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Jirsaraie RJ, Gatavins MM, Pines AR, Kandala S, Bijsterbosch JD, Marek S, Bogdan R, Barch DM, Sotiras A. Mapping the neurodevelopmental predictors of psychopathology. Mol Psychiatry 2024:10.1038/s41380-024-02682-7. [PMID: 39107582 DOI: 10.1038/s41380-024-02682-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 07/13/2024] [Accepted: 07/22/2024] [Indexed: 08/10/2024]
Abstract
Neuroimaging research has uncovered a multitude of neural abnormalities associated with psychopathology, but few prediction-based studies have been conducted during adolescence, and even fewer used neurobiological features that were extracted across multiple neuroimaging modalities. This gap in the literature is critical, as deriving accurate brain-based models of psychopathology is an essential step towards understanding key neural mechanisms and identifying high-risk individuals. As such, we trained adaptive tree-boosting algorithms on multimodal neuroimaging features from the Lifespan Human Connectome Developmental (HCP-D) sample that contained 956 participants between the ages of 8 to 22 years old. Our feature space consisted of 1037 anatomical, 1090 functional, and 192 diffusion MRI features, which were used to derive models that separately predicted internalizing symptoms, externalizing symptoms, and the general psychopathology factor. We found that multimodal models were the most accurate, but all brain-based models of psychopathology yielded out-of-sample predictions that were weakly correlated with actual symptoms (r2 < 0.15). White matter microstructural properties, including orientation dispersion indices and intracellular volume fractions, were the most predictive of general psychopathology, followed by cortical thickness and functional connectivity. Spatially, the most predictive features of general psychopathology were primarily localized within the default mode and dorsal attention networks. These results were mostly consistent across all dimensions of psychopathology, except orientation dispersion indices and the default mode network were not as heavily weighted in the prediction of internalizing and externalizing symptoms. Taken with prior literature, it appears that neurobiological features are an important part of the equation for predicting psychopathology but relying exclusively on neural markers is clearly not sufficient, especially among adolescent samples with subclinical symptoms. Consequently, risk factor models of psychopathology may benefit from incorporating additional sources of information that have also been shown to explain individual differences, such as psychosocial factors, environmental stressors, and genetic vulnerabilities.
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Affiliation(s)
- Robert J Jirsaraie
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Martins M Gatavins
- Lifespan Brain Institute, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam R Pines
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Sridhar Kandala
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Janine D Bijsterbosch
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Scott Marek
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- AI for Health Institute, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Ryan Bogdan
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
- Institute for Informatics, Data Science & Biostatistics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.
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Koochakpour K, Nytrø Ø, Leventhal BL, Sverre Westbye O, Brox Røst T, Koposov R, Frodl T, Clausen C, Stien L, Skokauskas N. A review of information sources and analysis methods for data driven decision aids in child and adolescent mental health services. Int J Med Inform 2024; 188:105479. [PMID: 38761460 DOI: 10.1016/j.ijmedinf.2024.105479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 06/16/2023] [Accepted: 05/08/2024] [Indexed: 05/20/2024]
Abstract
OBJECTIVE Clinical data analysis relies on effective methods and appropriate data. Recognizing distinctive clinical services and service functions may lead to improved decision-making. Our first objective is to categorize analytical methods, data sources, and algorithms used in current research on information analysis and decision support in child and adolescent mental health services (CAMHS). Our secondary objective is to identify the potential for data analysis in different clinical services and functions in which data-driven decision aids can be useful. MATERIALS AND METHODS We searched related studies in Science Direct and PubMed from 2018 to 2023(Jun), and also in ACM (Association for Computing Machinery) Digital Library, DBLP (Database systems and Logic Programming), and Google Scholar from 2018 to 2021. We have reviewed 39 studies and extracted types of analytical methods, information content, and information sources for decision-making. RESULTS In order to compare studies, we developed a framework for characterizing health services, functions, and data features. Most data sets in reviewed studies were small, with a median of 1,176 patients and 46,503 record entries. Structured data was used for all studies except two that used textual clinical notes. Most studies used supervised classification and regression. Service and situation-specific data analysis dominated among the studies, only two studies used temporal, or process features from the patient data. This paper presents and summarizes the utility, but not quality, of the studies according to the care situations and care providers to identify service functions where data-driven decision aids may be relevant. CONCLUSIONS Frameworks identifying services, functions, and care processes are necessary for characterizing and comparing electronic health record (EHR) data analysis studies. The majority of studies use features related to diagnosis and assessment and correspondingly have utility for intervention planning and follow-up. Profiling the disease severity of referred patients is also an important application area.
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Affiliation(s)
- Kaban Koochakpour
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
| | - Øystein Nytrø
- Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway; Department of Computer Science, The Arctic University of Norway (UiT), Tromsø, Norway
| | | | - Odd Sverre Westbye
- Regional Centre for Child and Youth Mental Health and Child Welfare (RKBU Central Norway), Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway; Department of Child and Adolescent Psychiatry, St. Olav's University Hospital, Trondheim, Norway
| | | | - Roman Koposov
- Regional Centre for Child and Youth Mental Health and Child Welfare (RKBU), The Arctic University of Norway (UiT), Tromsø, Norway
| | - Thomas Frodl
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
| | - Carolyn Clausen
- Regional Centre for Child and Youth Mental Health and Child Welfare (RKBU Central Norway), Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Line Stien
- Regional Centre for Child and Youth Mental Health and Child Welfare (RKBU Central Norway), Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Norbert Skokauskas
- Regional Centre for Child and Youth Mental Health and Child Welfare (RKBU Central Norway), Department of Mental Health, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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Hinzen W, Palaniyappan L. The 'L-factor': Language as a transdiagnostic dimension in psychopathology. Prog Neuropsychopharmacol Biol Psychiatry 2024; 131:110952. [PMID: 38280712 DOI: 10.1016/j.pnpbp.2024.110952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 12/20/2023] [Accepted: 01/23/2024] [Indexed: 01/29/2024]
Abstract
Thoughts and moods constituting our mental life incessantly change. When the steady flow of this dynamics diverges in clinical directions, the possible pathways involved are captured through discrete diagnostic labels. Yet a single vulnerable neurocognitive system may be causally involved in psychopathological deviations transdiagnostically. We argue that language viewed as integrating cortical functions is the best current candidate, whose forms of breakdown along its different dimensions are then manifest as symptoms - from prosodic abnormalities and rumination in depression to distortions of speech perception in verbal hallucinations, distortions of meaning and content in delusions, or disorganized speech in formal thought disorder. Spontaneous connected speech provides continuous objective readouts generating a highly accessible bio-behavioral marker with the potential of revolutionizing neuropsychological measurement. This argument turns language into a transdiagnostic 'L-factor' providing an analytical and mechanistic substrate for previously proposed latent general factors of psychopathology ('p-factor') and cognitive functioning ('c-factor'). Together with immense practical opportunities afforded by rapidly advancing natural language processing (NLP) technologies and abundantly available data, this suggests a new era of translational clinical psychiatry, in which both psychopathology and language may be rethought together.
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Affiliation(s)
- Wolfram Hinzen
- Department of Translation & Language Sciences, Universitat Pompeu Fabra, Barcelona, Spain; Institut Català de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal H4H1R3, Quebec, Canada; Robarts Research Institute & Lawson Health Research Institute, London, ON, Canada
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Hoy N, Lynch SJ, Waszczuk MA, Reppermund S, Mewton L. Transdiagnostic biomarkers of mental illness across the lifespan: A systematic review examining the genetic and neural correlates of latent transdiagnostic dimensions of psychopathology in the general population. Neurosci Biobehav Rev 2023; 155:105431. [PMID: 37898444 DOI: 10.1016/j.neubiorev.2023.105431] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/26/2023] [Accepted: 10/21/2023] [Indexed: 10/30/2023]
Abstract
This systematic review synthesizes evidence from research investigating the biological correlates of latent transdiagnostic dimensions of psychopathology (e.g., the p-factor, internalizing, externalizing) across the lifespan. Eligibility criteria captured genomic and neuroimaging studies investigating general and/or specific dimensions in general population samples across all age groups. MEDLINE, Embase, and PsycINFO were searched for relevant studies published up to March 2023 and 46 studies were selected for inclusion. The results revealed several biological correlates consistently associated with transdiagnostic dimensions of psychopathology, including polygenic scores for ADHD and neuroticism, global surface area and global gray matter volume. Shared and unique associations between symptom dimensions are highlighted, as are potential age-specific differences in biological associations. Findings are interpreted with reference to key methodological differences across studies. The included studies provide compelling evidence that the general dimension of psychopathology reflects common underlying genetic and neurobiological vulnerabilities that are shared across diverse manifestations of mental illness. Substantive interpretations of general psychopathology in the context of genetic and neurobiological evidence are discussed.
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Affiliation(s)
- Nicholas Hoy
- The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, Australia; Centre for Healthy Brain Ageing, University of New South Wales, Sydney, Australia.
| | - Samantha J Lynch
- The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, Australia; Department of Psychiatry, Université de Montréal, Montreal, Canada; Research Centre, CHU Sainte-Justine, Montreal, Canada
| | - Monika A Waszczuk
- Department of Psychology, Rosalind Franklin University of Medicine and Science, North Chicago, United States
| | - Simone Reppermund
- Centre for Healthy Brain Ageing, University of New South Wales, Sydney, Australia; Department of Developmental Disability Neuropsychiatry, University of New South Wales, Sydney, Australia
| | - Louise Mewton
- The Matilda Centre for Research in Mental Health and Substance Use, University of Sydney, Sydney, Australia
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Hu F, Chen AA, Horng H, Bashyam V, Davatzikos C, Alexander-Bloch A, Li M, Shou H, Satterthwaite TD, Yu M, Shinohara RT. Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage 2023; 274:120125. [PMID: 37084926 PMCID: PMC10257347 DOI: 10.1016/j.neuroimage.2023.120125] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 04/23/2023] Open
Abstract
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States.
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Vishnu Bashyam
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, United States
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; The Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
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