to sex-specific effects on physiological processes and behavior in adults with ADHD. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation

Author

Ge, Ruiyang, primary, Yu, Yuetong, additional, Qi, Yi Xuan, additional, Fan, Yu-nan, additional, Chen, Shiyu, additional, Gao, Chuntong, additional, Haas, Shalaila S, additional, New, Faye, additional, Boomsma, Dorret I, additional, Brodaty, Henry, additional, Brouwer, Rachel M, additional, Buckner, Randy, additional, Caseras, Xavier, additional, Crivello, Fabrice, additional, Crone, Eveline A, additional, Erk, Susanne, additional, Fisher, Simon E, additional, Franke, Barbara, additional, Glahn, David C, additional, Dannlowski, Udo, additional, Grotegerd, Dominik, additional, Gruber, Oliver, additional, Hulshoff Pol, Hilleke E, additional, Schumann, Gunter, additional, Tamnes, Christian K, additional, Walter, Henrik, additional, Wierenga, Lara M, additional, Jahanshad, Neda, additional, Thompson, Paul M, additional, Frangou, Sophia, additional, Agartz, Ingrid, additional, Asherson, Philip, additional, Ayesa-Arriola, Rosa, additional, Banaj, Nerisa, additional, Banaschewski, Tobias, additional, Baumeister, Sarah, additional, Bertolino, Alessandro, additional, Borgwardt, Stefan, additional, Bourque, Josiane, additional, Brandeis, Daniel, additional, Breier, Alan, additional, Buitelaar, Jan K, additional, Cannon, Dara M, additional, Cervenka, Simon, additional, Conrod, Patricia J, additional, Crespo-Facorro, Benedicto, additional, Davey, Christopher G, additional, de Haan, Lieuwe, additional, de Zubicaray, Greig I, additional, Di Giorgio, Annabella, additional, Frodl, Thomas, additional, Gruner, Patricia, additional, Gur, Raquel E, additional, Gur, Ruben C, additional, Harrison, Ben J, additional, Hatton, Sean N, additional, Hickie, Ian, additional, Howells, Fleur M, additional, Huyser, Chaim, additional, Jernigan, Terry L, additional, Jiang, Jiyang, additional, Joska, John A, additional, Kahn, René S, additional, Kalnin, Andrew J, additional, Kochan, Nicole A, additional, Koops, Sanne, additional, Kuntsi, Jonna, additional, Lagopoulos, Jim, additional, Lazaro, Luisa, additional, Lebedeva, Irina S, additional, Lochner, Christine, additional, Martin, Nicholas G, additional, Mazoyer, Bernard, additional, McDonald, Brenna C, additional, McDonald, Colm, additional, McMahon, Katie L, additional, Medland, Sarah, additional, Modabbernia, Amirhossein, additional, Mwangi, Benson, additional, Nakao, Tomohiro, additional, Nyberg, Lars, additional, Piras, Fabrizio, additional, Portella, Maria J, additional, Qiu, Jiang, additional, Roffman, Joshua L, additional, Sachdev, Perminder S, additional, Sanford, Nicole, additional, Satterthwaite, Theodore D, additional, Saykin, Andrew J, additional, Sellgren, Carl M, additional, Sim, Kang, additional, Smoller, Jordan W, additional, Soares, Jair C, additional, Sommer, Iris E, additional, Spalletta, Gianfranco, additional, Stein, Dan J, additional, Thomopoulos, Sophia I, additional, Tomyshev, Alexander S, additional, Tordesillas-Gutiérrez, Diana, additional, Trollor, Julian N, additional, van 't Ent, Dennis, additional, van den Heuvel, Odile A, additional, van Erp, Theo GM, additional, van Haren, Neeltje EM, additional, Vecchio, Daniela, additional, Veltman, Dick J, additional, Wang, Yang, additional, Weber, Bernd, additional, Wei, Dongtao, additional, Wen, Wei, additional, Westlye, Lars T, additional, Williams, Steven CR, additional, Wright, Margaret J, additional, Wu, Mon-Ju, additional, and Yu, Kevin, additional

Published
2024
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29. Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders: Evidence through univariate and multivariate mega-analysis including 6420 participants from the ENIGMA MDD working group

Author

Opel, Nils, Thalamuthu, Anbupalam, Milaneschi, Yuri, Grotegerd, Dominik, Flint, Claas, Leenings, Ramona, Goltermann, Janik, Richter, Maike, Hahn, Tim, Woditsch, Georg, Berger, Klaus, Hermesdorf, Marco, McIntosh, Andrew, Whalley, Heather C., Harris, Mathew A., MacMaster, Frank P., Walter, Henrik, Veer, Ilya M., Frodl, Thomas, Carballedo, Angela, Krug, Axel, Nenadic, Igor, Kircher, Tilo, Aleman, Andre, Groenewold, Nynke A., Stein, Dan J., Soares, Jair C., Zunta-Soares, Giovana B., Mwangi, Benson, Wu, Mon-Ju, Walter, Martin, Li, Meng, Harrison, Ben J., Davey, Christopher G., Cullen, Kathryn R., Klimes-Dougan, Bonnie, Mueller, Bryon A., Sämann, Philipp G., Penninx, Brenda, Nawijn, Laura, Veltman, Dick J., Aftanas, Lyubomir, Brak, Ivan V., Filimonova, Elena A., Osipov, Evgeniy A., Reneman, Liesbeth, Schrantee, Anouk, Grabe, Hans J., Van der Auwera, Sandra, Wittfeld, Katharina, Hosten, Norbert, Völzke, Henry, Sim, Kang, Gotlib, Ian H., Sacchet, Matthew D., Lagopoulos, Jim, Hatton, Sean N., Hickie, Ian, Pozzi, Elena, Thompson, Paul M., Jahanshad, Neda, Schmaal, Lianne, Baune, Bernhard T., and Dannlowski, Udo

Published
2021
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33. 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, 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., Saemann, 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., Voelzke, Henry, Whittle, Sarah, 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, 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, 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., Saemann, 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., Voelzke, 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

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 = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also 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 yield more encouraging prospects., Funding Agencies|NIH [U54 EB020403, R01MH116147, MH117601, R37 MH101495]; ERA-NET PRIOMEDCHILD FP 6 (EU) [11.32050.26]; University Medical Center Gottingen (UMG Startforderung); German Federal Ministry of Education and Research (Bundesministerium fuer Bildung und Forschung) [BMBF: 01 ZX 1507]; Ministerio de Ciencia e Innovacion of the Spanish Government; Instituto de Salud Carlos III through a 'Miguel Servet' research contract [CP16-0020]; National Research Plan (Plan Estatal de I + D + I 2016-2019); European Regional Development Fund (ERDF); Medical Research Council [G 1100629]; National Center for Mental Health (NCMH) - Health Research Wales [HS/14/20]; Johnson & Johnson Innovation; Swedish Medical Research Council [2017-00875, 2013-07434, 2019-01138]; ALF Grants, Region Ostergotland; National Institutes of Health [R01 CA193522, R01 NS073939]; MD Anderson Cancer Support Grant [P30CA016672]; Canadian Institutes for Health Research; Branch Out Neurological Foundation; Alberta Children's Hospital Foundation; Medical Research Council of South Africa; Intramural Research Program at the National Institute of Mental Health, National Institutes of Health (IRP-NIMH-NIH) [ZIA-MH002857]; GGZ Rivierduinen; LUMC; Generalitat de Catalunya [2014 SGR 1573]; Instituto de Salud Carlos III [CPII16/00018, PI14/01151, PI14/01148]; Gratama Foundation, the Netherlands [2012/35]; NIMH [R01 085667]; Dunn Research Foundation; NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London; National Health and Medical Research Council of Australia (NHMRC) [1064643, 1024570]; National Institute of Mental Health [K23MH090421]; Biotechnology Research Center [P41 RR008079]; National Alliance for Research on Schizophrenia and Depression; University of Minnesota Graduate School; Minnesota Medical Foundation; Brain and Behavior Research Foundation; National Health and Medical Research Council [1125504]; Geestkracht program of the Netherla

Published
2024
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34. Normative modelling of brain morphometry across the lifespan with CentileBrain:algorithm benchmarking and model optimisation

Author

Ge, Ruiyang, Yu, Yuetong, Qi, Yi Xuan, Fan, Yu nan, Chen, Shiyu, Gao, Chuntong, Haas, Shalaila S., New, Faye, Boomsma, Dorret I., Brodaty, Henry, Brouwer, Rachel M., Buckner, Randy, Caseras, Xavier, Crivello, Fabrice, Crone, Eveline A., Erk, Susanne, Fisher, Simon E., Franke, Barbara, Glahn, David C., Dannlowski, Udo, Grotegerd, Dominik, Gruber, Oliver, Hulshoff Pol, Hilleke E., Schumann, Gunter, Tamnes, Christian K., Walter, Henrik, Wierenga, Lara M., Jahanshad, Neda, Thompson, Paul M., Frangou, Sophia, Agartz, Ingrid, Asherson, Philip, Ayesa-Arriola, Rosa, Banaj, Nerisa, Banaschewski, Tobias, Baumeister, Sarah, Bertolino, Alessandro, Borgwardt, Stefan, Bourque, Josiane, Brandeis, Daniel, Breier, Alan, Buitelaar, Jan K., Cannon, Dara M., Cervenka, Simon, Conrod, Patricia J., Crespo-Facorro, Benedicto, Davey, Christopher G., de Haan, Lieuwe, de Zubicaray, Greig I., van Haren, Neeltje EM, Ge, Ruiyang, Yu, Yuetong, Qi, Yi Xuan, Fan, Yu nan, Chen, Shiyu, Gao, Chuntong, Haas, Shalaila S., New, Faye, Boomsma, Dorret I., Brodaty, Henry, Brouwer, Rachel M., Buckner, Randy, Caseras, Xavier, Crivello, Fabrice, Crone, Eveline A., Erk, Susanne, Fisher, Simon E., Franke, Barbara, Glahn, David C., Dannlowski, Udo, Grotegerd, Dominik, Gruber, Oliver, Hulshoff Pol, Hilleke E., Schumann, Gunter, Tamnes, Christian K., Walter, Henrik, Wierenga, Lara M., Jahanshad, Neda, Thompson, Paul M., Frangou, Sophia, Agartz, Ingrid, Asherson, Philip, Ayesa-Arriola, Rosa, Banaj, Nerisa, Banaschewski, Tobias, Baumeister, Sarah, Bertolino, Alessandro, Borgwardt, Stefan, Bourque, Josiane, Brandeis, Daniel, Breier, Alan, Buitelaar, Jan K., Cannon, Dara M., Cervenka, Simon, Conrod, Patricia J., Crespo-Facorro, Benedicto, Davey, Christopher G., de Haan, Lieuwe, de Zubicaray, Greig I., and van Haren, Neeltje EM

Abstract

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3–90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.

Published
2024

35. The self, neuroscience and psychosis study:Testing a neurophenomenological model of the onset of psychosis

Author

Krcmar, Marija, Wannan, Cassandra M.J., Lavoie, Suzie, Allott, Kelly, Davey, Christopher G., Yuen, Hok Pan, Whitford, Thomas, Formica, Melanie, Youn, Sarah, Shetty, Jashmina, Beedham, Rebecca, Rayner, Victoria, Murray, Graham, Polari, Andrea, Gawęda, Łukasz, Koren, Dan, Sass, Louis, Parnas, Josef, Rasmussen, Andreas R., McGorry, Patrick, Hartmann, Jessica A., Nelson, Barnaby, Krcmar, Marija, Wannan, Cassandra M.J., Lavoie, Suzie, Allott, Kelly, Davey, Christopher G., Yuen, Hok Pan, Whitford, Thomas, Formica, Melanie, Youn, Sarah, Shetty, Jashmina, Beedham, Rebecca, Rayner, Victoria, Murray, Graham, Polari, Andrea, Gawęda, Łukasz, Koren, Dan, Sass, Louis, Parnas, Josef, Rasmussen, Andreas R., McGorry, Patrick, Hartmann, Jessica A., and Nelson, Barnaby

Abstract

Aim Basic self disturbance is a putative core vulnerability marker of schizophrenia spectrum disorders. The primary aims of the Self, Neuroscience and Psychosis (SNAP) study are to: (1) empirically test a previously described neurophenomenological self-disturbance model of psychosis by examining the relationship between specific clinical, neurocognitive, and neurophysiological variables in UHR patients, and (2) develop a prediction model using these neurophenomenological disturbances for persistence or deterioration of UHR symptoms at 12-month follow-up. Methods SNAP is a longitudinal observational study. Participants include 400 UHR individuals, 100 clinical controls with no attenuated psychotic symptoms, and 50 healthy controls. All participants complete baseline clinical and neurocognitive assessments and electroencephalography. The UHR sample are followed up for a total of 24 months, with clinical assessment completed every 6 months. Results This paper presents the protocol of the SNAP study, including background rationale, aims and hypotheses, design, and assessment procedures. Conclusions The SNAP study will test whether neurophenomenological disturbances associated with basic self-disturbance predict persistence or intensification of UHR symptomatology over a 2-year follow up period, and how specific these disturbances are to a clinical population with attenuated psychotic symptoms. This may ultimately inform clinical care and pathoaetiological models of psychosis., Aim: Basic self disturbance is a putative core vulnerability marker of schizophrenia spectrum disorders. The primary aims of the Self, Neuroscience and Psychosis (SNAP) study are to: (1) empirically test a previously described neurophenomenological self-disturbance model of psychosis by examining the relationship between specific clinical, neurocognitive, and neurophysiological variables in UHR patients, and (2) develop a prediction model using these neurophenomenological disturbances for persistence or deterioration of UHR symptoms at 12-month follow-up. Methods: SNAP is a longitudinal observational study. Participants include 400 UHR individuals, 100 clinical controls with no attenuated psychotic symptoms, and 50 healthy controls. All participants complete baseline clinical and neurocognitive assessments and electroencephalography. The UHR sample are followed up for a total of 24 months, with clinical assessment completed every 6 months. Results: This paper presents the protocol of the SNAP study, including background rationale, aims and hypotheses, design, and assessment procedures. Conclusions: The SNAP study will test whether neurophenomenological disturbances associated with basic self-disturbance predict persistence or intensification of UHR symptomatology over a 2-year follow up period, and how specific these disturbances are to a clinical population with attenuated psychotic symptoms. This may ultimately inform clinical care and pathoaetiological models of psychosis.

Published
2024

37. An analysis of real‐time suicidal ideation and its relationship with retrospective reports among young people with borderline personality disorder.

Author

Andrewes, Holly E., Cavelti, Marialuisa, Hulbert, Carol, Cotton, Susan M., Betts, Jennifer K., Jackson, Henry J., McCutcheon, Louise, Gleeson, John, Davey, Christopher G., and Chanen, Andrew M.

Subjects
YOUNG adults, SUICIDAL ideation, BORDERLINE personality disorder, DIALECTICAL behavior therapy, MENTAL health services
Abstract

Introduction: This study aimed to analyze the real‐time variability of suicidal ideation intensity and the relationship between real‐time and retrospective reports of suicidal ideation made on the Beck Scale for Suicidal Ideation (BSS), among young people with borderline personality disorder (BPD). Methods: Young people (15–25‐year olds) with BPD (N = 46), recruited from two government‐funded mental health services, rated the intensity of their suicidal ideation six times per day for 7 days before completing the BSS. Results: For 70% of participants, suicidal ideation changed in intensity approximately five times across the week, both within and between days. BSS ratings were most highly correlated with the highest real‐time ratings of suicidal ideation. However, this was not significantly different from the relationship between the BSS and both the average and most recent ratings. Median ratings of suicidal ideation intensity were higher on the BSS compared with an equivalent question asked in real time. Conclusion: Findings suggest that young people with BPD experience high levels of fluctuation in their intensity of suicidal ideation across a week and that retrospective reports of suicidal ideation might be more reflective of the most intense experience of suicidal ideation across the week. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Altered resting-state functional connectome in major depressive disorder: a mega-analysis from the PsyMRI consortium

Author

Javaheripour, Nooshin, Li, Meng, Chand, Tara, Krug, Axel, Kircher, Tilo, Dannlowski, Udo, Nenadić, Igor, Hamilton, J. Paul, Sacchet, Matthew D., Gotlib, Ian H., Walter, Henrik, Frodl, Thomas, Grimm, Simone, Harrison, Ben J., Wolf, Christian Robert, Olbrich, Sebastian, van Wingen, Guido, Pezawas, Lukas, Parker, Gordon, Hyett, Matthew P., Sämann, Philipp G., Hahn, Tim, Steinsträter, Olaf, Jansen, Andreas, Yuksel, Dilara, Kämpe, Robin, Davey, Christopher G., Meyer, Bernhard, Bartova, Lucie, Croy, Ilona, Walter, Martin, and Wagner, Gerd

Published
2021
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43. The addition of fluoxetine to cognitive behavioural therapy for youth depression (YoDA-C): a randomised, double-blind, placebo-controlled, multicentre clinical trial

Author

Davey, Christopher G, Chanen, Andrew M, Hetrick, Sarah E, Cotton, Sue M, Ratheesh, Aswin, Amminger, Günter P, Koutsogiannis, John, Phelan, Mark, Mullen, Edward, Harrison, Ben J, Rice, Simon, Parker, Alexendra G, Dean, Olivia M, Weller, Amber, Kerr, Melissa, Quinn, Amelia L, Catania, Lisa, Kazantzis, Nikolaos, McGorry, Patrick D, and Berk, Michael

Published
2019
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47. The Study of Ketamine for Youth Depression (SKY-D): study protocol for a randomised controlled trial of low-dose ketamine for young people with major depressive disorder

Author

Schwartz, Orli, primary, Amminger, Paul, additional, Baune, Bernard T, additional, Bedi, Gillinder, additional, Berk, Michael, additional, Cotton, Sue M., additional, Daglas-Georgiou, Rothanthi, additional, Glozier, Nick, additional, Harrison, Ben, additional, Hermens, Daniel F., additional, Jennings, Emma, additional, Lagopoulos, Jim, additional, Loo, Colleen, additional, Mallawaarachchi, Sumudu, additional, Martin, Donel, additional, Phelan, Bethany, additional, Read, Nikki, additional, Rodgers, Anthony, additional, Schmaal, Lianne, additional, Somogyi, Andrew A., additional, Thurston, Lily, additional, Weller, Amber, additional, and Davey, Christopher G., additional

Published
2023
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