3 results
Mega-analysis of association between obesity and cortical morphology in bipolar disorders: ENIGMA study in 2832 participants
- Sean R. McWhinney, Christoph Abé, Martin Alda, Francesco Benedetti, Erlend Bøen, Caterina del Mar Bonnin, Tiana Borgers, Katharina Brosch, Erick J. Canales-Rodríguez, Dara M. Cannon, Udo Dannlowski, Ana M. Diaz-Zuluaga, Lorielle M.F. Dietze, Torbjørn Elvsåshagen, Lisa T. Eyler, Janice M. Fullerton, Jose M. Goikolea, Janik Goltermann, Dominik Grotegerd, Bartholomeus C. M. Haarman, Tim Hahn, Fleur M. Howells, Martin Ingvar, Neda Jahanshad, Tilo T. J. Kircher, Axel Krug, Rayus T. Kuplicki, Mikael Landén, Hannah Lemke, Benny Liberg, Carlos Lopez-Jaramillo, Ulrik F. Malt, Fiona M. Martyn, Elena Mazza, Colm McDonald, Genevieve McPhilemy, Sandra Meier, Susanne Meinert, Tina Meller, Elisa M. T. Melloni, Philip B. Mitchell, Leila Nabulsi, Igor Nenadic, Nils Opel, Roel A. Ophoff, Bronwyn J. Overs, Julia-Katharina Pfarr, Julian A. Pineda-Zapata, Edith Pomarol-Clotet, Joaquim Raduà, Jonathan Repple, Maike Richter, Kai G. Ringwald, Gloria Roberts, Alex Ross, Raymond Salvador, Jonathan Savitz, Simon Schmitt, Peter R. Schofield, Kang Sim, Dan J. Stein, Frederike Stein, Henk S. Temmingh, Katharina Thiel, Sophia I. Thomopoulos, Neeltje E. M. van Haren, Cristian Vargas, Eduard Vieta, Annabel Vreeker, Lena Waltemate, Lakshmi N. Yatham, Christopher R. K. Ching, Ole A. Andreassen, Paul M. Thompson, Tomas Hajek, for the ENIGMA Bipolar Disorder Working Group
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- Journal:
- Psychological Medicine / Volume 53 / Issue 14 / October 2023
- Published online by Cambridge University Press:
- 27 February 2023, pp. 6743-6753
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- Article
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- Open access
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Background:
Obesity is highly prevalent and disabling, especially in individuals with severe mental illness including bipolar disorders (BD). The brain is a target organ for both obesity and BD. Yet, we do not understand how cortical brain alterations in BD and obesity interact.
Methods:We obtained body mass index (BMI) and MRI-derived regional cortical thickness, surface area from 1231 BD and 1601 control individuals from 13 countries within the ENIGMA-BD Working Group. We jointly modeled the statistical effects of BD and BMI on brain structure using mixed effects and tested for interaction and mediation. We also investigated the impact of medications on the BMI-related associations.
Results:BMI and BD additively impacted the structure of many of the same brain regions. Both BMI and BD were negatively associated with cortical thickness, but not surface area. In most regions the number of jointly used psychiatric medication classes remained associated with lower cortical thickness when controlling for BMI. In a single region, fusiform gyrus, about a third of the negative association between number of jointly used psychiatric medications and cortical thickness was mediated by association between the number of medications and higher BMI.
Conclusions:We confirmed consistent associations between higher BMI and lower cortical thickness, but not surface area, across the cerebral mantle, in regions which were also associated with BD. Higher BMI in people with BD indicated more pronounced brain alterations. BMI is important for understanding the neuroanatomical changes in BD and the effects of psychiatric medications on the brain.
4 - COVIS
- Edited by Emmanuel M. Pothos, Swansea University, Andy J. Wills, University of Exeter
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- Book:
- Formal Approaches in Categorization
- Published online:
- 05 June 2012
- Print publication:
- 27 January 2011, pp 65-87
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Summary
Summary
The COVIS model of category learning assumes separate rule-based and procedural-learning categorization systems that compete for access to response production. The rule-based system selects and tests simple verbalizable hypotheses about category membership. The procedural-learning system gradually associates categorization responses with regions of perceptual space via reinforcement learning.
Description and motivation of COVIS
Despite the obvious importance of categorization to survival, and the varied nature of category-learning problems facing every animal, research on category learning has been narrowly focused (e.g., Markman & Ross, 2003). For example, the majority of category-learning studies have focused on situations in which two categories are relevant, the motor response is fixed, the nature and timing of feedback is constant (or ignored), and the only task facing the participant is the relevant categorization problem.
One reason for this narrow focus is that until recently, the goal of most categorization research has been to test predictions from purely cognitive models that assume a single category-learning system. In typical applications, the predictions of two competing single-system models were pitted against each other and simple goodness-of-fit was used to select a winner (Maddox & Ashby, 1993; McKinley & Nosofsky, 1995; Smith & Minda, 1998). During the past decade, however, two developments have begun to alter this landscape.
First, there are now many results suggesting that human categorization is mediated by multiple category-learning systems (Ashby & O'Brien, 2005; Ashby et al., 1998; Erickson & Kruschke, 1998; Love, Medin, & Gureckis, 2004; Reber et al., 2003).
Contributors
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- By Isabella Aboderin, W. Andrew Achenbaum, Katherine R. Allen, Toni C. Antonucci, Sara Arber, Claudine Attias‐Donfut, Paul B. Baltes, Sandhi Maria Barreto, Vern L. Bengtson, Simon Biggs, Joanna Bornat, Julie B. Boron, Mike Boulton, Clive E. Bowman, Marjolein Broese van Groenou, Edna Brown, Robert N. Butler, Bill Bytheway, Neena L. Chappell, Neil Charness, Kaare Christensen, Peter G. Coleman, Ingrid Arnet Connidis, Neal E. Cutler, Sara J. Czaja, Svein Olav Daatland, Lia Susana Daichman, Adam Davey, Bleddyn Davies, Freya Dittmann‐Kohli, Glen H. Elder, Carroll L. Estes, Mike Featherstone, Amy Fiske, Alexandra Freund, Daphna Gans, Linda K. George, Roseann Giarrusso, Chris Gilleard, Jay Ginn, Edlira Gjonça, Elena L. Grigorenko, Jaber F. Gubrium, Sarah Harper, Jutta Heckhausen, Akiko Hashimoto, Jon Hendricks, Mike Hepworth, Charlotte Ikels, James S. Jackson, Yuri Jang, Bernard Jeune, Malcolm L. Johnson, Randi S. Jones, Alexandre Kalache, Robert L. Kane, Rosalie A. Kane, Ingrid Keller, Rose Anne Kenny, Thomas B. L. Kirkwood, Kees Knipscheer, Martin Kohli, Gisela Labouvie‐Vief, Kristina Larsson, Shu‐Chen Li, Charles F. Longino, Ariela Lowenstein, Erick McCarthy, Gerald E. McClearn, Brendan McCormack, Elizabeth MacKinlay, Alfons Marcoen, Michael Marmot, Tom Margrain, Victor W. Marshall, Elizabeth A. Maylor, Ruud ter Meulen, Harry R. Moody, Robert A. Neimeyer, Demi Patsios, Margaret J. Penning, Stephen A. Petrill, Chris Phillipson, Leonard W. Poon, Norella M. Putney, Jill Quadagno, Pat Rabbitt, Jennifer Reid Keene, Sandra G. Reynolds, Steven R. Sabat, Clive Seale, Merril Silverstein, Hannes B. Staehelin, Ursula M. Staudinger, Robert J. Sternberg, Debra Street, Philip Taylor, Fleur Thomése, Mats Thorslund, Jinzhou Tian, Theo van Tilburg, Fernando M. Torres‐Gil, Josy Ubachs‐Moust, Christina Victor, K. Warner Shaie, Anthony M. Warnes, James L. Werth, Sherry L. Willis, François‐Charles Wolff, Bob Woods
- Edited by Malcolm L. Johnson, University of Bristol
- Edited in association with Vern L. Bengtson, University of Southern California, Peter G. Coleman, University of Southampton, Thomas B. L. Kirkwood, University of Newcastle upon Tyne
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- Book:
- The Cambridge Handbook of Age and Ageing
- Published online:
- 05 June 2016
- Print publication:
- 01 December 2005, pp xii-xvi
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