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Xing Y, Pearlson GD, Kochunov P, Calhoun VD, Du Y. Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia. Neuroimage 2024; 299:120839. [PMID: 39251116 DOI: 10.1016/j.neuroimage.2024.120839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 08/10/2024] [Accepted: 09/04/2024] [Indexed: 09/11/2024] Open
Abstract
Accurate diagnosis of mental disorders is expected to be achieved through the identification of reliable neuroimaging biomarkers with the help of cutting-edge feature selection techniques. However, existing feature selection methods often fall short in capturing the local structural characteristics among samples and effectively eliminating redundant features, resulting in inadequate performance in disorder prediction. To address this gap, we propose a novel supervised method named local-structure-preservation and redundancy-removal-based feature selection (LRFS), and then apply it to the identification of meaningful biomarkers for schizophrenia (SZ). LRFS method leverages graph-based regularization to preserve original sample similarity relationships during data transformation, thus retaining crucial local structure information. Additionally, it introduces redundancy-removal regularization based on interrelationships among features to exclude similar and redundant features from high-dimensional data. Moreover, LRFS method incorporates l2,1 sparse regularization that enables selecting a sparse and noise-robust feature subset. Experimental evaluations on eight public datasets with diverse properties demonstrate the superior performance of our method over nine popular feature selection methods in identifying discriminative features, with average classification accuracy gains ranging from 1.30 % to 9.11 %. Furthermore, the LRFS method demonstrates superior discriminability in four functional magnetic resonance imaging (fMRI) datasets from 708 healthy controls (HCs) and 537 SZ patients, with an average increase in classification accuracy ranging from 1.89 % to 9.24 % compared to other nine methods. Notably, our method reveals reproducible and significant changes in SZ patients relative to HCs across the four datasets, predominantly in the thalamus-related functional network connectivity, which exhibit a significant correlation with clinical symptoms. Convergence analysis, parameter sensitivity analysis, and ablation studies further demonstrate the effectiveness and robustness of our method. In short, our proposed feature selection method effectively identifies discriminative and reliable features that hold the potential to be biomarkers, paving the way for the elucidation of brain abnormalities and the advancement of precise diagnosis of mental disorders.
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Affiliation(s)
- Ying Xing
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neurobiology, Yale University, New Haven, CT, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center and Department of Psychiatry, University of Maryland, School of Medicine, Baltimore, MD, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China.
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2
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Romanovsky E, Choudhary A, Peles D, Abu-Akel A, Stern S. Uncovering convergence and divergence between autism and schizophrenia using genomic tools and patients' neurons. Mol Psychiatry 2024:10.1038/s41380-024-02740-0. [PMID: 39237719 DOI: 10.1038/s41380-024-02740-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Revised: 08/26/2024] [Accepted: 08/30/2024] [Indexed: 09/07/2024]
Abstract
Autism spectrum disorders (ASDs) are highly heritable and result in abnormal repetitive behaviors and impairment in communication and cognitive skills. Previous studies have focused on the genetic correlation between ASDs and other neuropsychiatric disorders, but an in-depth understanding of the correlation to other disorders is required. We conducted an extensive meta-analysis of common variants identified in ASDs by genome-wide association studies (GWAS) and compared it to the consensus genes and single nucleotide polymorphisms (SNPs) of Schizophrenia (SCZ). We found approximately 75% of the GWAS genes that are associated with ASD are also associated with SCZ. We further investigated the cellular phenotypes of neurons derived from induced pluripotent stem cell (iPSC) models in ASD and SCZ. Our findings revealed that ASD and SCZ neurons initially follow divergent developmental trajectories compared to control neurons. However, despite these early diametrical differences, both ASD and SCZ neurons ultimately display similar deficits in synaptic activity as they mature. This significant genetic overlap between ASD and SCZ, coupled with the convergence towards similar synaptic deficits, highlights the intricate interplay of genetic and developmental factors in shaping the shared underlying mechanisms of these complex neurodevelopmental and neuropsychiatric disorders.
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Affiliation(s)
- Eva Romanovsky
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - Ashwani Choudhary
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel
| | - David Peles
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel
| | - Ahmad Abu-Akel
- School of Psychological Sciences, University of Haifa, Haifa, Israel
- The Haifa Brain and Behavior Hub, University of Haifa, Haifa, Israel
| | - Shani Stern
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel.
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3
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Du Y, Yuan Z, Sui J, Calhoun VD. Common and unique brain aging patterns between females and males quantified by large-scale deep learning. Hum Brain Mapp 2024; 45:e70005. [PMID: 39225381 PMCID: PMC11369911 DOI: 10.1002/hbm.70005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 07/20/2024] [Accepted: 08/07/2024] [Indexed: 09/04/2024] Open
Abstract
There has been extensive evidence that aging affects human brain function. However, there is no complete picture of what brain functional changes are mostly related to normal aging and how aging affects brain function similarly and differently between males and females. Based on resting-state brain functional connectivity (FC) of 25,582 healthy participants (13,373 females) aged 49-76 years from the UK Biobank project, we employ deep learning with explainable AI to discover primary FCs related to progressive aging and reveal similarity and difference between females and males in brain aging. Using a nested cross-validation scheme, we conduct 4200 deep learning models to classify all paired age groups on the main data for females and males separately and then extract gender-common and gender-specific aging-related FCs. Next, we validate those FCs using additional 21,000 classifiers on the independent data. Our results support that aging results in reduced brain functional interactions for both females and males, primarily relating to the positive connectivity within the same functional domain and the negative connectivity between different functional domains. Regions linked to cognitive control show the most significant age-related changes in both genders. Unique aging effects in males and females mainly involve the interaction between cognitive control and the default mode, vision, auditory, and frontoparietal domains. Results also indicate females exhibit faster brain functional changes than males. Overall, our study provides new evidence about common and unique patterns of brain aging in females and males.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Zhen Yuan
- School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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4
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Zhang R, Ren J, Lei X, Wang Y, Chen X, Fu L, Li Q, Guo C, Teng X, Wu Z, Yu L, Wang D, Chen Y, Zhang C. Aberrant patterns of spontaneous brain activity in schizophrenia: A resting-state fMRI study and classification analysis. Prog Neuropsychopharmacol Biol Psychiatry 2024; 134:111066. [PMID: 38901758 DOI: 10.1016/j.pnpbp.2024.111066] [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: 01/17/2024] [Accepted: 06/17/2024] [Indexed: 06/22/2024]
Abstract
BACKGROUND Schizophrenia is a prevalent mental disorder, leading to severe disability. Currently, the absence of objective biomarkers hinders effective diagnosis. This study was conducted to explore the aberrant spontaneous brain activity and investigate the potential of abnormal brain indices as diagnostic biomarkers employing machine learning methods. METHODS A total of sixty-one schizophrenia patients and seventy demographically matched healthy controls were enrolled in this study. The static indices of resting-state functional magnetic resonance imaging (rs-fMRI) including amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were calculated to evaluate spontaneous brain activity. Subsequently, a sliding-window method was then used to conduct temporal dynamic analysis. The comparison of static and dynamic rs-fMRI indices between the patient and control groups was conducted using a two-sample t-test. Finally, the machine learning analysis was applied to estimate the diagnostic value of abnormal indices of brain activity. RESULTS Schizophrenia patients exhibited a significant increase ALFF value in inferior frontal gyrus, alongside significant decreases in fALFF values observed in left postcentral gyrus and right cerebellum posterior lobe. Pervasive aberrations in ReHo indices were observed among schizophrenia patients, particularly in frontal lobe and cerebellum. A noteworthy reduction in voxel-wise concordance of dynamic indices was observed across gray matter regions encompassing the bilateral frontal, parietal, occipital, temporal, and insular cortices. The classification analysis achieved the highest values for area under curve at 0.87 and accuracy at 81.28% when applying linear support vector machine and leveraging a combination of abnormal static and dynamic indices in the specified brain regions as features. CONCLUSIONS The static and dynamic indices of brain activity exhibited as potential neuroimaging biomarkers for the diagnosis of schizophrenia.
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Affiliation(s)
- Rong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Juanjuan Ren
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoxia Lei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yewei Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaochang Chen
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lirong Fu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qingyi Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chaoyue Guo
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinyue Teng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zenan Wu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lingfang Yu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dandan Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Chen
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Chen Zhang
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Lin D, Fu Z, Liu J, Perrone-Bizzozero N, Hutchison KE, Bustillo J, Du Y, Pearlson G, Calhoun VD. Association between the oral microbiome and brain resting state connectivity in schizophrenia. Schizophr Res 2024; 270:392-402. [PMID: 38986386 DOI: 10.1016/j.schres.2024.06.045] [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: 03/01/2023] [Revised: 05/03/2024] [Accepted: 06/24/2024] [Indexed: 07/12/2024]
Abstract
Recent microbiome-brain axis findings have shown evidence of the modulation of microbiome community as an environmental mediator in brain function and psychiatric illness. This work is focused on the role of the microbiome in understanding a rarely investigated environmental involvement in schizophrenia (SZ), especially in relation to brain circuit dysfunction. We leveraged high throughput microbial 16s rRNA sequencing and functional neuroimaging techniques to enable the delineation of microbiome-brain network links in SZ. N = 213 SZ and healthy control subjects were assessed for the oral microbiome. Among them, 139 subjects were scanned by resting-state functional magnetic resonance imaging (rsfMRI) to derive brain functional connectivity. We found a significant microbiome compositional shift in SZ beta diversity (weighted UniFrac distance, p = 6 × 10-3; Bray-Curtis distance p = 0.021). Fourteen microbial species involving pro-inflammatory and neurotransmitter signaling and H2S production, showed significant abundance alterations in SZ. Multivariate analysis revealed one pair of microbial and functional connectivity components showing a significant correlation of 0.46. Thirty five percent of microbial species and 87.8 % of brain functional network connectivity from each component also showed significant differences between SZ and healthy controls with strong performance in classifying SZ from healthy controls, with an area under curve (AUC) = 0.84 and 0.87, respectively. The results suggest a potential link between oral microbiome dysbiosis and brain functional connectivity alteration in relation to SZ, possibly through immunological and neurotransmitter signaling pathways and the hypothalamic-pituitary-adrenal axis, supporting for future work in characterizing the role of oral microbiome in mediating effects on SZ brain functional activity.
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Affiliation(s)
- Dongdong Lin
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia, Tech, Emory, Atlanta, GA 30303, United States of America.
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia, Tech, Emory, Atlanta, GA 30303, United States of America
| | - Jingyu Liu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia, Tech, Emory, Atlanta, GA 30303, United States of America
| | - Nora Perrone-Bizzozero
- Department of neuroscience, University of New Mexico, Albuquerque, NM, 87109, United States of America
| | - Kent E Hutchison
- Department of psychology and neuroscience, University of Colorado Boulder, Boulder, CO 80309, United States of America
| | - Juan Bustillo
- Department of psychiatry, University of New Mexico, Albuquerque, NM 87109, United States of America
| | - Yuhui Du
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia, Tech, Emory, Atlanta, GA 30303, United States of America
| | - Godfrey Pearlson
- Olin Research Center, Institute of Living Hartford, CT 06102, United States of America; Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, United States of America; Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06511, United States of America
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia, Tech, Emory, Atlanta, GA 30303, United States of America
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Du Y, Fang S, He X, Calhoun VD. A survey of brain functional network extraction methods using fMRI data. Trends Neurosci 2024; 47:608-621. [PMID: 38906797 DOI: 10.1016/j.tins.2024.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 05/04/2024] [Accepted: 05/23/2024] [Indexed: 06/23/2024]
Abstract
Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China.
| | - Songke Fang
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Xingyu He
- School of Computer and Information Technology, Shanxi University, Taiyuan, 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, USA
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7
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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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Morais S, d’Almeida OC, Caldeira S, Meneses S, Areias G, Girão V, Bettencourt C, Pereira DJ, Macedo A, Castelo-Branco M. Executive function in schizophrenia and autism in adults shares common components separating high and low performance groups. Front Psychiatry 2024; 15:1381526. [PMID: 38699455 PMCID: PMC11064061 DOI: 10.3389/fpsyt.2024.1381526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Accepted: 03/27/2024] [Indexed: 05/05/2024] Open
Abstract
The profile of executive function (EF) in adults with Schizophrenia (SCZ) and autism spectrum disorder (ASD) remains unclear. This study aims to ascertain if distinct EF patterns can be identified between each clinical condition by comparing the neuropsychological profile of adults with SCZ and ASD, for whom the differential diagnosis is still highly challenging. Forty-five individuals (15 SCZ, 15 ASD, 15 controls) matched for age, sex, education level, and handedness underwent intelligence evaluation and neuropsychological testing for working memory, inhibition, planning and set-shifting, and verbal fluency subdomains. Principal component analysis (2D-PCA) using variables representing 4 domains was employed to identify patterns in neuropsychological profiles. The ASD group had lower scores on the Digits Forward subtest compared to the SCZ group (7.2 ± 2.1 vs. 9.3 ± 1.9, p = 0.003; Cohen's d: 1.05). ASD also performed significantly worse on the Stroop Word Test compared to the control group (77.7± 17.9 vs. 98.0 ± 12.7, p = 0.009; Cohen's d: 1.31). No significant differences were observed between ASD and SCZ on other EF measures. The larger contributors for the dimensions in 2D-PCA were the Digits Forward subtest and Stroop Word Test. Still, there was substantial overlap between the clinical groups. This study suggests a high degree of similarity of EF between SCZ and ASD. Through four EF measures, the discrimination of low and high-functioning EF groups spanning both diagnostic categories may help to identify the individuals who could better benefit from cognitive rehabilitation strategies.
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Affiliation(s)
- Sofia Morais
- Psychiatry Department, Coimbra Hospital and University Centre, Coimbra, Portugal
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Otília C. d’Almeida
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
| | - Salomé Caldeira
- Psychology Department, Coimbra Hospital and University Centre, Coimbra, Portugal
| | - Sofia Meneses
- Psychology Department, Coimbra Hospital and University Centre, Coimbra, Portugal
| | - Graça Areias
- Psychology Department, Coimbra Hospital and University Centre, Coimbra, Portugal
| | - Vanessa Girão
- Faculty of Psychological and Education Sciences, University of Coimbra, Coimbra, Portugal
| | - Catarina Bettencourt
- Faculty of Psychological and Education Sciences, University of Coimbra, Coimbra, Portugal
- Centre for Research in Neuropsychology and Cognitive and Behavioral Intervention (CINEICC), Faculty of Psychological and Education Sciences, University of Coimbra, Coimbra, Portugal
| | - Daniela Jardim Pereira
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Neurorradiology Functional Area, Imaging Department, Coimbra Hospital and University Center, Coimbra, Portugal
| | - António Macedo
- Psychiatry Department, Coimbra Hospital and University Centre, Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Psychological Medicine, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Miguel Castelo-Branco
- Faculty of Medicine, University of Coimbra, Coimbra, Portugal
- Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), University of Coimbra, Coimbra, Portugal
- Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal
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9
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Fu Z, Batta I, Wu L, Abrol A, Agcaoglu O, Salman MS, Du Y, Iraji A, Shultz S, Sui J, Calhoun VD. Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities. Neuroimage 2024; 292:120617. [PMID: 38636639 DOI: 10.1016/j.neuroimage.2024.120617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/03/2024] [Accepted: 04/15/2024] [Indexed: 04/20/2024] Open
Abstract
A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States.
| | - Ishaan Batta
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Anees Abrol
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Oktay Agcaoglu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Mustafa S Salman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Sarah Shultz
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal 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, Georgia, United States
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10
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Chen Y, Li W, Lv L, Yue W. Shared Genetic Determinants of Schizophrenia and Autism Spectrum Disorder Implicate Opposite Risk Patterns: A Genome-Wide Analysis of Common Variants. Schizophr Bull 2024:sbae044. [PMID: 38616054 DOI: 10.1093/schbul/sbae044] [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: 04/16/2024]
Abstract
BACKGROUND AND HYPOTHESIS The synaptic pruning hypothesis posits that schizophrenia (SCZ) and autism spectrum disorder (ASD) may represent opposite ends of neurodevelopmental disorders: individuals with ASD exhibit an overabundance of synapses and connections while SCZ was characterized by excessive pruning of synapses and a reduction. Given the strong genetic predisposition of both disorders, we propose a shared genetic component, with certain loci having differential regulatory impacts. STUDY DESIGN Genome-Wide single nucleotide polymorphism (SNP) data of European descent from SCZ (N cases = 53 386, N controls = 77 258) and ASD (N cases = 18 381, N controls = 27 969) were analyzed. We used genetic correlation, bivariate causal mixture model, conditional false discovery rate method, colocalization, Transcriptome-Wide Association Study (TWAS), and Phenome-Wide Association Study (PheWAS) to investigate the genetic overlap and gene expression pattern. STUDY RESULTS We found a positive genetic correlation between SCZ and ASD (rg = .26, SE = 0.01, P = 7.87e-14), with 11 genomic loci jointly influencing both conditions (conjFDR <0.05). Functional analysis highlights a significant enrichment of shared genes during early to mid-fetal developmental stages. A notable genetic region on chromosome 17q21.31 (lead SNP rs2696609) showed strong evidence of colocalization (PP.H4.abf = 0.85). This SNP rs2696609 is linked to many imaging-derived brain phenotypes. TWAS indicated opposing gene expression patterns (primarily pseudogenes and long noncoding RNAs [lncRNAs]) for ASD and SCZ in the 17q21.31 region and some genes (LRRC37A4P, LINC02210, and DND1P1) exhibit considerable variation in the cerebellum across the lifespan. CONCLUSIONS Our findings support a shared genetic basis for SCZ and ASD. A common genetic variant, rs2696609, located in the Chr17q21.31 locus, may exert differential risk regulation on SCZ and ASD by altering brain structure. Future studies should focus on the role of pseudogenes, lncRNAs, and cerebellum in synaptic pruning and neurodevelopmental disorders.
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Affiliation(s)
- Yu Chen
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, Henan, China
- Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang Medical University, Xinxiang, Henan, China
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Wenqiang Li
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, Henan, China
- Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang Medical University, Xinxiang, Henan, China
| | - Luxian Lv
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
- Henan Key Lab of Biological Psychiatry, International Joint Research Laboratory for Psychiatry and Neuroscience of Henan, Xinxiang Medical University, Xinxiang, Henan, China
- Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder, Xinxiang Medical University, Xinxiang, Henan, China
- Henan Province People's Hospital, Zhengzhou, Henan, China
| | - Weihua Yue
- Institute of Mental Health, Peking University Sixth Hospital, Beijing, China
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
- Research Unit of Diagnosis and Treatment of Mood Cognitive Disorder (2018RU006), Chinese Academy of Medical Sciences, Beijing, China
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11
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Xing Y, van Erp TG, Pearlson GD, Kochunov P, Calhoun VD, Du Y. More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method. iScience 2024; 27:109319. [PMID: 38482500 PMCID: PMC10933544 DOI: 10.1016/j.isci.2024.109319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/17/2023] [Accepted: 02/19/2024] [Indexed: 04/26/2024] Open
Abstract
The integration of neuroimaging with artificial intelligence is crucial for advancing the diagnosis of mental disorders. However, challenges arise from incomplete matching between diagnostic labels and neuroimaging. Here, we propose a label-noise filtering-based dimensional prediction (LAMP) method to identify reliable biomarkers and achieve accurate prediction for mental disorders. Our method proposes to utilize a label-noise filtering model to automatically filter out unclear cases from a neuroimaging perspective, and then the typical subjects whose diagnostic labels align with neuroimaging measures are used to construct a dimensional prediction model to score independent subjects. Using fMRI data of schizophrenia patients and healthy controls (n = 1,245), our method yields consistent scores to independent subjects, leading to more distinguishable relabeled groups with an enhanced classification accuracy of 31.89%. Additionally, it enables the exploration of stable abnormalities in schizophrenia. In summary, our LAMP method facilitates the identification of reliable biomarkers and accurate diagnosis of mental disorders using neuroimages.
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Affiliation(s)
- Ying Xing
- School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
| | - Theo G.M. van Erp
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, Irvine, CA 92617, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA 92617, USA
| | - Godfrey D. Pearlson
- Departments of Psychiatry and of Neurobiology, Yale University, New Haven, CT 06519, USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT 06106, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center and Department of Psychiatry, University of Maryland, School of Medicine, Baltimore, MD 21201, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30030, USA
| | - Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China
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12
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Lin D, Fu Z, Liu J, Perrone-Bizzozero N, Hutchison KE, Bustillo J, Du Y, Pearlson G, Calhoun VD. Association between the oral microbiome and brain resting state connectivity in schizophrenia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.22.573165. [PMID: 38234846 PMCID: PMC10793457 DOI: 10.1101/2023.12.22.573165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Recent microbiome-brain axis findings have shown evidence of the modulation of microbiome community as an environmental mediator in brain function and psychiatric illness. This work is focused on the role of the microbiome in understanding a rarely investigated environmental involvement in schizophrenia (SZ), especially in relation to brain circuit dysfunction. We leveraged high throughput microbial 16s rRNA sequencing and functional neuroimaging techniques to enable the delineation of microbiome-brain network links in SZ. N=213 SZ and healthy control (HC) subjects were assessed for the oral microbiome. Among them, 139 subjects were scanned by resting-state functional magnetic resonance imaging (rsfMRI) to derive brain functional connectivity. We found a significant microbiome compositional shift in SZ beta diversity (weighted UniFrac distance, p= 6×10 -3 ; Bray-Curtis distance p = 0.021). Fourteen microbial species involving pro-inflammatory and neurotransmitter signaling and H 2 S production, showed significant abundance alterations in SZ. Multivariate analysis revealed one pair of microbial and functional connectivity components showing a significant correlation of 0.46. Thirty five percent of microbial species and 87.8% of brain functional network connectivity from each component also showed significant differences between SZ and HC with strong performance in classifying SZ from HC, with an area under curve (AUC) = 0.84 and 0.87, respectively. The results suggest a potential link between oral microbiome dysbiosis and brain functional connectivity alteration in relation to SZ, possibly through immunological and neurotransmitter signaling pathways and the hypothalamic-pituitary-adrenal axis, supporting for future work in characterizing the role of oral microbiome in mediating effects on SZ brain functional activity.
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13
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Haigh SM, Van Key L, Brosseau P, Eack SM, Leitman DI, Salisbury DF, Behrmann M. Assessing Trial-to-Trial Variability in Auditory ERPs in Autism and Schizophrenia. J Autism Dev Disord 2023; 53:4856-4871. [PMID: 36207652 PMCID: PMC10079782 DOI: 10.1007/s10803-022-05771-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2022] [Indexed: 01/12/2023]
Abstract
Sensory abnormalities are characteristic of autism and schizophrenia. In autism, greater trial-to-trial variability (TTV) in sensory neural responses suggest that the system is more unstable. However, these findings have only been identified in the amplitude and not in the timing of neural responses, and have not been fully explored in schizophrenia. TTV in event-related potential amplitudes and inter-trial coherence (ITC) were assessed in the auditory mismatch negativity (MMN) in autism, schizophrenia, and controls. MMN was largest in autism and smallest in schizophrenia, and TTV was greater in autism and schizophrenia compared to controls. There were no differences in ITC. Greater TTV appears to be characteristic of both autism and schizophrenia, implicating several neural mechanisms that could underlie sensory instability.
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Affiliation(s)
- Sarah M Haigh
- Department of Psychology and Center for Integrative Neuroscience, University of Nevada, Reno, Reno, NV, USA.
- Department of Psychology and the Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
| | - Laura Van Key
- Department of Psychology and Center for Integrative Neuroscience, University of Nevada, Reno, Reno, NV, USA
| | - Pat Brosseau
- Department of Psychology and the Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Shaun M Eack
- School of Social Work, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Dean F Salisbury
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Marlene Behrmann
- Department of Psychology and the Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA
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14
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Ozbek SU, Sut E, Bora E. Comparison of social cognition and neurocognition in schizophrenia and autism spectrum disorder: A systematic review and meta-analysis. Neurosci Biobehav Rev 2023; 155:105441. [PMID: 37923237 DOI: 10.1016/j.neubiorev.2023.105441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 10/14/2023] [Accepted: 10/26/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND This report aimed to compare group differences in social and non-social cognition in autism spectrum disorders (ASD) and schizophrenia, and examine the influence of age and other factors on group differences. METHODS Literature searches were conducted in Pubmed and Web of Science from January 1980 to August 2022. Original research articles reporting objective measures of cognition were selected. RESULTS 57 articles involving 1864 patients with schizophrenia and 1716 patients with ASD have been included. Schizophrenia was associated with more severe non-social-cognitive impairment, particularly in fluency (g=0.47;CI[0.17-0.76]) and processing speed domains (g=0.41;CI[0.20-0.62]). Poorer performance in social cognition (Z = 3.68,p = 0.0002) and non-social cognition (Z = 2.48,p = 0.01) in schizophrenia were significantly related to older age. ASD was associated with more severe social cognitive impairment when groups were matched for non-social-cognition (g=-0.18, p = 0.04) or reasoning/problem solving (g=-0,62; CI [-1,06-(-0.08)]. DISCUSSION While both disorders present with social and non-social cognitive impairments, the pattern and developmental trajectories of these deficits are different. The limitations included heterogeneity of the cognitive measures, and the lack of sufficient information about antipsychotic use.
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Affiliation(s)
| | - Ekin Sut
- Department of Psychiatry, Faculty of Medicine, Izmir, Turkey.
| | - Emre Bora
- Department of Psychiatry, Faculty of Medicine, Izmir, Turkey; Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton South, Victoria 3053, Australia; Department of Neurosciences, Health Sciences Institute, Dokuz Eylül University, Izmir, Turkey.
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15
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Liu L, Qi X, Cheng S, Meng P, Yang X, Pan C, Zhang N, Chen Y, Li C, Zhang H, Zhang Z, Zhang J, Cheng B, Wen Y, Jia Y, Liu H, Zhang F. Epigenetic analysis suggests aberrant cerebellum brain aging in old-aged adults with autism spectrum disorder and schizophrenia. Mol Psychiatry 2023; 28:4867-4876. [PMID: 37612365 DOI: 10.1038/s41380-023-02233-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 08/11/2023] [Accepted: 08/15/2023] [Indexed: 08/25/2023]
Abstract
The aberrant aging hypothesis of schizophrenia (SCZ) and autism spectrum disorder (ASD) has been proposed, and the DNA methylation (DNAm) clock, which is a cumulative evaluation of DNAm levels at age-related CpGs, could serve as a biological aging indicator. This study evaluated epigenetic brain aging of ASD and SCZ using Horvath's epigenetic clock, based on two public genome-wide DNA methylation datasets of post-mortem brain samples (NASD = 222; NSCZ = 142). Total subjects were further divided into subgroups by gender and age. The epigenetic age acceleration (AgeAccel) for each sample was calculated as the residual value resulting from the regression model and compared between groups. Results showed DNAm age has a strong correlation with chronological age in both datasets across multiple brain regions (P < 0.05). When divided into equally sized age groups, the AgeAccel of the cerebellum (CB) region from people over 45 years of age was greater compared to the control sample (AgeAccel of ASD vs control: 5.069 vs -6.249; P < 0.001). And a decelerated epigenetic aging process was observed in the CB region of individuals with SCZ aged 50-70 years (AgeAccel of SCZ vs control: -3.171 vs 2.418; P < 0.05). However, our results showed no significant difference in AgeAccel between ASD and control groups, and between SCZ and control groups in the total and gender-specific groups (P > 0.05). This study's results revealed some evidence for aberrant epigenetic CB brain aging in old-aged patients with ASD and SCZ, indicating a different pattern of CB aging in older adults with these two diseases. However, further studies of larger ASD and SCZ cohorts are necessary to make definitive conclusions on this observation.
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Affiliation(s)
- Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Xin Qi
- Precision Medicine Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, P. R. China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Na Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Yujing Chen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Chune Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Huijie Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Zhen Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Jingxi Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Huan Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, 710061, P. R. China.
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, P. R. China.
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16
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Hinault T, D'Argembeau A, Bowler DM, La Corte V, Desaunay P, Provasi J, Platel H, Tran The J, Charretier L, Giersch A, Droit-Volet S. Time processing in neurological and psychiatric conditions. Neurosci Biobehav Rev 2023; 154:105430. [PMID: 37871780 DOI: 10.1016/j.neubiorev.2023.105430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 10/18/2023] [Accepted: 10/19/2023] [Indexed: 10/25/2023]
Abstract
A central question in understanding cognition and pathology-related cognitive changes is how we process time. However, time processing difficulties across several neurological and psychiatric conditions remain seldom investigated. The aim of this review is to develop a unifying taxonomy of time processing, and a neuropsychological perspective on temporal difficulties. Four main temporal judgments are discussed: duration processing, simultaneity and synchrony, passage of time, and mental time travel. We present an integrated theoretical framework of timing difficulties across psychiatric and neurological conditions based on selected patient populations. This framework provides new mechanistic insights on both (a) the processes involved in each temporal judgement, and (b) temporal difficulties across pathologies. By identifying underlying transdiagnostic time-processing mechanisms, this framework opens fruitful avenues for future research.
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Affiliation(s)
- Thomas Hinault
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14032 Caen, France.
| | - Arnaud D'Argembeau
- Psychology and Neuroscience of Cognition Research Unit, University of Liège, F.R.S-FNRS, 4000 Liège, Belgium
| | - Dermot M Bowler
- Autism Research Group, City, University of London, EC1V 0HB London, United Kingdom
| | - Valentina La Corte
- Laboratoire Mémoire, Cerveau et Cognition (MC2Lab), UR 7536, Université de Paris cité, 92774 Boulogne-Billancourt, France; Institut Universitaire de France, 75231 Paris, France
| | - Pierre Desaunay
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14032 Caen, France; Service de Psychiatrie de l'enfant et de l'adolescent, CHU de Caen, 14000 Caen, France
| | - Joelle Provasi
- CHArt laboratory (Human and Artificial Cognition), EPHE-PSL, 75014 Paris, France
| | - Hervé Platel
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14032 Caen, France
| | - Jessica Tran The
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14032 Caen, France
| | - Laura Charretier
- Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, GIP Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, 14032 Caen, France
| | - Anne Giersch
- Cognitive Neuropsychology and Pathophysiology of Schizophrenia Laboratory, National Institute of Health and Medical Research, University of Strasbourg, 67081 Strasbourg, France
| | - Sylvie Droit-Volet
- Université Clermont Auvergne, LAPSCO, CNRS, UMR 6024, 60032 Clermont-Ferrand, France
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17
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Kilpatrick S, Irwin C, Singh KK. Human pluripotent stem cell (hPSC) and organoid models of autism: opportunities and limitations. Transl Psychiatry 2023; 13:217. [PMID: 37344450 PMCID: PMC10284884 DOI: 10.1038/s41398-023-02510-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 05/09/2023] [Accepted: 06/05/2023] [Indexed: 06/23/2023] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder caused by genetic or environmental perturbations during early development. Diagnoses are dependent on the identification of behavioral abnormalities that likely emerge well after the disorder is established, leaving critical developmental windows uncharacterized. This is further complicated by the incredible clinical and genetic heterogeneity of the disorder that is not captured in most mammalian models. In recent years, advancements in stem cell technology have created the opportunity to model ASD in a human context through the use of pluripotent stem cells (hPSCs), which can be used to generate 2D cellular models as well as 3D unguided- and region-specific neural organoids. These models produce profoundly intricate systems, capable of modeling the developing brain spatiotemporally to reproduce key developmental milestones throughout early development. When complemented with multi-omics, genome editing, and electrophysiology analysis, they can be used as a powerful tool to profile the neurobiological mechanisms underlying this complex disorder. In this review, we will explore the recent advancements in hPSC-based modeling, discuss present and future applications of the model to ASD research, and finally consider the limitations and future directions within the field to make this system more robust and broadly applicable.
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Affiliation(s)
- Savannah Kilpatrick
- Donald K. Johnson Eye Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Department of Biochemistry and Biomedical Science, McMaster University, Hamilton, ON, Canada
| | - Courtney Irwin
- Donald K. Johnson Eye Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Karun K Singh
- Donald K. Johnson Eye Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
- Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
- Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON, Canada.
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18
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Sun M, Gabrielson B, Akhonda MABS, Yang H, Laport F, Calhoun V, Adali T. A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis. SENSORS (BASEL, SWITZERLAND) 2023; 23:5333. [PMID: 37300060 PMCID: PMC10256022 DOI: 10.3390/s23115333] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 05/27/2023] [Accepted: 05/29/2023] [Indexed: 06/12/2023]
Abstract
Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data's true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the "shared" subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs.
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Affiliation(s)
- Mingyu Sun
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; (B.G.); (M.A.B.S.A.); (H.Y.); (F.L.)
| | - Ben Gabrielson
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; (B.G.); (M.A.B.S.A.); (H.Y.); (F.L.)
| | - Mohammad Abu Baker Siddique Akhonda
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; (B.G.); (M.A.B.S.A.); (H.Y.); (F.L.)
| | - Hanlu Yang
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; (B.G.); (M.A.B.S.A.); (H.Y.); (F.L.)
| | - Francisco Laport
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; (B.G.); (M.A.B.S.A.); (H.Y.); (F.L.)
- CITIC Research Center, University of A Coruña, 15008 A Coruña, Spain
| | - Vince 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;
| | - Tülay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA; (B.G.); (M.A.B.S.A.); (H.Y.); (F.L.)
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19
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Du Y, Guo Y, Calhoun VD. Aging brain shows joint declines in brain within-network connectivity and between-network connectivity: a large-sample study ( N > 6,000). Front Aging Neurosci 2023; 15:1159054. [PMID: 37273655 PMCID: PMC10233064 DOI: 10.3389/fnagi.2023.1159054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 04/21/2023] [Indexed: 06/06/2023] Open
Abstract
Introduction Numerous studies have shown that aging has important effects on specific functional networks of the brain and leads to brain functional connectivity decline. However, no studies have addressed the effect of aging at the whole-brain level by studying both brain functional networks (i.e., within-network connectivity) and their interaction (i.e., between-network connectivity) as well as their joint changes. Methods In this work, based on a large sample size of neuroimaging data including 6300 healthy adults aged between 49 and 73 years from the UK Biobank project, we first use our previously proposed priori-driven independent component analysis (ICA) method, called NeuroMark, to extract the whole-brain functional networks (FNs) and the functional network connectivity (FNC) matrix. Next, we perform a two-level statistical analysis method to identify robust aging-related changes in FNs and FNCs, respectively. Finally, we propose a combined approach to explore the synergistic and paradoxical changes between FNs and FNCs. Results Results showed that the enhanced FNCs mainly occur between different functional domains, involving the default mode and cognitive control networks, while the reduced FNCs come from not only between different domains but also within the same domain, primarily relating to the visual network, cognitive control network, and cerebellum. Aging also greatly affects the connectivity within FNs, and the increased within-network connectivity along with aging are mainly within the sensorimotor network, while the decreased within-network connectivity significantly involves the default mode network. More importantly, many significant joint changes between FNs and FNCs involve default mode and sub-cortical networks. Furthermore, most synergistic changes are present between the FNCs with reduced amplitude and their linked FNs, and most paradoxical changes are present in the FNCs with enhanced amplitude and their linked FNs. Discussion In summary, our study emphasizes the diversity of brain aging and provides new evidence via novel exploratory perspectives for non-pathological aging of the whole brain.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Yating Guo
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
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20
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Abrol A, Fu Z, Du Y, Wilson TW, Wang Y, Stephen JM, Calhoun VD. Developmental and aging resting functional magnetic resonance imaging brain state adaptations in adolescents and adults: A large N (>47K) study. Hum Brain Mapp 2023; 44:2158-2175. [PMID: 36629328 PMCID: PMC10028673 DOI: 10.1002/hbm.26200] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 12/02/2022] [Accepted: 12/28/2022] [Indexed: 01/12/2023] Open
Abstract
The brain's functional architecture and organization undergo continual development and modification throughout adolescence. While it is well known that multiple factors govern brain maturation, the constantly evolving patterns of time-resolved functional connectivity are still unclear and understudied. We systematically evaluated over 47,000 youth and adult brains to bridge this gap, highlighting replicable time-resolved developmental and aging functional brain patterns. The largest difference between the two life stages was captured in a brain state that indicated coherent strengthening and modularization of functional coupling within the auditory, visual, and motor subdomains, supplemented by anticorrelation with other subdomains in adults. This distinctive pattern, which we replicated in independent data, was consistently less modular or absent in children and presented a negative association with age in adults, thus indicating an overall inverted U-shaped trajectory. This indicates greater synchrony, strengthening, modularization, and integration of the brain's functional connections beyond adolescence, and gradual decline of this pattern during the healthy aging process. We also found evidence that the developmental changes may also bring along a departure from the canonical static functional connectivity pattern in favor of more efficient and modularized utilization of the vast brain interconnections. State-based statistical summary measures presented robust and significant group differences that also showed significant age-related associations. The findings reported in this article support the idea of gradual developmental and aging brain state adaptation processes in different phases of life and warrant future research via lifespan studies to further authenticate the projected time-resolved brain state trajectories.
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Affiliation(s)
- Anees Abrol
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Zening Fu
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
| | - Yuhui Du
- School of Computer & Information TechnologyShanxi UniversityTaiyuanChina
| | - Tony W. Wilson
- Boys Town National Research HospitalInstitute for Human NeuroscienceBoys TownNebraskaUSA
| | - Yu‐Ping Wang
- Department of Biomedical EngineeringTulane UniversityNew OrleansLouisianaUSA
- Department of Global Biostatistics and Data ScienceTulane UniversityNew OrleansLouisianaUSA
| | | | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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21
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Yang H, Vu T, Long Q, Calhoun V, Adali T. Identification of Homogeneous Subgroups from Resting-State fMRI Data. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23063264. [PMID: 36991975 PMCID: PMC10051904 DOI: 10.3390/s23063264] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/04/2023] [Accepted: 03/14/2023] [Indexed: 06/12/2023]
Abstract
The identification of homogeneous subgroups of patients with psychiatric disorders can play an important role in achieving personalized medicine and is essential to provide insights for understanding neuropsychological mechanisms of various mental disorders. The functional connectivity profiles obtained from functional magnetic resonance imaging (fMRI) data have been shown to be unique to each individual, similar to fingerprints; however, their use in characterizing psychiatric disorders in a clinically useful way is still being studied. In this work, we propose a framework that makes use of functional activity maps for subgroup identification using the Gershgorin disc theorem. The proposed pipeline is designed to analyze a large-scale multi-subject fMRI dataset with a fully data-driven method, a new constrained independent component analysis algorithm based on entropy bound minimization (c-EBM), followed by an eigenspectrum analysis approach. A set of resting-state network (RSN) templates is generated from an independent dataset and used as constraints for c-EBM. The constraints present a foundation for subgroup identification by establishing a connection across the subjects and aligning subject-wise separate ICA analyses. The proposed pipeline was applied to a dataset comprising 464 psychiatric patients and discovered meaningful subgroups. Subjects within the identified subgroups share similar activation patterns in certain brain areas. The identified subgroups show significant group differences in multiple meaningful brain areas including dorsolateral prefrontal cortex and anterior cingulate cortex. Three sets of cognitive test scores were used to verify the identified subgroups, and most of them showed significant differences across subgroups, which provides further confirmation of the identified subgroups. In summary, this work represents an important step forward in using neuroimaging data to characterize mental disorders.
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Affiliation(s)
- Hanlu Yang
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Trung Vu
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Qunfang Long
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA
| | - Vince 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
| | - Tülay Adali
- Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA
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22
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Wei L, Zhang Y, Zhai W, Wang H, Zhang J, Jin H, Feng J, Qin Q, Xu H, Li B, Liu J. Attenuated effective connectivity of large-scale brain networks in children with autism spectrum disorders. Front Neurosci 2022; 16:987248. [PMID: 36523439 PMCID: PMC9745118 DOI: 10.3389/fnins.2022.987248] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 11/07/2022] [Indexed: 11/29/2023] Open
Abstract
INTRODUCTION Understanding the neurological basis of autism spectrum disorder (ASD) is important for the diagnosis and treatment of this mental disorder. Emerging evidence has suggested aberrant functional connectivity of large-scale brain networks in individuals with ASD. However, whether the effective connectivity which measures the causal interactions of these networks is also impaired in these patients remains unclear. OBJECTS The main purpose of this study was to investigate the effective connectivity of large-scale brain networks in patients with ASD during resting state. MATERIALS AND METHODS The subjects were 42 autistic children and 127 age-matched normal children from the ABIDE II dataset. We investigated effective connectivity of 7 large-scale brain networks including visual network (VN), default mode network (DMN), cerebellum, sensorimotor network (SMN), auditory network (AN), salience network (SN), frontoparietal network (FPN), with spectral dynamic causality model (spDCM). Parametric empirical Bayesian (PEB) was used to perform second-level group analysis and furnished group commonalities and differences in effective connectivity. Furthermore, we analyzed the correlation between the strength of effective connectivity and patients' clinical characteristics. RESULTS For both groups, SMN acted like a hub network which demonstrated dense effective connectivity with other large-scale brain network. We also observed significant causal interactions within the "triple networks" system, including DMN, SN and FPN. Compared with healthy controls, children with ASD showed decreased effective connectivity among some large-scale brain networks. These brain networks included VN, DMN, cerebellum, SMN, and FPN. In addition, we also found significant negative correlation between the strength of the effective connectivity from right angular gyrus (ANG_R) of DMN to left precentral gyrus (PreCG_L) of SMN and ADOS-G or ADOS-2 module 4 stereotyped behaviors and restricted interest total (ADOS_G_STEREO_BEHAV) scores. CONCLUSION Our research provides new evidence for the pathogenesis of children with ASD from the perspective of effective connections within and between large-scale brain networks. The attenuated effective connectivity of brain networks may be a clinical neurobiological feature of ASD. Changes in effective connectivity of brain network in children with ASD may provide useful information for the diagnosis and treatment of the disease.
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Affiliation(s)
- Lei Wei
- Network Center, Air Force Medical University, Xi’an, China
| | - Yao Zhang
- Military Medical Center, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Wensheng Zhai
- School of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Huaning Wang
- Department of Psychiatry, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Junchao Zhang
- Network Center, Air Force Medical University, Xi’an, China
| | - Haojie Jin
- Network Center, Air Force Medical University, Xi’an, China
| | - Jianfei Feng
- Network Center, Air Force Medical University, Xi’an, China
| | - Qin Qin
- Network Center, Air Force Medical University, Xi’an, China
| | - Hao Xu
- Network Center, Air Force Medical University, Xi’an, China
| | - Baojuan Li
- School of Biomedical Engineering, Air Force Medical University, Xi’an, China
| | - Jian Liu
- Network Center, Air Force Medical University, Xi’an, China
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23
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Du Y, He X, Kochunov P, Pearlson G, Hong LE, van Erp TGM, Belger A, Calhoun VD. A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder. Hum Brain Mapp 2022; 43:3887-3903. [PMID: 35484969 PMCID: PMC9294304 DOI: 10.1002/hbm.25890] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 03/24/2022] [Accepted: 04/08/2022] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information TechnologyShanxi UniversityTaiyuanShanxiChina
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Xingyu He
- School of Computer and Information TechnologyShanxi UniversityTaiyuanShanxiChina
| | - Peter Kochunov
- Center for Brain Imaging ResearchUniversity of MarylandBaltimoreMarylandUSA
| | | | - L. Elliot Hong
- Center for Brain Imaging ResearchUniversity of MarylandBaltimoreMarylandUSA
| | - Theo G. M. van Erp
- Department of Psychiatry and Human BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of CaliforniaIrvineCaliforniaUSA
| | - Aysenil Belger
- Department of PsychiatryUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
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Abrol A, Calhoun V. Discovery and Replication of Time-Resolved Functional Network Connectivity Differences in Adolescence and Adulthood in over 50K fMRI Datasets. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1855-1858. [PMID: 36085722 DOI: 10.1109/embc48229.2022.9870916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
There remains an open question about whether and in what context brain function varies in adolescence and adulthood. In this work, we systematically study the functional brain networks of adolescents and adults, outlining the significant differences in the developing brain detected via time-resolved functional network connectivity (trFNC) derived from a fully automated independent component analysis pipeline applied to resting-state fMRI data in over 50K individuals. We then statistically analyze the transient, recurrent, and robust brain state profiles in both groups. We confirmed the results in independent replication datasets for both groups. Our findings indicate a strengthening of a state reflecting functional coupling within the visual, motor, and auditory domains and anticorrelation with all other domains in a unique adult state profile, a pattern consistently less modular in adolescents. This new insight into possible integration, strengthening, and modularization of resting-state brain connections beyond childhood convergently indicates that the highlighted temporal dynamics likely reflect robust differences in brain function in adolescents versus adults.
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25
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Colizzi M, Bortoletto R, Costa R, Bhattacharyya S, Balestrieri M. The Autism-Psychosis Continuum Conundrum: Exploring the Role of the Endocannabinoid System. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:5616. [PMID: 35565034 PMCID: PMC9105053 DOI: 10.3390/ijerph19095616] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/01/2022] [Accepted: 05/03/2022] [Indexed: 02/07/2023]
Abstract
Evidence indicates shared physiopathological mechanisms between autism and psychosis. In this regard, the endocannabinoid system has been suggested to modulate neural circuits during the early stage of neurodevelopment, with implications for both autism and psychosis. Nevertheless, such potential common markers of disease have been investigated in both autism and psychosis spectrum disorders, without considering the conundrum of differentiating the two groups of conditions in terms of diagnosis and treatment. Here, we systematically review all human and animal studies examining the endocannabinoid system and its biobehavioral correlates in the association between autism and psychosis. Studies indicate overlapping biobehavioral aberrancies between autism and schizophrenia, subject to correction by modulation of the endocannabinoid system. In addition, common cannabinoid-based pharmacological strategies have been identified, exerting epigenetic effects across genes controlling neural mechanisms shared between autism and schizophrenia. Interestingly, a developmental and transgenerational trajectory between autism and schizophrenia is supported by evidence that exogenous alteration of the endocannabinoid system promotes progression to inheritable psychosis phenotypes in the context of biobehavioral autism vulnerability. However, evidence for a diametral association between autism and psychosis is scant. Several clinical implications follow from evidence of a developmental continuum between autism and psychosis as a function of the endocannabinoid system dysregulation.
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Affiliation(s)
- Marco Colizzi
- Unit of Psychiatry, Department of Medicine (DAME), University of Udine, 33100 Udine, Italy;
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK;
| | - Riccardo Bortoletto
- Child and Adolescent Neuropsychiatry Unit, Maternal-Child Integrated Care Department, Integrated University Hospital of Verona, 37126 Verona, Italy;
| | - Rosalia Costa
- Community Mental Health Team, Friuli Centrale University Health Service (ASUFC), 33057 Palmanova, Italy;
| | - Sagnik Bhattacharyya
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK;
| | - Matteo Balestrieri
- Unit of Psychiatry, Department of Medicine (DAME), University of Udine, 33100 Udine, Italy;
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Multivariate Gaussian Copula Mutual Information to Estimate Functional Connectivity with Less Random Architecture. ENTROPY 2022; 24:e24050631. [PMID: 35626516 PMCID: PMC9141633 DOI: 10.3390/e24050631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/13/2022] [Accepted: 04/26/2022] [Indexed: 01/27/2023]
Abstract
Recognition of a brain region’s interaction is an important field in neuroscience. Most studies use the Pearson correlation to find the interaction between the regions. According to the experimental evidence, there is a nonlinear dependence between the activities of different brain regions that is ignored by Pearson correlation as a linear measure. Typically, the average activity of each region is used as input because it is a univariate measure. This dimensional reduction, i.e., averaging, leads to a loss of spatial information across voxels within the region. In this study, we propose using an information-theoretic measure, multivariate mutual information (mvMI), as a nonlinear dependence to find the interaction between regions. This measure, which has been recently proposed, simplifies the mutual information calculation complexity using the Gaussian copula. Using simulated data, we show that the using this measure overcomes the mentioned limitations. Additionally using the real resting-state fMRI data, we compare the level of significance and randomness of graphs constructed using different methods. Our results indicate that the proposed method estimates the functional connectivity more significantly and leads to a smaller number of random connections than the common measure, Pearson correlation. Moreover, we find that the similarity of the estimated functional networks of the individuals is higher when the proposed method is used.
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