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Zhu Y, Maikusa N, Radua J, Sämann PG, Fusar-Poli P, Agartz I, Andreassen OA, Bachman P, Baeza I, Chen X, Choi S, Corcoran CM, Ebdrup BH, Fortea A, Garani RR, Glenthøj BY, Glenthøj LB, Haas SS, Hamilton HK, Hayes RA, He Y, Heekeren K, Kasai K, Katagiri N, Kim M, Kristensen TD, Kwon JS, Lawrie SM, Lebedeva I, Lee J, Loewy RL, Mathalon DH, McGuire P, Mizrahi R, Mizuno M, Møller P, Nemoto T, Nordholm D, Omelchenko MA, Raghava JM, Røssberg JI, Rössler W, Salisbury DF, Sasabayashi D, Smigielski L, Sugranyes G, Takahashi T, Tamnes CK, Tang J, Theodoridou A, Tomyshev AS, Uhlhaas PJ, Værnes TG, van Amelsvoort TAMJ, Waltz JA, Westlye LT, Zhou JH, Thompson PM, Hernaus D, Jalbrzikowski M, Koike S. Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk. Mol Psychiatry 2024; 29:1465-1477. [PMID: 38332374 PMCID: PMC11189817 DOI: 10.1038/s41380-024-02426-7] [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: 08/16/2023] [Revised: 12/22/2023] [Accepted: 01/08/2024] [Indexed: 02/10/2024]
Abstract
Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.
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Affiliation(s)
- Yinghan Zhu
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Instituto de Salud Carlos III, Universitat de Barcelona, Barcelona, Spain
| | | | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Ingrid Agartz
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Peter Bachman
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
| | - Inmaculada Baeza
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neuroscience, 2017SGR-881, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Universitat de Barcelona, Barcelona, Spain
| | - Xiaogang Chen
- National Clinical Research Center for Mental Disorders and Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Sunah Choi
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - Cheryl M Corcoran
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Mental Illness Research, Education, and Clinical Center, James J Peters VA Medical Center, New York City, NY, USA
| | - Bjørn H Ebdrup
- Centre for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Centre Glostrup, Copenhagen University Hospital, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Adriana Fortea
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic Barcelona, Fundació Clínic Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Ranjini Rg Garani
- Douglas Research Center; Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Birte Yding Glenthøj
- Centre for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Centre Glostrup, Copenhagen University Hospital, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Louise Birkedal Glenthøj
- Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, University of Copenhagen Copenhagen, Copenhagen, Denmark
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Holly K Hamilton
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
- San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | - Rebecca A Hayes
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
| | - Ying He
- National Clinical Research Center for Mental Disorders and Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Karsten Heekeren
- Department of Psychiatry and Psychotherapy I, LVR-Hospital Cologne, Cologne, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The University of Tokyo Institute for Diversity and Adaptation of Human Mind, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence at The University of Tokyo Institutes for Advanced Study (WPI-IRCN), The University of Tokyo, Tokyo, Japan
| | - Naoyuki Katagiri
- Department of Neuropsychiatry, Toho University School of Medicine, Tokyok, Japan
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | - Tina D Kristensen
- Centre for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Centre Glostrup, Copenhagen University Hospital, Glostrup, Denmark
| | - Jun Soo Kwon
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | | | - Irina Lebedeva
- Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russian Federation
| | - Jimmy Lee
- Department of Psychosis, Institute of Mental Health, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Rachel L Loewy
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Daniel H Mathalon
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
- San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | - Philip McGuire
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Romina Mizrahi
- Douglas Research Center; Department of Psychiatry, McGill University, Montreal, QC, Canada
| | | | - Paul Møller
- Department for Mental Health Research and Development, Division of Mental Health and Addiction, Vestre Viken Hospital Trust, Drammen, Norway
| | - Takahiro Nemoto
- Department of Neuropsychiatry, Toho University School of Medicine, Tokyok, Japan
| | - Dorte Nordholm
- Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, University of Copenhagen Copenhagen, Copenhagen, Denmark
| | - Maria A Omelchenko
- Department of Youth Psychiatry, Mental Health Research Center, Moscow, Russian Federation
| | - Jayachandra M Raghava
- Centre for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Centre Glostrup, Copenhagen University Hospital, Glostrup, Denmark
- Department of Clinical Physiology, Nuclear Medicine and PET, Functional Imaging, University of Copenhagen Copenhagen, Copenhagen, Denmark
| | - Jan I Røssberg
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Wulf Rössler
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Dean F Salisbury
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Lukasz Smigielski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Child and Adolescent Psychiatry, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Gisela Sugranyes
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neuroscience, 2017SGR-881, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Universitat de Barcelona, Barcelona, Spain
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Christian K Tamnes
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Jinsong Tang
- Department of Psychiatry, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Zhejiang, China
- Key Laboratory of Medical Neurobiology of Zhejiang Province, School of Medicine, Zhejiang University, Zhejiang, China
| | - Anastasia Theodoridou
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Alexander S Tomyshev
- Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russian Federation
| | - Peter J Uhlhaas
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
| | - Tor G Værnes
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Early Intervention in Psychosis Advisory Unit for South-East Norway, TIPS Sør-Øst, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Therese A M J van Amelsvoort
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - James A Waltz
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore County, Baltimore, MD, USA
| | - Lars T Westlye
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Juan H Zhou
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Dennis Hernaus
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Maria Jalbrzikowski
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Cambridge, MA, USA
| | - Shinsuke Koike
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan.
- The University of Tokyo Institute for Diversity and Adaptation of Human Mind, The University of Tokyo, Tokyo, Japan.
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Gao T, Wang X, Cen H, Li X, Zhai Z, Lu C, Dong Y, Zhang S, Zhuo K, Xiang Q, Wang Y, Liu D. Cross-modal associative memory impairment in schizophrenia. Neuropsychologia 2023; 191:108721. [PMID: 37918479 DOI: 10.1016/j.neuropsychologia.2023.108721] [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: 05/03/2023] [Revised: 10/30/2023] [Accepted: 10/30/2023] [Indexed: 11/04/2023]
Abstract
Impaired associative memory function in patients with schizophrenia has received considerable attention. However, previous studies have primarily concentrated on unisensory materials, which limits our understanding of the broader implications of this impairment. In this study, we sought to expand on this knowledge by examining two types of associative memory domains in individuals with schizophrenia, leveraging both visual (Vis) and auditory (Aud) materials. A total of 32 patients with schizophrenia and 29 healthy controls were recruited to participate in the study. Each participant participated in an experiment composed of three paradigms in which different abstract materials (Aud-Aud, Aud-Vis, and Vis-Vis) were presented. Subsequently, the discriminability scores of the two groups were calculated and compared in different modal tasks. Results from the study indicated that individuals with schizophrenia demonstrated varying degrees of associative memory dysfunction in both the same and cross-modalities, with the latter having a significantly lower score than healthy controls (t = 4.120, p < 0.001). Additionally, the cross-modal associative memory function was significantly and negatively correlated with the severity of negative symptoms among individuals diagnosed with schizophrenia (r = -0.362, p = 0.042). This study provides evidence of abnormalities in the processing and memorization of information that integrates multiple sensory modalities in individuals with schizophrenia. This is of great significance for further understanding the cognitive symptoms and pathological mechanisms of schizophrenia, potentially guiding the development of relevant interventions and treatment methods.
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Affiliation(s)
- Tianhao Gao
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, 200040, China; Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; Clinical Center for Psychotic Disorders, National Center for Mental Disorders, Shanghai, 200030, China
| | - Xiaoliang Wang
- Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; Clinical Center for Psychotic Disorders, National Center for Mental Disorders, Shanghai, 200030, China
| | - Haixin Cen
- Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; Clinical Center for Psychotic Disorders, National Center for Mental Disorders, Shanghai, 200030, China
| | - Xuan Li
- Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; Clinical Center for Psychotic Disorders, National Center for Mental Disorders, Shanghai, 200030, China
| | - Zhaolin Zhai
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, 200040, China; Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; Clinical Center for Psychotic Disorders, National Center for Mental Disorders, Shanghai, 200030, China
| | - Chang Lu
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, 200040, China; Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; Clinical Center for Psychotic Disorders, National Center for Mental Disorders, Shanghai, 200030, China
| | - Yuke Dong
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, 200040, China; Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; Clinical Center for Psychotic Disorders, National Center for Mental Disorders, Shanghai, 200030, China
| | - Suzhen Zhang
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, 200040, China; Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; Clinical Center for Psychotic Disorders, National Center for Mental Disorders, Shanghai, 200030, China
| | - Kaiming Zhuo
- Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; Clinical Center for Psychotic Disorders, National Center for Mental Disorders, Shanghai, 200030, China
| | - Qiong Xiang
- Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; Clinical Center for Psychotic Disorders, National Center for Mental Disorders, Shanghai, 200030, China
| | - Yan Wang
- School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China.
| | - Dengtang Liu
- Department of Psychiatry, Huashan Hospital, Fudan University, Shanghai, 200040, China; Division of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China; Clinical Center for Psychotic Disorders, National Center for Mental Disorders, Shanghai, 200030, China; Institute of Mental Health, Fudan University, Shanghai, 200030, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, China.
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3
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Mohamed Saini S, Bousman CA, Mancuso SG, Cropley V, Van Rheenen TE, Lenroot RK, Bruggemann J, Weickert CS, Weickert TW, Sundram S, Everall IP, Pantelis C. Genetic variation in glutamatergic genes moderates the effects of childhood adversity on brain volume and IQ in treatment-resistant schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:59. [PMID: 37709784 PMCID: PMC10502098 DOI: 10.1038/s41537-023-00381-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 07/20/2023] [Indexed: 09/16/2023]
Affiliation(s)
- Suriati Mohamed Saini
- Department of Psychiatry, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Cheras, Kuala Lumpur, Malaysia.
- Department of Psychiatry, Hospital Canselor Tuanku Muhriz, Jalan Yaacob Latif, Cheras, Kuala Lumpur, Malaysia.
| | - Chad A Bousman
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, VIC, Australia
- Alberta Children's Hospital Research Institute, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Medical Genetics, Psychiatry, and Physiology and Pharmacology, The University of Calgary, Calgary, AB, Canada
| | - Serafino G Mancuso
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, VIC, Australia
| | - Vanessa Cropley
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, VIC, Australia
- Centre for Mental Health, Faculty of Health, Arts and Design, Swinburne University, Melbourne, VIC, Australia
| | - Tamsyn E Van Rheenen
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, VIC, Australia
- Centre for Mental Health, Faculty of Health, Arts and Design, Swinburne University, Melbourne, VIC, Australia
| | - Rhoshel K Lenroot
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
- Department of Psychiatry and Behavioural Science, University of New Mexico, Albuquerque, NM, USA
| | - Jason Bruggemann
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
- Neuroscience Research Australia, Randwick, NSW, Australia
- Schizophrenia Research Institute, Sydney, NSW, Australia
| | - Cynthia S Weickert
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, NY, USA
- Schizophrenia Research Laboratory, Neuroscience Research Australia, NSW, Australia
| | - Thomas W Weickert
- School of Psychiatry, University of New South Wales, Randwick, NSW, Australia
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, NY, USA
| | - Suresh Sundram
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, VIC, Australia
- Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, VIC, Australia
- Monash Medical Centre, Monash Health, Clayton, VIC, Australia
| | - Ian P Everall
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, VIC, Australia
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, VIC, Australia
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Western Centre for Health Research & Education, Sunshine Hospital, Western Health, St Albans, VIC, 3021, Australia
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Duda M, Faghiri A, Belger A, Bustillo JR, Ford JM, Mathalon DH, Mueller BA, Pearlson GD, Potkin SG, Preda A, Sui J, Van Erp TGM, Calhoun VD. Alterations in grey matter structure linked to frequency-specific cortico-subcortical connectivity in schizophrenia via multimodal data fusion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.05.547840. [PMID: 37461731 PMCID: PMC10350020 DOI: 10.1101/2023.07.05.547840] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Schizophrenia (SZ) is a complex psychiatric disorder that is currently defined by symptomatic and behavioral, rather than biological, criteria. Neuroimaging is an appealing avenue for SZ biomarker development, as several neuroimaging-based studies comparing individuals with SZ to healthy controls (HC) have shown measurable group differences in brain structure, as well as functional brain alterations in both static and dynamic functional network connectivity (sFNC and dFNC, respectively). The recently proposed filter-banked connectivity (FBC) method extends the standard dFNC sliding-window approach to estimate FNC within an arbitrary number of distinct frequency bands. The initial implementation used a set of filters spanning the full connectivity spectral range, providing a unified approach to examine both sFNC and dFNC in a single analysis. Initial FBC results found that individuals with SZ spend more time in a less structured, more disconnected low-frequency (i.e., static) FNC state than HC, as well as preferential SZ occupancy in high-frequency connectivity states, suggesting a frequency-specific component underpinning the functional dysconnectivity observed in SZ. Building on these findings, we sought to link such frequency-specific patterns of FNC to covarying data-driven structural brain networks in the context of SZ. Specifically, we employ a multi-set canonical correlation analysis + joint independent components analysis (mCCA + jICA) data fusion framework to study the connection between grey matter volume (GMV) maps and FBC states across the full connectivity frequency spectrum. Our multimodal analysis identified two joint sources that captured co-varying patterns of frequency-specific functional connectivity and alterations in GMV with significant group differences in loading parameters between the SZ group and HC. The first joint source linked frequency-modulated connections between the subcortical and sensorimotor networks and GMV alterations in the frontal and temporal lobes, while the second joint source identified a relationship between low-frequency cerebellar-sensorimotor connectivity and structural changes in both the cerebellum and motor cortex. Together, these results show a strong connection between cortico-subcortical functional connectivity at both high and low frequencies and alterations in cortical GMV that may be relevant to the pathogenesis and pathophysiology of SZ.
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Affiliation(s)
- Marlena Duda
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Juan R Bustillo
- Department of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Judith M Ford
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
| | - Daniel H Mathalon
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, California, USA
- Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, USA
| | - Bryon A Mueller
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, California, USA
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Theo G M Van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
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5
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Lotan A, Luza S, Opazo CM, Ayton S, Lane DJR, Mancuso S, Pereira A, Sundram S, Weickert CS, Bousman C, Pantelis C, Everall IP, Bush AI. Perturbed iron biology in the prefrontal cortex of people with schizophrenia. Mol Psychiatry 2023; 28:2058-2070. [PMID: 36750734 PMCID: PMC10575779 DOI: 10.1038/s41380-023-01979-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 01/10/2023] [Accepted: 01/20/2023] [Indexed: 02/09/2023]
Abstract
Despite loss of grey matter volume and emergence of distinct cognitive deficits in young adults diagnosed with schizophrenia, current treatments for schizophrenia do not target disruptions in late maturational reshaping of the prefrontal cortex. Iron, the most abundant transition metal in the brain, is essential to brain development and function, but in excess, it can impair major neurotransmission systems and lead to lipid peroxidation, neuroinflammation and accelerated aging. However, analysis of cortical iron biology in schizophrenia has not been reported in modern literature. Using a combination of inductively coupled plasma-mass spectrometry and western blots, we quantified iron and its major-storage protein, ferritin, in post-mortem prefrontal cortex specimens obtained from three independent, well-characterised brain tissue resources. Compared to matched controls (n = 85), among schizophrenia cases (n = 86) we found elevated tissue iron, unlikely to be confounded by demographic and lifestyle variables, by duration, dose and type of antipsychotic medications used or by copper and zinc levels. We further observed a loss of physiologic age-dependent iron accumulation among people with schizophrenia, in that the iron level among cases was already high in young adulthood. Ferritin, which stores iron in a redox-inactive form, was paradoxically decreased in individuals with the disorder. Such iron-ferritin uncoupling could alter free, chemically reactive, tissue iron in key reasoning and planning areas of the young-adult schizophrenia cortex. Using a prediction model based on iron and ferritin, our data provide a pathophysiologic link between perturbed cortical iron biology and schizophrenia and indicate that achievement of optimal cortical iron homeostasis could offer a new therapeutic target.
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Affiliation(s)
- Amit Lotan
- Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, 3010, Australia
- Department of Psychiatry and the Biological Psychiatry Laboratory, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Sandra Luza
- Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, 3010, Australia
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Carlton, VIC, Australia
| | - Carlos M Opazo
- Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, 3010, Australia.
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Carlton, VIC, Australia.
| | - Scott Ayton
- Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Darius J R Lane
- Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Serafino Mancuso
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Carlton, VIC, Australia
| | - Avril Pereira
- Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, 3010, Australia
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Carlton, VIC, Australia
| | - Suresh Sundram
- Department of Psychiatry, School of Clinical Sciences, Monash University, Melbourne, VIC, Australia
- Mental Health Program, Monash Health, Melbourne, VIC, Australia
| | - Cynthia Shannon Weickert
- Schizophrenia Research Laboratory, Neuroscience Research Australia, Randwick, NSW, Australia
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Chad Bousman
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Carlton, VIC, Australia
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Departments of Medical Genetics, Psychiatry, Physiology & Pharmacology, University of Calgary, Calgary, AB, Canada
- The Cooperative Research Centre (CRC) for Mental Health, Melbourne, VIC, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Carlton, VIC, Australia
- North Western Mental Health, Melbourne, VIC, Australia
| | - Ian P Everall
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Carlton, VIC, Australia
- North Western Mental Health, Melbourne, VIC, Australia
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ashley I Bush
- Melbourne Dementia Research Centre, Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, VIC, 3010, Australia.
- The Cooperative Research Centre (CRC) for Mental Health, Melbourne, VIC, Australia.
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6
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How Functional Connectivity Measures Affect the Outcomes of Global Neuronal Network Characteristics in Patients with Schizophrenia Compared to Healthy Controls. Brain Sci 2023; 13:brainsci13010138. [PMID: 36672119 PMCID: PMC9856389 DOI: 10.3390/brainsci13010138] [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: 11/21/2022] [Revised: 12/24/2022] [Accepted: 01/10/2023] [Indexed: 01/15/2023] Open
Abstract
Modern computational solutions used in the reconstruction of the global neuronal network arrangement seem to be particularly valuable for research on neuronal disconnection in schizophrenia. However, the vast number of algorithms used in these analyses may be an uncontrolled source of result inconsistency. Our study aimed to verify to what extent the characteristics of the global network organization in schizophrenia depend on the inclusion of a given type of functional connectivity measure. Resting-state EEG recordings from schizophrenia patients and healthy controls were collected. Based on these data, two identical procedures of graph-theory-based network arrangements were computed twice using two different functional connectivity measures (phase lag index, PLI, and phase locking value, PLV). Two series of between-group comparisons regarding global network parameters calculated on the basis of PLI or PLV gave contradictory results. In many cases, the values of a given network index based on PLI were higher in the patients, and the results based on PLV were lower in the patients than in the controls. Additionally, selected network measures were significantly different within the patient group when calculated from PLI or PLV. Our analysis shows that the selection of FC measures significantly affects the parameters of graph-theory-based neuronal network organization and might be an important source of disagreement in network studies on schizophrenia.
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7
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Watson AJ, Harrison L, Preti A, Wykes T, Cella M. Cognitive trajectories following onset of psychosis: a meta-analysis. Br J Psychiatry 2022; 221:714-721. [PMID: 36149012 DOI: 10.1192/bjp.2022.131] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Cognitive impairment is a core feature of schizophrenia, associated with poor functional outcomes. The course of cognitive function in the years following illness onset has remained a subject of debate, with a previous analysis finding no worsening, providing support for the neurodevelopmental model of schizophrenia. Since then, many more studies have reported on longitudinal cognitive performance in early psychosis, with some indicating deterioration, which does not align with this view. AIMS This study aims to quantitatively review the literature on the longitudinal trajectory of cognitive deficits in the years following psychosis onset, in comparison with healthy controls. It is the first to also synthesise longitudinal data on social cognition. METHOD Electronic databases ('PubMed', 'PsycInfo' and 'Scopus') were searched (to end September 2021). Meta-analyses of 25 longitudinal studies of cognition in early psychosis were conducted (1480 patients, 789 health controls). Unlike previous analyses, randomised controlled trials and those with multiple cognitive testing periods within the first year were excluded to minimise bias (PROSPERO, ID: CRD42021241525). RESULTS Small improvements were observed for global cognition (g = 0.25, 95% CI 0.17-0.33) and individual cognitive domains, but these were comparable with healthy controls and likely an artefact of practice effects. CONCLUSIONS There is no evidence of continued cognitive decline or improvement in the early years following psychosis onset, with a need for more studies over longer follow-up periods. Practice effects highlight the importance of including control samples in longitudinal and intervention studies. Further data are needed to evaluate the course of social cognition subdomains.
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Affiliation(s)
- Andrew J Watson
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; and South London and Maudsley NHS Foundation Trust, London, UK
| | - Lauren Harrison
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK
| | - Antonio Preti
- Dipartimento di Neuroscienze, Università degli studi di Torino, Italy
| | - Til Wykes
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; and South London and Maudsley NHS Foundation Trust, London, UK
| | - Matteo Cella
- Department of Psychology, Institute of Psychiatry, Psychology & Neuroscience, King's College London, UK; and South London and Maudsley NHS Foundation Trust, London, UK
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8
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Eratne D, Janelidze S, Malpas CB, Loi S, Walterfang M, Merritt A, Diouf I, Blennow K, Zetterberg H, Cilia B, Wannan C, Bousman C, Everall I, Zalesky A, Jayaram M, Thomas N, Berkovic SF, Hansson O, Velakoulis D, Pantelis C, Santillo A, Stehmann C, Cadwallader C, Fowler C, Ravanfar P, Farrand S, Keem M, Kang M, Watson R, Yassi N, Kaylor-Hughes C, Kanaan R, Perucca P, Vivash L, Ali R, O’Brien TJ, Masters CL, Collins S, Kelso W, Evans A, King A, Kwan P, Gunn J, Goranitis I, Pan T, Lewis C, Kalincik T. Plasma neurofilament light chain protein is not increased in treatment-resistant schizophrenia and first-degree relatives. Aust N Z J Psychiatry 2022; 56:1295-1305. [PMID: 35179048 DOI: 10.1177/00048674211058684] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
OBJECTIVE Schizophrenia, a complex psychiatric disorder, is often associated with cognitive, neurological and neuroimaging abnormalities. The processes underlying these abnormalities, and whether a subset of people with schizophrenia have a neuroprogressive or neurodegenerative component to schizophrenia, remain largely unknown. Examining fluid biomarkers of diverse types of neuronal damage could increase our understanding of these processes, as well as potentially provide clinically useful biomarkers, for example with assisting with differentiation from progressive neurodegenerative disorders such as Alzheimer and frontotemporal dementias. METHODS This study measured plasma neurofilament light chain protein (NfL) using ultrasensitive Simoa technology, to investigate the degree of neuronal injury in a well-characterised cohort of people with treatment-resistant schizophrenia on clozapine (n = 82), compared to first-degree relatives (an at-risk group, n = 37), people with schizophrenia not treated with clozapine (n = 13), and age- and sex-matched controls (n = 59). RESULTS We found no differences in NfL levels between treatment-resistant schizophrenia (mean NfL, M = 6.3 pg/mL, 95% confidence interval: [5.5, 7.2]), first-degree relatives (siblings, M = 6.7 pg/mL, 95% confidence interval: [5.2, 8.2]; parents, M after adjusting for age = 6.7 pg/mL, 95% confidence interval: [4.7, 8.8]), controls (M = 5.8 pg/mL, 95% confidence interval: [5.3, 6.3]) and not treated with clozapine (M = 4.9 pg/mL, 95% confidence interval: [4.0, 5.8]). Exploratory, hypothesis-generating analyses found weak correlations in treatment-resistant schizophrenia, between NfL and clozapine levels (Spearman's r = 0.258, 95% confidence interval: [0.034, 0.457]), dyslipidaemia (r = 0.280, 95% confidence interval: [0.064, 0.470]) and a negative correlation with weight (r = -0.305, 95% confidence interval: [-0.504, -0.076]). CONCLUSION Treatment-resistant schizophrenia does not appear to be associated with neuronal, particularly axonal degeneration. Further studies are warranted to investigate the utility of NfL to differentiate treatment-resistant schizophrenia from neurodegenerative disorders such as behavioural variant frontotemporal dementia, and to explore NfL in other stages of schizophrenia such as the prodome and first episode.
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Affiliation(s)
- Dhamidhu Eratne
- Neuropsychiatry, The Royal Melbourne Hospital, Parkville, VIC, Australia.,Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund, Sweden
| | - Charles B Malpas
- Clinical Outcomes Research Unit (CORe), Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, VIC, Australia.,Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC, Australia.,Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Samantha Loi
- Neuropsychiatry, The Royal Melbourne Hospital, Parkville, VIC, Australia.,Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia
| | - Mark Walterfang
- Neuropsychiatry, The Royal Melbourne Hospital, Parkville, VIC, Australia.,Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia
| | - Antonia Merritt
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia
| | - Ibrahima Diouf
- Clinical Outcomes Research Unit (CORe), Department of Medicine, The Royal Melbourne Hospital, The University of Melbourne, Melbourne, VIC, Australia.,Department of Neurology, The Royal Melbourne Hospital, Melbourne, VIC, Australia
| | - Kaj Blennow
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, Sahlgrenska University Hospital, University of Gothenburg, Mölndal, Sweden
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Institute of Neuroscience and Physiology, Sahlgrenska University Hospital, University of Gothenburg, Mölndal, Sweden.,Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK.,UK Dementia Research Institute, University College London (UCL), London, UK.,Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Brandon Cilia
- The University of Melbourne, Parkville, VIC, Australia
| | - Cassandra Wannan
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia
| | - Chad Bousman
- Departments of Medical Genetics, Psychiatry, and Physiology & Pharmacology, University of Calgary, Calgary, AB, Canada
| | - Ian Everall
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia
| | - Mahesh Jayaram
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia.,Mid West Area Mental Health Service, Melbourne Health, Sunshine, VIC, Australia
| | - Naveen Thomas
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia.,Mid West Area Mental Health Service, Melbourne Health, Sunshine, VIC, Australia
| | - Samuel F Berkovic
- Epilepsy Research Centre, Department of Medicine, Austin Health, The University of Melbourne, Heidelberg, VIC, Australia
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund, Sweden
| | - Dennis Velakoulis
- Neuropsychiatry, The Royal Melbourne Hospital, Parkville, VIC, Australia.,Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia.,Mid West Area Mental Health Service, Melbourne Health, Sunshine, VIC, Australia
| | - Alexander Santillo
- Clinical Memory Research Unit, Department of Clinical Sciences, Faculty of Medicine, Lund University, Lund, Sweden
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9
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Lalousis PA, Schmaal L, Wood SJ, Reniers RLEP, Barnes NM, Chisholm K, Griffiths SL, Stainton A, Wen J, Hwang G, Davatzikos C, Wenzel J, Kambeitz-Ilankovic L, Andreou C, Bonivento C, Dannlowski U, Ferro A, Lichtenstein T, Riecher-Rössler A, Romer G, Rosen M, Bertolino A, Borgwardt S, Brambilla P, Kambeitz J, Lencer R, Pantelis C, Ruhrmann S, Salokangas RKR, Schultze-Lutter F, Schmidt A, Meisenzahl E, Koutsouleris N, Dwyer D, Upthegrove R. Neurobiologically Based Stratification of Recent-Onset Depression and Psychosis: Identification of Two Distinct Transdiagnostic Phenotypes. Biol Psychiatry 2022; 92:552-562. [PMID: 35717212 PMCID: PMC10128104 DOI: 10.1016/j.biopsych.2022.03.021] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/04/2022] [Accepted: 03/01/2022] [Indexed: 12/17/2022]
Abstract
BACKGROUND Identifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity and provide better candidates for predictive modeling. We aimed to identify clusters across patients with recent-onset depression (ROD) and recent-onset psychosis (ROP) based on structural neuroimaging data. We hypothesized that these transdiagnostic clusters would identify patients with poor outcome and allow more accurate prediction of symptomatic remission than traditional diagnostic structures. METHODS HYDRA (Heterogeneity through Discriminant Analysis) was trained on whole-brain volumetric measures from 577 participants from the discovery sample of the multisite PRONIA study to identify neurobiologically driven clusters, which were then externally validated in the PRONIA replication sample (n = 404) and three datasets of chronic samples (Centre for Biomedical Research Excellence, n = 146; Mind Clinical Imaging Consortium, n = 202; Munich, n = 470). RESULTS The optimal clustering solution was two transdiagnostic clusters (cluster 1: n = 153, 67 ROP, 86 ROD; cluster 2: n = 149, 88 ROP, 61 ROD; adjusted Rand index = 0.618). The two clusters contained both patients with ROP and patients with ROD. One cluster had widespread gray matter volume deficits and more positive, negative, and functional deficits (impaired cluster), and one cluster revealed a more preserved neuroanatomical signature and more core depressive symptomatology (preserved cluster). The clustering solution was internally and externally validated and assessed for clinical utility in predicting 9-month symptomatic remission, outperforming traditional diagnostic structures. CONCLUSIONS We identified two transdiagnostic neuroanatomically informed clusters that are clinically and biologically distinct, challenging current diagnostic boundaries in recent-onset mental health disorders. These results may aid understanding of the etiology of poor outcome patients transdiagnostically and improve development of stratified treatments.
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Affiliation(s)
- Paris Alexandros Lalousis
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom.
| | - Lianne Schmaal
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Stephen J Wood
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Renate L E P Reniers
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom; Institute of Clinical Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Nicholas M Barnes
- Institute of Clinical Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Katharine Chisholm
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Department of Psychology, Aston University, Birmingham, United Kingdom
| | - Sian Lowri Griffiths
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom
| | - Alexandra Stainton
- Orygen, Parkville, Victoria, Australia; Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Junhao Wen
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Gyujoon Hwang
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Christos Davatzikos
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Julian Wenzel
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | | | - Carolina Bonivento
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Adele Ferro
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy
| | - Theresa Lichtenstein
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | | | - Georg Romer
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Marlene Rosen
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Alessandro Bertolino
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Stefan Borgwardt
- Department of Psychiatry and Psychotherapy, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany; Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Paolo Brambilla
- Department of Psychiatry, University of Basel, Basel, Switzerland; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, Ludwig Maxmilians University, Munich, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, University of Basel, Basel, Switzerland
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - Stephan Ruhrmann
- Department of Psychiatry, University of Basel, Basel, Switzerland
| | | | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, University of Düsseldorf, Düsseldorf, Germany; University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Department of Psychology and Mental Health, Faculty of Psychology, Airlangga University, Surabaya, Indonesia
| | - André Schmidt
- Department of Psychiatry, University of Basel, Basel, Switzerland
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, University of Düsseldorf, Düsseldorf, Germany
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maxmilians University, Munich, Germany
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig Maxmilians University, Munich, Germany
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom; Centre for Human Brain Health, University of Birmingham, Birmingham, United Kingdom; Birmingham Early Interventions Service, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, United Kingdom
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10
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Zhu Y, Nakatani H, Yassin W, Maikusa N, Okada N, Kunimatsu A, Abe O, Kuwabara H, Yamasue H, Kasai K, Okanoya K, Koike S. Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study. Schizophr Bull 2022; 48:563-574. [PMID: 35352811 PMCID: PMC9077435 DOI: 10.1093/schbul/sbac030] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
BACKGROUND AND HYPOTHESIS Machine learning approaches using structural magnetic resonance imaging (MRI) can be informative for disease classification; however, their applicability to earlier clinical stages of psychosis and other disease spectra is unknown. We evaluated whether a model differentiating patients with chronic schizophrenia (ChSZ) from healthy controls (HCs) could be applied to earlier clinical stages such as first-episode psychosis (FEP), ultra-high risk for psychosis (UHR), and autism spectrum disorders (ASDs). STUDY DESIGN Total 359 T1-weighted MRI scans, including 154 individuals with schizophrenia spectrum (UHR, n = 37; FEP, n = 24; and ChSZ, n = 93), 64 with ASD, and 141 HCs, were obtained using three acquisition protocols. Of these, data regarding ChSZ (n = 75) and HC (n = 101) from two protocols were used to build a classifier (training dataset). The remainder was used to evaluate the classifier (test, independent confirmatory, and independent group datasets). Scanner and protocol effects were diminished using ComBat. STUDY RESULTS The accuracy of the classifier for the test and independent confirmatory datasets were 75% and 76%, respectively. The bilateral pallidum and inferior frontal gyrus pars triangularis strongly contributed to classifying ChSZ. Schizophrenia spectrum individuals were more likely to be classified as ChSZ compared to ASD (classification rate to ChSZ: UHR, 41%; FEP, 54%; ChSZ, 70%; ASD, 19%; HC, 21%). CONCLUSION We built a classifier from multiple protocol structural brain images applicable to independent samples from different clinical stages and spectra. The predictive information of the classifier could be useful for applying neuroimaging techniques to clinical differential diagnosis and predicting disease onset earlier.
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Affiliation(s)
- Yinghan Zhu
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Hironori Nakatani
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- Department of Information Media Technology, School of Information and Telecommunication Engineering, Tokai University, 2-3-23, Takanawa, Minato-ku, Tokyo 108-8619, Japan
| | - Walid Yassin
- Department of Child Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Naohiro Okada
- The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Akira Kunimatsu
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Hitoshi Kuwabara
- Department of Psychiatry, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu City, Shizuoka 431-3192, Japan
| | - Hidenori Yamasue
- Department of Psychiatry, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu City, Shizuoka 431-3192, Japan
| | - Kiyoto Kasai
- The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Kazuo Okanoya
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Shinsuke Koike
- To whom correspondence should be addressed; Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan; tel: +81-3-5454-4327, fax: +81-3-5454-4327, e-mail:
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11
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Gutman BA, van Erp TG, Alpert K, Ching CRK, Isaev D, Ragothaman A, Jahanshad N, Saremi A, Zavaliangos‐Petropulu A, Glahn DC, Shen L, Cong S, Alnæs D, Andreassen OA, Doan NT, Westlye LT, Kochunov P, Satterthwaite TD, Wolf DH, Huang AJ, Kessler C, Weideman A, Nguyen D, Mueller BA, Faziola L, Potkin SG, Preda A, Mathalon DH, Bustillo J, Calhoun V, Ford JM, Walton E, Ehrlich S, Ducci G, Banaj N, Piras F, Piras F, Spalletta G, Canales‐Rodríguez EJ, Fuentes‐Claramonte P, Pomarol‐Clotet E, Radua J, Salvador R, Sarró S, Dickie EW, Voineskos A, Tordesillas‐Gutiérrez D, Crespo‐Facorro B, Setién‐Suero E, van Son JM, Borgwardt S, Schönborn‐Harrisberger F, Morris D, Donohoe G, Holleran L, Cannon D, McDonald C, Corvin A, Gill M, Filho GB, Rosa PGP, Serpa MH, Zanetti MV, Lebedeva I, Kaleda V, Tomyshev A, Crow T, James A, Cervenka S, Sellgren CM, Fatouros‐Bergman H, Agartz I, Howells F, Stein DJ, Temmingh H, Uhlmann A, de Zubicaray GI, McMahon KL, Wright M, Cobia D, Csernansky JG, Thompson PM, Turner JA, Wang L. A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium. Hum Brain Mapp 2022; 43:352-372. [PMID: 34498337 PMCID: PMC8675416 DOI: 10.1002/hbm.25625] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 07/13/2021] [Accepted: 07/14/2021] [Indexed: 01/06/2023] Open
Abstract
Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta-analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1-weighted structural MRI scans. Mass univariate meta-analyses revealed more-concave-than-convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more-convex-than-concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta-analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.
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Affiliation(s)
- Boris A. Gutman
- Department of Biomedical EngineeringIllinois Institute of TechnologyChicagoIllinoisUSA
- Institute for Information Transmission Problems (Kharkevich Institute)MoscowRussia
| | - Theo G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of California IrvineIrvineCaliforniaUSA
| | - Kathryn Alpert
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Christopher R. K. Ching
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Dmitry Isaev
- Department of Biomedical EngineeringDuke UniversityDurhamNorth CarolinaUSA
| | - Anjani Ragothaman
- Department of biomedical engineeringOregon Health and Science universityPortlandOregonUSA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Arvin Saremi
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Artemis Zavaliangos‐Petropulu
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - David C. Glahn
- Department of PsychiatryBoston Children's Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Li Shen
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Shan Cong
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Dag Alnæs
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Ole Andreas Andreassen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Nhat Trung Doan
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University 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
| | - Peter Kochunov
- Department of PsychiatryUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Theodore D. Satterthwaite
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Daniel H. Wolf
- Department of PsychiatryUniversity of Pennsylvania Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Alexander J. Huang
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Charles Kessler
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Andrea Weideman
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Dana Nguyen
- Department of PediatricsUniversity of California IrvineIrvineCaliforniaUSA
| | - Bryon A. Mueller
- Department of Psychiatry and Behavioral SciencesUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Lawrence Faziola
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Steven G. Potkin
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Adrian Preda
- Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA
| | - Daniel H. Mathalon
- Department of Psychiatry and Weill Institute for NeurosciencesUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Judith Ford Mental HealthVA San Francisco Healthcare SystemSan FranciscoCaliforniaUSA
| | - Juan Bustillo
- Departments of Psychiatry & NeuroscienceUniversity of New MexicoAlbuquerqueNew MexicoUSA
| | - Vince Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology]Emory UniversityAtlantaGeorgiaUSA
- Department of Electrical and Computer EngineeringThe University of New MexicoAlbuquerqueNew MexicoUSA
| | - Judith M. Ford
- Judith Ford Mental HealthVA San Francisco Healthcare SystemSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | | | - Stefan Ehrlich
- Division of Psychological & Social Medicine and Developmental NeurosciencesFaculty of Medicine, TU‐DresdenDresdenGermany
| | | | - Nerisa Banaj
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Fabrizio Piras
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Federica Piras
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
| | - Gianfranco Spalletta
- Laboratory of NeuropsychiatryIRCCS Santa Lucia FoundationRomeItaly
- Menninger Department of Psychiatry and Behavioral SciencesBaylor College of MedicineHoustonTexasUSA
| | | | | | | | - Joaquim Radua
- FIDMAG Germanes Hospitalàries Research FoundationCIBERSAMBarcelonaSpain
- Institut d'Investigacions Biomdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research FoundationCIBERSAMBarcelonaSpain
| | - Salvador Sarró
- FIDMAG Germanes Hospitalàries Research FoundationCIBERSAMBarcelonaSpain
| | - Erin W. Dickie
- Centre for Addiction and Mental Health (CAMH)TorontoCanada
| | | | | | | | | | | | - Stefan Borgwardt
- Department of PsychiatryUniversity of BaselBaselSwitzerland
- Department of Psychiatry and PsychotherapyUniversity of LübeckLübeckGermany
| | | | - Derek Morris
- Centre for Neuroimaging and Cognitive Genomics, Discipline of BiochemistryNational University of Ireland GalwayGalwayIreland
| | - Gary Donohoe
- Centre for Neuroimaging and Cognitive Genomics, School of PsychologyNational University of Ireland GalwayGalwayIreland
| | - Laurena Holleran
- Centre for Neuroimaging and Cognitive Genomics, School of PsychologyNational University of Ireland GalwayGalwayIreland
| | - Dara Cannon
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive GenomicsNational University of Ireland GalwayGalwayIreland
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive GenomicsNational University of Ireland GalwayGalwayIreland
| | - Aiden Corvin
- Neuropsychiatric Genetics Research Group, Department of PsychiatryTrinity College DublinDublinIreland
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
| | - Michael Gill
- Neuropsychiatric Genetics Research Group, Department of PsychiatryTrinity College DublinDublinIreland
- Trinity College Institute of NeuroscienceTrinity College DublinDublinIreland
| | - Geraldo Busatto Filho
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
| | - Pedro G. P. Rosa
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
| | - Mauricio H. Serpa
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
| | - Marcus V. Zanetti
- Laboratory of Psychiatric Neuroimaging (LIM‐21), Departamento e Instituto de PsiquiatriaHospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao PauloSao PauloSPBrazil
- Hospital Sirio‐LibanesSao PauloSPBrazil
| | - Irina Lebedeva
- Laboratory of Neuroimaging and Multimodal AnalysisMental Health Research CenterMoscowRussia
| | - Vasily Kaleda
- Department of Endogenous Mental DisordersMental Health Research CenterMoscowRussia
| | - Alexander Tomyshev
- Laboratory of Neuroimaging and Multimodal AnalysisMental Health Research CenterMoscowRussia
| | - Tim Crow
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Anthony James
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Simon Cervenka
- Centre for Psychiatry Reserach, Department of Clinical NeuroscienceKarolinska Institutet, & Stockholm Health Care Services, Region StockholmStockholmSweden
| | - Carl M Sellgren
- Department of Physiology and PharmacologyKarolinska InstitutetStockholmSweden
| | - Helena Fatouros‐Bergman
- Centre for Psychiatry Reserach, Department of Clinical NeuroscienceKarolinska Institutet, & Stockholm Health Care Services, Region StockholmStockholmSweden
| | - Ingrid Agartz
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Fleur Howells
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
- Neuroscience InstituteUniversity of Cape Town, Cape TownWCSouth Africa
| | - Dan J. Stein
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
- Neuroscience InstituteUniversity of Cape Town, Cape TownWCSouth Africa
- SA MRC Unit on Risk & Resilience in Mental DisordersUniversity of Cape TownCape TownWCSouth Africa
| | - Henk Temmingh
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
| | - Anne Uhlmann
- Department of Psychiatry and Mental Health, Faculty of Health SciencesUniversity of Cape TownCape TownWCSouth Africa
- Department of Child and Adolescent PsychiatryTU DresdenGermany
| | - Greig I. de Zubicaray
- School of Psychology, Faculty of HealthQueensland University of Technology (QUT)BrisbaneQLDAustralia
| | - Katie L. McMahon
- School of Clinical SciencesQueensland University of Technology (QUT)BrisbaneQLDAustralia
| | - Margie Wright
- Queensland Brain InstituteUniversity of QueenslandBrisbaneQLDAustralia
| | - Derin Cobia
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of Psychology and Neuroscience CenterBrigham Young UniversityProvoUtahUSA
| | - John G. Csernansky
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark & Mary Stevens Neuroimaging & Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | | | - Lei Wang
- Department of Psychiatry and Behavioral SciencesNorthwestern University Feinberg School of MedicineChicagoIllinoisUSA
- Department of Psychiatry and Behavioral HealthOhio State University Wexner Medical CenterColumbusOhioUSA
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12
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Plechawska-Wójcik M, Karczmarek P, Krukow P, Kaczorowska M, Tokovarov M, Jonak K. Recognition of Electroencephalography-Related Features of Neuronal Network Organization in Patients With Schizophrenia Using the Generalized Choquet Integrals. Front Neuroinform 2022; 15:744355. [PMID: 34970131 PMCID: PMC8712566 DOI: 10.3389/fninf.2021.744355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 11/09/2021] [Indexed: 11/13/2022] Open
Abstract
In this study, we focused on the verification of suitable aggregation operators enabling accurate differentiation of selected neurophysiological features extracted from resting-state electroencephalographic recordings of patients who were diagnosed with schizophrenia (SZ) or healthy controls (HC). We built the Choquet integral-based operators using traditional classification results as an input to the procedure of establishing the fuzzy measure densities. The dataset applied in the study was a collection of variables characterizing the organization of the neural networks computed using the minimum spanning tree (MST) algorithms obtained from signal-spaced functional connectivity indicators and calculated separately for predefined frequency bands using classical linear Granger causality (GC) measure. In the series of numerical experiments, we reported the results of classification obtained using numerous generalizations of the Choquet integral and other aggregation functions, which were tested to find the most appropriate ones. The obtained results demonstrate that the classification accuracy can be increased by 1.81% using the extended versions of the Choquet integral called in the literature, namely, generalized Choquet integral or pre-aggregation operators.
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Affiliation(s)
| | - Paweł Karczmarek
- Department of Computer Science, Lublin University of Technology, Lublin, Poland
| | - Paweł Krukow
- Department of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland
| | - Monika Kaczorowska
- Department of Computer Science, Lublin University of Technology, Lublin, Poland
| | - Mikhail Tokovarov
- Department of Computer Science, Lublin University of Technology, Lublin, Poland
| | - Kamil Jonak
- Department of Computer Science, Lublin University of Technology, Lublin, Poland.,Department of Clinical Neuropsychiatry, Medical University of Lublin, Lublin, Poland
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13
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Choy KHC, Luo JK, Wannan CMJ, Laskaris L, Merritt A, Syeda WT, Sexton PM, Christopoulos A, Pantelis C, Nithianantharajah J. Cognitive behavioral markers of neurodevelopmental trajectories in rodents. Transl Psychiatry 2021; 11:556. [PMID: 34718322 PMCID: PMC8557208 DOI: 10.1038/s41398-021-01662-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 09/13/2021] [Accepted: 09/27/2021] [Indexed: 12/20/2022] Open
Abstract
Between adolescence and adulthood, the brain critically undergoes maturation and refinement of synaptic and neural circuits that shape cognitive processing. Adolescence also represents a vulnerable period for the onset of symptoms in neurodevelopmental psychiatric disorders. Despite the wide use of rodent models to unravel neurobiological mechanisms underlying neurodevelopmental disorders, there is a surprising paucity of rigorous studies focusing on normal cognitive-developmental trajectories in such models. Here, we sought to behaviorally capture maturational changes in cognitive trajectories during adolescence and into adulthood in male and female mice using distinct behavioral paradigms. C57 BL/6J mice (4.5, 6, and 12 weeks of age) were assessed on three behavioral paradigms: drug-induced locomotor hyperactivity, prepulse inhibition, and a novel validated version of a visuospatial paired-associate learning touchscreen task. We show that the normal maturational trajectories of behavioral performance on these paradigms are dissociable. Responses in drug-induced locomotor hyperactivity and prepulse inhibition both displayed a 'U-shaped' developmental trajectory; lower during mid-adolescence relative to early adolescence and adulthood. In contrast, visuospatial learning and memory, memory retention, and response times indicative of motivational processing progressively improved with age. Our study offers a framework to investigate how insults at different developmental stages might perturb normal trajectories in cognitive development. We provide a brain maturational approach to understand resilience factors of brain plasticity in the face of adversity and to examine pharmacological and non-pharmacological interventions directed at ameliorating or rescuing perturbed trajectories in neurodevelopmental and neuropsychiatric disorders.
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Affiliation(s)
- K. H. Christopher Choy
- grid.1002.30000 0004 1936 7857Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Melbourne, VIC Australia
| | - Jiaqi K. Luo
- grid.418025.a0000 0004 0606 5526The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC Australia ,grid.1008.90000 0001 2179 088XDepartment of Florey Neuroscience, University of Melbourne, Melbourne, VIC Australia
| | - Cassandra M. J. Wannan
- grid.1008.90000 0001 2179 088XMelbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, VIC Australia
| | - Liliana Laskaris
- grid.1008.90000 0001 2179 088XMelbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, VIC Australia
| | - Antonia Merritt
- grid.1008.90000 0001 2179 088XMelbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, VIC Australia
| | - Warda T. Syeda
- grid.1008.90000 0001 2179 088XMelbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, VIC Australia
| | - Patrick M. Sexton
- grid.1002.30000 0004 1936 7857Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Melbourne, VIC Australia ,grid.1002.30000 0004 1936 7857ARC Centre for Cryo-electron Microscopy of Membrane Proteins, Monash Institute of Pharmaceutical Sciences, Monash University, Melbourne, VIC Australia
| | - Arthur Christopoulos
- Drug Discovery Biology, Monash Institute of Pharmaceutical Sciences, Monash University, Melbourne, VIC, Australia. .,ARC Centre for Cryo-electron Microscopy of Membrane Proteins, Monash Institute of Pharmaceutical Sciences, Monash University, Melbourne, VIC, Australia.
| | - Christos Pantelis
- The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia. .,Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, VIC, Australia.
| | - Jess Nithianantharajah
- The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia. .,Department of Florey Neuroscience, University of Melbourne, Melbourne, VIC, Australia.
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14
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Guma E, Bordignon PDC, Devenyi GA, Gallino D, Anastassiadis C, Cvetkovska V, Barry AD, Snook E, Germann J, Greenwood CMT, Misic B, Bagot RC, Chakravarty MM. Early or Late Gestational Exposure to Maternal Immune Activation Alters Neurodevelopmental Trajectories in Mice: An Integrated Neuroimaging, Behavioral, and Transcriptional Study. Biol Psychiatry 2021; 90:328-341. [PMID: 34053674 DOI: 10.1016/j.biopsych.2021.03.017] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/23/2021] [Accepted: 03/15/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Exposure to maternal immune activation (MIA) in utero is a risk factor for neurodevelopmental disorders later in life. The impact of the gestational timing of MIA exposure on downstream development remains unclear. METHODS We characterized neurodevelopmental trajectories of mice exposed to the viral mimetic poly I:C (polyinosinic:polycytidylic acid) either on gestational day 9 (early) or on day 17 (late) using longitudinal structural magnetic resonance imaging from weaning to adulthood. Using multivariate methods, we related neuroimaging and behavioral variables for the time of greatest alteration (adolescence/early adulthood) and identified regions for further investigation using RNA sequencing. RESULTS Early MIA exposure was associated with accelerated brain volume increases in adolescence/early adulthood that normalized in later adulthood in the striatum, hippocampus, and cingulate cortex. Similarly, alterations in anxiety-like, stereotypic, and sensorimotor gating behaviors observed in adolescence normalized in adulthood. MIA exposure in late gestation had less impact on anatomical and behavioral profiles. Multivariate maps associated anxiety-like, social, and sensorimotor gating deficits with volume of the dorsal and ventral hippocampus and anterior cingulate cortex, among others. The most transcriptional changes were observed in the dorsal hippocampus, with genes enriched for fibroblast growth factor regulation, autistic behaviors, inflammatory pathways, and microRNA regulation. CONCLUSIONS Leveraging an integrated hypothesis- and data-driven approach linking brain-behavior alterations to the transcriptome, we found that MIA timing differentially affects offspring development. Exposure in late gestation leads to subthreshold deficits, whereas exposure in early gestation perturbs brain development mechanisms implicated in neurodevelopmental disorders.
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Affiliation(s)
- Elisa Guma
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada; Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada.
| | - Pedro do Couto Bordignon
- Department of Psychology, McGill University, Montreal, Quebec, Canada; Ludmer Center for Neuroinformatics and Mental Health, Montreal, Quebec, Canada
| | - Gabriel A Devenyi
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Daniel Gallino
- Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Chloe Anastassiadis
- Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Institute of Medical Science & Collaborative Program in Neuroscience, University of Toronto, Toronto, Ontario, Canada
| | | | - Amadou D Barry
- Departments of Human Genetics and Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada; Ludmer Center for Neuroinformatics and Mental Health, Montreal, Quebec, Canada
| | - Emily Snook
- Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Jurgen Germann
- Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada; University Health Network, Toronto, Ontario, Canada
| | - Celia M T Greenwood
- Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, Quebec, Canada; Departments of Human Genetics and Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada; Ludmer Center for Neuroinformatics and Mental Health, Montreal, Quebec, Canada
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Rosemary C Bagot
- Department of Psychology, McGill University, Montreal, Quebec, Canada; Ludmer Center for Neuroinformatics and Mental Health, Montreal, Quebec, Canada
| | - M Mallar Chakravarty
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada; Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada.
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15
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Fronto-Parietal Gray Matter Volume Loss Is Associated with Decreased Working Memory Performance in Adolescents with a First Episode of Psychosis. J Clin Med 2021; 10:jcm10173929. [PMID: 34501377 PMCID: PMC8432087 DOI: 10.3390/jcm10173929] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/26/2021] [Accepted: 08/28/2021] [Indexed: 11/16/2022] Open
Abstract
Cognitive maturation during adolescence is modulated by brain maturation. However, it is unknown how these processes intertwine in early onset psychosis (EOP). Studies examining longitudinal brain changes and cognitive performance in psychosis lend support for an altered development of high-order cognitive functions, which parallels progressive gray matter (GM) loss over time, particularly in fronto-parietal brain regions. We aimed to assess this relationship in a subsample of 33 adolescents with first-episode EOP and 47 matched controls over 2 years. Backwards stepwise regression analyses were conducted to determine the association and predictive value of longitudinal brain changes over cognitive performance within each group. Fronto-parietal GM volume loss was positively associated with decreased working memory in adolescents with psychosis (frontal left (B = 0.096, p = 0.008); right (B = 0.089, p = 0.015); parietal left (B = 0.119, p = 0.007), right (B = 0.125, p = 0.015)) as a function of age. A particular decrease in frontal left GM volume best predicted a significant amount (22.28%) of the variance of decreased working memory performance over time, accounting for variance in age (14.9%). No such association was found in controls. Our results suggest that during adolescence, EOP individuals seem to follow an abnormal neurodevelopmental trajectory, in which fronto-parietal GM volume reduction is associated with the differential age-related working memory dysfunction in this group.
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16
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Koike S, Uematsu A, Sasabayashi D, Maikusa N, Takahashi T, Ohi K, Nakajima S, Noda Y, Hirano Y. Recent Advances and Future Directions in Brain MR Imaging Studies in Schizophrenia: Toward Elucidating Brain Pathology and Developing Clinical Tools. Magn Reson Med Sci 2021; 21:539-552. [PMID: 34408115 DOI: 10.2463/mrms.rev.2021-0050] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Schizophrenia is a common severe psychiatric disorder that affects approximately 1% of general population through the life course. Historically, in Kraepelin's time, schizophrenia was a disease unit conceptualized as dementia praecox; however, since then, the disease concept has changed. Recent MRI studies had shown that the neuropathology of the brain in this disorder was characterized by mild progression before and after the onset of the disease, and that the brain alterations were relatively smaller than assumed. Although genetic factors contribute to the brain alterations in schizophrenia, which are thought to be trait differences, other changes include factors that are common in psychiatric diseases. Furthermore, it has been shown that the brain differences specific to schizophrenia were relatively small compared to other changes, such as those caused by brain development, aging, and gender. In addition, compared to the disease and participant factors, machine and imaging protocol differences could affect MRI signals, which should be addressed in multi-site studies. Recent advances in MRI modalities, such as multi-shell diffusion-weighted imaging, magnetic resonance spectroscopy, and multimodal brain imaging analysis, may be candidates to sharpen the characterization of schizophrenia-specific factors and provide new insights. The Brain/MINDS Beyond Human Brain MRI (BMB-HBM) project has been launched considering the differences and noises irrespective of the disease pathologies and includes the future perspectives of MRI studies for various psychiatric and neurological disorders. The sites use restricted MRI machines and harmonized multi-modal protocols, standardized image preprocessing, and traveling subject harmonization. Data sharing to the public will be planned in FY 2024. In the future, we believe that combining a high-quality human MRI dataset with genetic data, randomized controlled trials, and MRI for non-human primates and animal models will enable us to understand schizophrenia, elucidate its neural bases and therapeutic targets, and provide tools for clinical application at bedside.
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Affiliation(s)
- Shinsuke Koike
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo.,University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM).,University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB).,The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), The University of Tokyo
| | - Akiko Uematsu
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences.,Research Center for Idling Brain Science (RCIBS), University of Toyama
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences.,Research Center for Idling Brain Science (RCIBS), University of Toyama
| | - Kazutaka Ohi
- Department of Psychiatry and Psychotherapy, Gifu University Graduate School of Medicine
| | | | - Yoshihiro Noda
- Department of Neuropsychiatry, Keio University School of Medicine
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University.,Institute of Industrial Science, The University of Tokyo
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17
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Fagerlund B, Pantelis C, Jepsen JRM, Raghava JM, Rostrup E, Thomas MB, Nielsen MØ, Bojesen K, Jensen KG, Stentebjerg-Decara M, Klauber DG, Rudå D, Ebdrup BH, Jessen K, Sigvard A, Tangmose K, Jeppesen P, Correll CU, Fink-Jensen A, Pagsberg AK, Glenthøj BY. Differential effects of age at illness onset on verbal memory functions in antipsychotic-naïve schizophrenia patients aged 12-43 years. Psychol Med 2021; 51:1570-1580. [PMID: 32156323 DOI: 10.1017/s0033291720000409] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND The typical onset of schizophrenia coincides with the maturational peak in cognition; however, for a significant proportion of patients the onset is before age 18 and after age 30 years. While cognitive deficits are considered core features of schizophrenia, few studies have directly examined the impact of age of illness onset on cognition. METHODS The aim of the study was to examine if the effects of age on cognition differ between healthy controls (HCs) and patients with schizophrenia at illness onset. We examined 156 first-episode antipsychotic-naïve patients across a wide age span (12-43 years), and 161 age- and sex-matched HCs. Diagnoses were made according to ICD-10 criteria. Cognition was assessed using the Brief Assessment of Cognition in Schizophrenia (BACS), and IQ was estimated using subtests from the Wechsler adult- or child-intelligence scales. Multivariate analysis of covariance (MANCOVA) was used to examine linear and quadratic effects of age on cognitive scores and interactions by group, including sex and parental socioeconomic status as covariates. RESULTS There was a significant overall effect of age on BACS and IQ (p < 0.001). Significant group-by-age interactions for verbal memory (for age-squared, p = 0.009), and digit sequencing (for age, p = 0.01; age-squared, p < 0.001), indicated differential age-related trajectories between patients and HCs. CONCLUSIONS Cognitive functions showing protracted maturation into adulthood, such as verbal memory and verbal working memory, may be particularly impaired in both early- and late-schizophrenia onset. Our findings indicate a potential interaction between the timing of neurodevelopmental maturation and a possible premature age effect in late-onset schizophrenia.
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Affiliation(s)
- Birgitte Fagerlund
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, Glostrup, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Christos Pantelis
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, Glostrup, Denmark
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
| | - Jens Richardt Møllegaard Jepsen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, Glostrup, Denmark
- Mental Health Services, Capital Region of Denmark, Child and Adolescent Mental Health Center, Copenhagen, Denmark
| | - Jayachandra Mitta Raghava
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, Glostrup, Denmark
- Functional Imaging Unit, Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet Glostrup, 2600 Glostrup, Denmark
| | - Egill Rostrup
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, Glostrup, Denmark
| | - Marie Bjerregaard Thomas
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, Glostrup, Denmark
| | - Mette Ødegaard Nielsen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, Glostrup, Denmark
- University of Copenhagen, Faculty of Health and Medical Sciences, Department of Clinical Medicine, Copenhagen, Denmark
| | - Kirsten Bojesen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, Glostrup, Denmark
| | - Karsten Gjessing Jensen
- Mental Health Services, Capital Region of Denmark, Child and Adolescent Mental Health Center, Copenhagen, Denmark
- University of Copenhagen, Faculty of Health and Medical Sciences, Department of Clinical Medicine, Copenhagen, Denmark
| | - Marie Stentebjerg-Decara
- Mental Health Services, Capital Region of Denmark, Child and Adolescent Mental Health Center, Copenhagen, Denmark
- University of Copenhagen, Faculty of Health and Medical Sciences, Department of Clinical Medicine, Copenhagen, Denmark
| | - Dea Gowers Klauber
- Mental Health Services, Capital Region of Denmark, Child and Adolescent Mental Health Center, Copenhagen, Denmark
- University of Copenhagen, Faculty of Health and Medical Sciences, Department of Clinical Medicine, Copenhagen, Denmark
| | - Ditte Rudå
- Mental Health Services, Capital Region of Denmark, Child and Adolescent Mental Health Center, Copenhagen, Denmark
- University of Copenhagen, Faculty of Health and Medical Sciences, Department of Clinical Medicine, Copenhagen, Denmark
| | - Bjørn H Ebdrup
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, Glostrup, Denmark
- University of Copenhagen, Faculty of Health and Medical Sciences, Department of Clinical Medicine, Copenhagen, Denmark
| | - Kasper Jessen
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, Glostrup, Denmark
| | - Anne Sigvard
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, Glostrup, Denmark
- University of Copenhagen, Faculty of Health and Medical Sciences, Department of Clinical Medicine, Copenhagen, Denmark
| | - Karen Tangmose
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, Glostrup, Denmark
- University of Copenhagen, Faculty of Health and Medical Sciences, Department of Clinical Medicine, Copenhagen, Denmark
| | - Pia Jeppesen
- Mental Health Services, Capital Region of Denmark, Child and Adolescent Mental Health Center, Copenhagen, Denmark
- University of Copenhagen, Faculty of Health and Medical Sciences, Department of Clinical Medicine, Copenhagen, Denmark
| | - Christoph U Correll
- The Zucker Hillside Hospital, Department of Psychiatry, Northwell Health, Glen Oaks, NY, USA
- Zucker School of Medicine at Hofstra/Northwell, Department of Psychiatry and Molecular Medicine, Hempstead, NY, USA
- Charité Universitätsmedizin Berlin, Department of Child and Adolescent Psychiatry, Berlin, Germany
| | - Anders Fink-Jensen
- University of Copenhagen, Faculty of Health and Medical Sciences, Department of Clinical Medicine, Copenhagen, Denmark
- Mental Health Center Copenhagen, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Anne Katrine Pagsberg
- Mental Health Services, Capital Region of Denmark, Child and Adolescent Mental Health Center, Copenhagen, Denmark
- University of Copenhagen, Faculty of Health and Medical Sciences, Department of Clinical Medicine, Copenhagen, Denmark
| | - Birte Yding Glenthøj
- Center for Neuropsychiatric Schizophrenia Research (CNSR) and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research (CINS), Mental Health Center Glostrup, Glostrup, Denmark
- University of Copenhagen, Faculty of Health and Medical Sciences, Department of Clinical Medicine, Copenhagen, Denmark
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18
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Avery SN, McHugo M, Armstrong K, Blackford JU, Woodward ND, Heckers S. Stable habituation deficits in the early stage of psychosis: a 2-year follow-up study. Transl Psychiatry 2021; 11:20. [PMID: 33414431 PMCID: PMC7791099 DOI: 10.1038/s41398-020-01167-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 12/01/2020] [Accepted: 12/09/2020] [Indexed: 01/29/2023] Open
Abstract
Neural habituation, the decrease in brain response to repeated stimuli, is a fundamental, highly conserved mechanism that acts as an essential filter for our complex sensory environment. Convergent evidence indicates neural habituation is disrupted in both early and chronic stages of schizophrenia, with deficits co-occurring in brain regions that show inhibitory dysfunction. As inhibitory deficits have been proposed to contribute to the onset and progression of illness, habituation may be an important treatment target. However, a crucial first step is clarifying whether habituation deficits progress with illness. In the present study, we measured neural habituation in 138 participants (70 early psychosis patients (<2 years of illness), 68 healthy controls), with 108 participants assessed longitudinally at both baseline and 2-year follow-up. At follow-up, all early psychosis patients met criteria for a schizophrenia spectrum disorder (i.e., schizophreniform disorder, schizophrenia, schizoaffective disorder). Habituation slopes (i.e., rate of fMRI signal change) to repeated images were computed for the anterior hippocampus, occipital cortex, and the fusiform face area. Habituation slopes were entered into a linear mixed model to test for effects of group and time by region. We found that early psychosis patients showed habituation deficits relative to healthy control participants across brain regions, and that these deficits were maintained, but did not worsen, over two years. These results suggest a stable period of habituation deficits in the early stage of schizophrenia.
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Affiliation(s)
- Suzanne N. Avery
- grid.412807.80000 0004 1936 9916Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - Maureen McHugo
- grid.412807.80000 0004 1936 9916Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - Kristan Armstrong
- grid.412807.80000 0004 1936 9916Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - Jennifer Urbano Blackford
- grid.412807.80000 0004 1936 9916Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA ,grid.413806.8Research Health Scientist, Research and Development, Department of Veterans Affairs Medical Center, Nashville, TN USA
| | - Neil D. Woodward
- grid.412807.80000 0004 1936 9916Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - Stephan Heckers
- grid.412807.80000 0004 1936 9916Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA
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19
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Rürup L, Mathes B, Schmiedt-Fehr C, Wienke AS, Özerdem A, Brand A, Basar-Eroglu C. Altered gamma and theta oscillations during multistable perception in schizophrenia. Int J Psychophysiol 2020; 155:127-139. [DOI: 10.1016/j.ijpsycho.2020.06.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 06/01/2020] [Accepted: 06/05/2020] [Indexed: 12/24/2022]
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20
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A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data. Transl Psychiatry 2020; 10:276. [PMID: 32778656 PMCID: PMC7417553 DOI: 10.1038/s41398-020-00962-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 07/14/2020] [Accepted: 07/22/2020] [Indexed: 11/24/2022] Open
Abstract
The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework. We included 138 antipsychotic-naïve, first-episode schizophrenia patients with data on psychopathology, cognition, electrophysiology, and structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Short-term treatment response was defined as change in Positive And Negative Syndrome Score (PANSS) after the initial antipsychotic treatment period. Baseline diagnostic classification algorithms also included data from 151 matched controls. Both approaches significantly classified patients from healthy controls with a balanced accuracy of 63.8% and 64.2%, respectively. Post-hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- nor long-term treatment response. Validation of the framework showed that choice of algorithm and parameter settings in the real data was successfully guided by results from the simulated data. In conclusion, this novel approach holds promise as an important step to minimize bias and obtain reliable results with modest sample sizes when independent replication samples are not available.
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21
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Avery SN, Armstrong K, McHugo M, Vandekar S, Blackford JU, Woodward ND, Heckers S. Relational Memory in the Early Stage of Psychosis: A 2-Year Follow-up Study. Schizophr Bull 2020; 47:75-86. [PMID: 32657351 PMCID: PMC7825006 DOI: 10.1093/schbul/sbaa081] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Relational memory, the ability to bind information into complex memories, is moderately impaired in early psychosis and severely impaired in chronic schizophrenia, suggesting relational memory may worsen throughout the course of illness. METHODS We examined relational memory in 66 early psychosis patients and 64 healthy control subjects, with 59 patients and 52 control subjects assessed longitudinally at baseline and 2-year follow-up. Relational memory was assessed with 2 complementary tasks, to test how individuals learn relationships between items (face-scene binding task) and make inferences about trained relationships (associative inference task). RESULTS The early psychosis group showed impaired relational memory in both tasks relative to the healthy control group. The ability to learn relationships between items remained impaired in early psychosis patients, while the ability to make inferences about trained relationships improved, although never reaching the level of healthy control performance. Early psychosis patients who did not progress to schizophrenia at follow-up had better relational memory than patients who did. CONCLUSIONS Relational memory impairments, some of which improve and are less severe in patients who do not progress to schizophrenia, are a target for intervention in early psychosis.
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Affiliation(s)
- Suzanne N Avery
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Kristan Armstrong
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Maureen McHugo
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Jennifer Urbano Blackford
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN,Department of Research and Development, Veterans Affairs Medical Center, Nashville, TN
| | - Neil D Woodward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Stephan Heckers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN,To whom correspondence should be addressed; Vanderbilt Psychiatric Hospital, 1601 23rd Avenue South, Room 3060, Nashville, TN 37212; tel: (615)-322-2665, fax: (615)-343-8400, e-mail:
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22
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Vanes LD, Mouchlianitis E, Patel K, Barry E, Wong K, Thomas M, Szentgyorgyi T, Joyce D, Shergill S. Neural correlates of positive and negative symptoms through the illness course: an fMRI study in early psychosis and chronic schizophrenia. Sci Rep 2019; 9:14444. [PMID: 31595009 PMCID: PMC6783468 DOI: 10.1038/s41598-019-51023-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 09/23/2019] [Indexed: 12/14/2022] Open
Abstract
Psychotic illness is associated with cognitive control deficits and abnormal recruitment of neural circuits subserving cognitive control. It is unclear to what extent this dysfunction underlies the development and/or maintenance of positive and negative symptoms typically observed in schizophrenia. In this study we compared fMRI activation on a standard Stroop task and its relationship with positive and negative symptoms in early psychosis (EP, N = 88) and chronic schizophrenia (CHR-SZ, N = 38) patients. CHR-SZ patients showed reduced frontal, striatal, and parietal activation across incongruent and congruent trials compared to EP patients. Higher positive symptom severity was associated with reduced activation across both trial types in supplementary motor area (SMA), middle temporal gyrus and cerebellum in EP, but not CHR-SZ patients. Higher negative symptom severity was associated with reduced cerebellar activation in EP, but not in CHR-SZ patients. A negative correlation between negative symptoms and activation in SMA and precentral gyrus was observed in EP patients and in CHR-SZ patients. The results suggest that the neural substrate of positive symptoms changes with illness chronicity, and that cognitive control related neural circuits may be most relevant in the initial development phase of positive symptoms. These findings also highlight a changing role for the cerebellum in the development and later maintenance of both positive and negative symptoms.
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Affiliation(s)
- Lucy D Vanes
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London, WC1N 3AR, United Kingdom.
| | - Elias Mouchlianitis
- Institute of Psychiatry, Psychology and Neuroscience, de Crespigny Park, London, SE5 8AF, United Kingdom
| | - Krisna Patel
- Institute of Psychiatry, Psychology and Neuroscience, de Crespigny Park, London, SE5 8AF, United Kingdom
| | - Erica Barry
- Institute Department of Clinical Sciences, Cornell University College of Veterinary Medicine, Ithaca, NY, USA
| | - Katie Wong
- Institute of Psychiatry, Psychology and Neuroscience, de Crespigny Park, London, SE5 8AF, United Kingdom
| | - Megan Thomas
- Institute of Psychiatry, Psychology and Neuroscience, de Crespigny Park, London, SE5 8AF, United Kingdom
| | - Timea Szentgyorgyi
- Institute of Psychiatry, Psychology and Neuroscience, de Crespigny Park, London, SE5 8AF, United Kingdom
| | - Dan Joyce
- Institute of Psychiatry, Psychology and Neuroscience, de Crespigny Park, London, SE5 8AF, United Kingdom
| | - Sukhwinder Shergill
- Institute of Psychiatry, Psychology and Neuroscience, de Crespigny Park, London, SE5 8AF, United Kingdom
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23
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Niznikiewicz MA. Neurobiological approaches to the study of clinical and genetic high risk for developing psychosis. Psychiatry Res 2019; 277:17-22. [PMID: 30926150 DOI: 10.1016/j.psychres.2019.02.009] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 02/04/2019] [Accepted: 02/04/2019] [Indexed: 01/12/2023]
Abstract
Research on neurobiological impairments in clinical and genetic high risk for developing psychosis individuals (CHR) has identified several brain abnormalities that impact both brain structure and function. The current review will discuss research examining brain abnormalities in clinical and genetic high risk for psychosis using magnetic resonance imaging (MRI) focusing on structural brain abnormalities, diffusion tensor imaging (DTI) focusing on the integrity of white matter tracks, functional MRI focusing on functional brain abnormalities, and EEG and event related potential (ERP) methodologies focusing on indices of cognitive dysfunction in CHR. Studies conducted across these different methodologies sought to identify brain regions and brain processes that would distinguish between those high risk individuals who converted to psychosis versus those who did not. In addition, in some of the studies, the distinction was made between individuals who converted to psychosis, those who did not, and those individuals who remained clinically symptomatic while not converting to psychosis. The brain regions most often identified as abnormal in this subject group were the brain areas often found abnormal in schizophrenia, including frontal and temporal regions. Similarly, several cognitive processes often found to be abnormal in schizophrenia have been also found impaired in CHR.
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Affiliation(s)
- Margaret A Niznikiewicz
- Harvard Medical School and Veterans Administration Boston, Healthcare System, United States.
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24
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Avery SN, McHugo M, Armstrong K, Blackford JU, Woodward ND, Heckers S. Disrupted Habituation in the Early Stage of Psychosis. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2019; 4:1004-1012. [PMID: 31445881 DOI: 10.1016/j.bpsc.2019.06.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 06/08/2019] [Accepted: 06/10/2019] [Indexed: 10/26/2022]
Abstract
BACKGROUND Learning and memory are impaired in schizophrenia. Some theories have proposed that one form of memory, habituation, is particularly impaired. Preliminary evidence suggests that memory impairment is associated with failed hippocampal habituation in patients with chronic schizophrenia. We studied how abnormal habituation of the hippocampus is related to relational memory deficits in the early stage of psychosis. METHODS We measured hippocampal activity in 62 patients with early psychosis and 70 healthy individuals using functional magnetic resonance imaging. Habituation was defined as the slope of functional magnetic resonance imaging signal change to repeated presentations of faces and objects. Relational memory ability was measured as the slope of preferential viewing during a face-scene pair eye movement task outside the scanner. RESULTS Patients with early psychosis showed impaired relational memory (p < .001) and less hippocampal habituation to objects (p = .01) than healthy control subjects. In the healthy control group, better relational memory was associated with faster anterior hippocampal habituation (faces, r = -.28, p = .03). In contrast, patients with early psychosis showed no brain-behavior relationship (r = .12, p = .40). CONCLUSIONS We found evidence for disrupted hippocampal habituation in the early stage of psychosis along with an altered association between hippocampal habituation and relational memory ability. These results suggest that neural habituation may provide a novel target for early cognitive interventions in psychosis.
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Affiliation(s)
- Suzanne N Avery
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee.
| | - Maureen McHugo
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Kristan Armstrong
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jennifer U Blackford
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Neil D Woodward
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Stephan Heckers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, Tennessee
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25
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Wienke AS, Basar-Eroglu C, Schmiedt-Fehr C, Mathes B. Novelty N2-P3a Complex and Theta Oscillations Reflect Improving Neural Coordination Within Frontal Brain Networks During Adolescence. Front Behav Neurosci 2018; 12:218. [PMID: 30319369 PMCID: PMC6170662 DOI: 10.3389/fnbeh.2018.00218] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Accepted: 08/29/2018] [Indexed: 12/02/2022] Open
Abstract
Adolescents are easily distracted by novel items than adults. Maturation of the frontal cortex and its integration into widely distributed brain networks may result in diminishing distractibility with the transition into young adulthood. The aim of this study was to investigate maturational changes of brain activity during novelty processing. We hypothesized that during adolescence, timing and task-relevant modulation of frontal cortex network activity elicited by novelty processing improves, concurrently with increasing cognitive control abilities. A visual novelty oddball task was utilized in combination with EEG measurements to investigate brain maturation between 8–28 years of age (n = 84). Developmental changes of the frontal N2-P3a complex and concurrent theta oscillations (4–7 Hz) elicited by rare and unexpected novel stimuli were analyzed using regression models. N2 amplitude decreased, P3a amplitude increased, and latency of both components decreased with age. Pre-stimulus amplitude of theta oscillations decreased, while inter-trial consistency, task-related amplitude modulation and inter-site connectivity of frontal theta oscillations increased with age. Targets, intertwined in a stimulus train with regular non-targets and novels, were detected faster with increasing age. These results indicate that neural processing of novel stimuli became faster and the neural activation pattern more precise in timing and amplitude modulation. Better inter-site connectivity further implicates that frontal brain maturation leads to global neural reorganization and better integration of frontal brain activity within widely distributed brain networks. Faster target detection indicated that these maturational changes in neural activation during novelty processing may result in diminished distractibility and increased cognitive control to pursue the task.
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Affiliation(s)
- Annika Susann Wienke
- Institute of Psychology and Cognition Research & Center of Cognitive Science, University of Bremen, Bremen, Germany
| | - Canan Basar-Eroglu
- Institute of Psychology and Cognition Research & Center of Cognitive Science, University of Bremen, Bremen, Germany.,Izmir University of Economy, Izmir, Turkey
| | - Christina Schmiedt-Fehr
- Institute of Psychology and Cognition Research & Center of Cognitive Science, University of Bremen, Bremen, Germany
| | - Birgit Mathes
- Institute of Psychology and Cognition Research & Center of Cognitive Science, University of Bremen, Bremen, Germany
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26
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Ganella EP, Seguin C, Pantelis C, Whittle S, Baune BT, Olver J, Amminger GP, McGorry PD, Cropley V, Zalesky A, Bartholomeusz CF. Resting-state functional brain networks in first-episode psychosis: A 12-month follow-up study. Aust N Z J Psychiatry 2018; 52:864-875. [PMID: 29806483 DOI: 10.1177/0004867418775833] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Schizophrenia is increasingly conceived as a disorder of brain network connectivity and organization. However, reports of network abnormalities during the early illness stage of psychosis are mixed. This study adopted a data-driven whole-brain approach to investigate functional connectivity and network architecture in a first-episode psychosis cohort relative to healthy controls and whether functional network properties changed abnormally over a 12-month period in first-episode psychosis. METHODS Resting-state functional connectivity was performed at two time points. At baseline, 29 first-episode psychosis individuals and 30 healthy controls were assessed, and at 12 months, 14 first-episode psychosis individuals and 20 healthy controls completed follow-up. Whole-brain resting-state functional connectivity networks were mapped for each individual and analyzed using graph theory to investigate whether network abnormalities associated with first-episode psychosis were evident and whether functional network properties changed abnormally over 12 months relative to controls. RESULTS This study found no evidence of abnormal resting-state functional connectivity or topology in first-episode psychosis individuals relative to healthy controls at baseline or at 12-months follow-up. Furthermore, longitudinal changes in network properties over a 12-month period did not significantly differ between first-episode psychosis individuals and healthy control. Network measures did not significantly correlate with symptomatology, duration of illness or antipsychotic medication. CONCLUSIONS This is the first study to show unaffected resting-state functional connectivity and topology in the early psychosis stage of illness. In light of previous literature, this suggests that a subgroup of first-episode psychosis individuals who have a neurotypical resting-state functional connectivity and topology may exist. Our preliminary longitudinal analyses indicate that there also does not appear to be deterioration in these network properties over a 12-month period. Future research in a larger sample is necessary to confirm our longitudinal findings.
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Affiliation(s)
- Eleni P Ganella
- 1 Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton South, VIC, Australia.,2 Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia.,3 The Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia.,4 The Cooperative Research Centre (CRC) for Mental Health, Carlton South, VIC, Australia.,5 NorthWestern Mental Health, Melbourne Health, Parkville, VIC, Australia
| | - Caio Seguin
- 1 Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton South, VIC, Australia
| | - Christos Pantelis
- 1 Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton South, VIC, Australia.,4 The Cooperative Research Centre (CRC) for Mental Health, Carlton South, VIC, Australia.,5 NorthWestern Mental Health, Melbourne Health, Parkville, VIC, Australia.,6 The Florey Institute of Neurosciences & Mental Health, Parkville, VIC, Australia.,7 Centre for Neural Engineering, Department of Electrical and Electronic Engineering, The University of Melbourne, Carlton South, VIC, Australia.,8 Melbourne School of Engineering, The University of Melbourne, Parkville, VIC, Australia
| | - Sarah Whittle
- 1 Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton South, VIC, Australia.,9 Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Bernhard T Baune
- 10 Discipline of Psychiatry, The University of Adelaide, Adelaide, SA, Australia
| | - James Olver
- 11 Department of Psychiatry, The University of Melbourne, Heidelberg, VIC, Australia
| | - G Paul Amminger
- 2 Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia.,3 The Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Patrick D McGorry
- 2 Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia.,3 The Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Vanessa Cropley
- 1 Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton South, VIC, Australia
| | - Andrew Zalesky
- 1 Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton South, VIC, Australia.,8 Melbourne School of Engineering, The University of Melbourne, Parkville, VIC, Australia
| | - Cali F Bartholomeusz
- 1 Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Carlton South, VIC, Australia.,2 Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia.,3 The Centre for Youth Mental Health, The University of Melbourne, Melbourne, VIC, Australia
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Takahashi T, Suzuki M. Brain morphologic changes in early stages of psychosis: Implications for clinical application and early intervention. Psychiatry Clin Neurosci 2018; 72:556-571. [PMID: 29717522 DOI: 10.1111/pcn.12670] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/23/2018] [Indexed: 12/20/2022]
Abstract
To date, a large number of magnetic resonance imaging (MRI) studies have been conducted in schizophrenia, which generally demonstrate gray matter reduction, predominantly in the frontal and temporo-limbic regions, as well as gross brain abnormalities (e.g., a deviated sulcogyral pattern). Although the causes as well as timing and course of these findings remain elusive, these morphologic changes (especially gross brain abnormalities and medial temporal lobe atrophy) are likely present at illness onset, possibly reflecting early neurodevelopmental abnormalities. In addition, longitudinal MRI studies suggest that patients with schizophrenia and related psychoses also have progressive gray matter reduction during the transition period from prodrome to overt psychosis, as well as initial periods after psychosis onset, while such changes may become almost stable in the chronic stage. These active brain changes during the early phases seem to be relevant to the development of clinical symptoms in a region-specific manner (e.g., superior temporal gyrus atrophy and positive psychotic symptoms), but may be at least partly ameliorated by antipsychotic medication. Recently, increasing evidence from MRI findings in individuals at risk for developing psychosis has suggested that those who subsequently develop psychosis have baseline brain changes, which could be at least partly predictive of later transition into psychosis. In this article, we selectively review previous MRI findings during the course of psychosis and also refer to the possible clinical applicability of these neuroimaging research findings, especially in the diagnosis of schizophrenia and early intervention for psychosis.
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Affiliation(s)
- Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
| | - Michio Suzuki
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
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Abstract
SummaryRevisions of international classification systems for mental disorders have focused on improving the reliability of diagnostic criteria. However, the uncertain validity of the current diagnostic categories means that they do not always fulfil their key purposes, namely to guide treatment and predict outcomes. This is especially true when traditional diagnostic approaches are applied to adolescents and young adults with emerging illnesses. A clinical staging model, similar to those used in general medicine, could improve diagnosis in psychiatry and aid treatment decision-making, especially if applied to individuals aged about 15–25 years, which is the peak age range for the onset of severe mental disorders. Staging models may offer a new framework for the development of interventions with high benefit and low risk, and for research into neurobiological and psychosocial risk factors. However, this approach is not without controversy: some experts oppose its introduction, some argue that it represents a transdiagnostic model, and some suggest it is only viable if disorder-specific models are used.Learning Objectives• Gain awareness of some limitations of current approaches to psychiatric diagnosis• Review the basic principles of clinical staging models used in general medicine• Understand current research on the use of staging models in psychiatry, and attempts to apply these models to bipolar disorders
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29
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Murphy BP, Brewer WJ. Early intervention in psychosis: strengths and limitations of services. ACTA ACUST UNITED AC 2018. [DOI: 10.1192/apt.bp.110.008573] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
SummaryEarly intervention services were established on the basis of a number of fundamental principles, including the notions that intervening in the early stages of psychosis alters illness trajectory and prognosis, that multicomponent interventions promote psychosocial recovery and reduce iatrogenic damage, and that early targeting of non-responders reduces treatment resistance. There is growing evidence of the benefits of specialised early intervention services. These include improved clinical, social and vocational outcomes, reduced in-patient stays and better engagement. Early intervention services can also significantly reduce the risk of a second episode and are highly valued by service users and carers. Duration of treatment appears to determine long-term outcome and there remains uncertainty about how long such intensive intervention should last and whether all patients need the same length of care. Budgetary constraints are pervasive and are particularly likely to affect prodrome clinics and community awareness programmes.
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Wannan CMJ, Bartholomeusz CF, Cropley VL, Van Rheenen TE, Panayiotou A, Brewer WJ, Proffitt TM, Henry L, Harris MG, Velakoulis D, McGorry P, Pantelis C, Wood SJ. Deterioration of visuospatial associative memory following a first psychotic episode: a long-term follow-up study. Psychol Med 2018; 48:132-141. [PMID: 28625185 DOI: 10.1017/s003329171700157x] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND Cognitive deficits are a core feature of schizophrenia, and impairments in most domains are thought to be stable over the course of the illness. However, cross-sectional evidence indicates that some areas of cognition, such as visuospatial associative memory, may be preserved in the early stages of psychosis, but become impaired in later established illness stages. This longitudinal study investigated change in visuospatial and verbal associative memory following psychosis onset. METHODS In total 95 first-episode psychosis (FEP) patients and 63 healthy controls (HC) were assessed on neuropsychological tests at baseline, with 38 FEP and 22 HCs returning for follow-up assessment at 5-11 years. Visuospatial associative memory was assessed using the Cambridge Neuropsychological Test Automated Battery Visuospatial Paired-Associate Learning task, and verbal associative memory was assessed using Verbal Paired Associates subtest of the Wechsler Memory Scale - Revised. RESULTS Visuospatial and verbal associative memory at baseline did not differ significantly between FEP patients and HCs. However, over follow-up, visuospatial associative memory deteriorated significantly for the FEP group, relative to healthy individuals. Conversely, verbal associative memory improved to a similar degree observed in HCs. In the FEP cohort, visuospatial (but not verbal) associative memory ability at baseline was associated with functional outcome at follow-up. CONCLUSIONS Areas of cognition that develop prior to psychosis onset, such as visuospatial and verbal associative memory, may be preserved early in the illness. Later deterioration in visuospatial memory ability may relate to progressive structural and functional brain abnormalities that occurs following psychosis onset.
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Affiliation(s)
- C M J Wannan
- Melbourne Neuropsychiatry Centre,Department of Psychiatry,The University of Melbourne & Melbourne Health,Carlton South, VIC,Australia
| | - C F Bartholomeusz
- Melbourne Neuropsychiatry Centre,Department of Psychiatry,The University of Melbourne & Melbourne Health,Carlton South, VIC,Australia
| | - V L Cropley
- Melbourne Neuropsychiatry Centre,Department of Psychiatry,The University of Melbourne & Melbourne Health,Carlton South, VIC,Australia
| | - T E Van Rheenen
- Melbourne Neuropsychiatry Centre,Department of Psychiatry,The University of Melbourne & Melbourne Health,Carlton South, VIC,Australia
| | - A Panayiotou
- Melbourne Neuropsychiatry Centre,Department of Psychiatry,The University of Melbourne & Melbourne Health,Carlton South, VIC,Australia
| | - W J Brewer
- Orygen, The National Centre of Excellence in Youth Mental Health,Parkville, Victoria,Australia
| | - T M Proffitt
- Orygen, The National Centre of Excellence in Youth Mental Health,Parkville, Victoria,Australia
| | - L Henry
- Orygen, The National Centre of Excellence in Youth Mental Health,Parkville, Victoria,Australia
| | - M G Harris
- School of Public Health,The University of Queensland,Herston, Queensland,Australia
| | - D Velakoulis
- Melbourne Neuropsychiatry Centre,Department of Psychiatry,The University of Melbourne & Melbourne Health,Carlton South, VIC,Australia
| | - P McGorry
- Orygen, The National Centre of Excellence in Youth Mental Health,Parkville, Victoria,Australia
| | - C Pantelis
- Melbourne Neuropsychiatry Centre,Department of Psychiatry,The University of Melbourne & Melbourne Health,Carlton South, VIC,Australia
| | - S J Wood
- Melbourne Neuropsychiatry Centre,Department of Psychiatry,The University of Melbourne & Melbourne Health,Carlton South, VIC,Australia
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Di Biase MA, Cropley VL, Baune BT, Olver J, Amminger GP, Phassouliotis C, Bousman C, McGorry PD, Everall I, Pantelis C, Zalesky A. White matter connectivity disruptions in early and chronic schizophrenia. Psychol Med 2017; 47:2797-2810. [PMID: 28528586 DOI: 10.1017/s0033291717001313] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND White matter disruptions in schizophrenia have been widely reported, but it remains unclear whether these abnormalities differ between illness stages. We mapped the connectome in patients with recently diagnosed and chronic schizophrenia and investigated the extent and overlap of white matter connectivity disruptions between these illness stages. METHODS Diffusion-weighted magnetic resonance images were acquired in recent-onset (n = 19) and chronic patients (n = 45) with schizophrenia, as well as age-matched controls (n = 87). Whole-brain fiber tracking was performed to quantify the strength of white matter connections. Connections were tested for significant streamline count reductions in recent-onset and chronic groups, relative to separate age-matched controls. Permutation tests were used to assess whether disrupted connections significantly overlapped between chronic and recent-onset patients. Linear regression was performed to test whether connectivity was strongest in controls, weakest in chronic patients, and midway between these extremities in recent-onset patients (controls > recent-onset > chronic). RESULTS Compared with controls, chronic patients displayed a widespread network of connectivity disruptions (p < 0.01). In contrast, connectivity reductions were circumscribed to the anterior fibers of the corpus callosum in recent-onset patients (p < 0.01). A significant proportion of disrupted connections in recent-onset patients (86%) coincided with disrupted connections in chronic patients (p < 0.01). Linear regression revealed that chronic patients displayed reduced connectivity relative to controls, while recent-onset patients showed an intermediate reduction compared with chronic patients (p < 0.01). CONCLUSIONS Connectome pathology in recent-onset patients with schizophrenia is confined to select tracts within a more extensive network of white matter connectivity disruptions found in chronic illness. These findings may suggest a trajectory of progressive deterioration of connectivity in schizophrenia.
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Affiliation(s)
- M A Di Biase
- Department of Psychiatry,Melbourne Neuropsychiatry Centre,The University of Melbourne and Melbourne Health,Carlton South, VIC,Australia
| | - V L Cropley
- Department of Psychiatry,Melbourne Neuropsychiatry Centre,The University of Melbourne and Melbourne Health,Carlton South, VIC,Australia
| | - B T Baune
- Discipline of Psychiatry,The University of Adelaide,SA,Australia
| | - J Olver
- Department of Psychiatry,The University of Melbourne,Parkville, VIC,Australia
| | - G P Amminger
- Orygen,The National Centre of Excellence in Youth Mental Health,VIC,Australia
| | - C Phassouliotis
- Department of Psychiatry,Melbourne Neuropsychiatry Centre,The University of Melbourne and Melbourne Health,Carlton South, VIC,Australia
| | - C Bousman
- Department of Psychiatry,Melbourne Neuropsychiatry Centre,The University of Melbourne and Melbourne Health,Carlton South, VIC,Australia
| | - P D McGorry
- North Western Mental Health,Melbourne Health,Parkville, VIC,Australia
| | - I Everall
- Department of Psychiatry,The University of Melbourne,Parkville, VIC,Australia
| | - C Pantelis
- Department of Psychiatry,Melbourne Neuropsychiatry Centre,The University of Melbourne and Melbourne Health,Carlton South, VIC,Australia
| | - A Zalesky
- Department of Psychiatry,Melbourne Neuropsychiatry Centre,The University of Melbourne and Melbourne Health,Carlton South, VIC,Australia
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32
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Oxytocin effects in schizophrenia: Reconciling mixed findings and moving forward. Neurosci Biobehav Rev 2017; 80:36-56. [PMID: 28506922 DOI: 10.1016/j.neubiorev.2017.05.007] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2016] [Revised: 05/06/2017] [Accepted: 05/09/2017] [Indexed: 12/22/2022]
Abstract
Schizophrenia is a severe mental illness that causes major functional impairment. Current pharmacologic treatments are inadequate, particularly for addressing negative and cognitive symptoms of the disorder. Oxytocin, a neuropeptide known to moderate social behaviors, has been investigated as a potential therapeutic for schizophrenia in recent years. Results have been decidedly mixed, leading to controversy regarding oxytocin's utility. In this review, we outline several considerations for interpreting the extant literature and propose a focused agenda for future work that builds on the most compelling findings regarding oxytocin effects in schizophrenia to date. Specifically, we examine underlying causes of heterogeneity in randomized clinical trials (RCTs) conducted thus far and highlight the complexity of the human oxytocin system. We then review evidence of oxytocin's effects on specific deficits in schizophrenia, arguing for further study using objective, precise outcome measures in order to determine whether oxytocin has the potential to improve functional impairment in schizophrenia.
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33
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Bartholomeusz CF, Cropley VL, Wannan C, Di Biase M, McGorry PD, Pantelis C. Structural neuroimaging across early-stage psychosis: Aberrations in neurobiological trajectories and implications for the staging model. Aust N Z J Psychiatry 2017; 51:455-476. [PMID: 27733710 DOI: 10.1177/0004867416670522] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
OBJECTIVE This review critically examines the structural neuroimaging evidence in psychotic illness, with a focus on longitudinal imaging across the first-episode psychosis and ultra-high-risk of psychosis illness stages. METHODS A thorough search of the literature involving specifically longitudinal neuroimaging in early illness stages of psychosis was conducted. The evidence supporting abnormalities in brain morphology and altered neurodevelopmental trajectories is discussed in the context of a clinical staging model. RESULTS In general, grey matter (and, to a lesser extent, white matter) declines across multiple frontal, temporal (especially superior regions), insular and parietal regions during the first episode of psychosis, which has a steeper trajectory than that of age-matched healthy counterparts. Although the ultra-high-risk of psychosis literature is considerably mixed, evidence indicates that certain volumetric structural aberrations predate psychotic illness onset (e.g. prefrontal cortex thinning), while other abnormalities present in ultra-high-risk of psychosis populations are potentially non-psychosis-specific (e.g. hippocampal volume reductions). CONCLUSION We highlight the advantages of longitudinal designs, discuss the implications such studies have on clinical staging and provide directions for future research.
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Affiliation(s)
- Cali F Bartholomeusz
- 1 Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia
- 2 Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- 3 Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Vanessa L Cropley
- 3 Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Cassandra Wannan
- 1 Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia
- 2 Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- 3 Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Maria Di Biase
- 3 Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Patrick D McGorry
- 1 Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia
- 2 Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Christos Pantelis
- 3 Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- 4 Centre for Neural Engineering, Department of Electrical and Electronic Engineering, The University of Melbourne, Carlton South, VIC, Australia
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Zarogianni E, Storkey AJ, Johnstone EC, Owens DGC, Lawrie SM. Improved individualized prediction of schizophrenia in subjects at familial high risk, based on neuroanatomical data, schizotypal and neurocognitive features. Schizophr Res 2017; 181:6-12. [PMID: 27613509 DOI: 10.1016/j.schres.2016.08.027] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 08/29/2016] [Accepted: 08/29/2016] [Indexed: 01/11/2023]
Abstract
To date, there are no reliable markers for predicting onset of schizophrenia in individuals at high risk (HR). Substantial promise is, however, shown by a variety of pattern classification approaches to neuroimaging data. Here, we examined the predictive accuracy of support vector machine (SVM) in later diagnosing schizophrenia, at a single-subject level, using a cohort of HR individuals drawn from multiply affected families and a combination of neuroanatomical, schizotypal and neurocognitive variables. Baseline structural magnetic resonance imaging (MRI), schizotypal and neurocognitive data from 17 HR subjects, who subsequently developed schizophrenia and a matched group of 17 HR subjects who did not make the transition, yet had psychotic symptoms, were included in the analysis. We employed recursive feature elimination (RFE), in a nested cross-validation scheme to identify the most significant predictors of disease transition and enhance diagnostic performance. Classification accuracy was 94% when a self-completed measure of schizotypy, a declarative memory test and structural MRI data were combined into a single learning algorithm; higher than when either quantitative measure was used alone. The discriminative neuroanatomical pattern involved gray matter volume differences in frontal, orbito-frontal and occipital lobe regions bilaterally as well as parts of the superior, medial temporal lobe and cerebellar regions. Our findings suggest that an early SVM-based prediction of schizophrenia is possible and can be improved by combining schizotypal and neurocognitive features with neuroanatomical variables. However, our predictive model needs to be tested by classifying a new, independent HR cohort in order to estimate its validity.
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Affiliation(s)
- Eleni Zarogianni
- Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, UK.
| | - Amos J Storkey
- Institute for Adaptive and Neural Computation, University of Edinburgh, UK
| | - Eve C Johnstone
- Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, UK
| | - David G C Owens
- Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, UK
| | - Stephen M Lawrie
- Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, UK
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Reniers RLEP, Lin A, Yung AR, Koutsouleris N, Nelson B, Cropley VL, Velakoulis D, McGorry PD, Pantelis C, Wood SJ. Neuroanatomical Predictors of Functional Outcome in Individuals at Ultra-High Risk for Psychosis. Schizophr Bull 2017; 43:449-458. [PMID: 27369472 PMCID: PMC5605267 DOI: 10.1093/schbul/sbw086] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Most individuals at ultra-high risk (UHR) for psychosis do not transition to frank illness. Nevertheless, many have poor clinical outcomes and impaired psychosocial functioning. This study used voxel-based morphometry to investigate if baseline grey and white matter brain densities at identification as UHR were associated with functional outcome at medium- to long-term follow-up. Participants were help-seeking UHR individuals (n = 109, 54M:55F) who underwent magnetic resonance imaging at baseline; functional outcome was assessed an average of 9.2 years later. Primary analysis showed that lower baseline grey matter density, but not white matter density, in bilateral frontal and limbic areas, and left cerebellar declive were associated with poorer functional outcome (Social and Occupational Functioning Assessment Scale [SOFAS]). These findings were independent of transition to psychosis or persistence of the at-risk mental state. Similar regions were significantly associated with lower self-reported levels of social functioning and increased negative symptoms at follow-up. Exploratory analyses showed that lower baseline grey matter densities in middle and inferior frontal gyri were significantly associated with decline in Global Assessment of Functioning (GAF) score over follow-up. There was no association between baseline grey matter density and IQ or positive symptoms at follow-up. The current findings provide novel evidence that those with the poorest functional outcomes have the lowest grey matter densities at identification as UHR, regardless of transition status or persistence of the at-risk mental state. Replication and validation of these findings may allow for early identification of poor functional outcome and targeted interventions.
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Affiliation(s)
| | - Ashleigh Lin
- Telethon Kids Institute, The University of Western Australia, Perth, Australia
| | - Alison R. Yung
- Institute of Brain Behaviour and Mental Health, University of Manchester, Manchester, UK
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University of Munich, Munich, Germany
| | - Barnaby Nelson
- Orygen, The National Centre of Excellence in Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Vanessa L. Cropley
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Dennis Velakoulis
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, Australia
| | - Patrick D. McGorry
- Orygen, The National Centre of Excellence in Youth Mental Health, The University of Melbourne, Melbourne, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, Australia;,Centre for Neural Engineering, Department of Electrical and Electronic Engineering, University of Melbourne, Victoria, Australia;,Florey Institute for Neuroscience & Mental Health, Victoria, Australia
| | - Stephen J. Wood
- School of Psychology, University of Birmingham, Birmingham, UK;,Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, Australia
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36
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The varieties of psychosis in multiple sclerosis: A systematic review of cases. Mult Scler Relat Disord 2017; 12:9-14. [DOI: 10.1016/j.msard.2016.12.012] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2016] [Revised: 12/21/2016] [Accepted: 12/23/2016] [Indexed: 11/30/2022]
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Pruessner M, Cullen AE, Aas M, Walker EF. The neural diathesis-stress model of schizophrenia revisited: An update on recent findings considering illness stage and neurobiological and methodological complexities. Neurosci Biobehav Rev 2017; 73:191-218. [DOI: 10.1016/j.neubiorev.2016.12.013] [Citation(s) in RCA: 131] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2016] [Revised: 12/09/2016] [Accepted: 12/12/2016] [Indexed: 01/29/2023]
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38
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Takano Y, Aoki Y, Yahata N, Kawakubo Y, Inoue H, Iwashiro N, Natsubori T, Koike S, Gonoi W, Sasaki H, Takao H, Kasai K, Yamasue H. Neural basis for inferring false beliefs and social emotions in others among individuals with schizophrenia and those at ultra-high risk for psychosis. Psychiatry Res Neuroimaging 2017; 259:34-41. [PMID: 27960147 DOI: 10.1016/j.pscychresns.2016.11.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Revised: 11/12/2016] [Accepted: 11/20/2016] [Indexed: 01/14/2023]
Abstract
Inferring beliefs and social emotions of others has different neural substrates and possibly different roles in the pathophysiology of different clinical phases of schizophrenia. The current study investigated the neural basis for inferring others' beliefs and social emotions, as individual concepts, in 17 subjects at ultra-high risk for psychosis (UHR), 16 patients with schizophrenia and 20 healthy controls. Brain activity significantly differed from normal in both the left superior temporal sulcus (STS) and the inferior frontal gyrus (IFG) in the schizophrenia group while inferring others' beliefs, whereas those of UHR group were in the middle of those in the schizophrenia and healthy-control groups. Brain activity during inferring others' social emotions significantly differed in both the left STS and right IFG among individuals at UHR; however, there was no significant difference in the schizophrenia group. In contrast, brain activity differed in the left IFG of those in both the schizophrenia and UHR groups while inferring social emotion. Regarding the difference in direction of the abnormality, both the UHR and schizophrenia groups were characterized by hyper-STS and hypo-IFG activations when inferring others' beliefs and emotions. These findings might reflect different aspects of the same pathophysiological process at different clinical phases of psychosis.
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Affiliation(s)
- Yosuke Takano
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Yuta Aoki
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; The Child Study Center at NYU Langone Medical Center, One Park Avenue, New York, NY 10016, USA
| | - Noriaki Yahata
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Yuki Kawakubo
- Department of Child Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Hideyuki Inoue
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Norichika Iwashiro
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Tatsunobu Natsubori
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Shinsuke Koike
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Wataru Gonoi
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Hiroki Sasaki
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Hidemasa Takao
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Hidenori Yamasue
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan; Department of Psychiatry, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashiku, Hamamatsu City 431-3192, Japan.
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Healey KM, Bartholomeusz CF, Penn DL. Deficits in social cognition in first episode psychosis: A review of the literature. Clin Psychol Rev 2016; 50:108-137. [PMID: 27771557 DOI: 10.1016/j.cpr.2016.10.001] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Revised: 09/23/2016] [Accepted: 10/08/2016] [Indexed: 11/18/2022]
Affiliation(s)
- Kristin M Healey
- Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
| | - Cali F Bartholomeusz
- Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, Victoria, Australia; The Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - David L Penn
- Department of Psychology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; School of Psychology, Australian Catholic University, Melbourne, VIC, Australia
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40
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Liberg B, Rahm C, Panayiotou A, Pantelis C. Brain change trajectories that differentiate the major psychoses. Eur J Clin Invest 2016; 46:658-74. [PMID: 27208657 DOI: 10.1111/eci.12641] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Accepted: 05/18/2016] [Indexed: 12/17/2022]
Abstract
BACKGROUND Bipolar disorder and schizophrenia are highly heritable, often chronic and debilitating psychotic disorders that can be difficult to differentiate clinically. Their brain phenotypes appear to overlap in both cross-sectional and longitudinal structural neuroimaging studies, with some evidence to suggest areas of differentiation with differing trajectories. The aim of this review was to investigate the notion that longitudinal trajectories of alterations in brain structure could differentiate the two disorders. DESIGN Narrative review. We searched MEDLINE and Web of Science databases in May 2016 for studies that used structural magnetic resonance imaging to investigate longitudinal between-group differences in bipolar disorder and schizophrenia. Ten studies met inclusion criteria, namely longitudinal structural magnetic resonance studies comparing bipolar disorder (or affective psychosis) and schizophrenia within the same study. RESULTS Our review of these studies implicates illness-specific trajectories of morphological change in total grey matter volume, and in regions of the frontal, temporal and cingulate cortices. The findings in schizophrenia suggest a trajectory involving progressive grey matter loss confined to fronto-temporal cortical regions. Preliminary findings identify a similar but less severely impacted trajectory in a number of regions in bipolar disorder, however, bipolar disorder is also characterized by differential involvement across cingulate subregions. CONCLUSION The small number of available studies must be interpreted with caution but provide initial evidence supporting the notion that bipolar disorder and schizophrenia have differential longitudinal trajectories that are influenced by brain maturation.
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Affiliation(s)
- Benny Liberg
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Carlton South, Vic., Australia.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Christoffer Rahm
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Carlton South, Vic., Australia.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Medicine Huddinge, Karolinska Institutet, Stockholm, Sweden
| | - Anita Panayiotou
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Carlton South, Vic., Australia.,Western Centre for Health Research & Education, Sunshine Hospital, University of Melbourne, St Albans, Vic., Australia.,Sunshine Hospital, Western Health, St Albans, Vic., Australia
| | - Christos Pantelis
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Carlton South, Vic., Australia.,Western Centre for Health Research & Education, Sunshine Hospital, University of Melbourne, St Albans, Vic., Australia.,Florey Institute for Neuroscience and Mental Health, Parkville, Vic., Australia.,Department of Electrical and Electronic Engineering, Centre for Neural Engineering, University of Melbourne, Parkville, Vic., Australia
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42
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Mathes B, Khalaidovski K, Wienke AS, Schmiedt-Fehr C, Basar-Eroglu C. Maturation of the P3 and concurrent oscillatory processes during adolescence. Clin Neurophysiol 2016; 127:2599-609. [PMID: 27291879 DOI: 10.1016/j.clinph.2016.04.019] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Revised: 04/12/2016] [Accepted: 04/23/2016] [Indexed: 10/21/2022]
Abstract
OBJECTIVE During adolescence event-related modulations of the neural response may increase. For slow event-related components, such as the P3, this developmental change may be masked due to increased amplitude levels of ongoing delta and theta oscillations in adolescents. METHODS In a cross-sectional study design, EEG was measured in 51 participants between 13 and 24years. A visual oddball paradigm was used to elicit the P3. Our analysis focused on fronto-parietal activations within the P3 time-window and the concurrent time-frequency characteristics in the delta (∼0.5-4Hz) and theta (∼4-7Hz) band. RESULTS The parietal P3 amplitude was similar across the investigated age range, while the amplitude at frontal regions increased with age. The pre-stimulus amplitudes of delta and theta oscillations declined with age, while post-stimulus amplitude enhancement and inter-trial phase coherence increased. These changes affected fronto-parietal electrode sites. CONCLUSIONS The parietal P3 maximum seemed comparable for adolescents and young adults. Detailed analysis revealed that within the P3 time-window brain maturation during adolescence may lead to reduced spontaneous slow-wave oscillations, increased amplitude modulation and time precision of event-related oscillations, and altered P3 scalp topography. SIGNIFICANCE Time-frequency analyses may help to distinguish selective neurodevelopmental changes within the P3 time window.
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Affiliation(s)
- Birgit Mathes
- University of Bremen, Institute of Psychology and Cognition Research, Bremen, Germany; Centre for Cognitive Science, Bremen, Germany.
| | - Ksenia Khalaidovski
- University of Bremen, Institute of Psychology and Cognition Research, Bremen, Germany; Centre for Cognitive Science, Bremen, Germany
| | - Annika S Wienke
- University of Bremen, Institute of Psychology and Cognition Research, Bremen, Germany; Centre for Cognitive Science, Bremen, Germany
| | - Christina Schmiedt-Fehr
- University of Bremen, Institute of Psychology and Cognition Research, Bremen, Germany; Centre for Cognitive Science, Bremen, Germany
| | - Canan Basar-Eroglu
- University of Bremen, Institute of Psychology and Cognition Research, Bremen, Germany; Centre for Cognitive Science, Bremen, Germany
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43
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Hyperactivity of caudate, parahippocampal, and prefrontal regions during working memory in never-medicated persons at clinical high-risk for psychosis. Schizophr Res 2016; 173:1-12. [PMID: 26965745 PMCID: PMC4836956 DOI: 10.1016/j.schres.2016.02.023] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2015] [Revised: 02/09/2016] [Accepted: 02/11/2016] [Indexed: 12/22/2022]
Abstract
BACKGROUND Deficits in working memory (WM) are a core feature of schizophrenia (SZ) and other psychotic disorders. We examined brain activity during WM in persons at clinical high risk (CHR) for psychosis. METHODS Thirty-seven CHR and 34 healthy control participants underwent functional MRI (fMRI) on a 3.0T scanner while performing an N-back WM task. The sample included a sub-sample of CHR participants who had no lifetime history of treatment with psychotropic medications (n=11). Data were analyzed using SPM8 (2-back>0-back contrast). Pearson correlations between brain activity, symptoms, and WM performance were examined. RESULTS The total CHR group and medication-naive CHR sub-sample were comparable to controls in most demographic features and in N-back WM performance, but had significantly lower IQ. Relative to controls, medication-naïve CHR showed hyperactivity in the left parahippocampus (PHP) and the left caudate during performance of the N-back WM task. Relative to medication-exposed CHR, medication naïve CHR exhibited hyperactivity in the left caudate and the right dorsolateral prefrontal cortex (DLPFC). DLPFC activity was significantly negatively correlated with WM performance. PHP, caudate and DLPFC activity correlated strongly with symptoms, but results did not withstand FDR-correction for multiple comparisons. When all CHR participants were combined (regardless of medication status), only trend-level PHP hyperactivity was observed in CHR relative to controls. CONCLUSIONS Medication-naïve CHR exhibit hyperactivity in regions that subserve WM. These regions are implicated in studies of schizophrenia and risk for psychosis. Results emphasize the importance of medication status in the interpretation of task - induced brain activity.
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Waltz J, Demro C, Schiffman J, Thompson E, Kline E, Reeves G, Xu Z, Gold J. Reinforcement Learning Performance and Risk for Psychosis in Youth. J Nerv Ment Dis 2015; 203:919-926. [PMID: 26588080 PMCID: PMC5483992 DOI: 10.1097/nmd.0000000000000420] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Early identification efforts for psychosis have thus far yielded many more individuals "at risk" than actually develop psychotic illness. Here, we test whether measures of reinforcement learning (RL), known to be impaired in chronic schizophrenia, are related to the severity of clinical risk symptoms. Because of the reliance of RL on dopamine-rich frontostriatal systems and evidence of dopamine system dysfunction in the psychosis prodrome, RL measures are of specific interest in this clinical population. The current study examines relationships between psychosis risk symptoms and RL task performance in a sample of adolescents and young adults (n = 70) receiving mental health services. We observed significant correlations between multiple measures of RL performance and measures of both positive and negative symptoms. These results suggest that RL measures may provide a psychosis risk signal in treatment-seeking youth. Further research is necessary to understand the potential predictive role of RL measures for conversion to psychosis.
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Affiliation(s)
- James Waltz
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, P.O. Box 21247, Baltimore, MD 21228
| | - Caroline Demro
- Department of Psychology, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA, 21250
| | - Jason Schiffman
- Department of Psychology, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA, 21250
| | - Elizabeth Thompson
- Department of Psychology, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA, 21250
| | - Emily Kline
- Department of Psychology, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD, USA, 21250
| | - Gloria Reeves
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, 701 W. Pratt Street, Baltimore, MD, US, 21201
| | - Ziye Xu
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, P.O. Box 21247, Baltimore, MD 21228
| | - James Gold
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, P.O. Box 21247, Baltimore, MD 21228
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45
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Wells R, Swaminathan V, Sundram S, Weinberg D, Bruggemann J, Jacomb I, Cropley V, Lenroot R, Pereira AM, Zalesky A, Bousman C, Pantelis C, Weickert CS, Weickert TW. The impact of premorbid and current intellect in schizophrenia: cognitive, symptom, and functional outcomes. NPJ SCHIZOPHRENIA 2015; 1:15043. [PMID: 27336046 PMCID: PMC4849463 DOI: 10.1038/npjschz.2015.43] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2015] [Revised: 09/15/2015] [Accepted: 10/06/2015] [Indexed: 11/13/2022]
Abstract
BACKGROUND Cognitive heterogeneity among people with schizophrenia has been defined on the basis of premorbid and current intelligence quotient (IQ) estimates. In a relatively large, community cohort, we aimed to independently replicate and extend cognitive subtyping work by determining the extent of symptom severity and functional deficits in each group. METHODS A total of 635 healthy controls and 534 patients with a diagnosis of schizophrenia or schizoaffective disorder were recruited through the Australian Schizophrenia Research Bank. Patients were classified into cognitive subgroups on the basis of the Wechsler Test of Adult Reading (a premorbid IQ estimate) and current overall cognitive abilities into preserved, deteriorated, and compromised groups using both clinical and empirical (k-means clustering) methods. Additional cognitive, functional, and symptom outcomes were compared among the resulting groups. RESULTS A total of 157 patients (29%) classified as 'preserved' performed within one s.d. of control means in all cognitive domains. Patients classified as 'deteriorated' (n=239, 44%) performed more than one s.d. below control means in all cognitive domains except estimated premorbid IQ and current visuospatial abilities. A separate 138 patients (26%), classified as 'compromised,' performed more than one s.d. below control means in all cognitive domains and displayed greater impairment than other groups on symptom and functional measures. CONCLUSIONS In the present study, we independently replicated our previous cognitive classifications of people with schizophrenia. In addition, we extended previous work by demonstrating worse functional outcomes and symptom severity in the compromised group.
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Affiliation(s)
- Ruth Wells
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Randwick, Sydney, NSW, Australia
| | - Vaidy Swaminathan
- Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
- Northern Psychiatry Research Centre, North Western Mental Health, Melbourne Health, Victoria, Australia
- Schizophrenia Research Institute, Sydney, NSW, Australia
- Molecular Psychopharmacology Laboratory, The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia
| | - Suresh Sundram
- Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
- Northern Psychiatry Research Centre, North Western Mental Health, Melbourne Health, Victoria, Australia
- Molecular Psychopharmacology Laboratory, The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia
| | - Danielle Weinberg
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Randwick, Sydney, NSW, Australia
| | - Jason Bruggemann
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Randwick, Sydney, NSW, Australia
| | - Isabella Jacomb
- Neuroscience Research Australia, Randwick, Sydney, NSW, Australia
| | - Vanessa Cropley
- Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
| | - Rhoshel Lenroot
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Randwick, Sydney, NSW, Australia
- Schizophrenia Research Institute, Sydney, NSW, Australia
| | - Avril M Pereira
- Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
- Molecular Psychopharmacology Laboratory, The Florey Institute of Neuroscience and Mental Health, Parkville, VIC, Australia
| | - Andrew Zalesky
- Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
| | - Chad Bousman
- Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
| | - Christos Pantelis
- Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia
- Schizophrenia Research Institute, Sydney, NSW, Australia
| | - Cynthia Shannon Weickert
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Randwick, Sydney, NSW, Australia
- Schizophrenia Research Institute, Sydney, NSW, Australia
| | - Thomas W Weickert
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
- Neuroscience Research Australia, Randwick, Sydney, NSW, Australia
- Schizophrenia Research Institute, Sydney, NSW, Australia
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Klauser P, Zhou J, Lim JK, Poh JS, Zheng H, Tng HY, Krishnan R, Lee J, Keefe RS, Adcock RA, Wood SJ, Fornito A, Chee MW. Lack of Evidence for Regional Brain Volume or Cortical Thickness Abnormalities in Youths at Clinical High Risk for Psychosis: Findings From the Longitudinal Youth at Risk Study. Schizophr Bull 2015; 41:1285-93. [PMID: 25745033 PMCID: PMC4601700 DOI: 10.1093/schbul/sbv012] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
There is cumulative evidence that young people in an "at-risk mental state" (ARMS) for psychosis show structural brain abnormalities in frontolimbic areas, comparable to, but less extensive than those reported in established schizophrenia. However, most available data come from ARMS samples from Australia, Europe, and North America while large studies from other populations are missing. We conducted a structural brain magnetic resonance imaging study from a relatively large sample of 69 ARMS individuals and 32 matched healthy controls (HC) recruited from Singapore as part of the Longitudinal Youth At-Risk Study (LYRIKS). We used 2 complementary approaches: a voxel-based morphometry and a surface-based morphometry analysis to extract regional gray and white matter volumes (GMV and WMV) and cortical thickness (CT). At the whole-brain level, we did not find any statistically significant difference between ARMS and HC groups concerning total GMV and WMV or regional GMV, WMV, and CT. The additional comparison of 2 regions of interest, hippocampal, and ventricular volumes, did not return any significant difference either. Several characteristics of the LYRIKS sample like Asian origins or the absence of current illicit drug use could explain, alone or in conjunction, the negative findings and suggest that there may be no dramatic volumetric or CT abnormalities in ARMS.
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Affiliation(s)
- Paul Klauser
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, Australia;,Monash Clinical and Imaging Neuroscience, School of Psychological Sciences & Monash Biomedical Imaging, Monash University, Clayton, Australia;,These authors contributed equally to the article
| | - Juan Zhou
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore, Singapore;
| | - Joseph K.W. Lim
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore, Singapore
| | - Joann S. Poh
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore, Singapore
| | - Hui Zheng
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore, Singapore
| | - Han Ying Tng
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore, Singapore
| | - Ranga Krishnan
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore, Singapore
| | - Jimmy Lee
- Department of General Psychiatry 1 and Research Division, Institute of Mental Health, Singapore, Singapore;,Office of Clinical Sciences, Duke-National University of Singapore Graduate Medical School, Singapore, Singapore
| | - Richard S.E. Keefe
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC
| | - R. Alison Adcock
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC;,Center for Cognitive Neuroscience, Duke University, Durham, NC
| | - Stephen J. Wood
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, Australia;,School of Psychology, University of Birmingham, Edgbaston, UK
| | - Alex Fornito
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, Australia;,Monash Clinical and Imaging Neuroscience, School of Psychological Sciences & Monash Biomedical Imaging, Monash University, Clayton, Australia
| | - Michael W.L. Chee
- Center for Cognitive Neuroscience, Neuroscience and Behavioral Disorders Program, Duke-National University of Singapore Graduate Medical School, Singapore, Singapore
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Yao Y, Palaniyappan L, Liddle P, Zhang J, Francis S, Feng J. Variability of structurally constrained and unconstrained functional connectivity in schizophrenia. Hum Brain Mapp 2015; 36:4529-38. [PMID: 26274628 PMCID: PMC4843947 DOI: 10.1002/hbm.22932] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2014] [Revised: 07/25/2015] [Accepted: 08/01/2015] [Indexed: 01/05/2023] Open
Abstract
Spatial variation in connectivity is an integral aspect of the brain's architecture. In the absence of this variability, the brain may act as a single homogenous entity without regional specialization. In this study, we investigate the variability in functional links categorized on the basis of the presence of direct structural paths (primary) or indirect paths mediated by one (secondary) or more (tertiary) brain regions ascertained by diffusion tensor imaging. We quantified the variability in functional connectivity using an unbiased estimate of unpredictability (functional connectivity entropy) in a neuropsychiatric disorder where structure-function relationship is considered to be abnormal; 34 patients with schizophrenia and 32 healthy controls underwent DTI and resting state functional MRI scans. Less than one-third (27.4% in patients, 27.85% in controls) of functional links between brain regions were regarded as direct primary links on the basis of DTI tractography, while the rest were secondary or tertiary. The most significant changes in the distribution of functional connectivity in schizophrenia occur in indirect tertiary paths with no direct axonal linkage in both early (P=0.0002, d=1.46) and late (P=1×10(-17), d=4.66) stages of schizophrenia, and are not altered by the severity of symptoms, suggesting that this is an invariant feature of this illness. Unlike those with early stage illness, patients with chronic illness show some additional reduction in the distribution of connectivity among functional links that have direct structural paths (P=0.08, d=0.44). Our findings address a critical gap in the literature linking structure and function in schizophrenia, and demonstrate for the first time that the abnormal state of functional connectivity preferentially affects structurally unconstrained links in schizophrenia. It also raises the question of a continuum of dysconnectivity ranging from less direct (structurally unconstrained) to more direct (structurally constrained) brain pathways underlying the progressive clinical staging and persistence of schizophrenia.
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Affiliation(s)
- Ye Yao
- Centre for Computational Systems BiologyFudan UniversityShanghaiPeople's Republic of China
- School of Mathematical SciencesFudan UniversityShanghaiPeople's Republic of China
- Department of Computer ScienceUniversity of WarwickCoventryUnited Kingdom
| | - Lena Palaniyappan
- Translational Neuroimaging in Mental Health, Division of Psychiatry & Applied PsychologyInstitute of Mental HealthNottinghamUnited Kingdom
- Early Intervention in Psychosis, Nottinghamshire Healthcare NHS Foundation TrustNottinghamUnited Kingdom
| | - Peter Liddle
- Translational Neuroimaging in Mental Health, Division of Psychiatry & Applied PsychologyInstitute of Mental HealthNottinghamUnited Kingdom
| | - Jie Zhang
- Centre for Computational Systems BiologyFudan UniversityShanghaiPeople's Republic of China
- Department of Medical ImagingJinling Hospital, Nanjing University School of MedicineNanjingPeople's Republic of China
| | - Susan Francis
- Sir Peter Mansfield Imaging Centre, School of Physics and AstronomyUniversity of NottinghamUnited Kingdom
| | - Jianfeng Feng
- Centre for Computational Systems BiologyFudan UniversityShanghaiPeople's Republic of China
- School of Mathematical SciencesFudan UniversityShanghaiPeople's Republic of China
- Department of Computer ScienceUniversity of WarwickCoventryUnited Kingdom
- Shanghai Center for Mathematical Sciences, Fudan UniversityShanghaiPeople's Republic of China
- School of Life Sciences and Collaborative Innovation Center for Brain ScienceFudan UniversityShanghaiPeople's Republic of China
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48
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Holper L, Aleksandrowicz A, Müller M, Ajdacic-Gross V, Haker H, Fallgatter AJ, Hagenmuller F, Rössler W, Kawohl W. Brain correlates of verbal fluency in subthreshold psychosis assessed by functional near-infrared spectroscopy. Schizophr Res 2015; 168:23-9. [PMID: 26277535 DOI: 10.1016/j.schres.2015.07.043] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Revised: 07/22/2015] [Accepted: 07/23/2015] [Indexed: 11/26/2022]
Abstract
The prevalence of subthreshold psychotic symptoms in the general population has gained increasing interest as a possible precursor of psychotic disorders. The goal of the present study was to evaluate whether neurobiological features of subthreshold psychotic symptoms can be detected using verbal fluency tasks and functional near-infrared spectroscopy (fNIRS). A large data set was obtained from the Zurich Program for Sustainable Development of Mental Health Services (ZInEP). Based on the SCL-90-R subscales 'Paranoid Ideation' and 'Psychoticism' a total sample of 188 subjects was assigned to four groups with different levels of subthreshold psychotic symptoms. All subjects completed a phonemic and semantic verbal fluency task while fNIRS was recorded over the prefrontal and temporal cortices. Results revealed larger hemodynamic (oxy-hemoglobin) responses to the phonemic and semantic conditions compared to the control condition over prefrontal and temporal cortices. Subjects with high subthreshold psychotic symptoms exhibited significantly reduced hemodynamic responses in both conditions compared to the control group. Further, connectivity between prefrontal and temporal cortices revealed significantly weaker patterns in subjects with high subthreshold psychotic symptoms compared to the control group, possibly indicating less incisive network connections associated with subthreshold psychotic symptoms. The present findings provide evidence that subthreshold forms of psychotic symptoms are associated with reduced hemodynamic responses and connectivity in prefrontal and temporal cortices during verbal fluency that can be identified using fNIRS.
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Affiliation(s)
- L Holper
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University Hospital of Psychiatry Zurich, Switzerland.
| | - A Aleksandrowicz
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University Hospital of Psychiatry Zurich, Switzerland; The Zurich Program for Sustainable Development of Mental Health Services, University Hospital of Psychiatry Zurich, Switzerland
| | - M Müller
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University Hospital of Psychiatry Zurich, Switzerland; The Zurich Program for Sustainable Development of Mental Health Services, University Hospital of Psychiatry Zurich, Switzerland
| | - V Ajdacic-Gross
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University Hospital of Psychiatry Zurich, Switzerland; The Zurich Program for Sustainable Development of Mental Health Services, University Hospital of Psychiatry Zurich, Switzerland
| | - H Haker
- The Zurich Program for Sustainable Development of Mental Health Services, University Hospital of Psychiatry Zurich, Switzerland; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Switzerland
| | - A J Fallgatter
- Department of Psychiatry Psychotherapy, University of Tübingen, Germany; LEAD Graduate School, University of Tübingen, Germany
| | - F Hagenmuller
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University Hospital of Psychiatry Zurich, Switzerland; The Zurich Program for Sustainable Development of Mental Health Services, University Hospital of Psychiatry Zurich, Switzerland
| | - W Rössler
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University Hospital of Psychiatry Zurich, Switzerland; The Zurich Program for Sustainable Development of Mental Health Services, University Hospital of Psychiatry Zurich, Switzerland; Institute of Psychiatry, Laboratory of Neuroscience, LIM27, University of Sao Paulo, Brazil
| | - W Kawohl
- Department of Psychiatry, Psychotherapy, and Psychosomatics, University Hospital of Psychiatry Zurich, Switzerland; The Zurich Program for Sustainable Development of Mental Health Services, University Hospital of Psychiatry Zurich, Switzerland
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Bora E, Pantelis C. Meta-analysis of Cognitive Impairment in First-Episode Bipolar Disorder: Comparison With First-Episode Schizophrenia and Healthy Controls. Schizophr Bull 2015; 41:1095-104. [PMID: 25616505 PMCID: PMC4535631 DOI: 10.1093/schbul/sbu198] [Citation(s) in RCA: 219] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Neurocognitive deficits are evident both in established schizophrenia and bipolar disorder (BP). However, it has been suggested that schizophrenia, but not BP, is characterized by neurodevelopmental abnormalities that can lead to cognitive deficits at the earliest stages of the illness. The aim of this meta-analytic review was to compare neurocognitive deficits in first-episode BP (FEBP) with healthy controls and first-episode schizophrenia (FES) patients. The current meta-analysis included a total of 22 adult studies and involved comparisons of 533 FEBP patients with 1417 healthy controls and 605 FEBP and 822 FES patients. FEBP patients were significantly impaired in all cognitive domains (d = 0.26-0.80) and individual tasks (d = 0.22-0.66) investigated. FES patients significantly underperformed FEBP patients in most cognitive domains (d = 0.05-0.63) and on individual tasks (d = 0.13-0.77). Neuropsychological impairment, which is comparable to chronic BP, was evident in FEBP. Similar to chronic patients, cognitive functions in FEBP lie intermediate between FES and healthy controls. Neurodevelopmental factors are likely to play a significant role not only in schizophrenia but also in BP.
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Affiliation(s)
- Emre Bora
- Department of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Victoria, Australia
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Cognitive intervention in early psychosis — preserving abilities versus remediating deficits. Curr Opin Behav Sci 2015. [DOI: 10.1016/j.cobeha.2015.02.008] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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