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Miskowiak KW, Obel ZK, Guglielmo R, Bonnin CDM, Bowie CR, Balanzá-Martínez V, Burdick KE, Carvalho AF, Dols A, Douglas K, Gallagher P, Kessing LV, Lafer B, Lewandowski KE, López-Jaramillo C, Martinez-Aran A, McIntyre RS, Porter RJ, Purdon SE, Schaffer A, Stokes PRA, Sumiyoshi T, Torres IJ, Van Rheenen TE, Yatham LN, Young AH, Vieta E, Hasler G. Efficacy and safety of established and off-label ADHD drug therapies for cognitive impairment or attention-deficit hyperactivity disorder symptoms in bipolar disorder: A systematic review by the ISBD Targeting Cognition Task Force. Bipolar Disord 2024; 26:216-239. [PMID: 38433530 DOI: 10.1111/bdi.13414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/05/2024]
Abstract
BACKGROUND Abnormalities in dopamine and norepinephrine signaling are implicated in cognitive impairments in bipolar disorder (BD) and attention-deficit hyperactivity disorder (ADHD). This systematic review by the ISBD Targeting Cognition Task Force therefore aimed to investigate the possible benefits on cognition and/or ADHD symptoms and safety of established and off-label ADHD therapies in BD. METHODS We included studies of ADHD medications in BD patients, which involved cognitive and/or safety measures. We followed the procedures of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 statement. Searches were conducted on PubMed, Embase and PsycINFO from inception until June 2023. Two authors reviewed the studies independently using the Revised Cochrane Collaboration's Risk of Bias tool for Randomized trials. RESULTS Seventeen studies were identified (N = 2136), investigating armodafinil (k = 4, N = 1581), methylphenidate (k = 4, N = 84), bupropion (k = 4, n = 249), clonidine (k = 1, n = 70), lisdexamphetamine (k = 1, n = 25), mixed amphetamine salts (k = 1, n = 30), or modafinil (k = 2, n = 97). Three studies investigated cognition, four ADHD symptoms, and 10 the safety. Three studies found treatment-related ADHD symptom reduction: two involved methylphenidate and one amphetamine salts. One study found a trend towards pro-cognitive effects of modafinil on some cognitive domains. No increased risk of (hypo)mania was observed. Five studies had low risk of bias, eleven a moderate risk, and one a serious risk of bias. CONCLUSIONS Methylphenidate or mixed amphetamine salts may improve ADHD symptoms in BD. However, there is limited evidence regarding the effectiveness on cognition. The medications produced no increased mania risk when used alongside mood stabilizers. Further robust studies are needed to assess cognition in BD patients receiving psychostimulant treatment alongside mood stabilizers.
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Affiliation(s)
- Kamilla W Miskowiak
- Neurocognition and Emotion in Affective Disorders (NEAD) Centre, Department of Psychology, University of Copenhagen | Mental Health Services, Capital Region of Denmark, Copenhagen, Denmark
- Copenhagen Affective Disorder Research Centre (CADIC), Psychiatric Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Zacharias K Obel
- Neurocognition and Emotion in Affective Disorders (NEAD) Centre, Department of Psychology, University of Copenhagen | Mental Health Services, Capital Region of Denmark, Copenhagen, Denmark
- Copenhagen Affective Disorder Research Centre (CADIC), Psychiatric Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Riccardo Guglielmo
- Psychiatry Research Unit, University of Fribourg, Fribourg, Switzerland
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), Section of Psychiatry, University of Genoa, Genoa, Italy
| | - Caterina Del Mar Bonnin
- Clinical Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
| | | | | | - Katherine E Burdick
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Andre F Carvalho
- IMPACT Strategic Research Centre (Innovation in Mental and Physical Health and Clinical Treatment), Deakin University, Geelong, Victoria, Australia
| | - Annemieke Dols
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Katie Douglas
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - Peter Gallagher
- Faculty of Medical Sciences, Translational and Clinical Research Institute, Newcastle University, Newcastle-upon-Tyne, UK
| | - Lars V Kessing
- Copenhagen Affective Disorder Research Centre (CADIC), Psychiatric Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Beny Lafer
- Bipolar Disorder Research Program, Institute of Psychiatry, Hospital das Clinicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Kathryn E Lewandowski
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA
- McLean Hospital, Schizophrenia and Bipolar Disorder Program, Belmont, Massachusetts, USA
| | - Carlos López-Jaramillo
- Research Group in Psychiatry, Department of Psychiatry, Universidad de Antioquia, Medellín, Colombia
| | - Anabel Martinez-Aran
- Clinical Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
| | - Roger S McIntyre
- Mood Disorders Psychopharmacology Unit, Brain and Cognition Discovery Foundation, University of Toronto, Toronto, Canada
| | - Richard J Porter
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - Scot E Purdon
- Department of Psychiatry, University of Alberta, Edmonton, Canada
| | - Ayal Schaffer
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Paul R A Stokes
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Tomiki Sumiyoshi
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Ivan J Torres
- Department of Psychiatry, University of British Columbia, Vancouver, Canada
| | - Tamsyn E Van Rheenen
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Carlton, Australia
- Centre for Mental Health, Faculty of Health, Arts and Design, Swinburne University, Melbourne, Australia
| | - Lakshmi N Yatham
- Department of Psychiatry, University of British Columbia, Vancouver, Canada
| | - Allan H Young
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Eduard Vieta
- Clinical Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, Barcelona, Spain
| | - Gregor Hasler
- Psychiatry Research Unit, University of Fribourg, Fribourg, Switzerland
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Cao M, Martin E, Li X. Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms. Transl Psychiatry 2023; 13:236. [PMID: 37391419 DOI: 10.1038/s41398-023-02536-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 06/19/2023] [Accepted: 06/21/2023] [Indexed: 07/02/2023] Open
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder in children and has a high chance of persisting in adulthood. The development of individualized, efficient, and reliable treatment strategies is limited by the lack of understanding of the underlying neural mechanisms. Diverging and inconsistent findings from existing studies suggest that ADHD may be simultaneously associated with multivariate factors across cognitive, genetic, and biological domains. Machine learning algorithms are more capable of detecting complex interactions between multiple variables than conventional statistical methods. Here we present a narrative review of the existing machine learning studies that have contributed to understanding mechanisms underlying ADHD with a focus on behavioral and neurocognitive problems, neurobiological measures including genetic data, structural magnetic resonance imaging (MRI), task-based and resting-state functional MRI (fMRI), electroencephalogram, and functional near-infrared spectroscopy, and prevention and treatment strategies. Implications of machine learning models in ADHD research are discussed. Although increasing evidence suggests that machine learning has potential in studying ADHD, extra precautions are still required when designing machine learning strategies considering the limitations of interpretability and generalization.
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Affiliation(s)
- Meng Cao
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | | | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA.
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Pacheco J, Garvey MA, Sarampote CS, Cohen ED, Murphy ER, Friedman-Hill SR. Annual Research Review: The contributions of the RDoC research framework on understanding the neurodevelopmental origins, progression and treatment of mental illnesses. J Child Psychol Psychiatry 2022; 63:360-376. [PMID: 34979592 PMCID: PMC8940667 DOI: 10.1111/jcpp.13543] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/29/2021] [Indexed: 12/22/2022]
Abstract
The National Institute of Mental Health (NIMH) proposed the Research Domain Criteria (RDoC) initiative as an alternate way to organize research of mental illnesses, by looking at dimensions of functioning rather than being tied to categorical diagnoses. This paper briefly discusses the motivation for and organization of RDoC, and then explores the NIMH portfolio and recent work to monitor the utility and progress that RDoC has afforded developmental research. To examine how RDoC has influenced the NIMH developmental research portfolio over the last decade, we employed a natural language processing algorithm to identify the number of developmental science grants classified as incorporating an RDoC approach. Additional portfolio analyses examine temporal trends in funded RDoC-relevant grants, publications and citations, and research training opportunities. Reflecting on how RDoC has influenced the focus of grant applications, we highlight examples from research on Attention-Deficit Hyperactivity Disorder (ADHD), childhood irritability, and Autism Spectrum Disorder (ASD). Lastly, we consider how the dimensional and transdiagnostic approaches emphasized in RDoC have facilitated research on personalized intervention for heterogeneous disorders and preventive/early interventions targeting emergent or subthreshold psychopathology.
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Affiliation(s)
- Jennifer Pacheco
- Division of Translational Research, National Institute of Mental Health
- RDoC Unit, National Institute of Mental Health
| | | | | | - Elan D. Cohen
- Office of Science Policy, Planning, and Communications, National Institute of Mental Health
| | - Eric R. Murphy
- Division of Translational Research, National Institute of Mental Health
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4
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Zhang M, Huang Y, Jiao J, Yuan D, Hu X, Yang P, Zhang R, Wen L, Situ M, Cai J, Sun X, Guo K, Huang X, Huang Y. Transdiagnostic symptom subtypes across autism spectrum disorders and attention deficit hyperactivity disorder: validated by measures of neurocognition and structural connectivity. BMC Psychiatry 2022; 22:102. [PMID: 35139813 PMCID: PMC8827180 DOI: 10.1186/s12888-022-03734-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 01/26/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUNDS Autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) are neurodevelopmental disorders that exhibit within-disorder heterogeneity and cross-disorder phenotypic overlap, thus suggesting that the current disease categories may not fully represent the etiologic essence of the disorders, especially for highly comorbid neurodevelopmental disorders. In this study, we explored the subtypes of a combined sample of ASD and ADHD by integrating measurements of behavior, cognition and brain imaging. METHODS A total of 164 participants, including 65 with ASD, 47 with ADHD, and 52 controls, were recruited. Unsupervised machine learning with an agglomerative hierarchical clustering algorithm was used to identify transdiagnostic symptom clusters. Neurocognition and brain structural connectivity measurements were used to assess symptom clusters. Mediation analysis was used to explore the relationship between transdiagnostic symptoms, neurocognition and brain structural connectivity. RESULTS We identified three symptom clusters that did not fall within the diagnostic boundaries of DSM. External measurements from neurocognition and neuroimaging domains supported distinct profiles, including fine motor function, verbal fluency, and structural connectivity in the corpus callosum between these symptom clusters, highlighting possible biomarkers for ASD and ADHD. Additionally, fine motor function was shown to mediate the relationship between the corpus callosum and perseveration symptoms. CONCLUSIONS In this transdiagnostic study on ASD and ADHD, we identified three subtypes showing meaningful associations between symptoms, neurocognition and brain white matter structural connectivity. The fine motor function and structural connectivity of corpus callosum might be used as biomarkers for neurodevelopmental disorders with social skill symptoms. The results of this study highlighted the importance of precise phenotyping and further supported the effects of fine motor intervention on ASD and ADHD.
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Affiliation(s)
- Manxue Zhang
- grid.412901.f0000 0004 1770 1022Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yan Huang
- grid.412901.f0000 0004 1770 1022Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Jian Jiao
- grid.412901.f0000 0004 1770 1022Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Danfeng Yuan
- grid.412901.f0000 0004 1770 1022Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xiao Hu
- grid.412901.f0000 0004 1770 1022Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Pingyuan Yang
- grid.412901.f0000 0004 1770 1022Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Rui Zhang
- grid.54549.390000 0004 0369 4060University of Electronic Science and Technology of China, Chengdu, China
| | - Liangjian Wen
- grid.54549.390000 0004 0369 4060University of Electronic Science and Technology of China, Chengdu, China
| | - Mingjing Situ
- grid.412901.f0000 0004 1770 1022Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Jia Cai
- grid.412901.f0000 0004 1770 1022Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xueli Sun
- grid.412901.f0000 0004 1770 1022Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Kuifang Guo
- grid.412901.f0000 0004 1770 1022Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xia Huang
- grid.412901.f0000 0004 1770 1022Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yi Huang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China. .,Brain Research Center, West China Hospital of Sichuan University, Chengdu, China.
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5
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Pelin H, Ising M, Stein F, Meinert S, Meller T, Brosch K, Winter NR, Krug A, Leenings R, Lemke H, Nenadić I, Heilmann-Heimbach S, Forstner AJ, Nöthen MM, Opel N, Repple J, Pfarr J, Ringwald K, Schmitt S, Thiel K, Waltemate L, Winter A, Streit F, Witt S, Rietschel M, Dannlowski U, Kircher T, Hahn T, Müller-Myhsok B, Andlauer TFM. Identification of transdiagnostic psychiatric disorder subtypes using unsupervised learning. Neuropsychopharmacology 2021; 46:1895-1905. [PMID: 34127797 PMCID: PMC8429672 DOI: 10.1038/s41386-021-01051-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/24/2021] [Accepted: 05/28/2021] [Indexed: 02/07/2023]
Abstract
Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.
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Affiliation(s)
- Helena Pelin
- Max Planck Institute of Psychiatry, Munich, Germany.
- International Max Planck Research School for Translational Psychiatry, Munich, Germany.
| | - Marcus Ising
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Frederike Stein
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Tina Meller
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Katharina Brosch
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Nils R Winter
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Axel Krug
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany
| | - Ramona Leenings
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Hannah Lemke
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Stefanie Heilmann-Heimbach
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
- Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
- Centre for Human Genetics, University of Marburg, Marburg, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Julia Pfarr
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
| | - Kai Ringwald
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Simon Schmitt
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Katharina Thiel
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Lena Waltemate
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Alexandra Winter
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Fabian Streit
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Stephanie Witt
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Marcella Rietschel
- Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-Universität Marburg, Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), Marburg, Germany
| | - Tim Hahn
- Institute for Translational Psychiatry, Westfälische Wilhelms-Universität Münster, Münster, Germany
| | - Bertram Müller-Myhsok
- Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
| | - Till F M Andlauer
- Max Planck Institute of Psychiatry, Munich, Germany.
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany.
- Global Computational Biology and Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany.
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Parks KMA, Cardy JEO, Woynaroski TG, Sehl CG, Stevenson RA. Investigating the Role of Inattention and/or Hyperactivity/impulsivity in Language and Social Functioning Using a Dimensional Approach. JOURNAL OF COMMUNICATION DISORDERS 2021; 89:106036. [PMID: 33249356 PMCID: PMC8862713 DOI: 10.1016/j.jcomdis.2020.106036] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 07/14/2020] [Accepted: 07/14/2020] [Indexed: 06/12/2023]
Abstract
The current study parsed out the distinct components of attention-deficit/hyperactivity disorder (ADHD) symptomatology to examine differential relations with language and social ability. Using a research domain criteria (RDoC) framework, we administered standardized tests and previously developed and validated questionnaires to assess levels of inattention and/or hyperactivity/impulsivity symptomatology, language, social responsivity and social competency in 98 young adults. Those with higher inattention and/or hyperactivity/impulsivity symptomatology had reduced language comprehension, social responsivity, and social competency. Inattention and hyperactivity/impulsivity both predicted language comprehension, but not language production. Interestingly, inattention uniquely contributed to social responsiveness and social competency, but hyperactivity/impulsivity did not. Findings suggest that inattention and/or hyperactivity/impulsivity symptoms, inattention in particular, may be especially important for social skills programs geared towards individuals with attention limitations.
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Affiliation(s)
- Kaitlyn M A Parks
- Western University, Department of Psychology, 1151 Richmond St London, N6A 3K7 London, ON, Canada; Western University, Brain and Mind Institute, 1151 Richmond St, London, ON, N6A 3K7, Canada.
| | - Janis E Oram Cardy
- Western University, Brain and Mind Institute, 1151 Richmond St, London, ON, N6A 3K7, Canada; Western University, School of Communication Sciences and Disorders, 1151 Richmond St, London, ON, N6A 3K7, Canada; Western University, Program in Neuroscience, 1151 Richmond St, London, ON, N6A 3K7, Canada.
| | - Tiffany G Woynaroski
- Vanderbilt University Medical Center, Department of Hearing & Speech Sciences, 2201 West End Ave, Nashville, 377235, TN, USA; Vanderbilt Kennedy Center, 2201 West End Ave, Nashville, 377235, TN, USA; Vanderbilt Brain Institute, 2201 West End Ave, Nashville, 377235, TN, USA.
| | - Claudia G Sehl
- Western University, Department of Psychology, 1151 Richmond St London, N6A 3K7 London, ON, Canada; Western University, Brain and Mind Institute, 1151 Richmond St, London, ON, N6A 3K7, Canada; University of Waterloo, Department of Psychology, 200 University Ave W, Waterloo, ON, N2L 3GL, Canada.
| | - Ryan A Stevenson
- Western University, Department of Psychology, 1151 Richmond St London, N6A 3K7 London, ON, Canada; Western University, Brain and Mind Institute, 1151 Richmond St, London, ON, N6A 3K7, Canada; Western University, Program in Neuroscience, 1151 Richmond St, London, ON, N6A 3K7, Canada; Western University, Department of Psychiatry, 1151 Richmond St, London, ON, N6A 3K7, Canada; York University, Centre for Vision Research, 4700 Keele St, Toronto, ON, M3J 1P3, Canada.
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Kaczkurkin AN, Moore TM, Sotiras A, Xia CH, Shinohara RT, Satterthwaite TD. Approaches to Defining Common and Dissociable Neurobiological Deficits Associated With Psychopathology in Youth. Biol Psychiatry 2020; 88:51-62. [PMID: 32087950 PMCID: PMC7305976 DOI: 10.1016/j.biopsych.2019.12.015] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 11/07/2019] [Accepted: 12/11/2019] [Indexed: 01/31/2023]
Abstract
Psychiatric disorders show high rates of comorbidity and nonspecificity of presenting clinical symptoms, while demonstrating substantial heterogeneity within diagnostic categories. Notably, many of these psychiatric disorders first manifest in youth. We review progress and next steps in efforts to parse heterogeneity in psychiatric symptoms in youths by identifying abnormalities within neural circuits. To address this fundamental challenge in psychiatry, a number of methods have been proposed. We provide an overview of these methods, broadly organized into dimensional versus categorical approaches and single-view versus multiview approaches. Dimensional approaches including factor analysis and canonical correlation analysis aim to capture dimensional associations between psychopathology and brain measures across a continuous spectrum from health to disease. In contrast, categorical approaches, such as clustering and community detection, aim to identify subtypes of individuals within a class of symptoms or brain features. We highlight several studies that apply these methods to samples of youths and discuss issues to consider when using these approaches. Finally, we end by highlighting avenues for future research.
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Affiliation(s)
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, Missouri; Institute for Informatics, Washington University School of Medicine in St. Louis, St. Louis, Missouri
| | - Cedric Huchuan Xia
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
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8
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Portugal LCL, Schrouff J, Stiffler R, Bertocci M, Bebko G, Chase H, Lockovitch J, Aslam H, Graur S, Greenberg T, Pereira M, Oliveira L, Phillips M, Mourão-Miranda J. Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach. Neuroimage Clin 2019; 23:101813. [PMID: 31082774 PMCID: PMC6517640 DOI: 10.1016/j.nicl.2019.101813] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 03/04/2019] [Accepted: 04/02/2019] [Indexed: 01/21/2023]
Abstract
BACKGROUND It is becoming increasingly clear that pathophysiological processes underlying psychiatric disorders categories are heterogeneous on many levels, including symptoms, disease course, comorbidity and biological underpinnings. This heterogeneity poses challenges for identifying biological markers associated with dimensions of symptoms and behaviour that could provide targets to guide treatment choice and novel treatment. In response, the research domain criteria (RDoC) (Insel et al., 2010) was developed to advocate a dimensional approach which omits any disease definitions, disorder thresholds, or cut-points for various levels of psychopathology to understanding the pathophysiological processes underlying psychiatry disorders. In the present study we aimed to apply pattern regression analysis to identify brain signatures during dynamic emotional face processing that are predictive of anxiety and depression symptoms in a continuum that ranges from normal to pathological levels, cutting across categorically-defined diagnoses. METHODS The sample was composed of one-hundred and fifty-four young adults (mean age=21.6 and s.d.=2.0, 103 females) consisting of eighty-two young adults seeking treatment for psychological distress that cut across categorically-defined diagnoses and 72 matched healthy young adults. Participants performed a dynamic face task involving fearful, angry and happy faces (and geometric shapes) while undergoing functional Magnetic Resonance Imaging (fMRI). Pattern regression analyses consisted of Gaussian Process Regression (GPR) implemented in the Pattern Recognition for Neuroimaging toolbox (PRoNTo). Predicted and actual clinical scores were compared using Pearson's correlation coefficient (r) and normalized mean squared error (MSE) to evaluate the models' performance. Permutation test was applied to estimate significance levels. RESULTS GPR identified patterns of neural activity to dynamic emotional face processing predictive of self-report anxiety in the whole sample, which covered a continuum that ranged from healthy to different levels of distress, including subthreshold to fully-syndromal psychiatric diagnoses. Results were significant using two different cross validation strategies (two-fold: r=0.28 (p-value=0.001), MSE=4.47 (p-value=0.001) and five fold r=0.28 (p-value=0.002), MSE=4.62 (p-value=0.003). The contributions of individual regions to the predictive model were very small, demonstrating that predictions were based on the overall pattern rather than on a small combination of regions. CONCLUSIONS These findings represent early evidence that neuroimaging techniques may inform clinical assessment of young adults irrespective of diagnoses by allowing accurate and objective quantitative estimation of psychopathology.
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Affiliation(s)
- Liana C L Portugal
- Centre for Medical Image Computing, Department of Computer Science, University College London, United Kingdom; Department of Physiology and Pharmacology, Federal Fluminense University, Niteroi, Brazil.
| | - Jessica Schrouff
- Centre for Medical Image Computing, Department of Computer Science, University College London, United Kingdom; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, United Kingdom
| | - Ricki Stiffler
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, United States
| | - Michele Bertocci
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, United States
| | - Genna Bebko
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, United States
| | - Henry Chase
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, United States
| | - Jeanette Lockovitch
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, United States
| | - Haris Aslam
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, United States
| | - Simona Graur
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, United States
| | - Tsafrir Greenberg
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, United States
| | - Mirtes Pereira
- Department of Physiology and Pharmacology, Federal Fluminense University, Niteroi, Brazil
| | - Leticia Oliveira
- Department of Physiology and Pharmacology, Federal Fluminense University, Niteroi, Brazil
| | - Mary Phillips
- Department of Psychiatry, Western Psychiatric Institute and Clinic, University of Pittsburgh Medical Center, University of Pittsburgh, Pittsburgh, United States; Department of Psychological Medicine, Cardiff University, Cardiff, United Kingdom
| | - Janaina Mourão-Miranda
- Centre for Medical Image Computing, Department of Computer Science, University College London, United Kingdom; Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, United Kingdom
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9
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Using Cluster Ensembles to Identify Psychiatric Patient Subgroups. Artif Intell Med 2019. [DOI: 10.1007/978-3-030-21642-9_31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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10
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Rossi A, Moura L, Miranda M, Muszkat M, Mello C, Bueno O. Latent class analysis of attention and white matter correlation in children with attention-deficit/hyperactivity disorder. Braz J Med Biol Res 2018; 51:e7653. [PMID: 30304132 PMCID: PMC6172928 DOI: 10.1590/1414-431x20187653] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 07/30/2018] [Indexed: 02/08/2023] Open
Abstract
This study aimed to explore attentional patterns among children with inattentive attention-deficit/hyperactivity disorder (ADHD-I) and children with typical development (TD), using a latent class analysis (LCA). Patterns of brain connectivity were also explored. The sample comprised 29 ADHD-I and 29 TD matched children. An LCA was conducted to reclassify subjects according to their attentional performance, considering cognitive measures of attention and behavioral symptoms, regardless of group of origin. The new clusters were then compared in respect to brain white matter measurements (extracted from diffusion tensor imaging). Participants were rearranged in 2 new latent classes, according to their performance in an attention task and the results of behavioral scales, resulting in groups with more homogeneous attentional profiles. A comparison of the 2 new classes using the white matter measurements revealed increased fractional anisotropy in the left inferior fronto-occipital fasciculus and left inferior longitudinal fasciculus for the class composed by participants with a higher risk of attentional problems. The findings indicated that it was possible to observe variability regarding neuropsychological profile, accompanied by underpinning neurobiological differences, even among individuals with the same disorder subtype - inattentive ADHD. This specific data-driven clustering analysis may help to enhance understanding of the pathophysiology of the disorder's phenotypes.
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Affiliation(s)
- A.S.U. Rossi
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, SP, Brasil
| | - L.M. Moura
- Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, SP, Brasil
| | - M.C. Miranda
- Departamento de Psicologia, Curso de Pós-graduação em Psicossomática, Universidade Ibirapuera, São Paulo, SP, Brasil
| | - M. Muszkat
- Programa de Educação e Saúde da Infância e Adolescência, Universidade Federal de São Paulo, Guarulhos, SP, Brasil
| | - C.B. Mello
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, SP, Brasil
| | - O.F.A. Bueno
- Departamento de Psicobiologia, Universidade Federal de São Paulo, São Paulo, SP, Brasil
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11
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Grisanzio KA, Goldstein-Piekarski AN, Wang MY, Rashed Ahmed AP, Samara Z, Williams LM. Transdiagnostic Symptom Clusters and Associations With Brain, Behavior, and Daily Function in Mood, Anxiety, and Trauma Disorders. JAMA Psychiatry 2018; 75:201-209. [PMID: 29197929 PMCID: PMC5838569 DOI: 10.1001/jamapsychiatry.2017.3951] [Citation(s) in RCA: 100] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
IMPORTANCE The symptoms that define mood, anxiety, and trauma disorders are highly overlapping across disorders and heterogeneous within disorders. It is unknown whether coherent subtypes exist that span multiple diagnoses and are expressed functionally (in underlying cognition and brain function) and clinically (in daily function). The identification of cohesive subtypes would help disentangle the symptom overlap in our current diagnoses and serve as a tool for tailoring treatment choices. OBJECTIVE To propose and demonstrate 1 approach for identifying subtypes within a transdiagnostic sample. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional study analyzed data from the Brain Research and Integrative Neuroscience Network Foundation Database that had been collected at the University of Sydney and University of Adelaide between 2006 and 2010 and replicated at Stanford University between 2013 and 2017. The study included 420 individuals with a primary diagnosis of major depressive disorder (n = 100), panic disorder (n = 53), posttraumatic stress disorder (n = 47), or no disorder (healthy control participants) (n = 220). Data were analyzed between October 2016 and October 2017. MAIN OUTCOMES AND MEASURES We followed a data-driven approach to achieve the primary study outcome of identifying transdiagnostic subtypes. First, machine learning with a hierarchical clustering algorithm was implemented to classify participants based on self-reported negative mood, anxiety, and stress symptoms. Second, the robustness and generalizability of the subtypes were tested in an independent sample. Third, we assessed whether symptom subtypes were expressed at behavioral and physiological levels of functioning. Fourth, we evaluated the clinically meaningful differences in functional capacity of the subtypes. Findings were interpreted relative to a complementary diagnostic frame of reference. RESULTS Four hundred twenty participants with a mean (SD) age of 39.8 (14.1) years were included in the final analysis; 256 (61.0%) were female. We identified 6 distinct subtypes characterized by tension (n=81; 19%), anxious arousal (n=55; 13%), general anxiety (n=38; 9%), anhedonia (n=29; 7%), melancholia (n=37; 9%), and normative mood (n=180; 43%), and these subtypes were replicated in an independent sample. Subtypes were expressed through differences in cognitive control (F5,383 = 5.13, P < .001, ηp2 = 0.063), working memory (F5,401 = 3.29, P = .006, ηp2 = 0.039), electroencephalography-recorded β power in a resting paradigm (F5,357 = 3.84, P = .002, ηp2 = 0.051), electroencephalography-recorded β power in an emotional paradigm (F5,365 = 3.56, P = .004, ηp2 = 0.047), social functional capacity (F5,414 = 21.33, P < .001, ηp2 = 0.205), and emotional resilience (F5,376 = 15.10, P < .001, ηp2 = 0.171). CONCLUSIONS AND RELEVANCE These findings offer a data-driven framework for identifying robust subtypes that signify specific, coherent, meaningful associations between symptoms, behavior, brain function, and observable real-world function, and that cut across DSM-IV-defined diagnoses of major depressive disorder, panic disorder, and posttraumatic stress disorder.
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Affiliation(s)
- Katherine A. Grisanzio
- Department of Psychiatry and Behavioral Sciences,
Stanford University, Stanford, California,Sierra-Pacific Mental Illness Research, Education, and
Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Andrea N. Goldstein-Piekarski
- Department of Psychiatry and Behavioral Sciences,
Stanford University, Stanford, California,Sierra-Pacific Mental Illness Research, Education, and
Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Michelle Yuyun Wang
- Brain Resource International Database, Brain Resource
Ltd, Woolloomooloo, Sydney, Australia
| | | | - Zoe Samara
- Department of Psychiatry and Behavioral Sciences,
Stanford University, Stanford, California,Sierra-Pacific Mental Illness Research, Education, and
Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
| | - Leanne M. Williams
- Department of Psychiatry and Behavioral Sciences,
Stanford University, Stanford, California,Sierra-Pacific Mental Illness Research, Education, and
Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, California
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12
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Elias LR, Miskowiak KW, Vale AMO, Köhler CA, Kjærstad HL, Stubbs B, Kessing LV, Vieta E, Maes M, Goldstein BI, Carvalho AF. Cognitive Impairment in Euthymic Pediatric Bipolar Disorder: A Systematic Review and Meta-Analysis. J Am Acad Child Adolesc Psychiatry 2017; 56:286-296. [PMID: 28335872 DOI: 10.1016/j.jaac.2017.01.008] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2016] [Revised: 12/21/2016] [Accepted: 01/25/2017] [Indexed: 12/14/2022]
Abstract
OBJECTIVE To perform a systematic review and meta-analysis of studies investigating neurocognition in euthymic youths with bipolar disorder (BD) compared to healthy controls (HCs). METHOD A systematic literature search was conducted in the PubMed/MEDLINE, PsycINFO, and EMBASE databases from inception up until March 23, 2016, for original peer-reviewed articles that investigated neurocognition in euthymic youths with BD compared to HCs. Effect sizes (ES) for individual tests were extracted. In addition, results were grouped according to cognitive domain. This review complied with the PRISMA statement guidelines. RESULTS A total of 24 studies met inclusion criteria (N = 1,146; 510 with BD). Overall, euthymic youths with BD were significantly impaired in verbal learning, verbal memory, working memory, visual learning, and visual memory, with moderate to large ESs (Hedge's g 0.76-0.99); significant impairments were not observed for attention/vigilance, reasoning and problem solving, and/or processing speed. Heterogeneity was moderate to large (I2 ≥ 50%) for most ES estimates. Differences in the definition of euthymia across studies explained the heterogeneity in the ES estimate for verbal learning and memory. We also found evidence for other potential sources of heterogeneity in several ES estimates including co-occurring attention-deficit/hyperactivity disorder (ADHD) and anxiety disorders, and the use of medications. In addition, the use of different neuropsychological tests appeared to contribute to heterogeneity of some estimates (e.g., attention/vigilance domain). CONCLUSION Euthymic youths with BD exhibit significant cognitive dysfunction encompassing verbal learning and memory, working memory, and/or visual learning and memory domains. These data indicate that for a subset of individuals with BD, neurodevelopmental factors may contribute to cognitive dysfunction.
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Affiliation(s)
- Liana R Elias
- Translational Psychiatry Research Group Federal University of Ceará, Fortaleza, CE, Brazil
| | - Kamilla W Miskowiak
- Psychiatric Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Antônio M O Vale
- Translational Psychiatry Research Group Federal University of Ceará, Fortaleza, CE, Brazil
| | - Cristiano A Köhler
- Translational Psychiatry Research Group Federal University of Ceará, Fortaleza, CE, Brazil
| | - Hanne L Kjærstad
- Psychiatric Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Brendon Stubbs
- Health Service and Population Research Department, Institute of Psychiatry, King's College London, UK, and South London and Maudsley National Health Service Foundation Trust, Denmark Hill, London
| | - Lars V Kessing
- Psychiatric Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Eduard Vieta
- Bipolar Disorder Program, Institute of Neuroscience, Hospital Clinic, University of Barcelona, Institut d'investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro para la Investigación Biomédica en Red de Salud Mental (CIBERSAM), Barcelona, Spain
| | - Michael Maes
- Chulalongkorn University, Bangkok, Thailand, and IMPACT Strategic Research Center, Barwon Health, Deakin University, Geelong, VIC, Australia
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Sunnybrook Health Sciences Centre, Toronto, Canada
| | - André F Carvalho
- Translational Psychiatry Research Group Federal University of Ceará, Fortaleza, CE, Brazil.
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13
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Carcone D, Ruocco AC. Six Years of Research on the National Institute of Mental Health's Research Domain Criteria (RDoC) Initiative: A Systematic Review. Front Cell Neurosci 2017; 11:46. [PMID: 28316565 PMCID: PMC5334510 DOI: 10.3389/fncel.2017.00046] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 02/13/2017] [Indexed: 01/01/2023] Open
Abstract
Six years have passed since the National Institute of Mental Health (NIMH) in the United States launched the Research Domain Criteria (RDoC) initiative. The RDoC introduces a framework for research on the biology of mental illness that integrates research findings across multiple levels of information. The framework outlines constructs that represent specific quantifiable dimensions of behavior (e.g., responses to acute threat, cognitive control) and corresponding units of analysis that can be used to study the constructs, beginning at the levels of genes, molecules, cells, circuits and physiology, and moving up to behaviors and self-reports. In this systematic review, a literature search was conducted to synthesize empirical research published since the proposal of the framework that incorporated the RDoC. Forty-eight peer-reviewed scholarly articles met eligibility criteria for the review. Studies differed according to whether they analyzed RDoC constructs and units of analysis within vs. between clinically-diagnosed and non-psychiatric samples. The most commonly studied constructs were subsumed within the domains of Negative Valence Systems, Positive Valence Systems and Cognitive Systems, providing initial results which primarily connected genetics, brain circuits and physiology research findings with behavior and self-reports. Prospects for future research adopting the RDoC matrix and utilizing a dimensional approach to studying the biology of mental illness are discussed.
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Affiliation(s)
- Dean Carcone
- Departments of Psychology and Psychological Clinical Science, University of Toronto Toronto, ON, Canada
| | - Anthony C Ruocco
- Departments of Psychology and Psychological Clinical Science, University of Toronto Toronto, ON, Canada
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14
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15
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Marquand AF, Wolfers T, Mennes M, Buitelaar J, Beckmann CF. Beyond Lumping and Splitting: A Review of Computational Approaches for Stratifying Psychiatric Disorders. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2016; 1:433-447. [PMID: 27642641 PMCID: PMC5013873 DOI: 10.1016/j.bpsc.2016.04.002] [Citation(s) in RCA: 108] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Revised: 04/06/2016] [Accepted: 04/06/2016] [Indexed: 01/03/2023]
Abstract
Heterogeneity is a key feature of all psychiatric disorders that manifests on many levels, including symptoms, disease course, and biological underpinnings. These form a substantial barrier to understanding disease mechanisms and developing effective, personalized treatments. In response, many studies have aimed to stratify psychiatric disorders, aiming to find more consistent subgroups on the basis of many types of data. Such approaches have received renewed interest after recent research initiatives, such as the National Institute of Mental Health Research Domain Criteria and the European Roadmap for Mental Health Research, both of which emphasize finding stratifications that are based on biological systems and that cut across current classifications. We first introduce the basic concepts for stratifying psychiatric disorders and then provide a methodologically oriented and critical review of the existing literature. This shows that the predominant clustering approach that aims to subdivide clinical populations into more coherent subgroups has made a useful contribution but is heavily dependent on the type of data used; it has produced many different ways to subgroup the disorders we review, but for most disorders it has not converged on a consistent set of subgroups. We highlight problems with current approaches that are not widely recognized and discuss the importance of validation to ensure that the derived subgroups index clinically relevant variation. Finally, we review emerging techniques-such as those that estimate normative models for mappings between biology and behavior-that provide new ways to parse the heterogeneity underlying psychiatric disorders and evaluate all methods to meeting the objectives of such as the National Institute of Mental Health Research Domain Criteria and Roadmap for Mental Health Research.
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Affiliation(s)
- Andre F. Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
- Department of Cognitive Neuroscience , Radboud University Medical Centre, Nijmegen
- Department of Neuroimaging (AFM), Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London, London
| | - Thomas Wolfers
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
| | - Maarten Mennes
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
| | - Jan Buitelaar
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
- Department of Cognitive Neuroscience , Radboud University Medical Centre, Nijmegen
- Karakter Child and Adolescent Psychiatric University Centre, Nijmegen, The Netherlands
| | - Christian F. Beckmann
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen
- Department of Cognitive Neuroscience , Radboud University Medical Centre, Nijmegen
- Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (CFB), University of Oxford, London, United Kingdom
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16
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Hall CL, Valentine AZ, Groom MJ, Walker GM, Sayal K, Daley D, Hollis C. The clinical utility of the continuous performance test and objective measures of activity for diagnosing and monitoring ADHD in children: a systematic review. Eur Child Adolesc Psychiatry 2016; 25:677-99. [PMID: 26620873 DOI: 10.1007/s00787-015-0798-x] [Citation(s) in RCA: 105] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Accepted: 11/14/2015] [Indexed: 11/25/2022]
Abstract
Attention-deficit hyperactivity disorder (ADHD) is typically diagnosed using clinical observation and subjective informant reports. Once children commence ADHD medication, robust monitoring is required to detect partial or non-responses. The extent to which neuropsychological continuous performance tests (CPTs) and objective measures of activity can clinically aid the assessment and titration process in ADHD is not fully understood. This review describes the current evidence base for the use of CPTs and objectively measured activity to support the diagnostic procedure and medication management for children with ADHD. Four databases (PsycINFO, Medline, Allied and Complementary Medicine (AMED), and PsycARTICLES) were systematically searched to understand the current evidence base for (1) the use of CPTs to aid clinical assessment of ADHD; (2) the use of CPTs to aid medication management; and (3) the clinical utility of objective measures of activity in ADHD. Sixty relevant articles were identified. The search revealed six commercially available CPTs that had been reported on for their clinical use. There were mixed findings with regard to the use of CPTs to assess and manage medication, with contrasting evidence on their ability to support clinical decision-making. There was a strong evidence base for the use of objective measures of activity to aid ADHD/non-ADHD group differentiation, which appears sensitive to medication effects and would also benefit from further research on their clinical utility. The findings suggest that combining CPTs and an objective measure of activity may be particularly useful as a clinical tool and worthy of further pursuit.
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Affiliation(s)
- Charlotte L Hall
- Division of Psychiatry and Applied Psychology, CLAHRC-EM, Institute of Mental Health, University of Nottingham Innovation Park, Triumph Road, Nottingham, NG7 2TU, UK.
| | - Althea Z Valentine
- Division of Psychiatry and Applied Psychology, CLAHRC-EM, Institute of Mental Health, University of Nottingham Innovation Park, Triumph Road, Nottingham, NG7 2TU, UK
| | - Madeleine J Groom
- Division of Psychiatry and Applied Psychology, Institute of Mental Health, University of Nottingham Innovation Park, Triumph Road, Nottingham, NG7 2TU, UK
| | - Gemma M Walker
- Division of Psychiatry and Applied Psychology, CLAHRC-EM, Institute of Mental Health, University of Nottingham Innovation Park, Triumph Road, Nottingham, NG7 2TU, UK
| | - Kapil Sayal
- Developmental Psychiatry, School of Medicine, University of Nottingham, Nottingham, UK
- Centre for ADHD and Neuro-developmental Disorders Across the Lifespan (CANDAL), Institute of Mental Health, University of Nottingham Innovation Park, Triumph Road, Nottingham, NG7 2TU, UK
| | - David Daley
- Centre for ADHD and Neuro-developmental Disorders Across the Lifespan (CANDAL), Institute of Mental Health, University of Nottingham Innovation Park, Triumph Road, Nottingham, NG7 2TU, UK
- School of Medicine, University of Nottingham, Nottingham, UK
| | - Chris Hollis
- Division of Psychiatry and Applied Psychology, CLAHRC-EM, Institute of Mental Health, University of Nottingham Innovation Park, Triumph Road, Nottingham, NG7 2TU, UK
- Centre for ADHD and Neuro-developmental Disorders Across the Lifespan (CANDAL), University of Nottingham, Nottingham, UK
- MindTech, Developmental Psychiatry, Queen's Medical Centre, University of Nottingham, Nottingham, NG7 2UH, UK
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17
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Wang LJ, Shyu YC, Yuan SS, Yang CJ, Yang KC, Lee TL, Lee SY. Attention-deficit hyperactivity disorder, its pharmacotherapy, and the risk of developing bipolar disorder: A nationwide population-based study in Taiwan. J Psychiatr Res 2016; 72:6-14. [PMID: 26519764 DOI: 10.1016/j.jpsychires.2015.10.014] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 08/19/2015] [Accepted: 10/16/2015] [Indexed: 10/22/2022]
Abstract
UNLABELLED In this study, we aimed to evaluate the relationship between attention-deficit/hyperactivity disorder (ADHD) during childhood and subsequent diagnoses of bipolar disorder (BD), as well as to determine whether the pharmacotherapy for ADHD (methylphenidate and atomoxetine) influence the risks of developing BD. A nationwide cohort of patients newly diagnosed with ADHD (n = 144,920) and age- and gender-matching controls (n = 144,920) were found in Taiwan's National Health Insurance database from January 2000 to December 2011. Both patients and controls were observed until December 31, 2011. To determine the effect that the duration of methylphenidate and atomoxetine exposure had on BD, the difference in the risk of developing BD was compared among non-users, short-term users (≤ 365 days), and long-term users (>365 days). In comparison to the control group, the ADHD group showed a significantly increased risk of developing BD (ADHD: 2.1% vs. CONTROLS 0.4%; aHR: 7.85, 95% CI: 7.09-8.70), and had a younger mean age at the time of first diagnosis (ADHD: 12.0 years vs. CONTROLS 18.8 years). Compared to ADHD patients that had never taken methylphenidate, patients with long-term use of methylphenidate were less likely to be diagnosed with BD (aOR: 0.72, 95% CI: 0.65-0.80). However, the duration of exposure to atomoxetine did not have a significant relationship to a BD diagnosis. The results suggested that a previous diagnosis of ADHD was a powerful indicator of BD, particularly juvenile-onset BD. Nevertheless, the exact mechanisms of the relationships among ADHD, its pharmacotherapy, and BD require further clarification in the future.
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Affiliation(s)
- Liang-Jen Wang
- Department of Child and Adolescent Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Yu-Chiau Shyu
- Community Medicine Research Center, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan; Institute of Molecular Biology, Academia Sinica, Nankang, Taipei, Taiwan
| | - Shin-Sheng Yuan
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Chun-Ju Yang
- Institute of Biopharmaceutical Sciences, National Yang-Ming University, Taipei, Taiwan; Community Medicine Research Center, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Kang-Chung Yang
- Genome and Systems Biology Degree Program, National Taiwan University, Taipei, Taiwan; Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Tung-Liang Lee
- Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Sheng-Yu Lee
- Department of Psychiatry, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan; Department of Psychiatry, College of Medicine and Hospital, National Cheng Kung University, Tainan, Taiwan.
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