15 results on '"Sarink, K."'
Search Results
2. Longitudinal structural brain changes in bipolar disorder: A multicenter neuroimaging study of 1232 individuals by the ENIGMA Bipolar Disorder Working Group
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Theophilus N. Akudjedu, Frederike Stein, Edith Pomarol-Clotet, Lars T. Westlye, Ulrik Fredrik Malt, Erlend Bøen, Fabian Breuer, Chao Suo, Tina Meller, Tim Hahn, Francesco Benedetti, Jose Manuel Goikolea, Silvia Alonso-Lana, Adam George White, Dag Alnæs, Julia-Katharina Pfarr, Beathe Haatveit, Sara Poletti, Kai Ringwald, Nathalia Zak, Benny Liberg, Kelvin Sarink, Giulia Tronchin, Yann Chye, Janice M. Fullerton, Orwa Dandash, Igor Nenadic, Caterina del Mar Bonnín, Elisa M T Melloni, Udo Dannlowski, Michael Berk, Dominik Grotegerd, Christopher R.K. Ching, Lukas Fisch, Torbjørn Elvsåshagen, Andreas Dahl, Martin Alda, Francesco Panicalli, Ingrid Agartz, Martin Ingvar, Bronwyn Overs, Joaquim Radua, Katharina Brosch, Alexander V. Lebedev, Kang Sim, Tilo Kircher, Leila Nabulsi, Dara M. Cannon, Erick J. Canales-Rodríguez, Paul M. Thompson, Nils Opel, Jonathan Repple, R. Salvador, Katharina Dohm, Philip B. Mitchell, Colm McDonald, Salvador Sarró, Rachel M. Brouwer, Ole A. Andreassen, Tomas Hajek, Mikael Landén, Simon Schmitt, Sophia I. Thomopoulos, Elena Rodriguez-Cano, Eduard Vieta, Ingrid Melle, Rhoshel K. Lenroot, Lakshmi N. Yatham, Sean R. McWhinney, Gloria Roberts, Christoph Abé, Walter Heindel, Abe, C., Ching, C. R. K., Liberg, B., Lebedev, A. V., Agartz, I., Akudjedu, T. N., Alda, M., Alnaes, D., Alonso-Lana, S., Benedetti, F., Berk, M., Boen, E., Bonnin, C. D. M., Breuer, F., Brosch, K., Brouwer, R. M., Canales-Rodriguez, E. J., Cannon, D. M., Chye, Y., Dahl, A., Dandash, O., Dannlowski, U., Dohm, K., Elvsashagen, T., Fisch, L., Fullerton, J. M., Goikolea, J. M., Grotegerd, D., Haatveit, B., Hahn, T., Hajek, T., Heindel, W., Ingvar, M., Sim, K., Kircher, T. T. J., Lenroot, R. K., Malt, U. F., Mcdonald, C., Mcwhinney, S. R., Melle, I., Meller, T., Melloni, E. M. T., Mitchell, P. B., Nabulsi, L., Nenadic, I., Opel, N., Overs, B. J., Panicalli, F., Pfarr, J. -K., Poletti, S., Pomarol-Clotet, E., Radua, J., Repple, J., Ringwald, K. G., Roberts, G., Rodriguez-Cano, E., Salvador, R., Sarink, K., Sarro, S., Schmitt, S., Stein, F., Suo, C., Thomopoulos, S. I., Tronchin, G., Vieta, E., Westlye, L. T., White, A. G., Yatham, L. N., Zak, N., Thompson, P. M., Andreassen, O. A., Landen, M., Complex Trait Genetics, and Amsterdam Neuroscience - Complex Trait Genetics
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Adult ,Male ,Longitudinal study ,medicine.medical_specialty ,Bipolar disorder ,Neuroimaging ,volume changes ,surface-based analysis ,Young Adult ,gray-matter ,Cortex (anatomy) ,Internal medicine ,medicine ,Humans ,Multicenter Studies as Topic ,Biological Psychiatry ,mri ,human cerebral-cortex ,Psychiatry ,medicine.diagnostic_test ,business.industry ,ENIGMA ,Brain ,Magnetic resonance imaging ,Cerebral Cortical Thinning ,Middle Aged ,cortical thickness ,medicine.disease ,Magnetic Resonance Imaging ,Neuroprogression ,Mania ,genetic influences ,medicine.anatomical_structure ,Mood ,Meta-analysis ,Cardiology ,lithium treatment ,Female ,medicine.symptom ,i disorder ,business ,metaanalysis - Abstract
Background: Bipolar disorder (BD) is associated with cortical and subcortical structural brain abnormalities. It is unclear whether such alterations progressively change over time, and how this is related to the number of mood episodes. To address this question, we analyzed a large and diverse international sample with longitudinal magnetic resonance imaging (MRI) and clinical data to examine structural brain changes over time in BD. Methods: Longitudinal structural MRI and clinical data from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) BD Working Group, including 307 patients with BD and 925 healthy control subjects, were collected from 14 sites worldwide. Male and female participants, aged 40 ± 17 years, underwent MRI at 2 time points. Cortical thickness, surface area, and subcortical volumes were estimated using FreeSurfer. Annualized change rates for each imaging phenotype were compared between patients with BD and healthy control subjects. Within patients, we related brain change rates to the number of mood episodes between time points and tested for effects of demographic and clinical variables. Results: Compared with healthy control subjects, patients with BD showed faster enlargement of ventricular volumes and slower thinning of the fusiform and parahippocampal cortex (0.18 < d < 0.22). More (hypo)manic episodes were associated with faster cortical thinning, primarily in the prefrontal cortex. Conclusions: In the hitherto largest longitudinal MRI study on BD, we did not detect accelerated cortical thinning but noted faster ventricular enlargements in BD. However, abnormal frontocortical thinning was observed in association with frequent manic episodes. Our study yields insights into disease progression in BD and highlights the importance of mania prevention in BD treatment.
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- 2022
3. Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders
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Committee, Writing, Disorder, Autism Spectrum, French, Leon, Grevet, Eugenio H, Groenewold, Nynke A, Grotegerd, Dominik, Gruber, Oliver, Gruner, Patricia, Guerrero-Pedraza, Amalia, Gur, Raquel E, Gur, Ruben C, Haar, Shlomi, Haarman, Bartholomeus C M, Thomopoulos, Sophia I, Haavik, Jan, Hahn, Tim, Hajek, Tomas, Harrison, Benjamin J, Harrison, Neil A, Hartman, Catharina A, Whalley, Heather C, Heslenfeld, Dirk J, Hibar, Derrek P, Hilland, Eva, Pozzi, Elena, Hirano, Yoshiyuki, Ho, Tiffany C, Hoekstra, Pieter J, Hoekstra, Liesbeth, Hohmann, Sarah, Hong, L. E., Höschl, Cyril, Høvik, Marie F, Howells, Fleur M, Nenadic, Igor, Abe, Yoshinari, Jalbrzikowski, Maria, James, Anthony C, Janssen, Joost, Jaspers-Fayer, Fern, Xu, Jian, Jonassen, Rune, Karkashadze, Georgii, King, Joseph A, Kircher, Tilo, Kirschner, Matthias, Abé, Christoph, Koch, Kathrin, Kochunov, Peter, Kohls, Gregor, Konrad, Kerstin, Krämer, Bernd, Krug, Axel, Kuntsi, Jonna, Kwon, Jun Soo, Landén, Mikael, Landrø, Nils I, Anticevic, Alan, Lazaro, Luisa, Lebedeva, Irina S, Leehr, Elisabeth J, Lera-Miguel, Sara, Lesch, Klaus-Peter, Lochner, Christine, Louza, Mario R, Luna, Beatriz, Lundervold, Astri J, MacMaster, Frank P, Alda, Martin, Maglanoc, Luigi A, Malpas, Charles B, Portella, Maria J, Marsh, Rachel, Martyn, Fiona M, Mataix-Cols, David, Mathalon, Daniel H, McCarthy, Hazel, McDonald, Colm, McPhilemy, Genevieve, Aleman, Andre, Meinert, Susanne, Menchón, José M, Minuzzi, Luciano, Mitchell, Philip B, Moreno, Carmen, Morgado, Pedro, Muratori, Filippo, Murphy, Clodagh M, Murphy, Declan, Mwangi, Benson, Alloza, Clara, Nabulsi, Leila, Nakagawa, Akiko, Nakamae, Takashi, Namazova, Leyla, Narayanaswamy, Janardhanan, Jahanshad, Neda, Nguyen, Danai D, Nicolau, Rosa, O'Gorman Tuura, Ruth L, O'Hearn, Kirsten, Alonso-Lana, Silvia, Oosterlaan, Jaap, Opel, Nils, Ophoff, Roel A, Oranje, Bob, García de la Foz, Victor Ortiz, Overs, Bronwyn J, Paloyelis, Yannis, Pantelis, Christos, Parellada, Mara, Pauli, Paul, Disorder, Bipolar, Ameis, Stephanie H, Picó-Pérez, Maria, Picon, Felipe A, Piras, Fabrizio, Piras, Federica, Plessen, Kerstin J, Pomarol-Clotet, Edith, Preda, Adrian, Puig, Olga, Quidé, Yann, Radua, Joaquim, Anagnostou, Evdokia, Ramos-Quiroga, J Antoni, Rasser, Paul E, Rauer, Lisa, Reddy, Janardhan, Redlich, Ronny, Reif, Andreas, Reneman, Liesbeth, Repple, Jonathan, Retico, Alessandra, Richarte, Vanesa, McIntosh, Andrew A, Richter, Anja, Rosa, Pedro G P, Rubia, Katya K, Hashimoto, Ryota, Sacchet, Matthew D, Salvador, Raymond, Santonja, Javier, Sarink, Kelvin, Sarró, Salvador, Satterthwaite, Theodore D, Arango, Celso, Sawa, Akira, Schall, Ulrich, Schofield, Peter R, Schrantee, Anouk, Seitz, Jochen, Serpa, Mauricio H, Setién-Suero, Esther, Shaw, Philip, Shook, Devon, Silk, Tim J, Arnold, Paul D, Sim, Kang, Simon, Schmitt, Simpson, Helen Blair, Singh, Aditya, Skoch, Antonin, Skokauskas, Norbert, Soares, Jair C, Soreni, Noam, Soriano-Mas, Carles, Spalletta, Gianfranco, Asherson, Philip, Spaniel, Filip, Lawrie, Stephen M, Stern, Emily R, Stewart, S Evelyn, Takayanagi, Yoichiro, Temmingh, Henk S, Tolin, David F, Tomecek, David, Tordesillas-Gutiérrez, Diana, Tosetti, Michela, Assogna, Francesca, Uhlmann, Anne, van Amelsvoort, Therese, van der Wee, Nic J A, van der Werff, Steven J A, van Haren, Neeltje E M, van Wingen, Guido A, Vance, Alasdair, Vázquez-Bourgon, Javier, Vecchio, Daniela, Venkatasubramanian, Ganesan, Auzias, Guillaume, Vieta, Eduard, Vilarroya, Oscar, Vives-Gilabert, Yolanda, Voineskos, Aristotle N, Völzke, Henry, von Polier, Georg G, Walton, Esther, Weickert, Thomas W, Weickert, Cynthia Shannon, Weideman, Andrea S, Ayesa-Arriola, Rosa, Wittfeld, Katharina, Wolf, Daniel H, Wu, Mon-Ju, Yang, T. T., Yang, Sikun, Yoncheva, Yuliya, Yun, Je-Yeon, Cheng, Yuqi, Zanetti, Marcus V, Ziegler, Georg C, Bakker, Geor, Franke, Barbara, Hoogman, Martine, Buitelaar, Jan K, van Rooij, Daan, Andreassen, Ole A, Ching, Christopher R K, Veltman, Dick J, Schmaal, Lianne, Stein, Dan J, van den Heuvel, Odile A, Disorder, Major Depressive, Banaj, Nerisa, Turner, Jessica A, van Erp, Theo G M, Pausova, Zdenka, Thompson, Paul M, Paus, Tomáš, Attention-Deficit/Hyperactivity Disorder, Banaschewski, Tobias, Bandeira, Cibele E, Baranov, Alexandr, Bargalló, Núria, Bau, Claiton H D, Baumeister, Sarah, Baune, Bernhard T, Bellgrove, Mark A, Benedetti, Francesco, Disorder, Obsessive-Compulsive, Bertolino, Alessandro, Boedhoe, Premika S W, Boks, Marco, Bollettini, Irene, Del Mar Bonnin, Caterina, Borgers, Tiana, Borgwardt, Stefan, Brandeis, Daniel, Brennan, Brian P, Bruggemann, Jason M, Groups, Schizophrenia ENIGMA Working, Bülow, Robin, Busatto, Geraldo F, Calderoni, Sara, Calhoun, Vince D, Calvo, Rosa, Canales-Rodríguez, Erick J, Cannon, Dara M, Carr, Vaughan J, Cascella, Nicola, Cercignani, Mara, Patel, Yash, Chaim-Avancini, Tiffany M, Christakou, Anastasia, Coghill, David, Conzelmann, Annette, Crespo-Facorro, Benedicto, Cubillo, Ana I, Cullen, Kathryn R, Cupertino, Renata B, Daly, Eileen, Dannlowski, Udo, Parker, Nadine, Davey, Christopher G, Denys, Damiaan, Deruelle, Christine, Di Giorgio, Annabella, Dickie, Erin W, Dima, Danai, Dohm, Katharina, Ehrlich, Stefan, Ely, Benjamin A, Erwin-Grabner, Tracy, Shin, Jean, Ethofer, Thomas, Fair, Damien A, Fallgatter, Andreas, Faraone, Stephen V, Fatjó-Vilas, Mar, Fedor, Jennifer M, Fitzgerald, Kate D, Ford, Judith M, Frodl, Thomas, Fu, Cynthia H Y, Howard, Derek, Fullerton, Janice M, Gabel, Matt C, Glahn, David C, Roberts, Gloria, Gogberashvili, Tinatin, Goikolea, Jose M, Gotlib, Ian H, Goya-Maldonado, Roberto, Grabe, Hans, Green, Melissa J, Patel, Y., Parker, N., Shin, J., Howard, D., French, L., Thomopoulos, S. I., Pozzi, E., Abe, Y., Abe, C., Anticevic, A., Alda, M., Aleman, A., Alloza, C., Alonso-Lana, S., Ameis, S. H., Anagnostou, E., Mcintosh, A. A., Arango, C., Arnold, P. D., Asherson, P., Assogna, F., Auzias, G., Ayesa-Arriola, R., Bakker, G., Banaj, N., Banaschewski, T., Bandeira, C. E., Baranov, A., Bargallo, N., Bau, C. H. D., Baumeister, S., Baune, B. T., Bellgrove, M. A., Benedetti, F., Bertolino, A., Boedhoe, P. S. W., Boks, M., Bollettini, I., Del Mar Bonnin, C., Borgers, T., Borgwardt, S., Brandeis, D., Brennan, B. P., Bruggemann, J. M., Bulow, R., Busatto, G. F., Calderoni, S., Calhoun, V. D., Calvo, R., Canales-Rodriguez, E. J., Cannon, D. M., Carr, V. J., Cascella, N., Cercignani, M., Chaim-Avancini, T. M., Christakou, A., Coghill, D., Conzelmann, A., Crespo-Facorro, B., Cubillo, A. I., Cullen, K. R., Cupertino, R. B., Daly, E., Dannlowski, U., Davey, C. G., Denys, D., Deruelle, C., Di Giorgio, A., Dickie, E. W., Dima, D., Dohm, K., Ehrlich, S., Ely, B. A., Erwin-Grabner, T., Ethofer, T., Fair, D. A., Fallgatter, A. J., Faraone, S. V., Fatjo-Vilas, M., Fedor, J. M., Fitzgerald, K. D., Ford, J. M., Frodl, T., Fu, C. H. Y., Fullerton, J. M., Gabel, M. C., Glahn, D. C., Roberts, G., Gogberashvili, T., Goikolea, J. M., Gotlib, I. H., Goya-Maldonado, R., Grabe, H. J., Green, M. J., Grevet, E. H., Groenewold, N. A., Grotegerd, D., Gruber, O., Gruner, P., Guerrero-Pedraza, A., Gur, R. E., Gur, R. C., Haar, S., Haarman, B. C. M., Haavik, J., Hahn, T., Hajek, T., Harrison, B. J., Harrison, N. A., Hartman, C. A., Whalley, H. C., Heslenfeld, D. J., Hibar, D. P., Hilland, E., Hirano, Y., Ho, T. C., Hoekstra, P. J., Hoekstra, L., Hohmann, S., Hong, L. E., Hoschl, C., Hovik, M. F., Howells, F. M., Nenadic, I., Jalbrzikowski, M., James, A. C., Janssen, J., Jaspers-Fayer, F., Xu, J., Jonassen, R., Karkashadze, G., King, J. A., Kircher, T., Kirschner, M., Koch, K., Kochunov, P., Kohls, G., Konrad, K., Kramer, B., Krug, A., Kuntsi, J., Kwon, J. S., Landen, M., Landro, N. I., Lazaro, L., Lebedeva, I. S., Leehr, E. J., Lera-Miguel, S., Lesch, K. -P., Lochner, C., Louza, M. R., Luna, B., Lundervold, A. J., Macmaster, F. P., Maglanoc, L. A., Malpas, C. B., Portella, M. J., Marsh, R., Martyn, F. M., Mataix-Cols, D., Mathalon, D. H., Mccarthy, H., Mcdonald, C., Mcphilemy, G., Meinert, S., Menchon, J. M., Minuzzi, L., Mitchell, P. B., Moreno, C., Morgado, P., Muratori, F., Murphy, C. M., Murphy, D., Mwangi, B., Nabulsi, L., Nakagawa, A., Nakamae, T., Namazova, L., Narayanaswamy, J., Jahanshad, N., Nguyen, D. D., Nicolau, R., O'Gorman Tuura, R. L., O'Hearn, K., Oosterlaan, J., Opel, N., Ophoff, R. A., Oranje, B., Garcia De La Foz, V. O., Overs, B. J., Paloyelis, Y., Pantelis, C., Parellada, M., Pauli, P., Pico-Perez, M., Picon, F. A., Piras, F., Plessen, K. J., Pomarol-Clotet, E., Preda, A., Puig, O., Quide, Y., Radua, J., Ramos-Quiroga, J. A., Rasser, P. E., Rauer, L., Reddy, J., Redlich, R., Reif, A., Reneman, L., Repple, J., Retico, A., Richarte, V., Richter, A., Rosa, P. G. P., Rubia, K. K., Hashimoto, R., Sacchet, M. D., Salvador, R., Santonja, J., Sarink, K., Sarro, S., Satterthwaite, T. D., Sawa, A., Schall, U., Schofield, P. R., Schrantee, A., Seitz, J., Serpa, M. H., Setien-Suero, E., Shaw, P., Shook, D., Silk, T. J., Sim, K., Simon, S., Simpson, H. B., Singh, A., Skoch, A., Skokauskas, N., Soares, J. C., Soreni, N., Soriano-Mas, C., Spalletta, G., Spaniel, F., Lawrie, S. M., Stern, E. R., Stewart, S. E., Takayanagi, Y., Temmingh, H. S., Tolin, D. F., Tomecek, D., Tordesillas-Gutierrez, D., Tosetti, M., Uhlmann, A., Van Amelsvoort, T., Van Der Wee, N. J. A., Van Der Werff, S. J. A., Van Haren, N. E. M., Van Wingen, G. A., Vance, A., Vazquez-Bourgon, J., Vecchio, D., Venkatasubramanian, G., Vieta, E., Vilarroya, O., Vives-Gilabert, Y., Voineskos, A. N., Volzke, H., Von Polier, G. G., Walton, E., Weickert, T. W., Weickert, C. S., Weideman, A. S., Wittfeld, K., Wolf, D. H., Wu, M. -J., Yang, T. T., Yang, K., Yoncheva, Y., Yun, J. -Y., Cheng, Y., Zanetti, M. V., Ziegler, G. C., Franke, B., Hoogman, M., Buitelaar, J. K., Van Rooij, D., Andreassen, O. A., Ching, C. R. K., Veltman, D. J., Schmaal, L., Stein, D. J., Van Den Heuvel, O. A., Turner, J. A., Van Erp, T. G. M., Pausova, Z., Thompson, P. M., Paus, T., Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Pediatric surgery, Radiology and nuclear medicine, Anatomy and neurosciences, Psychiatry, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Amsterdam Neuroscience - Neurodegeneration, Psychiatrie & Neuropsychologie, RS: MHeNs - R2 - Mental Health, MUMC+: MA Med Staf Spec Psychiatrie (9), Adult Psychiatry, ANS - Compulsivity, Impulsivity & Attention, General Paediatrics, ARD - Amsterdam Reproduction and Development, Radiology and Nuclear Medicine, APH - Personalized Medicine, ANS - Brain Imaging, ANS - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, University of Zurich, Clinical Cognitive Neuropsychiatry Research Program (CCNP), Clinical Neuropsychology, Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Child and Adolescent Psychiatry / Psychology, IBBA, and Cognitive Psychology
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Male ,Obsessive-Compulsive Disorder ,Bipolar Disorder ,Autism Spectrum Disorder ,Autism ,[SDV]Life Sciences [q-bio] ,Gene Expression ,cytology [Cerebral Cortex] ,Cohort Studies ,Fetal Development ,physiology [Gene Expression] ,2738 Psychiatry and Mental Health ,0302 clinical medicine ,diagnostic imaging [Cerebral Cortex] ,SCHIZOPHRENIA ,BRAIN ,Child ,Obsessive-compulsive disorder (OCD) ,Original Investigation ,Aged, 80 and over ,Cerebral Cortex ,0303 health sciences ,pathology [Depressive Disorder, Major] ,Principal Component Analysis ,Adolescent psychiatry ,10058 Department of Child and Adolescent Psychiatry ,Middle Aged ,diagnostic imaging [Obsessive-Compulsive Disorder] ,REGIONS ,Magnetic Resonance Imaging ,3. Good health ,FALSE DISCOVERY RATE ,Psychiatry and Mental health ,Autism spectrum disorder ,Schizophrenia ,growth & development [Cerebral Cortex] ,Child, Preschool ,Major depressive disorder ,diagnostic imaging [Schizophrenia] ,Esquizofrènia ,Female ,Psiquiatria infantil ,Psiquiatria de l'adolescència ,diagnostic imaging [Autism Spectrum Disorder] ,Adult ,medicine.medical_specialty ,Adolescent ,Human Development ,610 Medicine & health ,diagnostic imaging [Bipolar Disorder] ,pathology [Autism Spectrum Disorder] ,diagnostic imaging [Depressive Disorder, Major] ,03 medical and health sciences ,Young Adult ,All institutes and research themes of the Radboud University Medical Center ,Neuroimaging ,SDG 3 - Good Health and Well-being ,CEREBRAL-CORTEX ,Child psychiatry ,medicine ,Attention deficit hyperactivity disorder ,Humans ,Bipolar disorder ,ddc:610 ,Psychiatry ,pathology [Schizophrenia] ,030304 developmental biology ,Aged ,Depressive Disorder, Major ,Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7] ,business.industry ,DENDRITE ,Computational Biology ,Correction ,pathology [Attention Deficit Disorder with Hyperactivity] ,physiology [Fetal Development] ,medicine.disease ,PATHOLOGY ,pathology [Bipolar Disorder] ,pathology [Obsessive-Compulsive Disorder] ,10036 Medical Clinic ,Attention Deficit Disorder with Hyperactivity ,10054 Clinic for Psychiatry, Psychotherapy, and Psychosomatics ,Case-Control Studies ,DENSITY ,ORIGINS ,HIPPOCAMPUS ,diagnostic imaging [Attention Deficit Disorder with Hyperactivity] ,pathology [Cerebral Cortex] ,Autisme ,business ,Neuroscience ,030217 neurology & neurosurgery ,physiology [Human Development] - Abstract
[Importance] Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood., [Objective] To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia., [Design, Setting, and Participants] Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244., [Main Outcomes and Measures] Interregional profiles of group difference in cortical thickness between cases and controls., [Results] A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders., [Conclusions and Relevance] In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.
- Published
- 2021
4. deepbet: Fast brain extraction of T1-weighted MRI using Convolutional Neural Networks.
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Fisch L, Zumdick S, Barkhau C, Emden D, Ernsting J, Leenings R, Sarink K, Winter NR, Risse B, Dannlowski U, and Hahn T
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- Humans, Image Processing, Computer-Assisted methods, Databases, Factual, Neuroimaging methods, Magnetic Resonance Imaging methods, Brain diagnostic imaging, Deep Learning, Neural Networks, Computer
- Abstract
Background: Brain extraction in magnetic resonance imaging (MRI) data is an important segmentation step in many neuroimaging preprocessing pipelines. Image segmentation is one of the research fields in which deep learning had the biggest impact in recent years. Consequently, traditional brain extraction methods are now being replaced by deep learning-based methods., Method: Here, we used a unique dataset compilation comprising 7837 T1-weighted (T1w) MR images from 191 different OpenNeuro datasets in combination with advanced deep learning methods to build a fast, high-precision brain extraction tool called deepbet., Results: deepbet sets a novel state-of-the-art performance during cross-dataset validation with a median Dice score (DSC) of 99.0 on unseen datasets, outperforming the current best performing deep learning (DSC=97.9) and classic (DSC=96.5) methods. While current methods are more sensitive to outliers, deepbet achieves a Dice score of >97.4 across all 7837 images from 191 different datasets. This robustness was additionally tested in 5 external datasets, which included challenging clinical MR images. During visual exploration of each method's output which resulted in the lowest Dice score, major errors could be found for all of the tested tools except deepbet. Finally, deepbet uses a compute efficient variant of the UNet architecture, which accelerates brain extraction by a factor of ≈10 compared to current methods, enabling the processing of one image in ≈2 s on low level hardware., Conclusions: In conclusion, deepbet demonstrates superior performance and reliability in brain extraction across a wide range of T1w MR images of adults, outperforming existing top tools. Its high minimal Dice score and minimal objective errors, even in challenging conditions, validate deepbet as a highly dependable tool for accurate brain extraction. deepbet can be conveniently installed via "pip install deepbet" and is publicly accessible at https://github.com/wwu-mmll/deepbet., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)
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- 2024
- Full Text
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5. GateNet: A novel neural network architecture for automated flow cytometry gating.
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Fisch L, Heming M, Schulte-Mecklenbeck A, Gross CC, Zumdick S, Barkhau C, Emden D, Ernsting J, Leenings R, Sarink K, Winter NR, Dannlowski U, Wiendl H, Hörste GMZ, and Hahn T
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- Humans, Flow Cytometry methods, Neural Networks, Computer
- Abstract
Background and Objective: Flow cytometry is a widely used technique for identifying cell populations in patient-derived fluids, such as peripheral blood (PB) or cerebrospinal fluid (CSF). Despite its ubiquity in research and clinical practice, the process of gating, i.e., manually identifying cell types, is labor-intensive and error-prone. The objective of this study is to address this challenge by introducing GateNet, a neural network architecture designed for fully end-to-end automated gating without the need for correcting batch effects., Methods: For this study a unique dataset is used which comprises over 8,000,000 events from N = 127 PB and CSF samples which were manually labeled independently by four experts. Applying cross-validation, the classification performance of GateNet is compared to the human experts performance. Additionally, GateNet is applied to a publicly available dataset to evaluate generalization. The classification performance is measured using the F1 score., Results: GateNet achieves F1 scores ranging from 0.910 to 0.997 demonstrating human-level performance on samples unseen during training. In the publicly available dataset, GateNet confirms its generalization capabilities with an F1 score of 0.936. Importantly, we also show that GateNet only requires ≈10 samples to reach human-level performance. Finally, gating with GateNet only takes 15 microseconds per event utilizing graphics processing units (GPU)., Conclusions: GateNet enables fully end-to-end automated gating in flow cytometry, overcoming the labor-intensive and error-prone nature of manual adjustments. The neural network achieves human-level performance on unseen samples and generalizes well to diverse datasets. Notably, its data efficiency, requiring only ∼10 samples to reach human-level performance, positions GateNet as a widely applicable tool across various domains of flow cytometry., Competing Interests: Declaration of competing interest We, the undersigned, confirm that the manuscript represents our own work, is original and has not been copyrighted, published, submitted, or accepted for publication elsewhere. We further confirm that we all have fully read the manuscript and give consent to be co-authors of the manuscript., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
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- 2024
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6. A Systematic Evaluation of Machine Learning-Based Biomarkers for Major Depressive Disorder.
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Winter NR, Blanke J, Leenings R, Ernsting J, Fisch L, Sarink K, Barkhau C, Emden D, Thiel K, Flinkenflügel K, Winter A, Goltermann J, Meinert S, Dohm K, Repple J, Gruber M, Leehr EJ, Opel N, Grotegerd D, Redlich R, Nitsch R, Bauer J, Heindel W, Gross J, Risse B, Andlauer TFM, Forstner AJ, Nöthen MM, Rietschel M, Hofmann SG, Pfarr JK, Teutenberg L, Usemann P, Thomas-Odenthal F, Wroblewski A, Brosch K, Stein F, Jansen A, Jamalabadi H, Alexander N, Straube B, Nenadic I, Kircher T, Dannlowski U, and Hahn T
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- Humans, Female, Male, Diffusion Tensor Imaging, Cohort Studies, Reproducibility of Results, Magnetic Resonance Imaging, Biomarkers, Depressive Disorder, Major diagnostic imaging, Depressive Disorder, Major pathology
- Abstract
Importance: Biological psychiatry aims to understand mental disorders in terms of altered neurobiological pathways. However, for one of the most prevalent and disabling mental disorders, major depressive disorder (MDD), no informative biomarkers have been identified., Objective: To evaluate whether machine learning (ML) can identify a multivariate biomarker for MDD., Design, Setting, and Participants: This study used data from the Marburg-Münster Affective Disorders Cohort Study, a case-control clinical neuroimaging study. Patients with acute or lifetime MDD and healthy controls aged 18 to 65 years were recruited from primary care and the general population in Münster and Marburg, Germany, from September 11, 2014, to September 26, 2018. The Münster Neuroimaging Cohort (MNC) was used as an independent partial replication sample. Data were analyzed from April 2022 to June 2023., Exposure: Patients with MDD and healthy controls., Main Outcome and Measure: Diagnostic classification accuracy was quantified on an individual level using an extensive ML-based multivariate approach across a comprehensive range of neuroimaging modalities, including structural and functional magnetic resonance imaging and diffusion tensor imaging as well as a polygenic risk score for depression., Results: Of 1801 included participants, 1162 (64.5%) were female, and the mean (SD) age was 36.1 (13.1) years. There were a total of 856 patients with MDD (47.5%) and 945 healthy controls (52.5%). The MNC replication sample included 1198 individuals (362 with MDD [30.1%] and 836 healthy controls [69.9%]). Training and testing a total of 4 million ML models, mean (SD) accuracies for diagnostic classification ranged between 48.1% (3.6%) and 62.0% (4.8%). Integrating neuroimaging modalities and stratifying individuals based on age, sex, treatment, or remission status does not enhance model performance. Findings were replicated within study sites and also observed in structural magnetic resonance imaging within MNC. Under simulated conditions of perfect reliability, performance did not significantly improve. Analyzing model errors suggests that symptom severity could be a potential focus for identifying MDD subgroups., Conclusion and Relevance: Despite the improved predictive capability of multivariate compared with univariate neuroimaging markers, no informative individual-level MDD biomarker-even under extensive ML optimization in a large sample of diagnosed patients-could be identified.
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- 2024
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7. Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder.
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Hahn T, Winter NR, Ernsting J, Gruber M, Mauritz MJ, Fisch L, Leenings R, Sarink K, Blanke J, Holstein V, Emden D, Beisemann M, Opel N, Grotegerd D, Meinert S, Heindel W, Witt S, Rietschel M, Nöthen MM, Forstner AJ, Kircher T, Nenadic I, Jansen A, Müller-Myhsok B, Andlauer TFM, Walter M, van den Heuvel MP, Jamalabadi H, Dannlowski U, and Repple J
- Subjects
- Humans, Diffusion Tensor Imaging, Genetic Predisposition to Disease, Magnetic Resonance Imaging methods, Brain, Depressive Disorder, Major, Connectome
- Abstract
Many therapeutic interventions in psychiatry can be viewed as attempts to influence the brain's large-scale, dynamic network state transitions. Building on connectome-based graph analysis and control theory, Network Control Theory is emerging as a powerful tool to quantify network controllability-i.e., the influence of one brain region over others regarding dynamic network state transitions. If and how network controllability is related to mental health remains elusive. Here, from Diffusion Tensor Imaging data, we inferred structural connectivity and inferred calculated network controllability parameters to investigate their association with genetic and familial risk in patients diagnosed with major depressive disorder (MDD, n = 692) and healthy controls (n = 820). First, we establish that controllability measures differ between healthy controls and MDD patients while not varying with current symptom severity or remission status. Second, we show that controllability in MDD patients is associated with polygenic scores for MDD and psychiatric cross-disorder risk. Finally, we provide evidence that controllability varies with familial risk of MDD and bipolar disorder as well as with body mass index. In summary, we show that network controllability is related to genetic, individual, and familial risk in MDD patients. We discuss how these insights into individual variation of network controllability may inform mechanistic models of treatment response prediction and personalized intervention-design in mental health., (© 2023. The Author(s).)
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- 2023
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8. Towards a network control theory of electroconvulsive therapy response.
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Hahn T, Jamalabadi H, Nozari E, Winter NR, Ernsting J, Gruber M, Mauritz MJ, Grumbach P, Fisch L, Leenings R, Sarink K, Blanke J, Vennekate LK, Emden D, Opel N, Grotegerd D, Enneking V, Meinert S, Borgers T, Klug M, Leehr EJ, Dohm K, Heindel W, Gross J, Dannlowski U, Redlich R, and Repple J
- Abstract
Electroconvulsive Therapy (ECT) is arguably the most effective intervention for treatment-resistant depression. While large interindividual variability exists, a theory capable of explaining individual response to ECT remains elusive. To address this, we posit a quantitative, mechanistic framework of ECT response based on Network Control Theory (NCT). Then, we empirically test our approach and employ it to predict ECT treatment response. To this end, we derive a formal association between Postictal Suppression Index (PSI)-an ECT seizure quality index-and whole-brain modal and average controllability, NCT metrics based on white-matter brain network architecture, respectively. Exploiting the known association of ECT response and PSI, we then hypothesized an association between our controllability metrics and ECT response mediated by PSI. We formally tested this conjecture in N = 50 depressive patients undergoing ECT. We show that whole-brain controllability metrics based on pre-ECT structural connectome data predict ECT response in accordance with our hypotheses. In addition, we show the expected mediation effects via PSI. Importantly, our theoretically motivated metrics are at least on par with extensive machine learning models based on pre-ECT connectome data. In summary, we derived and tested a control-theoretic framework capable of predicting ECT response based on individual brain network architecture. It makes testable, quantitative predictions regarding individual therapeutic response, which are corroborated by strong empirical evidence. Our work might constitute a starting point for a comprehensive, quantitative theory of personalized ECT interventions rooted in control theory., (© The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences.)
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- 2023
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9. Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities.
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Winter NR, Leenings R, Ernsting J, Sarink K, Fisch L, Emden D, Blanke J, Goltermann J, Opel N, Barkhau C, Meinert S, Dohm K, Repple J, Mauritz M, Gruber M, Leehr EJ, Grotegerd D, Redlich R, Jansen A, Nenadic I, Nöthen MM, Forstner A, Rietschel M, Groß J, Bauer J, Heindel W, Andlauer T, Eickhoff SB, Kircher T, Dannlowski U, and Hahn T
- Subjects
- Adolescent, Adult, Aged, Biomarkers, Brain diagnostic imaging, Brain physiopathology, Case-Control Studies, Cohort Studies, Cross-Sectional Studies, Depression, Female, Humans, Magnetic Resonance Imaging methods, Middle Aged, Neuroimaging methods, Young Adult, Depressive Disorder, Major diagnostic imaging, Depressive Disorder, Major physiopathology
- Abstract
Importance: Identifying neurobiological differences between patients with major depressive disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta-analyses have raised concerns regarding the replicability and clinical relevance of brain alterations in depression., Objective: To quantify the upper bounds of univariate effect sizes, estimated predictive utility, and distributional dissimilarity of healthy individuals and those with depression across structural magnetic resonance imaging (MRI), diffusion-tensor imaging, and functional task-based as well as resting-state MRI, and to compare results with an MDD polygenic risk score (PRS) and environmental variables., Design, Setting, and Participants: This was a cross-sectional, case-control clinical neuroimaging study. Data were part of the Marburg-Münster Affective Disorders Cohort Study. Patients with depression and healthy controls were recruited from primary care and the general population in Münster and Marburg, Germany. Study recruitment was performed from September 11, 2014, to September 26, 2018. The sample comprised patients with acute and chronic MDD as well as healthy controls in the age range of 18 to 65 years. Data were analyzed from October 29, 2020, to April 7, 2022., Main Outcomes and Measures: Primary analyses included univariate partial effect size (η2), classification accuracy, and distributional overlapping coefficient for healthy individuals and those with depression across neuroimaging modalities, controlling for age, sex, and additional modality-specific confounding variables. Secondary analyses included patient subgroups for acute or chronic depressive status., Results: A total of 1809 individuals (861 patients [47.6%] and 948 controls [52.4%]) were included in the analysis (mean [SD] age, 35.6 [13.2] years; 1165 female patients [64.4%]). The upper bound of the effect sizes of the single univariate measures displaying the largest group difference ranged from partial η2 of 0.004 to 0.017, and distributions overlapped between 87% and 95%, with classification accuracies ranging between 54% and 56% across neuroimaging modalities. This pattern remained virtually unchanged when considering either only patients with acute or chronic depression. Differences were comparable with those found for PRS but substantially smaller than for environmental variables., Conclusions and Relevance: Results of this case-control study suggest that even for maximum univariate biological differences, deviations between patients with MDD and healthy controls were remarkably small, single-participant prediction was not possible, and similarity between study groups dominated. Biological psychiatry should facilitate meaningful outcome measures or predictive approaches to increase the potential for a personalization of the clinical practice.
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- 2022
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10. Longitudinal Structural Brain Changes in Bipolar Disorder: A Multicenter Neuroimaging Study of 1232 Individuals by the ENIGMA Bipolar Disorder Working Group.
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Abé C, Ching CRK, Liberg B, Lebedev AV, Agartz I, Akudjedu TN, Alda M, Alnæs D, Alonso-Lana S, Benedetti F, Berk M, Bøen E, Bonnin CDM, Breuer F, Brosch K, Brouwer RM, Canales-Rodríguez EJ, Cannon DM, Chye Y, Dahl A, Dandash O, Dannlowski U, Dohm K, Elvsåshagen T, Fisch L, Fullerton JM, Goikolea JM, Grotegerd D, Haatveit B, Hahn T, Hajek T, Heindel W, Ingvar M, Sim K, Kircher TTJ, Lenroot RK, Malt UF, McDonald C, McWhinney SR, Melle I, Meller T, Melloni EMT, Mitchell PB, Nabulsi L, Nenadić I, Opel N, Overs BJ, Panicalli F, Pfarr JK, Poletti S, Pomarol-Clotet E, Radua J, Repple J, Ringwald KG, Roberts G, Rodriguez-Cano E, Salvador R, Sarink K, Sarró S, Schmitt S, Stein F, Suo C, Thomopoulos SI, Tronchin G, Vieta E, Westlye LT, White AG, Yatham LN, Zak N, Thompson PM, Andreassen OA, and Landén M
- Subjects
- Adult, Brain diagnostic imaging, Brain pathology, Cerebral Cortical Thinning, Female, Humans, Magnetic Resonance Imaging, Male, Mania, Middle Aged, Multicenter Studies as Topic, Neuroimaging, Young Adult, Bipolar Disorder pathology
- Abstract
Background: Bipolar disorder (BD) is associated with cortical and subcortical structural brain abnormalities. It is unclear whether such alterations progressively change over time, and how this is related to the number of mood episodes. To address this question, we analyzed a large and diverse international sample with longitudinal magnetic resonance imaging (MRI) and clinical data to examine structural brain changes over time in BD., Methods: Longitudinal structural MRI and clinical data from the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) BD Working Group, including 307 patients with BD and 925 healthy control subjects, were collected from 14 sites worldwide. Male and female participants, aged 40 ± 17 years, underwent MRI at 2 time points. Cortical thickness, surface area, and subcortical volumes were estimated using FreeSurfer. Annualized change rates for each imaging phenotype were compared between patients with BD and healthy control subjects. Within patients, we related brain change rates to the number of mood episodes between time points and tested for effects of demographic and clinical variables., Results: Compared with healthy control subjects, patients with BD showed faster enlargement of ventricular volumes and slower thinning of the fusiform and parahippocampal cortex (0.18
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- 2022
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11. Seizure Prediction in Genetic Rat Models of Absence Epilepsy: Improved Performance through Multiple-Site Cortico-Thalamic Recordings Combined with Machine Learning.
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Budde B, Maksimenko V, Sarink K, Seidenbecher T, van Luijtelaar G, Hahn T, Pape HC, and Lüttjohann A
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- Animals, Disease Models, Animal, Electroencephalography methods, Machine Learning, Rats, Seizures, Epilepsy, Absence genetics
- Abstract
Seizure prediction is the grand challenge of epileptology. However, effort was devoted to prediction of focal seizures, while generalized seizures were regarded as stochastic events. Long-lasting local field potential (LFP) recordings containing several hundred generalized spike and wave discharges (SWDs), acquired at eight locations in the cortico-thalamic system of absence epileptic rats, were iteratively analyzed in all possible combinations of either two or three recording sites, by a wavelet-based algorithm, calculating the product of the wavelet-energy signaling increases in synchronicity. Sensitivity and false alarm rate of prediction were compared between various combinations, and wavelet spectra of true and false positive predictions were fed to a random forest machine learning algorithm to further differentiate between them. Wavelet analysis of intracortical and cortico-thalamic LFP traces showed a significantly smaller number of false alarms compared with intrathalamic combinations, while predictions based on recordings in Layers IV, V, and VI of the somatosensory-cortex significantly outreached all other combinations in terms of prediction sensitivity. In 24-h out-of-sample recordings of nine Genetic Absence Epilepsy Rats from Strasbourg (GAERS), containing diurnal fluctuations of SWD occurrence, classification of true and false positives by the trained random forest further reduced the false alarm rate by 71%, although at some trade-off between false alarms and sensitivity of prediction, as reflected in relatively low F1 score values. Results provide support for the cortical-focus theory of absence epilepsy and allow the conclusion that SWDs are predictable to some degree. The latter paves the way for the development of closed-loop SWD prediction-prevention systems. Suggestions for a possible translation to human data are outlined., (Copyright © 2022 Budde et al.)
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- 2022
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12. An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling.
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Hahn T, Ernsting J, Winter NR, Holstein V, Leenings R, Beisemann M, Fisch L, Sarink K, Emden D, Opel N, Redlich R, Repple J, Grotegerd D, Meinert S, Hirsch JG, Niendorf T, Endemann B, Bamberg F, Kröncke T, Bülow R, Völzke H, von Stackelberg O, Sowade RF, Umutlu L, Schmidt B, Caspers S, Kugel H, Kircher T, Risse B, Gaser C, Cole JH, Dannlowski U, and Berger K
- Abstract
The deviation between chronological age and age predicted from neuroimaging data has been identified as a sensitive risk marker of cross-disorder brain changes, growing into a cornerstone of biological age research. However, machine learning models underlying the field do not consider uncertainty, thereby confounding results with training data density and variability. Also, existing models are commonly based on homogeneous training sets, often not independently validated, and cannot be shared because of data protection issues. Here, we introduce an uncertainty-aware, shareable, and transparent Monte Carlo dropout composite quantile regression (MCCQR) Neural Network trained on N = 10,691 datasets from the German National Cohort. The MCCQR model provides robust, distribution-free uncertainty quantification in high-dimensional neuroimaging data, achieving lower error rates compared with existing models. In two examples, we demonstrate that it prevents spurious associations and increases power to detect deviant brain aging. We make the pretrained model and code publicly available.
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- 2022
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13. PHOTONAI-A Python API for rapid machine learning model development.
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Leenings R, Winter NR, Plagwitz L, Holstein V, Ernsting J, Sarink K, Fisch L, Steenweg J, Kleine-Vennekate L, Gebker J, Emden D, Grotegerd D, Opel N, Risse B, Jiang X, Dannlowski U, and Hahn T
- Subjects
- Algorithms, Datasets as Topic, Neural Networks, Computer, Workflow, Machine Learning, Software
- Abstract
PHOTONAI is a high-level Python API designed to simplify and accelerate machine learning model development. It functions as a unifying framework allowing the user to easily access and combine algorithms from different toolboxes into custom algorithm sequences. It is especially designed to support the iterative model development process and automates the repetitive training, hyperparameter optimization and evaluation tasks. Importantly, the workflow ensures unbiased performance estimates while still allowing the user to fully customize the machine learning analysis. PHOTONAI extends existing solutions with a novel pipeline implementation supporting more complex data streams, feature combinations, and algorithm selection. Metrics and results can be conveniently visualized using the PHOTONAI Explorer and predictive models are shareable in a standardized format for further external validation or application. A growing add-on ecosystem allows researchers to offer data modality specific algorithms to the community and enhance machine learning in the areas of the life sciences. Its practical utility is demonstrated on an exemplary medical machine learning problem, achieving a state-of-the-art solution in few lines of code. Source code is publicly available on Github, while examples and documentation can be found at www.photon-ai.com., Competing Interests: The authors have declared that no competing interests exist.
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- 2021
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14. From 'loose fitting' to high-performance, uncertainty-aware brain-age modelling.
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Hahn T, Fisch L, Ernsting J, Winter NR, Leenings R, Sarink K, Emden D, Kircher T, Berger K, and Dannlowski U
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- Humans, Uncertainty, Brain diagnostic imaging
- Published
- 2021
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15. Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders.
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Patel Y, Parker N, Shin J, Howard D, French L, Thomopoulos SI, Pozzi E, Abe Y, Abé C, Anticevic A, Alda M, Aleman A, Alloza C, Alonso-Lana S, Ameis SH, Anagnostou E, McIntosh AA, Arango C, Arnold PD, Asherson P, Assogna F, Auzias G, Ayesa-Arriola R, Bakker G, Banaj N, Banaschewski T, Bandeira CE, Baranov A, Bargalló N, Bau CHD, Baumeister S, Baune BT, Bellgrove MA, Benedetti F, Bertolino A, Boedhoe PSW, Boks M, Bollettini I, Del Mar Bonnin C, Borgers T, Borgwardt S, Brandeis D, Brennan BP, Bruggemann JM, Bülow R, Busatto GF, Calderoni S, Calhoun VD, Calvo R, Canales-Rodríguez EJ, Cannon DM, Carr VJ, Cascella N, Cercignani M, Chaim-Avancini TM, Christakou A, Coghill D, Conzelmann A, Crespo-Facorro B, Cubillo AI, Cullen KR, Cupertino RB, Daly E, Dannlowski U, Davey CG, Denys D, Deruelle C, Di Giorgio A, Dickie EW, Dima D, Dohm K, Ehrlich S, Ely BA, Erwin-Grabner T, Ethofer T, Fair DA, Fallgatter AJ, Faraone SV, Fatjó-Vilas M, Fedor JM, Fitzgerald KD, Ford JM, Frodl T, Fu CHY, Fullerton JM, Gabel MC, Glahn DC, Roberts G, Gogberashvili T, Goikolea JM, Gotlib IH, Goya-Maldonado R, Grabe HJ, Green MJ, Grevet EH, Groenewold NA, Grotegerd D, Gruber O, Gruner P, Guerrero-Pedraza A, Gur RE, Gur RC, Haar S, Haarman BCM, Haavik J, Hahn T, Hajek T, Harrison BJ, Harrison NA, Hartman CA, Whalley HC, Heslenfeld DJ, Hibar DP, Hilland E, Hirano Y, Ho TC, Hoekstra PJ, Hoekstra L, Hohmann S, Hong LE, Höschl C, Høvik MF, Howells FM, Nenadic I, Jalbrzikowski M, James AC, Janssen J, Jaspers-Fayer F, Xu J, Jonassen R, Karkashadze G, King JA, Kircher T, Kirschner M, Koch K, Kochunov P, Kohls G, Konrad K, Krämer B, Krug A, Kuntsi J, Kwon JS, Landén M, Landrø NI, Lazaro L, Lebedeva IS, Leehr EJ, Lera-Miguel S, Lesch KP, Lochner C, Louza MR, Luna B, Lundervold AJ, MacMaster FP, Maglanoc LA, Malpas CB, Portella MJ, Marsh R, Martyn FM, Mataix-Cols D, Mathalon DH, McCarthy H, McDonald C, McPhilemy G, Meinert S, Menchón JM, Minuzzi L, Mitchell PB, Moreno C, Morgado P, Muratori F, Murphy CM, Murphy D, Mwangi B, Nabulsi L, Nakagawa A, Nakamae T, Namazova L, Narayanaswamy J, Jahanshad N, Nguyen DD, Nicolau R, O'Gorman Tuura RL, O'Hearn K, Oosterlaan J, Opel N, Ophoff RA, Oranje B, García de la Foz VO, Overs BJ, Paloyelis Y, Pantelis C, Parellada M, Pauli P, Picó-Pérez M, Picon FA, Piras F, Piras F, Plessen KJ, Pomarol-Clotet E, Preda A, Puig O, Quidé Y, Radua J, Ramos-Quiroga JA, Rasser PE, Rauer L, Reddy J, Redlich R, Reif A, Reneman L, Repple J, Retico A, Richarte V, Richter A, Rosa PGP, Rubia KK, Hashimoto R, Sacchet MD, Salvador R, Santonja J, Sarink K, Sarró S, Satterthwaite TD, Sawa A, Schall U, Schofield PR, Schrantee A, Seitz J, Serpa MH, Setién-Suero E, Shaw P, Shook D, Silk TJ, Sim K, Simon S, Simpson HB, Singh A, Skoch A, Skokauskas N, Soares JC, Soreni N, Soriano-Mas C, Spalletta G, Spaniel F, Lawrie SM, Stern ER, Stewart SE, Takayanagi Y, Temmingh HS, Tolin DF, Tomecek D, Tordesillas-Gutiérrez D, Tosetti M, Uhlmann A, van Amelsvoort T, van der Wee NJA, van der Werff SJA, van Haren NEM, van Wingen GA, Vance A, Vázquez-Bourgon J, Vecchio D, Venkatasubramanian G, Vieta E, Vilarroya O, Vives-Gilabert Y, Voineskos AN, Völzke H, von Polier GG, Walton E, Weickert TW, Weickert CS, Weideman AS, Wittfeld K, Wolf DH, Wu MJ, Yang TT, Yang K, Yoncheva Y, Yun JY, Cheng Y, Zanetti MV, Ziegler GC, Franke B, Hoogman M, Buitelaar JK, van Rooij D, Andreassen OA, Ching CRK, Veltman DJ, Schmaal L, Stein DJ, van den Heuvel OA, Turner JA, van Erp TGM, Pausova Z, Thompson PM, and Paus T
- Subjects
- Adolescent, Adult, Aged, Aged, 80 and over, Attention Deficit Disorder with Hyperactivity diagnostic imaging, Autism Spectrum Disorder diagnostic imaging, Bipolar Disorder diagnostic imaging, Case-Control Studies, Cerebral Cortex cytology, Cerebral Cortex diagnostic imaging, Cerebral Cortex growth & development, Child, Child, Preschool, Cohort Studies, Computational Biology, Depressive Disorder, Major diagnostic imaging, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Obsessive-Compulsive Disorder diagnostic imaging, Principal Component Analysis, Schizophrenia diagnostic imaging, Young Adult, Attention Deficit Disorder with Hyperactivity pathology, Autism Spectrum Disorder pathology, Bipolar Disorder pathology, Cerebral Cortex pathology, Depressive Disorder, Major pathology, Fetal Development physiology, Gene Expression physiology, Human Development physiology, Obsessive-Compulsive Disorder pathology, Schizophrenia pathology
- Abstract
Importance: Large-scale neuroimaging studies have revealed group differences in cortical thickness across many psychiatric disorders. The underlying neurobiology behind these differences is not well understood., Objective: To determine neurobiologic correlates of group differences in cortical thickness between cases and controls in 6 disorders: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), and schizophrenia., Design, Setting, and Participants: Profiles of group differences in cortical thickness between cases and controls were generated using T1-weighted magnetic resonance images. Similarity between interregional profiles of cell-specific gene expression and those in the group differences in cortical thickness were investigated in each disorder. Next, principal component analysis was used to reveal a shared profile of group difference in thickness across the disorders. Analysis for gene coexpression, clustering, and enrichment for genes associated with these disorders were conducted. Data analysis was conducted between June and December 2019. The analysis included 145 cohorts across 6 psychiatric disorders drawn from the ENIGMA consortium. The numbers of cases and controls in each of the 6 disorders were as follows: ADHD: 1814 and 1602; ASD: 1748 and 1770; BD: 1547 and 3405; MDD: 2658 and 3572; OCD: 2266 and 2007; and schizophrenia: 2688 and 3244., Main Outcomes and Measures: Interregional profiles of group difference in cortical thickness between cases and controls., Results: A total of 12 721 cases and 15 600 controls, ranging from ages 2 to 89 years, were included in this study. Interregional profiles of group differences in cortical thickness for each of the 6 psychiatric disorders were associated with profiles of gene expression specific to pyramidal (CA1) cells, astrocytes (except for BD), and microglia (except for OCD); collectively, gene-expression profiles of the 3 cell types explain between 25% and 54% of variance in interregional profiles of group differences in cortical thickness. Principal component analysis revealed a shared profile of difference in cortical thickness across the 6 disorders (48% variance explained); interregional profile of this principal component 1 was associated with that of the pyramidal-cell gene expression (explaining 56% of interregional variation). Coexpression analyses of these genes revealed 2 clusters: (1) a prenatal cluster enriched with genes involved in neurodevelopmental (axon guidance) processes and (2) a postnatal cluster enriched with genes involved in synaptic activity and plasticity-related processes. These clusters were enriched with genes associated with all 6 psychiatric disorders., Conclusions and Relevance: In this study, shared neurobiologic processes were associated with differences in cortical thickness across multiple psychiatric disorders. These processes implicate a common role of prenatal development and postnatal functioning of the cerebral cortex in these disorders.
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- 2021
- Full Text
- View/download PDF
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