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Zhang X, Yan H, Yu H, Zhang Y, Tan HY, Zhang D, Yue W. The effects of environmental factors associated with childhood urbanicity on brain structure and cognition. BMC Psychiatry 2023; 23:598. [PMID: 37592210 PMCID: PMC10433654 DOI: 10.1186/s12888-023-05066-3] [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: 02/06/2023] [Accepted: 07/30/2023] [Indexed: 08/19/2023] Open
Abstract
Urbanization is a trend lasting for more than one century worldwide. Four hundred ninety male and female adult Chinese Han participants with different urban and rural childhoods were included in this study. Early-life urban environment was found benefit for total grey matter volume (GMV), dorsolateral prefrontal cortex (DLPFC) GMV, temporal pole (TP) GMV and cognition function, and negatively correlated with medial prefrontal cortex (MPFC) GMV. Regression analysis showed that maternal education was a protective factor for total and DLPFC GMVs, while having siblings was better for MPFC GMV. Total, DLPFC and TP GMVs acts mediation effects between childhood urbanicity and different cognitive domains. These findings may suggest some pros and cons on brain structure associated with childhood urbanicity and related environmental factors.
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Affiliation(s)
- Xiao Zhang
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, 51 Huayuanbei Road, Haidian District, Beijing, 100191, China.
- NHC Key Laboratory of Mental Health (Peking University), 51 Huayuanbei Road, Haidian District, Beijing, 100191, China.
| | - Hao Yan
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, 51 Huayuanbei Road, Haidian District, Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), 51 Huayuanbei Road, Haidian District, Beijing, 100191, China
| | - Hao Yu
- Department of Psychiatry, Jining Medical University, Jining, Shandong, China
| | - Yuyanan Zhang
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, 51 Huayuanbei Road, Haidian District, Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), 51 Huayuanbei Road, Haidian District, Beijing, 100191, China
| | - Hao Yang Tan
- Lieber Institute for Brain Development, Baltimore, MD, 21205, US
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, US
| | - Dai Zhang
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, 51 Huayuanbei Road, Haidian District, Beijing, 100191, China
- NHC Key Laboratory of Mental Health (Peking University), 51 Huayuanbei Road, Haidian District, Beijing, 100191, China
- PKU-IDG/McGovern Institute for Brain Research of Peking University, &Chinese Institute for Brain Research, 51 Huayuanbei Road, Haidian District, Beijing, 100191, China
| | - Weihua Yue
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, 51 Huayuanbei Road, Haidian District, Beijing, 100191, China.
- NHC Key Laboratory of Mental Health (Peking University), 51 Huayuanbei Road, Haidian District, Beijing, 100191, China.
- PKU-IDG/McGovern Institute for Brain Research of Peking University, &Chinese Institute for Brain Research, 51 Huayuanbei Road, Haidian District, Beijing, 100191, China.
- Research Unit of Diagnosis and Treatment of Mood Cognitive Disorder, Chinese Academy of Medical Sciences, 51 Huayuanbei Road, Haidian District, Beijing, 100191, China.
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Li L, Yu X, Sheng C, Jiang X, Zhang Q, Han Y, Jiang J. A review of brain imaging biomarker genomics in Alzheimer’s disease: implementation and perspectives. Transl Neurodegener 2022; 11:42. [PMID: 36109823 PMCID: PMC9476275 DOI: 10.1186/s40035-022-00315-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/24/2022] [Indexed: 11/25/2022] Open
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disease with phenotypic changes closely associated with both genetic variants and imaging pathology. Brain imaging biomarker genomics has been developed in recent years to reveal potential AD pathological mechanisms and provide early diagnoses. This technique integrates multimodal imaging phenotypes with genetic data in a noninvasive and high-throughput manner. In this review, we summarize the basic analytical framework of brain imaging biomarker genomics and elucidate two main implementation scenarios of this technique in AD studies: (1) exploring novel biomarkers and seeking mutual interpretability and (2) providing a diagnosis and prognosis for AD with combined use of machine learning methods and brain imaging biomarker genomics. Importantly, we highlight the necessity of brain imaging biomarker genomics, discuss the strengths and limitations of current methods, and propose directions for development of this research field.
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Cong S, Yao X, Xie L, Yan J, Shen L. Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts. Front Genet 2022; 12:782953. [PMID: 35237294 PMCID: PMC8884108 DOI: 10.3389/fgene.2021.782953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 11/16/2021] [Indexed: 11/29/2022] Open
Abstract
Background: Human brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. Methods: This study analyzes diffusion-weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. Results: Our empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. Discussion: These imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related complex diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.
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Affiliation(s)
- Shan Cong
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Linhui Xie
- Department of Electrical and Computer Engineering, School of Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States
| | - Jingwen Yan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Lee B, Yao X, Shen L. Genome-Wide association study of quantitative biomarkers identifies a novel locus for alzheimer's disease at 12p12.1. BMC Genomics 2022; 23:85. [PMID: 35086473 PMCID: PMC8796646 DOI: 10.1186/s12864-021-08269-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 12/14/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genetic study of quantitative biomarkers in Alzheimer's Disease (AD) is a promising method to identify novel genetic factors and relevant endophenotypes, which provides valuable information to deconvolute mechanistic complexity and better understand disease subtypes. RESULTS Using the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we performed a genome-wide association study (GWAS) between 565,373 single nucleotide polymorphisms (SNPs) and 16 key AD biomarkers from 1,576 subjects at four visits. We identified a novel locus rs5011804 at 12p12.1 significantly associated with several AD biomarkers, including three cognitive traits (CDRSB, FAQ, ADAS13) and one imaging trait (fusiform volume). Additional mediation and interaction analyses investigated the relationships among this SNP, relevant biomarkers, and clinical diagnosis, confirming and further elaborating the genetic effects seen in the GWAS. CONCLUSION Our GWAS not only affirms key AD genes but also suggests the promising role of the SNP rs5011804 due to its associations with several AD cognitive and imaging outcomes. The SNP rs5011804 has a reported association with adult asthma and slightly affects intracranial volume but has not been associated with AD before. Our novel findings contribute to a more comprehensive view of the molecular mechanism behind AD.
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Affiliation(s)
- Brian Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
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Hippocampal Subregion and Gene Detection in Alzheimer's Disease Based on Genetic Clustering Random Forest. Genes (Basel) 2021; 12:genes12050683. [PMID: 34062866 PMCID: PMC8147351 DOI: 10.3390/genes12050683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/29/2021] [Accepted: 04/29/2021] [Indexed: 01/18/2023] Open
Abstract
The distinguishable subregions that compose the hippocampus are differently involved in functions associated with Alzheimer's disease (AD). Thus, the identification of hippocampal subregions and genes that classify AD and healthy control (HC) groups with high accuracy is meaningful. In this study, by jointly analyzing the multimodal data, we propose a novel method to construct fusion features and a classification method based on the random forest for identifying the important features. Specifically, we construct the fusion features using the gene sequence and subregions correlation to reduce the diversity in same group. Moreover, samples and features are selected randomly to construct a random forest, and genetic algorithm and clustering evolutionary are used to amplify the difference in initial decision trees and evolve the trees. The features in resulting decision trees that reach the peak classification are the important "subregion gene pairs". The findings verify that our method outperforms well in classification performance and generalization. Particularly, we identified some significant subregions and genes, such as hippocampus amygdala transition area (HATA), fimbria, parasubiculum and genes included RYR3 and PRKCE. These discoveries provide some new candidate genes for AD and demonstrate the contribution of hippocampal subregions and genes to AD.
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