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Polemiti E, Hese S, Schepanski K, Yuan J, Schumann G. How does the macroenvironment influence brain and behaviour-a review of current status and future perspectives. Mol Psychiatry 2024; 29:3268-3286. [PMID: 38658771 PMCID: PMC11449798 DOI: 10.1038/s41380-024-02557-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024]
Abstract
The environment influences brain and mental health, both detrimentally and beneficially. Existing research has emphasised the individual psychosocial 'microenvironment'. Less attention has been paid to 'macroenvironmental' challenges, including climate change, pollution, urbanicity, and socioeconomic disparity. Notably, the implications of climate and pollution on brain and mental health have only recently gained prominence. With the advent of large-scale big-data cohorts and an increasingly dense mapping of macroenvironmental parameters, we are now in a position to characterise the relation between macroenvironment, brain, and behaviour across different geographic and cultural locations globally. This review synthesises findings from recent epidemiological and neuroimaging studies, aiming to provide a comprehensive overview of the existing evidence between the macroenvironment and the structure and functions of the brain, with a particular emphasis on its implications for mental illness. We discuss putative underlying mechanisms and address the most common exposures of the macroenvironment. Finally, we identify critical areas for future research to enhance our understanding of the aetiology of mental illness and to inform effective interventions for healthier environments and mental health promotion.
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Affiliation(s)
- Elli Polemiti
- Centre of Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience CCM, Charité-Universitätsmedizin Berlin, Berlin, Germany.
| | - Sören Hese
- Institute of Geography, Friedrich Schiller University Jena, Jena, Germany
| | | | - Jiacan Yuan
- Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences & CMA-FDU Joint Laboratory of Marine Meteorology & IRDR-ICOE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China
| | - Gunter Schumann
- Centre of Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience CCM, Charité-Universitätsmedizin Berlin, Berlin, Germany.
- Centre for Population Neuroscience and Stratified Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China.
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Costa T, Premi E, Borroni B, Manuello J, Cauda F, Duca S, Liloia D. Local functional connectivity abnormalities in mild cognitive impairment and Alzheimer's disease: A meta-analytic investigation using minimum Bayes factor activation likelihood estimation. Neuroimage 2024; 298:120798. [PMID: 39153521 DOI: 10.1016/j.neuroimage.2024.120798] [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: 04/24/2024] [Revised: 08/13/2024] [Accepted: 08/15/2024] [Indexed: 08/19/2024] Open
Abstract
Functional magnetic resonance imaging research employing regional homogeneity (ReHo) analysis has uncovered aberrant local brain connectivity in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) in comparison with healthy controls. However, the precise localization, extent, and possible overlap of these aberrations are still not fully understood. To bridge this gap, we applied a novel meta-analytic and Bayesian method (minimum Bayes Factor Activation Likelihood Estimation, mBF-ALE) for a systematic exploration of local functional connectivity alterations in MCI and AD brains. We extracted ReHo data via a standardized MEDLINE database search, which included 35 peer-reviewed experiments, 1,256 individuals with AD or MCI, 1,118 healthy controls, and 205 x-y-z coordinates of ReHo variation. We then separated the data into two distinct datasets: one for MCI and the other for AD. Two mBF-ALE analyses were conducted, thresholded at "very strong evidence" (mBF ≥ 150), with a minimum cluster size of 200 mm³. We also assessed the spatial consistency and sensitivity of our Bayesian results using the canonical version of the ALE algorithm. For MCI, we observed two clusters of ReHo decrease and one of ReHo increase. Decreased local connectivity was notable in the left precuneus (Brodmann area - BA 7) and left inferior temporal gyrus (BA 20), while increased connectivity was evident in the right parahippocampal gyrus (BA 36). The canonical ALE confirmed these locations, except for the inferior temporal gyrus. In AD, one cluster each of ReHo decrease and increase were found, with decreased connectivity in the right posterior cingulate cortex (BA 30 extending to BA 23) and increased connectivity in the left posterior cingulate cortex (BA 31). These locations were confirmed by the canonical ALE. The identification of these distinct functional connectivity patterns sheds new light on the complex pathophysiology of MCI and AD, offering promising directions for future neuroimaging-based interventions. Additionally, the use of a Bayesian framework for statistical thresholding enhances the robustness of neuroimaging meta-analyses, broadening its applicability to small datasets.
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Affiliation(s)
- Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy.
| | - Enrico Premi
- Stroke Unit, Department of Neurological and Vision Sciences, ASST Spedali Civili, Brescia, Italy
| | - Barbara Borroni
- Cognitive and Behavioural Neurology, Department of Clinical and Experimental Sciences, University of Brescia, Italy
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
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3
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Liloia D, Zamfira DA, Tanaka M, Manuello J, Crocetta A, Keller R, Cozzolino M, Duca S, Cauda F, Costa T. Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research. Neurosci Biobehav Rev 2024; 164:105791. [PMID: 38960075 DOI: 10.1016/j.neubiorev.2024.105791] [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: 10/26/2023] [Revised: 05/22/2024] [Accepted: 06/27/2024] [Indexed: 07/05/2024]
Abstract
Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Denisa Adina Zamfira
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Masaru Tanaka
- HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged (HUN-REN-SZTE), Danube Neuroscience Research Laboratory, Szeged, Hungary
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Annachiara Crocetta
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Mauro Cozzolino
- Department of Humanities, Philosophical and Educational Sciences, University of Salerno, Fisciano, Italy
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy
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Wei Y, Su W, Zhang T, Webler R, Tang X, Zheng Y, Tang Y, Xu L, Cui H, Zhu J, Qian Z, Ju M, Long B, Zhao J, Chen C, Zeng L, Zhang T, Wang J. Structural and functional abnormalities across clinical stages of psychosis: A multimodal neuroimaging investigation. Asian J Psychiatr 2024; 99:104153. [PMID: 39047353 DOI: 10.1016/j.ajp.2024.104153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2024] [Revised: 06/27/2024] [Accepted: 07/04/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND Structural and functional neurobiological abnormalities have been observed in schizophrenia. Previous studies have concentrated on specific illness stages, obscuring relationships between functional/structural changes and disorder progression. The present study aimed to quantify structural and functional abnormalities across different clinical stages using functional near-infrared spectroscopy (fNIRS) and structural magnetic resonance imaging (sMRI). METHODS Fifty-four participants with first-episode schizophrenia (FES), 120 with clinically high risk of psychosis (CHR), and 111 healthy controls (HCs) underwent functional near-infrared spectroscopy (fNIRS) to measure oxyhemoglobin (Oxy-Hb) during the verbal fluency task. Among them, 28FES, 64CHR and 55HC also finished sMRI. Oxy-Hb and gray matter volume (GMV) were compared among the three groups while controlling for covariates, including age, sex, years of education, and task performance. Mediation analysis was utilized to determine the mediating effect of GMV on Oxy-Hb and cognition. RESULTS Compared with the HC group, CHR and FES groups showed significantly reduced brain activity. However, there were no significant differences between the FES and CHR. Pronounced GMV increase in the right frontal pole area (F = 4.234, p = 0.016) was identified in the CHR and FES groups. Mediation analysis showed a significant mediation effect of the right frontal pole GMV between Channel 31 Oxy-Hb and processing speed (z = 2.105, p = 0.035) and attention/vigilance (z = 1.992, p = 0.046). CONCLUSIONS Brain activation and anatomical deficits were observed in different brain regions, suggesting that anatomical and functional abnormalities are dissociated in the early stages of psychosis. The relationship between neural activity and anatomy may reflect a specific pathophysiology related to cognitive deterioration in schizophrenia.
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Affiliation(s)
- Yanyan Wei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Wenjun Su
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Tingyu Zhang
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
| | - Ryan Webler
- Center for Brain Circuit Therapeutics, Brigham & Women's Hospital, Boston, MA, United States; Department of Psychiatry, Harvard Medical School, United States
| | - Xiaochen Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Yuchen Zheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Huiru Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Junjuan Zhu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Zhenying Qian
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Mingliang Ju
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Bin Long
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Jian Zhao
- Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Cheng Chen
- Department of Micro/Nano Electronics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lingyun Zeng
- Department of Psychiatric Rehabilitation, Shenzhen Kangning Hospital, ShenZhen, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China.
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Liloia D, Cauda F, Uddin LQ, Manuello J, Mancuso L, Keller R, Nani A, Costa T. Revealing the Selectivity of Neuroanatomical Alteration in Autism Spectrum Disorder via Reverse Inference. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:1075-1083. [PMID: 35131520 DOI: 10.1016/j.bpsc.2022.01.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/30/2021] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Although neuroimaging research has identified atypical neuroanatomical substrates in individuals with autism spectrum disorder (ASD), it is at present unclear whether and to what extent disorder-selective gray matter alterations occur in this spectrum of conditions. In fact, a growing body of evidence shows a substantial overlap between the pathomorphological changes across different brain diseases, which may complicate identification of reliable neural markers and differentiation of the anatomical substrates of distinct psychopathologies. METHODS Using a novel data-driven and Bayesian methodology with published voxel-based morphometry data (849 peer-reviewed experiments and 22,304 clinical subjects), this study performs the first reverse inference investigation to explore the selective structural brain alteration profile of ASD. RESULTS We found that specific brain areas exhibit a >90% probability of gray matter alteration selectivity for ASD: the bilateral precuneus (Brodmann area 7), right inferior occipital gyrus (Brodmann area 18), left cerebellar lobule IX and Crus II, right cerebellar lobule VIIIA, and right Crus I. Of note, many brain voxels that are selective for ASD include areas that are posterior components of the default mode network. CONCLUSIONS The identification of these spatial gray matter alteration patterns offers new insights into understanding the complex neurobiological underpinnings of ASD and opens attractive prospects for future neuroimaging-based interventions.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Franco Cauda
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin, Turin, Italy
| | - Lucina Q Uddin
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, California
| | - Jordi Manuello
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lorenzo Mancuso
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy
| | - Andrea Nani
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS-fMRI Research Group, Koelliker Hospital, and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin, Turin, Italy
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Polemiti E, Hese S, Schepanski K, Yuan J, Schumann G. How does the macroenvironment influence brain and behaviour - a review of current status and future perspectives. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.09.23296785. [PMID: 37873310 PMCID: PMC10593044 DOI: 10.1101/2023.10.09.23296785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
The environment influences mental health, both detrimentally and beneficially. Current research has emphasized the individual psychosocial 'microenvironment'. Less attention has been paid to 'macro-environmental' challenges including climate change, pollution, urbanicity and socioeconomic disparity. With the advent of large-scale big-data cohorts and an increasingly dense mapping of macroenvironmental parameters, we are now in a position to characterise the relation between macroenvironment, brain, and behaviour across different geographic and cultural locations globally. This review synthesises findings from recent epidemiological and neuroimaging studies, aiming to provide a comprehensive overview of the existing evidence between the macroenvironment and the structure and functions of the brain, with a particular emphasis on its implications for mental illness. We discuss putative underlying mechanisms and address the most common exposures of the macroenvironment. Finally, we identify critical areas for future research to enhance our understanding of the aetiology of mental illness and to inform effective interventions for healthier environments and mental health promotion.
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Affiliation(s)
- Elli Polemiti
- Centre of Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité, Universitätsmedizin Berlin, Germany
| | - Soeren Hese
- Institute of Geography, Friedrich Schiller University Jena, Germany
| | | | - Jiacan Yuan
- Department of Atmospheric and Oceanic Sciences & Institute of Atmospheric Sciences & CMA-FDU Joint Laboratory of Marine Meteorology & IRDR-ICOE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health, Fudan University, Shanghai, China
| | - Gunter Schumann
- Centre of Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité, Universitätsmedizin Berlin, Germany
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
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Li M, Deng W, Li Y, Zhao L, Ma X, Yu H, Li X, Meng Y, Wang Q, Du X, Sham PC, Palaniyappan L, Li T. Ameliorative patterns of grey matter in patients with first-episode and treatment-naïve schizophrenia. Psychol Med 2023; 53:3500-3510. [PMID: 35164887 PMCID: PMC10277763 DOI: 10.1017/s0033291722000058] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 12/30/2021] [Accepted: 01/05/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND Grey matter (GM) reduction is a consistent observation in established late stages of schizophrenia, but patients in the untreated early stages of illness display an increase as well as a decrease in GM distribution relative to healthy controls (HC). The relative excess of GM may indicate putative compensatory responses, though to date its relevance is unclear. METHODS 343 first-episode treatment-naïve patients with schizophrenia (FES) and 342 HC were recruited. Multivariate source-based morphometry was performed to identify covarying 'networks' of grey matter concentration (GMC). Neurocognitive scores using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and symptom burden using the Positive and Negative Symptoms Scale (PANSS) were obtained. Bivariate linear relationships between GMC and cognition/symptoms were studied. RESULTS Compared to healthy subjects, FES had prominently lower GMC in two components; the first consists of the anterior insula, inferior frontal gyrus, anterior cingulate and the second component with the superior temporal gyrus, precuneus, inferior/superior parietal lobule, cuneus, and lingual gyrus. Higher GMC was seen in adjacent areas of the middle and superior temporal gyrus, middle frontal gyrus, inferior parietal cortex and putamen. Greater GMC of this component was associated with lower duration of untreated psychosis, less severe positive symptoms and better performance on cognitive tests. CONCLUSIONS In untreated stages of schizophrenia, both a distributed lower and higher GMC is observable. While the higher GMC is relatively modest, it occurs across frontoparietal, temporal and subcortical regions in association with reduced illness burden suggesting a compensatory role for higher GMC in the early stages of schizophrenia.
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Affiliation(s)
- Mingli Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Wei Deng
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yinfei Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaohong Ma
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Hua Yu
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaojing Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Yajing Meng
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
| | - Xiangdong Du
- Suzhou Psychiatry Hospital, Affiliated Guangji Hospital of Soochow University, Suzhou, 215137, Jiangsu, China
| | - Pak Chung Sham
- Centre for Genomic Sciences and State Key Laboratory in Cognitive and Brain Sciences, LKS Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, China
| | - Lena Palaniyappan
- Robarts Research Institute & The Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada
- Department of Psychiatry, University of Western Ontario, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
| | - Tao Li
- Mental Health Center and Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, China
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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8
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Holton KM, Chan SY, Brockmeier AJ, Öngür D, Hall MH. Exploring the influence of functional architecture on cortical thickness networks in early psychosis - a longitudinal study. Neuroimage 2023; 274:120127. [PMID: 37086876 DOI: 10.1016/j.neuroimage.2023.120127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Revised: 04/17/2023] [Accepted: 04/19/2023] [Indexed: 04/24/2023] Open
Abstract
Cortical thickness reductions differ between individuals with psychotic disorders and comparison subjects even in early stages of illness. Whether these reductions covary as expected by functional network membership or simply by spatial proximity has not been fully elucidated. Through orthonormal projective non-negative matrix factorization, cortical thickness measurements in functionally-annotated regions from MRI scans of early-stage psychosis and matched healthy controls were reduced in dimensionality into features capturing positive covariance. Rather than matching the functional networks, the covarying regions in each feature displayed a more localized spatial organization. With Bayesian belief networks, the covarying regions per feature were arranged into a network topology to visualize the dependency structure and identify key driving regions. The features demonstrated diagnosis-specific differences in cortical thickness distributions per feature, identifying reduction-vulnerable spatial regions. Differences in key cortical thickness features between psychosis and control groups were delineated, as well as those between affective and non-affective psychosis. Clustering of the participants, stratified by diagnosis and clinical variables, characterized the clinical traits that define the cortical thickness patterns. Longitudinal follow-up revealed that in select clusters with low baseline cortical thickness, clinical traits improved over time. Our study represents a novel effort to characterize brain structure in relation to functional networks in healthy and clinical populations and to map patterns of cortical thickness alterations among ESP patients onto clinical variables for a better understanding of brain pathophysiology.
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Affiliation(s)
- Kristina M Holton
- Computational Neural Information Engineering Lab, University of Delaware, 139 The Green, Newark, DE 19716.
| | - Shi Yu Chan
- Psychosis Neurobiology Laboratory, McLean Hospital, 115 Mill St, Belmont, MA 02478; Division of Psychotic Disorders, McLean Hospital, 115 Mill St, Belmont, MA 02478
| | - Austin J Brockmeier
- Computational Neural Information Engineering Lab, University of Delaware, 139 The Green, Newark, DE 19716
| | - Dost Öngür
- Department of Psychiatry, Harvard Medical School, 25 Shattuck St, Boston, MA 02115; Division of Psychotic Disorders, McLean Hospital, 115 Mill St, Belmont, MA 02478
| | - Mei-Hua Hall
- Psychosis Neurobiology Laboratory, McLean Hospital, 115 Mill St, Belmont, MA 02478; Department of Psychiatry, Harvard Medical School, 25 Shattuck St, Boston, MA 02115; Division of Psychotic Disorders, McLean Hospital, 115 Mill St, Belmont, MA 02478.
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9
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Gray JP, Manuello J, Alexander-Bloch AF, Leonardo C, Franklin C, Choi KS, Cauda F, Costa T, Blangero J, Glahn DC, Mayberg HS, Fox PT. Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects. Neuroinformatics 2022; 21:443-455. [PMID: 36469193 DOI: 10.1007/s12021-022-09614-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/06/2022] [Indexed: 12/12/2022]
Abstract
Major depressive disorder (MDD) exhibits diverse symptomology and neuroimaging studies report widespread disruption of key brain areas. Numerous theories underpinning the network degeneration hypothesis (NDH) posit that neuropsychiatric diseases selectively target brain areas via meaningful network mechanisms rather than as indistinct disease effects. The present study tests the hypothesis that MDD is a network-based disorder, both structurally and functionally. Coordinate-based meta-analysis and Activation Likelihood Estimation (CBMA-ALE) were used to assess the convergence of findings from 92 previously published studies in depression. An extension of CBMA-ALE was then used to generate a node-and-edge network model representing the co-alteration of brain areas impacted by MDD. Standardized measures of graph theoretical network architecture were assessed. Co-alteration patterns among the meta-analytic MDD nodes were then tested in independent, clinical T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional (rs-fMRI) data. Differences in co-alteration profiles between MDD patients and healthy controls, as well as between controls and clinical subgroups of MDD patients, were assessed. A 65-node 144-edge co-alteration network model was derived for MDD. Testing of co-alteration profiles in replication data using the MDD nodes provided distinction between MDD and healthy controls in structural data. However, co-alteration profiles were not distinguished between patients and controls in rs-fMRI data. Improved distinction between patients and healthy controls was observed in clinically homogenous MDD subgroups in T1 data. MDD abnormalities demonstrated both structural and functional network architecture, though only structural networks exhibited between-groups differences. Our findings suggest improved utility of structural co-alteration networks for ongoing biomarker development.
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10
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Jia X, Wang J, Jiang W, Kong Z, Deng H, Lai W, Ye C, Guan F, Li P, Zhao M, Yang M. Common gray matter loss in the frontal cortex in patients with methamphetamine-associated psychosis and schizophrenia. Neuroimage Clin 2022; 36:103259. [PMID: 36510408 PMCID: PMC9668661 DOI: 10.1016/j.nicl.2022.103259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 10/08/2022] [Accepted: 11/02/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND AND HYPOTHESIS Methamphetamine (MA)-associated psychosis has become a public concern. However, its mechanism is not clear. Investigating similarities and differences between MA-associated psychosis and schizophrenia in brain alterations would be informative for neuropathology. STUDY DESIGN This study compared gray matter volumes of the brain across four participant groups: healthy controls (HC, n = 53), MA users without psychosis (MA, n = 22), patients with MA-associated psychosis (MAP, n = 34) and patients with schizophrenia (SCZ, n = 33). Clinical predictors of brain alterations, as well as association of brain alterations with psychotic symptoms and attention impairment were further investigated. STUDY RESULTS Compared with the HC, the MAP and the SCZ showed similar gray matter reductions in the frontal cortex, particularly in prefrontal areas. Moreover, a stepwise extension of gray matter reductions was exhibited across the MA - MAP - SCZ. Duration of abstinence was associated with regional volumetric recovery in the MAP, while this amendment in brain morphometry was not accompanied with symptom's remission. Illness duration of psychosis was among the predictive factors of regional gray matter reductions in both psychotic groups. Volume reductions were found to be associated with attention impairment in the SCZ, while this association was reversed in the MAP in frontal cortex. CONCLUSIONS This study suggested MA-associated psychosis and schizophrenia had common neuropathology in cognitive-related frontal cortices. A continuum of neuropathology between MA use and schizophrenia was tentatively implicated. Illness progressions and glial repairments could both play roles in neuropathological changes in MA-associated psychosis.
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Affiliation(s)
- Xiaojian Jia
- Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen Clinical Research Center for Mental Disorders, Shenzhen 518020, China
| | - Jianhong Wang
- Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen Clinical Research Center for Mental Disorders, Shenzhen 518020, China
| | - Wentao Jiang
- Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen Clinical Research Center for Mental Disorders, Shenzhen 518020, China
| | - Zhi Kong
- Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen Clinical Research Center for Mental Disorders, Shenzhen 518020, China
| | - Huan Deng
- School of International Education, Beijing University of Chemical Technology, Beijing 100029, China
| | - Wentao Lai
- Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen Clinical Research Center for Mental Disorders, Shenzhen 518020, China
| | - Caihong Ye
- Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen Clinical Research Center for Mental Disorders, Shenzhen 518020, China
| | - Fen Guan
- Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen Clinical Research Center for Mental Disorders, Shenzhen 518020, China
| | - Peng Li
- Peking University Sixth Hospital, Peking University, Beijing 100191, China
| | - Min Zhao
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Mei Yang
- Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen Clinical Research Center for Mental Disorders, Shenzhen 518020, China.
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11
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Liloia D, Crocetta A, Cauda F, Duca S, Costa T, Manuello J. Seeking Overlapping Neuroanatomical Alterations between Dyslexia and Attention-Deficit/Hyperactivity Disorder: A Meta-Analytic Replication Study. Brain Sci 2022; 12:brainsci12101367. [PMID: 36291301 PMCID: PMC9599506 DOI: 10.3390/brainsci12101367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/23/2022] [Accepted: 09/29/2022] [Indexed: 01/18/2023] Open
Abstract
The present work is a replication article based on the paper “Are there shared neural correlates between dyslexia and ADHD? A meta-analysis of voxel-based morphometry studies” by McGrath and Stoodley (2019). In the original research, the authors used activation likelihood estimation (ALE), a technique to perform coordinate-based meta-analysis (CBMA), to investigate the existence of brain regions undergoing gray matter alteration in association with both attention-deficit/hyper-activity disorder (ADHD) and dyslexia. Here, the same voxel-based morphometry dataset was analyzed, while using the permutation-subject images version of signed differential mapping (PSI-SDM) in place of ALE. Overall, the replication converged with the original paper in showing a limited overlap between the two conditions. In particular, no significant effect was found for dyslexia, therefore precluding any form of comparison between the two disorders. The possible influences of biological sex, age, and medication status were also ruled out. Our findings are in line with literature about gray matter alteration associated with ADHD and dyslexia, often showing conflicting results. Therefore, although neuropsychological and clinical evidence suggest some convergence between ADHD and dyslexia, more future research is sorely needed to reach a consensus on the neuroimaging domain in terms of patterns of gray matter alteration.
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Affiliation(s)
- Donato Liloia
- GCS fMRI Koelliker Group, Koelliker Hospital and University of Turin, 10124 Turin, Italy
- FOCUS Laboratory, Department of Psychology, University of Turin, 10124 Turin, Italy
| | - Annachiara Crocetta
- FOCUS Laboratory, Department of Psychology, University of Turin, 10124 Turin, Italy
| | - Franco Cauda
- GCS fMRI Koelliker Group, Koelliker Hospital and University of Turin, 10124 Turin, Italy
- FOCUS Laboratory, Department of Psychology, University of Turin, 10124 Turin, Italy
- Neuroscience Institute of Turin, 10043 Turin, Italy
- Correspondence: ; Tel.: +39-011-670-29-80; Fax: +39-011-814-62-31
| | - Sergio Duca
- GCS fMRI Koelliker Group, Koelliker Hospital and University of Turin, 10124 Turin, Italy
- FOCUS Laboratory, Department of Psychology, University of Turin, 10124 Turin, Italy
| | - Tommaso Costa
- GCS fMRI Koelliker Group, Koelliker Hospital and University of Turin, 10124 Turin, Italy
- FOCUS Laboratory, Department of Psychology, University of Turin, 10124 Turin, Italy
| | - Jordi Manuello
- GCS fMRI Koelliker Group, Koelliker Hospital and University of Turin, 10124 Turin, Italy
- FOCUS Laboratory, Department of Psychology, University of Turin, 10124 Turin, Italy
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12
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Barbu MC, Harris M, Shen X, Aleks S, Green C, Amador C, Walker R, Morris S, Adams M, Sandu A, McNeil C, Waiter G, Evans K, Campbell A, Wardlaw J, Steele D, Murray A, Porteous D, McIntosh A, Whalley H. Epigenome-wide association study of global cortical volumes in generation Scotland: Scottish family health study. Epigenetics 2022; 17:1143-1158. [PMID: 34738878 PMCID: PMC9542280 DOI: 10.1080/15592294.2021.1997404] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 10/18/2021] [Accepted: 10/21/2021] [Indexed: 01/22/2023] Open
Abstract
A complex interplay of genetic and environmental risk factors influence global brain structural alterations associated with brain health and disease. Epigenome-wide association studies (EWAS) of global brain imaging phenotypes have the potential to reveal the mechanisms of brain health and disease and can lead to better predictive analytics through the development of risk scores.We perform an EWAS of global brain volumes in Generation Scotland using peripherally measured whole blood DNA methylation (DNAm) from two assessments, (i) at baseline recruitment, ~6 years prior to MRI assessment (N = 672) and (ii) concurrent with MRI assessment (N=565). Four CpGs at baseline were associated with global cerebral white matter, total grey matter, and whole-brain volume (Bonferroni p≤7.41×10-8, βrange = -1.46x10-6 to 9.59 × 10-7). These CpGs were annotated to genes implicated in brain-related traits, including psychiatric disorders, development, and ageing. We did not find significant associations in the meta-analysis of the EWAS of the two sets concurrent with imaging at the corrected level.These findings reveal global brain structural changes associated with DNAm measured ~6 years previously, indicating a potential role of early DNAm modifications in brain structure. Although concurrent DNAm was not associated with global brain structure, the nominally significant findings identified here present a rationale for future investigation of associations between DNA methylation and structural brain phenotypes in larger population-based samples.
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Affiliation(s)
- Miruna Carmen Barbu
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Mat Harris
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Xueyi Shen
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Stolicyn Aleks
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Claire Green
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Carmen Amador
- Mrc Human Genetics Unit, Institute of Genetics and Cancer, the University of Edinburgh, UK
| | - Rosie Walker
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, the University of Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, UK
| | - Stewart Morris
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, the University of Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, UK
| | - Mark Adams
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
| | - Anca Sandu
- Aberdeen Biomedical Imaging Centre, The Institute of Medical Sciences, University of Aberdeen, UK
| | - Christopher McNeil
- Aberdeen Biomedical Imaging Centre, The Institute of Medical Sciences, University of Aberdeen, UK
| | - Gordon Waiter
- Aberdeen Biomedical Imaging Centre, The Institute of Medical Sciences, University of Aberdeen, UK
| | - Kathryn Evans
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, the University of Edinburgh, UK
- Centre for Clinical Brain Sciences, The University of Edinburgh, UK
| | - Archie Campbell
- Mrc Human Genetics Unit, Institute of Genetics and Cancer, the University of Edinburgh, UK
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, The University of Edinburgh, UK
| | - Douglas Steele
- Imaging Science and Technology, School of Medicine, University of Dundee, DundeeUK
| | - Alison Murray
- Aberdeen Biomedical Imaging Centre, The Institute of Medical Sciences, University of Aberdeen, UK
| | - David Porteous
- Mrc Human Genetics Unit, Institute of Genetics and Cancer, the University of Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, UK
| | - Andrew McIntosh
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, UK
| | - Heather Whalley
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, UK
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13
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Liu L, Wang YP, Wang Y, Zhang P, Xiong S. An enhanced multi-modal brain graph network for classifying neuropsychiatric disorders. Med Image Anal 2022; 81:102550. [PMID: 35872360 DOI: 10.1016/j.media.2022.102550] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 07/06/2022] [Accepted: 07/13/2022] [Indexed: 10/17/2022]
Abstract
It has been proven that neuropsychiatric disorders (NDs) can be associated with both structures and functions of brain regions. Thus, data about structures and functions could be usefully combined in a comprehensive analysis. While brain structural MRI (sMRI) images contain anatomic and morphological information about NDs, functional MRI (fMRI) images carry complementary information. However, efficient extraction and fusion of sMRI and fMRI data remains challenging. In this study, we develop an enhanced multi-modal graph convolutional network (MME-GCN) in a binary classification between patients with NDs and healthy controls, based on the fusion of the structural and functional graphs of the brain region. First, based on the same brain atlas, we construct structural and functional graphs from sMRI and fMRI data, respectively. Second, we use machine learning to extract important features from the structural graph network. Third, we use these extracted features to adjust the corresponding edge weights in the functional graph network. Finally, we train a multi-layer GCN and use it in binary classification task. MME-GCN achieved 93.71% classification accuracy on the open data set provided by the Consortium for Neuropsychiatric Phenomics. In addition, we analyzed the important features selected from the structural graph and verified them in the functional graph. Using MME-GCN, we found several specific brain connections important to NDs.
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Affiliation(s)
- Liangliang Liu
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China.
| | - Yu-Ping Wang
- Dthe Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA
| | - Yi Wang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
| | - Pei Zhang
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
| | - Shufeng Xiong
- College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, P.R. China
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14
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Kowalski J, Wypych M, Marchewka A, Dragan M. Brain structural correlates of cognitive-attentional syndrome - a Voxel-Based Morphometry Study. Brain Imaging Behav 2022; 16:1914-1918. [PMID: 35266100 DOI: 10.1007/s11682-022-00649-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/03/2022] [Indexed: 11/26/2022]
Abstract
Cognitive-attentional syndrome (CAS) is in the self-regulatory executive function model a set of cognitive and behavioural strategies aimed at regulating cognition and emotion originating from maladaptive metacognitive beliefs. Investigating the brain structure of people with high levels of CAS enables a better understanding of the syndrome and bridging between the metacognitive model of psychopathology and previous results on structural abnormalities in various psychological disorders. Participants with high (n=40) and low levels of CAS (n=44) underwent structural Magnetic Resonance Imaging session. Voxel-Based Morphometry analytical approach was used to compute grey matter volume (GMV) differences between the groups. The group with a high level of CAS had lower GMV in the dorsal part of the anterior cingulate cortex. Our results are in line with the self-regulatory executive function model of psychopathology, showing a link between CAS and lowered GMV in the brain region associated with the regulation of cognition and emotion. They are also in agreement with meta-analytical results on structural correlates of various psychological disorders.
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Affiliation(s)
- Joachim Kowalski
- Experimental Psychopathology Laboratory, Institute of Psychology, Polish Academy of Sciences, Jaracza 1, Warsaw, 00-378, Poland.
- Faculty of Psychology, University of Warsaw, Warsaw, Poland.
| | - Marek Wypych
- Laboratory of Brain Imaging, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Artur Marchewka
- Laboratory of Brain Imaging, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
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15
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Manuello J, Mancuso L, Liloia D, Cauda F, Duca S, Costa T. A co-alteration parceling of the cingulate cortex. Brain Struct Funct 2022; 227:1803-1816. [PMID: 35238998 PMCID: PMC9098570 DOI: 10.1007/s00429-022-02473-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 02/14/2022] [Indexed: 11/24/2022]
Abstract
The cingulate cortex is known to be a complex structure, involved in several cognitive and emotional functions, as well as being altered by a variety of brain disorders. This heterogeneity is reflected in the multiple parceling models proposed in the literature. At the present, sub-regions of the cingulate cortex had been identified taking into account functional and structural connectivity, as well as cytological and electrochemical properties. In the present work, we propose an innovative node-wise parceling approach based on meta-analytic Bayesian co-alteration. To this aim, 193 case-control voxel-based morphometry experiments were analyzed, and the Patel's κ index was used to assess probability of morphometric co-alteration between nodes placed in the cingulate cortex and in the rest of the brain. Hierarchical clustering was then applied to identify nodes in the cingulate cortex exhibiting a similar pattern of whole-brain co-alteration. The obtained dendrogram highlighted a robust fronto-parietal cluster compatible with the default mode network, and being supported by the interplay between the retrosplenial cortex and the anterior and posterior cingulate cortex, rarely described in the literature. This ensemble was further confirmed by the analysis of functional patterns. Leveraging on co-alteration to investigate cortical organization could, therefore, allow to combine multimodal information, resolving conflicting results sometimes coming from the separate use of singular modalities. Crucially, this provides a valuable way to understand the pathological brain using data driven, whole-brain informed and context-specific evidence in a way not yet explored in the field.
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Affiliation(s)
- Jordi Manuello
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Lorenzo Mancuso
- FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Donato Liloia
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy. .,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy.
| | - Franco Cauda
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy.,Neuroscience Institute of Turin, Turin, Italy
| | - Sergio Duca
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- GCS fMRI, Koelliker Hospital and University of Turin, Turin, Italy.,FOCUS Lab, Department of Psychology, University of Turin, Turin, Italy
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16
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Sami MB, Liddle P. Neurobiology of Psychosis and Schizophrenia 2021: Nottingham Meeting. Schizophr Bull 2022; 48:289-291. [PMID: 35064266 PMCID: PMC8886577 DOI: 10.1093/schbul/sbab152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Musa Basseer Sami
- Institute of Mental Health, University of Nottingham, Nottingham,UK
- Nottinghamshire Healthcare, NHS Foundation Trust, Nottingham, UK
- To whom correspondence should be addressed; Institute of Mental Health, University of Nottingham, Nottingham, UK; tel: +44 115 823 1294, e-mail:
| | - Peter Liddle
- Institute of Mental Health, University of Nottingham, Nottingham,UK
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17
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Interhemispheric co-alteration of brain homotopic regions. Brain Struct Funct 2021; 226:2181-2204. [PMID: 34170391 PMCID: PMC8354999 DOI: 10.1007/s00429-021-02318-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 06/07/2021] [Indexed: 11/11/2022]
Abstract
Asymmetries in gray matter alterations raise important issues regarding the pathological co-alteration between hemispheres. Since homotopic areas are the most functionally connected sites between hemispheres and gray matter co-alterations depend on connectivity patterns, it is likely that this relationship might be mirrored in homologous interhemispheric co-altered areas. To explore this issue, we analyzed data of patients with Alzheimer’s disease, schizophrenia, bipolar disorder and depressive disorder from the BrainMap voxel-based morphometry database. We calculated a map showing the pathological homotopic anatomical co-alteration between homologous brain areas. This map was compared with the meta-analytic homotopic connectivity map obtained from the BrainMap functional database, so as to have a meta-analytic connectivity modeling map between homologous areas. We applied an empirical Bayesian technique so as to determine a directional pathological co-alteration on the basis of the possible tendencies in the conditional probability of being co-altered of homologous brain areas. Our analysis provides evidence that: the hemispheric homologous areas appear to be anatomically co-altered; this pathological co-alteration is similar to the pattern of connectivity exhibited by the couples of homologues; the probability to find alterations in the areas of the left hemisphere seems to be greater when their right homologues are also altered than vice versa, an intriguing asymmetry that deserves to be further investigated and explained.
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18
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Liloia D, Mancuso L, Uddin LQ, Costa T, Nani A, Keller R, Manuello J, Duca S, Cauda F. Gray matter abnormalities follow non-random patterns of co-alteration in autism: Meta-connectomic evidence. Neuroimage Clin 2021; 30:102583. [PMID: 33618237 PMCID: PMC7903137 DOI: 10.1016/j.nicl.2021.102583] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/15/2020] [Accepted: 01/30/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical brain anatomy and connectivity. Graph-theoretical methods have mainly been applied to detect altered patterns of white matter tracts and functional brain activation in individuals with ASD. The network topology of gray matter (GM) abnormalities in ASD remains relatively unexplored. METHODS An innovative meta-connectomic analysis on voxel-based morphometry data (45 experiments, 1,786 subjects with ASD) was performed in order to investigate whether GM variations can develop in a distinct pattern of co-alteration across the brain. This pattern was then compared with normative profiles of structural and genetic co-expression maps. Graph measures of centrality and clustering were also applied to identify brain areas with the highest topological hierarchy and core sub-graph components within the co-alteration network observed in ASD. RESULTS Individuals with ASD exhibit a distinctive and topologically defined pattern of GM co-alteration that moderately follows the structural connectivity constraints. This was not observed with respect to the pattern of genetic co-expression. Hub regions of the co-alteration network were mainly left-lateralized, encompassing the precuneus, ventral anterior cingulate, and middle occipital gyrus. Regions of the default mode network appear to be central in the topology of co-alterations. CONCLUSION These findings shed new light on the pathobiology of ASD, suggesting a network-level dysfunction among spatially distributed GM regions. At the same time, this study supports pathoconnectomics as an insightful approach to better understand neuropsychiatric disorders.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lorenzo Mancuso
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA; Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy.
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy.
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy.
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19
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Palaniyappan L, Sukumar N. Reconsidering brain tissue changes as a mechanistic focus for early intervention in psychiatry. J Psychiatry Neurosci 2020; 45. [PMID: 33119489 PMCID: PMC7595740 DOI: 10.1503/jpn.200172] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
Affiliation(s)
- Lena Palaniyappan
- From the Robarts Research Institute, Western University (Palaniyappan); the Department of Psychiatry, Western University (Palaniyappan, Sukumar); the Lawson Health Research Institute, Imaging Division (Palaniyappan); and the Department of Medical Biophysics, Western University (Palaniyappan), London, Ont., Canada
| | - Niron Sukumar
- From the Robarts Research Institute, Western University (Palaniyappan); the Department of Psychiatry, Western University (Palaniyappan, Sukumar); the Lawson Health Research Institute, Imaging Division (Palaniyappan); and the Department of Medical Biophysics, Western University (Palaniyappan), London, Ont., Canada
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