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5 results on '"Helena van Nieuwenhuizen"'

1. Cortical and subcortical brain structure in generalized anxiety disorder: findings from 28 research sites in the ENIGMA-Anxiety Working Group

Author

Anita Harrewijn, Elise M. Cardinale, Nynke A. Groenewold, Janna Marie Bas-Hoogendam, Moji Aghajani, Kevin Hilbert, Narcis Cardoner, Daniel Porta-Casteràs, Savannah Gosnell, Ramiro Salas, Andrea P. Jackowski, Pedro M. Pan, Giovanni A. Salum, Karina S. Blair, James R. Blair, Mira Z. Hammoud, Mohammed R. Milad, Katie L. Burkhouse, K. Luan Phan, Heidi K. Schroeder, Jeffrey R. Strawn, Katja Beesdo-Baum, Neda Jahanshad, Sophia I. Thomopoulos, Randy Buckner, Jared A. Nielsen, Jordan W. Smoller, Jair C. Soares, Benson Mwangi, Mon-Ju Wu, Giovana B. Zunta-Soares, Michal Assaf, Gretchen J. Diefenbach, Paolo Brambilla, Eleonora Maggioni, David Hofmann, Thomas Straube, Carmen Andreescu, Rachel Berta, Erica Tamburo, Rebecca B. Price, Gisele G. Manfro, Federica Agosta, Elisa Canu, Camilla Cividini, Massimo Filippi, Milutin Kostić, Ana Munjiza Jovanovic, Bianca A. V. Alberton, Brenda Benson, Gabrielle F. Freitag, Courtney A. Filippi, Andrea L. Gold, Ellen Leibenluft, Grace V. Ringlein, Kathryn E. Werwath, Hannah Zwiebel, André Zugman, Hans J. Grabe, Sandra Van der Auwera, Katharina Wittfeld, Henry Völzke, Robin Bülow, Nicholas L. Balderston, Monique Ernst, Christian Grillon, Lilianne R. Mujica-Parodi, Helena van Nieuwenhuizen, Hugo D. Critchley, Elena Makovac, Matteo Mancini, Frances Meeten, Cristina Ottaviani, Tali M. Ball, Gregory A. Fonzo, Martin P. Paulus, Murray B. Stein, Raquel E. Gur, Ruben C. Gur, Antonia N. Kaczkurkin, Bart Larsen, Theodore D. Satterthwaite, Jennifer Harper, Michael Myers, Michael T. Perino, Chad M. Sylvester, Qiongru Yu, Ulrike Lueken, Dick J. Veltman, Paul M. Thompson, Dan J. Stein, Nic J. A. Van der Wee, Anderson M. Winkler, and Daniel S. Pine

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Abstract The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.

Published
2021
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2. Ketosis regulates K+ion channels, strengthening brain-wide signaling disrupted by age

Author

Helena van Nieuwenhuizen, Anthony G. Chesebro, Claire Polizu, Kieran Clarke, Helmut H. Strey, Corey Weistuch, and Lilianne R. Mujica-Parodi

Abstract

Aging is associated with impaired signaling between brain regions when measured using resting-state fMRI. This age-related destabilization and desynchronization of brain networks reverses itself when the brain switches from metabolizing glucose to ketones. Here, we probe the mechanistic basis for these effects. First, we established their neuronal basis using two datasets acquired from resting-state EEG (Lifespan:standard diet, 20-80 years, N = 201;Metabolic:individually weight-dosed and calorically-matched glucose and ketone ester challenge,μage= 26.9 ± 11.2 years, N = 36). Then, using the multi-scale Larter-Breakspear neural mass model, we identified the unique set of mechanistic parameters consistent with our clinical data. Together, our results implicate potassium (K+) gradient dysregulation as a mechanism for age-related neural desynchronization and its reversal with ketosis, the latter finding of which is consistent with direct measurement of ion channels.

Published
2023

3. Brain-Based Classification of Youth with Anxiety Disorders: an ENIGMA-ANXIETY Transdiagnostic Examination using Machine Learning

Author

Willem B. Bruin, Paul Zhutovsky, Guido van Wingen, Janna Marie Bas-Hoogendam, Nynke A. Groenewold, Kevin Hilbert, Anderson M. Winkler, André Zugman, Federica Agosta, Fredrik Åhs, Carmen Andreescu, Chase Antonacci, Takeshi Asami, Michal Assaf, Jacques Barber, Jochen Bauer, Shreya Bavdekar, Katja Beesdo-Baum, Francesco Benedetti, Rachel Bernstein, Johannes Björkstrand, Robert Blair, Karina S. Blair, Laura Blanco-Hinojo, Joscha Böhnlein, Paolo Brambilla, Rodrigo Bressan, Fabian Breuer, Marta Cano, Elisa Canu, Elise M Cardinale, Narcís Cardoner, Camilla Cividini, Henk Cremers, Udo Dannlowski, Gretchen J. Diefenbach, Katharina Domschke, Alexander Doruyter, Thomas Dresler, Angelika Erhardt, Massimo Filippi, Gregory Fonzo, Gabrielle Felice Freitag, Tomas Furmark, Tian Ge, Andrew J. Gerber, Savannah Gosnell, Hans J. Grabe, Dominik Grotegerd, Ruben C. Gur, Raquel E. Gur, Alfons O. Hamm, Laura K. M. Han, Jennifer Harper, Anita Harrewijn, Alexandre Heeren, David Hoffman, Andrea P. Jackowski, Neda Jahanshad, Laura Jett, Antonia N. Kaczkurkin, Parmis Khosravi, Ellen Kingsley, Tilo Kircher, Milutin Kostić, Bart Larsen, Sang-Hyuk Lee, Elisabeth Leehr, Ellen Leibenluft, Christine Lochner, Su Lui, Eleonora Maggioni, Gisele Gus Manfro, Kristoffer Månsson, Claire Marino, Frances Meeten, Barbara Milrod, Ana Munjiza, Benson Irungu, Michael Myers, Susanne Neufang, Jared Nielsen, Patricia Ohrmann, Cristina Ottaviani, Martin P Paulus, Michael T. Perino, K Luan Phan, Sara Poletti, Daniel Porta-Casteràs, Jesus Pujol, Andrea Reinecke, Grace Ringlein, Pavel Rjabtsenkov, Karin Roelofs, Ramiro Salas, Giovanni Salum, Theodore D. Satterthwaite, Elisabeth Schrammen, Lisa Sindermann, Jordan Smoller, Jair Soares, Rudolf Stark, Frederike Stein, thomas straube, Benjamin Straube, Jeffrey Strawn, Benjamin Suarez-Jimenez, Chad M. Sylvester, Ardesheer Talati, Sophia I Thomopoulos, Raşit Tükel, Helena van Nieuwenhuizen, Katy E. Werwath, Katharina Wittfeld, Barry Wright, Mon-Ju Wu, Yunbo Yang, Anna Zilverstand, Peter Zwanzger, Jennifer Blackford, Suzanne Avery, Jacqueline Clauss, Ulrike Lueken, Paul Thompson, Daniel Pine, Dan J. Stein, Nic van der Wee, Dick Veltman, and Moji Aghajani

Abstract

Neuroimaging studies point to neurostructural abnormalities in youth with anxiety disorders. Yet, findings are based on small-scale studies, often with small effect sizes, and have limited generalizability and clinical relevance. These issues have prompted a paradigm shift in the field towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically informed. Here, we built and validated neurostructural machine learning (ML) models for individual-level inferences based on the largest-ever multi-site neuroimaging sample of youth with anxiety disorders (age: 10-25 years, N=3,343 individuals from 32 global sites), as compiled by three ENIGMA Anxiety Working Groups: Panic Disorder (PD), Generalized Anxiety Disorder (GAD), and Social Anxiety Disorder (SAD). ML classifiers were trained on MRI-derived regional measures of cortical thickness, surface area, and subcortical volumes to classify patients and healthy controls (HC) for each anxiety disorder separately and across disorders (transdiagnostic classification). Modest, yet robust, classification performance was achieved for PD vs. HC (AUC=0.62), but other disorder-specific and transdiagnostic classifications were not significantly different from chance. However, above chance-level transdiagnostic classifications were obtained in exploratory subgroup analyses of male patients vs. male HC, unmedicated patients vs. HC, and patients with low anxiety severity vs. HC (AUC 0.59-0.63). The above chance-level classifications were based on plausible and specific neuroanatomical features in fronto-striato-limbic and temporo-parietal regions. This study provides a realistic estimate of classification performance in a large, ecologically valid, multi-site sample of youth with anxiety disorders, and may as such serve as a benchmark.

Published
2022

4. Cortical and subcortical brain structure in generalized anxiety disorder

Author

Paolo Brambilla, Tali M. Ball, Dan J. Stein, Courtney A. Filippi, Kathryn E. Werwath, Pedro Mario Pan, Heidi K. Schroeder, Hannah Zwiebel, Andrea Parolin Jackowski, Jared A. Nielsen, Savannah N. Gosnell, Hans J. Grabe, Christian Grillon, Paul M. Thompson, Gabrielle F. Freitag, Murray B. Stein, Karina S. Blair, Narcís Cardoner, Neda Jahanshad, Ana Munjiza Jovanovic, Cristina Ottaviani, Massimo Filippi, André Zugman, Katharina Wittfeld, Antonia N. Kaczkurkin, Camilla Cividini, Hugo D. Critchley, Nynke A. Groenewold, Elise M. Cardinale, Moji Aghajani, Bianca A.V. Alberton, Michael T. Perino, Anderson M. Winkler, Ellen Leibenluft, Sophia I. Thomopoulos, Mohammed R. Milad, Kevin Hilbert, Matteo Mancini, Randy L. Buckner, Henry Völzke, Robin Bülow, Carmen Andreescu, Milutin Kostić, Raquel E. Gur, Benson Mwangi, Daniel Porta-Casteràs, Michael J. Myers, Federica Agosta, Thomas Straube, Giovana Zunta-Soares, Gretchen J. Diefenbach, Martin P. Paulus, Erica Tamburo, Brenda E. Benson, Elena Makovac, Grace V. Ringlein, Andrea L. Gold, David Hofmann, Mon-Ju Wu, Jeffrey R. Strawn, Janna Marie Bas-Hoogendam, Dick J. Veltman, Eleonora Maggioni, Sandra Van der Auwera, Gregory A. Fonzo, Ramiro Salas, Giovanni Abrahão Salum, Daniel S. Pine, Gisele Gus Manfro, Bart Larsen, Monique Ernst, Nic J.A. van der Wee, Helena van Nieuwenhuizen, Mira Z. Hammoud, Ruben C. Gur, Katie L. Burkhouse, Chad M. Sylvester, Rachel Berta, Elisa Canu, Jair C. Soares, Theodore D. Satterthwaite, Qiongru Yu, Frances Meeten, Ulrike Lueken, Jordan W. Smoller, Michal Assaf, Lilianne R. Mujica-Parodi, K. Luan Phan, Jennifer Harper, Nicholas L. Balderston, James R. Blair, Anita Harrewijn, Rebecca B. Price, Katja Beesdo-Baum, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Anatomy and neurosciences, Amsterdam Neuroscience - Brain Imaging, Harrewijn, A., Cardinale, E. M., Groenewold, N. A., Bas-Hoogendam, J. M., Aghajani, M., Hilbert, K., Cardoner, N., Porta-Casteras, D., Gosnell, S., Salas, R., Jackowski, A. P., Pan, P. M., Salum, G. A., Blair, K. S., Blair, J. R., Hammoud, M. Z., Milad, M. R., Burkhouse, K. L., Phan, K. L., Schroeder, H. K., Strawn, J. R., Beesdo-Baum, K., Jahanshad, N., Thomopoulos, S. I., Buckner, R., Nielsen, J. A., Smoller, J. W., Soares, J. C., Mwangi, B., Wu, M. -J., Zunta-Soares, G. B., Assaf, M., Diefenbach, G. J., Brambilla, P., Maggioni, E., Hofmann, D., Straube, T., Andreescu, C., Berta, R., Tamburo, E., Price, R. B., Manfro, G. G., Agosta, F., Canu, E., Cividini, C., Filippi, M., Kostic, M., Munjiza Jovanovic, A., Alberton, B. A. V., Benson, B., Freitag, G. F., Filippi, C. A., Gold, A. L., Leibenluft, E., Ringlein, G. V., Werwath, K. E., Zwiebel, H., Zugman, A., Grabe, H. J., Van der Auwera, S., Wittfeld, K., Volzke, H., Bulow, R., Balderston, N. L., Ernst, M., Grillon, C., Mujica-Parodi, L. R., van Nieuwenhuizen, H., Critchley, H. D., Makovac, E., Mancini, M., Meeten, F., Ottaviani, C., Ball, T. M., Fonzo, G. A., Paulus, M. P., Stein, M. B., Gur, R. E., Gur, R. C., Kaczkurkin, A. N., Larsen, B., Satterthwaite, T. D., Harper, J., Myers, M., Perino, M. T., Sylvester, C. M., Yu, Q., Lueken, U., Veltman, D. J., Thompson, P. M., Stein, D. J., Van der Wee, N. J. A., Winkler, A. M., and Pine, D. S.

Subjects
Adult, Male, medicine.medical_specialty, endocrine system, Generalized anxiety disorder, Neurosciences. Biological psychiatry. Neuropsychiatry, Anxiety, Audiology, Article, Structural magnetic resonance imaging, Cellular and Molecular Neuroscience, Secondary analysis, medicine, Psychology, Humans, ddc:610, Cortical surface, generalized anxiety disorder, structural brain imaging, cortical thickness, Child, diagnostic imaging [Brain], Biological Psychiatry, Medication use, diagnostic imaging [Anxiety Disorders], medicine.diagnostic_test, business.industry, Brain, Small sample, Magnetic resonance imaging, medicine.disease, Magnetic Resonance Imaging, Anxiety Disorders, Psychiatry and Mental health, multicentric network, Female, medicine.symptom, business, RC321-571, Neuroscience
Abstract

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.

Published
2021

5. Metabolism modulates network synchrony in the aging brain

Author

Anar Amgalan, Lilianne R. Mujica-Parodi, Helena van Nieuwenhuizen, Botond Antal, Rostam M. Razban, Ken A. Dill, and Corey Weistuch

Subjects
Brain network, Aging, Multidisciplinary, Mechanism (biology), Brain activity and meditation, Functional connectivity, Models, Neurological, Brain, Biological Sciences, Biology, Magnetic Resonance Imaging, Younger adults, Connectome, Humans, Aging brain, sense organs, Collective property, skin and connective tissue diseases, Neuroscience, Brain aging
Abstract

Brain aging is associated with hypometabolism and global changes in functional connectivity. Using functional MRI (fMRI), we show that network synchrony, a collective property of brain activity, decreases with age. Applying quantitative methods from statistical physics, we provide a generative (Ising) model for these changes as a function of the average communication strength between brain regions. We find that older brains are closer to a critical point of this communication strength, in which even small changes in metabolism lead to abrupt changes in network synchrony. Finally, by experimentally modulating metabolic activity in younger adults, we show how metabolism alone—independent of other changes associated with aging—can provide a plausible candidate mechanism for marked reorganization of brain network topology.

Published
2021

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