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Karl V, Engen H, Beck D, Norbom LB, Ferschmann L, Aksnes ER, Kjelkenes R, Voldsbekk I, Andreassen OA, Alnæs D, Ladouceur CD, Westlye LT, Tamnes CK. The role of functional emotion circuits in distinct dimensions of psychopathology in youth. Transl Psychiatry 2024; 14:317. [PMID: 39095355 PMCID: PMC11297301 DOI: 10.1038/s41398-024-03036-1] [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: 09/13/2023] [Revised: 07/17/2024] [Accepted: 07/23/2024] [Indexed: 08/04/2024] Open
Abstract
Several mental disorders emerge during childhood or adolescence and are often characterized by socioemotional difficulties, including alterations in emotion perception. Emotional facial expressions are processed in discrete functional brain modules whose connectivity patterns encode emotion categories, but the involvement of these neural circuits in psychopathology in youth is poorly understood. This study examined the associations between activation and functional connectivity patterns in emotion circuits and psychopathology during development. We used task-based fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC, N = 1221, 8-23 years) and conducted generalized psycho-physiological interaction (gPPI) analyses. Measures of psychopathology were derived from an independent component analysis of questionnaire data. The results showed positive associations between identifying fearful, sad, and angry faces and depressive symptoms, and a negative relationship between sadness recognition and positive psychosis symptoms. We found a positive main effect of depressive symptoms on BOLD activation in regions overlapping with the default mode network, while individuals reporting higher levels of norm-violating behavior exhibited emotion-specific lower functional connectivity within regions of the salience network and between modules that overlapped with the salience and default mode network. Our findings illustrate the relevance of functional connectivity patterns underlying emotion processing for behavioral problems in children and adolescents.
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Affiliation(s)
- Valerie Karl
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway.
| | - Haakon Engen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Institute of Military Psychiatry Norwegian Armed Forces Joint Medical Services, Oslo, Norway
| | - Dani Beck
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Linn B Norbom
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Lia Ferschmann
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Eira R Aksnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Rikka Kjelkenes
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Irene Voldsbekk
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Dag Alnæs
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Cecile D Ladouceur
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Christian K Tamnes
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway
- NORMENT, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
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Liu Y, Lichtenstein P, Kotov R, Larsson H, D'Onofrio BM, Pettersson E. Exploring the genetic etiology across the continuum of the general psychopathology factor: a Swedish population-based family and twin study. Mol Psychiatry 2024:10.1038/s41380-024-02552-2. [PMID: 38600227 DOI: 10.1038/s41380-024-02552-2] [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: 08/24/2023] [Revised: 03/31/2024] [Accepted: 04/03/2024] [Indexed: 04/12/2024]
Abstract
Psychiatric comorbidity can be accounted for by a latent general psychopathology factor (p factor), which quantifies the variance that is shared to varying degrees by every dimension of psychopathology. It is unclear whether the entire continuum of the p factor shares the same genetic origin. We investigated whether mild, moderate, and extreme elevations on the p factor shared the same genetic etiology by, first, examining the linearity of the association between p factors across siblings (N = 580,891 pairs). Second, we estimated the group heritability in a twin sample (N = 17,170 pairs), which involves testing whether the same genetic variants influence both extreme and normal variations in the p factor. In both samples, the p factor was based on 10 register-based psychiatric diagnoses. Results showed that the association between siblings' p factors appeared linear, even into the extreme range. Likewise, the twin group heritabilities ranged from 0.42 to 0.45 (95% CI: 0.33-0.57) depending on the thresholds defining the probands (2-3.33 SD beyond the mean; >2 SD beyond the mean; >4.33 SD beyond the mean; and >5.33 SD beyond the mean), and these estimates were highly similar to the estimated individual differences heritability (0.41, 95% CI: 0.39-0.43), indicating that scores above and below these thresholds shared a common genetic origin. Together, these results suggest that the entire continuum of the p factor shares the same genetic origin, with common genetic variants likely playing an important role. This implies, first, genetic risk factors for the aspect that is shared between all forms of psychopathology (i.e., genetic risk factors for the p factor) might be generalizable between population-based cohorts with a higher prevalence of milder cases, and clinical samples with a preponderance of more severe cases. Second, prioritizing low-cost genome-wide association studies capable of identifying common genetic variants, rather than expensive whole genome sequencing that can identify rare variants, may increase the efficiency when studying the genetic architecture of the p factor.
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Affiliation(s)
- Yangjun Liu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Paul Lichtenstein
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Roman Kotov
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY, USA
| | - Henrik Larsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Brian M D'Onofrio
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Erik Pettersson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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3
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Desvaux T, Danna J, Velay JL, Frey A. From gifted to high potential and twice exceptional: A state-of-the-art meta-review. APPLIED NEUROPSYCHOLOGY. CHILD 2024; 13:165-179. [PMID: 37665678 DOI: 10.1080/21622965.2023.2252950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Despite the abundant literature on intelligence and high potential individuals, there is still a lack of international consensus on the terminology and clinical characteristics associated to this population. It has been argued that unstandardized use of diagnosis tools and research methods make comparisons and interpretations of scientific and epidemiological evidence difficult in this field. If multiple cognitive and psychological models have attempted to explain the mechanisms underlying high potentiality, there is a need to confront new scientific evidence with the old, to uproot a global understanding of what constitutes the neurocognitive profile of high-potential in gifted individuals. Another particularly relevant aspect of applied research on high potentiality concerns the challenges faced by individuals referred to as "twice exceptional" in the field of education and in their socio-affective life. Some individuals have demonstrated high forms of intelligence together with learning, affective or neurodevelopmental disorders posing the question as to whether compensating or exacerbating psycho-cognitive mechanisms might underlie their observed behavior. Elucidating same will prove relevant to questions concerning the possible need for differential diagnosis tools, specialized educational and clinical support. A meta-review of the latest findings from neuroscience to developmental psychology, might help in the conception and reviewing of intervention strategies.
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Affiliation(s)
- Tatiana Desvaux
- CNRS, Laboratoire de Neurosciences Cognitives, Aix-Marseille University, UMR 7291, Marseille, France
| | - J Danna
- CLLE, Université de Toulouse, CNRS, Toulouse, France
| | - J-L Velay
- CNRS, Laboratoire de Neurosciences Cognitives, Aix-Marseille University, UMR 7291, Marseille, France
| | - A Frey
- CNRS, Laboratoire de Neurosciences Cognitives, Aix-Marseille University, UMR 7291, Marseille, France
- INSPE of Aix-Marseille University, Marseille, France
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Huang X, Gao L, Xiao J, Li L, Shan X, Chen H, Chai X, Duan X. Family Environment Modulates Linkage of Transdiagnostic Psychiatric Phenotypes and Dissociable Brain Features in the Developing Brain. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00081-8. [PMID: 38537777 DOI: 10.1016/j.bpsc.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 02/26/2024] [Accepted: 03/16/2024] [Indexed: 06/04/2024]
Abstract
BACKGROUND Family environment has long been known for shaping brain function and psychiatric phenotypes, especially during childhood and adolescence. Accumulating neuroimaging evidence suggests that across different psychiatric disorders, common phenotypes may share common neural bases, indicating latent brain-behavior relationships beyond diagnostic categories. However, the influence of family environment on the brain-behavior relationship from a transdiagnostic perspective remains unknown. METHODS We included a community-based sample of 699 participants (ages 5-22 years) and applied partial least squares regression analysis to determine latent brain-behavior relationships from whole-brain functional connectivity and comprehensive phenotypic measures. Comparisons were made between diagnostic and nondiagnostic groups to help interpret the latent brain-behavior relationships. A moderation model was introduced to examine the potential moderating role of family factors in the estimated brain-behavior associations. RESULTS Four significant latent brain-behavior pairs were identified that reflected the relationship of dissociable brain network and general behavioral problems, cognitive and language skills, externalizing problems, and social dysfunction, respectively. The group comparisons exhibited interpretable variations across different diagnostic groups. A warm family environment was found to moderate the brain-behavior relationship of core symptoms in internalizing disorders. However, in neurodevelopmental disorders, family factors were not found to moderate the brain-behavior relationship of core symptoms, but they were found to affect the brain-behavior relationship in other domains. CONCLUSIONS Our findings leveraged a transdiagnostic analysis to investigate the moderating effects of family factors on brain-behavior associations, emphasizing the different roles that family factors play during this developmental period across distinct diagnostic groups.
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Affiliation(s)
- Xinyue Huang
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Leying Gao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Jinming Xiao
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Lei Li
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xiaolong Shan
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Huafu Chen
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Xiaoqian Chai
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.
| | - Xujun Duan
- Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China; MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
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Nerland S, Slapø NB, Barth C, Mørch-Johnsen L, Jørgensen KN, Beck D, Wortinger LA, Westlye LT, Jönsson EG, Andreassen OA, Maximov II, Geier OM, Agartz I. Current Auditory Hallucinations Are Not Associated With Specific White Matter Diffusion Alterations in Schizophrenia. SCHIZOPHRENIA BULLETIN OPEN 2024; 5:sgae008. [PMID: 39144116 PMCID: PMC11207682 DOI: 10.1093/schizbullopen/sgae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Background and Hypothesis Studies have linked auditory hallucinations (AH) in schizophrenia spectrum disorders (SCZ) to altered cerebral white matter microstructure within the language and auditory processing circuitry (LAPC). However, the specificity to the LAPC remains unclear. Here, we investigated the relationship between AH and DTI among patients with SCZ using diffusion tensor imaging (DTI). Study Design We included patients with SCZ with (AH+; n = 59) and without (AH-; n = 81) current AH, and 140 age- and sex-matched controls. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were extracted from 39 fiber tracts. We used principal component analysis (PCA) to identify general factors of variation across fiber tracts and DTI metrics. Regression models adjusted for sex, age, and age2 were used to compare tract-wise DTI metrics and PCA factors between AH+, AH-, and healthy controls and to assess associations with clinical characteristics. Study Results Widespread differences relative to controls were observed for MD and RD in patients without current AH. Only limited differences in 2 fiber tracts were observed between AH+ and controls. Unimodal PCA factors based on MD, RD, and AD, as well as multimodal PCA factors, differed significantly relative to controls for AH-, but not AH+. We did not find any significant associations between PCA factors and clinical characteristics. Conclusions Contrary to previous studies, DTI metrics differed mainly in patients without current AH compared to controls, indicating a widespread neuroanatomical distribution. This challenges the notion that altered DTI metrics within the LAPC is a specific feature underlying AH.
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Affiliation(s)
- Stener Nerland
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Nora Berz Slapø
- Norwegian Center for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Claudia Barth
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lynn Mørch-Johnsen
- Norwegian Center for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychiatry, Østfold Hospital, Grålum, Norway
- Department of Clinical Research, Østfold Hospital, Grålum, Norway
| | - Kjetil Nordbø Jørgensen
- Norwegian Center for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dani Beck
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Laura A Wortinger
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Erik G Jönsson
- Norwegian Center for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Ole A Andreassen
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ivan I Maximov
- Norwegian Center for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Oliver M Geier
- Department of Computational Radiology and Physics, Division of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
- Center for Lifespan Changes in Brain and Cognition (LCBC), Department of Psychology, University of Oslo, Oslo, Norway
| | - Ingrid Agartz
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Norwegian Center for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet and Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
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Feng G, Chen R, Zhao R, Li Y, Ma L, Wang Y, Men W, Gao J, Tan S, Cheng J, He Y, Qin S, Dong Q, Tao S, Shu N. Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture. Commun Biol 2023; 6:1257. [PMID: 38087047 PMCID: PMC10716168 DOI: 10.1038/s42003-023-05647-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.
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Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- BABRI Centre, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Rui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Rui Zhao
- College of Life Sciences, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Gene Resource and Molecular Development, Beijing, China
| | - Yuanyuan Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Leilei Ma
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yanpei Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Weiwei Men
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jiahong Gao
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing, China
| | - Jian Cheng
- School of Computer Science and Engineering, Beihang University, Beijing, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Shaozheng Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Sha Tao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
- BABRI Centre, Beijing Normal University, Beijing, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
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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: 0] [Impact Index Per Article: 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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8
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Misztal MC, Tio ES, Mohan A, Felsky D. Interactions between genetic risk for 21 neurodevelopmental and psychiatric disorders and sport activity on youth mental health. Psychiatry Res 2023; 330:115550. [PMID: 37973444 DOI: 10.1016/j.psychres.2023.115550] [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: 08/04/2023] [Revised: 10/06/2023] [Accepted: 10/16/2023] [Indexed: 11/19/2023]
Abstract
Childhood is a sensitive period where behavioral disturbances, determined by genetics and environmental factors including sport activity, may emerge and impact risk of mental illness in adulthood. We aimed to determine if participation in sports can mitigate genetic risk for neurodevelopmental and psychiatric disorders in youth. We analyzed 4975 unrelated European youth (ages 9-10) from the Adolescent Brain Cognitive Development Study. Our outcomes were eight Child Behavior Checklist (CBCL) scores, measured annually. Polygenic risk scores (PRSs) were calculated for 21 disorders, and sport frequency and type were summarized. PRSs and sport variables were tested for main effects and interactions against CBCL outcomes using linear models. Cross-sectionally, PRSs for attention-deficit/hyperactivity disorder and major depressive disorder were associated with increases in multiple CBCL outcomes. Participation in non-contact or team sports, as well as more frequent sport participation reduced all cross-sectional CBCL outcomes, whereas involvement in contact sports increased attention problems and rule-breaking behavior. Interactions revealed that more frequent exercise was significantly associated with less rule breaking behavior in individuals with high genetic risk for obsessive compulsive disorder. Associations with longitudinal CBCL outcomes demonstrated weaker effects. We highlight the importance of genetic context when considering sports as an intervention for early life behavioural problems.
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Affiliation(s)
- Melissa C Misztal
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Earvin S Tio
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Akshay Mohan
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada; Centre for Industrial Relations and Human Resources, University of Toronto, Toronto, ON, Canada
| | - Daniel Felsky
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada; Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
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9
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Parker N, Cheng W, Hindley GFL, Parekh P, Shadrin AA, Maximov II, Smeland OB, Djurovic S, Dale AM, Westlye LT, Frei O, Andreassen OA. Psychiatric disorders and brain white matter exhibit genetic overlap implicating developmental and neural cell biology. Mol Psychiatry 2023; 28:4924-4932. [PMID: 37759039 DOI: 10.1038/s41380-023-02264-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 09/06/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023]
Abstract
Improved understanding of the shared genetic architecture between psychiatric disorders and brain white matter may provide mechanistic insights for observed phenotypic associations. Our objective is to characterize the shared genetic architecture of bipolar disorder (BD), major depression (MD), and schizophrenia (SZ) with white matter fractional anisotropy (FA) and identify shared genetic loci to uncover biological underpinnings. We used genome-wide association study (GWAS) summary statistics for BD (n = 413,466), MD (n = 420,359), SZ (n = 320,404), and white matter FA (n = 33,292) to uncover the genetic architecture (i.e., polygenicity and discoverability) of each phenotype and their genetic overlap (i.e., genetic correlations, overlapping trait-influencing variants, and shared loci). This revealed that BD, MD, and SZ are at least 7-times more polygenic and less genetically discoverable than average FA. Even in the presence of weak genetic correlations (range = -0.05 to -0.09), average FA shared an estimated 42.5%, 43.0%, and 90.7% of trait-influencing variants as well as 12, 4, and 28 shared loci with BD, MD, and SZ, respectively. Shared variants were mapped to genes and tested for enrichment among gene-sets which implicated neurodevelopmental expression, neural cell types, myelin, and cell adhesion molecules. For BD and SZ, case vs control tract-level differences in FA associated with genetic correlations between those same tracts and the respective disorder (rBD = 0.83, p = 4.99e-7 and rSZ = 0.65, p = 5.79e-4). Genetic overlap at the tract-level was consistent with average FA results. Overall, these findings suggest a genetic basis for the involvement of brain white matter aberrations in the pathophysiology of psychiatric disorders.
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Affiliation(s)
- Nadine Parker
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Weiqiu Cheng
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Guy F L Hindley
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Psychosis Studies, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Pravesh Parekh
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey A Shadrin
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental disorders, University of Oslo, Oslo, Norway
| | - Ivan I Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Olav B Smeland
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Anders M Dale
- Multimodal Imaging Laboratory, University of California San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Lars T Westlye
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- K.G. Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
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10
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Voldsbekk I, Kjelkenes R, Dahl A, Holm MC, Lund MJ, Kaufmann T, Tamnes CK, Andreassen OA, Westlye LT, Alnæs D. Delineating disorder-general and disorder-specific dimensions of psychopathology from functional brain networks in a developmental clinical sample. Dev Cogn Neurosci 2023; 62:101271. [PMID: 37348146 PMCID: PMC10439505 DOI: 10.1016/j.dcn.2023.101271] [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: 04/26/2023] [Revised: 06/09/2023] [Accepted: 06/18/2023] [Indexed: 06/24/2023] Open
Abstract
The interplay between functional brain network maturation and psychopathology during development remains elusive. To establish the structure of psychopathology and its neurobiological mechanisms, mapping of both shared and unique functional connectivity patterns across developmental clinical populations is needed. We investigated shared associations between resting-state functional connectivity and psychopathology in children and adolescents aged 5-21 (n = 1689). Specifically, we used partial least squares (PLS) to identify latent variables (LV) between connectivity and both symptom scores and diagnostic information. We also investigated associations between connectivity and each diagnosis specifically, controlling for other diagnosis categories. PLS identified five significant LVs between connectivity and symptoms, mapping onto the psychopathology hierarchy. The first LV resembled a general psychopathology factor, followed by dimensions of internalising- externalising, neurodevelopment, somatic complaints, and thought problems. Another PLS with diagnostic data revealed one significant LV, resembling a cross-diagnostic case-control pattern. The diagnosis-specific PLS identified a unique connectivity pattern for autism spectrum disorder (ASD). All LVs were associated with distinct patterns of functional connectivity. These dimensions largely replicated in an independent sample (n = 420) from the same dataset, as well as to an independent cohort (n = 3504). This suggests that covariance in developmental functional brain networks supports transdiagnostic dimensions of psychopathology.
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Affiliation(s)
- Irene Voldsbekk
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway.
| | - Rikka Kjelkenes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Andreas Dahl
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Madelene C Holm
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Martina J Lund
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychiatry and Psychotherapy, University of Tübingen, Germany
| | - Christian K Tamnes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, & Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, & Department of Neurology, Oslo University Hospital, Oslo, Norway
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Kristiania University College, Oslo, Norway
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11
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Denckla CA, Dice ALE, Slopen N, Koenen KC, Tiemeier H. Mental health among bereaved youth in the ALSPAC birth cohort: Consideration of early sociodemographic precursors, cognitive ability, and type of loss. Dev Psychopathol 2023:1-12. [PMID: 37272542 PMCID: PMC10696131 DOI: 10.1017/s0954579423000512] [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] [Indexed: 06/06/2023]
Abstract
BACKGROUND Bereaved youth are at greater risk for adverse mental health outcomes, yet less is known about how social context shapes health for bereaved children. Ecosocial theory is employed to conceptualize bereavement in the context of sociodemographic factors. METHOD This longitudinal study used data from the Avon Longitudinal Study of Parents and Children. Of the 15,454 pregnancies enrolled, 5050 youth were still enrolled at age 16.5 and completed self-report questionnaires on life events and emotional/behavioral symptoms. RESULTS Sociodemographic precursors associated with parent, sibling, or close friend bereavement included maternal smoking, parental education levels, and financial difficulties. The significant yet small main effect of higher cognitive ability, assessed at age 8, on reduced emotional/behavioral symptoms at age 16.5 (β = -0.01, SE = 0.00, p < 0.001) did not interact with bereavement. Bereavement of a parent, sibling, or close friend was associated with a 0.19 point higher emotional/behavioral symptom log score compared to non-bereaved youth (95% CI: 0.10-0.28), across emotional, conduct, and hyperactivity subscales. CONCLUSIONS Descriptive findings suggest sociodemographic precursors are associated with bereavement. While there was an association between the bereavement of a parent, sibling, or close friend and elevated emotional/behavioral symptoms, cognitive ability did not moderate that effect.
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Affiliation(s)
- Christy A. Denckla
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Natalie Slopen
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Karestan C. Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health Boston, MA, USA
| | - Henning Tiemeier
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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12
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Izumi Y, Ishikawa M, Nakazawa T, Kunikata H, Sato K, Covey DF, Zorumski CF. Neurosteroids as stress modulators and neurotherapeutics: lessons from the retina. Neural Regen Res 2023; 18:1004-1008. [PMID: 36254981 PMCID: PMC9827771 DOI: 10.4103/1673-5374.355752] [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] [Indexed: 01/11/2023] Open
Abstract
Neurosteroids are rapidly emerging as important new therapies in neuropsychiatry, with one such agent, brexanolone, already approved for treatment of postpartum depression, and others on the horizon. These steroids have unique properties, including neuroprotective effects that could benefit a wide range of brain illnesses including depression, anxiety, epilepsy, and neurodegeneration. Over the past 25 years, our group has developed ex vivo rodent models to examine factors contributing to several forms of neurodegeneration in the retina. In the course of this work, we have developed a model of acute closed angle glaucoma that involves incubation of ex vivo retinas under hyperbaric conditions and results in neuronal and axonal changes that mimic glaucoma. We have used this model to determine neuroprotective mechanisms that could have therapeutic implications. In particular, we have focused on the role of both endogenous and exogenous neurosteroids in modulating the effects of acute high pressure. Endogenous allopregnanolone, a major stress-activated neurosteroid in the brain and retina, helps to prevent severe pressure-induced retinal excitotoxicity but is unable to protect against degenerative changes in ganglion cells and their axons under hyperbaric conditions. However, exogenous allopregnanolone, at a pharmacological concentration, completely preserves retinal structure and does so by combined effects on gamma-aminobutyric acid type A receptors and stimulation of the cellular process of macroautophagy. Surprisingly, the enantiomer of allopregnanolone, which is inactive at gamma-aminobutyric acid type A receptors, is equally retinoprotective and acts primarily via autophagy. Both enantiomers are also equally effective in preserving retinal structure and function in an in vivo glaucoma model. These studies in the retina have important implications for the ongoing development of allopregnanolone and other neurosteroids as therapeutics for neuropsychiatric illnesses.
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Affiliation(s)
- Yukitoshi Izumi
- Department of Psychiatry and Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, MO, USA
| | - Makoto Ishikawa
- Department of Ophthalmic Imaging and Information Analytics; Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Toru Nakazawa
- Department of Ophthalmic Imaging and Information Analytics; Department of Ophthalmology; Department of Retinal Disease Control; Department of Advanced Ophthalmic Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Hiroshi Kunikata
- Department of Ophthalmology; Department of Retinal Disease Control, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kota Sato
- Department of Ophthalmic Imaging and Information Analytics; Department of Advanced Ophthalmic Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Douglas F Covey
- Department of Psychiatry and Taylor Family Institute for Innovative Psychiatric Research; Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
| | - Charles F Zorumski
- Department of Psychiatry and Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, MO, USA
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13
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Retzler C, Hallam G, Johnson S, Retzler J. Person-centred Approaches to Psychopathology in the ABCD Study: Phenotypes and Neurocognitive Correlates. Res Child Adolesc Psychopathol 2023:10.1007/s10802-023-01065-w. [PMID: 37119331 PMCID: PMC10368562 DOI: 10.1007/s10802-023-01065-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 05/01/2023]
Abstract
Issues with classifying psychopathology using narrow diagnostic categories have prompted calls for the use of dimensional approaches. Yet questions remain about how closely dimensional approaches reflect the way symptoms cluster in individuals, whether known risk factors (e.g. preterm birth) produce distinct symptom phenotypes, and whether profiles reflecting symptom clusters are associated with neurocognitive factors. To identify distinct profiles of psychopathology, latent class analysis was applied to the syndrome scales of the parent-reported Child Behaviour Checklist for 11,381 9- and 10- year-olds from the Adolescent Brain Cognitive Development study. Four classes were identified, reflecting different profiles, to which children were assigned probabilistically; Class 1 (88.6%) reflected optimal functioning; Class 2 (7.1%), predominantly internalising; Class 3 (2.4%), predominantly externalising; and Class 4 (1.9%), universal difficulties. To investigate the presence of a possible preterm behavioural phenotype, the proportion of participants allocated to each class was cross-tabulated with gestational age category. No profile was specific to preterm birth. Finally, to assess the neurocognitive factors associated with class membership, elastic net regressions were conducted revealing a relatively distinct set of neurocognitive factors associated with each class. Findings support the use of large datasets to identify psychopathological profiles, explore phenotypes, and identify associated neurocognitive factors.
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Affiliation(s)
- Chris Retzler
- Department of Psychology, School of Human and Health Sciences, University of Huddersfield, Huddersfield, UK.
| | - Glyn Hallam
- Department of Psychology, School of Human and Health Sciences, University of Huddersfield, Huddersfield, UK
| | - Samantha Johnson
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Jenny Retzler
- Department of Psychology, School of Human and Health Sciences, University of Huddersfield, Huddersfield, UK
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14
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Voldsbekk I, Kjelkenes R, Wolfers T, Dahl A, Lund MJ, Kaufmann T, Fernandez-Cabello S, de Lange AMG, Tamnes CK, Andreassen OA, Westlye LT, Alnæs D. Shared pattern of impaired social communication and cognitive ability in the youth brain across diagnostic boundaries. Dev Cogn Neurosci 2023; 60:101219. [PMID: 36812678 PMCID: PMC9975702 DOI: 10.1016/j.dcn.2023.101219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/27/2023] [Accepted: 02/17/2023] [Indexed: 02/19/2023] Open
Abstract
BACKGROUND Abnormalities in brain structure are shared across diagnostic categories. Given the high rate of comorbidity, the interplay of relevant behavioural factors may also cross these classic boundaries. METHODS We aimed to detect brain-based dimensions of behavioural factors using canonical correlation and independent component analysis in a clinical youth sample (n = 1732, 64 % male, age: 5-21 years). RESULTS We identified two correlated patterns of brain structure and behavioural factors. The first mode reflected physical and cognitive maturation (r = 0.92, p = .005). The second mode reflected lower cognitive ability, poorer social skills, and psychological difficulties (r = 0.92, p = .006). Elevated scores on the second mode were a common feature across all diagnostic boundaries and linked to the number of comorbid diagnoses independently of age. Critically, this brain pattern predicted normative cognitive deviations in an independent population-based sample (n = 1253, 54 % female, age: 8-21 years), supporting the generalisability and external validity of the reported brain-behaviour relationships. CONCLUSIONS These results reveal dimensions of brain-behaviour associations across diagnostic boundaries, highlighting potent disorder-general patterns as the most prominent. In addition to providing biologically informed patterns of relevant behavioural factors for mental illness, this contributes to a growing body of evidence in favour of transdiagnostic approaches to prevention and intervention.
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Affiliation(s)
- Irene Voldsbekk
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway.
| | - Rikka Kjelkenes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,Department of Psychology, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,Department of Psychology, University of Oslo, Oslo, Norway
| | - Andreas Dahl
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,Department of Psychology, University of Oslo, Oslo, Norway
| | - Martina J. Lund
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,Department of Psychiatry and Psychotherapy, University of Tübingen, Germany
| | - Sara Fernandez-Cabello
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,Department of Psychology, University of Oslo, Oslo, Norway
| | - Ann-Marie G. de Lange
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,Department of Psychology, University of Oslo, Oslo, Norway,LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, CHUV and University of Lausanne, Lausanne, Switzerland,Department of Psychiatry, University of Oxford, Oxford, UK
| | - Christian K. Tamnes
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway,PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway,Department of Psychology, University of Oslo, Oslo, Norway,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Kristiania University College, Oslo, Norway.
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15
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Liu X, Tyler LK, Rowe JB, Tsvetanov KA. Multimodal fusion analysis of functional, cerebrovascular and structural neuroimaging in healthy aging subjects. Hum Brain Mapp 2022; 43:5490-5508. [PMID: 35855641 PMCID: PMC9704789 DOI: 10.1002/hbm.26025] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/24/2022] [Accepted: 07/07/2022] [Indexed: 01/15/2023] Open
Abstract
Brain aging is a complex process that requires a multimodal approach. Neuroimaging can provide insights into brain morphology, functional organization, and vascular dynamics. However, most neuroimaging studies of aging have focused on each imaging modality separately, limiting the understanding of interrelations between processes identified by different modalities and their relevance to cognitive decline in aging. Here, we used a data-driven multimodal approach, linked independent component analysis (ICA), to jointly analyze magnetic resonance imaging (MRI) of grey matter volume, cerebrovascular, and functional network topographies in relation to measures of fluid intelligence. Neuroimaging and cognitive data from the Cambridge Centre for Ageing and Neuroscience study were used, with healthy participants aged 18-88 years (main dataset n = 215 and secondary dataset n = 433). Using linked ICA, functional network activities were characterized in independent components but not captured in the same component as structural and cerebrovascular patterns. Split-sample (n = 108/107) and out-of-sample (n = 433) validation analyses using linked ICA were also performed. Global grey matter volume with regional cerebrovascular changes and the right frontoparietal network activity were correlated with age-related and individual differences in fluid intelligence. This study presents the insights from linked ICA to bring together measurements from multiple imaging modalities, with independent and additive information. We propose that integrating multiple neuroimaging modalities allows better characterization of brain pattern variability and changes associated with healthy aging.
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Affiliation(s)
- Xulin Liu
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
| | - Lorraine K. Tyler
- The Centre for Speech, Language and the Brain, Department of PsychologyUniversity of CambridgeCambridgeUK
| | - Cam‐CAN
- Cambridge Centre for Ageing and Neuroscience (Cam‐CAN), MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - James B. Rowe
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - Kamen A. Tsvetanov
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
- The Centre for Speech, Language and the Brain, Department of PsychologyUniversity of CambridgeCambridgeUK
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16
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Functional connectivity directionality between large-scale resting-state networks across typical and non-typical trajectories in children and adolescence. PLoS One 2022; 17:e0276221. [PMID: 36454744 PMCID: PMC9714732 DOI: 10.1371/journal.pone.0276221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 10/04/2022] [Indexed: 12/02/2022] Open
Abstract
Mental disorders often emerge during adolescence and have been associated with age-related differences in connection strengths of brain networks (static functional connectivity), manifesting in non-typical trajectories of brain development. However, little is known about the direction of information flow (directed functional connectivity) in this period of functional brain progression. We employed dynamic graphical models (DGM) to estimate directed functional connectivity from resting state functional magnetic resonance imaging data on 1143 participants, aged 6 to 17 years from the healthy brain network (HBN) sample. We tested for effects of age, sex, cognitive abilities and psychopathology on estimates of direction flow. Across participants, we show a pattern of reciprocal information flow between visual-medial and visual-lateral connections, in line with findings in adults. Investigating directed connectivity patterns between networks, we observed a positive association for age and direction flow from the cerebellar to the auditory network, and for the auditory to the sensorimotor network. Further, higher cognitive abilities were linked to lower information flow from the visual occipital to the default mode network. Additionally, examining the degree networks overall send and receive information to each other, we identified age-related effects implicating the right frontoparietal and sensorimotor network. However, we did not find any associations with psychopathology. Our results suggest that the directed functional connectivity of large-scale resting-state brain networks is sensitive to age and cognition during adolescence, warranting further studies that may explore directed relationships at rest and trajectories in more fine-grained network parcellations and in different populations.
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17
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Kjelkenes R, Wolfers T, Alnæs D, Norbom LB, Voldsbekk I, Holm M, Dahl A, Berthet P, Tamnes CK, Marquand AF, Westlye LT. Deviations from normative brain white and gray matter structure are associated with psychopathology in youth. Dev Cogn Neurosci 2022; 58:101173. [PMID: 36332329 PMCID: PMC9637865 DOI: 10.1016/j.dcn.2022.101173] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/10/2022] [Accepted: 10/31/2022] [Indexed: 11/30/2022] Open
Abstract
Combining imaging modalities and metrics that are sensitive to various aspects of brain structure and maturation may help identify individuals that show deviations in relation to same-aged peers, and thus benefit early-risk-assessment for mental disorders. We used one timepoint multimodal brain imaging, cognitive, and questionnaire data from 1280 eight- to twenty-one-year-olds from the Philadelphia Neurodevelopmental Cohort. We estimated age-related gray and white matter properties and estimated individual deviation scores using normative modeling. Next, we tested for associations between the estimated deviation scores, and with psychopathology domain scores and cognition. More negative deviations in DTI-based fractional anisotropy (FA) and the first principal eigenvalue of the diffusion tensor (L1) were associated with higher scores on psychosis positive and prodromal symptoms and general psychopathology. A more negative deviation in cortical thickness (CT) was associated with a higher general psychopathology score. Negative deviations in global FA, surface area, L1 and CT were also associated with poorer cognitive performance. No robust associations were found between the deviation scores based on CT and DTI. The low correlations between the different multimodal magnetic resonance imaging-based deviation scores suggest that psychopathological burden in adolescence can be mapped onto partly distinct neurobiological features.
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Affiliation(s)
- Rikka Kjelkenes
- Department of Psychology, University of Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway,Corresponding authors at: Department of Psychology, University of Oslo, Norway.
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway,Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway,Oslo New University College, Oslo, Norway
| | - Linn B. Norbom
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway,PROMENTA Research Center, Department of Psychology, University of Oslo, Norway,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Irene Voldsbekk
- Department of Psychology, University of Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway
| | - Madelene Holm
- Department of Psychology, University of Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway
| | - Andreas Dahl
- Department of Psychology, University of Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway
| | - Pierre Berthet
- Department of Psychology, University of Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway
| | - Christian K. Tamnes
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway,PROMENTA Research Center, Department of Psychology, University of Oslo, Norway,Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
| | - Andre F. Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands,Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, the Netherlands,Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, King’s College London, London, UK
| | - Lars T. Westlye
- Department of Psychology, University of Oslo, Norway,Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, University of Oslo, & Oslo University Hospital, Oslo, Norway,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Norway,Corresponding authors at: Department of Psychology, University of Oslo, Norway.
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18
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Brislin SJ, Martz ME, Joshi S, Duval ER, Gard A, Clark DA, Hyde LW, Hicks BM, Taxali A, Angstadt M, Rutherford S, Heitzeg MM, Sripada C. Differentiated nomological networks of internalizing, externalizing, and the general factor of psychopathology (' p factor') in emerging adolescence in the ABCD study. Psychol Med 2022; 52:3051-3061. [PMID: 33441214 PMCID: PMC9693677 DOI: 10.1017/s0033291720005103] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2020] [Revised: 11/04/2020] [Accepted: 12/03/2020] [Indexed: 01/05/2023]
Abstract
BACKGROUND Structural models of psychopathology consistently identify internalizing (INT) and externalizing (EXT) specific factors as well as a superordinate factor that captures their shared variance, the p factor. Questions remain, however, about the meaning of these data-driven dimensions and the interpretability and distinguishability of the larger nomological networks in which they are embedded. METHODS The sample consisted of 10 645 youth aged 9-10 years participating in the multisite Adolescent Brain and Cognitive Development (ABCD) Study. p, INT, and EXT were modeled using the parent-rated Child Behavior Checklist (CBCL). Patterns of associations were examined with variables drawn from diverse domains including demographics, psychopathology, temperament, family history of substance use and psychopathology, school and family environment, and cognitive ability, using instruments based on youth-, parent-, and teacher-report, and behavioral task performance. RESULTS p exhibited a broad pattern of statistically significant associations with risk variables across all domains assessed, including temperament, neurocognition, and social adversity. The specific factors exhibited more domain-specific patterns of associations, with INT exhibiting greater fear/distress and EXT exhibiting greater impulsivity. CONCLUSIONS In this largest study of hierarchical models of psychopathology to date, we found that p, INT, and EXT exhibit well-differentiated nomological networks that are interpretable in terms of neurocognition, impulsivity, fear/distress, and social adversity. These networks were, in contrast, obscured when relying on the a priori Internalizing and Externalizing dimensions of the CBCL scales. Our findings add to the evidence for the validity of p, INT, and EXT as theoretically and empirically meaningful broad psychopathology liabilities.
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Affiliation(s)
- Sarah J. Brislin
- Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI 48109, USA
| | - Meghan E. Martz
- Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI 48109, USA
| | - Sonalee Joshi
- Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI 48109, USA
| | - Elizabeth R. Duval
- Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI 48109, USA
| | - Arianna Gard
- Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI 48109, USA
| | - D. Angus Clark
- Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI 48109, USA
| | - Luke W. Hyde
- Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI 48109, USA
| | - Brian M. Hicks
- Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI 48109, USA
| | - Aman Taxali
- Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI 48109, USA
| | - Mike Angstadt
- Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI 48109, USA
| | - Saige Rutherford
- Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI 48109, USA
| | - Mary M. Heitzeg
- Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI 48109, USA
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan, 4250 Plymouth Rd, Ann Arbor, MI 48109, USA
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19
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Neumann A, Nolte IM, Pappa I, Ahluwalia TS, Pettersson E, Rodriguez A, Whitehouse A, van Beijsterveldt CEM, Benyamin B, Hammerschlag AR, Helmer Q, Karhunen V, Krapohl E, Lu Y, van der Most PJ, Palviainen T, St Pourcain B, Seppälä I, Suarez A, Vilor-Tejedor N, Tiesler CMT, Wang C, Wills A, Zhou A, Alemany S, Bisgaard H, Bønnelykke K, Davies GE, Hakulinen C, Henders AK, Hyppönen E, Stokholm J, Bartels M, Hottenga JJ, Heinrich J, Hewitt J, Keltikangas-Järvinen L, Korhonen T, Kaprio J, Lahti J, Lahti-Pulkkinen M, Lehtimäki T, Middeldorp CM, Najman JM, Pennell C, Power C, Oldehinkel AJ, Plomin R, Räikkönen K, Raitakari OT, Rimfeld K, Sass L, Snieder H, Standl M, Sunyer J, Williams GM, Bakermans-Kranenburg MJ, Boomsma DI, van IJzendoorn MH, Hartman CA, Tiemeier H. A genome-wide association study of total child psychiatric problems scores. PLoS One 2022; 17:e0273116. [PMID: 35994476 PMCID: PMC9394806 DOI: 10.1371/journal.pone.0273116] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 08/02/2022] [Indexed: 11/25/2022] Open
Abstract
Substantial genetic correlations have been reported across psychiatric disorders and numerous cross-disorder genetic variants have been detected. To identify the genetic variants underlying general psychopathology in childhood, we performed a genome-wide association study using a total psychiatric problem score. We analyzed 6,844,199 common SNPs in 38,418 school-aged children from 20 population-based cohorts participating in the EAGLE consortium. The SNP heritability of total psychiatric problems was 5.4% (SE = 0.01) and two loci reached genome-wide significance: rs10767094 and rs202005905. We also observed an association of SBF2, a gene associated with neuroticism in previous GWAS, with total psychiatric problems. The genetic effects underlying the total score were shared with common psychiatric disorders only (attention-deficit/hyperactivity disorder, anxiety, depression, insomnia) (rG > 0.49), but not with autism or the less common adult disorders (schizophrenia, bipolar disorder, or eating disorders) (rG < 0.01). Importantly, the total psychiatric problem score also showed at least a moderate genetic correlation with intelligence, educational attainment, wellbeing, smoking, and body fat (rG > 0.29). The results suggest that many common genetic variants are associated with childhood psychiatric symptoms and related phenotypes in general instead of with specific symptoms. Further research is needed to establish causality and pleiotropic mechanisms between related traits.
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Affiliation(s)
- Alexander Neumann
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - Ilja M. Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Irene Pappa
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Tarunveer S. Ahluwalia
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Erik Pettersson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Alina Rodriguez
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Andrew Whitehouse
- Telethon Kids Institute, University of Western Australia, Perth, Australia
| | | | - Beben Benyamin
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, School of Health Sciences, University of South Australia, Adelaide, Australia
- South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Anke R. Hammerschlag
- Child Health Research Centre, University of Queensland, Brisbane, QLD, Australia
- Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
| | - Quinta Helmer
- Netherlands Twin Register, Dept Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- Centre for Life-Course Health Research, University of Oulu, Oulu, Finland
| | - Eva Krapohl
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Yi Lu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter J. van der Most
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Teemu Palviainen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Beate St Pourcain
- MRC Integrative Epidemiology Unit, Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherland
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Ilkka Seppälä
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center—Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Anna Suarez
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Natalia Vilor-Tejedor
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- BarcelonaBeta Brain Research Center (BBRC)–Pasqual Maragall Foundation, Barcelona, Spain
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Carla M. T. Tiesler
- Institute of Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health, Neuherberg, Germany
- LMU–Ludwig-Maximilians-Universität Munich, Div. Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, University of Munich Medical Center, Munich, Germany
| | - Carol Wang
- School of Medicine and Public Health, Faculty of Medicine and Health, The University of Newcastle, Newcastle, New South Wales, Australia
| | - Amanda Wills
- Division of Substance Dependence, Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, United States of America
| | - Ang Zhou
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, School of Health Sciences, University of South Australia, Adelaide, Australia
- South Australian Health and Medical Research Institute, Adelaide, Australia
| | - Silvia Alemany
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- CIBER Epidemiology and Public Health (CIBERESP), Barcelona, Spain
| | - Hans Bisgaard
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Klaus Bønnelykke
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
| | - Gareth E. Davies
- Avera Institute for Human Genetics, Sioux Falls, South Dakota, United States of America
| | - Christian Hakulinen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Anjali K. Henders
- Institute of Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, School of Health Sciences, University of South Australia, Adelaide, Australia
- South Australian Health and Medical Research Institute, Adelaide, Australia
- Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Jakob Stokholm
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics, Naestved Hospital, Naestved, Denmark
| | - Meike Bartels
- Netherlands Twin Register, Dept Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | - Jouke-Jan Hottenga
- Netherlands Twin Register, Dept Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | - Joachim Heinrich
- LMU–Ludwig-Maximilians-Universität Munich, Div. Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, University of Munich Medical Center, Munich, Germany
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich, Germany
| | - John Hewitt
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, United States of America
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, United States of America
| | | | - Tellervo Korhonen
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Jari Lahti
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Marius Lahti-Pulkkinen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
- Department of Clinical Chemistry, Finnish Cardiovascular Research Center—Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Christel M. Middeldorp
- Child Health Research Centre, University of Queensland, Brisbane, QLD, Australia
- Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Child and Youth Mental Health Service, Children’s Health Queensland Hospital and Health Service, Brisbane, QLD, Australia
| | - Jackob M. Najman
- Public Health, University of Queensland, Brisbane, QLD, Australia
| | - Craig Pennell
- School of Medicine and Public Health, Faculty of Medicine and Health, The University of Newcastle, Newcastle, New South Wales, Australia
| | - Chris Power
- Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Albertine J. Oldehinkel
- Interdisciplinary Center Psychopathology and Emotion Regulation (IPCE), University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Robert Plomin
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Olli T. Raitakari
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
| | - Kaili Rimfeld
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Lærke Sass
- COPSAC, Copenhagen Prospective Studies on Asthma in Childhood, Herlev and Gentofte Hospital, University of Copenhagen, Copenhagen, Denmark
- Department of Pediatrics, Naestved Hospital, Naestved, Denmark
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Marie Standl
- LMU–Ludwig-Maximilians-Universität Munich, Div. Metabolic and Nutritional Medicine, Dr. von Hauner Children’s Hospital, University of Munich Medical Center, Munich, Germany
| | - Jordi Sunyer
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
- CIBER Epidemiology and Public Health (CIBERESP), Barcelona, Spain
- IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
| | - Gail M. Williams
- Child and Youth Mental Health Service, Children’s Health Queensland Hospital and Health Service, Brisbane, QLD, Australia
| | | | - Dorret I. Boomsma
- Netherlands Twin Register, Dept Biological Psychology, Vrije Universiteit, Amsterdam, The Netherlands
| | | | - Catharina A. Hartman
- Department of Psychiatry, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Social and Behavioral Science, Harvard TH Chan School of Public Health, Boston, MA, United States of America
- * E-mail:
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20
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Grazioplene RG, DeYoung CG, Hampson M, Anticevic A, Pittenger C. Obsessive compulsive symptom dimensions are linked to altered white-matter microstructure in a community sample of youth. Transl Psychiatry 2022; 12:328. [PMID: 35948535 PMCID: PMC9365814 DOI: 10.1038/s41398-022-02013-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 05/05/2022] [Accepted: 05/30/2022] [Indexed: 11/17/2022] Open
Abstract
Obsessive-compulsive symptoms (OCS) are common in school-aged children and predict the development of obsessive compulsive disorder (OCD). White-matter abnormalities have been described in OCD, but the white matter correlates of OCS in the developing brain are unclear. Some correlates of OCS (or a diagnosis of OCD) may reflect correlates of a transdiagnostic or even general psychopathology factor. We examined these questions in a large sample of typically developing youth (N = 1208), using a hierarchical analysis of fixel-based white matter measures in relation to OCS and general psychopathology. General psychopathology was associated with abnormalities in the posterior corpus callosum and forceps major in an age-dependent manner, suggesting altered maturation (specifically, hypermaturation in younger subjects). A unidimensional measure of OCS did not associate with any white-matter abnormalities, but analysis of separate OCS dimensions (derived from factor analysis within this sample) revealed the 'Bad Thoughts' dimension to associate with white-matter abnormalities in dorsal parietal white-matter and descending corticospinal tracts, and the 'Symmetry' dimension to associate with abnormalities in the anterior corpus callosum. Repetition/checking and Symmetry OCS were additionally associated with posterior abnormalities overlapping with the correlates of general psychopathology. Contamination symptoms had no white-matter correlates. Secondary analysis of fractional anisotropy (FA) revealed distinct white-matter abnormalities, suggesting that fixel-based and FA analyses identify distinct features of white matter relevant to psychopathology. These findings suggest that OCS dimensions correlate with dissociable abnormalities in white matter, implicating separable networks. Future studies should examine these white-matter signatures in a longitudinal framework.
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Affiliation(s)
| | - Colin G DeYoung
- University of Minnesota, Department of Psychology, Minneapolis, MN, USA
| | - Michelle Hampson
- Yale University, Department of Radiology and Biomedical Imaging, New Haven, CT, USA
| | - Alan Anticevic
- Yale University, Department of Psychiatry, New Haven, CT, USA
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21
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Mewton L, Lees B, Squeglia LM, Forbes MK, Sunderland M, Krueger R, Koch FC, Baillie A, Slade T, Hoy N, Teesson M. The relationship between brain structure and general psychopathology in preadolescents. J Child Psychol Psychiatry 2022; 63:734-744. [PMID: 34468031 PMCID: PMC8885925 DOI: 10.1111/jcpp.13513] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/23/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND An emerging body of literature has indicated that broad, transdiagnostic dimensions of psychopathology are associated with alterations in brain structure across the life span. The current study aimed to investigate the relationship between brain structure and broad dimensions of psychopathology in the critical preadolescent period when psychopathology is emerging. METHODS This study included baseline data from the Adolescent Brain and Cognitive Development (ABCD) Study® (n = 11,875; age range = 9-10 years; male = 52.2%). General psychopathology, externalizing, internalizing, and thought disorder dimensions were based on a higher-order model of psychopathology and estimated using Bayesian plausible values. Outcome variables included global and regional cortical volume, thickness, and surface area. RESULTS Higher levels of psychopathology across all dimensions were associated with lower volume and surface area globally, as well as widespread and pervasive alterations across the majority of cortical and subcortical regions studied, after adjusting for sex, race/ethnicity, parental education, income, and maternal psychopathology. The relationships between general psychopathology and brain structure were attenuated when adjusting for cognitive functioning. There were no statistically significant relationships between psychopathology and cortical thickness in this sample of preadolescents. CONCLUSIONS The current study identified lower cortical volume and surface area as transdiagnostic biomarkers for general psychopathology in preadolescence. Future research may focus on whether the widespread and pervasive relationships between general psychopathology and brain structure reflect cognitive dysfunction that is a feature across a range of mental illnesses.
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Affiliation(s)
- Louise Mewton
- Centre for Healthy Brain Ageing, University of New South Wales, Sydney, NSW, Australia
| | - Briana Lees
- The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, NSW, Australia
| | - Lindsay M Squeglia
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Miriam K Forbes
- Department of Psychology, Centre for Emotional Health, Macquarie University, Sydney, NSW, Australia
| | - Matthew Sunderland
- The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, NSW, Australia
| | | | - Forrest C Koch
- Centre for Healthy Brain Ageing, University of New South Wales, Sydney, NSW, Australia
| | - Andrew Baillie
- Sydney School of Health Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Tim Slade
- The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, NSW, Australia
| | - Nicholas Hoy
- Centre for Healthy Brain Ageing, University of New South Wales, Sydney, NSW, Australia
| | - Maree Teesson
- The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Sydney, NSW, Australia
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22
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Vieira BH, Pamplona GSP, Fachinello K, Silva AK, Foss MP, Salmon CEG. On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting. INTELLIGENCE 2022. [DOI: 10.1016/j.intell.2022.101654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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23
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Neumann A, Jolicoeur‐Martineau A, Szekely E, Sallis HM, O’Donnel K, Greenwood CM, Levitan R, Meaney MJ, Wazana A, Evans J, Tiemeier H. Combined polygenic risk scores of different psychiatric traits predict general and specific psychopathology in childhood. J Child Psychol Psychiatry 2022; 63:636-645. [PMID: 34389974 PMCID: PMC9291767 DOI: 10.1111/jcpp.13501] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/14/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Polygenic risk scores (PRSs) operationalize genetic propensity toward a particular mental disorder and hold promise as early predictors of psychopathology, but before a PRS can be used clinically, explanatory power must be increased and the specificity for a psychiatric domain established. To enable early detection, it is crucial to study these psychometric properties in childhood. We examined whether PRSs associate more with general or with specific psychopathology in school-aged children. Additionally, we tested whether psychiatric PRSs can be combined into a multi-PRS score for improved performance. METHODS We computed 16 PRSs based on GWASs of psychiatric phenotypes, but also neuroticism and cognitive ability, in mostly adult populations. Study participants were 9,247 school-aged children from three population-based cohorts of the DREAM-BIG consortium: ALSPAC (UK), The Generation R Study (Netherlands), and MAVAN (Canada). We associated each PRS with general and specific psychopathology factors, derived from a bifactor model based on self-report and parental, teacher, and observer reports. After fitting each PRS in separate models, we also tested a multi-PRS model, in which all PRSs are entered simultaneously as predictors of the general psychopathology factor. RESULTS Seven PRSs were associated with the general psychopathology factor after multiple testing adjustment, two with specific externalizing and five with specific internalizing psychopathology. PRSs predicted general psychopathology independently of each other, with the exception of depression and depressive symptom PRSs. Most PRSs associated with a specific psychopathology domain, were also associated with general child psychopathology. CONCLUSIONS The results suggest that PRSs based on current GWASs of psychiatric phenotypes tend to be associated with general psychopathology, or both general and specific psychiatric domains, but not with one specific psychopathology domain only. Furthermore, PRSs can be combined to improve predictive ability. PRS users should therefore be conscious of nonspecificity and consider using multiple PRSs simultaneously, when predicting psychiatric disorders.
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Affiliation(s)
- Alexander Neumann
- Department of Child and Adolescent PsychiatryErasmus University Medical CenterRotterdamThe Netherlands,Lady Davis Institute for Medical ResearchJewish General HospitalMontrealQCCanada,VIB Center for Molecular NeurologyVIBAntwerpBelgium,Department of Biomedical SciencesUniversity of AntwerpAntwerpBelgium
| | | | - Eszter Szekely
- Lady Davis Institute for Medical ResearchJewish General HospitalMontrealQCCanada,Department of PsychiatryMcGill University Faculty of MedicineMontrealQCCanada
| | - Hannah M. Sallis
- MRC Integrative Epidemiology UnitUniversity of BristolBristolUK,Centre for Academic Mental Health, Population Health SciencesBristol Medical SchoolUniversity of BristolBristolUK,School of Psychological ScienceUniversity of BristolBristolUK
| | - Kieran O’Donnel
- Department of Psychiatry and Sackler Program for Epigenetics and PsychobiologyMcGill UniversityMontrealQCCanada,Ludmer Centre for Neuroinformatics and Mental HealthMcGill UniversityMontrealQCCanada
| | - Celia M.T. Greenwood
- Lady Davis Institute for Medical ResearchJewish General HospitalMontrealQCCanada,Departments of Oncology, Human Genetics, and Epidemiology, Biostatistics and Occupational HealthMcGill UniversityMontrealQCCanada
| | - Robert Levitan
- Centre for Addiction and Mental HealthTorontoONCanada,Department of PsychiatryUniversity of TorontoTorontoONCanada
| | - Michael J. Meaney
- Department of PsychiatryMcGill University Faculty of MedicineMontrealQCCanada,Douglas Mental Health InstituteMontrealQCCanada,Singapore Institute for Clinical SciencesSingapore CitySingapore
| | - Ashley Wazana
- Lady Davis Institute for Medical ResearchJewish General HospitalMontrealQCCanada,Department of PsychiatryMcGill University Faculty of MedicineMontrealQCCanada,Centre for Child Development and Mental HealthJewish General HospitalMontrealQCCanada
| | - Jonathan Evans
- Centre for Academic Mental Health, Population Health SciencesBristol Medical SchoolUniversity of BristolBristolUK
| | - Henning Tiemeier
- Department of Child and Adolescent PsychiatryErasmus University Medical CenterRotterdamThe Netherlands,Department of Social and Behavioral SciencesHarvard T. H. Chan School of Public HealthBostonMAUSA
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24
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Astle DE, Holmes J, Kievit R, Gathercole SE. Annual Research Review: The transdiagnostic revolution in neurodevelopmental disorders. J Child Psychol Psychiatry 2022; 63:397-417. [PMID: 34296774 DOI: 10.1111/jcpp.13481] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/04/2021] [Indexed: 12/11/2022]
Abstract
Practitioners frequently use diagnostic criteria to identify children with neurodevelopmental disorders and to guide intervention decisions. These criteria also provide the organising framework for much of the research focussing on these disorders. Study design, recruitment, analysis and theory are largely built on the assumption that diagnostic criteria reflect an underlying reality. However, there is growing concern that this assumption may not be a valid and that an alternative transdiagnostic approach may better serve our understanding of this large heterogeneous population of young people. This review draws on important developments over the past decade that have set the stage for much-needed breakthroughs in understanding neurodevelopmental disorders. We evaluate contemporary approaches to study design and recruitment, review the use of data-driven methods to characterise cognition, behaviour and neurobiology, and consider what alternative transdiagnostic models could mean for children and families. This review concludes that an overreliance on ill-fitting diagnostic criteria is impeding progress towards identifying the barriers that children encounter, understanding underpinning mechanisms and finding the best route to supporting them.
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Affiliation(s)
- Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Joni Holmes
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Rogier Kievit
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.,Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Susan E Gathercole
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.,Department of Psychiatry, University of Cambridge, Cambridge, UK
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El Nagar Z, El Shahawi HH, Effat SM, El Sheikh MM, Adel A, Ibrahim YA, Aufa OM. Single episode brief psychotic disorder versus bipolar disorder: A diffusion tensor imaging and executive functions study. Schizophr Res Cogn 2022; 27:100214. [PMID: 34557386 PMCID: PMC8446778 DOI: 10.1016/j.scog.2021.100214] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 08/21/2021] [Accepted: 08/23/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Despite fast progress in neuroscientific approaches, the neurobiological continuum links psychotic spectrum, and affective disorder is obscure. White matter WM abnormalities found utilizing Diffusion Tensor Imaging (DTI) showing impaired communication in both disorders have been consistently demonstrated; however, direct comparisons of findings between them are scarce. This study aims to study WM abnormalities in single episode bipolar I disorder, and single episode brief psychotic disorder related to healthy control with the association of executive function. METHODS A cross-sectional case-control study was used to assess 60 subjects divided into 20 patients with single episode bipolar I disorder, 20 individuals with single episode brief psychotic disorder (both groups of patients were in remission), and 20 healthy controls. The present study examined the superior longitudinal fasciculus (SLF), and cingulum bundle fractional anisotropy (FA) determined from DTI images symmetrically and connected these results with cognitive functions as assessed by the trail making test (TMT) and Wisconsin card sorting test (WCST). RESULTS DTI data indicated that the psychotic group had a significant decrease in FA of the right SLF (p-value less than 0.001), left SLF (p-value less than 0.001), and left cingulum (p-value less than 0.001) than the bipolar I group. In terms of executive functioning, the psychotic group performed significantly worse than the bipolar I group on the TMT part B (p-value less than 0.001), the WCST (number of classifications fulfilled) (p-value less than 0.001), and perseverative errors (p-value less than 0.001). CONCLUSION Even after clinical remission, individuals with single episode brief psychotic disorder had more pronounced white matter impairments and executive function deficiencies than individuals with single episode bipolar I disorder.
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Affiliation(s)
- Zeinab El Nagar
- Institute of Psychiatry, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Heba H. El Shahawi
- Institute of Psychiatry, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Safeya M. Effat
- Institute of Psychiatry, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Mona M. El Sheikh
- Institute of Psychiatry, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Ahmed Adel
- Institute of Psychiatry, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Yosra A. Ibrahim
- Radiology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt
| | - Ola M. Aufa
- Institute of Psychiatry, Faculty of Medicine, Ain Shams University, Cairo, Egypt
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Mapping normative trajectories of cognitive function and its relation to psychopathology symptoms and genetic risk in youth. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022; 3:255-263. [PMID: 37124356 PMCID: PMC10140446 DOI: 10.1016/j.bpsgos.2022.01.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/08/2021] [Accepted: 01/16/2022] [Indexed: 12/19/2022] Open
Abstract
Background Adolescence hosts a sharp increase in the incidence of mental disorders. The prodromal phases are often characterized by cognitive deficits that predate disease onset by several years. Characterization of cognitive performance in relation to normative trajectories may have value for early risk assessment and monitoring. Methods Youth aged 8 to 21 years (N = 6481) from the Philadelphia Neurodevelopmental Cohort were included. Performance scores from a computerized neurocognitive battery were decomposed using principal component analysis, yielding a general cognitive score. Items reflecting various aspects of psychopathology from self-report questionnaires and collateral caregiver information were decomposed using independent component analysis, providing individual domain scores. Using normative modeling and Bayesian statistics, we estimated normative trajectories of cognitive function and tested for associations between cognitive deviance and psychopathological domain scores. In addition, we tested for associations with polygenic scores for mental and behavioral disorders often involving cognition, including schizophrenia, bipolar disorder, attention-deficit/hyperactivity disorder, and Alzheimer's disease. Results More negative normative cognitive deviations were associated with higher general psychopathology burden and domains reflecting positive and prodromal psychosis, attention problems, norm-violating behavior, and anxiety. In addition, better performance was associated with higher joint burden of depression, suicidal ideation, and negative psychosis symptoms. The analyses revealed no evidence for associations with polygenic scores. Conclusions Our results show that cognitive performance is associated with general and specific domains of psychopathology in youth. These findings support the close links between cognition and psychopathology in youth and highlight the potential of normative modeling for early risk assessment.
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Modabbernia A, Michelini G, Reichenberg A, Kotov R, Barch D, Frangou S. Neural Signatures of Data-Driven Psychopathology Dimensions at the Transition to Adolescence. Eur Psychiatry 2022; 65:e12. [PMID: 35067249 PMCID: PMC8853849 DOI: 10.1192/j.eurpsy.2021.2262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Background One of the challenges in human neuroscience is to uncover associations between brain organization and psychopathology in order to better understand the biological underpinnings of mental disorders. Here, we aimed to characterize the neural correlates of psychopathology dimensions obtained using two conceptually different data-driven approaches. Methods Dimensions of psychopathology that were either maximally dissociable or correlated were respectively extracted by independent component analysis (ICA) and exploratory factor analysis (EFA) applied to the Childhood Behavior Checklist items from 9- to 10-year-olds (n = 9983; 47.8% female, 50.8% white) participating in the Adolescent Brain Cognitive Development study. The patterns of brain morphometry, white matter integrity and resting-state connectivity associated with each dimension were identified using kernel-based regularized least squares and compared between dimensions using Spearman’s correlation coefficient. Results ICA identified three psychopathology dimensions, representing opposition–disinhibition, cognitive dyscontrol, and negative affect, with distinct brain correlates. Opposition–disinhibition was negatively associated with cortical surface area, cognitive dyscontrol was negatively associated with anatomical and functional dysconnectivity while negative affect did not show discernable associations with any neuroimaging measure. EFA identified three dimensions representing broad externalizing, neurodevelopmental, and broad Internalizing problems with partially overlapping brain correlates. All EFA-derived dimensions were negatively associated with cortical surface area, whereas measures of functional and structural connectivity were associated only with the neurodevelopmental dimension. Conclusions This study highlights the importance of cortical surface area and global connectivity for psychopathology in preadolescents and provides evidence for dissociable psychopathology dimensions with distinct brain correlates.
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Zhao Q, Voon V, Zhang L, Shen C, Zhang J, Feng J. The ABCD Study: Brain Heterogeneity in Intelligence During a Neurodevelopmental Transition Stage. Cereb Cortex 2022; 32:3098-3109. [PMID: 35037940 PMCID: PMC9290553 DOI: 10.1093/cercor/bhab403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 08/31/2021] [Accepted: 09/28/2021] [Indexed: 11/29/2022] Open
Abstract
A complex curvilinear relationship exists between intelligence and age during the neurodevelopment of cortical thickness. To parse out a more fine-grained relationship between intelligence and cortical thickness and surface area, we used a large-scale data set focusing on a critical transition juncture in neurodevelopment in preadolescence. Cortical thickness was derived from T1-weighted structural magnetic resonance images of a large sample of 9- and 11-year-old children from the Adolescent Brain Cognitive Development study. The NIH Toolbox Cognition Battery composite scores, which included fluid, crystallized, and total scores, were used to assess intelligence. Using a double generalized linear model, we assessed the independent association between the mean and dispersion of cortical thickness/surface area and intelligence. Higher intelligence in preadolescents was associated with higher mean cortical thickness in orbitofrontal and primary sensory cortices but with lower thickness in the dorsolateral and medial prefrontal cortex and particularly in the rostral anterior cingulate. The rostral anterior cingulate findings were particularly evident across all subscales of intelligence. Higher intelligence was also associated with greater interindividual similarity in the rostral cingulate. Intelligence during this key transition juncture in preadolescence appears to reflect a dissociation between the cortical development of basic cognitive processes and higher-order executive and motivational processes.
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Affiliation(s)
| | | | - Lingli Zhang
- Developmental and Behavioral Pediatric & Child Primary Care Department, Ministry of Education-Shanghai Key Laboratory of Children’s Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200092, PR China
| | - Chun Shen
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, PR China
| | - Jie Zhang
- Address correspondence to Professor Jianfeng Feng, Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, PR China. ; Professor Jie Zhang, Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, PR China.
| | - Jianfeng Feng
- Address correspondence to Professor Jianfeng Feng, Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, PR China. ; Professor Jie Zhang, Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, PR China.
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29
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The P-factor and its genomic and neural equivalents: an integrated perspective. Mol Psychiatry 2022; 27:38-48. [PMID: 33526822 PMCID: PMC8960404 DOI: 10.1038/s41380-021-01031-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 12/01/2020] [Accepted: 01/13/2021] [Indexed: 01/30/2023]
Abstract
Different psychiatric disorders and symptoms are highly correlated in the general population. A general psychopathology factor (or "P-factor") has been proposed to efficiently describe this covariance of psychopathology. Recently, genetic and neuroimaging studies also derived general dimensions that reflect densely correlated genomic and neural effects on behaviour and psychopathology. While these three types of general dimensions show striking parallels, it is unknown how they are conceptually related. Here, we provide an overview of these three general dimensions, and suggest a unified interpretation of their nature and underlying mechanisms. We propose that the general dimensions reflect, in part, a combination of heritable 'environmental' factors, driven by a dense web of gene-environment correlations. This perspective calls for an update of the traditional endophenotype framework, and encourages methodological innovations to improve models of gene-brain-environment relationships in all their complexity. We propose concrete approaches, which by taking advantage of the richness of current large databases will help to better disentangle the complex nature of causal factors underlying psychopathology.
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30
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Davis AD, Hassel S, Arnott SR, Hall GB, Harris JK, Zamyadi M, Downar J, Frey BN, Lam RW, Kennedy SH, Strother SC. Biophysical compartment models for single-shell diffusion MRI in the human brain: a model fitting comparison. Phys Med Biol 2021; 67. [PMID: 34965517 DOI: 10.1088/1361-6560/ac46de] [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: 09/05/2021] [Accepted: 12/29/2021] [Indexed: 11/11/2022]
Abstract
Clinically oriented studies commonly acquire diffusion MRI (dMRI) data with a single non-zero b-value (i.e. single-shell) and diffusion weighting ofb=1000 s/mm2. To produce microstructural parameter maps, the tensor model is usually used, despite known limitations. Although compartment models have demonstrated improved fits in multi-shell dMRI data, they are rarely used for single-shell parameter maps, where their effectiveness is unclear from the literature. Here, various compartment models combining isotropic balls and symmetric tensors were fitted to single-shell dMRI data to investigate model fitting optimization and extract the most information possible. Full testing was performed in 5 subjects, and 3 subjects with multi-shell data were included for comparison. The results were tested and confirmed in a further 50 subjects. The Markov chain Monte Carlo (MCMC) model fitting technique outperformed non-linear least squares. Using MCMC, the 2-fibre-orientation mono-exponential ball & stick model (BSME2) provided artifact-free, stable results, in little processing time. The analogous ball & zeppelin model (BZ2) also produced stable, low-noise parameter maps, though it required much greater computing resources (50 000 burn-in steps). In single-shell data, the gamma-distributed diffusivity ball & stick model (BSGD2) underperformed relative to other models, despite being an often-used software default. It produced artifacts in the diffusivity maps even with extremely long processing times. Neither increased diffusion weighting nor a greater number of gradient orientations improved BSGD2fits. In white matter (WM), the tensor produced the best fit as measured by Bayesian information criterion. This result contrasts with studies using multi-shell data. However, in crossing fibre regions the tensor confounded geometric effects with fractional anisotropy (FA): the planar/linear WM FA ratio was 49%, while BZ2and BSME2retained 76% and 83% of restricted fraction, respectively. As a result, the BZ2and BSME2models are strong candidates to optimize information extraction from single-shell dMRI studies.
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Affiliation(s)
- Andrew D Davis
- Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst St, Toronto, Ontario, M6A 2E1, CANADA
| | - Stefanie Hassel
- University of Calgary Cumming School of Medicine, 3330 Hospital Dr NW, Calgary, Alberta, T2N 4N1, CANADA
| | - Stephen R Arnott
- Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst St, Toronto, Ontario, M6A 2E1, CANADA
| | - Geoffrey B Hall
- Department of Psychology, Neuroscience & Behaviour, McMaster University, 1280 Main St W, Hamilton, Ontario, L8S 4K1, CANADA
| | - Jacqueline K Harris
- Department of Computing Science, University of Alberta, 8900 114 St NW, Edmonton, Alberta, T6G 2E8, CANADA
| | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst St, Toronto, Ontario, M6A 2E1, CANADA
| | - Jonathan Downar
- Institute of Medical Science, University of Toronto, 1 King's College Cir, Toronto, Ontario, M5S 1A8, CANADA
| | - Benicio N Frey
- McMaster University Department of Psychiatry and Behavioural Neurosciences, 100 West 5th Street, Hamilton, Ontario, L8N 3K7, CANADA
| | - Raymond W Lam
- Psychiatry, The University of British Columbia, 2255 Wesbrook Mall, Vancouver, British Columbia, V6T 2A1, CANADA
| | - Sidney H Kennedy
- University of Toronto Department of Psychiatry, 250 College Street, Toronto, Ontario, M5T 1R8, CANADA
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst St, Toronto, Ontario, M6A 2E1, CANADA
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Lund MJ, Alnæs D, de Lange AMG, Andreassen OA, Westlye LT, Kaufmann T. Brain age prediction using fMRI network coupling in youths and associations with psychiatric symptoms. Neuroimage Clin 2021; 33:102921. [PMID: 34959052 PMCID: PMC8718718 DOI: 10.1016/j.nicl.2021.102921] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 12/17/2021] [Accepted: 12/18/2021] [Indexed: 10/27/2022]
Abstract
OBJECTIVE Magnetic resonance imaging (MRI) has shown that estimated brain age is deviant from chronological age in various common brain disorders. Brain age estimation could be useful for investigating patterns of brain maturation and integrity, aiding to elucidate brain mechanisms underlying these heterogeneous conditions. Here, we examined functional brain age in two large samples of children and adolescents and its relation to mental health. METHODS We used resting-state fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC; n = 1126, age range 8-22 years) to estimate functional connectivity between brain networks, and utilized these as features for brain age prediction. We applied the prediction model to 1387 individuals (age range 8-22 years) in the Healthy Brain Network sample (HBN). In addition, we estimated brain age in PNC using a cross-validation framework. Next, we tested for associations between brain age gap and various aspects of psychopathology and cognitive performance. RESULTS Our model was able to predict age in the independent test samples, with a model performance of r = 0.54 for the HBN test set, supporting consistency in functional connectivity patterns between samples and scanners. Linear models revealed a significant association between brain age gap and psychopathology in PNC, where individuals with a lower estimated brain age, had a higher overall symptom burden. These associations were not replicated in HBN. DISCUSSION Our findings support the use of brain age prediction from fMRI-based connectivity. While requiring further extensions and validations, the approach may be instrumental for detecting brain phenotypes related to intrinsic connectivity and could assist in characterizing risk in non-typically developing populations.
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Affiliation(s)
- Martina J Lund
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway.
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; Bjørknes College, Oslo, Norway
| | - Ann-Marie G de Lange
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; LREN, Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, and Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychiatry and Psychotherapy, University of Tübingen, Germany.
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Sharma AA, Szaflarski JP. Neuroinflammation as a pathophysiological factor in the development and maintenance of functional seizures: A hypothesis. Epilepsy Behav Rep 2021; 16:100496. [PMID: 34917920 PMCID: PMC8645839 DOI: 10.1016/j.ebr.2021.100496] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 10/15/2021] [Accepted: 10/21/2021] [Indexed: 12/29/2022] Open
Abstract
Early-life stress may be a priming neuroinflammatory factor for later development of FS. Secondary trauma has emerged as an important predisposing factor for FS initiation. We propose an explanatory, two-hit hypothesis for FS development. The proposed hypothesis is based on findings from neuroimaging and biomarker studies.
The neurobiological underpinnings of functional seizure (FS) development and maintenance represent an active research area. Recent work has focused on hardware (brain structure) and software (brain function and connectivity). However, understanding whether FS are an adaptive consequence of changes in brain structure, function, and/or connectivity is important for identifying a causative mechanism and for FS treatment and prevention. Further, investigation must also uncover what causes these structural and functional phenomena. Pioneering work in the field of psychoneuroimmunology has established a strong, consistent link between psychopathology, immune dysfunction, and brain structure/function. Based on this and recent FS biomarker findings, we propose a new etiologic model of FS pathophysiology. We hypothesize that early-life stressors cause neuroinflammatory and neuroendocrine changes that prime the brain for later FS development following secondary trauma (e.g., traumatic brain injury or psychological trauma). This framework coalesces existing knowledge regarding brain aberrations underlying FS and established neurobiological theories on the pathophysiology of underlying psychiatric disorders. We also propose brain temperature mapping as a way of indirectly visualizing neuroinflammation in patients with FS, particularly in emotion regulation, fear processing, and sensory-motor integration circuits. We offer a foundation on which future research can be built, with clear recommendations for future studies.
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Affiliation(s)
- Ayushe A Sharma
- Departments of Neurology, University of Alabama at Birmingham (UAB) Heersink School of Medicine, Birmingham, AL, USA.,UAB Epilepsy Center (UABEC), Birmingham, AL, USA
| | - Jerzy P Szaflarski
- Departments of Neurology, University of Alabama at Birmingham (UAB) Heersink School of Medicine, Birmingham, AL, USA.,Departments of Neurosurgery, and University of Alabama at Birmingham (UAB) Heersink School of Medicine, Birmingham, AL, USA.,Departments of Neurobiology, University of Alabama at Birmingham (UAB) Heersink School of Medicine, Birmingham, AL, USA.,UAB Epilepsy Center (UABEC), Birmingham, AL, USA
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Midazolam Exposure Impedes Oligodendrocyte Development via the Translocator Protein and Impairs Myelination in Larval Zebrafish. Mol Neurobiol 2021; 59:93-106. [PMID: 34626343 DOI: 10.1007/s12035-021-02559-8] [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: 07/16/2021] [Accepted: 09/09/2021] [Indexed: 10/20/2022]
Abstract
Anesthetics are commonly used in various medical procedures. Accumulating evidence suggests that early-life anesthetics exposure in infants and children affects brain development, causing psychiatric and neurological disorders. However, the underlying mechanisms are poorly understood. Using zebrafish larvae as a model, we found that the proliferation and migration of oligodendrocyte progenitor cells (OPCs) were severely impaired by the exposure of midazolam (MDZ), an anesthetic widely used in pediatric surgery and intensive care medicine, leading to a reduction of oligodendroglial lineage cell in the dorsal spinal cord. This defect was mimicked by the bath application of translocator protein (TSPO) agonists and partially rescued by genetic downregulation of TSPO. Cell transplantation experiments showed that requirement of TSPO for MDZ-induced oligodendroglial lineage cell defects is cell-autonomous. Furthermore, transmission electron microscopy and in vivo electrophysiological recording experiments demonstrated that MDZ exposure caused axon hypomyelination and action potential propagation retardation, resulting in delayed behavior initiation. Thus, our findings reveal that MDZ affects oligodendroglial lineage cell development and myelination in young animals, raising the care about its clinic use in infants and children.
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Romer AL, Pizzagalli DA. Is executive dysfunction a risk marker or consequence of psychopathology? A test of executive function as a prospective predictor and outcome of general psychopathology in the adolescent brain cognitive development study®. Dev Cogn Neurosci 2021; 51:100994. [PMID: 34332330 PMCID: PMC8340137 DOI: 10.1016/j.dcn.2021.100994] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 07/08/2021] [Accepted: 07/19/2021] [Indexed: 12/30/2022] Open
Abstract
A general psychopathology ('p') factor captures shared variation across mental disorders. One hypothesis is that poor executive function (EF) contributes to p. Although EF is related to p concurrently, it is unclear whether EF predicts or is a consequence of p. For the first time, we examined prospective relations between EF and p in 9845 preadolescents (aged 9-12) from the Adolescent Brain Cognitive Development Study® longitudinally over two years. We identified higher-order factor models of psychopathology at baseline and one- and two-year follow-up waves. Consistent with previous research, a cross-sectional inverse relationship between EF and p emerged. Using residualized-change models, baseline EF prospectively predicted p factor scores two years later, controlling for prior p, sex, age, race/ethnicity, parental education, and family income. Baseline p factor scores also prospectively predicted change in EF two years later. Tests of specificity revealed that bi-directional prospective relations between EF and p were largely generalizable across externalizing, internalizing, neurodevelopmental, somatization, and detachment symptoms. EF consistently predicted change in externalizing and neurodevelopmental symptoms. These novel results suggest that executive dysfunction is both a risk marker and consequence of general psychopathology. EF may be a promising transdiagnostic intervention target to prevent the onset and maintenance of psychopathology.
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Affiliation(s)
- Adrienne L Romer
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA; Harvard Medical School, Belmont, MA, USA.
| | - Diego A Pizzagalli
- Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, MA, USA; Harvard Medical School, Belmont, MA, USA; McLean Imaging Center, McLean Hospital, Belmont, MA, USA
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Sydnor VJ, Larsen B, Bassett DS, Alexander-Bloch A, Fair DA, Liston C, Mackey AP, Milham MP, Pines A, Roalf DR, Seidlitz J, Xu T, Raznahan A, Satterthwaite TD. Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology. Neuron 2021; 109:2820-2846. [PMID: 34270921 PMCID: PMC8448958 DOI: 10.1016/j.neuron.2021.06.016] [Citation(s) in RCA: 226] [Impact Index Per Article: 75.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/24/2021] [Accepted: 06/11/2021] [Indexed: 12/11/2022]
Abstract
The human brain undergoes a prolonged period of cortical development that spans multiple decades. During childhood and adolescence, cortical development progresses from lower-order, primary and unimodal cortices with sensory and motor functions to higher-order, transmodal association cortices subserving executive, socioemotional, and mentalizing functions. The spatiotemporal patterning of cortical maturation thus proceeds in a hierarchical manner, conforming to an evolutionarily rooted, sensorimotor-to-association axis of cortical organization. This developmental program has been characterized by data derived from multimodal human neuroimaging and is linked to the hierarchical unfolding of plasticity-related neurobiological events. Critically, this developmental program serves to enhance feature variation between lower-order and higher-order regions, thus endowing the brain's association cortices with unique functional properties. However, accumulating evidence suggests that protracted plasticity within late-maturing association cortices, which represents a defining feature of the human developmental program, also confers risk for diverse developmental psychopathologies.
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Affiliation(s)
- Valerie J Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Electrical & Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, Institute of Child Development, College of Education and Human Development, Department of Pediatrics, Medical School, University of Minnesota, Minneapolis, MN 55414, USA
| | - Conor Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Allyson P Mackey
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY 10962, USA
| | - Adam Pines
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jakob Seidlitz
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, NIMH Intramural Research Program, NIH, Bethesda, MD 20892, USA
| | - Theodore D Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Mu J, Ma L, Ma S, Ding D, Li P, Ma X, Zhang M, Liu J. Neurological effects of hemodialysis on white matter microstructure in end-stage renal disease. NEUROIMAGE-CLINICAL 2021; 31:102743. [PMID: 34229157 PMCID: PMC8261074 DOI: 10.1016/j.nicl.2021.102743] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 06/21/2021] [Accepted: 06/22/2021] [Indexed: 11/29/2022]
Abstract
OBJECTIVES To detect the effects of hemodialysis (HD) on the central nervous system (CNS), the present study forces the memory storage capacity and the difference in white matter (WM) microstructure characteristics among end-stage renal disease (ESRD) participants before HD initiation (ESRD-BHD), ESRD participants with maintenance HD (ESRD-MHD), and healthy participants (HCs). METHODS Between 2016 and 2018, 56 ESRD-BHD, 39 ESRD-MHD, and 56 HCs were recruited for this study. The fractional anisotropy (FA) of tractography streamlines within the working memory network was investigated using a novel along-tracts analysis method. The relationship between WM microstructure and working memory scores, measured from an n-back task, were detected by multiple correlation analysis. RESULTS As compared with HCs, a significantly lower FA was found along part of the WM in the working memory network in ESRD-BHD. In the group-difference location of ESRD-BHD and HCs, the FA of ESRD-MHD was reversed to normal levels in HCs. However, the FA in a new location was differentially reduced across groups: highest in HCs, intermediate in ESRD-BHD, and lowest in ESRD-MHD. Correlation analysis showed that a longer reaction time correlated to a lower FA, according to the following pattern: ESRD-BHD > ESRD-MHD > HCs. CONCLUSION Despite the persisting abnormal brain structure, our findings suggest HD has a neuroprotective effect in ESRD patients.
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Affiliation(s)
- Junya Mu
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an 710126, People's Republic of China; Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, Xi'an 710126, People's Republic of China
| | - Liang Ma
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an 710126, People's Republic of China; Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, Xi'an 710126, People's Republic of China
| | - Shaohui Ma
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, People's Republic of China
| | - Dun Ding
- Department of Medical Imaging, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, People's Republic of China
| | - Peng Li
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, People's Republic of China; Department of Medical Imaging, Shaanxi Nuclear Geology 215 Hospital, Xianyang, People's Republic of China
| | - Xueying Ma
- The Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010000, People's Republic of China
| | - Ming Zhang
- Department of Medical Imaging, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, People's Republic of China.
| | - Jixin Liu
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an 710126, People's Republic of China; Engineering Research Center of Molecular & Neuroimaging, Ministry of Education, Xi'an 710126, People's Republic of China.
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Taquet M, Smith SM, Prohl AK, Peters JM, Warfield SK, Scherrer B, Harrison PJ. A structural brain network of genetic vulnerability to psychiatric illness. Mol Psychiatry 2021; 26:2089-2100. [PMID: 32372008 PMCID: PMC7644622 DOI: 10.1038/s41380-020-0723-7] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Revised: 03/17/2020] [Accepted: 03/30/2020] [Indexed: 12/31/2022]
Abstract
Psychiatry is undergoing a paradigm shift from the acceptance of distinct diagnoses to a representation of psychiatric illness that crosses diagnostic boundaries. How this transition is supported by a shared neurobiology remains largely unknown. In this study, we first identify single nucleotide polymorphisms (SNPs) associated with psychiatric disorders based on 136 genome-wide association studies. We then conduct a joint analysis of these SNPs and brain structural connectomes in 678 healthy children in the PING study. We discovered a strong, robust, and transdiagnostic mode of genome-connectome covariation which is positively and specifically correlated with genetic risk for psychiatric illness at the level of individual SNPs. Similarly, this mode is also significantly positively correlated with polygenic risk scores for schizophrenia, alcohol use disorder, major depressive disorder, a combined bipolar disorder-schizophrenia phenotype, and a broader cross-disorder phenotype, and significantly negatively correlated with a polygenic risk score for educational attainment. The resulting "vulnerability network" is shown to mediate the influence of genetic risks onto behaviors related to psychiatric vulnerability (e.g., marijuana, alcohol, and caffeine misuse, perceived stress, and impulsive behavior). Its anatomy overlaps with the default-mode network, with a network of cognitive control, and with the occipital cortex. These findings suggest that the brain vulnerability network represents an endophenotype funneling genetic risks for various psychiatric illnesses through a common neurobiological root. It may form part of the neural underpinning of the well-recognized but poorly explained overlap and comorbidity between psychiatric disorders.
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Affiliation(s)
- Maxime Taquet
- Department of Psychiatry, University of Oxford, Oxford, UK.
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, UK
| | - Anna K Prohl
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jurriaan M Peters
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Benoit Scherrer
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul J Harrison
- Department of Psychiatry, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
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Roelfs D, Alnæs D, Frei O, van der Meer D, Smeland OB, Andreassen OA, Westlye LT, Kaufmann T. Phenotypically independent profiles relevant to mental health are genetically correlated. Transl Psychiatry 2021; 11:202. [PMID: 33795632 PMCID: PMC8016894 DOI: 10.1038/s41398-021-01313-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/15/2021] [Accepted: 03/03/2021] [Indexed: 12/13/2022] Open
Abstract
Genome-wide association studies (GWAS) and family-based studies have revealed partly overlapping genetic architectures between various psychiatric disorders. Given clinical overlap between disorders, our knowledge of the genetic architectures underlying specific symptom profiles and risk factors is limited. Here, we aimed to derive distinct profiles relevant to mental health in healthy individuals and to study how these genetically relate to each other and to common psychiatric disorders. Using independent component analysis, we decomposed self-report mental health questionnaires from 136,678 healthy individuals of the UK Biobank, excluding data from individuals with a diagnosed neurological or psychiatric disorder, into 13 distinct profiles relevant to mental health, capturing different symptoms as well as social and risk factors underlying reduced mental health. Utilizing genotypes from 117,611 of those individuals with White British ancestry, we performed GWAS for each mental health profile and assessed genetic correlations between these profiles, and between the profiles and common psychiatric disorders and cognitive traits. We found that mental health profiles were genetically correlated with a wide range of psychiatric disorders and cognitive traits, with strongest effects typically observed between a given mental health profile and a disorder for which the profile is common (e.g. depression symptoms and major depressive disorder, or psychosis and schizophrenia). Strikingly, although the profiles were phenotypically uncorrelated, many of them were genetically correlated with each other. This study provides evidence that statistically independent mental health profiles partly share genetic underpinnings and show genetic overlap with psychiatric disorders, suggesting that shared genetics across psychiatric disorders cannot be exclusively attributed to the known overlapping symptomatology between the disorders.
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Affiliation(s)
- Daniel Roelfs
- NORMENT, KG Jebsen Centre for Neurodevelopmental Disorders, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Dag Alnæs
- NORMENT, KG Jebsen Centre for Neurodevelopmental Disorders, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Bjørknes College, Oslo, Norway
| | - Oleksandr Frei
- NORMENT, KG Jebsen Centre for Neurodevelopmental Disorders, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Dennis van der Meer
- NORMENT, KG Jebsen Centre for Neurodevelopmental Disorders, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Olav B Smeland
- NORMENT, KG Jebsen Centre for Neurodevelopmental Disorders, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre for Neurodevelopmental Disorders, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- NORMENT, KG Jebsen Centre for Neurodevelopmental Disorders, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Tobias Kaufmann
- NORMENT, KG Jebsen Centre for Neurodevelopmental Disorders, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany.
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Gong W, Beckmann CF, Smith SM. Phenotype discovery from population brain imaging. Med Image Anal 2021; 71:102050. [PMID: 33905882 PMCID: PMC8850869 DOI: 10.1016/j.media.2021.102050] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 03/15/2021] [Accepted: 03/23/2021] [Indexed: 12/20/2022]
Abstract
A multimodal independent component analysis approach is presented for performing data fusion in UK biobank scale dataset. This approach can estimate modes of population variability that enhance the ability to predict thousands of non-imaging phenotypes. This approach improves predictive power compared with widely-used analysis strategies, single-modality decompositions and existing IDPs. In UKB data, many interpretable associations with non-imaging phenotypes were identified.
Neuroimaging allows for the non-invasive study of the brain in rich detail. Data-driven discovery of patterns of population variability in the brain has the potential to be extremely valuable for early disease diagnosis and understanding the brain. The resulting patterns can be used as imaging-derived phenotypes (IDPs), and may complement existing expert-curated IDPs. However, population datasets, comprising many different structural and functional imaging modalities from thousands of subjects, provide a computational challenge not previously addressed. Here, for the first time, a multimodal independent component analysis approach is presented that is scalable for data fusion of voxel-level neuroimaging data in the full UK Biobank (UKB) dataset, that will soon reach 100,000 imaged subjects. This new computational approach can estimate modes of population variability that enhance the ability to predict thousands of phenotypic and behavioural variables using data from UKB and the Human Connectome Project. A high-dimensional decomposition achieved improved predictive power compared with widely-used analysis strategies, single-modality decompositions and existing IDPs. In UKB data (14,503 subjects with 47 different data modalities), many interpretable associations with non-imaging phenotypes were identified, including multimodal spatial maps related to fluid intelligence, handedness and disease, in some cases where IDP-based approaches failed.
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Affiliation(s)
- Weikang Gong
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
| | - Christian F Beckmann
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK; Radboud University Medical Centre, Department of Cognitive Neuroscience, Nijmegen, the Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Stephen M Smith
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
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Vanes LD, Dolan RJ. Transdiagnostic neuroimaging markers of psychiatric risk: A narrative review. NEUROIMAGE-CLINICAL 2021; 30:102634. [PMID: 33780864 PMCID: PMC8022867 DOI: 10.1016/j.nicl.2021.102634] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/03/2021] [Accepted: 03/12/2021] [Indexed: 02/07/2023]
Abstract
We review the literature on neural correlates of a general psychopathology factor General psychopathology relates to structural and functional neurodevelopment Disrupted network connectivity maturation may underlie psychiatric vulnerability
Several decades of neuroimaging research in psychiatry have shed light on structural and functional neural abnormalities associated with individual psychiatric disorders. However, there is increasing evidence for substantial overlap in the patterns of neural dysfunction seen across disorders, suggesting that risk for psychiatric illness may be shared across diagnostic boundaries. Gaining insights on the existence of shared neural mechanisms which may transdiagnostically underlie psychopathology is important for psychiatric research in order to tease apart the unique and common aspects of different disorders, but also clinically, so as to help identify individuals early on who may be biologically vulnerable to psychiatric disorder in general. In this narrative review, we first evaluate recent studies investigating the functional and structural neural correlates of a general psychopathology factor, which is thought to reflect the shared variance across common mental health symptoms and therefore index psychiatric vulnerability. We then link insights from this research to existing meta-analytic evidence for shared patterns of neural dysfunction across categorical psychiatric disorders. We conclude by providing an integrative account of vulnerability to mental illness, whereby delayed or disrupted maturation of large-scale networks (particularly default-mode, executive, and sensorimotor networks), and more generally between-network connectivity, results in a compromised ability to integrate and switch between internally and externally focused tasks.
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Affiliation(s)
- Lucy D Vanes
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, King's College London, United Kingdom.
| | - Raymond J Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, United Kingdom
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Mao N, Che K, Xie H, Li Y, Wang Q, Liu M, Wang Z, Lin F, Ma H, Zhuo Z. Abnormal information flow in postpartum depression: A resting-state functional magnetic resonance imaging study. J Affect Disord 2020; 277:596-602. [PMID: 32898821 DOI: 10.1016/j.jad.2020.08.060] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 07/24/2020] [Accepted: 08/25/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND Postpartum depression (PPD) is a common mental disorder among women. However, the brain information flow alteration in patients with PPD remains unclear. This study investigated the brain information flow characteristics of patients with PPD and their value for clinical evaluation by using support vector regression (SVR). METHODS Structural and resting-state functional magnetic resonance imaging data were acquired from 21 patients with PPD and 23 age-, educational level-, body mass index-, and menstruation-matched healthy controls. The preferred information flow direction between local brain regions and the preferred information flow direction index within local brain regions based on non-parametric multiplicative regression granger causality analysis were calculated to determine the global and local brain functional characteristics of the patients with PPD. Pearson's correlation analyses were performed to evaluate the relationship of the information flow characteristics with clinical scales. A predictive model for the mental state of the patients with PPD was established using SVR based on information flow characteristics. RESULTS The information flow patterns in the amygdala, cingulum gyrus, insula, hippocampus, frontal lobe, parietal lobe, and occipital lobe changed significantly in the patients with PPD. The preferred information flow direction between the amygdala and the temporal and frontal lobes significantly correlated with clinical scales. Prediction analysis shows that the information flow patterns can be used to assess depression in patients with PPD. LIMITATION This exploratory study has a small sample size with no longitudinal research. CONCLUSION The change in information flow pattern in the amygdala may play an important role in the neuropathological mechanism of PPD and may provide promising markers for clinical evaluation.
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Affiliation(s)
- Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, P. R. China
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, P. R. China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, P. R. China
| | - Yuna Li
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, P. R. China
| | - Qinglin Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, P. R. China
| | - Meijie Liu
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, P. R. China
| | - Zhongyi Wang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, P. R. China
| | - Fan Lin
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, P. R. China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, P. R. China.
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Beijing, 100044, P. R. China.
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Beck D, de Lange AMG, Maximov II, Richard G, Andreassen OA, Nordvik JE, Westlye LT. White matter microstructure across the adult lifespan: A mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction. Neuroimage 2020; 224:117441. [PMID: 33039618 DOI: 10.1016/j.neuroimage.2020.117441] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 09/11/2020] [Accepted: 10/05/2020] [Indexed: 12/22/2022] Open
Abstract
The macro- and microstructural architecture of human brain white matter undergoes substantial alterations throughout development and ageing. Most of our understanding of the spatial and temporal characteristics of these lifespan adaptations come from magnetic resonance imaging (MRI), including diffusion MRI (dMRI), which enables visualisation and quantification of brain white matter with unprecedented sensitivity and detail. However, with some notable exceptions, previous studies have relied on cross-sectional designs, limited age ranges, and diffusion tensor imaging (DTI) based on conventional single-shell dMRI. In this mixed cross-sectional and longitudinal study (mean interval: 15.2 months) including 702 multi-shell dMRI datasets, we combined complementary dMRI models to investigate age trajectories in healthy individuals aged 18 to 94 years (57.12% women). Using linear mixed effect models and machine learning based brain age prediction, we assessed the age-dependence of diffusion metrics, and compared the age prediction accuracy of six different diffusion models, including diffusion tensor (DTI) and kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), restriction spectrum imaging (RSI), spherical mean technique multi-compartment (SMT-mc), and white matter tract integrity (WMTI). The results showed that the age slopes for conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]) were largely consistent with previous research, and that the highest performing advanced dMRI models showed comparable age prediction accuracy to conventional DTI. Linear mixed effects models and Wilk's theorem analysis showed that the 'FA fine' metric of the RSI model and 'orientation dispersion' (OD) metric of the NODDI model showed the highest sensitivity to age. The results indicate that advanced diffusion models (DKI, NODDI, RSI, SMT mc, WMTI) provide sensitive measures of age-related microstructural changes of white matter in the brain that complement and extend the contribution of conventional DTI.
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Affiliation(s)
- Dani Beck
- Department of Psychology, University of Oslo, PO Box 1094 Blindern, 0317 Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Sunnaas Rehabilitation Hospital HT, Nesodden, Oslo, Norway.
| | - Ann-Marie G de Lange
- Department of Psychology, University of Oslo, PO Box 1094 Blindern, 0317 Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom
| | - Ivan I Maximov
- Department of Psychology, University of Oslo, PO Box 1094 Blindern, 0317 Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Geneviève Richard
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | | | - Lars T Westlye
- Department of Psychology, University of Oslo, PO Box 1094 Blindern, 0317 Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway.
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Cropley VL, Tian Y, Fernando K, Mansour L S, Pantelis C, Cocchi L, Zalesky A. Brain-Predicted Age Associates With Psychopathology Dimensions in Youths. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 6:410-419. [PMID: 32981878 DOI: 10.1016/j.bpsc.2020.07.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 07/05/2020] [Accepted: 07/20/2020] [Indexed: 11/29/2022]
Abstract
BACKGROUND This study aimed to investigate whether dimensional constructs of psychopathology relate to variation in patterns of brain development and to determine whether these constructs share common neurodevelopmental profiles. METHODS Psychiatric symptom ratings from 9312 youths (8-21 years old) from the Philadelphia Neurodevelopmental Cohort were parsed into 7 independent dimensions of clinical psychopathology representing conduct, anxiety, obsessive-compulsive, attention, depression, bipolar, and psychosis symptoms. Using a subset of this cohort with structural magnetic resonance imaging (n = 1313), a normative model of brain morphology was established and the model was then applied to predict the age of youths with clinical symptoms. We investigated whether the deviation of brain-predicted age from true chronological age, called the brain age gap, explained individual variation in each psychopathology dimension. RESULTS Individual variation in the brain age gap significantly associated with clinical dimensions representing psychosis (t = 3.16, p = .0016), obsessive-compulsive symptoms (t = 2.5, p = .01), and general psychopathology (t = 4.08, p < .0001). Greater symptom severity along these dimensions was associated with brain morphology that appeared older than expected for typically developing youths of the same age. Psychopathology dimensions clustered into 2 modules based on shared brain loci where putative accelerated neurodevelopment was most prominent. Patterns of morphological development were accelerated in frontal cortices for depression, psychosis, and conduct symptoms (module 1), whereas acceleration was most evident in subcortex and insula for the remaining dimensions (module 2). CONCLUSIONS Our findings suggest that increased brain age, particularly in frontal cortex and subcortical nuclei, underpins clinical psychosis and obsessive-compulsive symptoms in youths. Psychopathology dimensions share common neural substrates, despite representing clinically independent symptom profiles.
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Affiliation(s)
- Vanessa L Cropley
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia; Centre for Mental Health, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Melbourne, Victoria, Australia.
| | - Ye Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
| | - Kavisha Fernando
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia
| | - Sina Mansour L
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia; Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia; Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - Luca Cocchi
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia; Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
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Tønnesen S, Kaufmann T, de Lange AMG, Richard G, Doan NT, Alnæs D, van der Meer D, Rokicki J, Moberget T, Maximov II, Agartz I, Aminoff SR, Beck D, Barch DM, Beresniewicz J, Cervenka S, Fatouros-Bergman H, Craven AR, Flyckt L, Gurholt TP, Haukvik UK, Hugdahl K, Johnsen E, Jönsson EG, Kolskår KK, Kroken RA, Lagerberg TV, Løberg EM, Nordvik JE, Sanders AM, Ulrichsen K, Andreassen OA, Westlye LT. Brain Age Prediction Reveals Aberrant Brain White Matter in Schizophrenia and Bipolar Disorder: A Multisample Diffusion Tensor Imaging Study. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:1095-1103. [PMID: 32859549 DOI: 10.1016/j.bpsc.2020.06.014] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 06/15/2020] [Accepted: 06/26/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Schizophrenia (SZ) and bipolar disorder (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, patients with BD, and healthy control (HC) subjects across 10 cohorts. METHODS We trained 6 cross-validated models using different combinations of DTI data from 927 HC subjects (18-94 years of age) and applied the models to the test sets including 648 patients with SZ (18-66 years of age), 185 patients with BD (18-64 years of age), and 990 HC subjects (17-68 years of age), estimating the brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. RESULTS Tenfold cross-validation revealed high accuracy for all models. Compared with HC subjects, the model including all feature sets significantly overestimated the age of patients with SZ (Cohen's d = -0.29) and patients with BD (Cohen's d = 0.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy-based models showed larger group differences than the models based on other DTI-derived metrics. CONCLUSIONS Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.
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Affiliation(s)
- Siren Tønnesen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ann-Marie G de Lange
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom
| | - Geneviève Richard
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Nhat Trung Doan
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dag Alnæs
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Bjørknes University College, Oslo, Norway
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Jaroslav Rokicki
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Torgeir Moberget
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ivan I Maximov
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm Region, Stockholm, Sweden; Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Sofie R Aminoff
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Early Intervention in Psychosis Advisory Unit for South East Norway, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dani Beck
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Deanna M Barch
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, Missouri; Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri; Department of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri
| | - Justyna Beresniewicz
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; NORMENT, Haukeland University Hospital, Bergen, Norway
| | - Simon Cervenka
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm Region, Stockholm, Sweden; Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Helena Fatouros-Bergman
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm Region, Stockholm, Sweden; Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Alexander R Craven
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; NORMENT, Haukeland University Hospital, Bergen, Norway; Department of Clinical Engineering, Haukeland University Hospital, Bergen, Norway
| | - Lena Flyckt
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm Region, Stockholm, Sweden; Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | - Tiril P Gurholt
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Unn K Haukvik
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Adult Psychiatry Unit, Department of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Kenneth Hugdahl
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; NORMENT, Haukeland University Hospital, Bergen, Norway; Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Erik Johnsen
- Department of Clinical Medicine (K1), University of Bergen, Bergen, Norway
| | - Erik G Jönsson
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm Region, Stockholm, Sweden; Stockholm Health Care Services, Stockholm Region, Stockholm, Sweden
| | | | - Knut K Kolskår
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Sunnaas Rehabilitation Hospital HF, Nesodden, Norway
| | - Rune Andreas Kroken
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; NORMENT, Haukeland University Hospital, Bergen, Norway
| | - Trine V Lagerberg
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Else-Marie Løberg
- Department of Clinical Psychology, University of Bergen, Bergen, Norway; NORMENT, Haukeland University Hospital, Bergen, Norway; Department of Addiction Medicine, Haukeland University Hospital, Bergen, Norway
| | | | - Anne-Marthe Sanders
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Sunnaas Rehabilitation Hospital HF, Nesodden, Norway
| | - Kristine Ulrichsen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Sunnaas Rehabilitation Hospital HF, Nesodden, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
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Patterns of sociocognitive stratification and perinatal risk in the child brain. Proc Natl Acad Sci U S A 2020; 117:12419-12427. [PMID: 32409600 DOI: 10.1073/pnas.2001517117] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The expanding behavioral repertoire of the developing brain during childhood and adolescence is shaped by complex brain-environment interactions and flavored by unique life experiences. The transition into young adulthood offers opportunities for adaptation and growth but also increased susceptibility to environmental perturbations, such as the characteristics of social relationships, family environment, quality of schools and activities, financial security, urbanization and pollution, drugs, cultural practices, and values, that all act in concert with our genetic architecture and biology. Our multivariate brain-behavior mapping in 7,577 children aged 9 to 11 y across 585 brain imaging phenotypes and 617 cognitive, behavioral, psychosocial, and socioeconomic measures revealed three population modes of brain covariation, which were robust as assessed by cross-validation and permutation testing, taking into account siblings and twins, identified using genetic data. The first mode revealed traces of perinatal complications, including preterm and twin birth, eclampsia and toxemia, shorter period of breastfeeding, and lower cognitive scores, with higher cortical thickness and lower cortical areas and volumes. The second mode reflected a pattern of sociocognitive stratification, linking lower cognitive ability and socioeconomic status to lower cortical thickness, area, and volumes. The third mode captured a pattern related to urbanicity, with particulate matter pollution (PM25) inversely related to home value, walkability, and population density, associated with diffusion properties of white matter tracts. These results underscore the importance of a multidimensional and interdisciplinary understanding, integrating social, psychological, and biological sciences, to map the constituents of healthy development and to identify factors that may precede maladjustment and mental illness.
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Nieman DH, Chavez-Baldini U, Vulink NC, Smit DJA, van Wingen G, de Koning P, Sutterland AL, Mocking RJT, Bockting C, Verweij KJH, Lok A, Denys D. Protocol Across study: longitudinal transdiagnostic cognitive functioning, psychiatric symptoms, and biological parameters in patients with a psychiatric disorder. BMC Psychiatry 2020; 20:212. [PMID: 32393362 PMCID: PMC7216345 DOI: 10.1186/s12888-020-02624-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 04/26/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Patients with psychiatric disorders, such as major depressive disorder, schizophrenia or obsessive-compulsive disorder, often suffer from cognitive dysfunction. The nature of these dysfunctions and their relation with clinical symptoms and biological parameters is not yet clear. Traditionally, cognitive dysfunction is studied in patients with specific psychiatric disorders, disregarding the fact that cognitive deficits are shared across disorders. The Across study aims to investigate cognitive functioning and its relation with psychiatric symptoms and biological parameters transdiagnostically and longitudinally. METHODS The study recruits patients diagnosed with a variety of psychiatric disorders and has a longitudinal cohort design with an assessment at baseline and at one-year follow-up. The primary outcome measure is cognitive functioning. The secondary outcome measures include clinical symptoms, electroencephalographic, genetic and blood markers (e.g., fatty acids), and hair cortisol concentration levels. DISCUSSION The Across study provides an opportunity for a transdiagnostic, bottom-up, data-driven approach of investigating cognition in relation to symptoms and biological parameters longitudinally in patients with psychiatric disorders. The study may help to find new clusters of symptoms, biological markers, and cognitive dysfunctions that have better prognostic value than the current diagnostic categories. Furthermore, increased insight into the relationship among cognitive deficits, biological parameters, and psychiatric symptoms can lead to new treatment possibilities. TRIAL REGISTRATION Netherlands Trial Register (NTR): NL8170.
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Affiliation(s)
- Dorien H. Nieman
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, Netherlands
| | - UnYoung Chavez-Baldini
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, Netherlands
| | - Nienke C. Vulink
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, Netherlands
| | - Dirk J. A. Smit
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, Netherlands
| | - Guido van Wingen
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, Netherlands
| | - Pelle de Koning
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, Netherlands
| | - Arjen L. Sutterland
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, Netherlands
| | - Roel J. T. Mocking
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, Netherlands
| | - Claudi Bockting
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, Netherlands
| | - Karin J. H. Verweij
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, Netherlands
| | - Anja Lok
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, Netherlands
| | - Damiaan Denys
- grid.7177.60000000084992262Department of Psychiatry, Amsterdam University Medical Centers (location AMC), University of Amsterdam, Meibergdreef 5, 1105 AZ Amsterdam, Netherlands
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Maglanoc LA, Kaufmann T, van der Meer D, Marquand AF, Wolfers T, Jonassen R, Hilland E, Andreassen OA, Landrø NI, Westlye LT. Brain Connectome Mapping of Complex Human Traits and Their Polygenic Architecture Using Machine Learning. Biol Psychiatry 2020; 87:717-726. [PMID: 31858985 DOI: 10.1016/j.biopsych.2019.10.011] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/07/2019] [Accepted: 10/18/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Mental disorders and individual characteristics such as intelligence and personality are complex traits sharing a largely unknown neuronal basis. Their genetic architectures are highly polygenic and overlapping, which is supported by heterogeneous phenotypic expression and substantial clinical overlap. Brain network analysis provides a noninvasive means of dissecting biological heterogeneity, yet its sensitivity, specificity, and validity in assessing individual characteristics relevant for brain function and mental health and their genetic underpinnings in clinical applications remain a challenge. METHODS In a machine learning approach, we predicted individual scores for educational attainment, fluid intelligence and dimensional measures of depression, anxiety, and neuroticism using functional magnetic resonance imaging-based static and dynamic temporal synchronization between large-scale brain network nodes in 10,343 healthy individuals from the UK Biobank. In addition to using age and sex to serve as our reference point, we also predicted individual polygenic scores for related phenotypes and 13 different neuroticism traits and schizophrenia. RESULTS Beyond high accuracy for age and sex, supporting the biological sensitivity of the connectome-based features, permutation tests revealed above chance-level prediction accuracy for trait-level educational attainment and fluid intelligence. Educational attainment and fluid intelligence were mainly negatively associated with static brain connectivity in frontal and default mode networks, whereas age showed positive correlations with a more widespread pattern. In contrast, prediction accuracy was at chance level for depression, anxiety, neuroticism, and polygenic scores across traits. CONCLUSIONS These novel findings provide a benchmark for future studies linking the genetic architecture of individual and mental health traits with functional magnetic resonance imaging-based brain connectomics.
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Affiliation(s)
- Luigi A Maglanoc
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Department of Neuroimaging, Institute of Psychiatry, King's College London, London, United Kingdom
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Rune Jonassen
- Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway
| | - Eva Hilland
- Department of Psychology, University of Oslo, Oslo, Norway; Division of Psychiatry, Diakonhjemmet Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | | | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, & Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
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Abstract
An important advance in understanding and defining mental disorders has been the development of empirical approaches to mapping dimensions of dysfunction and their interrelatedness. Such empirical approaches have consistently observed intercorrelations among the many forms of psychopathology, leading to the identification of a general factor of psychopathology (the p factor). In this article, we review empirical support for p, including evidence for the stability and criterion validity of p. Further, we discuss the strong relationship between p and both the general factor of personality and the general factor of personality disorder, substantive interpretations of p, and the potential clinical utility of p. We posit that proposed substantive interpretations of p do not explain the full range of symptomatology typically included in p. The most plausible explanation is that p represents an index of impairment that has the potential to inform the duration and intensity of a client's mental health treatment.
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Affiliation(s)
- Gregory T Smith
- Department of Psychology, University of Kentucky, Lexington, Kentucky 40506, USA; , , , ,
| | - Emily A Atkinson
- Department of Psychology, University of Kentucky, Lexington, Kentucky 40506, USA; , , , ,
| | - Heather A Davis
- Department of Psychology, University of Kentucky, Lexington, Kentucky 40506, USA; , , , ,
| | - Elizabeth N Riley
- Department of Psychology, University of Kentucky, Lexington, Kentucky 40506, USA; , , , ,
| | - Joshua R Oltmanns
- Department of Psychology, University of Kentucky, Lexington, Kentucky 40506, USA; , , , ,
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Maglanoc LA, Kaufmann T, Jonassen R, Hilland E, Beck D, Landrø NI, Westlye LT. Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis. Hum Brain Mapp 2020; 41:241-255. [PMID: 31571370 PMCID: PMC7267936 DOI: 10.1002/hbm.24802] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Revised: 09/08/2019] [Accepted: 09/09/2019] [Indexed: 01/03/2023] Open
Abstract
Previous structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression. We used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting-state functional magnetic resonance imaging default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case-control status, and symptom loads for depression and anxiety with the resulting brain components. Univariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case-control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy. Multimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.
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Affiliation(s)
- Luigi A. Maglanoc
- Clinical Neuroscience Research Group, Department of PsychologyUniversity of OsloOsloNorway
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Tobias Kaufmann
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Rune Jonassen
- Faculty of Health SciencesOslo Metropolitan UniversityOsloNorway
| | - Eva Hilland
- Clinical Neuroscience Research Group, Department of PsychologyUniversity of OsloOsloNorway
- Division of PsychiatryDiakonhjemmet HospitalOsloNorway
| | - Dani Beck
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Nils Inge Landrø
- Clinical Neuroscience Research Group, Department of PsychologyUniversity of OsloOsloNorway
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
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Kebets V, Holmes AJ, Orban C, Tang S, Li J, Sun N, Kong R, Poldrack RA, Yeo BTT. Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology. Biol Psychiatry 2019; 86:779-791. [PMID: 31515054 DOI: 10.1016/j.biopsych.2019.06.013] [Citation(s) in RCA: 131] [Impact Index Per Article: 26.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2018] [Revised: 05/15/2019] [Accepted: 06/05/2019] [Indexed: 12/26/2022]
Abstract
BACKGROUND There is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which might miss possible links between psychopathology, cognitive processes, and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thereby neglecting the potential influence of huge swaths of the brain. METHODS A multivariate data-driven approach (partial least squares) was used to identify latent components linking a large set of clinical, cognitive, and personality measures to whole-brain resting-state functional connectivity patterns across 224 participants. The participants were either healthy (n = 110) or diagnosed with bipolar disorder (n = 40), attention-deficit/hyperactivity disorder (n = 37), schizophrenia (n = 29), or schizoaffective disorder (n = 8). In contrast to traditional case-control analyses, the diagnostic categories were not used in the partial least squares analysis but were helpful for interpreting the components. RESULTS Our analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction, and impulsivity. Each component was associated with a unique whole-brain resting-state functional connectivity signature and was shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensory-motor network and its connectivity with subcortical structures and cortical executive networks. CONCLUSIONS We identified three distinct dimensions with dissociable (but overlapping) whole-brain resting-state functional connectivity signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span diagnostic categories. Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks.
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Affiliation(s)
- Valeria Kebets
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, Connecticut; Department of Psychiatry, Yale University, New Haven, Connecticut; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts; Department of Psychiatry, Massachusetts General Hospital, Boston, Massachusetts
| | - Csaba Orban
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; Neuropsychopharmacology Unit, Centre for Psychiatry, Imperial College London, London, United Kingdom
| | - Siyi Tang
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; Department of Electrical Engineering, Stanford University, Stanford, California
| | - Jingwei Li
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | - Nanbo Sun
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | - Ru Kong
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore
| | | | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Clinical Imaging Research Centre, N.1 Institute for Health and Memory Networks Program, National University of Singapore, Singapore; Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore; Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.
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