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1. Large-scale analysis of structural brain asymmetries in schizophrenia via the ENIGMA consortium

Author

Schijven, Dick, Postema, Merel C, Fukunaga, Masaki, Matsumoto, Junya, Miura, Kenichiro, de Zwarte, Sonja MC, van Haren, Neeltje EM, Cahn, Wiepke, Hulshoff Pol, Hilleke E, Kahn, René S, Ayesa-Arriola, Rosa, Ortiz-García de la Foz, Víctor, Tordesillas-Gutierrez, Diana, Vázquez-Bourgon, Javier, Crespo-Facorro, Benedicto, Alnæs, Dag, Dahl, Andreas, Westlye, Lars T, Agartz, Ingrid, Andreassen, Ole A, Jönsson, Erik G, Kochunov, Peter, Bruggemann, Jason M, Catts, Stanley V, Michie, Patricia T, Mowry, Bryan J, Quidé, Yann, Rasser, Paul E, Schall, Ulrich, Scott, Rodney J, Carr, Vaughan J, Green, Melissa J, Henskens, Frans A, Loughland, Carmel M, Pantelis, Christos, Weickert, Cynthia Shannon, Weickert, Thomas W, de Haan, Lieuwe, Brosch, Katharina, Pfarr, Julia-Katharina, Ringwald, Kai G, Stein, Frederike, Jansen, Andreas, Kircher, Tilo TJ, Nenadić, Igor, Krämer, Bernd, Gruber, Oliver, Satterthwaite, Theodore D, Bustillo, Juan, Mathalon, Daniel H, Preda, Adrian, Calhoun, Vince D, Ford, Judith M, Potkin, Steven G, Chen, Jingxu, Tan, Yunlong, Wang, Zhiren, Xiang, Hong, Fan, Fengmei, Bernardoni, Fabio, Ehrlich, Stefan, Fuentes-Claramonte, Paola, Garcia-Leon, Maria Angeles, Guerrero-Pedraza, Amalia, Salvador, Raymond, Sarró, Salvador, Pomarol-Clotet, Edith, Ciullo, Valentina, Piras, Fabrizio, Vecchio, Daniela, Banaj, Nerisa, Spalletta, Gianfranco, Michielse, Stijn, van Amelsvoort, Therese, Dickie, Erin W, Voineskos, Aristotle N, Sim, Kang, Ciufolini, Simone, Dazzan, Paola, Murray, Robin M, Kim, Woo-Sung, Chung, Young-Chul, Andreou, Christina, Schmidt, André, Borgwardt, Stefan, McIntosh, Andrew M, Whalley, Heather C, Lawrie, Stephen M, du Plessis, Stefan, Luckhoff, Hilmar K, Scheffler, Freda, Emsley, Robin, Grotegerd, Dominik, Lencer, Rebekka, Dannlowski, Udo, Edmond, Jesse T, Rootes-Murdy, Kelly, Stephen, Julia M, Mayer, Andrew R, and Antonucci, Linda A

Subjects
Cerebral Cortex, Male, Neurosciences, Brain, brain imaging, cortical, Magnetic Resonance Imaging, Functional Laterality, Brain Disorders, subcortical, Mental Health, Clinical Research, Case-Control Studies, Schizophrenia, Humans, Female, Mental health, asymmetry
Abstract

Left-right asymmetry is an important organizing feature of the healthy brain that may be altered in schizophrenia, but most studies have used relatively small samples and heterogeneous approaches, resulting in equivocal findings. We carried out the largest case-control study of structural brain asymmetries in schizophrenia, with MRI data from 5,080 affected individuals and 6,015 controls across 46 datasets, using a single image analysis protocol. Asymmetry indexes were calculated for global and regional cortical thickness, surface area, and subcortical volume measures. Differences of asymmetry were calculated between affected individuals and controls per dataset, and effect sizes were meta-analyzed across datasets. Small average case-control differences were observed for thickness asymmetries of the rostral anterior cingulate and the middle temporal gyrus, both driven by thinner left-hemispheric cortices in schizophrenia. Analyses of these asymmetries with respect to the use of antipsychotic medication and other clinical variables did not show any significant associations. Assessment of age- and sex-specific effects revealed a stronger average leftward asymmetry of pallidum volume between older cases and controls. Case-control differences in a multivariate context were assessed in a subset of the data (N = 2,029), which revealed that 7% of the variance across all structural asymmetries was explained by case-control status. Subtle case-control differences of brain macrostructural asymmetry may reflect differences at the molecular, cytoarchitectonic, or circuit levels that have functional relevance for the disorder. Reduced left middle temporal cortical thickness is consistent with altered left-hemisphere language network organization in schizophrenia.

Published
2023

2. Brain ageing in schizophrenia: evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium

Author

Constantinides, Constantinos, Han, Laura KM, Alloza, Clara, Antonucci, Linda Antonella, Arango, Celso, Ayesa-Arriola, Rosa, Banaj, Nerisa, Bertolino, Alessandro, Borgwardt, Stefan, Bruggemann, Jason, Bustillo, Juan, Bykhovski, Oleg, Calhoun, Vince, Carr, Vaughan, Catts, Stanley, Chung, Young-Chul, Crespo-Facorro, Benedicto, Díaz-Caneja, Covadonga M, Donohoe, Gary, Plessis, Stefan Du, Edmond, Jesse, Ehrlich, Stefan, Emsley, Robin, Eyler, Lisa T, Fuentes-Claramonte, Paola, Georgiadis, Foivos, Green, Melissa, Guerrero-Pedraza, Amalia, Ha, Minji, Hahn, Tim, Henskens, Frans A, Holleran, Laurena, Homan, Stephanie, Homan, Philipp, Jahanshad, Neda, Janssen, Joost, Ji, Ellen, Kaiser, Stefan, Kaleda, Vasily, Kim, Minah, Kim, Woo-Sung, Kirschner, Matthias, Kochunov, Peter, Kwak, Yoo Bin, Kwon, Jun Soo, Lebedeva, Irina, Liu, Jingyu, Mitchie, Patricia, Michielse, Stijn, Mothersill, David, Mowry, Bryan, de la Foz, Víctor Ortiz-García, Pantelis, Christos, Pergola, Giulio, Piras, Fabrizio, Pomarol-Clotet, Edith, Preda, Adrian, Quidé, Yann, Rasser, Paul E, Rootes-Murdy, Kelly, Salvador, Raymond, Sangiuliano, Marina, Sarró, Salvador, Schall, Ulrich, Schmidt, André, Scott, Rodney J, Selvaggi, Pierluigi, Sim, Kang, Skoch, Antonin, Spalletta, Gianfranco, Spaniel, Filip, Thomopoulos, Sophia I, Tomecek, David, Tomyshev, Alexander S, Tordesillas-Gutiérrez, Diana, van Amelsvoort, Therese, Vázquez-Bourgon, Javier, Vecchio, Daniela, Voineskos, Aristotle, Weickert, Cynthia S, Weickert, Thomas, Thompson, Paul M, Schmaal, Lianne, van Erp, Theo GM, Turner, Jessica, Cole, James H, ENIGMA Schizophrenia Consortium, Dima, Danai, and Walton, Esther

Subjects
Adult, Male, Aging, ENIGMA Schizophrenia Consortium, Adolescent, Medical and Health Sciences, Young Adult, Clinical Research, Behavioral and Social Science, Humans, Prospective Studies, Aged, Psychiatry, Psychology and Cognitive Sciences, Neurosciences, Brain, Middle Aged, Biological Sciences, Serious Mental Illness, Magnetic Resonance Imaging, Brain Disorders, Mental Health, Good Health and Well Being, Neurological, Schizophrenia, Biomedical Imaging, Female, Mental health
Abstract

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18-72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18-73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen's d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.

Published
2023

3. Brain ageing in schizophrenia:evidence from 26 international cohorts via the ENIGMA Schizophrenia consortium

Author

Constantinides, Constantinos, Han, Laura K. M., Alloza, Clara, Antonucci, Linda Antonella, Arango, Celso, Ayesa-Arriola, Rosa, Banaj, Nerisa, Bertolino, Alessandro, Borgwardt, Stefan, Bruggemann, Jason, Bustillo, Juan, Bykhovski, Oleg, Calhoun, Vince, Carr, Vaughan, Catts, Stanley, Chung, Young-Chul, Crespo-Facorro, Benedicto, Díaz-Caneja, Covadonga M., Donohoe, Gary, Plessis, Stefan Du, Edmond, Jesse, Ehrlich, Stefan, Emsley, Robin, Eyler, Lisa T., Fuentes-Claramonte, Paola, Georgiadis, Foivos, Green, Melissa, Guerrero-Pedraza, Amalia, Ha, Minji, Hahn, Tim, Henskens, Frans A., Holleran, Laurena, Homan, Stephanie, Homan, Philipp, Jahanshad, Neda, Janssen, Joost, Ji, Ellen, Kaiser, Stefan, Kaleda, Vasily, Kim, Minah, Kim, Woo-Sung, Kirschner, Matthias, Kochunov, Peter, Kwak, Yoo Bin, Kwon, Jun Soo, Lebedeva, Irina, Liu, Jingyu, Mitchie, Patricia, Michielse, Stijn, Mothersill, David, Mowry, Bryan, de la Foz, Víctor Ortiz-García, Pantelis, Christos, Pergola, Giulio, Piras, Fabrizio, Pomarol-Clotet, Edith, Preda, Adrian, Quidé, Yann, Rasser, Paul E., Rootes-Murdy, Kelly, Salvador, Raymond, Sangiuliano, Marina, Sarró, Salvador, Schall, Ulrich, Schmidt, André, Scott, Rodney J., Selvaggi, Pierluigi, Sim, Kang, Skoch, Antonin, Spalletta, Gianfranco, Spaniel, Filip, Thomopoulos, Sophia I., Tomecek, David, Tomyshev, Alexander S., Tordesillas-Gutiérrez, Diana, van Amelsvoort, Therese, Vázquez-Bourgon, Javier, Vecchio, Daniela, Voineskos, Aristotle, Weickert, Cynthia S., Weickert, Thomas, Thompson, Paul M., Schmaal, Lianne, van Erp, Theo G. M., Turner, Jessica, Cole, James H., Du Plessis, Stefan, Bin Kwak, Yoo, Dima, Danai, Walton, Esther, Psychiatry, APH - Mental Health, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Neurochirurgie, RS: MHeNs - R3 - Neuroscience, MUMC+: MA Med Staf Spec Psychiatrie (9), RS: MHeNs - R2 - Mental Health, and Psychiatry 3

Subjects
Cellular and Molecular Neuroscience, Psychiatry and Mental health, SDG 3 - Good Health and Well-being, Molecular Biology
Abstract

Schizophrenia (SZ) is associated with an increased risk of life-long cognitive impairments, age-related chronic disease, and premature mortality. We investigated evidence for advanced brain ageing in adult SZ patients, and whether this was associated with clinical characteristics in a prospective meta-analytic study conducted by the ENIGMA Schizophrenia Working Group. The study included data from 26 cohorts worldwide, with a total of 2803 SZ patients (mean age 34.2 years; range 18–72 years; 67% male) and 2598 healthy controls (mean age 33.8 years, range 18–73 years, 55% male). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes and total intracranial volume, all derived from T1-weighted brain magnetic resonance imaging (MRI) scans. Deviations from a healthy brain ageing trajectory were assessed by the difference between brain-predicted age and chronological age (brain-predicted age difference [brain-PAD]). On average, SZ patients showed a higher brain-PAD of +3.55 years (95% CI: 2.91, 4.19; I2 = 57.53%) compared to controls, after adjusting for age, sex and site (Cohen’s d = 0.48). Among SZ patients, brain-PAD was not associated with specific clinical characteristics (age of onset, duration of illness, symptom severity, or antipsychotic use and dose). This large-scale collaborative study suggests advanced structural brain ageing in SZ. Longitudinal studies of SZ and a range of mental and somatic health outcomes will help to further evaluate the clinical implications of increased brain-PAD and its ability to be influenced by interventions.

Published
2023

4. Chronic Stroke Sensorimotor Impairment Is Related to Smaller Hippocampal Volumes: An ENIGMA Analysis

Author

Zavaliangos-Petropulu, Artemis, Lo, Bethany, Donnelly, Miranda R, Schweighofer, Nicolas, Lohse, Keith, Jahanshad, Neda, Barisano, Giuseppe, Banaj, Nerisa, Borich, Michael R, Boyd, Lara A, Buetefisch, Cathrin M, Byblow, Winston D, Cassidy, Jessica M, Charalambous, Charalambos C, Conforto, Adriana B, DiCarlo, Julie A, Dula, Adrienne N, Egorova-Brumley, Natalia, Etherton, Mark R, Feng, Wuwei, Fercho, Kelene A, Geranmayeh, Fatemeh, Hanlon, Colleen A, Hayward, Kathryn S, Hordacre, Brenton, Kautz, Steven A, Khlif, Mohamed Salah, Kim, Hosung, Kuceyeski, Amy, Lin, David J, Liu, Jingchun, Lotze, Martin, MacIntosh, Bradley J, Margetis, John L, Mohamed, Feroze B, Piras, Fabrizio, Ramos-Murguialday, Ander, Revill, Kate P, Roberts, Pamela S, Robertson, Andrew D, Schambra, Heidi M, Seo, Na Jin, Shiroishi, Mark S, Stinear, Cathy M, Soekadar, Surjo R, Spalletta, Gianfranco, Taga, Myriam, Tang, Wai Kwong, Thielman, Gregory T, Vecchio, Daniela, Ward, Nick S, Westlye, Lars T, Werden, Emilio, Winstein, Carolee, Wittenberg, George F, Wolf, Steven L, Wong, Kristin A, Yu, Chunshui, Brodtmann, Amy, Cramer, Steven C, Thompson, Paul M, and Liew, Sook-Lei

Subjects
Male, Aging, hippocampus, sensorimotor impairment, Stroke Rehabilitation, Neurosciences, Recovery of Function, Cardiorespiratory Medicine and Haematology, Hippocampus, stroke, Brain Disorders, Upper Extremity, Stroke, Cross-Sectional Studies, Quality of Life, Humans, Female, MRI
Abstract

Background Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippocampus is vulnerable to poststroke secondary degeneration and is involved in sensorimotor behavior but has not been widely studied within the context of poststroke upper-limb sensorimotor impairment. We investigated associations between non-lesioned hippocampal volume and upper limb sensorimotor impairment in people with chronic stroke, hypothesizing that smaller ipsilesional hippocampal volumes would be associated with greater sensorimotor impairment. Methods and Results Cross-sectional T1-weighted magnetic resonance images of the brain were pooled from 357 participants with chronic stroke from 18 research cohorts of the ENIGMA (Enhancing NeuoImaging Genetics through Meta-Analysis) Stroke Recovery Working Group. Sensorimotor impairment was estimated from the FMA-UE (Fugl-Meyer Assessment of Upper Extremity). Robust mixed-effects linear models were used to test associations between poststroke sensorimotor impairment and hippocampal volumes (ipsilesional and contralesional separately; Bonferroni-corrected, P

Published
2022

5. The thalamus and its subnuclei—a gateway to obsessive-compulsive disorder

Author

Weeland, Cees J., Kasprzak, Selina, Joode, Niels T. de, Abe, Yoshinari, Alonso, Pino, Ameis, Stephanie H., Anticevic, Alan, Arnold, Paul D., Balachander, Srinivas, Banaj, Nerisa, Bargalló Alabart, Núria​, Wang, Zhen, Watanabe, Anri, Wolters, Lidewij H., Xu, Xiufeng, Yun, Je-Yeon, Zhao, Qing, White, Tonya, Thompson, Paul M., Stein, Dan J., Heuvel, Odile A. van den, Vriend, Chris, ENIGMA-OCD Working Group, Batistuzzo, Marcelo C., Benedetti, Francesco, Beucke, Jan C., Bollettini, Irene, Brecke, Vilde, Brem, Silvia, Cappi, Carolina, Cheng, Yuqi, Cho, Kang Ik K., Costa, Daniel L. C., Dallaspezia, Sara, Denys, Damiaan, Eng, Goi Khia, Ferreira, Sónia, Feusner, Jamie D., Fontaine, Martine, Fouche, Jean Paul, Grazioplene, Rachael G., Gruner, Patricia, He, Mengxin, Hirano, Yoshiyuki, Hoexter, Marcelo Q., Huyser, Chaim, Hu, Hao, Jaspers-Fayer, Fern, Kathmann, Norbert, Kaufmann, Christian, Kim, Minah, Koch, Kathrin, Bin Kwak, Yoo, Kwon, Jun Soo, Lazaro, Luisa, Li, Chiang-Shan R., Lochner, Christine, Marsh, Rachel, Martínez Zalacaín, Ignacio, Mataix Cols, David, Menchón, Jose M., Minnuzi, Luciano, Moreira, Pedro Silva, Morgado, Pedro, Nakagawa, Akiko, Nakamae, Takashi, Narayanaswamy, Janardhanan C., Nurmi, Erika L., Ortiz, Ana E., Pariente, Jose C., Piacentini, John, Picó Pérez, Maria, Piras, Fabrizio, Piras, Federica, Pittenger, Christopher, Reddy, Y. C. Janardhan, Rodriguez Manrique, Daniela, Sakai, Yuki, Shimizu, Eiji, Shivakumar, Venkataram, Simpson, Helen Blair, Soreni, Noam, Soriano Mas, Carles, Sousa, Nuno, Spalletta, Gianfranco, Stern, Emily R., Stevens, Michael C., Stewart, S. Evelyn, Szeszko, Philip R., Takahashi, Jumpei, Tanamatis, Tais, Tang, Jinsong, Thorsen, Anders Lillevik, Tolin, David, Werf, Ysbrand D. van der, Van Marle, Hein, Wingen, Guido A. van, Vecchio, Daniela, Venkatasubramanian, G., Walitza, Susanne, Wang, Jicai, Child and Adolescent Psychiatry / Psychology, Adult Psychiatry, ANS - Compulsivity, Impulsivity & Attention, Child Psychiatry, Child and Adolescent Psychiatry & Psychosocial Care, ANS - Cellular & Molecular Mechanisms, ANS - Brain Imaging, Anatomy and neurosciences, Amsterdam Neuroscience - Compulsivity, Impulsivity & Attention, Psychiatry, Amsterdam Neuroscience - Systems & Network Neuroscience, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Amsterdam Neuroscience - Neurodegeneration, Amsterdam Neuroscience - Brain Imaging, Weeland, C. J., Kasprzak, S., de Joode, N. T., Abe, Y., Alonso, P., Ameis, S. H., Anticevic, A., Arnold, P. D., Balachander, S., Banaj, N., Bargallo, N., Batistuzzo, M. C., Benedetti, F., Beucke, J. C., Bollettini, I., Brecke, V., Brem, S., Cappi, C., Cheng, Y., Cho, K. I. K., Costa, D. L. C., Dallaspezia, S., Denys, D., Eng, G. K., Ferreira, S., Feusner, J. D., Fontaine, M., Fouche, J. -P., Grazioplene, R. G., Gruner, P., He, M., Hirano, Y., Hoexter, M. Q., Huyser, C., Hu, H., Jaspers-Fayer, F., Kathmann, N., Kaufmann, C., Kim, M., Koch, K., Bin Kwak, Y., Kwon, J. S., Lazaro, L., Li, C. -S. R., Lochner, C., Marsh, R., Martinez-Zalacain, I., Mataix-Cols, D., Menchon, J. M., Minnuzi, L., Moreira, P. S., Morgado, P., Nakagawa, A., Nakamae, T., Narayanaswamy, J. C., Nurmi, E. L., Ortiz, A. E., Pariente, J. C., Piacentini, J., Pico-Perez, M., Piras, F., Pittenger, C., Reddy, Y. C. J., Rodriguez-Manrique, D., Sakai, Y., Shimizu, E., Shivakumar, V., Simpson, H. B., Soreni, N., Soriano-Mas, C., Sousa, N., Spalletta, G., Stern, E. R., Stevens, M. C., Stewart, S. E., Szeszko, P. R., Takahashi, J., Tanamatis, T., Tang, J., Thorsen, A. L., Tolin, D., van der Werf, Y. D., van Marle, H., van Wingen, G. A., Vecchio, D., Venkatasubramanian, G., Walitza, S., Wang, J., Wang, Z., Watanabe, A., Wolters, L. H., Xu, X., Yun, J. -Y., Zhao, Q., White, T., Thompson, P. M., Stein, D. J., van den Heuvel, O. A., and Vriend, C.

Subjects
Adult, Obsessive-Compulsive Disorder, medicine.medical_specialty, Adolescent, Population, Thalamus, Audiology, Cellular and Molecular Neuroscience, Group differences, SDG 3 - Good Health and Well-being, Obsessive compulsive, Intracranial volume, Humans, Medicine, Brain magnetic resonance imaging, In patient, Child, education, Biological Psychiatry, education.field_of_study, business.industry, Symptom severity, Brain, Tàlem (Anatomia), Magnetic Resonance Imaging, Compulsive behavior, Psychiatry and Mental health, Psychiatric disorders, Conducta compulsiva, business, Neuroscience
Abstract

Larger thalamic volume has been found in children with obsessive-compulsive disorder (OCD) and children with clinical-level symptoms within the general population. Particular thalamic subregions may drive these differences. The ENIGMA-OCD working group conducted mega- and meta-analyses to study thalamic subregional volume in OCD across the lifespan. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2649 OCD patients and 2774 healthy controls across 29 sites (50 datasets) were processed using the FreeSurfer built-in ThalamicNuclei pipeline to extract five thalamic subregions. Volume measures were harmonized for site effects using ComBat before running separate multiple linear regression models for children, adolescents, and adults to estimate volumetric group differences. All analyses were pre-registered ( https://osf.io/73dvy ) and adjusted for age, sex and intracranial volume. Unmedicated pediatric OCD patients (, Translational Psychiatry, 12 (1), ISSN:2158-3188

Published
2022

6. Smaller spared subcortical nuclei are associated with worse post-stroke sensorimotor outcomes in 28 cohorts worldwide

Author

Liew, Sook-Lei, Zavaliangos-Petropulu, Artemis, Schweighofer, Nicolas, Jahanshad, Neda, Lang, Catherine E, Lohse, Keith R, Banaj, Nerisa, Barisano, Giuseppe, Baugh, Lee A, Bhattacharya, Anup K, Bigjahan, Bavrina, Borich, Michael R, Boyd, Lara A, Brodtmann, Amy, Buetefisch, Cathrin M, Byblow, Winston D, Cassidy, Jessica M, Charalambous, Charalambos C, Ciullo, Valentina, Conforto, Adriana B, Craddock, Richard C, Dula, Adrienne N, Egorova, Natalia, Feng, Wuwei, Fercho, Kelene A, Gregory, Chris M, Hanlon, Colleen A, Hayward, Kathryn S, Holguin, Jess A, Hordacre, Brenton, Hwang, Darryl H, Kautz, Steven A, Khlif, Mohamed Salah, Kim, Bokkyu, Kim, Hosung, Kuceyeski, Amy, Lo, Bethany, Liu, Jingchun, Lin, David, Lotze, Martin, MacIntosh, Bradley J, Margetis, John L, Mohamed, Feroze B, Nordvik, Jan Egil, Petoe, Matthew A, Piras, Fabrizio, Raju, Sharmila, Ramos-Murguialday, Ander, Revill, Kate P, Roberts, Pamela, Robertson, Andrew D, Schambra, Heidi M, Seo, Na Jin, Shiroishi, Mark S, Soekadar, Surjo R, Spalletta, Gianfranco, Stinear, Cathy M, Suri, Anisha, Tang, Wai Kwong, Thielman, Gregory T, Thijs, Vincent N, Vecchio, Daniela, Ward, Nick S, Westlye, Lars T, Winstein, Carolee J, Wittenberg, George F, Wong, Kristin A, Yu, Chunshui, Wolf, Steven L, Cramer, Steven C, Thompson, Paul M, Baugh, Lee, Gallaguet, Adrià Bermudo, Bhattacharya, Anup, Borich, Michael, Boyd, Lara, Brown, Truman, Buetefisch, Cathrin, Byblow, Winston, Cassidy, Jessica, Charalambous, Charalambos, Cloutier, Alison, Cole, James, Conforto, Adriana, Craddock, Richard, Cramer, Steven, Aguayo, Rosalia Dacosta, DiCarlo, Julie, Dimyan, Michael, Domin, Martin, Donnellly, Miranda, Dula, Adrienne, Edwardson, Matthew, Ermer, Elsa, Etherton, Mark, Fercho, Kelene, Ferris, Jennifer, Geranmayeh, Fatemeh, Gregory, Chris, Hadidchi, Shahram, Hanlon, Colleen, Hayes, Leticia, Hayward, Kathryn, Holguin, Jess, Hwang, Darryl, Jamison, Keith, Juliano, Julia, Kautz, Steven, Lang, Catherine, Lee, Jenny, Lohse, Keith, MacIntosh, Bradley, Margetis, John, Margulies, Daniel, Mataro, Maria, McGregor, Keith, Mohamed, Feroze, Nordvik, Jan, Olafson, Emily, Perera-LLuna, Alexandre, Petoe, Matthew, Phillips, Aaron, Revill, Kate, Robertson, Andrew, Rondina, Jane, Rost, Natalia, Sanossian, Nerses, Schambra, Heidi, Schranz, Christian, Sepehrband, Farshid, Shiroishi, Mark, Simon, Julia, Soekadar, Surjo, Srivastava, Shraddha, Stewart, Jill, Stinear, Cathy, Taga, Myriam, Thielman, Gregory, Thijs, Vincent, Thomopoulos, Sophia, Thompson, Paul, Warach, Steven, Ward, Nick, Werden, Emilio, Westlye, Lars, Wiest, Roland, Winstein, Carolee, Wittenberg, George, Wolf, Steven, Wong, Kristin, Liew, Sook-Lei, Zavaliangos-Petropulu, Artemis, Schweighofer, Nicolas, Jahanshad, Neda, Hordacre, Brenton, and Thompson, Paul M

Subjects
medicine.medical_specialty, Thalamus, Grey matter, Nucleus accumbens, rehabilitation, Physical medicine and rehabilitation, sensorimotor behaviour, Medicine, Mri brain, Stroke survivor, Chronic stroke, Stroke, subcortical volumes, business.industry, AcademicSubjects/SCI01870, Putamen, General Engineering, medicine.disease, stroke, Behavioral data, medicine.anatomical_structure, Ventricle, Post stroke, Original Article, AcademicSubjects/MED00310, business, MRI
Abstract

Up to two-thirds of stroke survivors experience persistent sensorimotor impairments. Recovery relies on the integrity of spared brain areas to compensate for damaged tissue. Deep grey matter structures play a critical role in the control and regulation of sensorimotor circuits. The goal of this work is to identify associations between volumes of spared subcortical nuclei and sensorimotor behaviour at different timepoints after stroke. We pooled high-resolution T1-weighted MRI brain scans and behavioural data in 828 individuals with unilateral stroke from 28 cohorts worldwide. Cross-sectional analyses using linear mixed-effects models related post-stroke sensorimotor behaviour to non-lesioned subcortical volumes (Bonferroni-corrected, P, Liew et al. report the first large-scale examination using high-resolution neuroimaging of subcortical nuclei and sensorimotor behaviour in 828 stroke patients from 28 cohorts worldwide. They discovered novel associations between post-stroke sensorimotor behaviour and specific subcortical nuclei, providing new insight for stroke rehabilitation., Graphical Abstract Graphical Abstract

Published
2022

7. Virtual Histology of Cortical Thickness and Shared Neurobiology in 6 Psychiatric Disorders

Author

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

Subjects
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

8. Smaller spared subcortical nuclei are associated with worse post-stroke sensorimotor outcomes in 28 cohorts worldwide

Author

Liew, Sook-Lei, Zavaliangos-Petropulu, Artemis, Schweighofer, Nicolas, Jahanshad, Neda, Lang, Catherine E, Lohse, Keith R, Banaj, Nerisa, Barisano, Giuseppe, Baugh, Lee A, Bhattacharya, Anup K, Bigjahan, Bavrina, Borich, Michael R, Boyd, Lara A, Brodtmann, Amy, Buetefisch, Cathrin M, Byblow, Winston D, Cassidy, Jessica M, Charalambous, Charalambos C, Ciullo, Valentina, Conforto, Adriana B, Craddock, Richard C, Dula, Adrienne N, Egorova, Natalia, Feng, Wuwei, Fercho, Kelene A, Gregory, Chris M, Hanlon, Colleen A, Hayward, Kathryn S, Holguin, Jess A, Hordacre, Brenton, Hwang, Darryl H, Kautz, Steven A, Khlif, Mohamed Salah, Kim, Bokkyu, Kim, Hosung, Kuceyeski, Amy, Lo, Bethany, Liu, Jingchun, Lin, David, Lotze, Martin, MacIntosh, Bradley J, Margetis, John L, Mohamed, Feroze B, Nordvik, Jan Egil, Petoe, Matthew A, Piras, Fabrizio, Raju, Sharmila, Ramos-Murguialday, Ander, Revill, Kate P, Roberts, Pamela, Robertson, Andrew D, Schambra, Heidi M, Seo, Na Jin, Shiroishi, Mark S, Soekadar, Surjo R, Spalletta, Gianfranco, Stinear, Cathy M, Suri, Anisha, Tang, Wai Kwong, Thielman, Gregory T, Thijs, Vincent N, Vecchio, Daniela, Ward, Nick S, Westlye, Lars T, Winstein, Carolee J, Wittenberg, George F, Wong, Kristin A, Yu, Chunshui, Wolf, Steven L, Cramer, Steven C, Thompson, Paul M, and ENIGMA Stroke Recovery Working Group

Subjects
subcortical volumes, Stroke, sensorimotor behaviour, Rehabilitation, Neurosciences, 2.1 Biological and endogenous factors, ENIGMA Stroke Recovery Working Group, Aetiology, stroke, MRI, rehabilitation, Brain Disorders
Abstract

Up to two-thirds of stroke survivors experience persistent sensorimotor impairments. Recovery relies on the integrity of spared brain areas to compensate for damaged tissue. Deep grey matter structures play a critical role in the control and regulation of sensorimotor circuits. The goal of this work is to identify associations between volumes of spared subcortical nuclei and sensorimotor behaviour at different timepoints after stroke. We pooled high-resolution T1-weighted MRI brain scans and behavioural data in 828 individuals with unilateral stroke from 28 cohorts worldwide. Cross-sectional analyses using linear mixed-effects models related post-stroke sensorimotor behaviour to non-lesioned subcortical volumes (Bonferroni-corrected, P

Published
2021

9. Supplemental_material – Supplemental material for Transcranial Direct Current Stimulation and Cognition in Neuropsychiatric Disorders: Systematic Review of the Evidence and Future Directions

Author

Ciullo, Valentina, Spalletta, Gianfranco, Caltagirone, Carlo, Nerisa Banaj, Vecchio, Daniela, Piras, Fabrizio, and Piras, Federica

Subjects
FOS: Clinical medicine, 110904 Neurology and Neuromuscular Diseases, Neuroscience
Abstract

Supplemental material, Supplemental_material for Transcranial Direct Current Stimulation and Cognition in Neuropsychiatric Disorders: Systematic Review of the Evidence and Future Directions by Valentina Ciullo, Gianfranco Spalletta, Carlo Caltagirone, Nerisa Banaj, Daniela Vecchio, Fabrizio Piras and Federica Piras in The Neuroscientist

Published
2020
Full Text
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11. Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

Author

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

Subjects
Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, Neurons and Cognition (q-bio.NC)
Abstract

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

Published
2018
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12. Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

Author

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

Subjects
deep learning, subcortical shape analysis, quality checking, deep learning, quality checking, subcortical shape analysis
Published
2018

13. Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing

Author

Ciullo, Valentina, Vecchio, Daniela, Gili, Tommaso, and Gianfranco Spalletta and Federica Piras

Subjects
schizophrenia, predictive timing, spatio-temporal predictive performance, structural connectivity, diffusion tensor imaging, complex network theory, insula, predictive timing, spatio-temporal predictive performance, structural connectivity, diffusion tensor imaging, complex network theory, insula, schizophrenia
Published
2018

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

Author

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

Subjects
FOS: Biological sciences, Quantitative Biology - Quantitative Methods, Quantitative Methods (q-bio.QM)
Abstract

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

Published
2017
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15. Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging

Author

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

Subjects
0301 basic medicine, Recall, Computer science, business.industry, shape analysis, Machine learning, computer.software_genre, Article, Support vector machine, 03 medical and health sciences, machine learning, 030104 developmental biology, 0302 clinical medicine, Neuroimaging, Medical imaging, Alternating decision tree, Radiologi och bildbehandling, Artificial intelligence, quality control, business, computer, 030217 neurology & neurosurgery, Radiology, Nuclear Medicine and Medical Imaging, Shape analysis (digital geometry)
Abstract

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

Published
2017

16. Eradication of Multidrug-Resistant Pseudomonas Biofilm with Pulsed Electric Fields

Author

Khan, Saiqa I., Blumrosen, Gaddi, Vecchio, Daniela, Golberg, Alexander, McCormack, Michael C., Yarmush, Martin L., Hamblin, Michael R., and Austen, William G.

Subjects
Disinfection, Microbial Viability, Electricity, Equipment and Supplies, Biofilms, Drug Resistance, Multiple, Bacterial, Luminescent Measurements, Optical Imaging, Pseudomonas aeruginosa, Microscopy, Electron, Scanning, Polypropylenes, Article
Abstract

Biofilm formation is a significant problem, accounting for over eighty percent of microbial infections in the body. Biofilm eradication is problematic due to increased resistance to antibiotics and antimicrobials as compared to planktonic cells. The purpose of this study was to investigate the effect of Pulsed Electric Fields (PEF) on biofilm-infected mesh. Prolene mesh was infected with bioluminescent Pseudomonas aeruginosa and treated with PEF using a concentric electrode system to derive, in a single experiment, the critical electric field strength needed to kill bacteria. The effect of the electric field strength and the number of pulses (with a fixed pulse length duration and frequency) on bacterial eradication was investigated. For all experiments, biofilm formation and disruption were confirmed with bioluminescent imaging and Scanning Electron Microscopy (SEM). Computation and statistical methods were used to analyze treatment efficiency and to compare it to existing theoretical models. In all experiments 1500 V are applied through a central electrode, with pulse duration of 50 μs, and pulse delivery frequency of 2 Hz. We found that the critical electric field strength (Ecr) needed to eradicate 100-80% of bacteria in the treated area was 121 ± 14 V/mm when 300 pulses were applied, and 235 ± 6.1 V/mm when 150 pulses were applied. The area at which 100-80% of bacteria were eradicated was 50.5 ± 9.9 mm(2) for 300 pulses, and 13.4 ± 0.65 mm(2) for 150 pulses. 80% threshold eradication was not achieved with 100 pulses. The results indicate that increased efficacy of treatment is due to increased number of pulses delivered. In addition, we that showed the bacterial death rate as a function of the electrical field follows the statistical Weibull model for 150 and 300 pulses. We hypothesize that in the clinical setting, combining systemic antibacterial therapy with PEF will yield a synergistic effect leading to improved eradication of mesh infections.

Published
2015

17. Eradication of multidrug-resistant A. baumannii in burn wounds by antiseptic pulsed electric field

Author

Golberg, Alexander, Broelsch, G. Felix, Vecchio, Daniela, Khan, Saiqa, Hamblin, Michael R., Austen, William G., Sheridan, Robert L., and Yarmush, Martin L.

Subjects
Article
Abstract

Emerging bacterial resistance to multiple drugs is an increasing problem in burn wound management. New non-pharmacologic interventions are needed for burn wound disinfection. Here we report on a novel physical method for disinfection: antiseptic pulsed electric field (PEF) applied externally to the infected burns. In a mice model, we show that PEF can reduce the load of multidrug resistant Acinetobacter baumannii present in a full thickness burn wound by more than four orders of magnitude, as detected by bioluminescence imaging. Furthermore, using a finite element numerical model, we demonstrate that PEF provides non-thermal, homogeneous, full thickness treatment for the burn wound, thus, overcoming the limitation of treatment depth for many topical antimicrobials. These modeling tools and our in vivo results will be extremely useful for further translation of the PEF technology to the clinical setting, as they provide the essential elements for planning of electrode design and treatment protocol.

Published
2014

18. Antimicrobial photodynamic therapy with RLP068 kills methicillin-resistant Staphylococcus aureus and improves wound healing in a mouse model of infected skin abrasion

Author

Vecchio, Daniela, Dai, Tianhong, Huang, Liyi, Fantetti, Lia, Roncucci, Gabrio, and Hamblin, Michael R.

Subjects
Methicillin-Resistant Staphylococcus aureus, Mice, Inbred BALB C, Wound Healing, Indoles, Photosensitizing Agents, Article, Disease Models, Animal, Mice, Anti-Infective Agents, Photochemotherapy, Luminescent Measurements, Organometallic Compounds, Animals, Female, Staphylococcal Skin Infections, Tolonium Chloride
Abstract

Photodynamic therapy (PDT) is an alternative treatment for infections that can kill drug resistant bacteria without damaging host-tissue. In this study we used bioluminescent methicillin-resistant Staphylococcus aureus, in a mouse skin abrasion model, to investigate the effect of PDT on bacterial inactivation and wound healing. RLP068/Cl, a tetracationic Zn(II)phthalocyanine derivative and toluidine blue (TBO) were used. The light-dose response of PDT to kill bacteria in vivo and the possible recurrence in the days post-treatment were monitored by real-time bioluminescence imaging, and wound healing by digital photography. The results showed PDT with RLP068/Cl (but not TBO) was able to kill bacteria, to inhibit bacterial re-growth after the treatment and to significantly accelerate the wound healing process (© 2013 WILEY-VCH Verlag GmbHCo. KGaA, Weinheim).

Published
2012

25. La consommation d’alcool pendant la période d’allaitement

Author

Del Vecchio, Daniela

Subjects
alcool, allaitement, sage-femme, représentations, partenaire, risques
Abstract

Ce travail de Bachelor s’intéresse aux représentations et aux comportements des femmes, en lien avec la gestion des risques liés à la consommation d’alcool pendant la période d’allaitement. Il aborde une problématique de santé publique encore peu explorée en Suisse. Objectifs Le but de ce travail est d’explorer les représentations, ainsi que les difficultés rencontrées par les femmes, dans la mise en place de changements en lien avec la gestion des risques liés à la consommation d’alcool. Cette étude s’intéresse également à l’influence du partenaire dans les choix adoptés par les femmes, ainsi qu’à l’implication de la sage-femme, dans le soutien de la mise en place de stratégies visant la réduction des risques. Méthodologie Ce travail répond à une collaboration avec l‘équipe de recherche ORACLE, qui est une étude qualitative financée par le Fonds National Scientifique. Il inclut l’analyse thématique de trois entretiens semi-dirigés menés auprès de trois femmes allaitantes. Résultats et conclusions : La norme sociale de la “bonne mère” régit les comportements et les choix des femmes en matière d’alcool et d’allaitement. Dans une société fortement influencée par la peur des risques, les connaissances profanes prévalent sur le savoir scientifique. Les partenaires et les professionnels de la santé paraissent peu impliqués dans le terrain de la gestion des risques liés à la consommation d’alcool. Leur attitude ne favorise pas le dépassement des difficultés rencontrées par les femmes.

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