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Carmichael J, Ponsford J, Gould KR, Tiego J, Forbes MK, Kotov R, Fornito A, Spitz G. A Transdiagnostic, Hierarchical Taxonomy of Psychopathology Following Traumatic Brain Injury (HiTOP-TBI). J Neurotrauma 2024. [PMID: 38970424 DOI: 10.1089/neu.2024.0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2024] Open
Abstract
Psychopathology, including depression, anxiety, and post-traumatic stress, is a significant yet inadequately addressed feature of moderate-severe traumatic brain injury (TBI). Progress in understanding and treating post-TBI psychopathology may be hindered by limitations associated with conventional diagnostic approaches, specifically the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD). The Hierarchical Taxonomy of Psychopathology (HiTOP) offers a promising, transdiagnostic alternative to psychiatric classification that may more effectively capture the experiences of individuals with TBI. However, HiTOP lacks validation in the TBI population. To address this gap, we administered a comprehensive questionnaire battery, including 56 scales assessing homogeneous symptom components and maladaptive traits within HiTOP, to 410 individuals with moderate-severe TBI. We evaluated the reliability and unidimensionality of each scale and revised those with psychometric problems. Using a top-down, exploratory latent variable approach (bass-ackwards modeling), we subsequently constructed a hierarchical model of psychopathological dimensions tailored to TBI. The results showed that, relative to norms, participants with moderate-severe TBI experienced greater problems in the established HiTOP internalizing and detachment spectra, but fewer problems with thought disorder and antagonism. Fourteen of the 56 scales demonstrated psychometric problems, which often appeared reflective of the TBI experience and associated disability. The Hierarchical Taxonomy of Psychopathology Following Traumatic Brain Injury (HiTOP-TBI) model encompassed broad internalizing and externalizing spectra, splitting into seven narrower dimensions: Detachment, Dysregulated Negative Emotionality, Somatic Symptoms, Compensatory and Phobic Reactions, Self-Harm and Psychoticism, Rigid Constraint, and Harmful Substance Use. This study presents the most comprehensive empirical classification of psychopathology after TBI to date. It introduces a novel, TBI-specific transdiagnostic questionnaire battery and model, which addresses the limitations of conventional DSM and ICD diagnoses. The empirical structure of psychopathology after TBI largely aligned with the established HiTOP model (e.g., a detachment spectrum). However, these constructs need to be interpreted in relation to the unique experiences associated with TBI (e.g., considering the injury's impact on the person's social functioning). By overcoming the limitations of conventional diagnostic approaches, the HiTOP-TBI model has the potential to accelerate our understanding of the causes, correlates, consequences, and treatment of psychopathology after TBI.
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Affiliation(s)
- Jai Carmichael
- Monash-Epworth Rehabilitation Research Centre, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Jennie Ponsford
- Monash-Epworth Rehabilitation Research Centre, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Kate Rachel Gould
- Monash-Epworth Rehabilitation Research Centre, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Jeggan Tiego
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Miriam K Forbes
- School of Psychological Sciences, Macquarie University, Sydney, Australia
| | - Roman Kotov
- Stony Brook University, New York, New York, USA
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Gershon Spitz
- Monash-Epworth Rehabilitation Research Centre, School of Psychological Sciences, Monash University, Melbourne, Australia
- Department of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia
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Zoupou E, Moore TM, Calkins ME, Gur RE, Gur RC, Scott JC. Domain-specific associations between psychopathology and neurocognitive functioning. Psychol Med 2024:1-11. [PMID: 38828712 DOI: 10.1017/s0033291724001302] [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: 06/05/2024]
Abstract
BACKGROUND Neurocognitive dysfunction is a transdiagnostic finding in psychopathology, but relationships among cognitive domains and general and specific psychopathology dimensions remain unclear. This study aimed to examine associations between cognition and psychopathology dimensions in a large youth cohort. METHOD The sample (N = 9350; age 8-21 years) was drawn from the Philadelphia Neurodevelopmental Cohort. Data from structured clinical interviews were modeled using bifactor confirmatory factor analysis (CFA), resulting in an overall psychopathology ('p') factor score and six orthogonal psychopathology dimensions: dysphoria/distress, obsessive-compulsive, behavioral/externalizing, attention-deficit/hyperactivity, phobias, and psychosis. Neurocognitive data were aggregated using correlated-traits CFA into five factors: executive functioning, memory, complex cognition, social cognition, and sensorimotor speed. We examined relationships among specific and general psychopathology dimensions and neurocognitive factors. RESULTS The final model showed both overall and specific associations between cognitive functioning and psychopathology, with acceptable fit (CFI = 0.91; TLI = 0.90; RMSEA = 0.024; SRMR = 0.054). Overall psychopathology and most psychopathology dimensions were negatively associated with neurocognitive functioning (phobias [p < 0.0005], behavioral/externalizing [p < 0.0005], attention-deficit/hyperactivity [p < 0.0005], psychosis [p < 0.0005 to p < 0.05]), except for dysphoria/distress and obsessive-compulsive symptoms, which were positively associated with complex cognition (p < 0.05 and p < 0.01, respectively). CONCLUSION By modeling a broad range of cognitive and psychopathology domains in a large, diverse sample of youth, we found aspects of neurocognitive functioning shared across clinical phenotypes, as well as domain-specific patterns. Findings support transdiagnostic examination of cognitive performance to parse variability in the link between neurocognitive functioning and clinical phenotypes.
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Affiliation(s)
- Eirini Zoupou
- Department of Psychiatry, Neurodevelopment and Psychosis Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tyler M Moore
- Department of Psychiatry, Neurodevelopment and Psychosis Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Monica E Calkins
- Department of Psychiatry, Neurodevelopment and Psychosis Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, Neurodevelopment and Psychosis Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, Neurodevelopment and Psychosis Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - J Cobb Scott
- Department of Psychiatry, Neurodevelopment and Psychosis Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- VISN4 MIRECC, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, USA
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Zoupou E, Moore TM, Kennedy KP, Calkins ME, Gorgone A, Sandro AD, Rush S, Lopez KC, Ruparel K, Daryoush T, Okoyeh P, Savino A, Troyan S, Wolf DH, Scott JC, Gur RE, Gur RC. Validation of the structured interview section of the penn computerized adaptive test for neurocognitive and clinical psychopathology assessment (CAT GOASSESS). Psychiatry Res 2024; 335:115862. [PMID: 38554493 PMCID: PMC11025108 DOI: 10.1016/j.psychres.2024.115862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 02/21/2024] [Accepted: 03/14/2024] [Indexed: 04/01/2024]
Abstract
Large-scale studies and burdened clinical settings require precise, efficient measures that assess multiple domains of psychopathology. Computerized adaptive tests (CATs) can reduce administration time without compromising data quality. We examined feasibility and validity of an adaptive psychopathology measure, GOASSESS, in a clinical community-based sample (N = 315; ages 18-35) comprising three groups: healthy controls, psychosis, mood/anxiety disorders. Assessment duration was compared between the Full and CAT GOASSESS. External validity was tested by comparing how the CAT and Full versions related to demographic variables, study group, and socioeconomic status. The relationships between scale scores and criteria were statistically compared within a mixed-model framework to account for dependency between relationships. Convergent validity was assessed by comparing scores of the CAT and the Full GOASSESS using Pearson correlations. The CAT GOASSESS reduced interview duration by more than 90 % across study groups and preserved relationships to external criteria and demographic variables as the Full GOASSESS. All CAT GOASSESS scales could replace those of the Full instrument. Overall, the CAT GOASSESS showed acceptable psychometric properties and demonstrated feasibility by markedly reducing assessment time compared to the Full GOASSESS. The adaptive version could be used in large-scale studies or clinical settings for intake screening.
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Affiliation(s)
- Eirini Zoupou
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Tyler M Moore
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Kelly P Kennedy
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Monica E Calkins
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Alesandra Gorgone
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Akira Di Sandro
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sage Rush
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Katherine C Lopez
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kosha Ruparel
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Tarlan Daryoush
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Paul Okoyeh
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew Savino
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Scott Troyan
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel H Wolf
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - J Cobb Scott
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; VISN 4 Mental Illness Research, Education, and Clinical Center at the Philadelphia VA Medical Center, PA, USA
| | - Raquel E Gur
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Brain Behavior Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Lifespan Brain Institute (LiBI), Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA.
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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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Stanton K, Balzen KM, DeFluri C, Brock P, Levin-Aspenson HF, Zimmerman M. Negative Mood Dysregulation Loads Strongly Onto Common Factors With Many Forms of Psychopathology: Considerations for Assessing Nonspecific Symptoms. Assessment 2024; 31:637-650. [PMID: 37232256 DOI: 10.1177/10731911231174471] [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] [Indexed: 05/27/2023]
Abstract
There have been proposals to expand definitions for categorical disorders and dimensionally conceptualized syndromes (e.g., psychopathy) to include negative mood lability and dysregulation (NMD). Factor analytic results are often presented in support of these proposals, and we provide factor analytic demonstrations across clinically oriented samples showing that NMD indicators load strongly onto factors with a range of psychopathology. This is unsurprising from a transdiagnostic perspective but shows that factor analysis could potentially be used to justify expanding definitions for specific constructs even though NMD indicators show strong, nonspecific loadings on psychopathology factors ranging widely in nature. Expanding construct definitions and assessment approaches to emphasize NMD also may negatively impact discriminant validity. We agree that targeting NMD is essential for comprehensive assessment, but our demonstrative analyses highlight a need for using factor analysis and other statistical methods in a careful, theoretically driven manner when evaluating psychopathology structure and developing measures.
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Pearlstein JG, Johnson SL, Timpano KR, Stamatis CA, Robison M, Carver CS. Emotion-related impulsivity across transdiagnostic dimensions of psychopathology. J Pers 2024; 92:342-360. [PMID: 36807053 DOI: 10.1111/jopy.12825] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 01/16/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023]
Abstract
OBJECTIVE Several dimensions have received attention for their potential role in explaining shared variance in transdiagnostic symptoms of psychopathology. We hypothesized emotion-related impulsivity, the trait-like tendency toward difficulty restraining responses to emotion, would relate to symptoms of psychopathology, with two separable dimensions of emotion-related impulsivity relating distinctly to internalizing and externalizing symptoms. METHOD Across two studies, we tested hypotheses using structural equation models of emotion-related impulsivity and multiple indicators of internalizing, externalizing, and thought symptoms. RESULTS In Study 1 (658 undergraduates), emotion-related impulsivity was highly correlated with the general psychopathology (p) factor. In study 2 (421 Mechanical Turk participants), models did not support a general p factor; however, we replicated the hypothesized associations of emotion-related impulsivity dimensions with internalizing and externalizing factors. Across both studies, forms of emotion-related impulsivity uniquely and differentially related to internalizing and externalizing symptoms. CONCLUSIONS Findings indicate emotion-related impulsivity may help explain transdiagnostic dimensions of psychopathology, such as the p factor.
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Affiliation(s)
- Jennifer G Pearlstein
- Department of Psychology, University of California, Berkeley, Berkeley, California, USA
- Department of Rehabilitation Medicine, University of Washington Medical Center, Seattle, Washington, USA
| | - Sheri L Johnson
- Department of Psychology, University of California, Berkeley, Berkeley, California, USA
| | | | - Caitlin A Stamatis
- Department of Psychology, University of Miami, Miami, USA
- Center for Behavioral Intervention Technologies, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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7
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Caspi A, Houts RM, Fisher HL, Danese A, Moffitt TE. The general factor of psychopathology (p): Choosing among competing models and interpreting p. Clin Psychol Sci 2024; 12:53-82. [PMID: 38236494 PMCID: PMC10794018 DOI: 10.1177/21677026221147872] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 12/04/2022] [Indexed: 01/19/2024]
Abstract
Over the past 10 years, the general factor of psychopathology, p, has attracted interest and scrutiny. We review the history of the idea that all mental disorders share something in common, p; how we arrived at this idea; and how it became conflated with a statistical representation, the Bi-Factor Model. We then leverage the Environmental Risk (E-Risk) longitudinal twin study to examine the properties and nomological network of different statistical representations of p. We find that p performed similarly regardless of how it was modelled, suggesting that if the sample and content are the same the resulting p factor will be similar. We suggest that the meaning of p is not to be found by dueling over statistical models but by conducting well-specified criterion-validation studies and developing new measurement approaches. We outline new directions to refresh research efforts to uncover what all mental disorders have in common.
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Affiliation(s)
- Avshalom Caspi
- Department of Psychology & Neuroscience, Duke University
- PROMENTA, Department of Psychology, University of Oslo
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London
| | | | - Helen L. Fisher
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London
- ESRC Centre for Society and Mental Health, Kings’ College London
| | - Andrea Danese
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London
- National and Specialist CAMHS Clinic for Trauma, Anxiety, and Depression, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Terrie E. Moffitt
- Department of Psychology & Neuroscience, Duke University
- PROMENTA, Department of Psychology, University of Oslo
- Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, & Neuroscience, King’s College London
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8
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Rotstein A, Fund S, Levine SZ, Reichenberg A, Goldenberg J. Is cognition integral to psychopathology? A population-based cohort study. Psychol Med 2023; 53:7350-7357. [PMID: 37114455 PMCID: PMC10719669 DOI: 10.1017/s0033291723000934] [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: 01/08/2023] [Revised: 03/11/2023] [Accepted: 03/27/2023] [Indexed: 04/29/2023]
Abstract
BACKGROUND Lower cognitive functioning has been documented across psychiatric disorders and hypothesized to be a core deficit of mental disorders. Situating psychopathology and cognition as part of a unitary construct is therefore important to understanding the etiology of psychiatric disorders. The current study aims to test competing structural models of psychopathology and cognition in a large national cohort of adolescents. METHODS The analytic sample consisted of 1189 participants aged 16-17 years, screened by the Israeli Draft Board. Psychopathology was assessed using a modified version of the Brief Symptom Inventory, and cognition was assessed based on four standardized test scores ((1) mathematical reasoning, concentration, and concept manipulation; (2) visual-spatial problem-solving skills and nonverbal abstract reasoning; (3) verbal understanding; (4) categorization and verbal abstraction). Confirmatory factor analysis was implemented to compare competing structural models of psychopathology with and without cognition. Sensitivity analyses examined the models in different subpopulations. RESULTS Confirmatory factor analysis indicated a better model fit of psychopathological symptoms without cognition (RMSEA = 0.037; TLI = 0.991; CFI = 0.992) than with cognition (RMSEA = 0.04-0.042; TLI = 0.987-0.988; CFI = 0.988-0.989). Sensitivity analyses supported the robustness of these results with a single exception. Among participants with low cognitive abilities (N = 139), models that integrated psychopathological symptoms with cognition had a better fit compared to models of psychopathology without cognition. CONCLUSIONS The current study suggests that cognition and psychopathology are, generally, independent constructs. However, within low cognitive abilities, cognition was integral to the structure of psychopathology. Our results point toward an increased vulnerability to psychopathology in individuals with low cognitive abilities and may provide valuable information for clinicians.
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Affiliation(s)
- Anat Rotstein
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Gerontology, University of Haifa, Haifa, Israel
| | - Suzanne Fund
- Department of Behavioral Sciences, Israel Defense Forces, Israel
| | | | - Abraham Reichenberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Environmental Medicine & Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Judy Goldenberg
- Department of Behavioral Sciences, Israel Defense Forces, Israel
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Libedinsky I, Helwegen K, Simón LG, Gruber M, Repple J, Kircher T, Dannlowski U, van den Heuvel MP. Quantifying brain connectivity signatures by means of polyconnectomic scoring. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.26.559327. [PMID: 37808808 PMCID: PMC10557693 DOI: 10.1101/2023.09.26.559327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
A broad range of neuropsychiatric disorders are associated with alterations in macroscale brain circuitry and connectivity. Identifying consistent brain patterns underlying these disorders by means of structural and functional MRI has proven challenging, partly due to the vast number of tests required to examine the entire brain, which can lead to an increase in missed findings. In this study, we propose polyconnectomic score (PCS) as a metric designed to quantify the presence of disease-related brain connectivity signatures in connectomes. PCS summarizes evidence of brain patterns related to a phenotype across the entire landscape of brain connectivity into a subject-level score. We evaluated PCS across four brain disorders (autism spectrum disorder, schizophrenia, attention deficit hyperactivity disorder, and Alzheimer's disease) and 14 studies encompassing ~35,000 individuals. Our findings consistently show that patients exhibit significantly higher PCS compared to controls, with effect sizes that go beyond other single MRI metrics ([min, max]: Cohen's d = [0.30, 0.87], AUC = [0.58, 0.73]). We further demonstrate that PCS serves as a valuable tool for stratifying individuals, for example within the psychosis continuum, distinguishing patients with schizophrenia from their first-degree relatives (d = 0.42, p = 4 × 10-3, FDR-corrected), and first-degree relatives from healthy controls (d = 0.34, p = 0.034, FDR-corrected). We also show that PCS is useful to uncover associations between brain connectivity patterns related to neuropsychiatric disorders and mental health, psychosocial factors, and body measurements.
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Affiliation(s)
- Ilan Libedinsky
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Koen Helwegen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Laura Guerrero Simón
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Goethe University, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, University of Marburg, Germany
- Center for Mind, Brain and Behavior (CMBB), University of Marburg and Justus Liebig University Giessen, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Child and Adolescent Psychiatry and Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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10
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Southward MW, Goh PK, Bansal PS. How to Align DBT and DBT Skills with Adolescent Externalizing Problems. CLINICAL PSYCHOLOGY-SCIENCE AND PRACTICE 2023; 30:264-267. [PMID: 38107600 PMCID: PMC10723818 DOI: 10.1037/cps0000162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Affiliation(s)
| | - Patrick K Goh
- Department of Psychology, University of Hawai'i at Mānoa
| | - Pevitr S Bansal
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco
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Dolan CV, Borsboom D. Interpretational issues with the bifactor model: a commentary on 'Defining the p-Factor: An Empirical Test of Five Leading Theories' by Southward, Cheavens, and Coccaro. Psychol Med 2023; 53:2744-2747. [PMID: 37039112 DOI: 10.1017/s0033291723000533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
Southward, Cheavens, and Coccaro (2022, Psychological Medicine) conducted an ambitious investigation aimed at determining the nature of the general p factor of psychopathology by considering the correlation between the p factor and five candidate constructs. Generally, in this area of research, the bifactor model is preferred to the second order common factor model. In this commentary, we identify several interpretational issues concerning the bifactor model, which are based on a realistic psychometric view of latent variables. These issues may hamper the study of the nature of p factor model using the bifactor model.
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Affiliation(s)
- Conor V Dolan
- Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Van der Boechorststraat 7-9, 1081 BT, Amsterdam, The Netherlands
| | - Denny Borsboom
- Department of Psychology, Faculty of Behavioral and Social Sciences, University of Amsterdam, Nieuwe Achtergracht 129-B, 1018WS Amsterdam, The Netherlands
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12
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Langwerden RJ, Van der Heijden PT, Claassen T, Derksen JJL, Egger JIM. The structure of dimensions of psychopathology in normative and clinical samples: Applying causal discovery to MMPI-2-RF scales to investigate clustering of psychopathology spectra and p-factors. Front Psychiatry 2022; 13:1026900. [PMID: 36440421 PMCID: PMC9686405 DOI: 10.3389/fpsyt.2022.1026900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 10/25/2022] [Indexed: 11/11/2022] Open
Abstract
We applied a Bayesian Constraint-based Causal Discovery method (BCCD) to examine the hierarchical structure of the Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF) Restructured Clinical (RC) scales. Two different general psychopathology super spectra (p-factor) scales were extracted from (1) all RC scales and (2) all RC scales except the RCd (Demoralization) scale. These p-factor scales were included in separate models to investigate the structure of dimensions of psychopathology in a normative (n = 3,242) and clinical (n = 2,466) sample, as well as the combined normative/clinical sample (N = 5,708), by applying the BCCD algorithm to obtain a data-driven reconstruction of the internal hierarchical structure of the MMPI-2-RF. Research on the underlying structure of the MMPI-2-RF has clinical relevance as well as conceptual relevance in the context of the HiTOP model. Results demonstrated that the syndromes measured with the RC-scales-in presence of a p-factor-cluster into six spectra: internalizing, disinhibited-externalizing, antagonistic-externalizing, thought disorder, detachment, and somatoform. These results may support a super spectrum construct, as it was necessary for obtaining a bottom-up reconstruction of this six-spectrum structure. We found support for superiority of a broad super spectrum with additional variance over and above demoralization, as it resulted in the clearest structure (i.e., clustering of the RC scales). Furthermore, our results indicate independent support for the bifactor structure model of psychopathology.
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Affiliation(s)
- Robbert J Langwerden
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands.,Community-Based Research Institute, Florida International University, Miami, FL, United States
| | - Paul T Van der Heijden
- Behavioral Science Institute, Radboud University Nijmegen, Nijmegen, Netherlands.,Centre for Adolescent Psychiatry, Reinier van Arkel Mental Health Institute, 's-Hertogenbosch, Netherlands
| | - Tom Claassen
- Institute for Computing and Information Sciences, Faculty of Science, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Jan J L Derksen
- Behavioral Science Institute, Radboud University Nijmegen, Nijmegen, Netherlands.,Faculty of Psychology and Educational Sciences, Department of Clinical and Life Span Psychology, Vrije Universiteit Brussels, Brussels, Belgium
| | - Jos I M Egger
- Centers of Excellence in Neuropsychiatry, Vincent van Gogh Mental Health Institute, Venray, Netherlands.,Stevig Specialized and Forensic Care for People With Intellectual Disabilities, Oostrom, Netherlands
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