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Your search keyword '"Lagerberg, Trine V."' showing total 5 results
Start Over Author "Lagerberg, Trine V." Publisher escholarship, university of california
5 results on '"Lagerberg, Trine V."'

1. In vivo hippocampal subfield volumes in bipolar disorder—A mega‐analysis from The Enhancing Neuro Imaging Genetics through Meta‐Analysis Bipolar Disorder Working Group

Author

Haukvik, Unn K, Gurholt, Tiril P, Nerland, Stener, Elvsåshagen, Torbjørn, Akudjedu, Theophilus N, Alda, Martin, Alnæs, Dag, Alonso‐Lana, Silvia, Bauer, Jochen, Baune, Bernhard T, Benedetti, Francesco, Berk, Michael, Bettella, Francesco, Bøen, Erlend, Bonnín, Caterina M, Brambilla, Paolo, Canales‐Rodríguez, Erick J, Cannon, Dara M, Caseras, Xavier, Dandash, Orwa, Dannlowski, Udo, Delvecchio, Giuseppe, Díaz‐Zuluaga, Ana M, Erp, Theo GM, Fatjó‐Vilas, Mar, Foley, Sonya F, Förster, Katharina, Fullerton, Janice M, Goikolea, José M, Grotegerd, Dominik, Gruber, Oliver, Haarman, Bartholomeus CM, Haatveit, Beathe, Hajek, Tomas, Hallahan, Brian, Harris, Mathew, Hawkins, Emma L, Howells, Fleur M, Hülsmann, Carina, Jahanshad, Neda, Jørgensen, Kjetil N, Kircher, Tilo, Krämer, Bernd, Krug, Axel, Kuplicki, Rayus, Lagerberg, Trine V, Lancaster, Thomas M, Lenroot, Rhoshel K, Lonning, Vera, López‐Jaramillo, Carlos, Malt, Ulrik F, McDonald, Colm, McIntosh, Andrew M, McPhilemy, Genevieve, Meer, Dennis, Melle, Ingrid, Melloni, Elisa MT, Mitchell, Philip B, Nabulsi, Leila, Nenadić, Igor, Oertel, Viola, Oldani, Lucio, Opel, Nils, Otaduy, Maria CG, Overs, Bronwyn J, Pineda‐Zapata, Julian A, Pomarol‐Clotet, Edith, Radua, Joaquim, Rauer, Lisa, Redlich, Ronny, Repple, Jonathan, Rive, Maria M, Roberts, Gloria, Ruhe, Henricus G, Salminen, Lauren E, Salvador, Raymond, Sarró, Salvador, Savitz, Jonathan, Schene, Aart H, Sim, Kang, Soeiro‐de‐Souza, Marcio G, Stäblein, Michael, Stein, Dan J, Stein, Frederike, Tamnes, Christian K, Temmingh, Henk S, Thomopoulos, Sophia I, Veltman, Dick J, Vieta, Eduard, Waltemate, Lena, Westlye, Lars T, Whalley, Heather C, Sämann, Philipp G, Thompson, Paul M, Ching, Christopher RK, Andreassen, Ole A, Agartz, Ingrid, and Group, ENIGMA Bipolar Disorder Working

Subjects
Biological Psychology, Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Psychology, Brain Disorders, Mental Health, Serious Mental Illness, Neurosciences, Biomedical Imaging, Bipolar Disorder, Mental health, Genetics, Hippocampus, Humans, Magnetic Resonance Imaging, Neuroimaging, ENIGMA Bipolar Disorder Working Group, bipolar disorder subtype, hippocampus, large-scale, lithium, psychosis, structural brain MRI, Cognitive Sciences, Experimental Psychology, Biological psychology, Cognitive and computational psychology
Abstract

The hippocampus consists of anatomically and functionally distinct subfields that may be differentially involved in the pathophysiology of bipolar disorder (BD). Here we, the Enhancing NeuroImaging Genetics through Meta-Analysis Bipolar Disorder workinggroup, study hippocampal subfield volumetry in BD. T1-weighted magnetic resonance imaging scans from 4,698 individuals (BD = 1,472, healthy controls [HC] = 3,226) from 23 sites worldwide were processed with FreeSurfer. We used linear mixed-effects models and mega-analysis to investigate differences in hippocampal subfield volumes between BD and HC, followed by analyses of clinical characteristics and medication use. BD showed significantly smaller volumes of the whole hippocampus (Cohen's d = -0.20), cornu ammonis (CA)1 (d = -0.18), CA2/3 (d = -0.11), CA4 (d = -0.19), molecular layer (d = -0.21), granule cell layer of dentate gyrus (d = -0.21), hippocampal tail (d = -0.10), subiculum (d = -0.15), presubiculum (d = -0.18), and hippocampal amygdala transition area (d = -0.17) compared to HC. Lithium users did not show volume differences compared to HC, while non-users did. Antipsychotics or antiepileptic use was associated with smaller volumes. In this largest study of hippocampal subfields in BD to date, we show widespread reductions in nine of 12 subfields studied. The associations were modulated by medication use and specifically the lack of differences between lithium users and HC supports a possible protective role of lithium in BD.

Published
2022

2. 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

3. 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

4. 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

5. Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium.

Author

van Erp, Theo GM, Walton, Esther, Hibar, Derrek P, Schmaal, Lianne, Jiang, Wenhao, Glahn, David C, Pearlson, Godfrey D, Yao, Nailin, Fukunaga, Masaki, Hashimoto, Ryota, Okada, Naohiro, Yamamori, Hidenaga, Bustillo, Juan R, Clark, Vincent P, Agartz, Ingrid, Mueller, Bryon A, Cahn, Wiepke, de Zwarte, Sonja MC, Hulshoff Pol, Hilleke E, Kahn, René S, Ophoff, Roel A, van Haren, Neeltje EM, Andreassen, Ole A, Dale, Anders M, Doan, Nhat Trung, Gurholt, Tiril P, Hartberg, Cecilie B, Haukvik, Unn K, Jørgensen, Kjetil N, Lagerberg, Trine V, Melle, Ingrid, Westlye, Lars T, Gruber, Oliver, Kraemer, Bernd, Richter, Anja, Zilles, David, Calhoun, Vince D, Crespo-Facorro, Benedicto, Roiz-Santiañez, Roberto, Tordesillas-Gutiérrez, Diana, Loughland, Carmel, Carr, Vaughan J, Catts, Stanley, Cropley, Vanessa L, Fullerton, Janice M, Green, Melissa J, Henskens, Frans A, Jablensky, Assen, Lenroot, Rhoshel K, Mowry, Bryan J, Michie, Patricia T, Pantelis, Christos, Quidé, Yann, Schall, Ulrich, Scott, Rodney J, Cairns, Murray J, Seal, Marc, Tooney, Paul A, Rasser, Paul E, Cooper, Gavin, Shannon Weickert, Cynthia, Weickert, Thomas W, Morris, Derek W, Hong, Elliot, Kochunov, Peter, Beard, Lauren M, Gur, Raquel E, Gur, Ruben C, Satterthwaite, Theodore D, Wolf, Daniel H, Belger, Aysenil, Brown, Gregory G, Ford, Judith M, Macciardi, Fabio, Mathalon, Daniel H, O'Leary, Daniel S, Potkin, Steven G, Preda, Adrian, Voyvodic, James, Lim, Kelvin O, McEwen, Sarah, Yang, Fude, Tan, Yunlong, Tan, Shuping, Wang, Zhiren, Fan, Fengmei, Chen, Jingxu, Xiang, Hong, Tang, Shiyou, Guo, Hua, Wan, Ping, Wei, Dong, Bockholt, Henry J, Ehrlich, Stefan, Wolthusen, Rick PF, King, Margaret D, Shoemaker, Jody M, Sponheim, Scott R, De Haan, Lieuwe, and Koenders, Laura

Subjects
Karolinska Schizophrenia Project, Brain, Frontal Lobe, Prefrontal Cortex, Temporal Lobe, Humans, Magnetic Resonance Imaging, Severity of Illness Index, Linear Models, Case-Control Studies, Schizophrenia, Age of Onset, Adolescent, Adult, Aged, Middle Aged, Child, Female, Male, Young Adult, Neuroimaging, Cortical, Imaging, Meta-analysis, Surface area, Thickness, Serious Mental Illness, Biomedical Imaging, Mental Health, Clinical Research, Neurosciences, Brain Disorders, Mental health, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry
Abstract

BackgroundThe profile of cortical neuroanatomical abnormalities in schizophrenia is not fully understood, despite hundreds of published structural brain imaging studies. This study presents the first meta-analysis of cortical thickness and surface area abnormalities in schizophrenia conducted by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) Schizophrenia Working Group.MethodsThe study included data from 4474 individuals with schizophrenia (mean age, 32.3 years; range, 11-78 years; 66% male) and 5098 healthy volunteers (mean age, 32.8 years; range, 10-87 years; 53% male) assessed with standardized methods at 39 centers worldwide.ResultsCompared with healthy volunteers, individuals with schizophrenia have widespread thinner cortex (left/right hemisphere: Cohen's d = -0.530/-0.516) and smaller surface area (left/right hemisphere: Cohen's d = -0.251/-0.254), with the largest effect sizes for both in frontal and temporal lobe regions. Regional group differences in cortical thickness remained significant when statistically controlling for global cortical thickness, suggesting regional specificity. In contrast, effects for cortical surface area appear global. Case-control, negative, cortical thickness effect sizes were two to three times larger in individuals receiving antipsychotic medication relative to unmedicated individuals. Negative correlations between age and bilateral temporal pole thickness were stronger in individuals with schizophrenia than in healthy volunteers. Regional cortical thickness showed significant negative correlations with normalized medication dose, symptom severity, and duration of illness and positive correlations with age at onset.ConclusionsThe findings indicate that the ENIGMA meta-analysis approach can achieve robust findings in clinical neuroscience studies; also, medication effects should be taken into account in future genetic association studies of cortical thickness in schizophrenia.

Published
2018

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