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Castro-de-Araujo LF, de Araujo JAP, Morais Xavier ÉF, Kanaan RAA. Feedback-loop between psychotic symptoms and brain volume: A cross-lagged panel model study. J Psychiatr Res 2023; 162:150-155. [PMID: 37156129 DOI: 10.1016/j.jpsychires.2023.05.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 05/01/2023] [Accepted: 05/02/2023] [Indexed: 05/10/2023]
Abstract
Brain structural changes are known to be associated with psychotic symptoms, with worse symptoms consistently associated with brain volume loss in some areas. It is not clear whether volume and symptoms interfere with each other over the course of psychosis. In this paper, we analyse the temporal relationships between psychosis symptom severity and total gray matter volume. We applied a cross-lagged panel model to a public dataset from the NUSDAST cohorts. The subjects were assessed at three-time points: baseline, 24 months, and 48 months. Psychosis symptoms were measured by SANS and SAPS scores. The cohort contained 673 subjects with schizophrenia, healthy subjects and their siblings. There were significant effects of symptom severity on total gray matter volume and vice-versa. The worse the psychotic symptoms, the smaller the total gray volume, and the smaller the volume, the worse the symptomatology. There is a bidirectional temporal relationship between symptoms of psychosis and brain volume.
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Affiliation(s)
- Luis Fs Castro-de-Araujo
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, P.O. Box 980126, Richmond, VA, 23298-0126, USA; Deptartment of Psychiatry, The University of Melbourne, Austin Health, Victoria, Australia.
| | | | - Érika Fialho Morais Xavier
- Center of Data and Knowledge Integration for Health (CIDACS), Fiocruz, R. Mundo, 121. Salvador, Bahia, Brazil
| | - Richard A A Kanaan
- Deptartment of Psychiatry, The University of Melbourne, Austin Health, Victoria, Australia
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Faria AV, Zhao Y, Ye C, Hsu J, Yang K, Cifuentes E, Wang L, Mori S, Miller M, Caffo B, Sawa A. Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup. Hum Brain Mapp 2020; 42:1034-1053. [PMID: 33377594 PMCID: PMC7856640 DOI: 10.1002/hbm.25276] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 09/29/2020] [Accepted: 10/18/2020] [Indexed: 02/06/2023] Open
Abstract
Multi‐institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels. We studied 87 first episode psychosis (FEP) patients and 62 healthy subjects by combining supervised integrated factor analysis (SIFA) with a novel pipeline for automated structure‐based analysis, an efficient and comprehensive method for dimensional data reduction that our group recently established. We integrated multiple MRI features (volume, DTI indices, resting state fMRI—rsfMRI) in the whole brain of each participant in an unbiased manner. The automated structure‐based analysis showed widespread DTI abnormalities in FEP and rs‐fMRI differences between FEP and healthy subjects mostly centered in thalamus. The combination of multiple modalities with SIFA was more efficient than the use of single modalities to stratify a subgroup of FEP (individuals with schizophrenia or schizoaffective disorder) that had more robust deficits from the overall FEP group. The information from multiple MRI modalities and analytical methods highlighted the thalamus as significantly abnormal in FEP. This study serves as a proof‐of‐concept for the potential of this methodology to reveal disease underpins and to stratify populations into more homogeneous sub‐groups.
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Affiliation(s)
- Andreia V Faria
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Yi Zhao
- Department of Biostatistics, Indiana University, School of Medicine, Indianapolis, Indiana, USA
| | - Chenfei Ye
- Department of Electronics and Information, Harbin Institute of Technology Shenzhen Graduate School, Guangdong, China
| | - Johnny Hsu
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kun Yang
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Elizabeth Cifuentes
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences and Radiology, Northwestern University, Evanston, Illinois, USA
| | - Susumu Mori
- Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michael Miller
- Department of Biomedical Engineering, The Whiting School of Engineering, Baltimore, Maryland, USA
| | - Brian Caffo
- Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Akira Sawa
- Department Psychiatry, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Biomedical Engineering, The Whiting School of Engineering, Baltimore, Maryland, USA.,Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.,Department of Mental Health, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA
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Castro-de-Araujo LF, Machado DB, Barreto ML, Kanaan RA. Subtyping schizophrenia based on symptomatology and cognition using a data driven approach. Psychiatry Res Neuroimaging 2020; 304:111136. [PMID: 32707455 PMCID: PMC7613209 DOI: 10.1016/j.pscychresns.2020.111136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 07/07/2020] [Accepted: 07/14/2020] [Indexed: 12/01/2022]
Abstract
Schizophrenia is a highly heterogeneous disorder, not only in its phenomenology but in its clinical course. This limits the usefulness of the diagnosis as a basis for both research and clinical management. Methods of reducing this heterogeneity may inform the diagnostic classification. With this in mind, we performed k-means clustering with symptom and cognitive measures to generate groups in a machine-driven way. We found that our data was best organised in three clusters: high cognitive performance, high positive symptomatology, low positive symptomatology. We hypothesized that these clusters represented biological categories, which we tested by comparing these groups in terms of brain volumetric information. We included all the groups in an ANCOVA analysis with post hoc tests, where brain volume areas were modelled as dependent variables, controlling for age and estimated intracranial volume. We found six brain volumes significantly differed between the clusters: left caudate, left cuneus, left lateral occipital, left inferior temporal, right lateral, and right pars opercularis. The k-means clustering provides a way of subtyping schizophrenia which appears to have a biological basis, though one that requires both replication and confirmation of its clinical significance.
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Affiliation(s)
- Luis Fs Castro-de-Araujo
- Center of Data and Knowledge Integration for Health (CIDACS). R. Mundo, 121, Salvador BA, Brazil; University of Melbourne, Department of Psychiatry, Austin Health. Studley Road, Heidelberg, Victoria, Australia.
| | - Daiane B Machado
- Center of Data and Knowledge Integration for Health (CIDACS). R. Mundo, 121, Salvador BA, Brazil; Centre for Global Mental health (CGMH), London School of Hygiene and Tropical Medicine. King's College London. David Goldberg Centre, De Crespigny Park, London United Kingdom
| | - Maurício L Barreto
- Center of Data and Knowledge Integration for Health (CIDACS). R. Mundo, 121, Salvador BA, Brazil; Institute of Collective Health, UFBA. Rua Basílio da Gama, Salvador BA Brazil.
| | - Richard Aa Kanaan
- University of Melbourne, Department of Psychiatry, Austin Health. Studley Road, Heidelberg, Victoria, Australia.
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Castro-de-Araujo LFS, Allin M, Picchioni MM, Mcdonald C, Pantelis C, Kanaan RAA. Schizophrenia moderates the relationship between white matter integrity and cognition. Schizophr Res 2018; 199:250-256. [PMID: 29602641 PMCID: PMC6179965 DOI: 10.1016/j.schres.2018.03.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 02/09/2018] [Accepted: 03/18/2018] [Indexed: 12/22/2022]
Abstract
Cognitive impairment is a primary feature of schizophrenia, with alterations in several cognitive domains appearing in the pre-morbid phase of the disorder. White matter microstructure is also affected in schizophrenia and considered to be related to cognition, but the relationship of the two is unclear. As interaction between cognition and white matter structure involves the interplay of several brain structures and cognitive abilities, investigative methods which can examine the interaction of multiple variables are preferred. A multiple-groups structural equation model (SEM) was used to assess the relationship between diffusion tension imaging data (fractional anisotropy of selected white matter tracts) and cognitive abilities of 196 subjects - 135 healthy subjects and 61 patients with schizophrenia. It was found that multiple-indicators, multiple-causes model best fitted the data analysed. Schizophrenia moderated the relation of white matter function on cognition with a large effect size. This paper extends previous work on modelling intelligence within a SEM framework by incorporating neurological elements into the model, and shows that white matter microstructure in patients with schizophrenia interacts with cognitive abilities.
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Affiliation(s)
- Luis F S Castro-de-Araujo
- CAPES Foundation, Ministry of Education of Brazil, Brasília-DF, Brazil; University of Melbourne, Department of Psychiatry, Austin Health, Heidelberg, Victoria, Australia.
| | - Mathew Allin
- Institute of Psychiatry, King's College, London, UK.
| | - Marco M Picchioni
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
| | - Colm Mcdonald
- National University of Ireland (NUI), Galway, Ireland.
| | - Christos Pantelis
- Department of Psychiatry, University of Melbourne, Parkville, VIC, Australia.
| | - Richard A A Kanaan
- University of Melbourne, Department of Psychiatry, Austin Health, Heidelberg, Victoria, Australia; Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
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