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Cheek CL, Lindner P, Grigorenko EL. Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods. Behav Genet 2024; 54:233-251. [PMID: 38336922 DOI: 10.1007/s10519-024-10177-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 01/24/2024] [Indexed: 02/12/2024]
Abstract
Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate to current deep learning approaches, highlighting each approach's strengths and weaknesses and elongating it with the field's development. We conclude by discussing selected remaining challenges and prospects for the field.
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Affiliation(s)
- Connor L Cheek
- Texas Institute for Evaluation, Measurement, and Statistics, University of Houston, Houston, TX, USA.
- Department of Physics, University of Houston, Houston, TX, USA.
| | - Peggy Lindner
- Texas Institute for Evaluation, Measurement, and Statistics, University of Houston, Houston, TX, USA
- Department of Information Science Technology, University of Houston, Houston, TX, USA
| | - Elena L Grigorenko
- Texas Institute for Evaluation, Measurement, and Statistics, University of Houston, Houston, TX, USA
- Department of Psychology, University of Houston, Houston, TX, USA
- Baylor College of Medicine, Houston, TX, USA
- Sirius University of Science and Technology, Sochi, Russia
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Cao H, Hong X, Tost H, Meyer-Lindenberg A, Schwarz E. Advancing translational research in neuroscience through multi-task learning. Front Psychiatry 2022; 13:993289. [PMID: 36465289 PMCID: PMC9714033 DOI: 10.3389/fpsyt.2022.993289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 10/24/2022] [Indexed: 11/18/2022] Open
Abstract
Translational research in neuroscience is increasingly focusing on the analysis of multi-modal data, in order to account for the biological complexity of suspected disease mechanisms. Recent advances in machine learning have the potential to substantially advance such translational research through the simultaneous analysis of different data modalities. This review focuses on one of such approaches, the so-called "multi-task learning" (MTL), and describes its potential utility for multi-modal data analyses in neuroscience. We summarize the methodological development of MTL starting from conventional machine learning, and present several scenarios that appear particularly suitable for its application. For these scenarios, we highlight different types of MTL algorithms, discuss emerging technological adaptations, and provide a step-by-step guide for readers to apply the MTL approach in their own studies. With its ability to simultaneously analyze multiple data modalities, MTL may become an important element of the analytics repertoire used in future neuroscience research and beyond.
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Affiliation(s)
- Han Cao
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Xudong Hong
- Department of Computer Vision and Machine Learning, Max Planck Institute for Informatics, Saarbrücken, Germany
- Department of Language Science and Technology, Saarland University, Saarbrücken, Germany
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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Meyer-Lindenberg A, Hirjak D. Schizophrenia as a categorical diagnosis: A view from the neural risk architecture. Schizophr Res 2022; 242:87-90. [PMID: 35086745 DOI: 10.1016/j.schres.2022.01.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 01/13/2022] [Accepted: 01/13/2022] [Indexed: 11/17/2022]
Affiliation(s)
- Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany.
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, Mannheim, Germany
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Sharma E, Jacob P, Murthy P, Jain S, Varghese M, Jayarajan D, Kumar K, Benegal V, Vaidya N, Zhang Y, Desrivieres S, Schumann G, Iyengar U, Holla B, Purushottam M, Chakrabarti A, Fernandes GS, Heron J, Hickman M, Kartik K, Kalyanram K, Rangaswamy M, Bharath RD, Barker G, Orfanos DP, Ahuja C, Thennarasu K, Basu D, Subodh BN, Kuriyan R, Kurpad SS, Kumaran K, Krishnaveni G, Krishna M, Singh RL, Singh LR, Toledano M. Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA): A developmental cohort study protocol. BMC Psychiatry 2020; 20:2. [PMID: 31898525 PMCID: PMC6941284 DOI: 10.1186/s12888-019-2373-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 11/26/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Low and middle-income countries like India with a large youth population experience a different environment from that of high-income countries. The Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA), based in India, aims to examine environmental influences on genomic variations, neurodevelopmental trajectories and vulnerability to psychopathology, with a focus on externalizing disorders. METHODS cVEDA is a longitudinal cohort study, with planned missingness design for yearly follow-up. Participants have been recruited from multi-site tertiary care mental health settings, local communities, schools and colleges. 10,000 individuals between 6 and 23 years of age, of all genders, representing five geographically, ethnically, and socio-culturally distinct regions in India, and exposures to variations in early life adversity (psychosocial, nutritional, toxic exposures, slum-habitats, socio-political conflicts, urban/rural living, mental illness in the family) have been assessed using age-appropriate instruments to capture socio-demographic information, temperament, environmental exposures, parenting, psychiatric morbidity, and neuropsychological functioning. Blood/saliva and urine samples have been collected for genetic, epigenetic and toxicological (heavy metals, volatile organic compounds) studies. Structural (T1, T2, DTI) and functional (resting state fMRI) MRI brain scans have been performed on approximately 15% of the individuals. All data and biological samples are maintained in a databank and biobank, respectively. DISCUSSION The cVEDA has established the largest neurodevelopmental database in India, comparable to global datasets, with detailed environmental characterization. This should permit identification of environmental and genetic vulnerabilities to psychopathology within a developmental framework. Neuroimaging and neuropsychological data from this study are already yielding insights on brain growth and maturation patterns.
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Affiliation(s)
- Eesha Sharma
- Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, Karnataka India
| | - Preeti Jacob
- Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, Karnataka India
| | - Pratima Murthy
- Department of Psychiatry, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, Karnataka India
| | - Sanjeev Jain
- Department of Psychiatry, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, Karnataka India
| | - Mathew Varghese
- Department of Psychiatry, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, Karnataka India
| | - Deepak Jayarajan
- Department of Psychiatry, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, Karnataka India
| | - Keshav Kumar
- Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, Karnataka India
| | - Vivek Benegal
- Department of Psychiatry, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, Karnataka India
| | - Nilakshi Vaidya
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychology, Psychiatry & Neuroscience, MRC SGDP Centre, King’s College London, London, UK
| | - Yuning Zhang
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychology, Psychiatry & Neuroscience, MRC SGDP Centre, King’s College London, London, UK
| | - Sylvane Desrivieres
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychology, Psychiatry & Neuroscience, MRC SGDP Centre, King’s College London, London, UK
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine, Institute of Psychology, Psychiatry & Neuroscience, MRC SGDP Centre, King’s College London, London, UK
| | - Udita Iyengar
- Department of Child & Adolescent Psychiatry, Institute of Psychology, Psychiatry & Neuroscience, King’s College London, London, UK
| | - Bharath Holla
- Department of Psychiatry, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, Karnataka India
| | - Meera Purushottam
- Molecular Genetics Laboratory, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, Karnataka India
| | - Amit Chakrabarti
- Regional Occupational Health Centre (ROHC), Eastern, ICMR-National Institute of Occupational Health (NIOH), Kolkata, West Bengal India
| | - Gwen Sascha Fernandes
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jon Heron
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Matthew Hickman
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Kamakshi Kartik
- Rishi Valley Rural Health Centre, Madanapalle, Chittoor, Andhra Pradesh India
| | - Kartik Kalyanram
- Rishi Valley Rural Health Centre, Madanapalle, Chittoor, Andhra Pradesh India
| | | | - Rose Dawn Bharath
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, Karnataka India
| | - Gareth Barker
- Department of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King’s College London, London, UK
| | | | - Chirag Ahuja
- Department of Radiodiagnosis and Imaging, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Kandavel Thennarasu
- Department of Biostatistics, National Institute of Mental Health and Neuro-Sciences (NIMHANS), Bangalore, Karnataka India
| | - Debashish Basu
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - B. N. Subodh
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research (PGIMER), Chandigarh, India
| | - Rebecca Kuriyan
- Division of Nutrition, St John’s Research Institute, Bengaluru, India
| | - Sunita Simon Kurpad
- Department of Psychiatry and Department of Medical Ethics, St. John’s Medical College and Hospital, Bengaluru, India
| | | | - Ghattu Krishnaveni
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | - Murali Krishna
- Foundation for Research and Advocacy in Mental Health, Mysore, India
| | - Rajkumar Lenin Singh
- Department of Psychiatry, Regional Institute of Medical Sciences (RIMS), Imphal, Manipur India
| | - L. Roshan Singh
- Department of Clinical Psychology, Regional Institute of Medical Sciences (RIMS), Imphal, Manipur India
| | - Mireille Toledano
- Faculty of Medicine, School of Public Health, Imperial College, London, UK
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Schwarz E, Doan NT, Pergola G, Westlye LT, Kaufmann T, Wolfers T, Brecheisen R, Quarto T, Ing AJ, Di Carlo P, Gurholt TP, Harms RL, Noirhomme Q, Moberget T, Agartz I, Andreassen OA, Bellani M, Bertolino A, Blasi G, Brambilla P, Buitelaar JK, Cervenka S, Flyckt L, Frangou S, Franke B, Hall J, Heslenfeld DJ, Kirsch P, McIntosh AM, Nöthen MM, Papassotiropoulos A, de Quervain DJF, Rietschel M, Schumann G, Tost H, Witt SH, Zink M, Meyer-Lindenberg A. Reproducible grey matter patterns index a multivariate, global alteration of brain structure in schizophrenia and bipolar disorder. Transl Psychiatry 2019; 9:12. [PMID: 30664633 PMCID: PMC6341112 DOI: 10.1038/s41398-018-0225-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Accepted: 07/16/2018] [Indexed: 12/18/2022] Open
Abstract
Schizophrenia is a severe mental disorder characterized by numerous subtle changes in brain structure and function. Machine learning allows exploring the utility of combining structural and functional brain magnetic resonance imaging (MRI) measures for diagnostic application, but this approach has been hampered by sample size limitations and lack of differential diagnostic data. Here, we performed a multi-site machine learning analysis to explore brain structural patterns of T1 MRI data in 2668 individuals with schizophrenia, bipolar disorder or attention-deficit/ hyperactivity disorder, and healthy controls. We found reproducible changes of structural parameters in schizophrenia that yielded a classification accuracy of up to 76% and provided discrimination from ADHD, through it lacked specificity against bipolar disorder. The observed changes largely indexed distributed grey matter alterations that could be represented through a combination of several global brain-structural parameters. This multi-site machine learning study identified a brain-structural signature that could reproducibly differentiate schizophrenia patients from controls, but lacked specificity against bipolar disorder. While this currently limits the clinical utility of the identified signature, the present study highlights that the underlying alterations index substantial global grey matter changes in psychotic disorders, reflecting the biological similarity of these conditions, and provide a roadmap for future exploration of brain structural alterations in psychiatric patients.
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Affiliation(s)
- Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
| | - Nhat Trung Doan
- Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Giulio Pergola
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, The Netherlands
- Donders Center for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands
| | - Ralph Brecheisen
- Maastricht University Medical Center, Maastricht, The Netherlands
| | - Tiziana Quarto
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Alex J Ing
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Pasquale Di Carlo
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Tiril P Gurholt
- Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | | | | | - Torgeir Moberget
- Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm County Council, Stockholm, Sweden
- Department of Psychiatry Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Marcella Bellani
- Section of Psychiatry, Azienda Ospedaliera Universitaria Integrata Verona, Verona, VR, Italy
- Department of Neurosciences, Biomedicine and Movements Sciences, University of Verona, Verona, VR, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- Institute of Psichiatry, Policlinico Bari, Azienda Ospedaliero Universitaria Consorziale Policlinico Bari, Bari, BA, Italy
| | - Giuseppe Blasi
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy
| | - Jan K Buitelaar
- Donders Institute for Brain, Cognition and Behaviour, Radboudumc, Nijmegen, The Netherlands
- Karakter Child and Adolescent Psychiatry University Center, Nijmegen, The Netherlands
| | - Simon Cervenka
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm County Council, Stockholm, Sweden
| | - Lena Flyckt
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, & Stockholm County Council, Stockholm, Sweden
| | - Sophia Frangou
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Barbara Franke
- Donders Institute for Brain, Cognition and Behaviour, Radboudumc, Nijmegen, The Netherlands
- Departments of Human Genetics and Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jeremy Hall
- Neuroscience and Mental Health Research Institute, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Dirk J Heslenfeld
- Department of Cognitive Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Peter Kirsch
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Heidelberg, Germany
- Bernstein Center for Computational Neuroscience Heidelberg-Mannheim, Mannheim, Germany
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, EH10 5HF, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, George Square, Edinburgh, EH8 9JZ, UK
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany
| | - Andreas Papassotiropoulos
- Division of Molecular Neuroscience, Department of Psychology, University of Basel, CH-4055, Basel, Switzerland
- Transfaculty Research Platform Molecular and Cognitive Neuroscience, University of Basel, Basel, Switzerland
- Psychiatric University Clinics, University of Basel, CH-4055, Basel, Switzerland
- Department Biozentrum, Life Sciences Training Facility, University of Basel, CH-4056, Basel, Switzerland
| | - Dominique J-F de Quervain
- Transfaculty Research Platform Molecular and Cognitive Neuroscience, University of Basel, Basel, Switzerland
- Psychiatric University Clinics, University of Basel, CH-4055, Basel, Switzerland
- Division of Cognitive Neuroscience, Department of Psychology, University of Basel, CH-4055, Basel, Switzerland
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Mathias Zink
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- District Hospital Mittelfranken, Department of Psychiatry, Psychotherapy and Psychosomatics, Ansbach, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
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Goodwin GM, Holmes EA, Andersson E, Browning M, Jones A, Lass-Hennemann J, Månsson KN, Moessnang C, Salemink E, Sanchez A, van Zutphen L, Visser RM. From neuroscience to evidence based psychological treatments - The promise and the challenge, ECNP March 2016, Nice, France. Eur Neuropsychopharmacol 2018; 28:317-333. [PMID: 29371024 PMCID: PMC5861996 DOI: 10.1016/j.euroneuro.2017.10.036] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 09/19/2017] [Accepted: 10/22/2017] [Indexed: 12/28/2022]
Abstract
This ECNP meeting was designed to build bridges between different constituencies of mental illness treatment researchers from a range of backgrounds with a specific focus on enhancing the development of novel, evidence based, psychological treatments. In particular we wished to explore the potential for basic neuroscience to support the development of more effective psychological treatments, just as this approach is starting to illuminate the actions of drugs. To fulfil this aim, a selection of clinical psychologists, psychiatrists and neuroscientists were invited to sit at the same table. The starting point of the meeting was the proposition that we know certain psychological treatments work, but we have only an approximate understanding of why they work. The first task in developing a coherent mental health science would therefore be to uncover the mechanisms (at all levels of analysis) of effective psychological treatments. Delineating these mechanisms, a task that will require input from both the clinic and the laboratory, will provide a key foundation for the rational optimisation of psychological treatments. As reviewed in this paper, the speakers at the meeting reviewed recent advances in the understanding of clinical and cognitive psychology, neuroscience, experimental psychopathology, and treatment delivery technology focussed primarily on anxiety disorders and depression. We started by asking three rhetorical questions: What has psychology done for treatment? What has technology done for psychology? What has neuroscience done for psychology? We then addressed how research in five broad research areas could inform the future development of better treatments: Attention, Conditioning, Compulsions and addiction, Emotional Memory, and Reward and emotional bias. Research in all these areas (and more) can be harnessed to neuroscience since psychological therapies are a learning process with a biological basis in the brain. Because current treatment approaches are not fully satisfactory, there is an imperative to understand why not. And when psychological therapies do work we need to understand why this is the case, and how we can improve them. We may be able to improve accessibility to treatment without understanding mechanisms. But for treatment innovation and improvement, mechanistic insights may actually help. Applying neuroscience in this way will become an additional mission for ECNP.
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Affiliation(s)
- Guy M Goodwin
- University Department of Psychiatry, University of Oxford and Oxford Health NHS Trust, Warneford Hospital, Oxford OX3 7JX, UK
| | - Emily A Holmes
- Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Erik Andersson
- Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden
| | - Michael Browning
- University Department of Psychiatry, University of Oxford and Oxford Health NHS Trust, Warneford Hospital, Oxford OX3 7JX, UK
| | - Andrew Jones
- Psychological Sciences, University of Liverpool, Bedford St South, Liverpool L697ZA, UK
| | - Johanna Lass-Hennemann
- Division of Clinical Psychology and Psychotherapy, Department of Psychology, Saarland University, D- 66123 Saarbrucken, Germany
| | - Kristoffer Nt Månsson
- Department of Psychology, Stockholm University, SE-106 91, Department of Clinical Neuroscience, Karolinska Institutet, SE-171 77 Stockholm, Sweden; Department of Psychology, Uppsala University, SE-75105, Uppsala, Sweden
| | - Carolin Moessnang
- Central Institute of Mental Health, Medical Faculty Mannheim/Heidelberg University, J5, 68159 Mannheim, Germany
| | - Elske Salemink
- Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129B, Amsterdam, the Netherlands
| | - Alvaro Sanchez
- Ghent University, Department of Experimental Clinical and Health Psychology, Henri Dunantlaan 2, B-9000 Ghent, Belgium
| | - Linda van Zutphen
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Universiteitssingel 40; 6229 ER, Maastricht University, Maastricht, the Netherlands
| | - Renée M Visser
- Medical Research Council Cognition & Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, UK
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Chen J, Schwarz E. The role of blood-based biomarkers in advancing personalized therapy of schizophrenia. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2017. [DOI: 10.1080/23808993.2017.1400906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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Pergola G, Quarto T, Papalino M, Di Carlo P, Selvaggi P, Gelao B, Blasi G, Bertolino A. Association of Inter-individual Differences in Imaging Markers with Schizophrenia Phenotypes. Eur Psychiatry 2017. [DOI: 10.1016/j.eurpsy.2017.01.193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
IntroductionNeuroimaging studies have identified several candidate biomarkers of schizophrenia. However, it is unclear whether the considerable variability in these neurobiological correlates between patients can be translated into the clinical setting.ObjectivesWe aimed to identify neuroimaging predictors of clinical course in patients with schizophrenia. Combined with the identification of genetically determined markers of schizophrenia risk, our studies aimed to elucidate the biological basis and the clinical relevance of inter-individual variability between patients.MethodsWe included over 150 patients with schizophrenia and 279 healthy volunteers across five neuroimaging centers in the framework of the IMAGEMEND project [1]. We performed multiple studies on MRI scans using random forests and ROC curves to predict clinical course. Data from healthy controls served to normalize the data from the clinical population and to provide a benchmark for the findings.ResultsWe identified ensembles of neuroimaging markers and of genetic variants predictive of clinical course. Results highlight that (i) brain imaging carries significant clinical information, (ii) clinical information at baseline can considerably increase prediction accuracy.ConclusionThe methodological challenges and the results will be discussed in the context of recent findings from other multi-site studies. We conclude that brain imaging data on their own right are relevant to stratify patients in terms of clinical course; however, complementing these data with other modalities such as genetics and clinical information is necessary to further develop the field towards clinical application of the predictions.Disclosure of interestGiulio Pergola is the academic supervisor of a Hoffmann-La Roche Collaboration grant that partially funds his salary.
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Epistasis in Neuropsychiatric Disorders. Trends Genet 2017; 33:256-265. [PMID: 28268034 DOI: 10.1016/j.tig.2017.01.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2016] [Revised: 01/25/2017] [Accepted: 01/27/2017] [Indexed: 12/12/2022]
Abstract
The contribution of epistasis to human disease remains unclear. However, several studies have now identified epistatic interactions between common variants that increase the risk of a neuropsychiatric disorder, while there is growing evidence that genetic interactions contribute to the pathogenicity of rare, multigenic copy-number variants (CNVs) that have been observed in patients. This review discusses the current evidence for epistatic events and genetic interactions in neuropsychiatric disorders, how paradigm shifts in the phenotypic classification of patients would empower the search for epistatic effects, and how network and cellular models might be employed to further elucidate relevant epistatic interactions.
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Chen J, Guest PC, Schwarz E. The Utility of Multiplex Assays for Identification of Proteomic Signatures in Psychiatry. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 974:131-138. [DOI: 10.1007/978-3-319-52479-5_8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Sutcliffe G, Harneit A, Tost H, Meyer-Lindenberg A. Neuroimaging Intermediate Phenotypes of Executive Control Dysfunction in Schizophrenia. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2016; 1:218-229. [DOI: 10.1016/j.bpsc.2016.03.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2015] [Revised: 03/11/2016] [Accepted: 03/14/2016] [Indexed: 01/10/2023]
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