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Pu F, Chen W, Li C, Fu J, Gao W, Ma C, Cao X, Zhang L, Hao M, Zhou J, Huang R, Ma Y, Hu K, Liu Z. Heterogeneous associations of multiplexed environmental factors and multidimensional aging metrics. Nat Commun 2024; 15:4921. [PMID: 38858361 PMCID: PMC11164970 DOI: 10.1038/s41467-024-49283-0] [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] [Received: 10/24/2023] [Accepted: 05/31/2024] [Indexed: 06/12/2024] Open
Abstract
Complicated associations between multiplexed environmental factors and aging are poorly understood. We manipulated aging using multidimensional metrics such as phenotypic age, brain age, and brain volumes in the UK Biobank. Weighted quantile sum regression was used to examine the relative individual contributions of multiplexed environmental factors to aging, and self-organizing maps (SOMs) were used to examine joint effects. Air pollution presented a relatively large contribution in most cases. We also found fair heterogeneities in which the same environmental factor contributed inconsistently to different aging metrics. Particulate matter contributed the most to variance in aging, while noise and green space showed considerable contribution to brain volumes. SOM identified five subpopulations with distinct environmental exposure patterns and the air pollution subpopulation had the worst aging status. This study reveals the heterogeneous associations of multiplexed environmental factors with multidimensional aging metrics and serves as a proof of concept when analyzing multifactors and multiple outcomes.
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Affiliation(s)
- Fan Pu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Weiran Chen
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Chenxi Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Jingqiao Fu
- Ocean College, Zhejiang University, Zhoushan, 316021, Zhejiang, China
| | - Weijing Gao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Chao Ma
- School of Economics and Management, Southeast University, Nanjing, 211189, Jiangsu, China
| | - Xingqi Cao
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Lingzhi Zhang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China
| | - Meng Hao
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai, 200433, China
| | - Jin Zhou
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention, Ministry of Education, China Medical University; Department of Biostatistics and Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China
| | - Rong Huang
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention, Ministry of Education, China Medical University; Department of Biostatistics and Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China
| | - Yanan Ma
- Key Laboratory of Environmental Stress and Chronic Disease Control & Prevention, Ministry of Education, China Medical University; Department of Biostatistics and Epidemiology, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China.
| | - Kejia Hu
- Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, 310058, Zhejiang, China.
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2
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Holla B, Mahadevan J, Ganesh S, Sud R, Janardhanan M, Balachander S, Strom N, Mattheisen M, Sullivan PF, Huang H, Zandi P, Benegal V, Reddy YJ, Jain S, Purushottam M, Viswanath B. A cross ancestry genetic study of psychiatric disorders from India. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.25.24306377. [PMID: 38712191 PMCID: PMC11071591 DOI: 10.1101/2024.04.25.24306377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Genome-wide association studies across diverse populations may help validate and confirm genetic contributions to risk of disease. We estimated the extent of population stratification as well as the predictive accuracy of polygenic scores (PGS) derived from European samples to a data set from India. We analysed 2685 samples from two data sets, a population neurodevelopmental study (cVEDA) and a hospital-based sample of bipolar affective disorder (BD) and obsessive-compulsive disorder (OCD). Genotyping was conducted using Illumina's Global Screening Array. Population structure was examined with principal component analysis (PCA), uniform manifold approximation and projection (UMAP), support vector machine (SVM) ancestry predictions, and admixture analysis. PGS were calculated from the largest available European discovery GWAS summary statistics for BD, OCD, and externalizing traits using two Bayesian methods that incorporate local linkage disequilibrium structures (PGS-CS-auto) and functional genomic annotations (SBayesRC). Our analyses reveal global and continental PCA overlap with other South Asian populations. Admixture analysis revealed a north-south genetic axis within India (FST 1.6%). The UMAP partially reconstructed the contours of the Indian subcontinent. The Bayesian PGS analyses indicates moderate-to-high predictive power for BD. This was despite the cross-ancestry bias of the discovery GWAS dataset, with the currently available data. However, accuracy for OCD and externalizing traits was much lower. The predictive accuracy was perhaps influenced by the sample size of the discovery GWAS and phenotypic heterogeneity across the syndromes and traits studied. Our study results highlight the accuracy and generalizability of newer PGS models across ancestries. Further research, across diverse populations, would help understand causal mechanisms that contribute to psychiatric syndromes and traits.
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Affiliation(s)
- Bharath Holla
- Department of Integrative Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Jayant Mahadevan
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Suhas Ganesh
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Reeteka Sud
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Meghana Janardhanan
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Srinivas Balachander
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Nora Strom
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
| | - Manuel Mattheisen
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Dalhousie University, Department of Community Health and Epidemiology & Faculty of Computer Science, Halifax, Nova Scotia, Canada
- University Hospital of Psychiatry and Psychotherapy, University of Bern
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Genetics and Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA02114, USA
| | - Peter Zandi
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Vivek Benegal
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Yc Janardhan Reddy
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Sanjeev Jain
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Meera Purushottam
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Biju Viswanath
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
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3
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Vaidya N, Marquand AF, Nees F, Siehl S, Schumann G. The impact of psychosocial adversity on brain and behaviour: an overview of existing knowledge and directions for future research. Mol Psychiatry 2024:10.1038/s41380-024-02556-y. [PMID: 38658773 DOI: 10.1038/s41380-024-02556-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024]
Abstract
Environmental experiences play a critical role in shaping the structure and function of the brain. Its plasticity in response to different external stimuli has been the focus of research efforts for decades. In this review, we explore the effects of adversity on brain's structure and function and its implications for brain development, adaptation, and the emergence of mental health disorders. We are focusing on adverse events that emerge from the immediate surroundings of an individual, i.e., microenvironment. They include childhood maltreatment, peer victimisation, social isolation, affective loss, domestic conflict, and poverty. We also take into consideration exposure to environmental toxins. Converging evidence suggests that different types of adversity may share common underlying mechanisms while also exhibiting unique pathways. However, they are often studied in isolation, limiting our understanding of their combined effects and the interconnected nature of their impact. The integration of large, deep-phenotyping datasets and collaborative efforts can provide sufficient power to analyse high dimensional environmental profiles and advance the systematic mapping of neuronal mechanisms. This review provides a background for future research, highlighting the importance of understanding the cumulative impact of various adversities, through data-driven approaches and integrative multimodal analysis techniques.
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Affiliation(s)
- Nilakshi Vaidya
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany.
| | - Andre F Marquand
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Frauke Nees
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | - Sebastian Siehl
- Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel, Germany
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Department of Psychiatry and Clinical Neuroscience, 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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4
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Meredith WJ, Silvers JA. Experience-dependent neurodevelopment of self-regulation in adolescence. Dev Cogn Neurosci 2024; 66:101356. [PMID: 38364507 PMCID: PMC10878838 DOI: 10.1016/j.dcn.2024.101356] [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: 08/31/2023] [Revised: 12/18/2023] [Accepted: 02/06/2024] [Indexed: 02/18/2024] Open
Abstract
Adolescence is a period of rapid biobehavioral change, characterized in part by increased neural maturation and sensitivity to one's environment. In this review, we aim to demonstrate that self-regulation skills are tuned by adolescents' social, cultural, and socioeconomic contexts. We discuss adjacent literatures that demonstrate the importance of experience-dependent learning for adolescent development: environmental contextual influences and training paradigms that aim to improve regulation skills. We first highlight changes in prominent limbic and cortical regions-like the amygdala and medial prefrontal cortex-as well as structural and functional connectivity between these areas that are associated with adolescents' regulation skills. Next, we consider how puberty, the hallmark developmental milestone in adolescence, helps instantiate these biobehavioral adaptations. We then survey the existing literature demonstrating the ways in which cultural, socioeconomic, and interpersonal contexts drive behavioral and neural adaptation for self-regulation. Finally, we highlight promising results from regulation training paradigms that suggest training may be especially efficacious for adolescent samples. In our conclusion, we highlight some exciting frontiers in human self-regulation research as well as recommendations for improving the methodological implementation of developmental neuroimaging studies and training paradigms.
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Affiliation(s)
- Wesley J Meredith
- Department of Psychology, University of California, Los Angeles, 1285 Franz Hall, Los Angeles, CA, USA.
| | - Jennifer A Silvers
- Department of Psychology, University of California, Los Angeles, 1285 Franz Hall, Los Angeles, CA, USA
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5
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Kashyap R, Holla B, Bhattacharjee S, Sharma E, Mehta UM, Vaidya N, Bharath RD, Murthy P, Basu D, Nanjayya SB, Singh RL, Lourembam R, Chakrabarti A, Kartik K, Kalyanram K, Kumaran K, Krishnaveni G, Krishna M, Kuriyan R, Kurpad SS, Desrivieres S, Purushottam M, Barker G, Orfanos DP, Hickman M, Heron J, Toledano M, Schumann G, Benegal V. Childhood adversities characterize the heterogeneity in the brain pattern of individuals during neurodevelopment. Psychol Med 2024:1-13. [PMID: 38509831 DOI: 10.1017/s0033291724000710] [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: 03/22/2024]
Abstract
BACKGROUND Several factors shape the neurodevelopmental trajectory. A key area of focus in neurodevelopmental research is to estimate the factors that have maximal influence on the brain and can tip the balance from typical to atypical development. METHODS Utilizing a dissimilarity maximization algorithm on the dynamic mode decomposition (DMD) of the resting state functional MRI data, we classified subjects from the cVEDA neurodevelopmental cohort (n = 987, aged 6-23 years) into homogeneously patterned DMD (representing typical development in 809 subjects) and heterogeneously patterned DMD (indicative of atypical development in 178 subjects). RESULTS Significant DMD differences were primarily identified in the default mode network (DMN) regions across these groups (p < 0.05, Bonferroni corrected). While the groups were comparable in cognitive performance, the atypical group had more frequent exposure to adversities and faced higher abuses (p < 0.05, Bonferroni corrected). Upon evaluating brain-behavior correlations, we found that correlation patterns between adversity and DMN dynamic modes exhibited age-dependent variations for atypical subjects, hinting at differential utilization of the DMN due to chronic adversities. CONCLUSION Adversities (particularly abuse) maximally influence the DMN during neurodevelopment and lead to the failure in the development of a coherent DMN system. While DMN's integrity is preserved in typical development, the age-dependent variability in atypically developing individuals is contrasting. The flexibility of DMN might be a compensatory mechanism to protect an individual in an abusive environment. However, such adaptability might deprive the neural system of the faculties of normal functioning and may incur long-term effects on the psyche.
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Affiliation(s)
- Rajan Kashyap
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Bharath Holla
- Department of Integrative Medicine, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Sagarika Bhattacharjee
- Department of Neurophysiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Eesha Sharma
- Department of Child and Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Urvakhsh Meherwan Mehta
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Nilakshi Vaidya
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, PONS Centre, Charité Mental Health, Germany
- Department of Psychiatry, Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Rose Dawn Bharath
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Pratima Murthy
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Debashish Basu
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | | | | | - Roshan Lourembam
- Department of Psychiatry, Regional Institute of Medical Sciences, Imphal, India
| | - Amit Chakrabarti
- Division of Mental Health, ICMR-Centre for Ageing and Mental Health, Kolkata, India
| | - Kamakshi Kartik
- Rishi Valley Rural Health Centre, Madanapalle, Chittoor, India
| | | | - Kalyanaraman Kumaran
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
- MRC Lifecourse Epidemiology Unit, University of Southampton, UK
| | - Ghattu Krishnaveni
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | - Murali Krishna
- Health Equity Cluster, Institute of Public Health, Bangalore, India
| | - Rebecca Kuriyan
- Division of Nutrition, St John's Research Institute, Bengaluru, India
| | - Sunita Simon Kurpad
- Department of Psychiatry & Department of Medical Ethics, St John's Research Institute, Bengaluru, India
| | - Sylvane Desrivieres
- SGDP Centre, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | - Meera Purushottam
- Molecular Genetics Laboratory, National Institute of Mental Health and Neurosciences, Bengaluru, India
| | - Gareth Barker
- Department of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | | | - Matthew Hickman
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Jon Heron
- Center for Public Health, Bristol Medical School, University of Bristol, Bristol, UK
| | - Mireille Toledano
- MRC Centre for Environment and Health, School of Public Health, Imperial College, London, UK
| | - Gunter Schumann
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, PONS Centre, Charité Mental Health, Germany
- PONS Centre, Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Vivek Benegal
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, India
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Basu D, Ghosh A, Naskar C, Balachander S, Fernandes G, Vaidya N, Kumaran K, Krishna M, Barker GJ, Sharma E, Murthy P, Holla B, Jain S, Orfanos DP, Kalyanram K, Purushottam M, Bharath RD, Varghese M, Thennarasu K, Chakrabarti A, Singh RL, Singh RL, Nanjayya SB, Ahuja CK, Kartik K, Krishnaveni G, Kuriyan R, Kurpad SS, Desrivieres S, Iyengar U, Zhang Y, Hickman M, Spiers A, Toledano M, Schumann G, Benegal V. Risk clustering and psychopathology from a multi-center cohort of Indian children, adolescents, and young adults. Dev Psychopathol 2023; 35:800-808. [PMID: 35393927 DOI: 10.1017/s0954579422000050] [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] [Indexed: 11/06/2022]
Abstract
Developmental adversities early in life are associated with later psychopathology. Clustering may be a useful approach to group multiple diverse risks together and study their relation with psychopathology. To generate risk clusters of children, adolescents, and young adults, based on adverse environmental exposure and developmental characteristics, and to examine the association of risk clusters with manifest psychopathology. Participants (n = 8300) between 6 and 23 years were recruited from seven sites in India. We administered questionnaires to elicit history of previous exposure to adverse childhood environments, family history of psychiatric disorders in first-degree relatives, and a range of antenatal and postnatal adversities. We used these variables to generate risk clusters. Mini-International Neuropsychiatric Interview-5 was administered to evaluate manifest psychopathology. Two-step cluster analysis revealed two clusters designated as high-risk cluster (HRC) and low-risk cluster (LRC), comprising 4197 (50.5%) and 4103 (49.5%) participants, respectively. HRC had higher frequencies of family history of mental illness, antenatal and neonatal risk factors, developmental delays, history of migration, and exposure to adverse childhood experiences than LRC. There were significantly higher risks of any psychiatric disorder [Relative Risk (RR) = 2.0, 95% CI 1.8-2.3], externalizing (RR = 4.8, 95% CI 3.6-6.4) and internalizing disorders (RR = 2.6, 95% CI 2.2-2.9), and suicidality (2.3, 95% CI 1.8-2.8) in HRC. Social-environmental and developmental factors could classify Indian children, adolescents and young adults into homogeneous clusters at high or low risk of psychopathology. These biopsychosocial determinants of mental health may have practice, policy and research implications for people in low- and middle-income countries.
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Affiliation(s)
- Debasish Basu
- Department of Psychiatry, Postgraduate Institute of Medical Education & Research, Chandigarh, India
| | - Abhishek Ghosh
- Drug Deaddiction and Treatment Center, Department of Psychiatry, Postgraduate Institute of Medical Education & Research, Chandigarh, India
| | - Chandrima Naskar
- Department of Psychiatry, Postgraduate Institute of Medical Education & Research, Chandigarh, India
| | - Srinivas Balachander
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Gwen Fernandes
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Nilakshi Vaidya
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
- Centre for Population Neuroscience and Precision Medicine, Charité Mental Health, Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin, Berlin, Germany
| | - Kalyanaraman Kumaran
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
| | - Murali Krishna
- Foundation for Research and Advocacy in Mental Health, Mysore, India
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | - Eesha Sharma
- Department of Child & Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Pratima Murthy
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Bharath Holla
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Sanjeev Jain
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | | | - Kartik Kalyanram
- Rishi Valley, Rural Health Centre, Madanapalle, Andhra Pradesh, India
| | - Meera Purushottam
- Molecular Genetics Laboratory, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Rose Dawn Bharath
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Mathew Varghese
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Kandavel Thennarasu
- Department of Biostatistics, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Amit Chakrabarti
- Centre on Non-Communicable Diseases Division of Non-Communicable Diseases (NCD) Indian Council of Medical Research (ICMR), Kolkata, India
| | - Rajkumar Lenin Singh
- Department of Psychiatry, Regional Institute of Medical Sciences, Imphal, Manipur, India
| | - Roshan Lourembam Singh
- Department of Psychology, Regional Institute of Medical Sciences, Imphal, Manipur, India
| | | | - Chirag Kamal Ahuja
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education & Research, Chandigarh, India
| | - Kamakshi Kartik
- Rishi Valley, Rural Health Centre, Madanapalle, Andhra Pradesh, India
| | - Ghattu Krishnaveni
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | - Rebecca Kuriyan
- Division of Nutrition, St John's Research Institute, Bangalore, India
| | - Sunita Simon Kurpad
- Department of Psychiatry & Department of Medical Ethics, St. John's Medical College & Hospital, Bangalore, India
| | - Sylvane Desrivieres
- Centre for Population Neuroscience and Precision Medicine, MRC Social, Genetic, Developmental Psychiatry Centre, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | - Udita Iyengar
- Centre for Population Neuroscience and Precision Medicine, MRC Social, Genetic, Developmental Psychiatry Centre, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | - Yuning Zhang
- Centre for Population Neuroscience and Precision Medicine, MRC Social, Genetic, Developmental Psychiatry Centre, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | | | - Alex Spiers
- Department for Epidemiology and Biostatistics, School of Public Health, Imperial College of Science, London, UK
| | - Mireille Toledano
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine, Charité Mental Health, Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin, Berlin, Germany
- Department of Psychiatry and Psychotherapy and PONS Centre, Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China
| | - Vivek Benegal
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
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7
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Vaidya N, Holla B, Heron J, Sharma E, Zhang Y, Fernandes G, Iyengar U, Spiers A, Yadav A, Das S, Roy S, Ahuja CK, Barker GJ, Basu D, Bharath RD, Hickman M, Jain S, Kalyanram K, Kartik K, Krishna M, Krishnaveni G, Kumaran K, Kuriyan R, Murthy P, Papadopoulos Orfanos D, Purushottam M, Kurpad SS, Singh L, Singh R, Subodh BN, Toledano M, Walter H, Desrivières S, Chakrabarti A, Benegal V, Schumann G. Neurocognitive Analysis of Low-level Arsenic Exposure and Executive Function Mediated by Brain Anomalies Among Children, Adolescents, and Young Adults in India. JAMA Netw Open 2023; 6:e2312810. [PMID: 37171822 PMCID: PMC10182429 DOI: 10.1001/jamanetworkopen.2023.12810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 03/20/2023] [Indexed: 05/13/2023] Open
Abstract
Importance Arsenic, a contaminant of groundwater and irrigated crops, is a global public health hazard. Exposure to low levels of arsenic through food extends well beyond the areas with high arsenic content in water. Objective To identify cognitive impairments following commonly prevalent low-level arsenic exposure and characterize their underlying brain mechanisms. Design, Setting, and Participants This multicenter population-based cohort study analyzed cross-sectional data of the Indian Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA) cohort, recruited between November 4, 2016, and May 4, 2019. Participants aged 6 to 23 years were characterized using deep phenotyping measures of behavior, neuropsychology, psychopathology, brain neuroimaging, and exposure to developmental adversities and environmental neurotoxins. All analyses were performed between June 1, 2020, and December 31, 2021. Exposure Arsenic levels were measured in urine as an index of exposure. Main Outcomes and Measures Executive function measured using the cVEDA neuropsychological battery, gray matter volume (GMV) from T1-weighted magnetic resonance imaging, and functional network connectivity measures from resting state functional magnetic resonance imaging. Results A total of 1014 participants aged 6 to 23 years (589 male [58.1%]; mean [SD] age, 14.86 [4.79] years) were included from 5 geographic locations. Sparse-partial least squares analysis was used to describe a negative association of arsenic exposure with executive function (r = -0.12 [P = 5.4 × 10-4]), brain structure (r = -0.20 [P = 1.8 × 10-8]), and functional connectivity (within network, r = -0.12 [P = 7.5 × 10-4]; between network, r = -0.23 [P = 1.8 × 10-10]). Alterations in executive function were partially mediated by GMV (b = -0.004 [95% CI, -0.007 to -0.002]) and within-network functional connectivity (b = -0.004 [95% CI, -0.008 to -0.002]). Socioeconomic status and body mass index moderated the association between arsenic and GMV, such that the association was strongest in participants with lower socioeconomic status and body mass index. Conclusions and Relevance The findings of this cross-sectional study suggest that low-level arsenic exposure was associated with alterations in executive functioning and underlying brain correlates. These results indicate potential detrimental consequences of arsenic exposure that are below the currently recommended guidelines and may extend beyond endemic risk areas. Precision medicine approaches to study global mental health vulnerabilities highlight widespread but potentially modifiable risk factors and a mechanistic understanding of the impact of low-level arsenic exposure on brain development.
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Affiliation(s)
- Nilakshi Vaidya
- Centre for Population Neuroscience and Precision Medicine, Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Bharath Holla
- Department of Psychiatry and Integrative Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Jon Heron
- Centre for Public Health, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Eesha Sharma
- Department of Child & Adolescent Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Yuning Zhang
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Gwen Fernandes
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Udita Iyengar
- Institute of Psychology, Psychiatry & Neuroscience, King’s College London, London, United Kingdom
| | - Alex Spiers
- MRC (Medical Research Council) Centre for Environment and Health, Imperial College London, London, United Kingdom
| | - Anupa Yadav
- Indian Council of Medical Research–Centre on Noncommunicable Diseases, Kolkata, India
| | - Surajit Das
- Indian Council of Medical Research–Centre on Noncommunicable Diseases, Kolkata, India
| | - Sanjit Roy
- Indian Council of Medical Research–Centre on Noncommunicable Diseases, Kolkata, India
| | - Chirag K. Ahuja
- Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Gareth J. Barker
- Department of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King’s College London, London, United Kingdom
| | - Debasish Basu
- Department of Psychiatry, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Rose Dawn Bharath
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Matthew Hickman
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Sanjeev Jain
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | | | | | - Murali Krishna
- Foundation for Research and Advocacy in Mental Health, Mysore, India
| | - Ghattu Krishnaveni
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | | | - Rebecca Kuriyan
- Division of Nutrition, St John’s Research Institute, Bangalore, India
| | - Pratima Murthy
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Dimitri Papadopoulos Orfanos
- NeuroSpin, Commissariat à l’Énergie Atomique et aux Énergies Alternatives, Université Paris-Saclay, Paris, France
| | - Meera Purushottam
- Molecular Genetics Laboratory, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Sunita Simon Kurpad
- Department of Psychiatry, St John’s Medical College and Hospital, Bangalore, India
- Department of Medical Ethics, St John’s Medical College and Hospital, Bangalore, India
| | - Lenin Singh
- Regional Institute of Medical Sciences, Imphal, Manipur, India
| | - Roshan Singh
- Regional Institute of Medical Sciences, Imphal, Manipur, India
| | - B. N. Subodh
- Department of Psychiatry, Postgraduate Institute of Medical Education and Research, Chandigarh, India
| | - Mireille Toledano
- MRC (Medical Research Council) Centre for Environment and Health, Imperial College London, London, United Kingdom
- Mohn Centre for Children’s Health and Wellbeing, School of Public Health, Imperial College London, London, United Kingdom
| | - Henrik Walter
- Psychiatry, Psychiatric Neuroscience and Neurophilosophy, Research Division of Mind and Brain, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Sylvane Desrivières
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Amit Chakrabarti
- Indian Council of Medical Research–Centre on Noncommunicable Diseases, Kolkata, India
| | - Vivek Benegal
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine, Department of Psychiatry and Neuroscience, Charité Universitätsmedizin Berlin, Berlin, Germany
- Centre for Population Neuroscience and Precision Medicine, Institute for Science and Technology of Brain-Inspired Intelligence, Fudan University, Shanghai, China
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8
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Gazula H, Rootes-Murdy K, Holla B, Basodi S, Zhang Z, Verner E, Kelly R, Murthy P, Chakrabarti A, Basu D, Bhagyalakshmi Nanjayya S, Lenin Singh R, Lourembam Singh R, Kalyanram K, Kartik K, Kalyanaraman K, Ghattu K, Kuriyan R, Kurpad SS, Barker GJ, Bharath RD, Desrivieres S, Purushottam M, Orfanos DP, Sharma E, Hickman M, Toledano M, Vaidya N, Banaschewski T, Bokde ALW, Flor H, Grigis A, Garavan H, Gowland P, Heinz A, Brühl R, Martinot JL, Paillére Martinot ML, Artiges E, Nees F, Paus T, Poustka L, Fröhner JH, Robinson L, Smolka MN, Walter H, Winterer J, Whelan R, Turner JA, Sarwate AD, Plis SM, Benegal V, Schumann G, Calhoun VD. Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains. Neuroinformatics 2023; 21:287-301. [PMID: 36434478 DOI: 10.1007/s12021-022-09604-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/29/2022] [Indexed: 11/27/2022]
Abstract
With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks. In this study, we implement the neuromark pipeline in COINSTAC, an open-source neuroimaging framework for collaborative/decentralized analysis. Decentralized exploratory analysis of nearly 2000 resting-state functional magnetic resonance imaging datasets collected at different sites across two cohorts and co-located in different countries was performed to study the resting brain functional network connectivity changes in adolescents who smoke and consume alcohol. Results showed hypoconnectivity across the majority of networks including sensory, default mode, and subcortical domains, more for alcohol than smoking, and decreased low frequency power. These findings suggest that global reduced synchronization is associated with both tobacco and alcohol use. This proof-of-concept work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.
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Affiliation(s)
- Harshvardhan Gazula
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical School, Boston, USA.
| | - Kelly Rootes-Murdy
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Bharath Holla
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India.
- Integrative Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India.
| | - Sunitha Basodi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Zuo Zhang
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, SE5 8AF, United Kingdom
| | - Eric Verner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Ross Kelly
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Pratima Murthy
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | | | - Debasish Basu
- Department of Psychiatry, Postgraduate Institute of Medical Education & Research, Chandigarh, India
| | | | - Rajkumar Lenin Singh
- Department of Psychiatry, Regional Institute of Medical Sciences, Imphal, Manipur, India
| | - Roshan Lourembam Singh
- Department of Psychology, Regional Institute of Medical Sciences, Imphal, Manipur, India
| | | | | | | | - Krishnaveni Ghattu
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | - Rebecca Kuriyan
- Division of Nutrition, St John's Research Institute, Bengaluru, India
| | - Sunita Simon Kurpad
- Department of Psychiatry & Department of Medical Ethics, St. John's Medical College & Hospital, Bangalore, India
| | - Gareth J Barker
- Department of Neuroimaging, Institute of Psychology, Psychiatry & Neuroscience, King's College London, London, UK
| | - Rose Dawn Bharath
- Department of Neuroimaging and Interventional Radiology, NIMHANS, Bengaluru, India
| | - Sylvane Desrivieres
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, SE5 8AF, United Kingdom
| | | | | | - Eesha Sharma
- Department of Child & Adolescent Psychiatry, NIMHANS, Bengaluru, India
| | | | - Mireille Toledano
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Nilakshi Vaidya
- Centre for Addiction Medicine, NIMHANS, Bengaluru, India
- Centre for Population Neuroscience and Precision Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, 68159, Germany
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Herta Flor
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, 68131, Germany
| | | | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, Burlington, Vermont, 05405, USA
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, United Kingdom
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Rüdiger Brühl
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli;, Gif-sur-Yvette, France
| | - Marie-Laure Paillére Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli;, Gif-sur-Yvette, France
- AP-HP. Sorbonne Université, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM U A10 "Trajectoires développementales en psychiatrie"; Université Paris-Saclay, Ecole Normale supérieure Paris-Saclay, CNRS, Centre Borelli;, Gif-sur-Yvette, France
- Psychiatry Department, EPS Barthélémy Durand, Etampes, France
| | - Frauke Nees
- Centre for Population Neuroscience and Precision Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, 68159, Germany
- Institute of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | - Tomás Paus
- Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, Göttingen, 37075, Germany
| | - Juliane H Fröhner
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Lauren Robinson
- Department of Psychological Medicine, Section for Eating Disorders, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Jeanne Winterer
- Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Jessica A Turner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Anand D Sarwate
- Department of Electrical and Computer Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Sergey M Plis
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vivek Benegal
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | - Gunter Schumann
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology & Neuroscience, SGDP Centre, King's College London, London, SE5 8AF, United Kingdom
- Centre for Population Neuroscience and Precision Medicine (PONS), Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
- Institute for Science and Technology of Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, P.R. China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
- Department of Psychology, Georgia State University, Atlanta, GA, USA
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9
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Environmental neuroscience linking exposome to brain structure and function underlying cognition and behavior. Mol Psychiatry 2023; 28:17-27. [PMID: 35790874 DOI: 10.1038/s41380-022-01669-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 06/02/2022] [Accepted: 06/09/2022] [Indexed: 01/07/2023]
Abstract
Individual differences in human brain structure, function, and behavior can be attributed to genetic variations, environmental exposures, and their interactions. Although genome-wide association studies have identified many genetic variants associated with brain imaging phenotypes, environmental exposures associated with these phenotypes remain largely unknown. Here, we propose that environmental neuroscience should pay more attention on exploring the associations between lifetime environmental exposures (exposome) and brain imaging phenotypes and identifying both cumulative environmental effects and their vulnerable age windows during the life course. Exposome-neuroimaging association studies face several challenges including the accurate measurement of the totality of environmental exposures varied in space and time, the highly correlated structure of the exposome, and the lack of standardized approaches for exposome-wide association studies. By agnostically scanning the effects of environmental exposures on brain imaging phenotypes and their interactions with genomic variations, exposome-neuroimaging association analyses will improve our understanding of causal factors associated with individual differences in brain structure and function as well as their relations with cognitive abilities and neuropsychiatric disorders.
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10
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Meredith WJ, Cardenas-Iniguez C, Berman MG, Rosenberg MD. Effects of the physical and social environment on youth cognitive performance. Dev Psychobiol 2022; 64:e22258. [PMID: 35452534 DOI: 10.1002/dev.22258] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 11/05/2021] [Accepted: 01/03/2022] [Indexed: 01/19/2023]
Abstract
Individual differences in children's cognitive abilities impact life and health outcomes. What factors influence these individual differences during development? Here, we test whether children's environments predict cognitive performance, independent of well-characterized socioeconomic effects. We analyzed data from 9002 9- to 10-year olds from the Adolescent Brain Cognitive Development Study, an ongoing longitudinal study with community samples across the United States. Using youth- and caregiver-report questionnaires and national database registries (e.g., neighborhood crime, walkability), we defined principal components summarizing children's home, school, neighborhood, and cultural environments. In two independent samples (ns = 3475, 5527), environmental components explained unique variance in children's general cognitive ability, executive functioning, and learning/memory abilities. Furthermore, increased neighborhood enrichment was associated with an attenuated relationship between sociodemographics and general cognitive abilities. Thus, the environment accounts for unique variance in cognitive performance in children and should be considered alongside sociodemographic factors to better understand brain functioning and behavior across development.
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Affiliation(s)
- Wesley J Meredith
- Department of Psychology, University of Chicago, Chicago, Illinois, USA.,Department of Psychology, University of California, Los Angeles, California, USA
| | | | - Marc G Berman
- Department of Psychology, University of Chicago, Chicago, Illinois, USA.,Neuroscience Institute, University of Chicago, Chicago, Illinois, USA
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, Chicago, Illinois, USA.,Neuroscience Institute, University of Chicago, Chicago, Illinois, USA
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11
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Chen LZ, Holmes AJ, Zuo XN, Dong Q. Neuroimaging brain growth charts: A road to mental health. PSYCHORADIOLOGY 2021; 1:272-286. [PMID: 35028568 PMCID: PMC8739332 DOI: 10.1093/psyrad/kkab022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/03/2021] [Accepted: 12/17/2021] [Indexed: 12/30/2022]
Abstract
Mental disorders are common health concerns and contribute to a heavy global burden on our modern society. It is challenging to identify and treat them timely. Neuroimaging evidence suggests the incidence of various psychiatric and behavioral disorders is closely related to the atypical development of brain structure and function. The identification and understanding of atypical brain development provide chances for clinicians to detect mental disorders earlier, perhaps even prior to onset, and treat them more precisely. An invaluable and necessary method in identifying and monitoring atypical brain development are growth charts of typically developing individuals in the population. The brain growth charts can offer a series of standard references on typical neurodevelopment, representing an important resource for the scientific and medical communities. In the present paper, we review the relationship between mental disorders and atypical brain development from a perspective of normative brain development by surveying the recent progress in the development of brain growth charts, including four aspects on growth chart utility: 1) cohorts, 2) measures, 3) mechanisms, and 4) clinical translations. In doing so, we seek to clarify the challenges and opportunities in charting brain growth, and to promote the application of brain growth charts in clinical practice.
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Affiliation(s)
- Li-Zhen Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT 06511, USA
- Department of Psychiatry, Yale University, New Haven, CT 06511, USA
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- National Basic Science Data Center, Beijing 100190, China
- Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
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12
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Liu S, Wang YS, Zhang Q, Zhou Q, Cao LZ, Jiang C, Zhang Z, Yang N, Dong Q, Zuo XN. Chinese Color Nest Project : An accelerated longitudinal brain-mind cohort. Dev Cogn Neurosci 2021; 52:101020. [PMID: 34653938 PMCID: PMC8517840 DOI: 10.1016/j.dcn.2021.101020] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 10/02/2021] [Accepted: 10/07/2021] [Indexed: 12/12/2022] Open
Abstract
The ongoing Chinese Color Nest Project (CCNP) was established to create normative charts for brain structure and function across the human lifespan, and link age-related changes in brain imaging measures to psychological assessments of behavior, cognition, and emotion using an accelerated longitudinal design. In the initial stage, CCNP aims to recruit 1520 healthy individuals (6-90 years), which comprises three phases: developing (devCCNP: 6-18 years, N = 480), maturing (matCCNP: 20-60 years, N = 560) and aging (ageCCNP: 60-84 years, N = 480). In this paper, we present an overview of the devCCNP, including study design, participants, data collection and preliminary findings. The devCCNP has acquired data with three repeated measurements from 2013 to 2017 in Southwest University, Chongqing, China (CCNP-SWU, N = 201). It has been accumulating baseline data since July 2018 and the second wave data since September 2020 in Chinese Academy of Sciences, Beijing, China (CCNP-CAS, N = 168). Each participant in devCCNP was followed up for 2.5 years at 1.25-year intervals. The devCCNP obtained longitudinal neuroimaging, biophysical, social, behavioral and cognitive data via MRI, parent- and self-reported questionnaires, behavioral assessments, and computer tasks. Additionally, data were collected on children's learning, daily life and emotional states during the COVID-19 pandemic in 2020. We address data harmonization across the two sites and demonstrated its promise of characterizing the growth curves for the overall brain morphometry using multi-center longitudinal data. CCNP data will be shared via the National Science Data Bank and requests for further information on collaboration and data sharing are encouraged.
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Affiliation(s)
- Siman Liu
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yin-Shan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qing Zhang
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Quan Zhou
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Li-Zhi Cao
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chao Jiang
- School of Psychology, Capital Normal University, Beijing 100048, China
| | - Zhe Zhang
- Department of Psychology, College of Education, Hebei Normal University, Shijiazhuang 05024, Hebei, China
| | - Ning Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Xi-Nian Zuo
- Research Center for Lifespan Development of Mind and Brain, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Developmental Population Neuroscience Research Center, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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13
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Large, open datasets for human connectomics research: Considerations for reproducible and responsible data use. Neuroimage 2021; 244:118579. [PMID: 34536537 DOI: 10.1016/j.neuroimage.2021.118579] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 08/27/2021] [Accepted: 09/14/2021] [Indexed: 12/19/2022] Open
Abstract
Large, open datasets have emerged as important resources in the field of human connectomics. In this review, the evolution of data sharing involving magnetic resonance imaging is described. A summary of the challenges and progress in conducting reproducible data analyses is provided, including description of recent progress made in the development of community guidelines and recommendations, software and data management tools, and initiatives to enhance training and education. Finally, this review concludes with a discussion of ethical conduct relevant to analyses of large, open datasets and a researcher's responsibility to prevent further stigmatization of historically marginalized racial and ethnic groups. Moving forward, future work should include an enhanced emphasis on the social determinants of health, which may further contextualize findings among diverse population-based samples. Leveraging the progress to date and guided by interdisciplinary collaborations, the future of connectomics promises to be an impressive era of innovative research, yielding a more inclusive understanding of brain structure and function.
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14
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Fernandes GS, Spiers A, Vaidya N, Zhang Y, Sharma E, Holla B, Heron J, Hickman M, Murthy P, Chakrabarti A, Basu D, Subodh BN, Singh L, Singh R, Kalyanram K, Kartik K, Kumaran K, Krishnaveni G, Kuriyan R, Kurpad S, Barker GJ, Bharath RD, Desrivieres S, Purushottam M, Orfanos DP, Toledano MB, Schumann G, Benegal V. Adverse childhood experiences and substance misuse in young people in India: results from the multisite cVEDA cohort. BMC Public Health 2021; 21:1920. [PMID: 34686158 PMCID: PMC8539836 DOI: 10.1186/s12889-021-11892-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 09/07/2021] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND Adverse childhood experiences (ACEs) increases vulnerability to externalising disorders such as substance misuse. The study aims to determine the prevalence of ACEs and its association with substance misuse. METHODS Data from the Consortium on Vulnerability to Externalising Disorders and Addictions (cVEDA) in India was used (n = 9010). ACEs were evaluated using the World Health Organisation (WHO) Adverse Childhood Experiences International Questionnaire whilst substance misuse was assessed using the WHO Alcohol, Smoking and Substance Involvement Screening Test. A random-effects, two-stage individual patient data meta-analysis explained the associations between ACEs and substance misuse with adjustments for confounders such as sex and family structure. RESULTS 1 in 2 participants reported child maltreatment ACEs and family level ACEs. Except for sexual abuse, males report more of every individual childhood adversity and are more likely to report misusing substances compared with females (87.3% vs. 12.7%). In adolescents, family level ACEs (adj OR 4.2, 95% CI 1.5-11.7) and collective level ACEs (adj OR 6.6, 95% CI 1.4-31.1) show associations with substance misuse whilst in young adults, child level ACEs such as maltreatment show similar strong associations (adj OR 2.0, 95% CI 1.1-3.5). CONCLUSION ACEs such as abuse and domestic violence are strongly associated with substance misuse, most commonly tobacco, in adolescent and young adult males in India. The results suggest enhancing current ACE resilience programmes and 'trauma-informed' approaches to tackling longer-term impact of ACEs in India. FUNDING Newton Bhabha Grant jointly funded by the Medical Research Council, UK (MR/N000390/1) and the Indian Council of Medical Research (ICMR/MRC-UK/3/M/2015-NCD-I).
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Affiliation(s)
- G S Fernandes
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK.
| | - A Spiers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - N Vaidya
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India.,Centre for Population Neuroscience and Precision Medicine, Kings College London, London, UK
| | - Y Zhang
- Centre for Innovation in Mental Health, Department of Psychology, University of Southampton, Southampton, UK
| | - E Sharma
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - B Holla
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - J Heron
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - M Hickman
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2BN, UK
| | - P Murthy
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - A Chakrabarti
- ICMR-Centre on Non-Communicable Diseases, Kolkata, India
| | - D Basu
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - B N Subodh
- Department of Psychiatry, Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - L Singh
- Department of Psychiatry, Regional Institute of Medical Sciences (RIMS), Imphal, Manipur, India
| | - R Singh
- Department of Psychiatry, Regional Institute of Medical Sciences (RIMS), Imphal, Manipur, India
| | - K Kalyanram
- Rishi Valley Rural Health Centre, Madanapalle, Chittoor, Andhra Pradesh, India
| | - K Kartik
- Rishi Valley Rural Health Centre, Madanapalle, Chittoor, Andhra Pradesh, India
| | - K Kumaran
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | - G Krishnaveni
- Epidemiology Research Unit, CSI Holdsworth Memorial Hospital, Mysore, India
| | - R Kuriyan
- Department of Psychiatry and Medical Ethics, St John's Medical College & Hospital, Bangalore, India
| | - S Kurpad
- Department of Psychiatry & Department of Medical Ethics, St. John's Medical College & Hospital, Bangalore, India
| | - G J Barker
- Centre for Population Neuroscience and Precision Medicine, Kings College London, London, UK.,Department of Neuroimaging, King's College London, London, UK
| | - R D Bharath
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - S Desrivieres
- Centre for Population Neuroscience and Precision Medicine, Kings College London, London, UK
| | - M Purushottam
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - D P Orfanos
- NeuroSpin, CEA, Université Paris-Saclay, Paris, France
| | - M B Toledano
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
| | - G Schumann
- Centre for Population Neuroscience and Precision Medicine, Kings College London, London, UK
| | - V Benegal
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences, Bangalore, India
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15
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Decentralized Multisite VBM Analysis During Adolescence Shows Structural Changes Linked to Age, Body Mass Index, and Smoking: a COINSTAC Analysis. Neuroinformatics 2021; 19:553-566. [PMID: 33462781 DOI: 10.1007/s12021-020-09502-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/17/2020] [Indexed: 12/23/2022]
Abstract
There has been an upward trend in developing frameworks that enable neuroimaging researchers to address challenging questions by leveraging data across multiple sites all over the world. One such open-source framework is the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC) that works on Windows, macOS, and Linux operating systems and leverages containerized analysis pipelines to analyze neuroimaging data stored locally across multiple physical locations without the need for pooling the data at any point during the analysis. In this paper, the COINSTAC team partnered with a data collection consortium to implement the first-ever decentralized voxelwise analysis of brain imaging data performed outside the COINSTAC development group. Decentralized voxel-based morphometry analysis of over 2000 structural magnetic resonance imaging data sets collected at 14 different sites across two cohorts and co-located in different countries was performed to study the structural changes in brain gray matter which linked to age, body mass index (BMI), and smoking. Results produced by the decentralized analysis were consistent with and extended previous findings in the literature. In particular, a widespread cortical gray matter reduction (resembling a 'default mode network' pattern) and hippocampal increase with age, bilateral increases in the hypothalamus and basal ganglia with BMI, and cingulate and thalamic decreases with smoking. This work provides a critical real-world test of the COINSTAC framework in a "Large-N" study. It showcases the potential benefits of performing multivoxel and multivariate analyses of large-scale neuroimaging data located at multiple sites.
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16
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Holla B, Taylor PA, Glen DR, Lee JA, Vaidya N, Mehta UM, Venkatasubramanian G, Pal PK, Saini J, Rao NP, Ahuja CK, Kuriyan R, Krishna M, Basu D, Kalyanram K, Chakrabarti A, Orfanos DP, Barker GJ, Cox RW, Schumann G, Bharath RD, Benegal V. A series of five population-specific Indian brain templates and atlases spanning ages 6-60 years. Hum Brain Mapp 2020; 41:5164-5175. [PMID: 32845057 PMCID: PMC7670651 DOI: 10.1002/hbm.25182] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 07/27/2020] [Accepted: 08/10/2020] [Indexed: 12/16/2022] Open
Abstract
Anatomical brain templates are commonly used as references in neurological MRI studies, for bringing data into a common space for group‐level statistics and coordinate reporting. Given the inherent variability in brain morphology across age and geography, it is important to have templates that are as representative as possible for both age and population. A representative‐template increases the accuracy of alignment, decreases distortions as well as potential biases in final coordinate reports. In this study, we developed and validated a new set of T1w Indian brain templates (IBT) from a large number of brain scans (total n = 466) acquired across different locations and multiple 3T MRI scanners in India. A new tool in AFNI, make_template_dask.py, was created to efficiently make five age‐specific IBTs (ages 6–60 years) as well as maximum probability map (MPM) atlases for each template; for each age‐group's template–atlas pair, there is both a “population‐average” and a “typical” version. Validation experiments on an independent Indian structural and functional‐MRI dataset show the appropriateness of IBTs for spatial normalization of Indian brains. The results indicate significant structural differences when comparing the IBTs and MNI template, with these differences being maximal along the Anterior–Posterior and Inferior–Superior axes, but minimal Left–Right. For each age‐group, the MPM brain atlases provide reasonably good representation of the native‐space volumes in the IBT space, except in a few regions with high intersubject variability. These findings provide evidence to support the use of age and population‐specific templates in human brain mapping studies.
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Affiliation(s)
- Bharath Holla
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Paul A Taylor
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, Maryland, USA
| | - Daniel R Glen
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, Maryland, USA
| | - John A Lee
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, Maryland, USA
| | - Nilakshi Vaidya
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India.,Centre for Population Neuroscience and Stratified Medicine (PONS), SGDP Centre, IoPPN, KCL, London, UK
| | | | | | - Pramod Kumar Pal
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Jitender Saini
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Naren P Rao
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Chirag K Ahuja
- Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | - Rebecca Kuriyan
- St. John's Medical College and Research Institute, Bengaluru, India
| | - Murali Krishna
- CSI Holdsworth Memorial Hospital, Mysore, India.,Foundation for Research and Advocacy in Mental Health, Mysore, India
| | - Debashish Basu
- Post Graduate Institute of Medical Education and Research, Chandigarh, India
| | | | | | | | - Gareth J Barker
- Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London (KCL), London, UK
| | - Robert W Cox
- Scientific and Statistical Computing Core, NIMH, NIH, Bethesda, Maryland, USA
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), SGDP Centre, IoPPN, KCL, London, UK
| | - Rose Dawn Bharath
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Vivek Benegal
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
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17
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Licinio J, Wong ML. Molecular Psychiatry, August 2020: new impact factor, and highlights of recent advances in psychiatry, including an overview of the brain's response to stress during infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Mol Psychiatry 2020; 25:1606-1610. [PMID: 32724165 PMCID: PMC7385469 DOI: 10.1038/s41380-020-0845-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 07/10/2020] [Accepted: 07/10/2020] [Indexed: 11/17/2022]
Affiliation(s)
- Julio Licinio
- State University of New York, Upstate Medical University, Syracuse, NY, 13210, USA.
| | - Ma-Li Wong
- State University of New York, Upstate Medical University, Syracuse, NY, 13210, USA
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18
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Palk A, Illes J, Thompson PM, Stein DJ. Ethical issues in global neuroimaging genetics collaborations. Neuroimage 2020; 221:117208. [PMID: 32736000 DOI: 10.1016/j.neuroimage.2020.117208] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 06/09/2020] [Accepted: 07/24/2020] [Indexed: 10/23/2022] Open
Abstract
Neuroimaging genetics is a rapidly developing field that combines neuropsychiatric genetics studies with imaging modalities to investigate how genetic variation influences brain structure and function. As both genetic and imaging technologies improve further, their combined power may hold translational potential in terms of improving psychiatric nosology, diagnosis, and treatment. While neuroimaging genetics studies offer a number of scientific advantages, they also face challenges. In response to some of these challenges, global neuroimaging genetics collaborations have been created to pool and compare brain data and replicate study findings. Attention has been paid to ethical issues in genetics, neuroimaging, and multi-site collaborative research, respectively, but there have been few substantive discussions of the ethical issues generated by the confluence of these areas in global neuroimaging genetics collaborations. Our discussion focuses on two areas: benefits and risks of global neuroimaging genetics collaborations and the potential impact of neuroimaging genetics research findings in low- and middle-income countries. Global neuroimaging genetics collaborations have the potential to enhance relations between countries and address global mental health challenges, however there are risks regarding inequity, exploitation and data sharing. Moreover, neuroimaging genetics research in low- and middle-income countries must address the issue of feedback of findings and the risk of essentializing and stigmatizing interpretations of mental disorders. We conclude by examining how the notion of solidarity, informed by an African Ethics framework, may justify some of the suggestions made in our discussion.
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Affiliation(s)
- Andrea Palk
- Department of Philosophy, Stellenbosch University, Bag X1, Matieland, Stellenbosch, 7602, South Africa.
| | - Judy Illes
- Neuroethics Canada, Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Paul M Thompson
- Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Dan J Stein
- SA MRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry & Neuroscience Institute, University of Cape Town, Groote Schuur Hospital, Cape Town 7925, South Africa
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