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Du Y, Niu J, Xing Y, Li B, Calhoun VD. Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade. Schizophr Bull 2024:sbae110. [PMID: 38982882 DOI: 10.1093/schbul/sbae110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. STUDY DESIGN The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. STUDY RESULTS Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. CONCLUSIONS We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ju Niu
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ying Xing
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Bang Li
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Vince D Calhoun
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, 30303, GA, USA
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Ren H, Li J, Zhou J, Chen X, Tang J, Li Z, Wang Q. Grey matter volume reduction in the frontotemporal cortex associated with persistent verbal auditory hallucinations in Chinese patients with chronic schizophrenia: Insights from a 3 T magnetic resonance imaging study. Schizophr Res 2024; 269:123-129. [PMID: 38772324 DOI: 10.1016/j.schres.2024.05.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 04/03/2024] [Accepted: 05/13/2024] [Indexed: 05/23/2024]
Abstract
BACKGROUND Persistent auditory verbal hallucinations (pAVHs) are a fundamental manifestation of schizophrenia (SCZ), yet the exact connection between pAVHs and brain structure remains contentious. This study aims to explore the potential correlation between pAVHs and alterations in grey matter volume (GMV) within specific brain regions among individuals diagnosed with SCZ. METHODS 76 SCZ patients with pAVHs (pAVH group), 57 SCZ patients without AVHs (non-AVH group), and 83 healthy controls (HC group) were investigated using 3 T magnetic resonance imaging. The P3 hallucination item of the Positive and Negative Syndrome Scale was used to assess the severity of pAVHs. Voxel-based morphometry was used to analyze the GMV profile between the three groups. RESULTS Compared to the non-AVH and HC groups, the pAVH group exhibited extensive reduction in GMV within the frontotemporal cortex. Conversely, no significant difference in GMV was observed between the non-AVH and HC groups. The severity of pAVHs showed a negative correlation with GMV in several regions, including the right fusiform, right inferior temporal, right medial orbitofrontal, right superior frontal, and right temporal pole (p = 0.0036, Bonferroni correction). Stepwise linear regression analysis revealed that GMV in the right temporal pole (β = -0.29, p = 0.001) and right fusiform (β = -0.21, p = 0.01) were significantly associated with the severity of pAVHs. CONCLUSIONS Widespread reduction in GMV is observed within the frontotemporal cortex, particularly involving the right temporal pole and right fusiform, which potentially contribute to the pathogenesis of pAVHs in individuals with chronic SCZ.
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Affiliation(s)
- Honghong Ren
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China; Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China; Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Jinguang Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Jun Zhou
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Xiaogang Chen
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Jinsong Tang
- Department of Psychiatry, Sir Run-Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Zongchang Li
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China.
| | - Qianjin Wang
- Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China; Shandong Provincial Hospital, Shandong University, Jinan, Shandong, China; Department of Psychiatry, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China.
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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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Wang K, Fu Y, Li L, Zhang L, Huang M, Yan W, Shan X, Yan Z, Lu Y. Gut Microbiota Moderates Multimodal Brain Structure-Function Integration and Behavioral Cognition in Growth Hormone Deficient Children. Neuroendocrinology 2024; 114:698-708. [PMID: 38679006 DOI: 10.1159/000539097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/23/2024] [Indexed: 05/01/2024]
Abstract
INTRODUCTION Previous brain studies of growth hormone deficiency (GHD) often used single-modal neuroimaging, missing the complexity captured by multimodal data. Growth hormone affects gut microbiota and metabolism in GHD. However, from a gut-brain axis (GBA) perspective, the relationship between abnormal GHD brain development and microbiota alterations remains unclear. The ultimate goal is to uncover the manifestations underlying GBA abnormalities in GHD and idiopathic short stature (ISS). METHODS Participants included 23 GHD and 25 ISS children. The fusion independent component analysis was applied to integrate multimodal brain data (high-resolution structural, diffusion tensor, and resting-state functional MRI) covering regional homogeneity (ReHo), amplitude of low frequency fluctuations (ALFF), and white matter fractional anisotropy (FA). Gut microbiome diversity and metabolites were analyzed using 16S sequencing and proton nuclear magnetic resonance (1H-NMR). Associations between multimodal neuroimaging and cognition were assessed using moderation analysis. RESULTS Six independent components (IC) of ReHo, ALFF, and FA differed significantly between GHD and ISS patients, with three functional components linked to the processing speed index. GHD individuals showed higher levels of acetate, nicotinate, and lysine in microbiota metabolism. Higher alpha diversity in GHD strengthened connections between ReHo-IC1, ReHo-IC5, ALFF-IC1, and the processing speed index, while increasing agathobacter levels in ISS weakened the link between ALFF-IC1 and the speech comprehension index. CONCLUSIONS Our findings uncover differing brain structure and functional fusion in GHD, alongside microbiota metabolism of short-chain fatty acids. Additionally, microbiome influences connections between neuroimaging and cognition, offering insight into diverse GBA patterns in GHD and ISS, enhancing our understanding of the disease's pathophysiology and interventions.
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Affiliation(s)
- Keren Wang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yuchuan Fu
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Lan Li
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Lingfeng Zhang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mei Huang
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Weihao Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiaoou Shan
- Department of Pediatric Endocrinology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhihan Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Structural Malformations in Children of Zhejiang Province, Wenzhou, China
- Wenzhou Key Laboratory of Structural and Functional Imaging, Wenzhou, China
| | - Yi Lu
- Department of Radiology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Structural Malformations in Children of Zhejiang Province, Wenzhou, China
- Wenzhou Key Laboratory of Structural and Functional Imaging, Wenzhou, China
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Sui J, Zhi D, Calhoun VD. Data-driven multimodal fusion: approaches and applications in psychiatric research. PSYCHORADIOLOGY 2023; 3:kkad026. [PMID: 38143530 PMCID: PMC10734907 DOI: 10.1093/psyrad/kkad026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/08/2023] [Accepted: 11/21/2023] [Indexed: 12/26/2023]
Abstract
In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.
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Affiliation(s)
- Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Emory University and Georgia State University, Atlanta, GA 30303, United States
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Porter A, Fei S, Damme KSF, Nusslock R, Gratton C, Mittal VA. A meta-analysis and systematic review of single vs. multimodal neuroimaging techniques in the classification of psychosis. Mol Psychiatry 2023; 28:3278-3292. [PMID: 37563277 PMCID: PMC10618094 DOI: 10.1038/s41380-023-02195-9] [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: 10/03/2022] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND Psychotic disorders are characterized by structural and functional abnormalities in brain networks. Neuroimaging techniques map and characterize such abnormalities using unique features (e.g., structural integrity, coactivation). However, it is unclear if a specific method, or a combination of modalities, is particularly effective in identifying differences in brain networks of someone with a psychotic disorder. METHODS A systematic meta-analysis evaluated machine learning classification of schizophrenia spectrum disorders in comparison to healthy control participants using various neuroimaging modalities (i.e., T1-weighted imaging (T1), diffusion tensor imaging (DTI), resting state functional connectivity (rs-FC), or some combination (multimodal)). Criteria for manuscript inclusion included whole-brain analyses and cross-validation to provide a complete picture regarding the predictive ability of large-scale brain systems in psychosis. For this meta-analysis, we searched Ovid MEDLINE, PubMed, PsychInfo, Google Scholar, and Web of Science published between inception and March 13th 2023. Prediction results were averaged for studies using the same dataset, but parallel analyses were run that included studies with pooled sample across many datasets. We assessed bias through funnel plot asymmetry. A bivariate regression model determined whether differences in imaging modality, demographics, and preprocessing methods moderated classification. Separate models were run for studies with internal prediction (via cross-validation) and external prediction. RESULTS 93 studies were identified for quantitative review (30 T1, 9 DTI, 40 rs-FC, and 14 multimodal). As a whole, all modalities reliably differentiated those with schizophrenia spectrum disorders from controls (OR = 2.64 (95%CI = 2.33 to 2.95)). However, classification was relatively similar across modalities: no differences were seen across modalities in the classification of independent internal data, and a small advantage was seen for rs-FC studies relative to T1 studies in classification in external datasets. We found large amounts of heterogeneity across results resulting in significant signs of bias in funnel plots and Egger's tests. Results remained similar, however, when studies were restricted to those with less heterogeneity, with continued small advantages for rs-FC relative to structural measures. Notably, in all cases, no significant differences were seen between multimodal and unimodal approaches, with rs-FC and unimodal studies reporting largely overlapping classification performance. Differences in demographics and analysis or denoising were not associated with changes in classification scores. CONCLUSIONS The results of this study suggest that neuroimaging approaches have promise in the classification of psychosis. Interestingly, at present most modalities perform similarly in the classification of psychosis, with slight advantages for rs-FC relative to structural modalities in some specific cases. Notably, results differed substantially across studies, with suggestions of biased effect sizes, particularly highlighting the need for more studies using external prediction and large sample sizes. Adopting more rigorous and systematized standards will add significant value toward understanding and treating this critical population.
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Affiliation(s)
- Alexis Porter
- Department of Psychology, Northwestern University, Evanston, IL, USA.
| | - Sihan Fei
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Katherine S F Damme
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Institute for Innovations in Developmental Sciences, Northwestern University, Evanston and Chicago, IL, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Caterina Gratton
- Department of Psychology, Florida State University, Tallahassee, FL, USA
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA
- Institute for Innovations in Developmental Sciences, Northwestern University, Evanston and Chicago, IL, USA
- Department of Psychiatry, Northwestern University, Chicago, IL, USA
- Medical Social Sciences, Northwestern University, Chicago, IL, USA
- Institute for Policy Research, Northwestern University, Chicago, IL, USA
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Zhang S, She S, Qiu Y, Li Z, Wu X, Hu H, Zheng W, Huang R, Wu H. Multi-modal MRI measures reveal sensory abnormalities in major depressive disorder patients: A surface-based study. Neuroimage Clin 2023; 39:103468. [PMID: 37473494 PMCID: PMC10372163 DOI: 10.1016/j.nicl.2023.103468] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/17/2023] [Accepted: 07/05/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Multi-modal magnetic resonance imaging (MRI) measures are supposed to be able to capture different brain neurobiological aspects of major depressive disorder (MDD). A fusion analysis of structural and functional modalities may better reveal the disease biomarker specific to the MDD disease. METHODS We recruited 30 MDD patients and 30 matched healthy controls (HC). For each subject, we acquired high-resolution brain structural images and resting-state fMRI (rs-fMRI) data using a 3 T MRI scanner. We first extracted the brain morphometric measures, including the cortical volume (CV), cortical thickness (CT), and surface area (SA), for each subject from the structural images, and then detected the structural clusters showing significant between-group differences in each measure using the surface-based morphology (SBM) analysis. By taking the identified structural clusters as seeds, we performed seed-based functional connectivity (FC) analyses to determine the regions with abnormal FC in the patients. Based on a logistic regression model, we performed a classification analysis by selecting these structural and functional cluster-wise measures as features to distinguish the MDD patients from the HC. RESULTS The MDD patients showed significantly lower CV in a cluster involving the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), and lower SA in three clusters involving the bilateral STG, temporal pole gyrus, and entorhinal cortex, and the left inferior temporal gyrus, and fusiform gyrus, than the controls. No significant difference in CT was detected between the two groups. By taking the above-detected clusters as seeds to perform the seed-based FC analysis, we found that the MDD patients showed significantly lower FC between STG/MTG (CV's cluster) and two clusters located in the bilateral visual cortices than the controls. The logistic regression model based on the structural and functional features reached a classification accuracy of 86.7% (p < 0.001) between MDD and controls. CONCLUSION The present study showed sensory abnormalities in MDD patients using the multi-modal MRI analysis. This finding may act as a disease biomarker distinguishing MDD patients from healthy individuals.
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Affiliation(s)
- Shufei Zhang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Shenglin She
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Yidan Qiu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Zezhi Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Xiaoyan Wu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Huiqing Hu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Wei Zheng
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Ruiwang Huang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China.
| | - Huawang Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China.
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Qin X, Huang H, Liu Y, Zheng F, Zhou Y, Wang H. Increased Functional Connectivity Involving the Parahippocampal Gyrus in Patients with Schizophrenia during Theory of Mind Processing: A Psychophysiological Interaction Study. Brain Sci 2023; 13:brainsci13040692. [PMID: 37190657 DOI: 10.3390/brainsci13040692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/11/2023] [Accepted: 04/18/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Theory of Mind (ToM) is an ability to infer the mental state of others, which plays an important role during social events. Previous studies have shown that ToM deficits exist frequently in schizophrenia, which may result from abnormal activity in brain regions related to sociality. However, the interactions between brain regions during ToM processing in schizophrenia are still unclear. Therefore, in this study, we investigated functional connectivity during ToM processing in patients with schizophrenia, using functional magnetic resonance imaging (fMRI). METHODS A total of 36 patients with schizophrenia and 33 healthy controls were recruited to complete a ToM task from the Human Connectome Project (HCP) during fMRI scanning. Psychophysiological interaction (PPI) analysis was applied to explore functional connectivity. RESULTS Patients with schizophrenia were less accurate than healthy controls in judging social stimuli from non-social stimuli (Z = 2.31, p = 0.021), and displayed increased activity in the right inferior frontal gyrus and increased functional connectivity between the bilateral middle temporal gyrus and the ipsilateral parahippocampal gyrus during ToM processing (AlphaSim corrected p < 0.05). CONCLUSIONS Here, we showed that the brain regions related to sociality interact more with the parahippocampal gyrus in patients with schizophrenia during ToM processing, which may reflect a possible compensatory pathway of ToM deficits in schizophrenia. Our study provides a new idea for ToM deficits in schizophrenia, which could be helpful to better understand social cognition of schizophrenia.
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Affiliation(s)
- Xucong Qin
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Huan Huang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Ying Liu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Fanfan Zheng
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yuan Zhou
- Department of Psychology, University of Chinese Academy of Sciences, Beijing 100101, China
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China
| | - Huiling Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
- Hubei Provincial Key Laboratory of Developmentally Originated Disease, Wuhan 430060, China
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Wei Y, Xue K, Yang M, Wang H, Chen J, Han S, Wang X, Li H, Zhang Y, Song X, Cheng J. Aberrant Cerebello-Thalamo-Cortical Functional and Effective Connectivity in First-Episode Schizophrenia With Auditory Verbal Hallucinations. Schizophr Bull 2022; 48:1336-1343. [PMID: 36029238 PMCID: PMC9673260 DOI: 10.1093/schbul/sbab142] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The thalamus is known to be impaired in schizophrenia patients with auditory verbal hallucinations (AVHs). Abnormal filtering function of the thalamus has been found in schizophrenia patients with AVHs. However, a whole-structure approach has commonly been adopted when investigating thalamic dysconnectivity in patients with AVHs, and it remains unclear which thalamic nucleus is the critical structure underlying AVHs. Here, we investigated voxel-wise resting-state functional connectivity (rsFC) of the thalamic nucleus in drug-naïve patients with first-episode schizophrenia (FES) with AVHs. In addition, dynamic causal modeling was applied to compute effective connectivity and estimate causal relationships that could explain aberrant rsFC. Compared with the FES patients without AVH (NAVH) and normal controls, patients with AVHs had weaker rsFC of the bilateral medial pulvinar (PuM) nucleus-cerebellum. Moreover, compared with the normal control group, the AVH and NAVH groups had significantly stronger rsFC of the bilateral PuM nucleus-cerebral cortex, as well as weaker rsFC of the right medial geniculate nucleus-cerebral cortex. Compared with the NAVH and normal control groups, dynamic causal modeling revealed significantly stronger effective connectivity from the left PuM nucleus to the right inferior frontal gyrus in the AVH group. These findings indicate that the critical structure in the thalamus underlying AVHs is the PuM nucleus, and provide direct evidence that the cerebello-thalamo-cortical circuit is associated with AVHs.
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Affiliation(s)
- Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Kangkang Xue
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Meng Yang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Huan Wang
- Hefei National Laboratory for Physical Sciences at Microscale, School of Life Science, University of Science and Technology of China, Hefei 230027, China
| | - Jingli Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Xiaoxiao Wang
- Hefei National Lab for Physical Sciences at the Microscale and Centers for Biomedical Engineering, University of Science and Technology of China, Hefei 230027,China
| | - Hong Li
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052,China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Xueqin Song
- Department of Psychiatry, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052,China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
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10
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Dabiri M, Dehghani Firouzabadi F, Yang K, Barker PB, Lee RR, Yousem DM. Neuroimaging in schizophrenia: A review article. Front Neurosci 2022; 16:1042814. [PMID: 36458043 PMCID: PMC9706110 DOI: 10.3389/fnins.2022.1042814] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/28/2022] [Indexed: 11/16/2022] Open
Abstract
In this review article we have consolidated the imaging literature of patients with schizophrenia across the full spectrum of modalities in radiology including computed tomography (CT), morphologic magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), and magnetoencephalography (MEG). We look at the impact of various subtypes of schizophrenia on imaging findings and the changes that occur with medical and transcranial magnetic stimulation (TMS) therapy. Our goal was a comprehensive multimodality summary of the findings of state-of-the-art imaging in untreated and treated patients with schizophrenia. Clinical imaging in schizophrenia is used to exclude structural lesions which may produce symptoms that may mimic those of patients with schizophrenia. Nonetheless one finds global volume loss in the brains of patients with schizophrenia with associated increased cerebrospinal fluid (CSF) volume and decreased gray matter volume. These features may be influenced by the duration of disease and or medication use. For functional studies, be they fluorodeoxyglucose positron emission tomography (FDG PET), rs-fMRI, task-based fMRI, diffusion tensor imaging (DTI) or MEG there generally is hypoactivation and disconnection between brain regions. However, these findings may vary depending upon the negative or positive symptomatology manifested in the patients. MR spectroscopy generally shows low N-acetylaspartate from neuronal loss and low glutamine (a neuroexcitatory marker) but glutathione may be elevated, particularly in non-treatment responders. The literature in schizophrenia is difficult to evaluate because age, gender, symptomatology, comorbidities, therapy use, disease duration, substance abuse, and coexisting other psychiatric disorders have not been adequately controlled for, even in large studies and meta-analyses.
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Affiliation(s)
- Mona Dabiri
- Department of Radiology, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Kun Yang
- Department of Psychiatry, Molecular Psychiatry Program, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Peter B. Barker
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institution, Baltimore, MD, United States
| | - Roland R. Lee
- Department of Radiology, UCSD/VA Medical Center, San Diego, CA, United States
| | - David M. Yousem
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institution, Baltimore, MD, United States
- *Correspondence: David M. Yousem,
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11
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Yang Y, Sun Y, Zhang Y, Jin X, Li Z, Ding M, Shi H, Liu Q, Zhang L, Su X, Shao M, Song M, Zhang Y, Li W, Yue W, Liu B, Lv L. Abnormal patterns of regional homogeneity and functional connectivity across the adolescent first-episode, adult first-episode and adult chronic schizophrenia. Neuroimage Clin 2022; 36:103198. [PMID: 36116163 PMCID: PMC9486119 DOI: 10.1016/j.nicl.2022.103198] [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: 05/18/2022] [Revised: 09/03/2022] [Accepted: 09/13/2022] [Indexed: 01/10/2023]
Abstract
Functional deficits in schizophrenia (SZ) are observed prior to the onset of psychosis and differ at different stages of SZ. However, there is a paucity of studies focused on adolescent first-episode SZ (AOS), adult first-episode SZ (AFES), and adult chronic SZ (CHSZ). In this study, we investigated regional activity and corresponding functional connectivity alterations that have aimed to compare the three disease stages simultaneously. The subjects comprised 49 patients with AOS, 57 patients with AFES, 51 patients with CHSZ, 41 adolescent healthy controls, and 138 adult healthy controls. We compared regional homogeneity (ReHo) between patients at each disease stage with matched healthy controls. We focused on the shared brain regions that showed significant differences between SZ patients at the three different disease stages and healthy controls. Further analysis was conducted to explore whether the patterns of the whole brain functional connectivity alterations were similar. The putamen and medial frontal gyrus (MFG) showed consistently abnormal patterns in AOS, AFES, and CHSZ. Commonly decreased ReHo values in the MFG and increased ReHo values in the bilateral putamen were found in AOS, AFES, and CHSZ. Functional connectivity of MFG remained common abnormality in different SZ stage. In conclusion, ReHo abnormalities in the MFG and the putamen may be common abnormal patterns of brain function in the three different stages of SZ. The vmPFC-dlPFC FC abnormality common occurs in adolescence and adulthood.. This study may provide a more comprehensive understanding of the neurodevelopmental abnormality across the AOS, AFES, and CHSZ.
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Affiliation(s)
- Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Yuqing Sun
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China,Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yuliang Zhang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China,Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xueyan Jin
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Zheng Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Minli Ding
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China
| | - Han Shi
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China
| | - Qing Liu
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Luwen Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Xi Su
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Minglong Shao
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Meng Song
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Yan Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Wenqiang Li
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China
| | - Weihua Yue
- Institute of Mental Health, Peking University, Beijing 100191, China,Key Laboratory for Mental Health, Ministry of Health, Beijing 100191, China
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China,Chinese Institute for Brain Research, Beijing 102206, China,Corresponding authors at: The Second Affiliated Hospital of Xinxiang Medical University, No.388, Jianshe Middle Road, Xinxiang 453002, China.
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, China,Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, China,International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang 453002, China,Corresponding authors at: The Second Affiliated Hospital of Xinxiang Medical University, No.388, Jianshe Middle Road, Xinxiang 453002, China.
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12
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Yue W, Huang H, Duan J. Potential diagnostic biomarkers for schizophrenia. MEDICAL REVIEW (BERLIN, GERMANY) 2022; 2:385-416. [PMID: 37724326 PMCID: PMC10388817 DOI: 10.1515/mr-2022-0009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 06/20/2022] [Indexed: 09/20/2023]
Abstract
Schizophrenia (SCH) is a complex and severe mental disorder with high prevalence, disability, mortality and carries a heavy disease burden, the lifetime prevalence of SCH is around 0.7%-1.0%, which has a profound impact on the individual and society. In the clinical practice of SCH, key problems such as subjective diagnosis, experiential treatment, and poor overall prognosis are still challenging. In recent years, some exciting discoveries have been made in the research on objective biomarkers of SCH, mainly focusing on genetic susceptibility genes, metabolic indicators, immune indices, brain imaging, electrophysiological characteristics. This review aims to summarize the biomarkers that may be used for the prediction and diagnosis of SCH.
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Affiliation(s)
- Weihua Yue
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, China
- National Clinical Research Center for Mental Disorders & NHC Key Laboratory of Mental Health (Peking University) and Chinese Academy of Medical Sciences Research Unit, Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Jubao Duan
- Center for Psychiatric Genetics, NorthShore University Health System, Evanston, IL, USA
- Department of Psychiatry and Behavioral Neurosciences, University of Chicago, Chicago, IL, USA
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13
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Zhang S, Wang Y, Zheng S, Seger C, Zhong S, Huang H, Hu H, Chen G, Chen L, Jia Y, Huang L, Huang R. Multimodal MRI reveals alterations of the anterior insula and posterior cingulate cortex in bipolar II disorders: A surface-based approach. Prog Neuropsychopharmacol Biol Psychiatry 2022; 116:110533. [PMID: 35151795 DOI: 10.1016/j.pnpbp.2022.110533] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 11/29/2021] [Accepted: 02/05/2022] [Indexed: 10/19/2022]
Abstract
BACKGROUND Bipolar disorder (BD) is a mental disorder with severe implications for those affected and their families. Previous studies detected brain structural and functional alterations in BD patients. However, very few studies conducted a multimodal MRI fusion analysis, and little is known about the role of common anomalies in the connectivity of BD. METHODS We collected sMRI, rs-fMRI, and DTI data from 56 patients with unmedicated BD-II depression and 72 age-, sex- and handedness-matched healthy controls. We applied data-driven approaches to analyze multimodal MRI data and detected brain areas with significant group differences in cortical thickness (CT), amplitude of low frequency fluctuations (ALFF), and fractional anisotropy (FA) of the superficial white matter. We observed the common abnormal areas and took these areas as seeds to analyze the resting-state functional connectivity (RSFC) patterns in BD patients by overlapping these abnormal areas. RESULTS The BD patients showed two common abnormal areas: (1) the left anterior insula (AI) with abnormal CT and FA, and (2) the left posterior cingulate cortex (PCC) with abnormal CT and ALFF. Seed-based analyses showed RSFC between the left AI and left occipital sensory cortex, the left AI and left superior and inferior parietal cortex, and the left PCC and right medial prefrontal cortex were uniformly lower in the BD patients than controls. Correlation analyses showed negative correction between AI's FA and disease episodes and between AI's FA and disease duration in depressed BD-II patients. CONCLUSIONS We observed abnormal brain structural and functional properties in the left AI and left PCC in BD patients. The abnormal RSFC patterns may suggest sensory and cognitive dysfunction in BD.
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Affiliation(s)
- Shufei Zhang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China.
| | - Senning Zheng
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Carol Seger
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China; Department of Psychology and Program in Molecular, Cellular, and Integrative Neuroscience, Colorado State University, Fort Collins, CO 80523, USA
| | - Shuming Zhong
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Huiyuan Huang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Huiqing Hu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Guanmao Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Lixiang Chen
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Yanbin Jia
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Li Huang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Ruiwang Huang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China.
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14
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Zhao M, Yan W, Luo N, Zhi D, Fu Z, Du Y, Yu S, Jiang T, Calhoun VD, Sui J. An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data. Med Image Anal 2022; 78:102413. [PMID: 35305447 PMCID: PMC9035078 DOI: 10.1016/j.media.2022.102413] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 10/27/2021] [Accepted: 03/01/2022] [Indexed: 12/30/2022]
Abstract
Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functional network connectivity (FNC) calculated by TC cross-correlation. TCs reflect the temporal dynamics of brain activity and the FNC characterizes temporal coherence across intrinsic brain networks. Both features have been used as input to deep learning approaches with decent results. However, few studies have tried to leverage their complementary information to learn optimal representations at multiple facets. Motivated by this, we proposed a Hybrid Deep Learning Framework integrating brain Connectivity and Activity (HDLFCA) together by combining convolutional recurrent neural network (C-RNN) and deep neural network (DNN), aiming to improve classification accuracy and interpretability simultaneously. Specifically, C-RNNAM was proposed to extract temporal dynamic dependencies with an attention module (AM) to automatically learn discriminative knowledge from TC nodes, while DNN was applied to identify the most group-discriminative FNC patterns with layer-wise relevance propagation (LRP). Then, both prediction outputs were concatenated to build a new feature matrix, generating the final decision by logistic regression. The effectiveness of HDLFCA was validated on both multi-site schizophrenia (SZ, n ∼ 1100) and public autism datasets (ABIDE, n ∼ 1522) by outperforming 12 alternative models at 2.8-8.9% accuracy, including 8 models using either static FNC or TCs and 4 models using dynamic FNC. Appreciable classification accuracy was achieved for HC vs. SZ (85.3%) and HC vs. Autism (72.4%) respectively. More importantly, the most group-discriminative brain regions can be easily attributed and visualized, providing meaningful biological interpretability and highlighting the great potential of the proposed HDLFCA model in the identification of valid neuroimaging biomarkers.
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Affiliation(s)
- Min Zhao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Weizheng Yan
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - 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
| | - 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; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
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15
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Huang Y, Wang W, Hei G, Yang Y, Long Y, Wang X, Xiao J, Xu X, Song X, Gao S, Shao T, Huang J, Wang Y, Zhao J, Wu R. Altered regional homogeneity and cognitive impairments in first-episode schizophrenia: A resting-state fMRI study. Asian J Psychiatr 2022; 71:103055. [PMID: 35303593 DOI: 10.1016/j.ajp.2022.103055] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/11/2022] [Accepted: 02/27/2022] [Indexed: 01/18/2023]
Abstract
BACKGROUND Patients with schizophrenia consistently present pervasive cognitive deficits, but the neurobiological mechanism of cognitive impairments remains unclear. By analyzing regional homogeneity (ReHo) of resting-state functional Magnetic Resonance Imaging, this study aimed to explore the association between brain functional alterations and cognitive deficits in first-episode schizophrenia (FES) with a relatively large sample. METHODS A total of 187 patients with FES and 100 healthy controls from 3 independent cohorts underwent resting-state functional magnetic resonance scans. The MATRICS Consensus Cognitive Battery (MCCB) was used to assess cognitive function. Partial correlation analysis was performed between abnormal ReHo values and the severity of symptoms and cognitive deficits. RESULTS Compared with healthy controls, ReHo values increased in right superior frontal cortex and decreased in right anterior cingulate cortex (ACC), left middle occipital gyrus (MOG), left cuneus, right posterior cingulate cortex (PCC), and right superior occipital gyrus in schizophrenia patients. ReHo values in ACC, PCC and superior occipital gyrus were correlated with PANSS scores. In addition, ReHo values in ACC and MOG were negatively correlated with working memory; left cuneus was positively correlated with multiple cognitive domains (speed of processing, attention/vigilance and social cognition); PCC was positively correlated with verbal learning; right superior occipital gyrus was positively correlated with speed of processing and social cognition. CONCLUSION In conclusion, we found widespread ReHo alterations and cognitive dysfunction in FES. And the pathophysiology mechanism of a wide range of cognitive deficits may be related to abnormal spontaneous brain activity.
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Affiliation(s)
- Yuyan Huang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Weiyan Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Gangrui Hei
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
| | - Ye Yang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Yujun Long
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Xiaoyi Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Jingmei Xiao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210000, Jiangsu, China
| | - Xueqin Song
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, China
| | - Shuzhan Gao
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210000, Jiangsu, China
| | - Tiannan Shao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Jing Huang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Ying Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Jingping Zhao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China
| | - Renrong Wu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, Hunan, China.
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16
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Feng A, Luo N, Zhao W, Calhoun VD, Jiang R, Zhi D, Shi W, Jiang T, Yu S, Xu Y, Liu S, Sui J. Multimodal brain deficits shared in early-onset and adult-onset schizophrenia predict positive symptoms regardless of illness stage. Hum Brain Mapp 2022; 43:3486-3497. [PMID: 35388581 PMCID: PMC9248316 DOI: 10.1002/hbm.25862] [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] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 03/10/2022] [Accepted: 03/23/2022] [Indexed: 11/25/2022] Open
Abstract
Incidence of schizophrenia (SZ) has two predominant peaks, in adolescent and young adult. Early‐onset schizophrenia provides an opportunity to explore the neuropathology of SZ early in the disorder and without the confound of antipsychotic mediation. However, it remains unexplored what deficits are shared or differ between adolescent early‐onset (EOS) and adult‐onset schizophrenia (AOS) patients. Here, based on 529 participants recruited from three independent cohorts, we explored AOS and EOS common and unique co‐varying patterns by jointly analyzing three MRI features: fractional amplitude of low‐frequency fluctuations (fALFF), gray matter (GM), and functional network connectivity (FNC). Furthermore, a prediction model was built to evaluate whether the common deficits in drug‐naive SZ could be replicated in chronic patients. Results demonstrated that (1) both EOS and AOS patients showed decreased fALFF and GM in default mode network, increased fALFF and GM in the sub‐cortical network, and aberrant FNC primarily related to middle temporal gyrus; (2) the commonly identified regions in drug‐naive SZ correlate with PANSS positive significantly, which can also predict PANSS positive in chronic SZ with longer duration of illness. Collectively, results suggest that multimodal imaging signatures shared by two types of drug‐naive SZ are also associated with positive symptom severity in chronic SZ and may be vital for understanding the progressive schizophrenic brain structural and functional deficits.
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Affiliation(s)
- Aichen Feng
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Wentao Zhao
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Vince D Calhoun
- Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA
| | - Rongtao Jiang
- Department of Radiology and Biomedical imaging, Yale University, New Haven, Connecticut, USA
| | - Dongmei Zhi
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Weiyang Shi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Shan Yu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yong Xu
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Sha Liu
- Department of Psychiatry, First Clinical Medical College/ First Hospital of Shanxi Medical University, Taiyuan, China
| | - Jing Sui
- Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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17
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Zhu J, Wang C, Qian Y, Cai H, Zhang S, Zhang C, Zhao W, Zhang T, Zhang B, Chen J, Liu S, Yu Y. Multimodal neuroimaging fusion biomarkers mediate the association between gut microbiota and cognition. Prog Neuropsychopharmacol Biol Psychiatry 2022; 113:110468. [PMID: 34736997 DOI: 10.1016/j.pnpbp.2021.110468] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 10/25/2021] [Accepted: 10/29/2021] [Indexed: 02/06/2023]
Abstract
Background The field of microbiota-gut-brain research in animals has progressed, while the exact nature of gut microbiota-brain-cognition relationship in humans is not completely elucidated, likely due to small sample sizes and single neuroimaging modality utilized to delineate limited aspects of the brain. We aimed to comprehensively investigate such association in a large sample using multimodal MRI. Methods Fecal samples were collected from 157 healthy young adults and 16S sequencing was used to assess gut microbial diversity and enterotypes. Five brain imaging measures, including regional homogeneity (ReHo) and functional connectivity density (FCD) from resting-state functional MRI, cerebral blood flow (CBF) from arterial spin labeling, gray matter volume (GMV) from structural MRI, and fractional anisotropy (FA) from diffusion tensor imaging, were jointly analyzed with a data-driven multivariate fusion method. Cognition was evaluated by 3-back and digit span tasks. Results We found significant associations of gut microbial diversity with ReHo, FCD, CBF, and GMV within the frontoparietal, default mode and visual networks, as well as with FA in a distributed set of juxtacortical white matter regions. In addition, there were FCD, CBF, GMV, and FA differences between Prevotella- versus Bacteroides-enterotypes in females and between Prevotella- versus Ruminococcaceae-enterotypes in males. Moreover, the identified neuroimaging fusion biomarkers could mediate the associations between microbial diversity and cognition. Conclusions Our findings not only expand existing knowledge of the microbiota-gut-brain axis, but also have potential clinical and translational implications by exposing the gut microbiota as a promising treatment and prevention target for cognitive impairment and related brain disorders.
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Affiliation(s)
- Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Chunli Wang
- Department of Clinical Laboratory, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yinfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Shujun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Cun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Wenming Zhao
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Tingting Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Biao Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Jingyao Chen
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Siyu Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Research Center of Clinical Medical Imaging, Anhui Province, Hefei 230032, China; Anhui Provincial Institute of Translational Medicine, Hefei 230032, China.
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Buchsbaum MS, Mitelman SA, Christian BT, Merrill BM, Buchsbaum BR, Mitelman D, Mukherjee J, Lehrer DS. Four-modality imaging of unmedicated subjects with schizophrenia: 18F-fluorodeoxyglucose and 18F-fallypride PET, diffusion tensor imaging, and MRI. Psychiatry Res Neuroimaging 2022; 320:111428. [PMID: 34954446 DOI: 10.1016/j.pscychresns.2021.111428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 12/14/2021] [Accepted: 12/16/2021] [Indexed: 12/28/2022]
Abstract
Diminished prefrontal function, dopaminergic abnormalities in the striatum and thalamus, reductions in white matter integrity and frontotemporal gray matter deficits are the most replicated findings in schizophrenia. We used four imaging modalities (18F-fluorodeoxyglucose and 18F-fallypride PET, diffusion tensor imaging, structural MRI) in 19 healthy and 25 schizophrenia subjects to assess the relationship between functional (dopamine D2/D3 receptor binding potential, glucose metabolic rate) and structural (fractional anisotropy, MRI) correlates of schizophrenia and their additive diagnostic prediction potential. Multivariate ANOVA was used to compare structural and functional image sets for identification of schizophrenia. Integration of data from all four modalities yielded better predictive power than less inclusive combinations, specifically in the thalamus, left dorsolateral prefrontal and temporal regions. Among the modalities, fractional anisotropy showed highest discrimination in white matter whereas 18F-fallypride binding showed highest discrimination in gray matter. Structural and functional modalities displayed comparable discriminative power but different topography, with higher sensitivity of structural modalities in the left prefrontal region. Combination of functional and structural imaging modalities with inclusion of both gray and white matter appears most effective in diagnostic discrimination. The highest sensitivity of 18F-fallypride PET to gray matter changes in schizophrenia supports the primacy of dopaminergic abnormalities in its pathophysiology.
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Affiliation(s)
- Monte S Buchsbaum
- Departments of Psychiatry and Radiology, University of California, Irvine and San Diego, 11388 Sorrento Valley Road, San Diego, CA 92121, United States
| | - Serge A Mitelman
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, United States; Department of Psychiatry, Division of Child and Adolescent Psychiatry, Elmhurst Hospital Center, 79-01 Broadway, Elmhurst, NY 11373, United States.
| | - Bradley T Christian
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, 1500 Highland Avenue, Room T231, Madison, WI 53705, United States
| | - Brian M Merrill
- Department of Psychiatry, Boonshoft School of Medicine, Wright State University, East Medical Plaza, Dayton, OH 45408, United States
| | - Bradley R Buchsbaum
- The Rotman Research Institute, Baycrest Centre for Geriatric Care and Department of Psychiatry, University of Toronto, 3560 Bathurst St., Toronto, Ontario, Canada, M6A 2E1
| | - Danielle Mitelman
- The Arnold & Marie Schwartz College of Pharmacy and Health Sciences, Long Island University, 75 Dekalb Avenue, Brooklyn, NY 11201, United States
| | - Jogeshwar Mukherjee
- Department of Radiological Sciences, Preclinical Imaging, University of California, Irvine School of Medicine, Irvine, CA 92697
| | - Douglas S Lehrer
- Department of Psychiatry, Boonshoft School of Medicine, Wright State University, East Medical Plaza, Dayton, OH 45408, United States
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19
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Antonucci LA, Fazio L, Pergola G, Blasi G, Stolfa G, Di Palo P, Mucci A, Rocca P, Brasso C, di Giannantonio M, Maria Giordano G, Monteleone P, Pompili M, Siracusano A, Bertolino A, Galderisi S, Maj M. Joint structural-functional magnetic resonance imaging features are associated with diagnosis and real-world functioning in patients with schizophrenia. Schizophr Res 2022; 240:193-203. [PMID: 35032904 DOI: 10.1016/j.schres.2021.12.029] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 10/20/2021] [Accepted: 12/22/2021] [Indexed: 12/28/2022]
Abstract
OBJECTIVE Earlier evidence suggested that structural-functional covariation in schizophrenia patients (SCZ) is associated with cognition, a predictor of functioning. Moreover, studies suggested that functional brain abnormalities of schizophrenia may be related with structural network features. However, only few studies have investigated the relationship between structural-functional covariation and both diagnosis and functioning in SCZ. We hypothesized that structural-functional covariation networks associated with diagnosis are related to real-world functioning in SCZ. METHODS We performed joint Independent Component Analysis on T1 images and resting-state fMRI-based Degree Centrality (DC) maps from 89 SCZ and 285 controls. Structural-functional covariation networks in which we found a main effect of diagnosis underwent correlation analysis to investigate their relationship with functioning. Covariation networks showing a significant association with both diagnosis and functioning underwent univariate analysis to better characterize group-level differences at the spatial level. RESULTS A structural-functional covariation network characterized by frontal, temporal, parietal and thalamic structural estimates significantly covaried with temporo-parietal resting-state DC. Compared with controls, SCZ had reduced structural-functional covariation within this network (pFDR = 0.005). The same measure correlated positively with both social and occupational functioning (both pFDR = 0.042). Univariate analyses revealed grey matter deviations in SCZ compared with controls within this structural-functional network in hippocampus, cerebellum, thalamus, orbito-frontal cortex, and insula. No group differences were found in DC. CONCLUSIONS Findings support the existence of a phenotypical association between group-level differences and inter-individual heterogeneity of functional deficits in SCZ. Given that only the joint structural/functional analysis revealed this association, structural-functional covariation may be a potentially relevant schizophrenia phenotype.
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Affiliation(s)
- Linda A Antonucci
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Leonardo Fazio
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giulio Pergola
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Blasi
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Stolfa
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Piergiuseppe Di Palo
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Armida Mucci
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Paola Rocca
- Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
| | - Claudio Brasso
- Department of Neuroscience, Section of Psychiatry, University of Turin, Turin, Italy
| | | | | | - Palmiero Monteleone
- Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", Section of Neuroscience, University of Salerno, Salerno, Italy
| | - Maurizio Pompili
- Department of Neurosciences, Mental Health, and Sensory Organs, S. Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Alberto Siracusano
- Department of Systems Medicine, Psychiatry and Clinical Psychology Unit, Tor Vergata University of Rome, Rome, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy.
| | - Silvana Galderisi
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Mario Maj
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
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20
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Zhang S, He Z, Du L, Zhang Y, Yu S, Wang R, Hu X, Jiang X, Zhang T. Joint Analysis of Functional and Structural Connectomes Between Preterm and Term Infant Brains via Canonical Correlation Analysis With Locality Preserving Projection. Front Neurosci 2021; 15:724391. [PMID: 34690672 PMCID: PMC8526737 DOI: 10.3389/fnins.2021.724391] [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: 06/13/2021] [Accepted: 08/24/2021] [Indexed: 12/16/2022] Open
Abstract
Preterm is a worldwide problem that affects infants' lives significantly. Moreover, the early impairment is more than limited to isolated brain regions but also to global and profound negative outcomes later, such as cognitive disorder. Therefore, seeking the differences of brain connectome between preterm and term infant brains is a vital step for understanding the developmental impairment caused by preterm. Existing studies revealed that studying the relationship between brain function and structure, and further investigating their differentiable connectomes between preterm and term infant brains is a way to comprehend and unveil the differences that occur in the preterm infant brains. Therefore, in this article, we proposed a novel canonical correlation analysis (CCA) with locality preserving projection (LPP) approach to investigate the relationship between brain functional and structural connectomes and how such a relationship differs between preterm and term infant brains. CCA is proposed to study the relationship between functional and structural connections, while LPP is adopted to identify the distinguishing features from the connections which can differentiate the preterm and term brains. After investigating the whole brain connections on a fine-scale connectome approach, we successfully identified 89 functional and 97 structural connections, which mostly contributed to differentiate preterm and term infant brains from the functional MRI (fMRI) and diffusion MRI (dMRI) of the public developing Human Connectome Project (dHCP) dataset. By further exploring those identified connections, the results innovatively revealed that the identified functional connections are short-range and within the functional network. On the contrary, the identified structural connections are usually remote connections across different functional networks. In addition, these connectome-level results show the new insights that longitudinal functional changes could deviate from longitudinal structural changes in the preterm infant brains, which help us better understand the brain-behavior changes in preterm infant brains.
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Affiliation(s)
- Shu Zhang
- Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Zhibin He
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Lei Du
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Yin Zhang
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Sigang Yu
- Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Ruoyang Wang
- Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi’an, China
| | - Xintao Hu
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Xi Jiang
- School of Life Sciences and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi’an, China
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21
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Cheng W, Luo N, Zhang Y, Zhang X, Tan H, Zhang D, Sui J, Yue W, Yan H. DNA Methylation and Resting Brain Function Mediate the Association between Childhood Urbanicity and Better Speed of Processing. Cereb Cortex 2021; 31:4709-4718. [PMID: 33987663 PMCID: PMC8408435 DOI: 10.1093/cercor/bhab117] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 04/07/2021] [Accepted: 04/09/2021] [Indexed: 01/10/2023] Open
Abstract
Urbanicity has been suggested to affect cognition, but the underlying mechanism remains unknown. We examined whether epigenetic modification (DNA methylation, DNAm), and brain white matter fiber integrity (fractional anisotropy, FA) or local spontaneous brain function activity (regional homogeneity, ReHo) play roles in the association between childhood urbanicity and cognition based on 497 healthy Chinese adults. We found significant correlation between childhood urbanicity and better cognitive performance. Multiset canonical correlation analysis (mCCA) identified an intercorrelated DNAm-FA-ReHo triplet, which showed significant pairwise correlations (DNAm-FA: Bonferroni-adjusted P, Pbon = 4.99E-03, rho = 0.216; DNAm-ReHo: Pbon = 4.08E-03, rho = 0.239; ReHo-FA: Pbon = 1.68E-06, rho = 0.328). Causal mediation analysis revealed that 1) ReHo mediated 10.86% childhood urbanicity effects on the speed of processing and 2) childhood urbanicity alters ReHo through DNA methylation in the cadherin and Wnt signaling pathways (mediated effect: 48.55%). The mediation effect of increased ReHo in the superior temporal gyrus underlying urbanicity impact on a better speed of processing was further validated in an independent cohort. Our work suggests a mediation role for ReHo, particularly increased brain activity in the superior temporal gyrus, in the urbanicity-associated speed of processing.
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Affiliation(s)
- Weiqiu Cheng
- Peking University Sixth Hospital/Institute of Mental Health, Beijing 100191, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuyanan Zhang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing 100191, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Xiao Zhang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing 100191, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Haoyang Tan
- Lieber Institute for Brain Development, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Dai Zhang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing 100191, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
- Tsinghua University-Peking University Joint Center for Life Sciences, Beijing 100871, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Jing Sui
- University of Chinese Academy of Sciences, Beijing 100049, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Weihua Yue
- Peking University Sixth Hospital/Institute of Mental Health, Beijing 100191, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Hao Yan
- Peking University Sixth Hospital/Institute of Mental Health, Beijing 100191, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
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22
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Akhonda MABS, Gabrielson B, Bhinge S, Calhoun VD, Adali T. Disjoint subspaces for common and distinct component analysis: Application to the fusion of multi-task FMRI data. J Neurosci Methods 2021; 358:109214. [PMID: 33957159 DOI: 10.1016/j.jneumeth.2021.109214] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 04/12/2021] [Accepted: 04/29/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND Data-driven methods such as independent component analysis (ICA) makes very few assumptions on the data and the relationships of multiple datasets, and hence, are attractive for the fusion of medical imaging data. Two important extensions of ICA for multiset fusion are the joint ICA (jICA) and the multiset canonical correlation analysis and joint ICA (MCCA-jICA) techniques. Both approaches assume identical mixing matrices, emphasizing components that are common across the multiple datasets. However, in general, one would expect to have components that are both common across the datasets and distinct to each dataset. NEW METHOD We propose a general framework, disjoint subspace analysis using ICA (DS-ICA), which identifies and extracts not only the common but also the distinct components across multiple datasets. A key component of the method is the identification of these subspaces and their separation before subsequent analyses, which helps establish better model match and provides flexibility in algorithm and order choice. COMPARISON We compare DS-ICA with jICA and MCCA-jICA through both simulations and application to multiset functional magnetic resonance imaging (fMRI) task data collected from healthy controls as well as patients with schizophrenia. RESULTS The results show DS-ICA estimates more components discriminative between healthy controls and patients than jICA and MCCA-jICA, and with higher discriminatory power showing activation differences in meaningful regions. When applied to a classification framework, components estimated by DS-ICA results in higher classification performance for different dataset combinations than the other two methods. CONCLUSION These results demonstrate that DS-ICA is an effective method for fusion of multiple datasets.
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Affiliation(s)
- M A B S Akhonda
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, 21250 MD, USA.
| | - Ben Gabrielson
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, 21250 MD, USA
| | - Suchita Bhinge
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, 21250 MD, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, 30303 GA, USA
| | - Tülay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, 21250 MD, USA
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23
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Yang F, Ma H, Yuan J, Wei Y, Xu L, Zhang Y, Kang C, Yang J. Correlation of abnormalities in resting state fMRI with executive functioning in chronic schizophrenia. Psychiatry Res 2021; 299:113862. [PMID: 33735738 DOI: 10.1016/j.psychres.2021.113862] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 03/06/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Although previous studies have consistently demonstrated that neurocognitive and social cognitive impairments are commonly observed in schizophrenia, the neural substrates of deficits of cognitive function remain unclear, especially for the chronic schizophrenia. There has been little resting-state functional magnetic resonance imaging (rs-fMRI) study of cognitive function in chronic schizophrenia. In this study we aimed to investigate the changes of rs-fMRI signals with regional homogeneity (ReHo), and explore the correlations between abnormal regional activity and cognitive function in chronic schizophrenia. METHODS Altogether 76 subjects, 37 patients with chronic schizophrenia and 39 normal controls matched approximately for age, gender and education level were enrolled. All subjects were evaluated psychotic symptoms by Positive and Negative Syndrome Scale (PANSS) and cognitive function by Wisconsin Card Sorting Test (WCST). Conventional MRI and rs-fMRI were performed in all subjects. ReHo was calculated to measure the temporal synchronization of a given voxel and its neighboring voxels based on Kendall coefficient of concordance (KCC) in the rs-fMRI. RESULTS For the numbers of achieved categories, percentage of conceptual level response in the scores of WCST, the patient group was significantly lower than the control group (p<0.05). For the total errors, perseverative errors, non-perseverative errors, the patient group was significantly higher than the control group (p<0.05). Significant differences in ReHo were found in 11 regions (included five activated and five with decreased activity in the cerebrum and one with decreased activity in the cerebellum) in the chronic schizophrenia patients when compared with the normal controls. The ReHo map clusters that were significantly different between the two groups showed no significant correlation with clinical symptoms. Correlation of the whole brain with subscores of PANSS-T, PANSS-P, PANSS-N and WCST were significantly found in some regions. CONCLUSIONS The study identified five increased and six decreased spontaneous synchrony in the cerebrum and cerebellum in chronic schizophrenia patients compared to the normal matched controls, which were associated with positive, negative symptoms, and deficits of executive functioning.
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Affiliation(s)
- Fan Yang
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China; Department of Psychiatry, Inner Mongolia People's Hospital, Inner Mongolia 010020, China
| | - Huan Ma
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China; Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming 650018, China
| | - Jing Yuan
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China
| | - Yujun Wei
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China
| | - Li Xu
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China
| | - Yan Zhang
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China
| | - Chuanyuan Kang
- Department of Psychosomatic Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, China
| | - Jianzhong Yang
- Department of Psychiatry, The Second Affiliated Hospital of Kunming Medical University, Kunming 650101, China.
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24
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Guo X, Liu L, Li T, Zhao Q, Li H, Huang F, Wang Y, Qian Q, Cao Q, Wang Y, Calhoun VD, Sui J, Sun L. Inhibition-directed multimodal imaging fusion patterns in adults with ADHD and its potential underlying "gene-brain-cognition" relationship. CNS Neurosci Ther 2021; 27:664-673. [PMID: 33724699 PMCID: PMC8111492 DOI: 10.1111/cns.13625] [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: 10/21/2020] [Revised: 01/27/2021] [Accepted: 01/30/2021] [Indexed: 11/30/2022] Open
Abstract
Aims Inhibition deficits have been suggested to be a core cognitive impairment in attention‐deficit/hyperactivity disorder (ADHD). Exploring imaging patterns and the potential genetic components associated with inhibition deficits would definitely promote our understanding of the neuropathological mechanism of ADHD. This study aims to investigate the multimodal imaging fusion features related to inhibition deficits in adults with ADHD (aADHD) and to make an exploratory analysis of the role of inhibition‐related gene, NOS1, on those brain alterations. Methods Specifically, multisite canonical correlation analysis with reference plus joint independent component analysis (MCCAR + jICA) was conducted to identify the joint co‐varying gray matter volume (GMV) and the functional connectivity (FC) features related to inhibition in 69 aADHD and 44 healthy controls. Then, mediation analysis was employed to detect the relationship among inhibition‐related imaging features, NOS1 ex1f‐VNTR genotypes, and inhibition. Results Inhibition‐directed multimodal imaging fusion patterns of aADHD were reduced GMV and FC in inhibition network and increased GMV and FC in default mode network. The results showed a significant indirect effect of NOS1 ex1f‐VNTR on inhibition via FC component [effect size = −0.54 (SE = 0.29), 95% CI = −1.16 to −0.01]. In addition, the results indicated a significant indirect effect of GMV on the inhibition via FC component [effect size = 0.43 (SE = 0.23), 95% CI = 0.12 to 1.00]. Conclusion The findings suggested that reduced GMV and FC in inhibition network and increased GMV and FC in default mode network were jointly responsible for inhibition deficits in aADHD. Both the NOS1 ex1f‐VNTR genotypes and GMV might influence the inhibition through the mediation effect of the aforementioned FC (NOS1/GMV→FC→Inhibition).
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Affiliation(s)
- Xiaojie Guo
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Lu Liu
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Tiantian Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Qihua Zhao
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Hui Li
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Fang Huang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Yanfei Wang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Qiujin Qian
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Qingjiu Cao
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - Yufeng Wang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
| | - 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
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Brain Science, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Li Sun
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China.,National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health (Peking University, Beijing, China
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Ma M, Zhang Y, Zhang X, Yan H, Zhang D, Yue W. Common and Distinct Alterations of Cognitive Function and Brain Structure in Schizophrenia and Major Depressive Disorder: A Pilot Study. Front Psychiatry 2021; 12:705998. [PMID: 34354618 PMCID: PMC8329352 DOI: 10.3389/fpsyt.2021.705998] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 06/03/2021] [Indexed: 01/10/2023] Open
Abstract
Objective: Numerous studies indicate that schizophrenia (SCZ) and major depressive disorder (MDD) share pathophysiological characteristics. Investigating the neurobiological features of psychiatric-affective disorders may facilitate the diagnosis of psychiatric disorders. Hence, we aimed to explore whether patients with SCZ and patients with MDD had the similar or distinct cognitive impairments and GMV alterations to further understand their underlying pathophysiological mechanisms. Methods: We recruited a total of 52 MDD patients, 64 SCZ patients, and 65 healthy controls (HCs). The Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery was used to assess cognitive functions. In addition, voxel-based morphometry (VBM) analysis was used to evaluate the gray matter volume (GMV) by using MRI scanning. One-way ANOVA and post-hoc tests were used to find the differences among the MDD, SCZ, and HCs. Finally, we explored the correlation between structural alterations and cognitive functions. Results: Compared with that of HCs, processing speed was impaired in both patients with SCZ and patients with MDD (F = 49.505, p < 0.001). SCZ patients displayed impaired cognitive performance in all dimensions of cognitive functions compared with HCs (p < 0.001, except social cognition, p = 0.043, Bonferroni corrected). Whole-brain VBM analysis showed that both SCZ and MDD groups had reductions of GMV in the medial superior frontal cortex (cluster-level FWE p < 0.05). Patients with SCZ exhibited declining GMV in the anterior cingulate cortex and right middle frontal cortex (MFC) compared with HCs and MDD patients (cluster-level FWE p < 0.05). The mean values of GMV in the right MFC had a positive correlation with the attention/vigilance function in patients with MDD (p = 0.014, partial. r = 0.349, without Bonferroni correction). Conclusions: In total, our study found that MDD and SCZ groups had common cognitive impairments and brain structural alterations, but the SCZ group exhibited more severe impairment than the MDD group in both fields. The above findings may provide a potential support for recognizing the convergent and divergent brain neural pathophysiological mechanisms between MDD and SCZ.
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Affiliation(s)
- Mengying Ma
- Institute of Mental Health, The Sixth Hospital, Peking University, Beijing, China.,Key Laboratory of Mental Health, Ministry of Health & National Clinical Research Center for Mental Disorders, Peking University, Beijing, China
| | - Yuyanan Zhang
- Institute of Mental Health, The Sixth Hospital, Peking University, Beijing, China.,Key Laboratory of Mental Health, Ministry of Health & National Clinical Research Center for Mental Disorders, Peking University, Beijing, China
| | - Xiao Zhang
- Institute of Mental Health, The Sixth Hospital, Peking University, Beijing, China.,Key Laboratory of Mental Health, Ministry of Health & National Clinical Research Center for Mental Disorders, Peking University, Beijing, China
| | - Hao Yan
- Institute of Mental Health, The Sixth Hospital, Peking University, Beijing, China.,Key Laboratory of Mental Health, Ministry of Health & National Clinical Research Center for Mental Disorders, Peking University, Beijing, China
| | - Dai Zhang
- Institute of Mental Health, The Sixth Hospital, Peking University, Beijing, China.,Key Laboratory of Mental Health, Ministry of Health & National Clinical Research Center for Mental Disorders, Peking University, Beijing, China.,PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Weihua Yue
- Institute of Mental Health, The Sixth Hospital, Peking University, Beijing, China.,Key Laboratory of Mental Health, Ministry of Health & National Clinical Research Center for Mental Disorders, Peking University, Beijing, China.,PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
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26
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Distinct Associations of Cognitive Impairments and Reduced Gray Matter Volumes in Remitted Patients with Schizophrenia and Bipolar Disorder. Neural Plast 2020; 2020:8859388. [PMID: 33381163 PMCID: PMC7748913 DOI: 10.1155/2020/8859388] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 11/04/2020] [Accepted: 11/30/2020] [Indexed: 12/12/2022] Open
Abstract
Background Cognitive impairments are documented in schizophrenia (SZ) and bipolar disorder (BD) and may be related to gray matter volumes (GMVs). Thus, this study is aimed at exploring whether the association between cognitive impairments and GMV alterations is similar in patients with SZ and BD and understanding the underlying neurobiological mechanisms. Methods A total of 137 adult subjects (46 with SZ, 35 with BD, and 56 age-, sex-, and education-matched healthy controls (HC)) completed the MATRICS Consensus Cognitive Battery (MCCB) and structural magnetic resonance imaging scanning. We performed group comparisons of the cognitive impairments, the GMV alterations, and the association between them. Results Compared with HC, the patients with SZ and BD showed shared deficits in 4 cognitive domains (i.e., processing speed, working memory, problem solving, and social cognition) and the composite. SZ and BD had commonly decreased GMVs, mainly in the insula, superior temporal pole, amygdala, anterior cingulate, and frontal cortices (superior, middle, opercular inferior, and orbital frontal gyrus). No correlation between MCCB scores and GMVs was detected in SZ. However, for BD, working memory was relevant to the right hemisphere (i.e., right insula, amygdala, superior temporal pole, and medial and dorsolateral superior frontal gyrus). Limitations. The major limitations were that not all patients were the first-episode status and no medication. Conclusions The association was mainly limited to the BD group. Thus, the underlying pathophysiology of the cognitive deficits, in terms of GMV alterations, may be diverse between two disorders.
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27
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Mi WF, Tabarak S, Wang L, Zhang SZ, Lin X, Du LT, Liu Z, Bao YP, Gao XJ, Zhang WH, Wang XQ, Fan TT, Li LZ, Hao XN, Fu Y, Shi Y, Guo LH, Sun HQ, Liu L, Si TM, Zhang HY, Lu L, Li SX. Effects of agomelatine and mirtazapine on sleep disturbances in major depressive disorder: evidence from polysomnographic and resting-state functional connectivity analyses. Sleep 2020; 43:5837058. [PMID: 32406918 DOI: 10.1093/sleep/zsaa092] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 05/04/2020] [Indexed: 11/14/2022] Open
Abstract
To investigate effects of agomelatine and mirtazapine on sleep disturbances in patients with major depressive disorder. A total of 30 depressed patients with sleep disturbances, 27 of which completed the study, took agomelatine or mirtazapine for 8 weeks. Subjective scales were administered, and polysomnography was performed at baseline and at the end of week 1 and 8. Functional magnetic resonance imaging was performed at baseline and at the end of week 8. Compared with baseline, scores on the Hamilton Depression Scale, Hamilton Anxiety Scale, Pittsburgh Sleep Quality Index, Sleep Dysfunction Rating Scale, and Insomnia Severity Index after 8 weeks of treatment significantly decreased in both groups, with no significant differences between groups, accompanied by significant increases in total sleep time, sleep efficiency, and rapid eye movement (REM) sleep and significant decrease in wake after sleep onset. Mirtazapine treatment increased N3 sleep at week 1 compared with agomelatine treatment, but this difference disappeared at week 8. The increases in the percentage and duration of N3 sleep were positively correlated with increases in connectivity between right dorsal lateral prefrontal cortex (dlPFC) and right precuneus and between left posterior cingulate cortex and right precuneus in both groups, respectively. Functional connectivity (FC) between right dlPFC and left precuneus in mirtazapine group was higher compared with agomelatine group after 8 weeks of treatment. These findings indicated that both agomelatine and mirtazapine improved sleep in depressed patients, and the effect of mirtazapine was greater than agomelatine with regard to rapidly increasing N3 sleep and gradually improving FC in the brain.
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Affiliation(s)
- Wei-Feng Mi
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Serik Tabarak
- Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Li Wang
- Xuanwu Hospital Capital Medical University, Xicheng, Beijing, China
| | - Su-Zhen Zhang
- Department of Psychiatry, Huzhou 3rd Hospital, Huzhou, Zhejiang, China
| | - Xiao Lin
- Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Lan-Ting Du
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Zhen Liu
- Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China.,Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Yan-Ping Bao
- Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China.,Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Xue-Jiao Gao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Wei-Hua Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Xue-Qin Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Teng-Teng Fan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Ling-Zhi Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Xiao-Nan Hao
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Yi Fu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Ying Shi
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Li-Hua Guo
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Hong-Qiang Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Lin Liu
- Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China.,Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Tian-Mei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Hong-Yan Zhang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Haidian, Beijing, China
| | - Lin Lu
- Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China.,Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China
| | - Su-Xia Li
- Beijing Key laboratory of Drug Dependence, National Institute on Drug Dependence, Peking University, Beijing, China.,Department of Pharmacology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
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28
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Zhang X, Liu W, Guo F, Li C, Wang X, Wang H, Yin H, Zhu Y. Disrupted structural covariance network in first episode schizophrenia patients: Evidence from a large sample MRI-based morphometric study. Schizophr Res 2020; 224:24-32. [PMID: 33203611 DOI: 10.1016/j.schres.2020.11.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 09/30/2020] [Accepted: 11/02/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Recent progress in neuroscience research has provided evidence that schizophrenia is a disease that involves dysconnectivity of brain networks. Widespread gray matter loss was commonly observed but how these gray matter abnormalities are characterized at the large-scale network-level in schizophrenia, especially patients with first-episode (FE-SCZ) remains unclear. METHODS In this study, gray matter structural network aberrations were investigated by applying structural covariance network analysis to 193 first episode schizophrenia patients and 178 age and gender-matched healthy controls (HCs). The mean gray matter volume in seed regions relating to eight specific networks (visual, auditory, sensorimotor, speech, semantic, default-mode, executive control, and salience) were extracted, and voxel-wise analyses of covariance were conducted to compare the association between whole-brain gray matter volume and each seed region for FE-SCZ and HCs. RESULTS The auditory network was less extended in FE-SCZ compared with HCs, with a significant decrease in the structural association between the Hesch's gyrus and the middle frontal gyrus and the superior frontal gyrus. Hyperconnectivity was observed in executive control network with a significant increase in the structural association between the dorsal lateral prefrontal cortex and the superior frontal gyrus and supplementary motor area. CONCLUSION Our research shows that seed based structural covariance analysis can well characterize multiple large-scale networks, the observed changes might underly the hallucinations and cognitive impairments observed in FE-SCZ. Given that these patients were experiencing their first episode of schizophrenia, our findings suggest that such structural network deficits are present at an early stage in this disorder.
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Affiliation(s)
- Xiao Zhang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Wenming Liu
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Fan Guo
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Chen Li
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Xingrui Wang
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
| | - Yuanqiang Zhu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi, China.
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29
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Falakshahi H, Vergara VM, Liu J, Mathalon DH, Ford JM, Voyvodic J, Mueller BA, Belger A, McEwen S, Potkin SG, Preda A, Rokham H, Sui J, Turner JA, Plis S, Calhoun VD. Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia. IEEE Trans Biomed Eng 2020; 67:2572-2584. [PMID: 31944934 PMCID: PMC7538162 DOI: 10.1109/tbme.2020.2964724] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Multimodal measurements of the same phenomena provide complementary information and highlight different perspectives, albeit each with their own limitations. A focus on a single modality may lead to incorrect inferences, which is especially important when a studied phenomenon is a disease. In this paper, we introduce a method that takes advantage of multimodal data in addressing the hypotheses of disconnectivity and dysfunction within schizophrenia (SZ). METHODS We start with estimating and visualizing links within and among extracted multimodal data features using a Gaussian graphical model (GGM). We then propose a modularity-based method that can be applied to the GGM to identify links that are associated with mental illness across a multimodal data set. Through simulation and real data, we show our approach reveals important information about disease-related network disruptions that are missed with a focus on a single modality. We use functional MRI (fMRI), diffusion MRI (dMRI), and structural MRI (sMRI) to compute the fractional amplitude of low frequency fluctuations (fALFF), fractional anisotropy (FA), and gray matter (GM) concentration maps. These three modalities are analyzed using our modularity method. RESULTS Our results show missing links that are only captured by the cross-modal information that may play an important role in disconnectivity between the components. CONCLUSION We identified multimodal (fALFF, FA and GM) disconnectivity in the default mode network area in patients with SZ, which would not have been detectable in a single modality. SIGNIFICANCE The proposed approach provides an important new tool for capturing information that is distributed among multiple imaging modalities.
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30
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Yan W, Zhang R, Zhou M, Lu S, Li W, Xie S, Zhang N. Relationships between abnormal neural activities and cognitive impairments in patients with drug-naive first-episode schizophrenia. BMC Psychiatry 2020; 20:283. [PMID: 32503481 PMCID: PMC7275517 DOI: 10.1186/s12888-020-02692-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 05/21/2020] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Prior resting state functional Magnetic Resonance Imaging studies (rs-fMRI) via the regional homogeneity (ReHo) method have demonstrated inconsistent and conflicting results because of several confounding factors, such as small sample size, medicinal influence, and illness duration. Relationships between ReHo measures and cognitive impairments in patients with drug-naive First-Episode Schizophrenia (dn-FES) are rarely reported. This study was conducted to explore the correlations between ReHo measures and cognitive deficits and clinical symptoms in patients with dn-FES. METHODS A total of 69 patients with dn-FES and 74 healthy controls were recruited. MATRICS Consensus Cognitive Battery (MCCB), Wechsler Adult Intelligence Scale (WAIS), and Positive And Negative Syndrome Scale (PANSS) were used to assess cognitive function, Intelligence Quotient (IQ), and clinical symptoms, respectively. The correlations between ReHo maps and cognitive deficits and the severity of symptoms were examined using strict correlation analysis. RESULTS ReHo values in right Middle Frontal Gyrus (MFG) and Superior Frontal Gyrus (SFG) increased in dn-FES group, whereas ReHo values in right cuneus decreased. Correlation analysis showed that the ReHo values in right MFG positively correlated with attention/vigilance impairments, social cognition deficits, and the severity of clinical manifestations. CONCLUSIONS These findings suggested that abnormal spontaneous activities in right MFG reflect illness severity and cognitive deficits, which also serve as a basis for establishing objective diagnostic markers and might be a clinical intervention target for treating patients with schizophrenia.
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Affiliation(s)
- Wei Yan
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029 China
| | - Rongrong Zhang
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029 China
| | - Min Zhou
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029 China
| | - Shuiping Lu
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029 China
| | - Wenmei Li
- grid.453246.20000 0004 0369 3615School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, 210023 China ,grid.453246.20000 0004 0369 3615College of Telecommunications & Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, 210003 China ,Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, Nanjing, 210023 China
| | - Shiping Xie
- grid.89957.3a0000 0000 9255 8984Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029 China
| | - Ning Zhang
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, 210029, China.
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31
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Qi S, Bustillo J, Turner JA, Jiang R, Zhi D, Fu Z, Deramus TP, Vergara V, Ma X, Yang X, Stevens M, Zhuo C, Xu Y, Calhoun VD, Sui J. The relevance of transdiagnostic shared networks to the severity of symptoms and cognitive deficits in schizophrenia: a multimodal brain imaging fusion study. Transl Psychiatry 2020; 10:149. [PMID: 32424299 PMCID: PMC7235018 DOI: 10.1038/s41398-020-0834-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Revised: 04/06/2020] [Accepted: 04/28/2020] [Indexed: 02/05/2023] Open
Abstract
Schizophrenia (SZ) is frequently concurrent with substance use, depressive symptoms, social communication and attention deficits. However, the relationship between common brain networks (e.g., SZ vs. substance use, SZ vs. depression, SZ vs. developmental disorders) with SZ on specific symptoms and cognition is unclear. Symptom scores were used as a reference to guide fMRI-sMRI fusion for SZ (n = 94), substance use with drinking (n = 313), smoking (n = 104), major depressive disorder (MDD, n = 260), developmental disorders with autism spectrum disorder (ASD, n = 421) and attention-deficit/hyperactivity disorder (ADHD, n = 244) respectively. Common brain regions were determined by overlapping the symptom-related components between SZ and these other groups. Correlation between the identified common brain regions and cognition/symptoms in an independent SZ dataset (n = 144) was also performed. Results show that (1): substance use was related with cognitive deficits in schizophrenia through gray matter volume (GMV) in anterior cingulate cortex and thalamus; (2) depression was linked to PANSS negative dimensions and reasoning in SZ through a network involving caudate-thalamus-middle/inferior temporal gyrus in GMV; (3) developmental disorders pattern was correlated with poor attention, speed of processing and reasoning in SZ through inferior temporal gyrus in GMV. This study reveals symptom driven transdiagnostic shared networks between SZ and other mental disorders via multi-group data mining, indicating that some potential common underlying brain networks associated with schizophrenia differently with respect to symptoms and cognition. These results have heuristic value and advocate specific approaches to refine available treatment strategies for comorbid conditions in schizophrenia.
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Affiliation(s)
- Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303 USA
| | - Juan Bustillo
- grid.266832.b0000 0001 2188 8502Department of Psychiatry, University of New Mexico, Albuquerque, NM 87131 USA
| | - Jessica A. Turner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303 USA ,grid.256304.60000 0004 1936 7400Department of Psychology, Georgia State University, Atlanta, GA 30302 USA
| | - Rongtao Jiang
- grid.9227.e0000000119573309Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, 100190 Beijing, China
| | - Dongmei Zhi
- grid.9227.e0000000119573309Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, 100190 Beijing, China
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303 USA
| | - Thomas P. Deramus
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303 USA
| | - Victor Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303 USA
| | - Xiaohong Ma
- grid.412901.f0000 0004 1770 1022Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041 Chengdu, China ,grid.412901.f0000 0004 1770 1022Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041 Chengdu, China
| | - Xiao Yang
- grid.412901.f0000 0004 1770 1022Psychiatric Laboratory and Mental Health Center, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041 Chengdu, China ,grid.412901.f0000 0004 1770 1022Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041 Chengdu, China
| | - Mike Stevens
- Olin Neuropsychiatry Research Center, Hartford, CT 06106 USA
| | - Chuanjun Zhuo
- grid.216938.70000 0000 9878 7032Department of Psychiatry, Nankai University Affiliated Anding Hospital, 300222 Tianjin, China
| | - Yong Xu
- grid.263452.40000 0004 1798 4018Department of Humanities and Social Science, Shanxi Medical University, 030001 Taiyuan, China
| | - 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 30303 USA ,grid.256304.60000 0004 1936 7400Department of Psychology, Georgia State University, Atlanta, GA 30302 USA
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,University of Chinese Academy of Sciences, 100190, Beijing, China. .,Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, 100190, Beijing, China.
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Predicting response to electroconvulsive therapy combined with antipsychotics in schizophrenia using multi-parametric magnetic resonance imaging. Schizophr Res 2020; 216:262-271. [PMID: 31826827 DOI: 10.1016/j.schres.2019.11.046] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 09/04/2019] [Accepted: 11/25/2019] [Indexed: 12/23/2022]
Abstract
Electroconvulsive therapy (ECT) has been shown to be effective in schizophrenia, particularly when rapid symptom reduction is needed or in cases of resistance to drug treatment. However, there are no markers available to predict response to ECT. Here, we examine whether multi-parametric magnetic resonance imaging (MRI)-based radiomic features can predict response to ECT for individual patients. A total of 57 treatment-resistant schizophrenia patients, or schizophrenia patients with an acute episode or suicide attempts were randomly divided into primary (42 patients) and test (15 patients) cohorts. We collected T1-weighted structural MRI and diffusion MRI for 57 patients before receiving ECT and extracted 600 radiomic features for feature selection and prediction. To predict a continuous improvement in symptoms (ΔPANSS), the prediction process was performed with a support vector regression model based on a leave-one-out cross-validation framework in primary cohort and was tested in test cohort. The multi-parametric MRI-based radiomic model, including four structural MRI feature from left inferior frontal gyrus, right insula, left middle temporal gyrus and right superior temporal gyrus respectively and six diffusion MRI features from tracts connecting frontal or temporal gyrus possessed a low root mean square error of 15.183 in primary cohort and 14.980 in test cohort. The Pearson's correlation coefficients between predicted and actual values were 0.671 and 0.777 respectively. These results demonstrate that multi-parametric MRI-based radiomic features may predict response to ECT for individual patients. Such features could serve as prognostic neuroimaging biomarkers that provide a critical step toward individualized treatment response prediction in schizophrenia.
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33
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Gao S, Ming Y, Wang J, Gu Y, Ni S, Lu S, Zhang R, Sun J, Zhang N, Xu X. Enhanced Prefrontal Regional Homogeneity and Its Correlations With Cognitive Dysfunction/Psychopathology in Patients With First-Diagnosed and Drug-Naive Schizophrenia. Front Psychiatry 2020; 11:580570. [PMID: 33192722 PMCID: PMC7649771 DOI: 10.3389/fpsyt.2020.580570] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 09/14/2020] [Indexed: 01/10/2023] Open
Abstract
Background: Schizophrenia, regarded as a neurodevelopmental disorder, is characterized by positive symptoms, negative symptoms, and cognitive dysfunction. Investigating the spontaneous brain activity in patients with schizophrenia can help us understand the underlying pathophysiologic mechanism of schizophrenia. However, results concerning abnormal neural activities and their correlations with cognitive dysfunction/psychopathology of patients with schizophrenia were inconsistent. Methods: We recruited 57 first-diagnosed and drug-naive patients with schizophrenia and 50 matched healthy controls underwent magnetic resonance imaging. The Positive and Negative Syndrome Scale (PANSS) and the MATRICS Consensus Cognitive Battery were used to assess the psychopathology/cognitive dysfunction. Regional homogeneity (ReHo) was used to explore neural activities. Correlation analyses were calculated between abnormal ReHo values and PANSS scores/standardized cognitive scores. Lastly, support vector machine analyses were conducted to evaluate the accuracy of abnormal ReHo values in distinguishing patients with schizophrenia from healthy controls. Results: Patients with schizophrenia showed cognitive dysfunction, and increased ReHo values in the right gyrus rectus, right inferior frontal gyrus/insula and left inferior frontal gyrus/insula compared with those of healthy controls. The ReHo values in the right inferior frontal gyrus/insula were positively correlated with negative symptom scores and negatively correlated with Hopkins verbal learning test-revised/verbal learning. Our results showed that the combination of increased ReHo values in the left inferior frontal gyrus/insula and right gyrus rectus had 78.5% (84/107) accuracy, 85.96% (49/57) sensitivity, and 70.00% specificity, which were higher than other combinations. Conclusions: Hyperactivities were primarily located in the prefrontal regions, and increased ReHo values in the right inferior frontal gyrus/insula might reflect the severity of negative symptoms and verbal learning abilities. The combined increases of ReHo values in these regions might be an underlying biomarker in differentiating patients with schizophrenia from healthy controls.
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Affiliation(s)
- Shuzhan Gao
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Yidan Ming
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Jiayin Wang
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Yuan Gu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Sulin Ni
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Shuiping Lu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Rongrong Zhang
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Jing Sun
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Ning Zhang
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.,Department of Psychiatry, Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China.,Department of Psychiatry, Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing, China
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34
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Yan W, Calhoun V, Song M, Cui Y, Yan H, Liu S, Fan L, Zuo N, Yang Z, Xu K, Yan J, Lv L, Chen J, Chen Y, Guo H, Li P, Lu L, Wan P, Wang H, Wang H, Yang Y, Zhang H, Zhang D, Jiang T, Sui J. Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data. EBioMedicine 2019; 47:543-552. [PMID: 31420302 PMCID: PMC6796503 DOI: 10.1016/j.ebiom.2019.08.023] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 08/09/2019] [Accepted: 08/09/2019] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information. METHODS Motivated by the ability of recurrent neural networks (RNN) in capturing dynamic information of time sequences, we propose a multi-scale RNN model, which enables classification between 558 schizophrenia and 542 healthy controls by using time courses of fMRI independent components (ICs) directly. To increase interpretability, we also propose a leave-one-IC-out looping strategy for estimating the top contributing ICs. FINDINGS Accuracies of 83·2% and 80·2% were obtained respectively for the multi-site pooling and leave-one-site-out transfer classification. Subsequently, dorsal striatum and cerebellum components contribute the top two group-discriminative time courses, which is true even when adopting different brain atlases to extract time series. INTERPRETATION This is the first attempt to apply a multi-scale RNN model directly on fMRI time courses for classification of mental disorders, and shows the potential for multi-scale RNN-based neuroimaging classifications. FUND: Natural Science Foundation of China, the Strategic Priority Research Program of the Chinese Academy of Sciences, National Institutes of Health Grants, National Science Foundation.
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Affiliation(s)
- Weizheng Yan
- National Laboratory of Pattern Recognition and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Atlanta 30303, GA, USA
| | - Ming Song
- National Laboratory of Pattern Recognition and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yue Cui
- National Laboratory of Pattern Recognition and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hao Yan
- Peking University Sixth Hospital, Institute of Mental Health, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing 100191, China
| | - Shengfeng Liu
- National Laboratory of Pattern Recognition and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lingzhong Fan
- National Laboratory of Pattern Recognition and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Nianming Zuo
- National Laboratory of Pattern Recognition and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhengyi Yang
- National Laboratory of Pattern Recognition and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kaibin Xu
- National Laboratory of Pattern Recognition and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jun Yan
- Peking University Sixth Hospital, Institute of Mental Health, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing 100191, China
| | - Luxian Lv
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, Henan, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, Henan, China
| | - Jun Chen
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, China
| | - Yunchun Chen
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, Shaanxi, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian 463000, Henan, China
| | - Peng Li
- Peking University Sixth Hospital, Institute of Mental Health, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing 100191, China
| | - Lin Lu
- Peking University Sixth Hospital, Institute of Mental Health, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing 100191, China
| | - Ping Wan
- Zhumadian Psychiatric Hospital, Zhumadian 463000, Henan, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, The Fourth Military Medical University, Xi'an 710032, Shaanxi, China
| | - Huiling Wang
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei, China
| | - Yongfeng Yang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, Henan, China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang 453002, Henan, China; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China
| | - Hongxing Zhang
- Department of Psychiatry, Henan Mental Hospital, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang 453002, Henan, China; Department of Psychology, Xinxiang Medical University, Xinxiang 453002, Henan, China
| | - Dai Zhang
- Peking University Sixth Hospital, Institute of Mental Health, Beijing 100191, China; Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing 100191, China; Center for Life Sciences/PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Tianzi Jiang
- National Laboratory of Pattern Recognition and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, Sichuan, China; Queensland Brain Institute, University of Queensland, Brisbane 4072, QLD, Australia; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
| | - Jing Sui
- National Laboratory of Pattern Recognition and Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
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35
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Finotelli P, Forlim CG, Klock L, Pini A, Bächle J, Stoll L, Giemsa P, Fuchs M, Schoofs N, Montag C, Dulio P, Gallinat J, Kühn S. New Graph-Theoretical-Multimodal Approach Using Temporal and Structural Correlations Reveals Disruption in the Thalamo-Cortical Network in Patients with Schizophrenia. Brain Connect 2019; 9:760-769. [PMID: 31232080 DOI: 10.1089/brain.2018.0654] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Schizophrenia has been understood as a network disease with altered functional and structural connectivity in multiple brain networks compatible to the extremely broad spectrum of psychopathological, cognitive, and behavioral symptoms in this disorder. When building brain networks, functional and structural networks are typically modeled independently: Functional network models are based on temporal correlations among brain regions, whereas structural network models are based on anatomical characteristics. Combining both features may give rise to more realistic and reliable models of brain networks. In this study, we applied a new flexible graph-theoretical-multimodal model called FD (F, the functional connectivity matrix, and D, the structural matrix) to construct brain networks combining functional, structural, and topological information of magnetic resonance imaging (MRI) measurements (structural and resting-state imaging) to patients with schizophrenia (n = 35) and matched healthy individuals (n = 41). As a reference condition, the traditional pure functional connectivity (pFC) analysis was carried out. By using the FD model, we found disrupted connectivity in the thalamo-cortical network in schizophrenic patients, whereas the pFC model failed to extract group differences after multiple comparison correction. We interpret this observation as evidence that the FD model is superior to conventional connectivity analysis, by stressing relevant features of the whole-brain connectivity, including functional, structural, and topological signatures. The FD model can be used in future research to model subtle alterations of functional and structural connectivity, resulting in pronounced clinical syndromes and major psychiatric disorders. Lastly, FD is not limited to the analysis of resting-state functional MRI, and it can be applied to electro-encephalography, magneto-encephalography, etc.
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Affiliation(s)
- Paolo Finotelli
- Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Caroline Garcia Forlim
- Clinic and Policlinic for Psychiatry and Psychotherapy, University Medical Center Hamburg - Eppendorf, Hamburg, Germany
| | - Leonie Klock
- Clinic and Policlinic for Psychiatry and Psychotherapy, University Medical Center Hamburg - Eppendorf, Hamburg, Germany
| | - Alessia Pini
- Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Johanna Bächle
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Laura Stoll
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Patrick Giemsa
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Marie Fuchs
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Nikola Schoofs
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Christiane Montag
- Department of Psychiatry and Psychotherapy, Charité University Medicine and St. Hedwig-Krankenhaus, Berlin, Germany
| | - Paolo Dulio
- Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Jürgen Gallinat
- Clinic and Policlinic for Psychiatry and Psychotherapy, University Medical Center Hamburg - Eppendorf, Hamburg, Germany
| | - Simone Kühn
- Clinic and Policlinic for Psychiatry and Psychotherapy, University Medical Center Hamburg - Eppendorf, Hamburg, Germany
- Lise-Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
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36
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Luo N, Tian L, Calhoun VD, Chen J, Lin D, Vergara VM, Rao S, Yang J, Zhuo C, Xu Y, Turner JA, Zhang F, Sui J. Brain function, structure and genomic data are linked but show different sensitivity to duration of illness and disease stage in schizophrenia. Neuroimage Clin 2019; 23:101887. [PMID: 31176952 PMCID: PMC6558215 DOI: 10.1016/j.nicl.2019.101887] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 05/28/2019] [Accepted: 05/29/2019] [Indexed: 01/05/2023]
Abstract
The progress of schizophrenia at various stages is an intriguing question, which has been explored to some degree using single-modality brain imaging data, e.g. gray matter (GM) or functional connectivity (FC). However it remains unclear how those changes from different modalities are correlated with each other and if the sensitivity to duration of illness and disease stages across modalities is different. In this work, we jointly analyzed FC, GM volume and single nucleotide polymorphisms (SNPs) data of 159 individuals including healthy controls (HC), drug-naïve first-episode schizophrenia (FESZ) and chronic schizophrenia patients (CSZ), aiming to evaluate the links among SNP, FC and GM patterns, and their sensitivity to duration of illness and disease stages in schizophrenia. Our results suggested: 1) both GM and FC highlighted impairments in hippocampal, temporal gyrus and cerebellum in schizophrenia, which were significantly correlated with genes like SATB2, GABBR2, PDE4B, CACNA1C etc. 2) GM and FC presented gradually decrease trend (HC > FESZ>CSZ), while SNP indicated a non-gradual variation trend with un-significant group difference observed between FESZ and CSZ; 3) Group difference between HC and FESZ of FC was more remarkable than GM, and FC presented a stronger negative correlation with duration of illness than GM (p = 0.0006). Collectively, these results highlight the benefit of leveraging multimodal data and provide additional clues regarding the impact of mental illness at various disease stages.
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Affiliation(s)
- Na Luo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lin Tian
- Wuxi Mental Health Center, Wuxi 214000, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, GA 30303, USA; The Mind Research Network, Albuquerque, NM 87106, USA
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, GA 30303, USA
| | - Dongdong Lin
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, GA 30303, USA
| | - Victor M Vergara
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): {Georgia State University, Georgia Institute of Technology, and Emory University}, Atlanta, GA 30303, USA
| | - Shuquan Rao
- School of Life Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, China
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory (PNGC-Lab), Tianjin Mental Health Center, Nankai University Affiliated Anding Hospital, Tianjin 300222, China
| | - Yong Xu
- Department of Psychiatry, First Clinical Medical College, First Hospital of Shanxi Medical University, Taiyuan 030000, China
| | - Jessica A Turner
- Department of Psychology, Neuroscience Institute, Georgia State University, Atlanta, GA, USA
| | | | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
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37
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Qi S, Sui J, Chen J, Liu J, Jiang R, Silva R, Iraji A, Damaraju E, Salman M, Lin D, Fu Z, Zhi D, Turner JA, Bustillo J, Ford JM, Mathalon DH, Voyvodic J, McEwen S, Preda A, Belger A, Potkin SG, Mueller BA, Adali T, Calhoun VD. Parallel group ICA+ICA: Joint estimation of linked functional network variability and structural covariation with application to schizophrenia. Hum Brain Mapp 2019; 40:3795-3809. [PMID: 31099151 DOI: 10.1002/hbm.24632] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 04/15/2019] [Accepted: 05/06/2019] [Indexed: 12/18/2022] Open
Abstract
There is growing evidence that rather than using a single brain imaging modality to study its association with physiological or symptomatic features, the field is paying more attention to fusion of multimodal information. However, most current multimodal fusion approaches that incorporate functional magnetic resonance imaging (fMRI) are restricted to second-level 3D features, rather than the original 4D fMRI data. This trade-off is that the valuable temporal information is not utilized during the fusion step. Here we are motivated to propose a novel approach called "parallel group ICA+ICA" that incorporates temporal fMRI information from group independent component analysis (GICA) into a parallel independent component analysis (ICA) framework, aiming to enable direct fusion of first-level fMRI features with other modalities (e.g., structural MRI), which thus can detect linked functional network variability and structural covariations. Simulation results show that the proposed method yields accurate intermodality linkage detection regardless of whether it is strong or weak. When applied to real data, we identified one pair of significantly associated fMRI-sMRI components that show group difference between schizophrenia and controls in both modalities, and this linkage can be replicated in an independent cohort. Finally, multiple cognitive domain scores can be predicted by the features identified in the linked component pair by our proposed method. We also show these multimodal brain features can predict multiple cognitive scores in an independent cohort. Overall, results demonstrate the ability of parallel GICA+ICA to estimate joint information from 4D and 3D data without discarding much of the available information up front, and the potential for using this approach to identify imaging biomarkers to study brain disorders.
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Affiliation(s)
- Shile Qi
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, and University of Chinese Academy of Sciences, Beijing, China.,Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Beijing, China
| | - Jiayu Chen
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Jingyu Liu
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, and University of Chinese Academy of Sciences, Beijing, China
| | - Rogers Silva
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Armin Iraji
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Eswar Damaraju
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Mustafa Salman
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Dongdong Lin
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Zening Fu
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia
| | - Dongmei Zhi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, and University of Chinese Academy of Sciences, Beijing, China
| | - Jessica A Turner
- Department of Psychology, Georgia State University, Atlanta, Georgia
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Judith M Ford
- Department of Psychiatry, University of California, San Francisco, San Francisco, California
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, San Francisco, San Francisco, California
| | - James Voyvodic
- Department of Radiology, Duke University, Durham, North Carolina
| | - Sarah McEwen
- Department of Psychiatry, School of Medicine at University of California, San Diego, La Jolla, California
| | - Adrian Preda
- Department of Psychiatry, University of California Irvine, Irvine, California
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina School of Medicine, Chapel Hill, North Carolina
| | - Steven G Potkin
- Department of Psychiatry, University of California Irvine, Irvine, California
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, Minnesota
| | - Tulay Adali
- Department of CSEE, University of Maryland, Baltimore Country, Baltimore, Maryland
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Tri-institutional Center for Translational Research in Neuroimaging and Data Sciences (TReNDS) [Georgia State, Georgia Tech, Emory], Atlanta, Georgia.,Department of Psychology, Georgia State University, Atlanta, Georgia.,Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico.,Department of ECE, University of New Mexico, Albuquerque, New Mexico
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38
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Sui J, Qi S, van Erp TGM, Bustillo J, Jiang R, Lin D, Turner JA, Damaraju E, Mayer AR, Cui Y, Fu Z, Du Y, Chen J, Potkin SG, Preda A, Mathalon DH, Ford JM, Voyvodic J, Mueller BA, Belger A, McEwen SC, O'Leary DS, McMahon A, Jiang T, Calhoun VD. Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion. Nat Commun 2018; 9:3028. [PMID: 30072715 PMCID: PMC6072778 DOI: 10.1038/s41467-018-05432-w] [Citation(s) in RCA: 107] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2017] [Accepted: 07/04/2018] [Indexed: 01/06/2023] Open
Abstract
Cognitive impairment is a feature of many psychiatric diseases, including schizophrenia. Here we aim to identify multimodal biomarkers for quantifying and predicting cognitive performance in individuals with schizophrenia and healthy controls. A supervised learning strategy is used to guide three-way multimodal magnetic resonance imaging (MRI) fusion in two independent cohorts including both healthy individuals and individuals with schizophrenia using multiple cognitive domain scores. Results highlight the salience network (gray matter, GM), corpus callosum (fractional anisotropy, FA), central executive and default-mode networks (fractional amplitude of low-frequency fluctuation, fALFF) as modality-specific biomarkers of generalized cognition. FALFF features are found to be more sensitive to cognitive domain differences, while the salience network in GM and corpus callosum in FA are highly consistent and predictive of multiple cognitive domains. These modality-specific brain regions define-in three separate cohorts-promising co-varying multimodal signatures that can be used as predictors of multi-domain cognition.
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Affiliation(s)
- Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
- The Mind Research Network, Albuquerque, NM, 87106, USA.
- University of Chinese Academy of Sciences, 100049, Beijing, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
| | - Shile Qi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Theo G M van Erp
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, 92697, USA
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Dongdong Lin
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Jessica A Turner
- The Mind Research Network, Albuquerque, NM, 87106, USA
- Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, 30302, USA
| | | | - Andrew R Mayer
- The Mind Research Network, Albuquerque, NM, 87106, USA
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Yue Cui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Zening Fu
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Yuhui Du
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Jiayu Chen
- The Mind Research Network, Albuquerque, NM, 87106, USA
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, 92697, USA
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, 92697, USA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, San Francisco, CA, 94143, USA
- San Francisco VA Medical Center, San Francisco, CA, 94143, USA
| | - Judith M Ford
- Department of Psychiatry, University of California, San Francisco, CA, 94143, USA
- San Francisco VA Medical Center, San Francisco, CA, 94143, USA
| | - James Voyvodic
- Department of Radiology, Brain Imaging and Analysis Center, Duke University, Durham, NC, 27710, USA
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, 55454, USA
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Sarah C McEwen
- Department of Psychiatry, University of California, San Diego, CA, 92093, USA
| | - Daniel S O'Leary
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa, IA, 52242, USA
| | - Agnes McMahon
- USC Stevens Neuroimaging and Informatics Institute, University of Southern California, San Diego, CA, 90033, USA
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
- University of Chinese Academy of Sciences, 100049, Beijing, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, 87106, USA.
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA.
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA.
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