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Vieira S, Bolton TAW, Schöttner M, Baecker L, Marquand A, Mechelli A, Hagmann P. Multivariate brain-behaviour associations in psychiatric disorders. Transl Psychiatry 2024; 14:231. [PMID: 38824172 PMCID: PMC11144193 DOI: 10.1038/s41398-024-02954-4] [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: 07/26/2023] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 06/03/2024] Open
Abstract
Mapping brain-behaviour associations is paramount to understand and treat psychiatric disorders. Standard approaches involve investigating the association between one brain and one behavioural variable (univariate) or multiple variables against one brain/behaviour feature ('single' multivariate). Recently, large multimodal datasets have propelled a new wave of studies that leverage on 'doubly' multivariate approaches capable of parsing the multifaceted nature of both brain and behaviour simultaneously. Within this movement, canonical correlation analysis (CCA) and partial least squares (PLS) emerge as the most popular techniques. Both seek to capture shared information between brain and behaviour in the form of latent variables. We provide an overview of these methods, review the literature in psychiatric disorders, and discuss the main challenges from a predictive modelling perspective. We identified 39 studies across four diagnostic groups: attention deficit and hyperactive disorder (ADHD, k = 4, N = 569), autism spectrum disorders (ASD, k = 6, N = 1731), major depressive disorder (MDD, k = 5, N = 938), psychosis spectrum disorders (PSD, k = 13, N = 1150) and one transdiagnostic group (TD, k = 11, N = 5731). Most studies (67%) used CCA and focused on the association between either brain morphology, resting-state functional connectivity or fractional anisotropy against symptoms and/or cognition. There were three main findings. First, most diagnoses shared a link between clinical/cognitive symptoms and two brain measures, namely frontal morphology/brain activity and white matter association fibres (tracts between cortical areas in the same hemisphere). Second, typically less investigated behavioural variables in multivariate models such as physical health (e.g., BMI, drug use) and clinical history (e.g., childhood trauma) were identified as important features. Finally, most studies were at risk of bias due to low sample size/feature ratio and/or in-sample testing only. We highlight the importance of carefully mitigating these sources of bias with an exemplar application of CCA.
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Affiliation(s)
- S Vieira
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- Center for Research in Neuropsychology and Cognitive Behavioral Intervention, Faculty of Psychology and Educational Sciences, University of Coimbra, Coimbra, Portugal.
| | - T A W Bolton
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- Neurosurgery Service and Gamma Knife Center, Lausanne University Hospital, Lausanne, Switzerland
| | - M Schöttner
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - L Baecker
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - A Marquand
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department for Cognitive Neuroscience, Radboud University Medical Center Nijmegen, Nijmegen, The Netherlands
- Department of Neuroimaging, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, London, UK
| | - A Mechelli
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - P Hagmann
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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Onitsuka T, Hirano Y, Nakazawa T, Ichihashi K, Miura K, Inada K, Mitoma R, Yasui-Furukori N, Hashimoto R. Toward recovery in schizophrenia: Current concepts, findings, and future research directions. Psychiatry Clin Neurosci 2022; 76:282-291. [PMID: 35235256 DOI: 10.1111/pcn.13342] [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] [Received: 11/25/2021] [Revised: 02/17/2022] [Accepted: 02/25/2022] [Indexed: 11/30/2022]
Abstract
Schizophrenia was initially defined as "dementia praecox" by E. Kraepelin, which implies progressive deterioration. However, recent studies have revealed that early effective intervention may lead to social and functional recovery in schizophrenia. In this review, we provide an overview of current concepts in schizophrenia and pathophysiological hypotheses. In addition, we present recent findings from clinical and basic research on schizophrenia. Recent neuroimaging and neurophysiological studies have consistently revealed specific biological differences in the structure and function of the brain in those with schizophrenia. From a basic research perspective, to determine the essential pathophysiology underlying schizophrenia, it is crucial that findings from all lines of inquiry-induced pluripotent stem cell (iPSC)-derived neural cells from patients, murine models expressing genetic mutations identified in patients, and patient clinical data-be integrated to contextualize the analysis results. However, the findings remain insufficient to serve as a diagnostic tool or a biomarker for predicting schizophrenia-related outcomes. Collaborations to conduct clinical research based on the patients' and their families' values are just beginning, and further development is expected.
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Affiliation(s)
- Toshiaki Onitsuka
- Department of Neuroimaging Psychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.,Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Takanobu Nakazawa
- Department of Bioscience, Tokyo University of Agriculture, Tokyo, Japan
| | - Kayo Ichihashi
- Department of Neuropsychiatry, The University of Tokyo Hospital, Tokyo, Japan
| | - Kenichiro Miura
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Ken Inada
- Department of Psychiatry, Tokyo Women's Medical University, Tokyo, Japan.,Department of Psychiatry, Kitasato University School of Medicine, Kanagawa, Japan
| | - Ryo Mitoma
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Norio Yasui-Furukori
- Department of Psychiatry, Dokkyo Medical University School of Medicine, Tochigi, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
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Li Q, Jiang L, Qiao K, Hu Y, Chen B, Zhang X, Ding Y, Yang Z, Li C. INCloud: integrated neuroimaging cloud for data collection, management, analysis and clinical translations. Gen Psychiatr 2022; 34:e100651. [PMID: 35028522 PMCID: PMC8705204 DOI: 10.1136/gpsych-2021-100651] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 11/22/2021] [Indexed: 12/29/2022] Open
Abstract
Background Neuroimaging techniques provide rich and accurate measures of brain structure and function, and have become one of the most popular methods in mental health and neuroscience research. Rapidly growing neuroimaging research generates massive amounts of data, bringing new challenges in data collection, large-scale data management, efficient computing requirements and data mining and analyses. Aims To tackle the challenges and promote the application of neuroimaging technology in clinical practice, we developed an integrated neuroimaging cloud (INCloud). INCloud provides a full-stack solution for the entire process of large-scale neuroimaging data collection, management, analysis and clinical applications. Methods INCloud consists of data acquisition systems, a data warehouse, automatic multimodal image quality check and processing systems, a brain feature library, a high-performance computing cluster and computer-aided diagnosis systems (CADS) for mental disorders. A unique design of INCloud is the brain feature library that converts the unit of data management from image to image features such as hippocampal volume. Connecting the CADS to the scientific database, INCloud allows the accumulation of scientific data to continuously improve the accuracy of objective diagnosis of mental disorders. Results Users can manage and analyze neuroimaging data on INCloud, without the need to download them to the local device. INCloud users can query, manage, analyze and share image features based on customized criteria. Several examples of 'mega-analyses' based on the brain feature library are shown. Conclusions Compared with traditional neuroimaging acquisition and analysis workflow, INCloud features safe and convenient data management and sharing, reduced technical requirements for researchers, high-efficiency computing and data mining, and straightforward translations to clinical service. The design and implementation of the system are also applicable to imaging research platforms in other fields.
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Affiliation(s)
- Qingfeng Li
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lijuan Jiang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kaini Qiao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Hu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bing Chen
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Xiaochen Zhang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yue Ding
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhi Yang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Institute of Psychological and Behavioral Sciences, Shanghai Jiao Tong University, Shanghai, China.,Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Chunbo Li
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Institute of Psychological and Behavioral Sciences, Shanghai Jiao Tong University, Shanghai, China.,Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.,Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences, Shanghai, China
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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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Zhuang X, Yang Z, Cordes D. A technical review of canonical correlation analysis for neuroscience applications. Hum Brain Mapp 2020; 41:3807-3833. [PMID: 32592530 PMCID: PMC7416047 DOI: 10.1002/hbm.25090] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 05/23/2020] [Indexed: 12/11/2022] Open
Abstract
Collecting comprehensive data sets of the same subject has become a standard in neuroscience research and uncovering multivariate relationships among collected data sets have gained significant attentions in recent years. Canonical correlation analysis (CCA) is one of the powerful multivariate tools to jointly investigate relationships among multiple data sets, which can uncover disease or environmental effects in various modalities simultaneously and characterize changes during development, aging, and disease progressions comprehensively. In the past 10 years, despite an increasing number of studies have utilized CCA in multivariate analysis, simple conventional CCA dominates these applications. Multiple CCA-variant techniques have been proposed to improve the model performance; however, the complicated multivariate formulations and not well-known capabilities have delayed their wide applications. Therefore, in this study, a comprehensive review of CCA and its variant techniques is provided. Detailed technical formulation with analytical and numerical solutions, current applications in neuroscience research, and advantages and limitations of each CCA-related technique are discussed. Finally, a general guideline in how to select the most appropriate CCA-related technique based on the properties of available data sets and particularly targeted neuroscience questions is provided.
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Affiliation(s)
- Xiaowei Zhuang
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNevadaUSA
| | - Zhengshi Yang
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNevadaUSA
| | - Dietmar Cordes
- Cleveland Clinic Lou Ruvo Center for Brain HealthLas VegasNevadaUSA
- University of ColoradoBoulderColoradoUSA
- Department of Brain HealthUniversity of NevadaLas VegasNevadaUSA
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