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Voineskos AN, Hawco C, Neufeld NH, Turner JA, Ameis SH, Anticevic A, Buchanan RW, Cadenhead K, Dazzan P, Dickie EW, Gallucci J, Lahti AC, Malhotra AK, Öngür D, Lencz T, Sarpal DK, Oliver LD. Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions. World Psychiatry 2024; 23:26-51. [PMID: 38214624 PMCID: PMC10786022 DOI: 10.1002/wps.21159] [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] [Indexed: 01/13/2024] Open
Abstract
Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.
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Affiliation(s)
- Aristotle N Voineskos
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nicholas H Neufeld
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cundill Centre for Child and Youth Depression and McCain Centre for Child, Youth and Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Alan Anticevic
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kristin Cadenhead
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Julia Gallucci
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anil K Malhotra
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Dost Öngür
- McLean Hospital/Harvard Medical School, Belmont, MA, USA
| | - Todd Lencz
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
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Miranda J, Paules C, Noell G, Youssef L, Paternina-Caicedo A, Crovetto F, Cañellas N, Garcia-Martín ML, Amigó N, Eixarch E, Faner R, Figueras F, Simões RV, Crispi F, Gratacós E. Similarity network fusion to identify phenotypes of small-for-gestational-age fetuses. iScience 2023; 26:107620. [PMID: 37694157 PMCID: PMC10485038 DOI: 10.1016/j.isci.2023.107620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 04/19/2023] [Accepted: 08/09/2023] [Indexed: 09/12/2023] Open
Abstract
Fetal growth restriction (FGR) affects 5-10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results confirm the existence of two subtypes of human FGR with distinct molecular and clinical features based on multiomic analysis. In addition, we demonstrated that clusters generated by machine learning significantly outperform single data subtype analysis and biologically support the current clinical classification in predicting adverse maternal and neonatal outcomes. Our approach can aid in the refinement of clinical classification systems for FGR supported by molecular and clinical signatures.
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Affiliation(s)
- Jezid Miranda
- BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
- Department of Obstetrics and Gynecology, Faculty of Medicine, Universidad de Cartagena, Cartagena de Indias, Colombia
| | - Cristina Paules
- BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
- Aragon Institute of Health Research (IIS Aragon), Obstetrics Department, Hospital Clínico Universitario Lozano Blesa, Zaragoza, Spain
| | - Guillaume Noell
- University of Barcelona, Biomedicine Department, IDIBAPS, Centre for Biomedical Research on Respiratory Diseases (CIBERES), Barcelona, Spain
| | - Lina Youssef
- BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | | | - Francesca Crovetto
- BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Nicolau Cañellas
- Metabolomics Platform, IISPV, DEEiA, Universidad Rovira i Virgili, Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), Tarragona, Spain
| | - María L. Garcia-Martín
- BIONAND, Andalusian Centre for Nanomedicine and Biotechnology, Junta de Andalucía, Universidad de Málaga, Málaga, Spain
| | | | - Elisenda Eixarch
- BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Rosa Faner
- University of Barcelona, Biomedicine Department, IDIBAPS, Centre for Biomedical Research on Respiratory Diseases (CIBERES), Barcelona, Spain
| | - Francesc Figueras
- BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Rui V. Simões
- BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
- Institute for Research & Innovation in Health (i3S), University of Porto, Porto, Portugal
| | - Fàtima Crispi
- BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Eduard Gratacós
- BCNatal – Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Deu), IDIBAPS, University of Barcelona, and Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
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Felsky D, Cannitelli A, Pipitone J. Whole Person Modeling: a transdisciplinary approach to mental health research. DISCOVER MENTAL HEALTH 2023; 3:16. [PMID: 37638348 PMCID: PMC10449734 DOI: 10.1007/s44192-023-00041-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 08/10/2023] [Indexed: 08/29/2023]
Abstract
The growing global burden of mental illness has prompted calls for innovative research strategies. Theoretical models of mental health include complex contributions of biological, psychosocial, experiential, and other environmental influences. Accordingly, neuropsychiatric research has self-organized into largely isolated disciplines working to decode each individual contribution. However, research directly modeling objective biological measurements in combination with cognitive, psychological, demographic, or other environmental measurements is only now beginning to proliferate. This review aims to (1) to describe the landscape of modern mental health research and current movement towards integrative study, (2) to provide a concrete framework for quantitative integrative research, which we call Whole Person Modeling, (3) to explore existing and emerging techniques and methods used in Whole Person Modeling, and (4) to discuss our observations about the scarcity, potential value, and untested aspects of highly transdisciplinary research in general. Whole Person Modeling studies have the potential to provide a better understanding of multilevel phenomena, deliver more accurate diagnostic and prognostic tests to aid in clinical decision making, and test long standing theoretical models of mental illness. Some current barriers to progress include challenges with interdisciplinary communication and collaboration, systemic cultural barriers to transdisciplinary career paths, technical challenges in model specification, bias, and data harmonization, and gaps in transdisciplinary educational programs. We hope to ease anxiety in the field surrounding the often mysterious and intimidating world of transdisciplinary, data-driven mental health research and provide a useful orientation for students or highly specialized researchers who are new to this area.
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Affiliation(s)
- Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
- Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON Canada
- Rotman Research Institute, Baycrest Hospital, Toronto, ON Canada
- Faculty of Medicine, McMaster University, Hamilton, ON Canada
| | - Alyssa Cannitelli
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8 Canada
- Faculty of Medicine, McMaster University, Hamilton, ON Canada
| | - Jon Pipitone
- Department of Psychiatry, Queen’s University, Kingston, ON Canada
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Wang C, Hayes R, Roeder K, Jalbrzikowski M. Neurobiological Clusters Are Associated With Trajectories of Overall Psychopathology in Youth. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:852-863. [PMID: 37121399 PMCID: PMC10792597 DOI: 10.1016/j.bpsc.2023.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/22/2023] [Accepted: 04/13/2023] [Indexed: 05/02/2023]
Abstract
BACKGROUND Integrating multiple neuroimaging modalities to identify clusters of individuals and then associating these clusters with psychopathology is a promising approach for understanding neurobiological mechanisms that underlie psychopathology and the extent to which these features are associated with clinical symptoms. METHODS We leveraged neuroimaging data from T1-weighted, diffusion-weighted, and resting-state functional magnetic resonance images from the Adolescent Brain Cognitive Development (ABCD) Study (N = 8035) and used similarity network fusion and spectral clustering to identify subgroups of participants. We examined neuroimaging measures as a function of clustering profiles using 1, 2, or 3 imaging modalities (i.e., data combinations), calculated the stability of the clustering assignment in each respective data combination, and compared the consistency of clusters across different data combinations. We then compared the extent to which clusters were associated with overall psychopathology at the baseline assessment and at 2 yearly follow-up visits. RESULTS Each data combination resulted in optimal clusters ranging from 2 to 4 subgroups for each data combination. Clusters were stable across subsampling of the ABCD Study cohort. Widespread structural measures (surface area, fractional anisotropy, and mean diffusivity) were important features contributing to clustering across different data combinations. Five of the seven data combinations were associated with overall psychopathology, both at baseline and over time (d = 0.08-0.41). Generally, lower global cortical volume and surface area, widespread reduced fractional anisotropy, and increased radial diffusivity were associated with increased overall psychopathology. CONCLUSIONS Profiles constructed from neuroimaging data combinations are associated with concurrent and future psychopathology trajectories.
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Affiliation(s)
- Catherine Wang
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Rebecca Hayes
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, Massachusetts
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania; Department of Computational Biology, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Maria Jalbrzikowski
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, Massachusetts; Department of Psychiatry, Harvard Medical School, Boston, Massachusetts.
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Seitz-Holland J, Nägele FL, Kubicki M, Pasternak O, Cho KIK, Hough M, Mulert C, Shenton ME, Crow TJ, James ACD, Lyall AE. Shared and distinct white matter abnormalities in adolescent-onset schizophrenia and adolescent-onset psychotic bipolar disorder. Psychol Med 2023; 53:4707-4719. [PMID: 35796024 PMCID: PMC11119277 DOI: 10.1017/s003329172200160x] [Citation(s) in RCA: 1] [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] [Indexed: 11/06/2022]
Abstract
BACKGROUND While adolescent-onset schizophrenia (ADO-SCZ) and adolescent-onset bipolar disorder with psychosis (psychotic ADO-BPD) present a more severe clinical course than their adult forms, their pathophysiology is poorly understood. Here, we study potentially state- and trait-related white matter diffusion-weighted magnetic resonance imaging (dMRI) abnormalities along the adolescent-onset psychosis continuum to address this need. METHODS Forty-eight individuals with ADO-SCZ (20 female/28 male), 15 individuals with psychotic ADO-BPD (7 female/8 male), and 35 healthy controls (HCs, 18 female/17 male) underwent dMRI and clinical assessments. Maps of extracellular free-water (FW) and fractional anisotropy of cellular tissue (FAT) were compared between individuals with psychosis and HCs using tract-based spatial statistics and FSL's Randomise. FAT and FW values were extracted, averaged across all voxels that demonstrated group differences, and then utilized to test for the influence of age, medication, age of onset, duration of illness, symptom severity, and intelligence. RESULTS Individuals with adolescent-onset psychosis exhibited pronounced FW and FAT abnormalities compared to HCs. FAT reductions were spatially more widespread in ADO-SCZ. FW increases, however, were only present in psychotic ADO-BPD. In HCs, but not in individuals with adolescent-onset psychosis, FAT was positively related to age. CONCLUSIONS We observe evidence for cellular (FAT) and extracellular (FW) white matter abnormalities in adolescent-onset psychosis. Although cellular white matter abnormalities were more prominent in ADO-SCZ, such alterations may reflect a shared trait, i.e. neurodevelopmental pathology, present across the psychosis spectrum. Extracellular abnormalities were evident in psychotic ADO-BPD, potentially indicating a more dynamic, state-dependent brain reaction to psychosis.
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Affiliation(s)
- Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Felix L. Nägele
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Kang Ik K. Cho
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Morgan Hough
- SANE POWIC, University Department of Psychiatry, Warneford Hospital, Oxford, UK
- Highfield Unit, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Christoph Mulert
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, University of Hamburg, Hamburg, Germany
- Centre for Psychiatry and Psychotherapy, Justus-Liebig-University, Giessen, Germany
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Timothy J. Crow
- SANE POWIC, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Anthony C. D. James
- SANE POWIC, University Department of Psychiatry, Warneford Hospital, Oxford, UK
- Highfield Unit, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Amanda E. Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Yang M, Matan-Lithwick S, Wang Y, De Jager PL, Bennett DA, Felsky D. Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing. Brain Commun 2023; 5:fcad110. [PMID: 37082508 PMCID: PMC10110975 DOI: 10.1093/braincomms/fcad110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 02/17/2023] [Accepted: 04/03/2023] [Indexed: 04/07/2023] Open
Abstract
Molecular subtyping of brain tissue provides insights into the heterogeneity of common neurodegenerative conditions, such as Alzheimer's disease. However, existing subtyping studies have mostly focused on single data modalities and only those individuals with severe cognitive impairment. To address these gaps, we applied similarity network fusion, a method capable of integrating multiple high-dimensional multi-omic data modalities simultaneously, to an elderly sample spanning the full spectrum of cognitive ageing trajectories. We analyzed human frontal cortex brain samples characterized by five omic modalities: bulk RNA sequencing (18 629 genes), DNA methylation (53 932 CpG sites), histone acetylation (26 384 peaks), proteomics (7737 proteins) and metabolomics (654 metabolites). Similarity network fusion followed by spectral clustering was used for subtype detection, and subtype numbers were determined by Eigen-gap and rotation cost statistics. Normalized mutual information determined the relative contribution of each modality to the fused network. Subtypes were characterized by associations with 13 age-related neuropathologies and cognitive decline. Fusion of all five data modalities (n = 111) yielded two subtypes (n S1 = 53, n S2 = 58), which were nominally associated with diffuse amyloid plaques; however, this effect was not significant after correction for multiple testing. Histone acetylation (normalized mutual information = 0.38), DNA methylation (normalized mutual information = 0.18) and RNA abundance (normalized mutual information = 0.15) contributed most strongly to this network. Secondary analysis integrating only these three modalities in a larger subsample (n = 513) indicated support for both three- and five-subtype solutions, which had significant overlap, but showed varying degrees of internal stability and external validity. One subtype showed marked cognitive decline, which remained significant even after correcting for tests across both three- and five-subtype solutions (p Bonf = 5.9 × 10-3). Comparison to single-modality subtypes demonstrated that the three-modal subtypes were able to uniquely capture cognitive variability. Comprehensive sensitivity analyses explored influences of sample size and cluster number parameters. We identified highly integrative molecular subtypes of ageing derived from multiple high dimensional, multi-omic data modalities simultaneously. Fusing RNA abundance, DNA methylation, and histone acetylation measures generated subtypes that were associated with cognitive decline. This work highlights the potential value and challenges of multi-omic integration in unsupervised subtyping of post-mortem brain.
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Affiliation(s)
- Mu Yang
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Stuart Matan-Lithwick
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Yanling Wang
- Rush Alzheimer’s Disease Center, Rush University, Chicago, IL 60612, USA
| | - Philip L De Jager
- The Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY 10033, USA
| | - David A Bennett
- Rush Alzheimer’s Disease Center, Rush University, Chicago, IL 60612, USA
| | - Daniel Felsky
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
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Hadaya L, Dimitrakopoulou K, Vanes LD, Kanel D, Fenn-Moltu S, Gale-Grant O, Counsell SJ, Edwards AD, Saqi M, Batalle D, Nosarti C. Parsing brain-behavior heterogeneity in very preterm born children using integrated similarity networks. Transl Psychiatry 2023; 13:108. [PMID: 37012252 PMCID: PMC10070645 DOI: 10.1038/s41398-023-02401-w] [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] [Received: 10/24/2022] [Revised: 03/14/2023] [Accepted: 03/17/2023] [Indexed: 04/05/2023] Open
Abstract
Very preterm birth (VPT; ≤32 weeks' gestation) is associated with altered brain development and cognitive and behavioral difficulties across the lifespan. However, heterogeneity in outcomes among individuals born VPT makes it challenging to identify those most vulnerable to neurodevelopmental sequelae. Here, we aimed to stratify VPT children into distinct behavioral subgroups and explore between-subgroup differences in neonatal brain structure and function. 198 VPT children (98 females) previously enrolled in the Evaluation of Preterm Imaging Study (EudraCT 2009-011602-42) underwent Magnetic Resonance Imaging at term-equivalent age and neuropsychological assessments at 4-7 years. Using an integrative clustering approach, we combined neonatal socio-demographic, clinical factors and childhood socio-emotional and executive function outcomes, to identify distinct subgroups of children based on their similarity profiles in a multidimensional space. We characterized resultant subgroups using domain-specific outcomes (temperament, psychopathology, IQ and cognitively stimulating home environment) and explored between-subgroup differences in neonatal brain volumes (voxel-wise Tensor-Based-Morphometry), functional connectivity (voxel-wise degree centrality) and structural connectivity (Tract-Based-Spatial-Statistics). Results showed two- and three-cluster data-driven solutions. The two-cluster solution comprised a 'resilient' subgroup (lower psychopathology and higher IQ, executive function and socio-emotional scores) and an 'at-risk' subgroup (poorer behavioral and cognitive outcomes). No neuroimaging differences between the resilient and at-risk subgroups were found. The three-cluster solution showed an additional third 'intermediate' subgroup, displaying behavioral and cognitive outcomes intermediate between the resilient and at-risk subgroups. The resilient subgroup had the most cognitively stimulating home environment and the at-risk subgroup showed the highest neonatal clinical risk, while the intermediate subgroup showed the lowest clinical, but the highest socio-demographic risk. Compared to the intermediate subgroup, the resilient subgroup displayed larger neonatal insular and orbitofrontal volumes and stronger orbitofrontal functional connectivity, while the at-risk group showed widespread white matter microstructural alterations. These findings suggest that risk stratification following VPT birth is feasible and could be used translationally to guide personalized interventions aimed at promoting children's resilience.
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Affiliation(s)
- Laila Hadaya
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, Faculty of Life Sciences & Medicine, King's College London, London, UK
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Konstantina Dimitrakopoulou
- Translational Bioinformatics Platform, NIHR Biomedical Research Centre, Guy's and St. Thomas' NHS Foundation Trust and King's College London, London, UK
| | - Lucy D Vanes
- Centre for Neuroimaging Sciences, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Dana Kanel
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, Faculty of Life Sciences & Medicine, King's College London, London, UK
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Sunniva Fenn-Moltu
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, Faculty of Life Sciences & Medicine, King's College London, London, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Oliver Gale-Grant
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, Faculty of Life Sciences & Medicine, King's College London, London, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Serena J Counsell
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, Faculty of Life Sciences & Medicine, King's College London, London, UK
| | - A David Edwards
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, Faculty of Life Sciences & Medicine, King's College London, London, UK
| | - Mansoor Saqi
- Translational Bioinformatics Platform, NIHR Biomedical Research Centre, Guy's and St. Thomas' NHS Foundation Trust and King's College London, London, UK
| | - Dafnis Batalle
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, Faculty of Life Sciences & Medicine, King's College London, London, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK
| | - Chiara Nosarti
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, Faculty of Life Sciences & Medicine, King's College London, London, UK.
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry Psychology and Neuroscience, King's College London, London, UK.
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8
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Enrico P, Delvecchio G, Turtulici N, Aronica R, Pigoni A, Squarcina L, Villa FM, Perlini C, Rossetti MG, Bellani M, Lasalvia A, Bonetto C, Scocco P, D'Agostino A, Torresani S, Imbesi M, Bellini F, Veronese A, Bocchio-Chiavetto L, Gennarelli M, Balestrieri M, Colombo GI, Finardi A, Ruggeri M, Furlan R, Brambilla P. A machine learning approach on whole blood immunomarkers to identify an inflammation-associated psychosis onset subgroup. Mol Psychiatry 2023; 28:1190-1200. [PMID: 36604602 DOI: 10.1038/s41380-022-01911-1] [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] [Received: 07/11/2022] [Revised: 11/29/2022] [Accepted: 12/07/2022] [Indexed: 01/07/2023]
Abstract
Psychosis onset is a transdiagnostic event that leads to a range of psychiatric disorders, which are currently diagnosed through clinical observation. The integration of multimodal biological data could reveal different subtypes of psychosis onset to target for the personalization of care. In this study, we tested the existence of subgroups of patients affected by first-episode psychosis (FEP) with a possible immunopathogenic basis. To do this, we designed a data-driven unsupervised machine learning model to cluster a sample of 127 FEP patients and 117 healthy controls (HC), based on the peripheral blood expression levels of 12 psychosis-related immune gene transcripts. To validate the model, we applied a resampling strategy based on the half-splitting of the total sample with random allocation of the cases. Further, we performed a post-hoc univariate analysis to verify the clinical, cognitive, and structural brain correlates of the subgroups identified. The model identified and validated two distinct clusters: 1) a FEP cluster characterized by the high expression of inflammatory and immune-activating genes (IL1B, CCR7, IL12A and CXCR3); 2) a cluster consisting of an equal number of FEP and HC subjects, which did not show a relative over or under expression of any immune marker (balanced subgroup). None of the subgroups was related to specific symptoms dimensions or longitudinal diagnosis of affective vs non-affective psychosis. FEP patients included in the balanced immune subgroup showed a thinning of the left supramarginal and superiorfrontal cortex (FDR-adjusted p-values < 0.05). Our results demonstrated the existence of a FEP patients' subgroup identified by a multivariate pattern of immunomarkers involved in inflammatory activation. This evidence may pave the way to sample stratification in clinical studies aiming to develop diagnostic tools and therapies targeting specific immunopathogenic pathways of psychosis.
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Affiliation(s)
- Paolo Enrico
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.,Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Nunzio Turtulici
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Rosario Aronica
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alessandro Pigoni
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.,Social and Affective Neuroscience Group, MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Filippo M Villa
- Child Psychopathology Unit, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Lecco, Italy
| | - Cinzia Perlini
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Clinical Psychology, University of Verona, Verona, Italy.,USD Clinical Psychology, Azienda Ospedaliera Universitaria Integrata (AOUI) of Verona, Verona, Italy
| | - Maria G Rossetti
- Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Marcella Bellani
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy.,UOC of Psychiatry, Azienda Ospedaliera Universitaria Integrata (AOUI) of Verona, Verona, Italy
| | - Antonio Lasalvia
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Chiara Bonetto
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy
| | - Paolo Scocco
- Department of Mental Health, AULSS 6 Euganea, Padua, Italy
| | - Armando D'Agostino
- Department of Health Sciences, San Paolo University Hospital, University of Milan, Milano, Milan, Italy
| | - Stefano Torresani
- Department of Psychiatry, ULSS, Bolzano Suedtiroler Sanitaetbetrieb- Azienda Sanitaria dell'Alto Adige, Bolzano, Italy
| | | | | | | | - Luisella Bocchio-Chiavetto
- Faculty of Psychology, eCampus University, Novedrate, Como, Italy.,Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio, Fatebenefratelli, Brescia, Italy
| | - Massimo Gennarelli
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio, Fatebenefratelli, Brescia, Italy.,Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | | | - Gualtiero I Colombo
- Centro Cardiologico Monzino IRCCS, Immunology and Functional Genomics Unit, Milan, Italy
| | - Annamaria Finardi
- Clinical Neuroimmunology Unit, Institute of Experimental Neurology, IRCCS Ospedale San Raffaele, Milano, Italy
| | - Mirella Ruggeri
- Department of Neurosciences, Biomedicine and Movement Sciences, Section of Psychiatry, University of Verona, Verona, Italy.,UOC of Psychiatry, Azienda Ospedaliera Universitaria Integrata (AOUI) of Verona, Verona, Italy
| | - Roberto Furlan
- Clinical Neuroimmunology Unit, Institute of Experimental Neurology, IRCCS Ospedale San Raffaele, Milano, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy. .,Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.
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9
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Chen J, Patil KR, Yeo BTT, Eickhoff SB. Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research. Biol Psychiatry 2023; 93:18-28. [PMID: 36307328 DOI: 10.1016/j.biopsych.2022.07.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/06/2022] [Accepted: 07/28/2022] [Indexed: 11/18/2022]
Abstract
Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.
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Affiliation(s)
- Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China; Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences & Engineering Programme, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-universität Düsseldorf, Düsseldorf, Germany
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10
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Wang Y, Tang S, Ma R, Zamit I, Wei Y, Pan Y. Multi-modal intermediate integrative methods in neuropsychiatric disorders: A review. Comput Struct Biotechnol J 2022; 20:6149-6162. [DOI: 10.1016/j.csbj.2022.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 11/04/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
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11
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Oliver LD, Hawco C, Viviano JD, Voineskos AN. From the Group to the Individual in Schizophrenia Spectrum Disorders: Biomarkers of Social Cognitive Impairments and Therapeutic Translation. Biol Psychiatry 2022; 91:699-708. [PMID: 34799097 DOI: 10.1016/j.biopsych.2021.09.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/11/2021] [Accepted: 09/11/2021] [Indexed: 12/23/2022]
Abstract
People with schizophrenia spectrum disorders (SSDs) often experience persistent social cognitive impairments, associated with poor functional outcome. There are currently no approved treatment options for these debilitating symptoms, highlighting the need for novel therapeutic strategies. Work to date has elucidated differential social processes and underlying neural circuitry affected in SSDs, which may be amenable to modulation using neurostimulation. Further, advances in functional connectivity mapping and electric field modeling may be used to identify individualized treatment targets to maximize the impact of brain stimulation on social cognitive networks. Here, we review literature supporting a roadmap for translating functional connectivity biomarker discovery to individualized treatment development for social cognitive impairments in SSDs. First, we outline the relevance of social cognitive impairments in SSDs. We review machine learning approaches for dimensional brain-behavior biomarker discovery, emphasizing the importance of individual differences. We synthesize research showing that brain stimulation techniques, such as repetitive transcranial magnetic stimulation, can be used to target relevant networks. Further, functional connectivity-based individualized targeting may enhance treatment response. We then outline recent approaches to account for neuroanatomical variability and optimize coil positioning to individually maximize target engagement. Overall, the synthesized literature provides support for the utility and feasibility of this translational approach to precision treatment. The proposed roadmap to translate biomarkers of social cognitive impairments to individualized treatment is currently under evaluation in precision-guided trials. Such a translational approach may also be applicable across conditions and generalizable for the development of individualized neurostimulation targeting other behavioral deficits.
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Affiliation(s)
- Lindsay D Oliver
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Joseph D Viviano
- Mila-Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
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12
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Evidence of shared and distinct functional and structural brain signatures in schizophrenia and autism spectrum disorder. Commun Biol 2021; 4:1073. [PMID: 34521980 PMCID: PMC8440519 DOI: 10.1038/s42003-021-02592-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 08/06/2021] [Indexed: 02/08/2023] Open
Abstract
Schizophrenia (SZ) and autism spectrum disorder (ASD) share considerable clinical features and intertwined historical roots. It is greatly needed to explore their similarities and differences in pathophysiologic mechanisms. We assembled a large sample size of neuroimaging data (about 600 SZ patients, 1000 ASD patients, and 1700 healthy controls) to study the shared and unique brain abnormality of the two illnesses. We analyzed multi-scale brain functional connectivity among functional networks and brain regions, intra-network connectivity, and cerebral gray matter density and volume. Both SZ and ASD showed lower functional integration within default mode and sensorimotor domains, but increased interaction between cognitive control and default mode domains. The shared abnormalties in intra-network connectivity involved default mode, sensorimotor, and cognitive control networks. Reduced gray matter volume and density in the occipital gyrus and cerebellum were observed in both illnesses. Interestingly, ASD had overall weaker changes than SZ in the shared abnormalities. Interaction between visual and cognitive regions showed disorder-unique deficits. In summary, we provide strong neuroimaging evidence of the convergent and divergent changes in SZ and ASD that correlated with clinical features.
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13
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Cao J, Chai-Zhang TC, Huang Y, Eshel MN, Kong J. Potential scalp stimulation targets for mental disorders: evidence from neuroimaging studies. J Transl Med 2021; 19:343. [PMID: 34376209 PMCID: PMC8353731 DOI: 10.1186/s12967-021-02993-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 07/17/2021] [Indexed: 11/16/2022] Open
Abstract
Mental disorders widely contribute to the modern global disease burden, creating a significant need for improvement of treatments. Scalp stimulation methods (such as scalp acupuncture and transcranial electrical stimulation) have shown promising results in relieving psychiatric symptoms. However, neuroimaging findings haven’t been well-integrated into scalp stimulation treatments. Identifying surface brain regions associated with mental disorders would expand target selection and the potential for these interventions as treatments for mental disorders. In this study, we performed large-scale meta-analyses separately on eight common mental disorders: attention deficit hyperactivity disorder, anxiety disorder, autism spectrum disorder, bipolar disorder, compulsive disorder, major depression, post-traumatic stress disorder and schizophrenia; utilizing modern neuroimaging literature to summarize disorder-associated surface brain regions, and proposed neuroimaging-based target protocols. We found that the medial frontal gyrus, the supplementary motor area, and the dorsal lateral prefrontal cortex are commonly involved in the pathophysiology of mental disorders. The target protocols we proposed may provide new brain targets for scalp stimulation in the treatment of mental disorders, and facilitate its clinical application.
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Affiliation(s)
- Jin Cao
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Thalia Celeste Chai-Zhang
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Yiting Huang
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Maya Nicole Eshel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Jian Kong
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA.
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14
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Moreau CA, Ching CR, Kumar K, Jacquemont S, Bearden CE. Structural and functional brain alterations revealed by neuroimaging in CNV carriers. Curr Opin Genet Dev 2021; 68:88-98. [PMID: 33812299 PMCID: PMC8205978 DOI: 10.1016/j.gde.2021.03.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 02/01/2021] [Accepted: 03/09/2021] [Indexed: 01/21/2023]
Abstract
Copy Number Variants (CNVs) are associated with elevated rates of neuropsychiatric disorders. A 'genetics-first' approach, involving the CNV effects on the brain, irrespective of clinical symptomatology, allows investigation of mechanisms underlying neuropsychiatric disorders in the general population. Recent years have seen an increasing number of larger multisite neuroimaging studies investigating the effect of CNVs on structural and functional brain endophenotypes. Alterations overlap with those found in idiopathic psychiatric conditions but effect sizes are twofold to fivefold larger. Here we review new CNV-associated structural and functional brain alterations and outline the future of neuroimaging genomics research, with particular emphasis on developing new resources for the study of high-risk CNVs and rare genomic variants.
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Affiliation(s)
- Clara A Moreau
- Sainte-Justine Hospital Research Center, Montreal, Canada; Department of Pediatrics, University of Montreal, Montreal, Canada; Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Canada; Human Genetics and Cognitive Functions, CNRS UMR 3571, Université de Paris, Institut Pasteur, Paris, France
| | - Christopher Rk Ching
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of the University of Southern California, USA
| | - Kuldeep Kumar
- Sainte-Justine Hospital Research Center, Montreal, Canada
| | - Sebastien Jacquemont
- Sainte-Justine Hospital Research Center, Montreal, Canada; Department of Pediatrics, University of Montreal, Montreal, Canada.
| | - Carrie E Bearden
- Semel Institute for Neuroscience and Human Behavior, Departments of Psychiatry and Biobehavioral Sciences and Psychology, University of California, Los Angeles, USA.
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15
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Hong SJ, Sisk LM, Caballero C, Mekhanik A, Roy AK, Milham MP, Gee DG. Decomposing complex links between the childhood environment and brain structure in school-aged youth. Dev Cogn Neurosci 2021; 48:100919. [PMID: 33556882 PMCID: PMC7868609 DOI: 10.1016/j.dcn.2021.100919] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 10/26/2020] [Accepted: 01/18/2021] [Indexed: 12/22/2022] Open
Abstract
Childhood experiences play a profound role in conferring risk and resilience for brain and behavioral development. However, how different facets of the environment shape neurodevelopment remains largely unknown. Here we sought to decompose heterogeneous relationships between environmental factors and brain structure in 989 school-aged children from the Adolescent Brain Cognitive Development Study. We applied a cross-modal integration and clustering approach called 'Similarity Network Fusion', which combined two brain morphometrics (i.e., cortical thickness and myelin-surrogate markers), and key environmental factors (i.e., trauma exposure, neighborhood safety, school environment, and family environment) to identify homogeneous subtypes. Depending on the subtyping resolution, results identified two or five subgroups, each characterized by distinct brain structure-environment profiles. Notably, more supportive caregiving and school environments were associated with greater myelination, whereas less supportive caregiving, higher family conflict and psychopathology, and higher perceived neighborhood safety were observed with greater cortical thickness. These subtypes were highly reproducible and predicted externalizing symptoms and overall mental health problems. Our findings support the theory that distinct environmental exposures are differentially associated with alterations in structural neurodevelopment. Delineating more precise associations between risk factors, protective factors, and brain development may inform approaches to enhance risk identification and optimize interventions targeting specific experiences.
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Affiliation(s)
- Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Lucinda M Sisk
- Department of Psychology, Yale University, New Haven, CT, USA
| | | | - Anthony Mekhanik
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Amy K Roy
- Department of Psychology, Fordham University, Bronx, NY, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | - Dylan G Gee
- Department of Psychology, Yale University, New Haven, CT, USA.
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16
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Oliver LD, Moxon-Emre I, Lai MC, Grennan L, Voineskos AN, Ameis SH. Social Cognitive Performance in Schizophrenia Spectrum Disorders Compared With Autism Spectrum Disorder: A Systematic Review, Meta-analysis, and Meta-regression. JAMA Psychiatry 2021; 78:281-292. [PMID: 33291141 PMCID: PMC7724568 DOI: 10.1001/jamapsychiatry.2020.3908] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
IMPORTANCE Schizophrenia spectrum disorders (SSDs) and autism spectrum disorder (ASD) both feature social cognitive deficits; however, these disorders historically have been examined separately using a range of tests and subdomain focus and at different time points in the life span. Moving beyond diagnostic categories and characterizing social cognitive deficits can enhance understanding of shared pathways across these disorders. OBJECTIVE To investigate how deficits in social cognitive domains diverge or overlap between SSDs and ASD based on the extant literature. DATA SOURCES Literature searches were conducted in MEDLINE, PsycInfo, Embase, and Web of Science from database inception until July 26, 2020. STUDY SELECTION Original research articles were selected that reported performance-based measures of social cognition in both SSDs and ASD samples. Selected articles also had to be published in English and use International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, DSM-IV, or more recent diagnostic criteria. DATA EXTRACTION AND SYNTHESIS This systematic review and meta-analysis was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-analyses and Meta-analysis of Observational Studies in Epidemiology reporting guidelines, including data extraction and quality assessment using a modified version of the Newcastle-Ottawa Scale. Data were pooled using a random-effects model. MAIN OUTCOMES AND MEASURES Effect sizes were calculated as Hedges g (SSDs vs ASD). The primary outcomes were performance on emotion processing tasks, theory of mind (ToM) tasks, and the Reading the Mind in the Eyes Test (RMET) in SSDs compared with ASD. Meta-regressions were performed for age difference, publication year, quality assessment scores, and antipsychotic medication use. RESULTS Of the 4175 screened articles, 36 studies directly comparing social cognitive performance in individuals with SSDs vs ASD were included in the qualitative analysis (n = 1212 for SSDs groups and n = 1109 for ASD groups), and 33 studies were included in the quantitative analyses (n = 1113 for SSDs groups and n = 1015 for ASD groups). Most study participants were male (number of studies [k] = 36, 72% [878 of 1212] in SSDs groups and 82% [907 of 1109] in ASD groups), and age (k = 35) was older in SSDs groups (mean [SD], 28.4 [9.5] years) than in ASD groups (mean [SD], 23.3 [7.6] years). Included studies highlighted the prevalence of small, male-predominant samples and a paucity of cross-disorder clinical measures. The meta-analyses revealed no statistically significant differences between SSDs and ASD on emotion processing measures (k = 15; g = 0.12 [95% CI, -0.07 to 0.30]; P = .21; I2 = 51.0%; 1 outlier excluded), ToM measures (k = 17; g = -0.01 [95% CI, -0.21 to 0.19]; P = .92; I2 = 56.5%; 1 outlier excluded), or the RMET (k = 13; g = 0.25 [95% CI, -0.04 to 0.53]; P = .10; I2 = 75.3%). However, SSDs vs ASD performance differences between studies were statistically significantly heterogeneous, which was only minimally explained by potential moderators. CONCLUSIONS AND RELEVANCE In this analysis, similar levels of social cognitive impairment were present, on average, in individuals with SSDs and ASD. Cross-disorder studies of social cognition, including larger samples, consensus batteries, and consistent reporting of measures, as well as data across multiple levels of analysis, are needed to help identify subgroups within and across disorders that may be more homogeneous in etiology and treatment response.
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Affiliation(s)
- Lindsay D. Oliver
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Iska Moxon-Emre
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Meng-Chuan Lai
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada,Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada,Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom,Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Laura Grennan
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Aristotle N. Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada,Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Stephanie H. Ameis
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada,Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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17
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Hawco C, Dickie EW, Jacobs G, Daskalakis ZJ, Voineskos AN. Moving beyond the mean: Subgroups and dimensions of brain activity and cognitive performance across domains. Neuroimage 2021; 231:117823. [PMID: 33549760 DOI: 10.1016/j.neuroimage.2021.117823] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 01/29/2021] [Accepted: 01/31/2021] [Indexed: 01/08/2023] Open
Abstract
Human neuroimaging during cognitive tasks has provided unique and important insights into the neurobiology of cognition. However, the vast majority of research relies on group aggregate or average statistical maps of activity, which do not fully capture the rich intersubject variability in brain function. In order to fully understand the neurobiology of cognitive processes, it is necessary to explore the range of variability in activation patterns across individuals. To better characterize individual variability, hierarchical clustering was performed separately on six fMRI tasks in 822 participants from the Human Connectome Project. Across all tasks, clusters ranged from a predominantly 'deactivating' pattern towards a more 'activating' pattern of brain activity, with significant differences in out-of-scanner cognitive test scores between clusters. Cluster stability was assessed via a resampling approach; a cluster probability matrix was generated, as the probability of any pair of participants clustering together when both were present in a random subsample. Rather than forming distinct clusters, participants fell along a spectrum or into pseudo-clusters without clear boundaries. A principal components analysis of the cluster probability matrix revealed three components explaining over 90% of the variance in clustering. Plotting participants in this lower-dimensional 'similarity space' revealed manifolds of variations along an S 'snake' shaped spectrum or a folded circle or 'tortilla' shape. The 'snake' shape was present in tasks where individual variability related to activity along covarying networks, while the 'tortilla' shape represented multiple networks which varied independently.
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Affiliation(s)
- Colin Hawco
- Centre for Addiction and Mental Health, Campbell Family Mental Health Institute, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
| | - Erin W Dickie
- Centre for Addiction and Mental Health, Campbell Family Mental Health Institute, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Grace Jacobs
- Centre for Addiction and Mental Health, Campbell Family Mental Health Institute, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, ON, Canada
| | - Zafiris J Daskalakis
- Centre for Addiction and Mental Health, Campbell Family Mental Health Institute, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Aristotle N Voineskos
- Centre for Addiction and Mental Health, Campbell Family Mental Health Institute, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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18
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Integration of brain and behavior measures for identification of data-driven groups cutting across children with ASD, ADHD, or OCD. Neuropsychopharmacology 2021; 46:643-653. [PMID: 33168947 PMCID: PMC8027842 DOI: 10.1038/s41386-020-00902-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 09/25/2020] [Accepted: 10/26/2020] [Indexed: 11/08/2022]
Abstract
Autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD) are clinically and biologically heterogeneous neurodevelopmental disorders (NDDs). The objective of the present study was to integrate brain imaging and behavioral measures to identify new brain-behavior subgroups cutting across these disorders. A subset of the data from the Province of Ontario Neurodevelopmental Disorder (POND) Network was used including participants with different NDDs (aged 6-16 years) that underwent cross-sectional T1-weighted and diffusion-weighted magnetic resonance imaging (MRI) scanning on the same 3T scanner, and behavioral/cognitive assessments. Similarity Network Fusion was applied to integrate cortical thickness, subcortical volume, white matter fractional anisotropy (FA), and behavioral measures in 176 children with ASD, ADHD or OCD with complete data that passed quality control. Normalized mutual information was used to determine top contributing model features. Bootstrapping, out-of-model outcome measures and supervised machine learning were each used to examine stability and evaluate the new groups. Cortical thickness in socio-emotional and attention/executive networks and inattention symptoms comprised the top ten features driving participant similarity and differences between four transdiagnostic groups. Subcortical volumes (pallidum, nucleus accumbens, thalamus) were also different among groups, although white matter FA showed limited differences. Features driving participant similarity remained stable across resampling, and the new groups showed significantly different scores on everyday adaptive functioning. Our findings open the possibility of studying new data-driven groups that represent children with NDDs more similar to each other than others within their own diagnostic group. Future work is needed to build on this early attempt through replication of the current findings in independent samples and testing longitudinally for prognostic value.
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Markello RD, Shafiei G, Tremblay C, Postuma RB, Dagher A, Misic B. Multimodal phenotypic axes of Parkinson's disease. NPJ PARKINSONS DISEASE 2021; 7:6. [PMID: 33402689 PMCID: PMC7785730 DOI: 10.1038/s41531-020-00144-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 11/19/2020] [Indexed: 12/15/2022]
Abstract
Individuals with Parkinson’s disease present with a complex clinical phenotype, encompassing sleep, motor, cognitive, and affective disturbances. However, characterizations of PD are typically made for the “average” patient, ignoring patient heterogeneity and obscuring important individual differences. Modern large-scale data sharing efforts provide a unique opportunity to precisely investigate individual patient characteristics, but there exists no analytic framework for comprehensively integrating data modalities. Here we apply an unsupervised learning method—similarity network fusion—to objectively integrate MRI morphometry, dopamine active transporter binding, protein assays, and clinical measurements from n = 186 individuals with de novo Parkinson’s disease from the Parkinson’s Progression Markers Initiative. We show that multimodal fusion captures inter-dependencies among data modalities that would otherwise be overlooked by field standard techniques like data concatenation. We then examine how patient subgroups derived from the fused data map onto clinical phenotypes, and how neuroimaging data is critical to this delineation. Finally, we identify a compact set of phenotypic axes that span the patient population, demonstrating that this continuous, low-dimensional projection of individual patients presents a more parsimonious representation of heterogeneity in the sample compared to discrete biotypes. Altogether, these findings showcase the potential of similarity network fusion for combining multimodal data in heterogeneous patient populations.
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Affiliation(s)
- Ross D Markello
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada.
| | - Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Christina Tremblay
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Ronald B Postuma
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, Canada.
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Oliver LD, Hawco C, Homan P, Lee J, Green MF, Gold JM, DeRosse P, Argyelan M, Malhotra AK, Buchanan RW, Voineskos AN. Social Cognitive Networks and Social Cognitive Performance Across Individuals With Schizophrenia Spectrum Disorders and Healthy Control Participants. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 6:1202-1214. [PMID: 33579663 DOI: 10.1016/j.bpsc.2020.11.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 11/17/2020] [Accepted: 11/30/2020] [Indexed: 11/24/2022]
Abstract
BACKGROUND Schizophrenia spectrum disorders (SSDs) feature social cognitive deficits, although their neural basis remains unclear. Social cognitive performance may relate to neural circuit activation patterns more than to diagnosis, which would have important prognostic and therapeutic implications. The current study aimed to determine how functional connectivity within and between social cognitive networks relates to social cognitive performance across individuals with SSDs and healthy control participants. METHODS Participants with SSDs (n = 164) and healthy control participants (n = 117) completed the Empathic Accuracy task during functional magnetic resonance imaging as well as lower-level (e.g., emotion recognition) and higher-level (e.g., theory of mind) social cognitive measures outside the scanner. Functional connectivity during the Empathic Accuracy task was analyzed using background connectivity and graph theory. Data-driven social cognitive networks were identified across participants. Regression analyses were used to examine network connectivity-performance relationships across individuals. Positive and negative within- and between-network connectivity strengths were also compared in poor versus good social cognitive performers and in SSD versus control groups. RESULTS Three social cognitive networks were identified: motor resonance, affect sharing, and mentalizing. Regression and group-based analyses demonstrated reduced between-network negative connectivity, or segregation, and greater within- and between-network positive connectivity in worse social cognitive performers. There were no significant effects of diagnostic group on within- or between-network connectivity. CONCLUSIONS These findings suggest that the neural circuitry of social cognitive performance may exist dimensionally. Across participants, better social cognitive performance was associated with greater segregation between social cognitive networks, whereas poor versus good performers may compensate via hyperconnectivity within and between social cognitive networks.
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Affiliation(s)
- Lindsay D Oliver
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Philipp Homan
- University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland; Division of Psychiatry Research, Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York; Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York; Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York
| | - Junghee Lee
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles; Department of Veterans Affairs, Desert Pacific Mental Illness Research, Education, and Clinical Center, Los Angeles, California; Department of Psychiatry and Behavioral Neurobiology, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Michael F Green
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles; Department of Veterans Affairs, Desert Pacific Mental Illness Research, Education, and Clinical Center, Los Angeles, California
| | - James M Gold
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Pamela DeRosse
- Division of Psychiatry Research, Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York; Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York; Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York
| | - Miklos Argyelan
- Division of Psychiatry Research, Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York; Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York; Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York
| | - Anil K Malhotra
- Division of Psychiatry Research, Zucker Hillside Hospital, Division of Northwell Health, Glen Oaks, New York; Department of Psychiatry, The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York; Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, New York
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, 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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22
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Voineskos AN, Jacobs GR, Ameis SH. Neuroimaging Heterogeneity in Psychosis: Neurobiological Underpinnings and Opportunities for Prognostic and Therapeutic Innovation. Biol Psychiatry 2020; 88:95-102. [PMID: 31668548 PMCID: PMC7075720 DOI: 10.1016/j.biopsych.2019.09.004] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2019] [Revised: 08/01/2019] [Accepted: 09/03/2019] [Indexed: 11/22/2022]
Abstract
Heterogeneity in symptom presentation, outcomes, and treatment response has long been problematic for researchers aiming to identify biological markers of schizophrenia or psychosis. However, there is increasing recognition that there may likely be no such general illness markers, which is consistent with the notion of a group of schizophrenia(s) that may have both shared and unique neurobiological pathways. Instead, strategies aiming to capitalize on or leverage such heterogeneity may help uncover neurobiological pathways that may then be used to stratify groups of patients for prognostic purposes or for therapeutic trials. A shift toward larger sample sizes with adequate statistical power to overcome small effect sizes and disentangle the shared variance among different brain-imaging or behavioral variables has become a priority for the field. In addition, recognition that two individuals with the same clinical diagnosis may be more different from each other (at brain, genetic, and behavioral levels) than from another individual in a different disorder or nonclinical control group-coupled with computational advances-has catapulted data-driven efforts forward. Emerging challenges for this new approach include longitudinal stability of new subgroups, demonstration of validity, and replicability. The "litmus test" will be whether computational approaches that are successfully identifying groups of patients who share features in common, more than current DSM diagnostic constructs, also provide better prognostic accuracy over time and in addition lead to enhancements in treatment response and outcomes. These are the factors that matter most to patients, families, providers, and payers.
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Affiliation(s)
- Aristotle N Voineskos
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada.
| | - Grace R Jacobs
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Stephanie H Ameis
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; Brain and Mental Health Program, Hospital for Sick Children, Toronto, Canada
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Hong SJ, Vogelstein JT, Gozzi A, Bernhardt BC, Yeo BTT, Milham MP, Di Martino A. Toward Neurosubtypes in Autism. Biol Psychiatry 2020; 88:111-128. [PMID: 32553193 DOI: 10.1016/j.biopsych.2020.03.022] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 03/25/2020] [Accepted: 03/28/2020] [Indexed: 12/22/2022]
Abstract
There is a consensus that substantial heterogeneity underlies the neurobiology of autism spectrum disorder (ASD). As such, it has become increasingly clear that a dissection of variation at the molecular, cellular, and brain network domains is a prerequisite for identifying biomarkers. Neuroimaging has been widely used to characterize atypical brain patterns in ASD, although findings have varied across studies. This is due, at least in part, to a failure to account for neurobiological heterogeneity. Here, we summarize emerging data-driven efforts to delineate more homogeneous ASD subgroups at the level of brain structure and function-that is, neurosubtyping. We break this pursuit into key methodological steps: the selection of diagnostic samples, neuroimaging features, algorithms, and validation approaches. Although preliminary and methodologically diverse, current studies generally agree that at least 2 to 4 distinct ASD neurosubtypes may exist. Their identification improved symptom prediction and diagnostic label accuracy above and beyond group average comparisons. Yet, this nascent literature has shed light onto challenges and gaps. These include 1) the need for wider and more deeply transdiagnostic samples collected while minimizing artifacts (e.g., head motion), 2) quantitative and unbiased methods for feature selection and multimodal fusion, 3) greater emphasis on algorithms' ability to capture hybrid dimensional and categorical models of ASD, and 4) systematic independent replications and validations that integrate different units of analyses across multiple scales. Solutions aimed to address these challenges and gaps are discussed for future avenues leading toward a comprehensive understanding of the mechanisms underlying ASD heterogeneity.
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Affiliation(s)
- Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, New York
| | - Joshua T Vogelstein
- Department of Biomedical Engineering Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, Maryland
| | - Alessandro Gozzi
- Functional Neuroimaging Laboratory, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - B T Thomas Yeo
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Department of Electrical and Computer Engineering, Center for Sleep and Cognition, Clinical Imaging Research Centre, N.1 Institute for Health, National University of Singapore; NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore; Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, New York
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Severance EG, Yolken RH. Tracking a dysregulated gut-brain axis with biomarkers of the microbiome. Biomark Neuropsychiatry 2020. [DOI: 10.1016/j.bionps.2019.100009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
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Asif M, Martiniano HFMC, Marques AR, Santos JX, Vilela J, Rasga C, Oliveira G, Couto FM, Vicente AM. Identification of biological mechanisms underlying a multidimensional ASD phenotype using machine learning. Transl Psychiatry 2020; 10:43. [PMID: 32066720 PMCID: PMC7026098 DOI: 10.1038/s41398-020-0721-1] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 12/27/2019] [Accepted: 01/08/2020] [Indexed: 12/05/2022] Open
Abstract
The complex genetic architecture of Autism Spectrum Disorder (ASD) and its heterogeneous phenotype makes molecular diagnosis and patient prognosis challenging tasks. To establish more precise genotype-phenotype correlations in ASD, we developed a novel machine-learning integrative approach, which seeks to delineate associations between patients' clinical profiles and disrupted biological processes, inferred from their copy number variants (CNVs) that span brain genes. Clustering analysis of the relevant clinical measures from 2446 ASD cases in the Autism Genome Project identified two distinct phenotypic subgroups. Patients in these clusters differed significantly in ADOS-defined severity, adaptive behavior profiles, intellectual ability, and verbal status, the latter contributing the most for cluster stability and cohesion. Functional enrichment analysis of brain genes disrupted by CNVs in these ASD cases identified 15 statistically significant biological processes, including cell adhesion, neural development, cognition, and polyubiquitination, in line with previous ASD findings. A Naive Bayes classifier, generated to predict the ASD phenotypic clusters from disrupted biological processes, achieved predictions with a high precision (0.82) but low recall (0.39), for a subset of patients with higher biological Information Content scores. This study shows that milder and more severe clinical presentations can have distinct underlying biological mechanisms. It further highlights how machine-learning approaches can reduce clinical heterogeneity by using multidimensional clinical measures, and establishes genotype-phenotype correlations in ASD. However, predictions are strongly dependent on patient's information content. Findings are therefore a first step toward the translation of genetic information into clinically useful applications, and emphasize the need for larger datasets with very complete clinical and biological information.
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Affiliation(s)
- Muhammad Asif
- grid.422270.10000 0001 2287 695XInstituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, LASIGE, Universidade de Lisboa, Lisboa, Portugal
| | - Hugo F. M. C. Martiniano
- grid.422270.10000 0001 2287 695XInstituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal
| | - Ana Rita Marques
- grid.422270.10000 0001 2287 695XInstituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal
| | - João Xavier Santos
- grid.422270.10000 0001 2287 695XInstituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal
| | - Joana Vilela
- grid.422270.10000 0001 2287 695XInstituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal
| | - Celia Rasga
- grid.422270.10000 0001 2287 695XInstituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal
| | - Guiomar Oliveira
- grid.28911.330000000106861985Unidade de Neurodesenvolvimento e Autismo (UNDA), Serviço do Centro de Desenvolvimento da Criança, Centro de Investigação e Formação Clínica, Hospital Pediátrico, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal ,grid.8051.c0000 0000 9511 4342Faculty of Medicine, Institute for Biomedical Imaging and Life Sciences, Universidade de Coimbra, Coimbra, Portugal ,grid.8051.c0000 0000 9511 4342Faculty of Medicine, University Clinic of Pediatrics, University of Coimbra, Coimbra, Portugal
| | - Francisco M. Couto
- grid.9983.b0000 0001 2181 4263Faculdade de Ciências, LASIGE, Universidade de Lisboa, Lisboa, Portugal
| | - Astrid M. Vicente
- grid.422270.10000 0001 2287 695XInstituto Nacional de Saúde Doutor Ricardo Jorge, Avenida Padre Cruz, 1649-016 Lisboa, Portugal ,grid.9983.b0000 0001 2181 4263Faculdade de Ciências, BioISI - Biosystems & Integrative Sciences Institute, Universidade de Lisboa, Lisboa, Portugal
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Abu-Akel A, Philip RCM, Lawrie SM, Johnstone EC, Stanfield AC. Categorical and Dimensional Approaches to Examining the Joint Effect of Autism and Schizotypal Personality Disorder on Sustained Attention. Front Psychiatry 2020; 11:798. [PMID: 32848955 PMCID: PMC7426517 DOI: 10.3389/fpsyt.2020.00798] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 07/24/2020] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Accumulating evidence for the co-occurrence autism spectrum disorder (ASD) and schizotypal personality disorder (SPD) at both the diagnostic and symptom levels raises important questions about the nature of their association and the effect of their co-occurrence on the individual's phenotype and functional outcome. Research comparing adults with ASD and SPD, as well as the impact of their co-occurrence on outcomes is extremely limited. We investigated executive functioning in terms of response inhibition and sustained attention, candidate endophenotypes of both conditions, in adults with ASD, SPD, comorbid ASD and SPD, and neurotypical adults using both categorical and dimensional approaches. METHODS A total of 88 adults (Mean Age = 37.54; SD = 10.17): ASD (n = 26; M/F = 20/6); SPD (n = 20; M/F = 14/6); comorbid ASD and SPD (n=9; M/F=6/3) and neurotypicals (n=33; M/F=23/10) completed the Sustained Attention to Response Task (SART) in both its fixed and random forms. Positive and autistic symptom severity was assessed with the positive subscale of the Positive and Negative Syndrome Scale (PANSSpos) and the PANSS Autism Severity Score (PAUSS), respectively. RESULTS Controlling for full scale IQ, working memory and medication dosage, group analyses revealed that the comorbid group committed fewer omission errors than the ASD group on the fixed SART, and fewer omission errors than the ASD and SPD groups on the random SART. The individual difference analyses of the entire sample revealed that the PANSSpos and PAUSS interactively reduced omission errors in both the fixed and random SARTs, as well as increased d' scores, indicative of improved overall performance. We observed no significant results for commission errors or reaction time. CONCLUSIONS Concurrent elevated levels of autistic and positive psychotic symptoms seem to be associated with improved sustained attention abilities (reduced omission errors) but not inhibition (commission errors). Our findings highlight the importance of investigating the concurrent effect of ASD and SPD at both the symptom and diagnostic levels, and raise important questions for future research regarding the clinical and behavioral phenotypes of adults with dual diagnosis and, more generally, about the nature of the relationship between ASD and SPD.
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Affiliation(s)
- Ahmad Abu-Akel
- Institute of Psychology, University of Lausanne, Lausanne, Switzerland
| | - Ruth C M Philip
- Tailor Ed Foundation, Edinburgh, United Kingdom.,Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen M Lawrie
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Eve C Johnstone
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew C Stanfield
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom.,Patrick Wild Centre, University of Edinburgh, Edinburgh, United Kingdom
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Hu Z, Ying X, Huang L, Zhao Y, Zhou D, Liu J, Zhong J, Huang T, Zhang W, Cheng F, Duan S. Association of human serotonin receptor 4 promoter methylation with autism spectrum disorder. Medicine (Baltimore) 2020; 99:e18838. [PMID: 31977880 PMCID: PMC7004686 DOI: 10.1097/md.0000000000018838] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Human serotonin receptor 4 (HTR4) encodes a 5-HT4 receptor involved in learning, memory, depression, anxiety, and feeding behavior. The aim of this study was to investigate the association between the deoxyribonucleic acid (DNA) methylation of HTR4 promoter and autism spectrum disorder (ASD), a disease characterized by communication disorder and repetitive or restrictive behavior.Peripheral blood DNA was obtained from 61 ASD children and 66 healthy children, and the DNA methylation of HTR4 promoter was assessed by quantitative methylation-specific polymerase chain reaction. We used percentage of methylated reference (PMR) to represent DNA methylation level.Due to significant age differences between ASD cases and controls (3 [2, 5] years and 6 [5, 6] years, P = 3.34E-10), we used binary logistic regression analysis for adjustment. Our results showed that the DNA methylation levels of HTR4 promoter were significantly lower in children with ASD than in healthy children (median PMR: 66.23% vs 94.31%,P = .028, age-adjusted P = .034). In addition, the DNA methylation of HTR4 promoter was inversely associated with age in male ASD cases (total cases: r = -0.283, P = .027; male cases: r = -0.431, P = .002; female cases: r = -0.108, P = .752). Dual-luciferase reporter gene assay showed that the reporter gene expression in the strain with recombinant pGL3-promoter-HTR4 plasmid was significantly higher than that in the strain with pGL3-promoter plasmid (fold change = 2.01, P = .0065), indicating that the HTR4 promoter fragment may contain transcription factors to upregulate promoter activity.Our study suggested that hypomethylation of the HTR4 promoter is a potential biomarker for predicting the risk of male ASD.
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Affiliation(s)
- Zhenyu Hu
- Ningbo Kangning Hospital, Ningbo Key Laboratory of Behavioral Neuroscience
| | - Xiuru Ying
- Medical Genetics Center, School of Medicine, Ningbo University, Ningbo, Zhejiang, China
| | - Ling Huang
- Ningbo Kangning Hospital, Ningbo Key Laboratory of Behavioral Neuroscience
| | - Yuanzhi Zhao
- Ningbo Kangning Hospital, Ningbo Key Laboratory of Behavioral Neuroscience
| | - Dongsheng Zhou
- Ningbo Kangning Hospital, Ningbo Key Laboratory of Behavioral Neuroscience
| | - Jing Liu
- Medical Genetics Center, School of Medicine, Ningbo University, Ningbo, Zhejiang, China
| | - Jie Zhong
- Medical Genetics Center, School of Medicine, Ningbo University, Ningbo, Zhejiang, China
| | - Tianyi Huang
- Medical Genetics Center, School of Medicine, Ningbo University, Ningbo, Zhejiang, China
| | - Wenwu Zhang
- Ningbo Kangning Hospital, Ningbo Key Laboratory of Behavioral Neuroscience
| | - Fang Cheng
- Ningbo Kangning Hospital, Ningbo Key Laboratory of Behavioral Neuroscience
| | - Shiwei Duan
- Medical Genetics Center, School of Medicine, Ningbo University, Ningbo, Zhejiang, China
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Ivleva EI, Turkozer HB, Sweeney JA. Imaging-Based Subtyping for Psychiatric Syndromes. Neuroimaging Clin N Am 2019; 30:35-44. [PMID: 31759570 DOI: 10.1016/j.nic.2019.09.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Despite considerable research evidence demonstrating significant neurobiological alterations in psychiatric disorders, incorporating neuroimaging approaches into clinical practice remains challenging. There is an urgent need for biologically validated psychiatric disease constructs that can inform diagnostic algorithms and targeted treatment development. In this article, we present a conceptual review of the most robust and impactful findings from studies that use neuroimaging methods in efforts to define distinct disease subtypes, while emphasizing cross-diagnostic and dimensional approaches. In addition, we discuss current challenges in psychoradiology and outline potential future strategies for clinically applicable translation.
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Affiliation(s)
- Elena I Ivleva
- Department of Psychiatry, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, NC5, Dallas, TX 75390, USA.
| | - Halide B Turkozer
- Department of Psychiatry, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, NC5, Dallas, TX 75390, USA
| | - John A Sweeney
- Department of Psychiatry, University of Cincinnati, 2600 Clifton Avenue, Cincinnati, OH 45221, USA
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Millman ZB, Gold JM, Mittal VA, Schiffman J. The Critical Need for Help-Seeking Controls in Clinical High-Risk Research. Clin Psychol Sci 2019; 7:1171-1189. [PMID: 33614257 PMCID: PMC7891463 DOI: 10.1177/2167702619855660] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Despite rapidly growing knowledge of the clinical high-risk (CHR) state for psychosis, the vast majority of case-control studies have relied on healthy volunteers as a reference point for drawing inferences about the CHR construct. Researchers have long recognized that results generated from this design are limited by significant interpretive concerns, yet little attention has been given to how these concerns affect the growing field of CHR research. We argue that overreliance on healthy controls in CHR research threatens the validity of inferences concerning group differences, hinders advances in understanding the development of psychosis, and limits clinical progress. We suggest that the combined use of healthy and help-seeking (i.e., psychiatric) controls is a necessary step for the next generation of CHR research. We then evaluate methods for help-seeking control studies, identify the available CHR studies that have used such designs, discuss select findings in this literature, and offer recommendations for research.
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Affiliation(s)
| | - James M. Gold
- Maryland Psychiatric Research Center, University of Maryland School of Medicine
| | - Vijay A. Mittal
- Department of Psychology, Northwestern University
- Department of Psychiatry, Northwestern University
- Institute for Policy Research, Northwestern University
- Medical Social Sciences, Northwestern University
- Institute for Innovations in Developmental Sciences, Northwestern University
| | - Jason Schiffman
- Department of Psychology, University of Maryland, Baltimore County
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Zitnik M, Nguyen F, Wang B, Leskovec J, Goldenberg A, Hoffman MM. Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2019; 50:71-91. [PMID: 30467459 PMCID: PMC6242341 DOI: 10.1016/j.inffus.2018.09.012] [Citation(s) in RCA: 215] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
New technologies have enabled the investigation of biology and human health at an unprecedented scale and in multiple dimensions. These dimensions include myriad properties describing genome, epigenome, transcriptome, microbiome, phenotype, and lifestyle. No single data type, however, can capture the complexity of all the factors relevant to understanding a phenomenon such as a disease. Integrative methods that combine data from multiple technologies have thus emerged as critical statistical and computational approaches. The key challenge in developing such approaches is the identification of effective models to provide a comprehensive and relevant systems view. An ideal method can answer a biological or medical question, identifying important features and predicting outcomes, by harnessing heterogeneous data across several dimensions of biological variation. In this Review, we describe the principles of data integration and discuss current methods and available implementations. We provide examples of successful data integration in biology and medicine. Finally, we discuss current challenges in biomedical integrative methods and our perspective on the future development of the field.
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Affiliation(s)
- Marinka Zitnik
- Department of Computer Science, Stanford University,
Stanford, CA, USA
| | - Francis Nguyen
- Department of Medical Biophysics, University of Toronto,
Toronto, ON, Canada
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Bo Wang
- Hikvision Research Institute, Santa Clara, CA, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University,
Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Anna Goldenberg
- Genetics & Genome Biology, SickKids Research Institute,
Toronto, ON, Canada
- Department of Computer Science, University of Toronto,
Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
| | - Michael M. Hoffman
- Department of Medical Biophysics, University of Toronto,
Toronto, ON, Canada
- Princess Margaret Cancer Centre, Toronto, ON, Canada
- Department of Computer Science, University of Toronto,
Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
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Lombardo MV, Lai MC, Baron-Cohen S. Big data approaches to decomposing heterogeneity across the autism spectrum. Mol Psychiatry 2019; 24:1435-1450. [PMID: 30617272 PMCID: PMC6754748 DOI: 10.1038/s41380-018-0321-0] [Citation(s) in RCA: 225] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 10/30/2018] [Accepted: 11/12/2018] [Indexed: 12/27/2022]
Abstract
Autism is a diagnostic label based on behavior. While the diagnostic criteria attempt to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame research examining multi-level heterogeneity in autism. Theoretical concepts such as 'spectrum' or 'autisms' reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case-control models are suboptimal for tackling heterogeneity. Big data are an important ingredient for furthering our understanding of heterogeneity in autism. In addition to being 'feature-rich', big data should be both 'broad' (i.e., large sample size) and 'deep' (i.e., multiple levels of data collected on the same individuals). These characteristics increase the likelihood that the study results are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model's utility can be measured by its ability to explain clinically or mechanistically important phenomena, and also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing changes within individuals. While progress can be made with 'supervised' models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. A better understanding of how to model heterogeneity between autistic people will facilitate progress towards precision medicine for symptoms that cause suffering, and person-centered support.
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Affiliation(s)
- Michael V Lombardo
- Department of Psychology, University of Cyprus, Nicosia, Cyprus.
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Centre for Addiction and Mental Health and The Hospital for Sick Children, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
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Lai MC, Kassee C, Besney R, Bonato S, Hull L, Mandy W, Szatmari P, Ameis SH. Prevalence of co-occurring mental health diagnoses in the autism population: a systematic review and meta-analysis. Lancet Psychiatry 2019; 6:819-829. [PMID: 31447415 DOI: 10.1016/s2215-0366(19)30289-5] [Citation(s) in RCA: 654] [Impact Index Per Article: 130.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 07/01/2019] [Accepted: 07/02/2019] [Indexed: 12/26/2022]
Abstract
BACKGROUND Co-occurring mental health or psychiatric conditions are common in autism, impairing quality of life. Reported prevalences of co-occurring mental health or psychiatric conditions in people with autism range widely. Improved prevalence estimates and identification of moderators are needed to enhance recognition and care, and to guide future research. METHODS In this systematic review and meta-analysis, we searched MEDLINE, Embase, PsycINFO, Scopus, Web of Science, and grey literature for publications between Jan 1, 1993, and Feb 1, 2019, in English or French, that reported original research using an observational design on the prevalence of co-occurring mental health conditions in people with autism and reported confirmed clinical diagnoses of the co-occurring conditions and autism using DSM or ICD criteria. For co-occurring mental health conditions reported with at least 15 datapoints (studies), we assessed risk of bias and we determined pooled estimates of prevalence for different co-occurring conditions in autism using random-effects models, and descriptively compared these with prevalence estimates for the general population from the literature (post hoc). We investigated heterogeneity in prevalence estimates using random-effects meta-regression models. This systematic review is registered with PROSPERO, CRD42018103176. FINDINGS Of 9746 unique studies identified, 432 were selected for full-text review. 100 studies were eligible for inclusion in our qualitative synthesis, of which 96 were included in our meta-analyses. 11 categories of co-occurring conditions were investigated, of which eight conditions were included in the meta-analyses and three were descriptively synthesised (ie, trauma and stressor-related disorders, substance-related and addictive disorders, and gender dysphoria). From our meta-analyses, we found overall pooled prevalence estimates of 28% (95% CI 25-32) for attention-deficit hyperactivity disorder; 20% (17-23) for anxiety disorders; 13% (9-17) for sleep-wake disorders; 12% (10-15) for disruptive, impulse-control, and conduct disorders; 11% (9-13) for depressive disorders; 9% (7-10) for obsessive-compulsive disorder; 5% (3-6) for bipolar disorders; and 4% (3-5) for schizophrenia spectrum disorders. Estimates in clinical sample-based studies were higher than in population-based and registry-based studies, and these estimates were mostly higher than those in the general population (post hoc). Age, gender, intellectual functioning, and country of study were associated with heterogeneity in prevalence estimates, yet remaining heterogeneity not explained was still substantial (all I2 >95%). INTERPRETATION Co-occurring mental health conditions are more prevalent in the autism population than in the general population. Careful assessment of mental health is an essential component of care for all people on the autism spectrum and should be integrated into clinical practice. FUNDING Academic Scholars Awards, Department of Psychiatry, University of Toronto; O'Brien Scholars Program, Slaight Family Child and Youth Mental Health Innovation Fund, and The Catherine and Maxwell Meighen Foundation via the Centre for Addiction and Mental Health Foundation.
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Affiliation(s)
- Meng-Chuan Lai
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Centre for Brain and Mental Health and Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada; Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK; Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.
| | - Caroline Kassee
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Richard Besney
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Sarah Bonato
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Laura Hull
- Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - William Mandy
- Research Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | - Peter Szatmari
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Centre for Brain and Mental Health and Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada; Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Stephanie H Ameis
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada; Centre for Brain and Mental Health and Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada; Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
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Douw L, van Dellen E, Gouw AA, Griffa A, de Haan W, van den Heuvel M, Hillebrand A, Van Mieghem P, Nissen IA, Otte WM, Reijmer YD, Schoonheim MM, Senden M, van Straaten ECW, Tijms BM, Tewarie P, Stam CJ. The road ahead in clinical network neuroscience. Netw Neurosci 2019; 3:969-993. [PMID: 31637334 PMCID: PMC6777944 DOI: 10.1162/netn_a_00103] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 07/23/2019] [Indexed: 12/15/2022] Open
Abstract
Clinical network neuroscience, the study of brain network topology in neurological and psychiatric diseases, has become a mainstay field within clinical neuroscience. Being a multidisciplinary group of clinical network neuroscience experts based in The Netherlands, we often discuss the current state of the art and possible avenues for future investigations. These discussions revolve around questions like "How do dynamic processes alter the underlying structural network?" and "Can we use network neuroscience for disease classification?" This opinion paper is an incomplete overview of these discussions and expands on ten questions that may potentially advance the field. By no means intended as a review of the current state of the field, it is instead meant as a conversation starter and source of inspiration to others.
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Affiliation(s)
- Linda Douw
- Department of Anatomy and Neuroscience, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Edwin van Dellen
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Alida A. Gouw
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alessandra Griffa
- Connectome Lab, Department of Neuroscience, section Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Willem de Haan
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Martijn van den Heuvel
- Connectome Lab, Department of Neuroscience, section Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Ida A. Nissen
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Willem M. Otte
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
- Department of Pediatric Neurology, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D. Reijmer
- Department of Neurology, Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Menno M. Schoonheim
- Department of Anatomy and Neuroscience, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Mario Senden
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Elisabeth C. W. van Straaten
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Betty M. Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Prejaas Tewarie
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Department of Neurology, Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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Rabany L, Brocke S, Calhoun VD, Pittman B, Corbera S, Wexler BE, Bell MD, Pelphrey K, Pearlson GD, Assaf M. Dynamic functional connectivity in schizophrenia and autism spectrum disorder: Convergence, divergence and classification. Neuroimage Clin 2019; 24:101966. [PMID: 31401405 PMCID: PMC6700449 DOI: 10.1016/j.nicl.2019.101966] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 05/15/2019] [Accepted: 07/31/2019] [Indexed: 01/16/2023]
Abstract
BACKGROUND Over the recent years there has been a growing debate regarding the extent and nature of the overlap in neuropathology between schizophrenia (SZ) and autism spectrum disorder (ASD). Dynamic functional network connectivity (dFNC) is a recent analysis method that explores temporal patterns of functional connectivity (FC). We compared resting-state dFNC in SZ, ASD and healthy controls (HC), characterized the associations between temporal patterns and symptoms, and performed a three-way classification analysis based on dFNC indices. METHODS Resting-state fMRI was collected from 100 young adults: 33 SZ, 33 ASD, 34 HC. Independent component analysis (ICA) was performed, followed by dFNC analysis (window = 33 s, step = 1TR, k-means clustering). Temporal patterns were compared between groups, correlated with symptoms, and classified via cross-validated three-way discriminant analysis. RESULTS Both clinical groups displayed an increased fraction of time (FT) spent in a state of weak, intra-network connectivity [p < .001] and decreased FT in a highly-connected state [p < .001]. SZ further showed decreased number of transitions between states [p < .001], decreased FT in a widely-connected state [p < .001], increased dwell time (DT) in the weakly-connected state [p < .001], and decreased DT in the highly-connected state [p = .001]. Social behavior scores correlated with DT in the widely-connected state in SZ [r = 0.416, p = .043], but not ASD. Classification correctly identified SZ at high rates (81.8%), while ASD and HC at lower rates. CONCLUSIONS Results indicate a severe and pervasive pattern of temporal aberrations in SZ (specifically, being "stuck" in a state of weak connectivity), that distinguishes SZ participants from both ASD and HC, and is associated with clinical symptoms.
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Affiliation(s)
- Liron Rabany
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA.
| | - Sophy Brocke
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Vince D Calhoun
- Mind Research Network, Albuquerque, NM, USA; University of New Mexico, Department of ECE, Albuquerque, NM, USA; Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA
| | - Brian Pittman
- Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA
| | - Silvia Corbera
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA; Central Connecticut State University, Department of Psychological Science, New Britain, CT, USA
| | - Bruce E Wexler
- Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA
| | - Morris D Bell
- Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA; VA Connecticut Healthcare System West Haven, CT, USA
| | - Kevin Pelphrey
- Autism and Neurodevelopment Disorders Institute, George Washington University and Children's National Medical Center, DC, USA
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA; Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA; Yale University School of Medicine, Department of Neuroscience, New Haven, CT, USA
| | - Michal Assaf
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA; Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA
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Brunton BW, Beyeler M. Data-driven models in human neuroscience and neuroengineering. Curr Opin Neurobiol 2019; 58:21-29. [PMID: 31325670 DOI: 10.1016/j.conb.2019.06.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Accepted: 06/22/2019] [Indexed: 12/26/2022]
Abstract
Discoveries in modern human neuroscience are increasingly driven by quantitative understanding of complex data. Data-intensive approaches to modeling have promise to dramatically advance our understanding of the brain and critically enable neuroengineering capabilities. In this review, we provide an accessible primer to modern modeling approaches and highlight recent data-driven discoveries in the domains of neuroimaging, single-neuron and neuronal population responses, and device neuroengineering. Further, we suggest that meaningful progress requires the community to tackle open challenges in the realms of model interpretability and generalizability, training pipelines of data-fluent human neuroscientists, and integrated consideration of data ethics.
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Affiliation(s)
- Bingni W Brunton
- Department of Biology, University of Washington, Seattle, WA 98195, USA; Institute for Neuroengineering, University of Washington, Seattle, WA 98195, USA; eScience Institute, University of Washington, Seattle, WA 98195, USA
| | - Michael Beyeler
- Institute for Neuroengineering, University of Washington, Seattle, WA 98195, USA; eScience Institute, University of Washington, Seattle, WA 98195, USA; Department of Psychology, University of Washington, Seattle, WA 98195, USA
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Mitelman SA. Transdiagnostic neuroimaging in psychiatry: A review. Psychiatry Res 2019; 277:23-38. [PMID: 30639090 DOI: 10.1016/j.psychres.2019.01.026] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 01/07/2019] [Accepted: 01/07/2019] [Indexed: 01/10/2023]
Abstract
Transdiagnostic approach has a long history in neuroimaging, predating its recent ascendance as a paradigm for new psychiatric nosology. Various psychiatric disorders have been compared for commonalities and differences in neuroanatomical features and activation patterns, with different aims and rationales. This review covers both structural and functional neuroimaging publications with direct comparison of different psychiatric disorders, including schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, conduct disorder, anorexia nervosa, and bulimia nervosa. Major findings are systematically presented along with specific rationales for each comparison.
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Affiliation(s)
- Serge A Mitelman
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Department of Psychiatry, Division of Child and Adolescent Psychiatry, Elmhurst Hospital Center, 79-01 Broadway, Elmhurst, NY 11373, USA.
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Crouse JJ, Moustafa AA, Bogaty SER, Hickie IB, Hermens DF. Parcellating cognitive heterogeneity in early psychosis-spectrum illnesses: A cluster analysis. Schizophr Res 2018; 202:91-98. [PMID: 30042029 DOI: 10.1016/j.schres.2018.06.060] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2017] [Revised: 04/04/2018] [Accepted: 06/24/2018] [Indexed: 01/27/2023]
Abstract
Cognitive impairment is argued to represent a core feature of psychosis-spectrum illnesses. However, within-diagnosis heterogeneity is common, and risk factors for poor cognition remain to be examined after statistically accounting for heterogeneity. Accordingly, we used a data-driven technique (cluster analysis) to empirically-derive cognitive clusters across diagnoses and examined whether concurrent substance use or a history of a neurodevelopmental/behavioral disorder differed between clusters. Data from 135 young help-seekers (aged 12-30 years) with a psychosis-spectrum illness were retrospectively analyzed. Ward's hierarchical cluster analysis classified three cognitive clusters characterized by: (1) normal-range; (2) mixed; and (3) grossly-impaired performance. Despite mostly comparable clinical and demographic measures, cluster 1 had superior socio-occupational functioning and the highest estimated premorbid IQ, followed sequentially by clusters 2 and 3. Proportions of cannabis and amphetamine users did not differ significantly across clusters, nor did rates of patients with a neurodevelopmental/behavioral disorder history. Cluster 3 was however comprised of fewer 'risky' drinkers, possibly reflecting reduced opportunity for social drinking associated with cognitive impairment. Estimated premorbid IQ predicted cluster membership (2 vs. 1 & 3 vs. 1), as did clinician-rated socio-occupational functioning and 'not being enrolled in school or tertiary education' (3 vs. 1). Our results suggest that concurrent substance use and history of a neurodevelopmental/behavioral disorder do not adequately explain cluster-level cognitive variance in this sample. Future work should integrate neurobiological measures associated with cognition (e.g. white matter integrity) to discern whether clusters reflect neurobiological subtypes better representative of pathophysiology than present symptom-based classifications.
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Affiliation(s)
- Jacob J Crouse
- Youth Mental Health Team, Brain & Mind Centre, The University of Sydney, NSW, Australia.
| | - Ahmed A Moustafa
- MARCS Institute for Brain, Behaviour & Development, NSW, Australia; School of Social Sciences & Psychology, Western Sydney University, NSW, Australia; Department of Social Sciences, College of Arts and Sciences, Qatar University, Doha, Qatar
| | - Sophia E R Bogaty
- Youth Mental Health Team, Brain & Mind Centre, The University of Sydney, NSW, Australia
| | - Ian B Hickie
- Youth Mental Health Team, Brain & Mind Centre, The University of Sydney, NSW, Australia
| | - Daniel F Hermens
- Youth Mental Health Team, Brain & Mind Centre, The University of Sydney, NSW, Australia; Sunshine Coast Mind & Neuroscience - Thompson Institute, University of the Sunshine Coast, QLD, Australia
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