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2. ESTIGMA E ACEITAÇÃO: EXPLORANDO AS DIMENSÕES SOCIAIS DO PÉ TORTO CONGÊNITO NA INFÂNCIA

Author

Oliva, Amanda de Oliveira, primary, Rosa, Pedro Cruz Garcia, additional, Furlan, Jullia Baccar, additional, Bueno, Nayara Souza, additional, Alssuffi, José Eduardo Azero, additional, Figueiredo, Ellen Victória, additional, Castro, Gabrielly Garcia de, additional, Becegatto, Tatiana Ferrari, additional, Silva, Ana Luara Barcelos, additional, Cavalcante, Flavia Roberta Posterli, additional, Criado, Luiza Bottaro, additional, Sampar, Augusto César Aparecido Vitoratto, additional, Soares, Gustavo Mayo, additional, Dall'Agnol, Vitor Toribio, additional, and Netto, Mauricio Lopes da Silva, additional

Published
2024
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3. Joining by electromagnetic forming

Author

Geier, Martin, primary, Paese, Evandro, additional, dos Santos, Tiago, additional, Homrich, Roberto Petry, additional, Rosa, Pedro, additional, and Rossi, Rodrigo, additional

Published
2024
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4. The Impact of a Curriculum for Resilience Promotion in Deaf Children and Adolescents

Author

Freitas, Eunice, Simões, Celeste, Mineiro, Ana, Rosa, Pedro J., and Santos, Anabela C.

Abstract

Resilience is an interactive process involving internal skills that should be promoted, especially in the early stages of development. This study aims to adapt and implement two themes from the European Curriculum for Resilience Promotion -- RESCUR, namely, 'Developing Communication Skills' and 'Establishing and Maintaining Healthy Relationships', for deaf and hard-of-hearing (DHH) students. The study included 37 children and adolescents from three Portuguese regions and its impact was evaluated through the perspectives of the students, their guardians and their teachers. Each 90-minute session was implemented weekly. The sessions followed the RESCUR curriculum structure with necessary adaptations to the mindfulness activities, stories, role-play and worksheets. The mean scores increased from pre- to post-intervention assessment on all instruments, namely, KIDSCREEN-10 (children/adolescents), KIDSCREEN-10 (guardians) and CYRM-28 (teachers). The implementation of adapted curricula promoting resilience seems to be beneficial to DHH children, allowing the development of specific resilience-associated skills, and thus enhancing health, well-being and quality of life.

Published
2023
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11. The Human Affectome

Author

Schiller, Daniela, Yu, Alessandra N.C., Alia-Klein, Nelly, Becker, Susanne, Cromwell, Howard C., Dolcos, Florin, Eslinger, Paul J., Frewen, Paul, Kemp, Andrew H., Pace-Schott, Edward F., Raber, Jacob, Silton, Rebecca L., Stefanova, Elka, Williams, Justin H.G., Abe, Nobuhito, Aghajani, Moji, Albrecht, Franziska, Alexander, Rebecca, Anders, Silke, Aragón, Oriana R., Arias, Juan A., Arzy, Shahar, Aue, Tatjana, Baez, Sandra, Balconi, Michela, Ballarini, Tommaso, Bannister, Scott, Banta, Marlissa C., Barrett, Karen Caplovitz, Belzung, Catherine, Bensafi, Moustafa, Booij, Linda, Bookwala, Jamila, Boulanger-Bertolus, Julie, Boutros, Sydney Weber, Bräscher, Anne-Kathrin, Bruno, Antonio, Busatto, Geraldo, Bylsma, Lauren M., Caldwell-Harris, Catherine, Chan, Raymond C.K., Cherbuin, Nicolas, Chiarella, Julian, Cipresso, Pietro, Critchley, Hugo, Croote, Denise E., Demaree, Heath A., Denson, Thomas F., Depue, Brendan, Derntl, Birgit, Dickson, Joanne M., Dolcos, Sanda, Drach-Zahavy, Anat, Dubljević, Olga, Eerola, Tuomas, Ellingsen, Dan-Mikael, Fairfield, Beth, Ferdenzi, Camille, Friedman, Bruce H., Fu, Cynthia H.Y., Gatt, Justine M., de Gelder, Beatrice, Gendolla, Guido H.E., Gilam, Gadi, Goldblatt, Hadass, Gooding, Anne Elizabeth Kotynski, Gosseries, Olivia, Hamm, Alfons O., Hanson, Jamie L., Hendler, Talma, Herbert, Cornelia, Hofmann, Stefan G., Ibanez, Agustin, Joffily, Mateus, Jovanovic, Tanja, Kahrilas, Ian J., Kangas, Maria, Katsumi, Yuta, Kensinger, Elizabeth, Kirby, Lauren A.J., Koncz, Rebecca, Koster, Ernst H.W., Kozlowska, Kasia, Krach, Sören, Kret, Mariska E., Krippl, Martin, Kusi-Mensah, Kwabena, Ladouceur, Cecile D., Laureys, Steven, Lawrence, Alistair, Li, Chiang-shan R., Liddell, Belinda J., Lidhar, Navdeep K., Lowry, Christopher A., Magee, Kelsey, Marin, Marie-France, Mariotti, Veronica, Martin, Loren J., Marusak, Hilary A., Mayer, Annalina V., Merner, Amanda R., Minnier, Jessica, Moll, Jorge, Morrison, Robert G., Moore, Matthew, Mouly, Anne-Marie, Mueller, Sven C., Mühlberger, Andreas, Murphy, Nora A., Muscatello, Maria Rosaria Anna, Musser, Erica D., Newton, Tamara L., Noll-Hussong, Michael, Norrholm, Seth Davin, Northoff, Georg, Nusslock, Robin, Okon-Singer, Hadas, Olino, Thomas M., Ortner, Catherine, Owolabi, Mayowa, Padulo, Caterina, Palermo, Romina, Palumbo, Rocco, Palumbo, Sara, Papadelis, Christos, Pegna, Alan J., Pellegrini, Silvia, Peltonen, Kirsi, Penninx, Brenda W.J.H., Pietrini, Pietro, Pinna, Graziano, Lobo, Rosario Pintos, Polnaszek, Kelly L., Polyakova, Maryna, Rabinak, Christine, Helene Richter, S., Richter, Thalia, Riva, Giuseppe, Rizzo, Amelia, Robinson, Jennifer L., Rosa, Pedro, Sachdev, Perminder S., Sato, Wataru, Schroeter, Matthias L., Schweizer, Susanne, Shiban, Youssef, Siddharthan, Advaith, Siedlecka, Ewa, Smith, Robert C., Soreq, Hermona, Spangler, Derek P., Stern, Emily R., Styliadis, Charis, Sullivan, Gavin B., Swain, James E., Urben, Sébastien, Van den Stock, Jan, vander Kooij, Michael A., van Overveld, Mark, Van Rheenen, Tamsyn E., VanElzakker, Michael B., Ventura-Bort, Carlos, Verona, Edelyn, Volk, Tyler, Wang, Yi, Weingast, Leah T., Weymar, Mathias, Williams, Claire, Willis, Megan L., Yamashita, Paula, Zahn, Roland, Zupan, Barbra, and Lowe, Leroy

Published
2024
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12. A meta‐analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium

Author

Gutman, Boris A, Erp, Theo GM, Alpert, Kathryn, Ching, Christopher RK, Isaev, Dmitry, Ragothaman, Anjani, Jahanshad, Neda, Saremi, Arvin, Zavaliangos‐Petropulu, Artemis, Glahn, David C, Shen, Li, Cong, Shan, Alnæs, Dag, Andreassen, Ole Andreas, Doan, Nhat Trung, Westlye, Lars T, Kochunov, Peter, Satterthwaite, Theodore D, Wolf, Daniel H, Huang, Alexander J, Kessler, Charles, Weideman, Andrea, Nguyen, Dana, Mueller, Bryon A, Faziola, Lawrence, Potkin, Steven G, Preda, Adrian, Mathalon, Daniel H, Bustillo, Juan, Calhoun, Vince, Ford, Judith M, Walton, Esther, Ehrlich, Stefan, Ducci, Giuseppe, Banaj, Nerisa, Piras, Fabrizio, Piras, Federica, Spalletta, Gianfranco, Canales‐Rodríguez, Erick J, Fuentes‐Claramonte, Paola, Pomarol‐Clotet, Edith, Radua, Joaquim, Salvador, Raymond, Sarró, Salvador, Dickie, Erin W, Voineskos, Aristotle, Tordesillas‐Gutiérrez, Diana, Crespo‐Facorro, Benedicto, Setién‐Suero, Esther, Son, Jacqueline Mayoral, Borgwardt, Stefan, Schönborn‐Harrisberger, Fabienne, Morris, Derek, Donohoe, Gary, Holleran, Laurena, Cannon, Dara, McDonald, Colm, Corvin, Aiden, Gill, Michael, Filho, Geraldo Busatto, Rosa, Pedro GP, Serpa, Mauricio H, Zanetti, Marcus V, Lebedeva, Irina, Kaleda, Vasily, Tomyshev, Alexander, Crow, Tim, James, Anthony, Cervenka, Simon, Sellgren, Carl M, Fatouros‐Bergman, Helena, Agartz, Ingrid, Howells, Fleur, Stein, Dan J, Temmingh, Henk, Uhlmann, Anne, Zubicaray, Greig I, McMahon, Katie L, Wright, Margie, Cobia, Derin, Csernansky, John G, Thompson, Paul M, Turner, Jessica A, and Wang, Lei

Subjects
Brain Disorders, Mental Health, Neurosciences, Schizophrenia, Mental health, Good Health and Well Being, Amygdala, Corpus Striatum, Hippocampus, Humans, Multicenter Studies as Topic, Neuroimaging, Thalamus, schizophrenia, structure, subcortical shape, Cognitive Sciences, Experimental Psychology
Abstract

Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.

Published
2022

13. Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium

Author

Dwyer, Dominic B., Chand, Ganesh B., Pigoni, Alessandro, Khuntia, Adyasha, Wen, Junhao, Antoniades, Mathilde, Hwang, Gyujoon, Erus, Guray, Doshi, Jimit, Srinivasan, Dhivya, Varol, Erdem, Kahn, Rene S., Schnack, Hugo G., Meisenzahl, Eva, Wood, Stephen J., Zhuo, Chuanjun, Sotiras, Aristeidis, Shinohara, Russell T., Shou, Haochang, Fan, Yong, Schaulfelberger, Maristela, Rosa, Pedro, Lalousis, Paris A., Upthegrove, Rachel, Kaczkurkin, Antonia N., Moore, Tyler M., Nelson, Barnaby, Gur, Raquel E., Gur, Ruben C., Ritchie, Marylyn D., Satterthwaite, Theodore D., Murray, Robin M., Di Forti, Marta, Ciufolini, Simone, Zanetti, Marcus V., Wolf, Daniel H., Pantelis, Christos, Crespo-Facorro, Benedicto, Busatto, Geraldo F., Davatzikos, Christos, Koutsouleris, Nikolaos, and Dazzan, Paola

Published
2023
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14. Analysis of structural brain asymmetries in attention‐deficit/hyperactivity disorder in 39 datasets

Author

Postema, Merel C, Hoogman, Martine, Ambrosino, Sara, Asherson, Philip, Banaschewski, Tobias, Bandeira, Cibele E, Baranov, Alexandr, Bau, Claiton HD, Baumeister, Sarah, Baur‐Streubel, Ramona, Bellgrove, Mark A, Biederman, Joseph, Bralten, Janita, Brandeis, Daniel, Brem, Silvia, Buitelaar, Jan K, Busatto, Geraldo F, Castellanos, Francisco X, Cercignani, Mara, Chaim‐Avancini, Tiffany M, Chantiluke, Kaylita C, Christakou, Anastasia, Coghill, David, Conzelmann, Annette, Cubillo, Ana I, Cupertino, Renata B, de Zeeuw, Patrick, Doyle, Alysa E, Durston, Sarah, Earl, Eric A, Epstein, Jeffery N, Ethofer, Thomas, Fair, Damien A, Fallgatter, Andreas J, Faraone, Stephen V, Frodl, Thomas, Gabel, Matt C, Gogberashvili, Tinatin, Grevet, Eugenio H, Haavik, Jan, Harrison, Neil A, Hartman, Catharina A, Heslenfeld, Dirk J, Hoekstra, Pieter J, Hohmann, Sarah, Høvik, Marie F, Jernigan, Terry L, Kardatzki, Bernd, Karkashadze, Georgii, Kelly, Clare, Kohls, Gregor, Konrad, Kerstin, Kuntsi, Jonna, Lazaro, Luisa, Lera‐Miguel, Sara, Lesch, Klaus‐Peter, Louza, Mario R, Lundervold, Astri J, Malpas, Charles B, Mattos, Paulo, McCarthy, Hazel, Namazova‐Baranova, Leyla, Nicolau, Rosa, Nigg, Joel T, Novotny, Stephanie E, Weiss, Eileen Oberwelland, Tuura, Ruth L O'Gorman, Oosterlaan, Jaap, Oranje, Bob, Paloyelis, Yannis, Pauli, Paul, Picon, Felipe A, Plessen, Kerstin J, Ramos‐Quiroga, J Antoni, Reif, Andreas, Reneman, Liesbeth, Rosa, Pedro GP, Rubia, Katya, Schrantee, Anouk, Schweren, Lizanne JS, Seitz, Jochen, Shaw, Philip, Silk, Tim J, Skokauskas, Norbert, Vila, Juan C Soliva, Stevens, Michael C, Sudre, Gustavo, Tamm, Leanne, Tovar‐Moll, Fernanda, van Erp, Theo GM, Vance, Alasdair, Vilarroya, Oscar, Vives‐Gilabert, Yolanda, von Polier, Georg G, Walitza, Susanne, Yoncheva, Yuliya N, Zanetti, Marcus V, Ziegler, Georg C, Glahn, David C, and Jahanshad, Neda

Subjects
Biological Psychology, Psychology, Pediatric, Brain Disorders, Mental Health, Neurosciences, Attention Deficit Hyperactivity Disorder (ADHD), Pediatric Research Initiative, Aetiology, 2.1 Biological and endogenous factors, Mental health, Adolescent, Adult, Attention Deficit Disorder with Hyperactivity, Autism Spectrum Disorder, Brain, Caudate Nucleus, Child, Humans, Magnetic Resonance Imaging, Attention&#8208, deficit, hyperactivity disorder, brain asymmetry, brain laterality, structural MRI, large&#8208, scale data, ENIGMA ADHD Working Group, Attention-deficit, large-scale data, Clinical Sciences, Cognitive Sciences, Developmental & Child Psychology, Clinical sciences, Applied and developmental psychology, Clinical and health psychology
Abstract

ObjectiveSome studies have suggested alterations of structural brain asymmetry in attention-deficit/hyperactivity disorder (ADHD), but findings have been contradictory and based on small samples. Here, we performed the largest ever analysis of brain left-right asymmetry in ADHD, using 39 datasets of the ENIGMA consortium.MethodsWe analyzed asymmetry of subcortical and cerebral cortical structures in up to 1,933 people with ADHD and 1,829 unaffected controls. Asymmetry Indexes (AIs) were calculated per participant for each bilaterally paired measure, and linear mixed effects modeling was applied separately in children, adolescents, adults, and the total sample, to test exhaustively for potential associations of ADHD with structural brain asymmetries.ResultsThere was no evidence for altered caudate nucleus asymmetry in ADHD, in contrast to prior literature. In children, there was less rightward asymmetry of the total hemispheric surface area compared to controls (t = 2.1, p = .04). Lower rightward asymmetry of medial orbitofrontal cortex surface area in ADHD (t = 2.7, p = .01) was similar to a recent finding for autism spectrum disorder. There were also some differences in cortical thickness asymmetry across age groups. In adults with ADHD, globus pallidus asymmetry was altered compared to those without ADHD. However, all effects were small (Cohen's d from -0.18 to 0.18) and would not survive study-wide correction for multiple testing.ConclusionPrior studies of altered structural brain asymmetry in ADHD were likely underpowered to detect the small effects reported here. Altered structural asymmetry is unlikely to provide a useful biomarker for ADHD, but may provide neurobiological insights into the trait.

Published
2021

15. Characterizing neuroanatomic heterogeneity in people with and without ADHD based on subcortical brain volumes

Author

Li, Ting, van Rooij, Daan, Mota, Nina Roth, Buitelaar, Jan K, Ambrosino, Sara, Banaschewski, Tobias, Bandeira, Cibele E, Bau, Claiton HD, Baumeister, Sarah, Baur‐Streubel, Ramona, Bellgrove, Mark A, Biederman, Joseph, Bralten, Janita, Bramati, Ivanei E, Brandeis, Daniel, Berm, Silvia, Busatto, Geraldo F, Calvo, Anna, Castellanos, Francisco X, Cercignani, Mara, Chantiluke, Kaylita C, Christakou, Anastasia, Coghill, David, Conzelmann, Annette, Cubillo, Ana I, Cupertino, Renata B, de Zeeuw, Parick, Durston, Sarah, Earl, Eric A, Epstein, Jeffery N, Ethofer, Thomas, Fallgatter, Andreas J, Fair, Damien A, Faraone, Stephen V, Frodl, Thomas, Gabel, Matt C, Gogberashvili, Tinatin, Grevet, Eugenio H, Haavik, Jan, Harrison, Neil A, Hartman, Catharina A, Heslenfeld, Dirk J, Hoekstra, Pieter J, Høvik, Marie F, Jahanshad, Neda, Kardatzki, Bernd, Karkashadze, Georgii, Kelly, Clare, Kohls, Gregor, Konrad, Kerstin, Kuntsi, Jonna, Lazaro, Luisa, Lera‐Miguel, Sara, Lesch, Klaus‐Peter, Louza, Mario R, Lundervold, Astri J, Malpas, Charles B, Mattos, Paulo, McCarthy, Hazel, Nicolau, Rosa, Nigg, Joel T, Tuura, Ruth L O'Gorman, Oosterlaan, Jaap, Oranje, Bob, Paloyelis, Yannis, Pauli, Paul, Picon, Felipe A, Plessen, Kerstin J, Ramos‐Quiroga, J Antoni, Reif, Andreas, Reneman, Liesbeth, Rosa, Pedro GP, Rubia, Katya, Schrantee, Anouk, Schweren, Lizanne JS, Seitz, Jochen, Shaw, Philip, Silk, Tim J, Skokauskas, Norbert, Vila, Juan Carlos Soliva, Soloveva, Anastasiia, Stevens, Michael C, Sudre, Gustavo, Tamm, Leanne, Thompson, Paul M, Tovar‐Moll, Fernanda, van Erp, Theo GM, Vance, Alasdair, Vilarroya, Oscar, Vives‐Gilabert, Yolanda, von Polier, Georg G, Walitza, Susanne, Yoncheva, Yuliya N, Zanetti, Marcus V, Ziegler, Georg C, Anikin, Anatoly, Asherson, Philip, Baranov, Alexandr, Chaim‐Avanicini, Tiffany, and Dale, Anders M

Subjects
Biological Psychology, Psychology, Mental Health, Pediatric, Brain Disorders, Attention Deficit Hyperactivity Disorder (ADHD), Clinical Research, Neurosciences, Adult, Attention Deficit Disorder with Hyperactivity, Brain, Case-Control Studies, Female, Humans, Magnetic Resonance Imaging, Male, Thalamus, ADHD, subcortical volume, neuroanatomic heterogeneity, community detection, effect sizes, ENIGMA ADHD Working Group, Clinical Sciences, Cognitive Sciences, Developmental & Child Psychology, Clinical sciences, Applied and developmental psychology, Clinical and health psychology
Abstract

BackgroundAttention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder. Neuroanatomic heterogeneity limits our understanding of ADHD's etiology. This study aimed to parse heterogeneity of ADHD and to determine whether patient subgroups could be discerned based on subcortical brain volumes.MethodsUsing the large ENIGMA-ADHD Working Group dataset, four subsamples of 993 boys with and without ADHD and to subsamples of 653 adult men, 400 girls, and 447 women were included in analyses. We applied exploratory factor analysis (EFA) to seven subcortical volumes in order to constrain the complexity of the input variables and ensure more stable clustering results. Factor scores derived from the EFA were used to build networks. A community detection (CD) algorithm clustered participants into subgroups based on the networks.ResultsExploratory factor analysis revealed three factors (basal ganglia, limbic system, and thalamus) in boys and men with and without ADHD. Factor structures for girls and women differed from those in males. Given sample size considerations, we concentrated subsequent analyses on males. Male participants could be separated into four communities, of which one was absent in healthy men. Significant case-control differences of subcortical volumes were observed within communities in boys, often with stronger effect sizes compared to the entire sample. As in the entire sample, none were observed in men. Affected men in two of the communities presented comorbidities more frequently than those in other communities. There were no significant differences in ADHD symptom severity, IQ, and medication use between communities in either boys or men.ConclusionsOur results indicate that neuroanatomic heterogeneity in subcortical volumes exists, irrespective of ADHD diagnosis. Effect sizes of case-control differences appear more pronounced at least in some of the subgroups.

Published
2021

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

Author

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

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

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

Published
2021

22. Contributors

Author

Abrunheiro, Sérgio Filipe Silva, primary, Alsaywid, Basim S., additional, Augusto, Berta Maria Jesus, additional, Barroca, Marta, additional, Damásio, Manuel José, additional, de Almeida Melo, Pedro Miguel, additional, Espanha, Rita, additional, Esteves, Sandra Laia, additional, Fernandes, Carlos Manuel Santos, additional, Fonseca, Rui Brito, additional, Franco Santos, Diogo, additional, Garcia, Francisco, additional, Henriques, Sara, additional, Housawi, Abdulrahman, additional, Lima, Helena, additional, Lytras, Miltiadis D., additional, Martins, Patricia, additional, Marujo, Helena Águeda, additional, Mheidly, Nour, additional, Morgado, Tânia Manuel Moço, additional, Neves, Hugo Leiria, additional, Nunes, Cecilia, additional, Oliveira, Hernâni Zão, additional, Patraquim, Nuno, additional, Pereira Sousa, Joana Sofia Dias, additional, Pinheiro, Diana, additional, Rodrigues, Manuela Soares, additional, Rodrigues, Patrícia, additional, Rosa, Pedro Joel, additional, Silva, Rosa Carla Gomes, additional, Vaz de Almeida, Cristina, additional, Veiga, Ana, additional, and Ventura, Filipa Isabel Quaresma Santos, additional

Published
2023
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26. Using structural MRI to identify bipolar disorders – 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Author

Nunes, Abraham, Schnack, Hugo G, Ching, Christopher RK, Agartz, Ingrid, Akudjedu, Theophilus N, Alda, Martin, Alnæs, Dag, Alonso-Lana, Silvia, Bauer, Jochen, Baune, Bernhard T, Bøen, Erlend, Bonnin, Caterina del Mar, Busatto, Geraldo F, Canales-Rodríguez, Erick J, Cannon, Dara M, Caseras, Xavier, Chaim-Avancini, Tiffany M, Dannlowski, Udo, Díaz-Zuluaga, Ana M, Dietsche, Bruno, Doan, Nhat Trung, Duchesnay, Edouard, Elvsåshagen, Torbjørn, Emden, Daniel, Eyler, Lisa T, Fatjó-Vilas, Mar, Favre, Pauline, Foley, Sonya F, Fullerton, Janice M, Glahn, David C, Goikolea, Jose M, Grotegerd, Dominik, Hahn, Tim, Henry, Chantal, Hibar, Derrek P, Houenou, Josselin, Howells, Fleur M, Jahanshad, Neda, Kaufmann, Tobias, Kenney, Joanne, Kircher, Tilo TJ, Krug, Axel, Lagerberg, Trine V, Lenroot, Rhoshel K, López-Jaramillo, Carlos, Machado-Vieira, Rodrigo, Malt, Ulrik F, McDonald, Colm, Mitchell, Philip B, Mwangi, Benson, Nabulsi, Leila, Opel, Nils, Overs, Bronwyn J, Pineda-Zapata, Julian A, Pomarol-Clotet, Edith, Redlich, Ronny, Roberts, Gloria, Rosa, Pedro G, Salvador, Raymond, Satterthwaite, Theodore D, Soares, Jair C, Stein, Dan J, Temmingh, Henk S, Trappenberg, Thomas, Uhlmann, Anne, van Haren, Neeltje EM, Vieta, Eduard, Westlye, Lars T, Wolf, Daniel H, Yüksel, Dilara, Zanetti, Marcus V, Andreassen, Ole A, Thompson, Paul M, and Hajek, Tomas

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Brain Disorders, Biomedical Imaging, Neurosciences, Mental Health, Clinical Research, Mental health, Bipolar Disorder, Brain, Humans, Machine Learning, Magnetic Resonance Imaging, Neuroimaging, ENIGMA Bipolar Disorders Working Group, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

Published
2020

27. Designing and Prototyping of SDN Switch for Application-Driven Approach

Author

Molinos, Diego Nunes, Oliveira, Romerson Deiny, Freitas, Marcelo Silva, de Souza Neto, Natal Vieira, de Almeida, Marcelo Barros, de Oliveira Silva, Flávio, Rosa, Pedro Frosi, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Hussain, Farookh, editor, and Enokido, Tomoya, editor

Published
2022
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28. Machinability of the 18Ni300 Additively Manufactured Maraging Steel Based on Orthogonal Cutting Tests

Author

Silva, Tiago E. F., Rosa, Pedro A. R., Reis, Ana R., de Jesus, Abílio M. P., Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, di Mare, Francesca, Series Editor, Machado, José, editor, Soares, Filomena, editor, and Ottaviano, Erika, editor

Published
2022
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29. Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals

Author

Favre, Pauline, Pauling, Melissa, Stout, Jacques, Hozer, Franz, Sarrazin, Samuel, Abé, Christoph, Alda, Martin, Alloza, Clara, Alonso-Lana, Silvia, Andreassen, Ole A, Baune, Bernhard T, Benedetti, Francesco, Busatto, Geraldo F, Canales-Rodríguez, Erick J, Caseras, Xavier, Chaim-Avancini, Tiffany Moukbel, Ching, Christopher RK, Dannlowski, Udo, Deppe, Michael, Eyler, Lisa T, Fatjo-Vilas, Mar, Foley, Sonya F, Grotegerd, Dominik, Hajek, Tomas, Haukvik, Unn K, Howells, Fleur M, Jahanshad, Neda, Kugel, Harald, Lagerberg, Trine V, Lawrie, Stephen M, Linke, Julia O, McIntosh, Andrew, Melloni, Elisa MT, Mitchell, Philip B, Polosan, Mircea, Pomarol-Clotet, Edith, Repple, Jonathan, Roberts, Gloria, Roos, Annerine, Rosa, Pedro GP, Salvador, Raymond, Sarró, Salvador, Schofield, Peter R, Serpa, Mauricio H, Sim, Kang, Stein, Dan J, Sussmann, Jess E, Temmingh, Henk S, Thompson, Paul M, Verdolini, Norma, Vieta, Eduard, Wessa, Michele, Whalley, Heather C, Zanetti, Marcus V, Leboyer, Marion, Mangin, Jean-François, Henry, Chantal, Duchesnay, Edouard, and Houenou, Josselin

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Serious Mental Illness, Neurosciences, Bipolar Disorder, Biomedical Imaging, Mental Health, Clinical Research, Mental health, Adult, Brain, Corpus Callosum, Diffusion Tensor Imaging, Female, Humans, Male, Neural Pathways, White Matter, ENIGMA Bipolar Disorder Working Group, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological psychology
Abstract

Fronto-limbic white matter (WM) abnormalities are assumed to lie at the heart of the pathophysiology of bipolar disorder (BD); however, diffusion tensor imaging (DTI) studies have reported heterogeneous results and it is not clear how the clinical heterogeneity is related to the observed differences. This study aimed to identify WM abnormalities that differentiate patients with BD from healthy controls (HC) in the largest DTI dataset of patients with BD to date, collected via the ENIGMA network. We gathered individual tensor-derived regional metrics from 26 cohorts leading to a sample size of N = 3033 (1482 BD and 1551 HC). Mean fractional anisotropy (FA) from 43 regions of interest (ROI) and average whole-brain FA were entered into univariate mega- and meta-analyses to differentiate patients with BD from HC. Mega-analysis revealed significantly lower FA in patients with BD compared with HC in 29 regions, with the highest effect sizes observed within the corpus callosum (R2 = 0.041, Pcorr

Published
2019

30. Correction: Widespread white matter microstructural abnormalities in bipolar disorder: evidence from mega- and meta-analyses across 3033 individuals

Author

Favre, Pauline, Pauling, Melissa, Stout, Jacques, Hozer, Franz, Sarrazin, Samuel, Abé, Christoph, Alda, Martin, Alloza, Clara, Alonso-Lana, Silvia, Andreassen, Ole A, Baune, Bernhard T, Benedetti, Francesco, Busatto, Geraldo F, Canales-Rodríguez, Erick J, Caseras, Xavier, Chaim-Avancini, Tiffany Moukbel, Ching, Christopher RK, Dannlowski, Udo, Deppe, Michael, Eyler, Lisa T, Fatjo-Vilas, Mar, Foley, Sonya F, Grotegerd, Dominik, Hajek, Tomas, Haukvik, Unn K, Howells, Fleur M, Jahanshad, Neda, Kugel, Harald, Lagerberg, Trine V, Lawrie, Stephen M, Linke, Julia O, McIntosh, Andrew, Melloni, Elisa MT, Mitchell, Philip B, Polosan, Mircea, Pomarol-Clotet, Edith, Repple, Jonathan, Roberts, Gloria, Roos, Annerine, Rosa, Pedro GP, Salvador, Raymond, Sarró, Salvador, Schofield, Peter R, Serpa, Mauricio H, Sim, Kang, Stein, Dan J, Sussmann, Jess E, Temmingh, Henk S, Thompson, Paul M, Verdolini, Norma, Vieta, Eduard, Wessa, Michele, Whalley, Heather C, Zanetti, Marcus V, Leboyer, Marion, Mangin, Jean-François, Henry, Chantal, Duchesnay, Edouard, and Houenou, Josselin

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Neurosciences, Psychology, ENIGMA Bipolar Disorder Working Group, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Biological psychology
Abstract

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Published
2019

34. Subtly altered topological asymmetry of brain structural covariance networks in autism spectrum disorder across 43 datasets from the ENIGMA consortium

Author

Sha, Zhiqiang, van Rooij, Daan, Anagnostou, Evdokia, Arango, Celso, Auzias, Guillaume, Behrmann, Marlene, Bernhardt, Boris, Bolte, Sven, Busatto, Geraldo F., Calderoni, Sara, Calvo, Rosa, Daly, Eileen, Deruelle, Christine, Duan, Meiyu, Duran, Fabio Luis Souza, Durston, Sarah, Ecker, Christine, Ehrlich, Stefan, Fair, Damien, Fedor, Jennifer, Fitzgerald, Jacqueline, Floris, Dorothea L., Franke, Barbara, Freitag, Christine M., Gallagher, Louise, Glahn, David C., Haar, Shlomi, Hoekstra, Liesbeth, Jahanshad, Neda, Jalbrzikowski, Maria, Janssen, Joost, King, Joseph A., Lazaro, Luisa, Luna, Beatriz, McGrath, Jane, Medland, Sarah E., Muratori, Filippo, Murphy, Declan G. M., Neufeld, Janina, O’Hearn, Kirsten, Oranje, Bob, Parellada, Mara, Pariente, Jose C., Postema, Merel C., Remnelius, Karl Lundin, Retico, Alessandra, Rosa, Pedro Gomes Penteado, Rubia, Katya, Shook, Devon, Tammimies, Kristiina, Taylor, Margot J., Tosetti, Michela, Wallace, Gregory L., Zhou, Fengfeng, Thompson, Paul M., Fisher, Simon E., Buitelaar, Jan K., and Francks, Clyde

Published
2022
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36. Deep Learning for Quality Control of Subcortical Brain 3D Shape Models

Author

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

Subjects
Quantitative Biology - Neurons and Cognition
Abstract

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

Published
2018

37. Brain Imaging of the Cortex in ADHD: A Coordinated Analysis of Large-Scale Clinical and Population-Based Samples

Author

Hoogman, Martine, Muetzel, Ryan, Guimaraes, Joao P, Shumskaya, Elena, Mennes, Maarten, Zwiers, Marcel P, Jahanshad, Neda, Sudre, Gustavo, Wolfers, Thomas, Earl, Eric A, Soliva Vila, Juan Carlos, Vives-Gilabert, Yolanda, Khadka, Sabin, Novotny, Stephanie E, Hartman, Catharina A, Heslenfeld, Dirk J, Schweren, Lizanne JS, Ambrosino, Sara, Oranje, Bob, de Zeeuw, Patrick, Chaim-Avancini, Tiffany M, Rosa, Pedro GP, Zanetti, Marcus V, Malpas, Charles B, Kohls, Gregor, von Polier, Georg G, Seitz, Jochen, Biederman, Joseph, Doyle, Alysa E, Dale, Anders M, van Erp, Theo GM, Epstein, Jeffery N, Jernigan, Terry L, Baur-Streubel, Ramona, Ziegler, Georg C, Zierhut, Kathrin C, Schrantee, Anouk, Høvik, Marie F, Lundervold, Astri J, Kelly, Clare, McCarthy, Hazel, Skokauskas, Norbert, O’Gorman Tuura, Ruth L, Calvo, Anna, Lera-Miguel, Sara, Nicolau, Rosa, Chantiluke, Kaylita C, Christakou, Anastasia, Vance, Alasdair, Cercignani, Mara, Gabel, Matt C, Asherson, Philip, Baumeister, Sarah, Brandeis, Daniel, Hohmann, Sarah, Bramati, Ivanei E, Tovar-Moll, Fernanda, Fallgatter, Andreas J, Kardatzki, Bernd, Schwarz, Lena, Anikin, Anatoly, Baranov, Alexandr, Gogberashvili, Tinatin, Kapilushniy, Dmitry, Solovieva, Anastasia, El Marroun, Hanan, White, Tonya, Karkashadze, Georgii, Namazova-Baranova, Leyla, Ethofer, Thomas, Mattos, Paulo, Banaschewski, Tobias, Coghill, David, Plessen, Kerstin J, Kuntsi, Jonna, Mehta, Mitul A, Paloyelis, Yannis, Harrison, Neil A, Bellgrove, Mark A, Silk, Tim J, Cubillo, Ana I, Rubia, Katya, Lazaro, Luisa, Brem, Silvia, Walitza, Susanne, Frodl, Thomas, Zentis, Mariam, Castellanos, Francisco X, Yoncheva, Yuliya N, Haavik, Jan, Reneman, Liesbeth, Conzelmann, Annette, Lesch, Klaus-Peter, Pauli, Paul, Reif, Andreas, Tamm, Leanne, Konrad, Kerstin, Oberwelland Weiss, Eileen, Busatto, Geraldo F, and Louza, Mario R

Subjects
Pediatric, Neurosciences, Mental Health, Attention Deficit Hyperactivity Disorder (ADHD), Behavioral and Social Science, Clinical Research, Brain Disorders, Pediatric Research Initiative, Aetiology, 2.1 Biological and endogenous factors, Mental health, Adolescent, Adult, Age Factors, Attention Deficit Disorder with Hyperactivity, Case-Control Studies, Cerebral Cortex, Child, Child, Preschool, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neuroimaging, Psychiatric Status Rating Scales, Sex Factors, Young Adult, Attention Deficit Hyperactivity Disorder, Cortical Surface Area, Cortical Thickness, Imaging, Meta-Analysis, Neuroanatomy, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

ObjectiveNeuroimaging studies show structural alterations of various brain regions in children and adults with attention deficit hyperactivity disorder (ADHD), although nonreplications are frequent. The authors sought to identify cortical characteristics related to ADHD using large-scale studies.MethodsCortical thickness and surface area (based on the Desikan-Killiany atlas) were compared between case subjects with ADHD (N=2,246) and control subjects (N=1,934) for children, adolescents, and adults separately in ENIGMA-ADHD, a consortium of 36 centers. To assess familial effects on cortical measures, case subjects, unaffected siblings, and control subjects in the NeuroIMAGE study (N=506) were compared. Associations of the attention scale from the Child Behavior Checklist with cortical measures were determined in a pediatric population sample (Generation-R, N=2,707).ResultsIn the ENIGMA-ADHD sample, lower surface area values were found in children with ADHD, mainly in frontal, cingulate, and temporal regions; the largest significant effect was for total surface area (Cohen's d=-0.21). Fusiform gyrus and temporal pole cortical thickness was also lower in children with ADHD. Neither surface area nor thickness differences were found in the adolescent or adult groups. Familial effects were seen for surface area in several regions. In an overlapping set of regions, surface area, but not thickness, was associated with attention problems in the Generation-R sample.ConclusionsSubtle differences in cortical surface area are widespread in children but not adolescents and adults with ADHD, confirming involvement of the frontal cortex and highlighting regions deserving further attention. Notably, the alterations behave like endophenotypes in families and are linked to ADHD symptoms in the population, extending evidence that ADHD behaves as a continuous trait in the population. Future longitudinal studies should clarify individual lifespan trajectories that lead to nonsignificant findings in adolescent and adult groups despite the presence of an ADHD diagnosis.

Published
2019

38. 10Kin1day: A Bottom-Up Neuroimaging Initiative

Author

van den Heuvel, Martijn P, Scholtens, Lianne H, van der Burgh, Hannelore K, Agosta, Federica, Alloza, Clara, Arango, Celso, Auyeung, Bonnie, Baron-Cohen, Simon, Basaia, Silvia, Benders, Manon JNL, Beyer, Frauke, Booij, Linda, Braun, Kees PJ, Filho, Geraldo Busatto, Cahn, Wiepke, Cannon, Dara M, Chaim-Avancini, Tiffany M, Chan, Sandra SM, Chen, Eric YH, Crespo-Facorro, Benedicto, Crone, Eveline A, Dannlowski, Udo, de Zwarte, Sonja MC, Dietsche, Bruno, Donohoe, Gary, Du Plessis, Stefan, Durston, Sarah, Díaz-Caneja, Covadonga M, Díaz-Zuluaga, Ana M, Emsley, Robin, Filippi, Massimo, Frodl, Thomas, Gorges, Martin, Graff, Beata, Grotegerd, Dominik, Gąsecki, Dariusz, Hall, Julie M, Holleran, Laurena, Holt, Rosemary, Hopman, Helene J, Jansen, Andreas, Janssen, Joost, Jodzio, Krzysztof, Jäncke, Lutz, Kaleda, Vasiliy G, Kassubek, Jan, Masouleh, Shahrzad Kharabian, Kircher, Tilo, Koevoets, Martijn GJC, Kostic, Vladimir S, Krug, Axel, Lawrie, Stephen M, Lebedeva, Irina S, Lee, Edwin HM, Lett, Tristram A, Lewis, Simon JG, Liem, Franziskus, Lombardo, Michael V, Lopez-Jaramillo, Carlos, Margulies, Daniel S, Markett, Sebastian, Marques, Paulo, Martínez-Zalacaín, Ignacio, McDonald, Colm, McIntosh, Andrew M, McPhilemy, Genevieve, Meinert, Susanne L, Menchón, José M, Montag, Christian, Moreira, Pedro S, Morgado, Pedro, Mothersill, David O, Mérillat, Susan, Müller, Hans-Peter, Nabulsi, Leila, Najt, Pablo, Narkiewicz, Krzysztof, Naumczyk, Patrycja, Oranje, Bob, de la Foz, Victor Ortiz-Garcia, Peper, Jiska S, Pineda, Julian A, Rasser, Paul E, Redlich, Ronny, Repple, Jonathan, Reuter, Martin, Rosa, Pedro GP, Ruigrok, Amber NV, Sabisz, Agnieszka, Schall, Ulrich, Seedat, Soraya, Serpa, Mauricio H, Skouras, Stavros, Soriano-Mas, Carles, Sousa, Nuno, Szurowska, Edyta, Tomyshev, Alexander S, Tordesillas-Gutierrez, Diana, Valk, Sofie L, and van den Berg, Leonard H

Subjects
Biological Psychology, Psychology, Biomedical Imaging, Brain Disorders, Neurosciences, Neurological, MRI, connectome analysis, diffusion weighted MRI, brain, network, Clinical Sciences, Clinical sciences, Biological psychology
Abstract

We organized 10Kin1day, a pop-up scientific event with the goal to bring together neuroimaging groups from around the world to jointly analyze 10,000+ existing MRI connectivity datasets during a 3-day workshop. In this report, we describe the motivation and principles of 10Kin1day, together with a public release of 8,000+ MRI connectome maps of the human brain.

Published
2019

43. Self-healing in the Scope of Software-Based Computer and Mobile Networks

Author

de Souza Neto, Natal Vieira, Oliveira, Daniel Ricardo Cunha, Gonçalves, Maurício Amaral, de Oliveira Silva, Flávio, Rosa, Pedro Frosi, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Ferguson, Donald, editor, Pahl, Claus, editor, and Helfert, Markus, editor

Published
2021
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44. NEA: An SDN Switch Architecture Suitable for Application-Oriented MAC

Author

Molinos, Diego Nunes, Oliveira, Romerson Deiny, Freitas, Marcelo Silva, de Almeida, Marcelo Barros, Rosa, Pedro Frosi, de Oliveira Silva, Flavio, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Woungang, Isaac, editor, and Enokido, Tomoya, editor

Published
2021
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45. A Proposal for Application-to-Application Network Addressing in Clean-Slate Architectures

Author

Chaves, Alisson O., Rosa, Pedro Frosi, Silva, Flávio O., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Woungang, Isaac, editor, and Enokido, Tomoya, editor

Published
2021
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46. Deploying Scalable and Stable XDP-Based Network Slices Through NASOR Framework for Low-Latency Applications

Author

Moreira, Rodrigo, Rosa, Pedro Frosi, Aguiar, Rui Luis Andrade, de Oliveira Silva, Flávio, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Barolli, Leonard, editor, Woungang, Isaac, editor, and Enokido, Tomoya, editor

Published
2021
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47. Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging

Author

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

Subjects
Quantitative Biology - Quantitative Methods
Abstract

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

Published
2017

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