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Desrivières S, Zhang Z, Robinson L, Whelan R, Jollans L, Wang Z, Nees F, Chu C, Bobou M, Du D, Cristea I, Banaschewski T, Barker G, Bokde A, Grigis A, Garavan H, Heinz A, Bruhl R, Martinot JL, Martinot MLP, Artiges E, Orfanos DP, Poustka L, Hohmann S, Millenet S, Fröhner J, Smolka M, Vaidya N, Walter H, Winterer J, Broulidakis M, van Noort B, Stringaris A, Penttilä J, Grimmer Y, Insensee C, Becker A, Zhang Y, King S, Sinclair J, Schumann G, Schmidt U. Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder. Res Sq 2024:rs.3.rs-3777784. [PMID: 38352452 PMCID: PMC10862965 DOI: 10.21203/rs.3.rs-3777784/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
This study uses machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder (MDD), and alcohol use disorder (AUD). Utilizing case-control samples (ages 18-25 years) and a longitudinal population-based sample (n=1,851), the models, incorporating diverse data domains, achieved high accuracy in classifying EDs, MDD, and AUD from healthy controls. The area under the receiver operating characteristic curves (AUC-ROC [95% CI]) reached 0.92 [0.86-0.97] for AN and 0.91 [0.85-0.96] for BN, without relying on body mass index as a predictor. The classification accuracies for MDD (0.91 [0.88-0.94]) and AUD (0.80 [0.74-0.85]) were also high. Each data domain emerged as accurate classifiers individually, with personality distinguishing AN, BN, and their controls with AUC-ROCs ranging from 0.77 to 0.89. The models demonstrated high transdiagnostic potential, as those trained for EDs were also accurate in classifying AUD and MDD from healthy controls, and vice versa (AUC-ROCs, 0.75-0.93). Shared predictors, such as neuroticism, hopelessness, and symptoms of attention-deficit/hyperactivity disorder, were identified as reliable classifiers. For risk prediction in the longitudinal population sample, the models exhibited moderate performance (AUC-ROCs, 0.64-0.71), highlighting the potential of combining multi-domain data for precise diagnostic and risk prediction applications in psychiatry.
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Kenney JPM, Milena Rueda-Delgado L, Hanlon EO, Jollans L, Kelleher I, Healy C, Dooley N, McCandless C, Frodl T, Leemans A, Lebel C, Whelan R, Cannon M. Neuroanatomical markers of psychotic experiences in adolescents: A machine-learning approach in a longitudinal population-based sample. Neuroimage Clin 2022; 34:102983. [PMID: 35287090 PMCID: PMC8920932 DOI: 10.1016/j.nicl.2022.102983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 11/25/2022]
Abstract
It is important to identify accurate markers of psychiatric illness to aid early prediction of disease course. Subclinical psychotic experiences (PEs) are important risk factors for later mental ill-health and suicidal behaviour. This study used machine learning to investigate neuroanatomical markers of PEs in early and later stages of adolescence. Machine learning using logistic regression using Elastic Net regularization was applied to T1-weighted and diffusion MRI data to classify adolescents with subclinical psychotic experiences vs. controls across 3 timepoints (Time 1:11-13 years, n = 77; Time 2:14-16 years, n = 56; Time 3:18-20 years, n = 40). Neuroimaging data classified adolescents aged 11-13 years with current PEs vs. controls returning an AROC of 0.62, significantly better than a null model, p = 1.73e-29. Neuroimaging data also classified those with PEs at 18-20 years (AROC = 0.59;P = 7.19e-10) but performance was at chance level at 14-16 years (AROC = 0.50). Left hemisphere frontal regions were top discriminant classifiers for 11-13 years-old adolescents with PEs, particularly pars opercularis. Those with future PEs at 18-20 years-old were best distinguished from controls based on left frontal regions, right-hemisphere medial lemniscus, cingulum bundle, precuneus and genu of the corpus callosum (CC). Deviations from normal adolescent brain development in young people with PEs included an acceleration in the typical pattern of reduction in left frontal thickness and right parietal curvature, and accelerated progression of microstructural changes in right white matter and corpus callosum. These results emphasise the importance of multi-modal analysis for understanding adolescent PEs and provide important new insights into early phenotypes for psychotic experiences.
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Affiliation(s)
- Joanne P M Kenney
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; School of Psychology, Dublin City University, Dublin, Ireland
| | - Laura Milena Rueda-Delgado
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland
| | - Erik O Hanlon
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; Department of Psychiatry, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin 2, Ireland
| | - Lee Jollans
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
| | - Ian Kelleher
- Department of Psychiatry, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin 2, Ireland
| | - Colm Healy
- Department of Psychiatry, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin 2, Ireland
| | - Niamh Dooley
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; Department of Psychiatry, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin 2, Ireland
| | - Conor McCandless
- Department of Psychiatry, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin 2, Ireland
| | - Thomas Frodl
- School of Medicine, Trinity College Dublin, Dublin 2, Ireland
| | - Alexander Leemans
- Images Sciences Institute, University Medical Center Utrecht, The Netherlands
| | - Catherine Lebel
- Alberta Children's Hospital Research Institute and the Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Robert Whelan
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland
| | - Mary Cannon
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin 2, Ireland; Department of Psychiatry, Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin 2, Ireland
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Martinelli S, Anderzhanova EA, Bajaj T, Wiechmann S, Dethloff F, Weckmann K, Heinz DE, Ebert T, Hartmann J, Geiger TM, Döngi M, Hafner K, Pöhlmann ML, Jollans L, Philipsen A, Schmidt SV, Schmidt U, Maccarrone G, Stein V, Hausch F, Turck CW, Schmidt MV, Gellner AK, Kuster B, Gassen NC. Stress-primed secretory autophagy promotes extracellular BDNF maturation by enhancing MMP9 secretion. Nat Commun 2021; 12:4643. [PMID: 34330919 PMCID: PMC8324795 DOI: 10.1038/s41467-021-24810-5] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 07/07/2021] [Indexed: 11/23/2022] Open
Abstract
The stress response is an essential mechanism for maintaining homeostasis, and its disruption is implicated in several psychiatric disorders. On the cellular level, stress activates, among other mechanisms, autophagy that regulates homeostasis through protein degradation and recycling. Secretory autophagy is a recently described pathway in which autophagosomes fuse with the plasma membrane rather than with lysosomes. Here, we demonstrate that glucocorticoid-mediated stress enhances secretory autophagy via the stress-responsive co-chaperone FK506-binding protein 51. We identify the matrix metalloproteinase 9 (MMP9) as one of the proteins secreted in response to stress. Using cellular assays and in vivo microdialysis, we further find that stress-enhanced MMP9 secretion increases the cleavage of pro-brain-derived neurotrophic factor (proBDNF) to its mature form (mBDNF). BDNF is essential for adult synaptic plasticity and its pathway is associated with major depression and posttraumatic stress disorder. These findings unravel a cellular stress adaptation mechanism that bears the potential of opening avenues for the understanding of the pathophysiology of stress-related disorders.
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Affiliation(s)
- Silvia Martinelli
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.
| | - Elmira A Anderzhanova
- Research Group Neurohomeostasis, Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
- Department of Stress Neurobiology and Neurogenetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Thomas Bajaj
- Research Group Neurohomeostasis, Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Svenja Wiechmann
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Emil-Erlenmeyer-Forum 5, Freising, Germany
- German Cancer Consortium (DKTK), Munich, Germany
- German Cancer Center (DKFZ), Heidelberg, Germany
| | - Frederik Dethloff
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Metabolomics Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Katja Weckmann
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Daniel E Heinz
- Research Group Neurohomeostasis, Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
- Department of Stress Neurobiology and Neurogenetics, Max Planck Institute of Psychiatry, Munich, Germany
| | - Tim Ebert
- Research Group Neurohomeostasis, Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Jakob Hartmann
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, MA, USA
| | - Thomas M Geiger
- Institute for Organic Chemistry and Biochemistry, Technische Universität Darmstadt, Darmstadt, Germany
| | - Michael Döngi
- Institut für Physiologie II, University of Bonn, Bonn, Germany
| | - Kathrin Hafner
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Max L Pöhlmann
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Lee Jollans
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Alexandra Philipsen
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | | | - Ulrike Schmidt
- Research Group Molecular and Clinical Psychotraumatology, Department of Psychiatry and Psychotherapy, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
- Research Group Traumatic Stress & Neurodegeneration & PTSD Treatment Unit, Department of Psychiatry and Psychotherapy, University Medical Center Göttingen (UMG), Göttingen, Germany
- Department of Psychiatry and Neuropsychology, Maastricht University Medical Centre, School for Mental Health and Neuroscience, Maastricht, The Netherlands
| | - Giuseppina Maccarrone
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Valentin Stein
- Institut für Physiologie II, University of Bonn, Bonn, Germany
| | - Felix Hausch
- Institute for Organic Chemistry and Biochemistry, Technische Universität Darmstadt, Darmstadt, Germany
| | - Christoph W Turck
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
| | - Mathias V Schmidt
- Research Group Neurobiology of Stress Resilience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Anne-Kathrin Gellner
- Institut für Physiologie II, University of Bonn, Bonn, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Emil-Erlenmeyer-Forum 5, Freising, Germany
- German Cancer Consortium (DKTK), Munich, Germany
- German Cancer Center (DKFZ), Heidelberg, Germany
- Bavarian Center for Biomolecular Mass Spectrometry, Freising, Germany
| | - Nils C Gassen
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany.
- Research Group Neurohomeostasis, Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany.
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Rueda-Delgado LM, O'Halloran L, Enz N, Ruddy KL, Kiiski H, Bennett M, Farina F, Jollans L, Vahey N, Whelan R. Brain event-related potentials predict individual differences in inhibitory control. Int J Psychophysiol 2021; 163:22-34. [PMID: 30936044 DOI: 10.1016/j.ijpsycho.2019.03.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 03/21/2019] [Accepted: 03/26/2019] [Indexed: 11/18/2022]
Abstract
Stop-signal reaction time (SSRT), the time needed to cancel an already-initiated motor response, quantifies individual differences in inhibitory control. Electrophysiological correlates of SSRT have primarily focused on late event-related potential (ERP) components over midline scalp regions from successfully inhibited stop trials. SSRT is robustly associated with the P300, there is mixed evidence for N200 involvement, and there is little information on the role of early ERP components. Here, machine learning was first used to interrogate ERPs during both successful and failed stop trials from 64 scalp electrodes at 4 ms resolution (n = 148). The most predictive model included data from both successful and failed stop trials, with a cross-validated Pearson's r of 0.32 between measured and predicted SSRT, significantly higher than null models. From successful stop trials, spatio-temporal features overlapping the N200 in right frontal areas and the P300 in frontocentral areas predicted SSRT, as did early ERP activity (<200 ms). As a demonstration of the reproducibility of these findings, the application of this model to a separate dataset of 97 participants was also significant (r = 0.29). These results show that ERPs during failed stops are relevant to SSRT, and that both early and late ERP activity contribute to individual differences in SSRT. Notably, the right lateralized N200, which predicted SSRT here, is not often observed in neurotypical adults. Both the ascending slope and peak of the P300 component predicted SSRT. These results were replicable, both within the training sample and when applied to ERPs from a separate dataset.
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Affiliation(s)
| | - L O'Halloran
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
| | - N Enz
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
| | - K L Ruddy
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
| | - H Kiiski
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
| | - M Bennett
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
| | - F Farina
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
| | - L Jollans
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
| | - N Vahey
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
| | - R Whelan
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland.
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5
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Boyle R, Jollans L, Rueda-Delgado LM, Rizzo R, Yener GG, McMorrow JP, Knight SP, Carey D, Robertson IH, Emek-Savaş DD, Stern Y, Kenny RA, Whelan R. Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis. Brain Imaging Behav 2021; 15:327-345. [PMID: 32141032 DOI: 10.1007/s11682-020-00260-3] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Brain-predicted age difference scores are calculated by subtracting chronological age from 'brain' age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylül University (n = 175), the Cognitive Reserve/Reference Ability Neural Network study (n = 380), and The Irish Longitudinal Study on Ageing (n = 487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.
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Affiliation(s)
- Rory Boyle
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Lee Jollans
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, München, Germany
| | - Laura M Rueda-Delgado
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Rossella Rizzo
- Physics Department, University of Calabria, Rende, CS, Italy
| | - Görsev G Yener
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, İzmir, Turkey
- Department of Neurology, Dokuz Eylul University Medical School, İzmir, Turkey
- Brain Dynamics Multidisciplinary Research Center, Dokuz Eylul University, İzmir, Turkey
| | - Jason P McMorrow
- Centre for Advanced Medical Imaging, St. James's Hospital, Dublin 8, Ireland
- School of Medicine, Trinity College Dublin, Dublin 2, Ireland
| | - Silvin P Knight
- School of Medicine, Trinity College Dublin, Dublin 2, Ireland
- The Irish Longitudinal Study on Ageing, Trinity College Dublin, Dublin 2, Ireland
| | - Daniel Carey
- The Irish Longitudinal Study on Ageing, Trinity College Dublin, Dublin 2, Ireland
- Department of Medical Gerontology, Trinity College Dublin, Dublin 2, Ireland
| | - Ian H Robertson
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
- Global Brain Health Institute, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Derya D Emek-Savaş
- Department of Neurosciences, Institute of Health Sciences, Dokuz Eylul University, İzmir, Turkey
- Global Brain Health Institute, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
- Department of Psychology, Faculty of Letters, Dokuz Eylul University, İzmir, Turkey
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of Neurology, Columbia University, New York, NY, USA
| | - Rose Anne Kenny
- School of Medicine, Trinity College Dublin, Dublin 2, Ireland
- The Irish Longitudinal Study on Ageing, Trinity College Dublin, Dublin 2, Ireland
- Mercer's Institute for Successful Ageing, St. James's Hospital, Dublin 8, Ireland
| | - Robert Whelan
- Trinity College Institute of Neuroscience, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland.
- Global Brain Health Institute, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland.
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6
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Tzovara A, Amarreh I, Borghesani V, Chakravarty MM, DuPre E, Grefkes C, Haugg A, Jollans L, Lee HW, Newman SD, Olsen RK, Ratnanather JT, Rippon G, Uddin LQ, Vega MLB, Veldsman M, White T, Badhwar A. Embracing diversity and inclusivity in an academic setting: Insights from the Organization for Human Brain Mapping. Neuroimage 2021; 229:117742. [PMID: 33454405 DOI: 10.1016/j.neuroimage.2021.117742] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 01/07/2021] [Accepted: 01/10/2021] [Indexed: 12/17/2022] Open
Abstract
Scientific research aims to bring forward innovative ideas and constantly challenges existing knowledge structures and stereotypes. However, women, ethnic and cultural minorities, as well as individuals with disabilities, are systematically discriminated against or even excluded from promotions, publications, and general visibility. A more diverse workforce is more productive, and thus discrimination has a negative impact on science and the wider society, as well as on the education, careers, and well-being of individuals who are discriminated against. Moreover, the lack of diversity at scientific gatherings can lead to micro-aggressions or harassment, making such meetings unpleasant, or even unsafe environments for early career and underrepresented scientists. At the Organization for Human Brain Mapping (OHBM), we recognized the need for promoting underrepresented scientists and creating diverse role models in the field of neuroimaging. To foster this, the OHBM has created a Diversity and Inclusivity Committee (DIC). In this article, we review the composition and activities of the DIC that have promoted diversity within OHBM, in order to inspire other organizations to implement similar initiatives. Activities of the committee over the past four years have included (a) creating a code of conduct, (b) providing diversity and inclusivity education for OHBM members, (c) organizing interviews and symposia on diversity issues, and (d) organizing family-friendly activities and providing childcare grants during the OHBM annual meetings. We strongly believe that these activities have brought positive change within the wider OHBM community, improving inclusivity and fostering diversity while promoting rigorous, ground-breaking science. These positive changes could not have been so rapidly implemented without the enthusiastic support from the leadership, including OHBM Council and Program Committee, and the OHBM Special Interest Groups (SIGs), namely the Open Science, Student and Postdoc, and Brain-Art SIGs. Nevertheless, there remains ample room for improvement, in all areas, and even more so in the area of targeted attempts to increase inclusivity for women, individuals with disabilities, members of the LGBTQ+ community, racial/ethnic minorities, and individuals of lower socioeconomic status or from low and middle-income countries. Here, we present an overview of the DIC's composition, its activities, future directions and challenges. Our goal is to share our experiences with a wider audience to provide information to other organizations and institutions wishing to implement similar comprehensive diversity initiatives. We propose that scientific organizations can push the boundaries of scientific progress only by moving beyond existing power structures and by integrating principles of equity and inclusivity in their core values.
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Affiliation(s)
- Athina Tzovara
- Institute for Computer Science, University of Bern, Neubrückstrasse 10, CH-3012 Bern, Switzerland; Helen Wills Neuroscience Institute, University of California Berkeley, USA; Sleep Wake Epilepsy Center
- NeuroTec, Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland.
| | | | - Valentina Borghesani
- Memory and Aging Center, Department of Neurology, University of California San Francisco
| | - M Mallar Chakravarty
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Research Centre; Departments of Psychiatry and Biological and Biomedical Engineering at McGill University
| | - Elizabeth DuPre
- NeuroDataScience - ORIGAMI laboratory, McGill University, Montreal, Canada
| | - Christian Grefkes
- University of Cologne, Medical Faculty, and Department of Neurology, University Hospital Cologne, Germany; Institute of Medicine and Neuroscience, Cognitive Neurology (INM-3), Juelich Research Center, Germany
| | - Amelie Haugg
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zurich, Zurich, Switzerland
| | - Lee Jollans
- Department of Translational Research in Psychiatry; Max Planck Institute of Psychiatry; Munich, Germany
| | - Hyang Woon Lee
- Departments of Neurology, Medical Science, Computational Medicine and System Health & Engineering Major, Ewha Womans University School of Medicine and Ewha Medical Research Institute, Seoul, South Korea
| | - Sharlene D Newman
- Alabama Life Research Institute, University of Alabama, Tuscaloosa, AL, USA
| | - Rosanna K Olsen
- Rotman Research Institute, Baycrest Health Sciences, and Department of Psychology, University of Toronto
| | - J Tilak Ratnanather
- Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Gina Rippon
- Aston Brain Centre, Aston University, Birmingham B4 7ET, UK
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Maria L Bringas Vega
- University of Electronic Sciences and Technology of China, Chengdu China; Cuban Neuroscience Center, La Habana, Cuba
| | - Michele Veldsman
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Tonya White
- Department of Child and Adolescent Psychiatry, Erasmus University Medical Centre, Rotterdam; Department of Radiology and Nuclear Medicine, Erasmus University Medical Centre, Rotterdam
| | - AmanPreet Badhwar
- Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Université de Montréal, Montréal, Quebec H3W 1W5, Canada; Université de Montréal, Département de pharmacologie et physiologie, Montreal, Canada.
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7
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Brückl TM, Spoormaker VI, Sämann PG, Brem AK, Henco L, Czamara D, Elbau I, Grandi NC, Jollans L, Kühnel A, Leuchs L, Pöhlchen D, Schneider M, Tontsch A, Keck ME, Schilbach L, Czisch M, Lucae S, Erhardt A, Binder EB. The biological classification of mental disorders (BeCOME) study: a protocol for an observational deep-phenotyping study for the identification of biological subtypes. BMC Psychiatry 2020; 20:213. [PMID: 32393358 PMCID: PMC7216390 DOI: 10.1186/s12888-020-02541-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 03/10/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND A major research finding in the field of Biological Psychiatry is that symptom-based categories of mental disorders map poorly onto dysfunctions in brain circuits or neurobiological pathways. Many of the identified (neuro) biological dysfunctions are "transdiagnostic", meaning that they do not reflect diagnostic boundaries but are shared by different ICD/DSM diagnoses. The compromised biological validity of the current classification system for mental disorders impedes rather than supports the development of treatments that not only target symptoms but also the underlying pathophysiological mechanisms. The Biological Classification of Mental Disorders (BeCOME) study aims to identify biology-based classes of mental disorders that improve the translation of novel biomedical findings into tailored clinical applications. METHODS BeCOME intends to include at least 1000 individuals with a broad spectrum of affective, anxiety and stress-related mental disorders as well as 500 individuals unaffected by mental disorders. After a screening visit, all participants undergo in-depth phenotyping procedures and omics assessments on two consecutive days. Several validated paradigms (e.g., fear conditioning, reward anticipation, imaging stress test, social reward learning task) are applied to stimulate a response in a basic system of human functioning (e.g., acute threat response, reward processing, stress response or social reward learning) that plays a key role in the development of affective, anxiety and stress-related mental disorders. The response to this stimulation is then read out across multiple levels. Assessments comprise genetic, molecular, cellular, physiological, neuroimaging, neurocognitive, psychophysiological and psychometric measurements. The multilevel information collected in BeCOME will be used to identify data-driven biologically-informed categories of mental disorders using cluster analytical techniques. DISCUSSION The novelty of BeCOME lies in the dynamic in-depth phenotyping and omics characterization of individuals with mental disorders from the depression and anxiety spectrum of varying severity. We believe that such biology-based subclasses of mental disorders will serve as better treatment targets than purely symptom-based disease entities, and help in tailoring the right treatment to the individual patient suffering from a mental disorder. BeCOME has the potential to contribute to a novel taxonomy of mental disorders that integrates the underlying pathomechanisms into diagnoses. TRIAL REGISTRATION Retrospectively registered on June 12, 2019 on ClinicalTrials.gov (TRN: NCT03984084).
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Affiliation(s)
- Tanja M. Brückl
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Victor I. Spoormaker
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Philipp G. Sämann
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Anna-Katharine Brem
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany ,grid.38142.3c000000041936754XBerenson-Allen Center for Noninvasive Brain Stimulation and Division for Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - Lara Henco
- grid.419548.50000 0000 9497 5095Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Darina Czamara
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Immanuel Elbau
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Norma C. Grandi
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Lee Jollans
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Anne Kühnel
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany ,grid.419548.50000 0000 9497 5095International Max Planck Research School – Translational Psychiatry (IMPRS-TP), Max Planck Institute of Psychiatry, Munich, Germany
| | - Laura Leuchs
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Dorothee Pöhlchen
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany ,grid.419548.50000 0000 9497 5095International Max Planck Research School – Translational Psychiatry (IMPRS-TP), Max Planck Institute of Psychiatry, Munich, Germany
| | - Maximilian Schneider
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Alina Tontsch
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Martin E. Keck
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Leonhard Schilbach
- grid.419548.50000 0000 9497 5095Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Michael Czisch
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Susanne Lucae
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Angelika Erhardt
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Elisabeth B. Binder
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany ,grid.189967.80000 0001 0941 6502Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, USA
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8
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O'Halloran L, Rueda‐Delgado LM, Jollans L, Cao Z, Boyle R, Vaughan C, Coey P, Whelan R. Inhibitory-control event-related potentials correlate with individual differences in alcohol use. Addict Biol 2020; 25:e12729. [PMID: 30919532 DOI: 10.1111/adb.12729] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 12/28/2018] [Accepted: 01/23/2019] [Indexed: 12/16/2022]
Abstract
Impulsivity is a multidimensional construct that is related to different aspects of alcohol use, abuse, and dependence. Inhibitory control, one facet of impulsivity, can be assayed using the stop-signal task (SST) and quantified behaviorally via the stop-signal reaction time (SSRT) and electrophysiologically using event-related potentials (ERPs). Research on the relationship between alcohol use and SSRTs, and between alcohol use and inhibitory-control ERPs, is mixed. Here, adult alcohol users (n = 79), with a wide range of scores on the Alcohol Use Disorders Identification Test (AUDIT), completed the SST under electroencephalography (EEG) (70% of participants had AUDIT total scores greater than or equal to 8). Other measures, including demographic, self-report, and task-based measures of impulsivity, personality, and psychological factors, were also recorded. A machine-learning method with penalized linear regression was used to correlate individual differences in alcohol use with impulsivity measures. Four separate models were tested, with out-of-sample validation used to quantify performance. ERPs alone statistically predicted alcohol use (cross-validated r = 0.28), with both early and late ERP components contributing to the model (larger N2, but smaller P3, amplitude). Behavioral data from a wide range of impulsivity measures were also associated with alcohol use (r = 0.37). SSRT was a relatively weak statistical predictor, whereas the Stroop interference effect was relatively strong. The addition of nonimpulsivity behavioral measures did not improve the correlation (r = 0.34) and was similar when ERPs were combined with non-ERP data (r = 0.29). These findings show that inhibitory control ERPs are robustly correlated individual differences in alcohol use.
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Affiliation(s)
| | | | - Lee Jollans
- School of PsychologyTrinity College Dublin Dublin Ireland
| | - Zhipeng Cao
- School of PsychologyTrinity College Dublin Dublin Ireland
| | - Rory Boyle
- School of PsychologyTrinity College Dublin Dublin Ireland
| | | | - Phillip Coey
- School of PsychologyTrinity College Dublin Dublin Ireland
| | - Robert Whelan
- School of PsychologyTrinity College Dublin Dublin Ireland
- Global Brain Health InstituteTrinity College Dublin Dublin Ireland
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9
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Jollans L, Boyle R, Artiges E, Banaschewski T, Desrivières S, Grigis A, Martinot JL, Paus T, Smolka MN, Walter H, Schumann G, Garavan H, Whelan R. Quantifying performance of machine learning methods for neuroimaging data. Neuroimage 2019; 199:351-365. [PMID: 31173905 DOI: 10.1016/j.neuroimage.2019.05.082] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Revised: 05/21/2019] [Accepted: 05/30/2019] [Indexed: 01/18/2023] Open
Abstract
Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the relative efficacy of different machine learning regression algorithms for different types of neuroimaging data are not known. Here, we sought to quantify the performance of a variety of machine learning algorithms for use with neuroimaging data with various sample sizes, feature set sizes, and predictor effect sizes. The contribution of additional machine learning techniques - embedded feature selection and bootstrap aggregation (bagging) - to model performance was also quantified. Five machine learning regression methods - Gaussian Process Regression, Multiple Kernel Learning, Kernel Ridge Regression, the Elastic Net and Random Forest, were examined with both real and simulated MRI data, and in comparison to standard multiple regression. The different machine learning regression algorithms produced varying results, which depended on sample size, feature set size, and predictor effect size. When the effect size was large, the Elastic Net, Kernel Ridge Regression and Gaussian Process Regression performed well at most sample sizes and feature set sizes. However, when the effect size was small, only the Elastic Net made accurate predictions, but this was limited to analyses with sample sizes greater than 400. Random Forest also produced a moderate performance for small effect sizes, but could do so across all sample sizes. Machine learning techniques also improved prediction accuracy for multiple regression. These data provide empirical evidence for the differential performance of various machines on neuroimaging data, which are dependent on number of sample size, features and effect size.
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Affiliation(s)
- Lee Jollans
- School of Psychology, Trinity College Dublin, Dublin, Ireland; Department of Translational Research in Psychiatry, Max-Planck Institute of Psychiatry, Munich, Germany
| | - Rory Boyle
- School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry", University Paris Sud, University Paris Descartes - Sorbonne Paris Cité, and Psychiatry Department 91G16, Orsay Hospital, France
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159, Mannheim, Germany
| | - Sylvane Desrivières
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191, Gif-sur-Yvette, France
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry", University Paris Sud, University Paris Descartes - Sorbonne Paris Cité, and Maison de Solenn, Paris, France
| | - Tomáš Paus
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital and Departments of Psychology and Psychiatry, University of Toronto, Toronto, Ontario, M6A 2E1, Canada
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Gunter Schumann
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont, Burlington, USA
| | - Robert Whelan
- School of Psychology, Trinity College Dublin, Dublin, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.
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10
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Rai LA, O'Halloran L, Jollans L, Vahey N, O'Brolchain C, Whelan R. Individual differences in learning from probabilistic reward and punishment predicts smoking status. Addict Behav 2019; 88:73-76. [PMID: 30149293 DOI: 10.1016/j.addbeh.2018.08.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 08/10/2018] [Accepted: 08/14/2018] [Indexed: 11/24/2022]
Abstract
INTRODUCTION The ability to update reward and punishment contingencies is a fundamental aspect of effective decision-making, requiring the ability to successfully adapt to the changing demands of one's environment. In the case of nicotine addiction, research has predominantly focused on reward- and punishment-based learning processes among current smokers relative to non-smokers, whereas less is known about these processes in former smokers. METHODS In a total sample of 105 students, we used the Probabilistic Selection Task to examine differences in reinforcement learning among 41 current smokers, 29 ex-smokers, and 35 non-smokers. The PST was comprised of a training and test phase that allowed for the comparison of learning from positive versus negative feedback. RESULTS The test phase of the Probabilistic Selection Task significantly predicted smoking status. Current and non-smokers were classified with moderate accuracy, whereas ex-smokers were typically misclassified as smokers. Lower rates of learning from rewards were associated with an increased likelihood of being a smoker or an ex-smoker compared with being a non-smoker. Higher rates of learning from punishment were associated with an increased likelihood of being a smoker relative to non-smoker. However, learning from punishment did not predict ex-smoker status. CONCLUSIONS Current smokers and ex-smokers were less likely to learn from rewards, supporting the hypothesis that deficient reward processing is a feature of chronic addiction. In addition, current smokers were more sensitive to punishment than ex-smokers, contradicting some recent findings.
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O'Halloran L, Pennie B, Jollans L, Kiiski H, Vahey N, Rai L, Bradley L, Lalor R, Whelan R. A Combination of Impulsivity Subdomains Predict Alcohol Intoxication Frequency. Alcohol Clin Exp Res 2018; 42:1530-1540. [PMID: 29905967 DOI: 10.1111/acer.13779] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 05/07/2018] [Indexed: 02/07/2023]
Abstract
BACKGROUND Impulsivity, broadly characterized as the tendency to act prematurely without foresight, is linked to alcohol misuse in college students. However, impulsivity is a multidimensional construct and different subdomains likely underlie different patterns of alcohol misuse. Here, we quantified the association between alcohol intoxication frequency and alcohol consumption frequency and choice, action, cognitive, and trait domains of impulsivity. METHODS University student drinkers (n = 106) completed a battery of demographic and alcohol-related items, as well as self-report and task-based measures indexing different facets of impulsivity. Two orthogonal latent factors, intoxication frequency and alcohol consumption frequency, were generated. Their validity was demonstrated with respect to adverse consequences of alcohol use. Machine learning with penalized regression and feature selection was then utilized to predict intoxication and alcohol consumption frequency using all impulsivity subdomains. Out-of-sample validation was used to quantify model performance. RESULTS Impulsivity measures alone were significant predictors of intoxication frequency, but not consumption frequency. Propensity for increased intoxication frequency was characterized by increased trait impulsivity, including the Disinhibition subscale of the Sensation Seeking Scale, Attentional and Non-planning subscales of the Barratt Impulsiveness Scale, increased task-based cognitive impulsivity (response time variability), and increased choice impulsivity (steeper delay discounting on a delay discounting questionnaire). A model combining impulsivity domains with other risk factors (gender; nicotine, cannabis, and other drug use; executive functioning; and learning processes) was also significant but did not outperform the model comprising of impulsivity alone. CONCLUSIONS Intoxication frequency, but not consumption frequency, was characterized by a number of impulsivity subdomains.
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Affiliation(s)
| | - Brian Pennie
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
| | - Lee Jollans
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
| | - Hanni Kiiski
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
| | - Nigel Vahey
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
| | - Laura Rai
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
| | - Louisa Bradley
- School of Psychology, University College Dublin, Dublin 4, Ireland
| | - Robert Lalor
- School of Psychology, University College Dublin, Dublin 4, Ireland
| | - Robert Whelan
- School of Psychology, Trinity College Dublin, Dublin 2, Ireland
- Global Brain Health Institute, Trinity College Dublin, Dublin 2, Ireland
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Abstract
Despite abundant research into the neurobiology of mental disorders, to date neurobiological insights have had very little impact on psychiatric diagnosis or treatment. In this review, we contend that the search for neuroimaging biomarkers-neuromarkers-of mental disorders is a highly promising avenue toward improved psychiatric healthcare. However, many of the traditional tools used for psychiatric neuroimaging are inadequate for the identification of neuromarkers. Specifically, we highlight the need for larger samples and for multivariate analysis. Approaches such as machine learning are likely to be beneficial for interrogating high-dimensional neuroimaging data. We suggest that broad, population-based study designs will be important for developing neuromarkers of mental disorders, and will facilitate a move away from a phenomenological definition of mental disorder categories and toward psychiatric nosology based on biological evidence. We provide an outline of how the development of neuromarkers should occur, emphasizing the need for tests of external and construct validity, and for collaborative research efforts. Finally, we highlight some concerns regarding the development, and use of, neuromarkers in psychiatric healthcare.
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Affiliation(s)
- Lee Jollans
- School of Psychology and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Robert Whelan
- School of Psychology and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
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13
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O'Halloran L, Nymberg C, Jollans L, Garavan H, Whelan R. The potential of neuroimaging for identifying predictors of adolescent alcohol use initiation and misuse. Addiction 2017; 112:719-726. [PMID: 27917536 DOI: 10.1111/add.13629] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Revised: 07/04/2016] [Accepted: 10/12/2016] [Indexed: 12/29/2022]
Abstract
BACKGROUND AND AIMS Dysfunction in brain regions underlying impulse control, reward processing and executive function have been associated previously with adolescent alcohol misuse. However, identifying pre-existing neurobiological risk factors, as distinct from changes arising from early alcohol-use, is difficult. Here, we outline how neuroimaging data can identify the neural predictors of adolescent alcohol-use initiation and misuse by using prospective longitudinal studies to follow initially alcohol-naive individuals over time and by neuroimaging adolescents with inherited risk factors for alcohol misuse. METHOD A comprehensive narrative of the literature regarding neuroimaging studies published between 2010 and 2016 focusing on predictors of adolescent alcohol use initiation and misuse. FINDINGS Prospective, longitudinal neuroimaging studies have identified pre-existing differences between adolescents who remained alcohol-naive and those who transitioned subsequently to alcohol use. Both functional and structural grey matter differences were observed in temporal and frontal regions, including reduced brain activity in the superior frontal gyrus and temporal lobe, and thinner temporal cortices of future alcohol users. Interactions between brain function and genetic predispositions have been identified, including significant association found between the Ras protein-specific guanine nucleotide releasing factor 2 (RASGRF2) gene and reward-related striatal functioning. CONCLUSIONS Neuroimaging predictors of alcohol use have shown modest utility to date. Future research should use out-of-sample performance as a quantitative measure of a predictor's utility. Neuroimaging data should be combined across multiple modalities, including structural information such as volumetrics and cortical thickness, in conjunction with white-matter tractography. A number of relevant neurocognitive systems should be assayed; particularly, inhibitory control, reward processing and executive functioning. Combining a rich magnetic resonance imaging data set could permit the generation of neuroimaging risk scores, which could potentially yield targeted interventions.
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Affiliation(s)
| | - Charlotte Nymberg
- Department for Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Lee Jollans
- School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - Robert Whelan
- School of Psychology, Trinity College Dublin, Dublin, Ireland.,Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
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14
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Jollans L, Whelan R, Venables L, Turnbull OH, Cella M, Dymond S. Computational EEG modelling of decision making under ambiguity reveals spatio-temporal dynamics of outcome evaluation. Behav Brain Res 2017; 321:28-35. [DOI: 10.1016/j.bbr.2016.12.033] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2016] [Revised: 12/19/2016] [Accepted: 12/23/2016] [Indexed: 01/08/2023]
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15
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Jollans L, Whelan R. The Clinical Added Value of Imaging: A Perspective From Outcome Prediction. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2016; 1:423-432. [DOI: 10.1016/j.bpsc.2016.04.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 04/06/2016] [Accepted: 04/28/2016] [Indexed: 01/02/2023]
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16
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Jollans L, Zhipeng C, Icke I, Greene C, Kelly C, Banaschewski T, Bokde ALW, Bromberg U, Büchel C, Cattrell A, Conrod PJ, Desrivières S, Flor H, Frouin V, Gallinat J, Garavan H, Gowland P, Heinz A, Ittermann B, Martinot JL, Artiges E, Nees F, Papadopoulos Orfanos D, Paus T, Smolka MN, Walter H, Schumann G, Whelan R. Ventral Striatum Connectivity During Reward Anticipation in Adolescent Smokers. Dev Neuropsychol 2016; 41:6-21. [PMID: 27074029 DOI: 10.1080/87565641.2016.1164172] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Substance misusers, including adolescent smokers, often have reduced reward system activity during processing of non-drug rewards. Using a psychophysiological interaction approach, we examined functional connectivity with the ventral striatum during reward anticipation in a large (N = 206) sample of adolescent smokers. Increased smoking frequency was associated with (1) increased connectivity with regions involved in saliency and valuation, including the orbitofrontal cortex and (2) reduced connectivity between the ventral striatum and regions associated with inhibition and risk aversion, including the right inferior frontal gyrus. These results demonstrate that functional connectivity during reward processing is relevant to adolescent addiction.
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Affiliation(s)
- Lee Jollans
- a Department of Psychology , University College Dublin , Dublin , Ireland
| | - Cao Zhipeng
- a Department of Psychology , University College Dublin , Dublin , Ireland
| | - Ilknur Icke
- b Bioimaging, School of Medicine , Boston University , Boston , Massachusetts
| | - Ciara Greene
- a Department of Psychology , University College Dublin , Dublin , Ireland
| | - Clare Kelly
- c Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neurosciences , Trinity College Dublin , Dublin , Ireland
| | - Tobias Banaschewski
- d Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim , Heidelberg University , Mannheim , Germany
| | - Arun L W Bokde
- c Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neurosciences , Trinity College Dublin , Dublin , Ireland
| | - Uli Bromberg
- e University Medical Centre Hamburg-Eppendorf , Hamburg , Germany
| | - Christian Büchel
- e University Medical Centre Hamburg-Eppendorf , Hamburg , Germany
| | - Anna Cattrell
- h Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience , King's College London , London , United Kingdom
| | - Patricia J Conrod
- f Department of Psychiatry , Universite de Montreal, CHU Ste Justine Hospital , Montreal , Canada.,g Department of Psychological Medicine and Psychiatry, Institute of Psychiatry, Psychology & Neuroscience , King's College London , London , United Kingdom
| | - Sylvane Desrivières
- u Medical Research Council-Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience , King's College London , London , United Kingdom
| | - Herta Flor
- i Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim , Heidelberg University , Mannheim , Germany
| | - Vincent Frouin
- j Neurospin, Commissariat à l'Energie Atomique , CEA-Saclay Center , Paris , France
| | - Jürgen Gallinat
- k Department of Psychiatry and Psychotherapy , University Medical Center Hamburg-Eppendorf (UKE) , Hamburg , Germany
| | - Hugh Garavan
- l Departments of Psychiatry and Psychology , University of Vermont , Burlington , Vermont
| | - Penny Gowland
- m Sir Peter Mansfield Imaging Centre School of Physics and Astronomy , University of Nottingham , University Park , Nottingham , United Kingdom
| | - Andreas Heinz
- n Department of Psychiatry and Psychotherapy, Campus Charité Mitte , Charité, Universitätsmedizin Berlin , Berlin , Germany
| | - Bernd Ittermann
- o Physikalisch-Technische Bundesanstalt (PTB) , Braunschweig and Berlin , Germany
| | - Jean-Luc Martinot
- p Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry," University Paris Sud, University Paris Descartes-Sorbonne Paris Cité and Maison de Solenn , Paris , France
| | - Eric Artiges
- q Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry," University Paris Sud, University Paris Descartes-Sorbonne Paris Cité and Psychiatry Department 91G16, Orsay Hospital , Paris , France
| | - Frauke Nees
- d Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim , Heidelberg University , Mannheim , Germany.,i Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim , Heidelberg University , Mannheim , Germany
| | | | - Tomáš Paus
- r Rotman Research Institute, Baycrest and Departments of Psychology and Psychiatry , University of Toronto , Toronto , Canada
| | - Michael N Smolka
- s Department of Psychiatry and Neuroimaging Center , Technische Universität Dresden , Dresden , Germany
| | - Henrik Walter
- n Department of Psychiatry and Psychotherapy, Campus Charité Mitte , Charité, Universitätsmedizin Berlin , Berlin , Germany
| | - Gunter Schumann
- t Department of Psychiatry , Universite de Montreal, CHU Ste Justine Hospital , Montreal , Canada
| | - Robert Whelan
- a Department of Psychology , University College Dublin , Dublin , Ireland
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