51
|
Wang J, Wang Y, Ou Q, Yang S, Jing J, Fang J. Computer gaming alters resting-state brain networks, enhancing cognitive and fluid intelligence in players: evidence from brain imaging-derived phenotypes-wide Mendelian randomization. Cereb Cortex 2024; 34:bhae061. [PMID: 38436466 DOI: 10.1093/cercor/bhae061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 03/05/2024] Open
Abstract
The debate on whether computer gaming enhances players' cognitive function is an ongoing and contentious issue. Aiming to delve into the potential impacts of computer gaming on the players' cognitive function, we embarked on a brain imaging-derived phenotypes (IDPs)-wide Mendelian randomization (MR) study, utilizing publicly available data from a European population. Our findings indicate that computer gaming has a positive impact on fluid intelligence (odds ratio [OR] = 6.264, P = 4.361 × 10-10, 95% confidence interval [CI] 3.520-11.147) and cognitive function (OR = 3.322, P = 0.002, 95% CI 1.563-7.062). Out of the 3062 brain IDPs analyzed, only one phenotype, IDP NET100 0378, was significantly influenced by computer gaming (OR = 4.697, P = 1.10 × 10-5, 95% CI 2.357-9.361). Further MR analysis suggested that alterations in the IDP NET100 0378 caused by computer gaming may be a potential factor affecting fluid intelligence (OR = 1.076, P = 0.041, 95% CI 1.003-1.153). Our MR study lends support to the notion that computer gaming can facilitate the development of players' fluid intelligence by enhancing the connectivity between the motor cortex in the resting-state brain and key regions such as the left dorsolateral prefrontal cortex and the language center.
Collapse
Affiliation(s)
- Jiadong Wang
- Department of Clinical Medicine, Hangzhou City University School of Medicine, 50 Huzhou Street, Hangzhou 310015, China
| | - Yu Wang
- Department of Clinical Medicine, The Second Clinical Medical College, Zhejiang Chinese Medical University, 548 Binwen Street, Hangzhou 310053, China
| | - Qian Ou
- Department of Basic Medical Sciences, Zhejiang University School of Medicine, 866 Yvhangtang Street, Hangzhou 310018, China
| | - Sengze Yang
- School of Economics and Management, Harbin University of Science and Technology, 4 Linyuan Street, Harbin 150080, China
| | - Jiajie Jing
- Department of Clinical Medicine, Hangzhou City University School of Medicine, 50 Huzhou Street, Hangzhou 310015, China
| | - Jiaqi Fang
- Department of Clinical Medicine, Hangzhou City University School of Medicine, 50 Huzhou Street, Hangzhou 310015, China
| |
Collapse
|
52
|
Gao W, Zhu WW, Yu YH, Wang J. Plasma homocysteine level, estradiol level, and brain atrophy: a Mendelian randomization study. Cereb Cortex 2024; 34:bhae112. [PMID: 38517173 DOI: 10.1093/cercor/bhae112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/28/2024] [Accepted: 03/01/2024] [Indexed: 03/23/2024] Open
Abstract
OBJECTIVES Observational studies link elevated plasma homocysteine (Hcy) with vascular disease. Our aim was to assess the gender difference in the association between the plasma tHcy level and brain atrophy and identify the possible influencer. We employed Mendelian randomization (MR) to explore the causal relationship between plasma tHcy level, estradiol level, and brain atrophy. METHODS A total of 687 patients with brain atrophy were included, and gender-specific subgroup analyses in association between tHcy and brain atrophy are conducted. From genome-wide association studies, we selected genetic variants (P < 5 × 10-8) for the plasma tHcy level and estradiol level. We investigated the degree of brain atrophy (including gray matter volume and total brain volume) in the UK biobank (n = 7,916). The inverse variance-weighted and several sensitivity MR regression analyses were carried out. RESULTS The plasma tHcy level was significantly associated with brain atrophy for females, but not for males. An MR study showed that there was little evidence of the causal link between elevated plasma tHcy and brain atrophy. On the other hand, we found evidence to support causality for genetically decreased estradiol with higher risk of brain atrophy. Furthermore, genetic predisposition to elevated plasma tHcy was associated with a lower estradiol level. CONCLUSIONS The influence of estradiol on the association between tHcy and brain atrophy deserves further investigation.
Collapse
Affiliation(s)
- Wen Gao
- Department of Epidemiology and Health Statistics, School of Public Health, Guangzhou Medical University, Guangzhou 511436, China
| | - Wei-Wen Zhu
- Department of Neurology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510260, China
| | - Ya-Huan Yu
- Department of Epidemiology and Health Statistics, School of Public Health, Guangzhou Medical University, Guangzhou 511436, China
| | - Juan Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Guangzhou Medical University, Guangzhou 511436, China
| |
Collapse
|
53
|
Li P, Wang Y, Tian D, Liu M, Zhu X, Wang Y, Huang C, Bai Y, Wu Y, Wei W, Tian S, Li Y, Qiao Y, Yang J, Cao S, Cong C, Zhao L, Su J, Wang M. Joint Exposure to Ambient Air Pollutants, Genetic Risk, and Ischemic Stroke: A Prospective Analysis in UK Biobank. Stroke 2024; 55:660-669. [PMID: 38299341 DOI: 10.1161/strokeaha.123.044935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/20/2023] [Indexed: 02/02/2024]
Abstract
BACKGROUND Our primary objective was to assess the association between joint exposure to various air pollutants and the risk of ischemic stroke (IS) and the modification of the genetic susceptibility. METHODS This observational cohort study included 307 304 British participants from the United Kingdom Biobank, who were stroke-free and possessed comprehensive baseline data on genetics, air pollutant exposure, alcohol consumption, and dietary habits. All participants were initially enrolled between 2006 and 2010 and were followed up until 2022. An air pollution score was calculated to assess joint exposure to 5 ambient air pollutants, namely particulate matter with diameters equal to or <2.5 µm, ranging from 2.5 to 10 µm, equal to or <10 µm, as well as nitrogen oxide and nitrogen dioxide. To evaluate individual genetic risk, a polygenic risk score for IS was calculated for each participant. We adjusted for demographic, social, economic, and health covariates. Cox regression models were utilized to estimate the associations between air pollution exposure, polygenic risk score, and the incidence of IS. RESULTS Over a median follow-up duration of 13.67 years, a total of 2476 initial IS events were detected. The hazard ratios (95% CI) of IS for per 10 µg/m3 increase in particulate matter with diameters equal to or <2.5 µm, ranging from 2.5 to 10 µm, equal to or <10 µm, nitrogen dioxide, and nitrogen oxide were 1.73 (1.33-2.14), 1.24 (0.88-1.70), 1.13 (0.89-1.33), 1.03 (0.98-1.08), and 1.04 (1.02-1.07), respectively. Furthermore, individuals in the highest quintile of the air pollution score exhibited a 29% to 66% higher risk of IS compared with those in the lowest quintile. Notably, participants with both high polygenic risk score and air pollution score had a 131% (95% CI, 85%-189%) greater risk of IS than participants with low polygenic risk score and air pollution score. CONCLUSIONS Our findings suggested that prolonged joint exposure to air pollutants may contribute to an increased risk of IS, particularly among individuals with elevated genetic susceptibility to IS.
Collapse
Affiliation(s)
- Panlong Li
- Department of Medical Imaging (P.L., Y.B., Y. Wu, W.W., M.W.), Henan Provincial People's Hospital and Zhengzhou University People's Hospital, China
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, China (P.L., X.Z., Yanfeng Wang, C.H.)
| | - Ying Wang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China (Ying Wang)
- School of Public Health, Zhengzhou University (Ying Wang)
| | - Dandan Tian
- Department of Hypertension (D.T., M.L.), Henan Provincial People's Hospital and Zhengzhou University People's Hospital, China
| | - Min Liu
- Department of Hypertension (D.T., M.L.), Henan Provincial People's Hospital and Zhengzhou University People's Hospital, China
| | - Xirui Zhu
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, China (P.L., X.Z., Yanfeng Wang, C.H.)
| | - Yanfeng Wang
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, China (P.L., X.Z., Yanfeng Wang, C.H.)
| | - Chun Huang
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, China (P.L., X.Z., Yanfeng Wang, C.H.)
| | - Yan Bai
- Department of Medical Imaging (P.L., Y.B., Y. Wu, W.W., M.W.), Henan Provincial People's Hospital and Zhengzhou University People's Hospital, China
- Laboratory of Brain Science and Brain-Like Intelligence Technology, Biomedical Research Institute, Henan Academy of Science, China (Y.B.)
| | - Yaping Wu
- Department of Medical Imaging (P.L., Y.B., Y. Wu, W.W., M.W.), Henan Provincial People's Hospital and Zhengzhou University People's Hospital, China
| | - Wei Wei
- Department of Medical Imaging (P.L., Y.B., Y. Wu, W.W., M.W.), Henan Provincial People's Hospital and Zhengzhou University People's Hospital, China
| | - Shan Tian
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Yuna Li
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Yuan Qiao
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Junting Yang
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Shanshan Cao
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Chaohua Cong
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Lei Zhao
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Jingjing Su
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, China (S.T., Y.L., Y.Q., J.Y., S.C., C.C., L.Z., J.S.)
| | - Meiyun Wang
- Department of Medical Imaging (P.L., Y.B., Y. Wu, W.W., M.W.), Henan Provincial People's Hospital and Zhengzhou University People's Hospital, China
| |
Collapse
|
54
|
Goltermann O, Alagöz G, Molz B, Fisher SE. Neuroimaging genomics as a window into the evolution of human sulcal organization. Cereb Cortex 2024; 34:bhae078. [PMID: 38466113 PMCID: PMC10926775 DOI: 10.1093/cercor/bhae078] [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: 10/23/2023] [Revised: 02/03/2024] [Accepted: 02/04/2024] [Indexed: 03/12/2024] Open
Abstract
Primate brain evolution has involved prominent expansions of the cerebral cortex, with largest effects observed in the human lineage. Such expansions were accompanied by fine-grained anatomical alterations, including increased cortical folding. However, the molecular bases of evolutionary alterations in human sulcal organization are not yet well understood. Here, we integrated data from recently completed large-scale neuroimaging genetic analyses with annotations of the human genome relevant to various periods and events in our evolutionary history. These analyses identified single-nucleotide polymorphism (SNP) heritability enrichments in fetal brain human-gained enhancer (HGE) elements for a number of sulcal structures, including the central sulcus, which is implicated in human hand dexterity. We zeroed in on a genomic region that harbors DNA variants associated with left central sulcus shape, an HGE element, and genetic loci involved in neurogenesis including ZIC4, to illustrate the value of this approach for probing the complex factors contributing to human sulcal evolution.
Collapse
Affiliation(s)
- Ole Goltermann
- Max Planck School of Cognition, Stephanstrasse 1a, 04103 Leipzig, Germany
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands
- Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany
| | - Gökberk Alagöz
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands
| | - Barbara Molz
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Wundtlaan 1, 6525 XD Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition & Behaviour, Radboud University, Thomas van Aquinostraat 4, 6525 GD Nijmegen, The Netherlands
| |
Collapse
|
55
|
Papazoglou S, Ashtarayeh M, Oeschger JM, Callaghan MF, Does MD, Mohammadi S. Insights and improvements in correspondence between axonal volume fraction measured with diffusion-weighted MRI and electron microscopy. NMR IN BIOMEDICINE 2024; 37:e5070. [PMID: 38098204 DOI: 10.1002/nbm.5070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 09/25/2023] [Accepted: 10/19/2023] [Indexed: 02/17/2024]
Abstract
Biophysical diffusion-weighted imaging (DWI) models are increasingly used in neuroscience to estimate the axonal water fraction (f AW ), which in turn is key for noninvasive estimation of the axonal volume fraction (f A ). These models require thorough validation by comparison with a reference method, for example, electron microscopy (EM). While EM studies often neglect the unmyelinated axons and solely report the fraction of myelinated axons, in DWI both myelinated and unmyelinated axons contribute to the DWI signal. However, DWI models often include simplifications, for example, the neglect of differences in the compartmental relaxation times or fixed diffusivities, which in turn might affect the estimation off AW . We investigate whether linear calibration parameters (scaling and offset) can improve the comparability between EM- and DWI-based metrics off A . To this end, we (a) used six DWI models based on the so-called standard model of white matter (WM), including two models with fixed compartmental diffusivities (e.g., neurite orientation dispersion and density imaging, NODDI) and four models that fitted the compartmental diffusivities (e.g., white matter tract integrity, WMTI), and (b) used a multimodal data set including ex vivo diffusion DWI and EM data in mice with a broad dynamic range of fibre volume metrics. We demonstrated that the offset is associated with the volume fraction of unmyelinated axons and the scaling factor is associated with different compartmentalT 2 and can substantially enhance the comparability between EM- and DWI-based metrics off A . We found that DWI models that fitted compartmental diffusivities provided the most accurate estimates of the EM-basedf A . Finally, we introduced a more efficient hybrid calibration approach, where only the offset is estimated but the scaling is fixed to a theoretically predicted value. Using this approach, a similar one-to-one correspondence to EM was achieved for WMTI. The method presented can pave the way for use of validated DWI-based models in clinical research and neuroscience.
Collapse
Affiliation(s)
- Sebastian Papazoglou
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Max Planck Research Group MR Physics, Max Planck Institute for Human Development, Berlin, Germany
| | - Mohammad Ashtarayeh
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Malte Oeschger
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Martina F Callaghan
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Mark D Does
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Electrical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Siawoosh Mohammadi
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Max Planck Research Group MR Physics, Max Planck Institute for Human Development, Berlin, Germany
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| |
Collapse
|
56
|
Wang X, Chen Q, Liu Y, Sun J, Li J, Zhao P, Cai L, Liu W, Yang Z, Wang Z, Lv H. Causal relationship between multiparameter brain MRI phenotypes and age: evidence from Mendelian randomization. Brain Commun 2024; 6:fcae077. [PMID: 38529357 PMCID: PMC10963122 DOI: 10.1093/braincomms/fcae077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 01/05/2024] [Accepted: 02/29/2024] [Indexed: 03/27/2024] Open
Abstract
To explore the causal relationship between age and brain health (cortical atrophy, white matter integrity, white matter hyperintensities and cerebral microbleeds in various brain regions) related multiparameter imaging features using two-sample Mendelian randomization. Age was determined as chronological age of the subject. Cortical volume, white matter micro-integrity, white matter hyperintensity volume and cerebral microbleeds of each brain region were included as phenotypes for brain health. Age and imaging of brain health related genetic data were analysed to determine the causal relationship using inverse-variance weighted model, validated by heterogeneity and horizontal pleiotropy variables. Age is causally related to increased volumes of white matter hyperintensities (β = 0.151). For white matter micro-integrity, fibres of the inferior cerebellar peduncle (axial diffusivity β = -0.128, orientation dispersion index β = 0.173), cerebral peduncle (axial diffusivity β = -0.136), superior fronto-occipital fasciculus (isotropic volume fraction β = 0.163) and fibres within the limbic system were causally deteriorated. We also detected decreased cortical thickness of multiple frontal and temporal regions (P < 0.05). Microbleeds were not related with aging (P > 0.05). Aging is a threat of brain health, leading to cortical atrophy mainly in the frontal lobes, as well as the white matter degeneration especially abnormal hyperintensity and deteriorated white matter integrity around the hippocampus.
Collapse
Affiliation(s)
- Xinghao Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Qian Chen
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Yawen Liu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Jing Sun
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Jia Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Pengfei Zhao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Linkun Cai
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Wenjuan Liu
- Department of Radiology, Aerospace Center Hospital, Beijing 100089, China
- Peking University Aerospace School of Clinical Medicine, Beijing 100089, China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| |
Collapse
|
57
|
Li F, Zhao Q, Tang T, Liu Y, Wang Z, Wang Z, Han X, Xu Z, Chang Y, Li Y. Brain imaging derived phenotypes: a biomarker for the onset of inflammatory bowel disease and a potential mediator of mental complications. Front Immunol 2024; 15:1359540. [PMID: 38469291 PMCID: PMC10925669 DOI: 10.3389/fimmu.2024.1359540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 02/14/2024] [Indexed: 03/13/2024] Open
Abstract
Background and aims Inflammatory bowel disease (IBD), mainly categorized into Crohn's disease (CD) and ulcerative colitis (UC), is a chronic relapsing gastrointestinal disorder that significantly impairs patients' quality of life. IBD patients often experience comorbidities such as anxiety and depression, and the underlying mechanisms and treatment strategies remain areas of investigation. Methods We conducted a Mendelian randomization(MR) analysis utilizing brain image derived phenotypes (IDP) from the UK Biobank database to investigate the causal relationships between IBD and alterations in brain structural morphology and connectivity of neural tracts. This study aimed to identify biological evidence linking IBD to psychiatric disorders such as anxiety and depression. Results Specifically, the volume of grey matter in the Left Frontal Orbital Cortex exhibited a negative association with the onset of Crohn's disease (odds ratio (OR) [95% confidence interval (CI)]: 0.315[0.180~0.551], adjusted P=0.001), while the volume of the superior frontal cortex in the right hemisphere showed a positive correlation with the development of Ulcerative colitis (OR [95% CI]: 2.285[1.793~2.911], adjusted P<0.001), and the volume of lateral occipital cortex in the left hemisphere demonstrated a positive relationship with Crohn's disease onset (OR [95% CI]: 1.709[1.671~1.747], adjusted P<0.001). In the context of reverse causality, the onset of UC or CD has led to alterations in imaging derived phenotypes associated with five disorders (anxiety, depression, schizophrenia, bipolar disorder, pain) and three functions (memory, emotion, language). Conclusion Our study has demonstrated a causal relationship between IBD and IDPs. IDPs may serve as potential biomarkers for the progression of IBD and as predictive intermediaries for the development of neurological diseases in IBD patients.
Collapse
Affiliation(s)
- Fan Li
- Department of Gastroenterology, The First Hospital of Jilin University, Changchun, China
- Norman Bethune Health Science Center, Jilin University, Changchun, China
| | - Qi Zhao
- Department of Gastroenterology, The First Hospital of Jilin University, Changchun, China
- Norman Bethune Health Science Center, Jilin University, Changchun, China
| | - Tongyu Tang
- Department of Gastroenterology, The First Hospital of Jilin University, Changchun, China
| | - Yuyuan Liu
- Department of Gastroenterology, The First Hospital of Jilin University, Changchun, China
- Norman Bethune Health Science Center, Jilin University, Changchun, China
| | - Zhaodi Wang
- Department of Gastroenterology, The First Hospital of Jilin University, Changchun, China
- Norman Bethune Health Science Center, Jilin University, Changchun, China
| | - Zhi Wang
- Department of Gastroenterology, The First Hospital of Jilin University, Changchun, China
- Norman Bethune Health Science Center, Jilin University, Changchun, China
| | - Xiaoping Han
- Department of Gastroenterology, The First Hospital of Jilin University, Changchun, China
- Norman Bethune Health Science Center, Jilin University, Changchun, China
| | - Zifeng Xu
- Department of Gastroenterology, The First Hospital of Jilin University, Changchun, China
- Norman Bethune Health Science Center, Jilin University, Changchun, China
| | - Yu Chang
- Department of Gastroenterology, The First Hospital of Jilin University, Changchun, China
- Norman Bethune Health Science Center, Jilin University, Changchun, China
| | - Yuqin Li
- Department of Gastroenterology, The First Hospital of Jilin University, Changchun, China
| |
Collapse
|
58
|
Wang X, Zhu Z, Sun J, Jia L, Cai L, Chen Q, Yang W, Wang Y, Zhang Y, Guo S, Liu W, Yang Z, Zhao P, Wang Z, Lv H. Changes in iron load in specific brain areas lead to neurodegenerative diseases of the central nervous system. Prog Neuropsychopharmacol Biol Psychiatry 2024; 129:110903. [PMID: 38036035 DOI: 10.1016/j.pnpbp.2023.110903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 11/24/2023] [Accepted: 11/24/2023] [Indexed: 12/02/2023]
Abstract
The causes of neurodegenerative diseases remain largely elusive, increasing their personal and societal impacts. To reveal the causal effects of iron load on Parkinson's disease (PD), Alzheimer's disease (AD), amyotrophic lateral sclerosis and multiple sclerosis, we used Mendelian randomisation and brain imaging data from a UK Biobank genome-wide association study of 39,691 brain imaging samples (predominantly of European origin). Using susceptibility-weighted images, which reflect iron load, we analysed genetically significant brain regions. Inverse variance weighting was used as the main estimate, while MR Egger and weighted median were used to detect heterogeneity and pleiotropy. Nine clear associations were obtained. For AD and PD, an increased iron load was causative: the right pallidum for AD and the right caudate, left caudate and right accumbens for PD. However, a reduced iron load was identified in the right and left caudate for multiple sclerosis, the bilateral hippocampus for mixed vascular dementia and the left thalamus and bilateral accumbens for subcortical vascular dementia. Thus, changes in iron load in different brain regions have causal effects on neurodegenerative diseases. Our results are crucial for understanding the pathogenesis and investigating the treatment of these diseases.
Collapse
Affiliation(s)
- Xinghao Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Zaimin Zhu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, People's Republic of China
| | - Jing Sun
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Li Jia
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Linkun Cai
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China; School of Biological Science and Medical Engineering, Beihang University, No.37 XueYuan Road, Beijing 100191, People's Republic of China
| | - Qian Chen
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Wenbo Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Yiling Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Yufan Zhang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Sihui Guo
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Wenjuan Liu
- Department of Radiology, Aerospace Center Hospital, Beijing, People's Republic of China; Peking University Aerospace School of Clinical Medicine, Beijing 100049, People's Republic of China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Pengfei Zhao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China.
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, No. 95 YongAn Road, Beijing 100050, People's Republic of China.
| |
Collapse
|
59
|
Busch EL, Rapuano KM, Anderson KM, Rosenberg MD, Watts R, Casey BJ, Haxby JV, Feilong M. Dissociation of Reliability, Heritability, and Predictivity in Coarse- and Fine-Scale Functional Connectomes during Development. J Neurosci 2024; 44:e0735232023. [PMID: 38148152 PMCID: PMC10866091 DOI: 10.1523/jneurosci.0735-23.2023] [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: 03/14/2023] [Revised: 10/09/2023] [Accepted: 11/16/2023] [Indexed: 12/28/2023] Open
Abstract
The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9-10 years; n = 1,115 individuals; both sexes; 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition.
Collapse
Affiliation(s)
- Erica L Busch
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - Kristina M Rapuano
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - Kevin M Anderson
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, Chicago, Illinois, 60637
| | - Richard Watts
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - B J Casey
- Department of Psychology, Yale University, New Haven, Connecticut, 06510
| | - James V Haxby
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, 03755
| | - Ma Feilong
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire, 03755
| |
Collapse
|
60
|
Wang S, Li T, Zhao B, Dai W, Yao Y, Li C, Li T, Zhu H, Zhang H. Identification and validation of supervariants reveal novel loci associated with human white matter microstructure. Genome Res 2024; 34:20-33. [PMID: 38190638 PMCID: PMC10904010 DOI: 10.1101/gr.277905.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 12/05/2023] [Indexed: 01/10/2024]
Abstract
As an essential part of the central nervous system, white matter coordinates communications between different brain regions and is related to a wide range of neurodegenerative and neuropsychiatric disorders. Previous genome-wide association studies (GWASs) have uncovered loci associated with white matter microstructure. However, GWASs suffer from limited reproducibility and difficulties in detecting multi-single-nucleotide polymorphism (multi-SNP) and epistatic effects. In this study, we adopt the concept of supervariants, a combination of alleles in multiple loci, to account for potential multi-SNP effects. We perform supervariant identification and validation to identify loci associated with 22 white matter fractional anisotropy phenotypes derived from diffusion tensor imaging. To increase reproducibility, we use United Kingdom (UK) Biobank White British (n = 30,842) data for discovery and internal validation, and UK Biobank White but non-British (n = 1927) data, Europeans from the Adolescent Brain Cognitive Development study (n = 4399) data, and Europeans from the Human Connectome Project (n = 319) data for external validation. We identify 23 novel loci on the discovery set that have not been reported in the previous GWASs on white matter microstructure. Among them, three supervariants on genomic regions 5q35.1, 8p21.2, and 19q13.32 have P-values lower than 0.05 in the meta-analysis of the three independent validation data sets. These supervariants contain genetic variants located in genes that have been related to brain structures, cognitive functions, and neuropsychiatric diseases. Our findings provide a better understanding of the genetic architecture underlying white matter microstructure.
Collapse
Affiliation(s)
- Shiying Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Ting Li
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104-1686, USA
| | - Wei Dai
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Yisha Yao
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA
| | - Cai Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Heping Zhang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut 06510, USA;
| |
Collapse
|
61
|
Wen X, Qu D, Liu D, Shu Y, Zhao S, Wu G, Wang Y, Cui Z, Zhang X, Chen R. Brain structural and functional signatures of multi-generational family history of suicidal behaviors in preadolescent children. Mol Psychiatry 2024; 29:484-495. [PMID: 38102486 DOI: 10.1038/s41380-023-02342-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/21/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023]
Abstract
Parent-child transmission of suicidal behaviors has been extensively studied, but the investigation of a three-generation family suicide risk paradigm remains limited. In this study, we aimed to explore the behavioral and brain signatures of multi-generational family history of suicidal behaviors (FHoS) in preadolescents, utilizing a longitudinal design and the dataset from Adolescent Brain and Cognitive DevelopmentSM Study (ABCD Study®), which comprised 4 years of data and includes a total of 9,653 preadolescents. Our findings revealed that multi-generational FHoS was significantly associated with an increased risk of problematic behaviors and suicidal behaviors (suicide ideation and suicide attempt) in offspring. Interestingly, the problematic behaviors were further identified as a mediator in the multi-generational transmission of suicidal behaviors. Additionally, we observed alterations in brain structure within superior temporal gyrus (STG), precentral/postcentral cortex, posterior parietal cortex (PPC), cingulate cortex (CC), and planum temporale (PT), as well as disrupted functional connectivity of default mode network (DMN), ventral attention network (VAN), dorsal attention network (DAN), fronto-parietal network (FPN), and cingulo-opercular network (CON) among preadolescents with FHoS. These results provide compelling longitudinal evidence at the population level, highlighting the associations between multi-generational FHoS and maladaptive behavioral and neurodevelopmental outcomes in offspring. These findings underscore the need for early preventive measures aimed at mitigating the familial transmission of suicide risk and reducing the global burden of deaths among children and adolescents.
Collapse
Affiliation(s)
- Xue Wen
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Diyang Qu
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Dongyu Liu
- Vanke School of Public Health, Tsinghua University, Beijing, China
- Institute for Healthy China, Tsinghua University, Beijing, China
| | - Yinuo Shu
- Chinese Institute for Brain Research, Beijing, China
| | - Shaoling Zhao
- Chinese Institute for Brain Research, Beijing, China
| | - Guowei Wu
- Chinese Institute for Brain Research, Beijing, China
| | - Yuanyuan Wang
- Key Laboratory of Brain, Cognition and Education Sciences; School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, China.
| | - Xiaoqian Zhang
- Wulituo Hospital of Shijingshan District, Beijing, China.
| | - Runsen Chen
- Vanke School of Public Health, Tsinghua University, Beijing, China.
- Institute for Healthy China, Tsinghua University, Beijing, China.
| |
Collapse
|
62
|
Bell AM. The evolution of decision-making mechanisms under competing demands. Trends Ecol Evol 2024; 39:141-151. [PMID: 37783626 PMCID: PMC10922085 DOI: 10.1016/j.tree.2023.09.007] [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: 04/17/2023] [Revised: 09/06/2023] [Accepted: 09/08/2023] [Indexed: 10/04/2023]
Abstract
Animals in nature are constantly managing multiple demands, and decisions about how to adjust behavior in response to ecologically relevant demands is critical for fitness. Evidence for behavioral correlations across functional contexts (behavioral syndromes) and growing appreciation for shared proximate substrates of behavior prompts novel questions about the existence of distinct neural, molecular, and genetic mechanisms involved in decision-making. Those proximate mechanisms are likely to be an important target of selection, but little is known about how they evolve, their evolutionary history, or where they harbor genetic variation. Herein I provide a conceptual framework for understanding the evolution of mechanisms for decision-making, highlighting insights on decision-making in humans and model organisms, and sketch an emerging synthesis.
Collapse
Affiliation(s)
- Alison M Bell
- Department of Evolution, Ecology and Behavior, 505 S. Goodwin Ave, Urbana, IL 61801, USA.
| |
Collapse
|
63
|
Chen J, Li T, Zhao B, Chen H, Yuan C, Garden GA, Wu G, Zhu H. The interaction effects of age, APOE and common environmental risk factors on human brain structure. Cereb Cortex 2024; 34:bhad472. [PMID: 38112569 PMCID: PMC10793588 DOI: 10.1093/cercor/bhad472] [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: 05/04/2023] [Revised: 10/09/2023] [Accepted: 11/06/2023] [Indexed: 12/21/2023] Open
Abstract
Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.
Collapse
Affiliation(s)
- Jie Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill NC 27514, United States
| | - Tengfei Li
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Bingxin Zhao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, 265 South 37th Street, 3rd & 4th Floors, Philadelphia, PA 19104-1686, United States
| | - Hui Chen
- School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, China
| | - Changzheng Yuan
- School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, China
- Department of Nutrition, Harvard T H Chan School of Public Health, 665 Huntington Avenue Boston, MA, 02115, United States
| | - Gwenn A Garden
- Department of Neurology, School of Medicine, University of North Carolina at Chapel Hill, 170 Manning Drive Chapel Hill, NC 27599-7025, United States
| | - Guorong Wu
- Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States
- Departments of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave #3260, Chapel Hill, NC 27599, United States
- Departments of Computer Science, University of North Carolina at Chapel Hill, 201 South Columbia Street, Chapel Hill, NC 27599, United States
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Dr, Chapel Hill, NC 27599, United States
- Carolina Institute for Developmental Disabilities, 101 Renee Lynne Ct, Carrboro, NC 27510, United States
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill NC 27514, United States
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Chapel Hill, NC 27599, United States
- Departments of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave #3260, Chapel Hill, NC 27599, United States
- Departments of Computer Science, University of North Carolina at Chapel Hill, 201 South Columbia Street, Chapel Hill, NC 27599, United States
- Departments of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27514, United States
| |
Collapse
|
64
|
Coffman C, Feczko E, Larsen B, Tervo-Clemmens B, Conan G, Lundquist JT, Houghton A, Moore LA, Weldon K, McCollum R, Perrone AJ, Fayzullobekova B, Madison TJ, Earl E, Dominguez OM, Fair DA, Basu S. Heritability estimation of subcortical volumes in a multi-ethnic multi-site cohort study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.11.575231. [PMID: 38260520 PMCID: PMC10802572 DOI: 10.1101/2024.01.11.575231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Heritability of regional subcortical brain volumes (rSBVs) describes the role of genetics in middle and inner brain development. rSBVs are highly heritable in adults but are not characterized well in adolescents. The Adolescent Brain Cognitive Development study (ABCD), taken over 22 US sites, provides data to characterize the heritability of subcortical structures in adolescence. In ABCD, site-specific effects co-occur with genetic effects which can bias heritability estimates. Existing methods adjusting for site effects require additional steps to adjust for site effects and can lead to inconsistent estimation. We propose a random-effect model-based method of moments approach that is a single step estimator and is a theoretically consistent estimator even when sites are imbalanced and performs well under simulations. We compare methods on rSBVs from ABCD. The proposed approach yielded heritability estimates similar to previous results derived from single-site studies. The cerebellum cortex and hippocampus were the most heritable regions (> 50%).
Collapse
Affiliation(s)
- Christian Coffman
- Division of Biostatistics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Bart Larsen
- Department of Pediatrics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Brenden Tervo-Clemmens
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Gregory Conan
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Jacob T. Lundquist
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Audrey Houghton
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Lucille A. Moore
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Kimberly Weldon
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Rae McCollum
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Anders J. Perrone
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Begim Fayzullobekova
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Thomas J. Madison
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Eric Earl
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Oscar Miranda Dominguez
- Department of Pediatrics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Damien A. Fair
- Department of Pediatrics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Saonli Basu
- Division of Biostatistics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| |
Collapse
|
65
|
Chen J, Iraji A, Fu Z, Andrés-Camazón P, Thapaliya B, Liu J, Calhoun VD. Dynamic fusion of genomics and functional network connectivity in UK biobank reveals static and time-varying SNP manifolds. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.09.24301013. [PMID: 38260328 PMCID: PMC10802663 DOI: 10.1101/2024.01.09.24301013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Many psychiatric and neurological disorders show significant heritability, indicating strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) measures functional temporal coupling between brain networks in a time-varying manner and has proven to identify disease-related changes in the brain. However, it remains largely unclear how genetic risk contributes to brain dysconnectivity that further manifests into clinical symptoms. The current work aimed to address this gap by proposing a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically tuned SNP manifolds by linking static SNPs to dynamic functional information of the brain. The sliding window approach was utilized to estimate four dFNC states and compute subject-level state-specific dFNC features. Each state of dFNC features were then combined with 12946 SZ risk SNPs for jICA decomposition, resulting in four parallel fusions in 32861 European ancestry individuals within the UK Biobank cohort. The identified joint SNP-dFNC components were further validated for SZ relevance in an aggregated SZ cohort, and compared for across-state similarity to indicate level of dynamism. The results supported that dynamic fusion yielded "static" and "dynamic" components (i.e., high and low across-state similarity, respectively) for SNP and dFNC modalities. As expected, the SNP components presented a mixture of static and dynamic manifolds, with the latter largely driven by fusion with dFNC. We also showed that some of the dynamic SNP manifolds uniquely elicited by fusion with state-specific dFNC features complemented each other in terms of biological interpretation. This dynamic fusion framework thus allows expanding the SNP modality to manifolds in the time dimension, which provides a unique lens to elicit unique SNP correlates of dFNC otherwise unseen, promising additional insights on how genetic risk links to disease-related dysconnectivity.
Collapse
Affiliation(s)
- Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
| | - Pablo Andrés-Camazón
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, Madrid, Spain
| | - Bishal Thapaliya
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): (Georgia State University, Georgia Institute of Technology, and Emory University), Atlanta, GA, USA
- Department of Computer Science, Georgia State University, Atlanta, GA, USA
| |
Collapse
|
66
|
Yang Z, Wen J, Abdulkadir A, Cui Y, Erus G, Mamourian E, Melhem R, Srinivasan D, Govindarajan ST, Chen J, Habes M, Masters CL, Maruff P, Fripp J, Ferrucci L, Albert MS, Johnson SC, Morris JC, LaMontagne P, Marcus DS, Benzinger TLS, Wolk DA, Shen L, Bao J, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering. Nat Commun 2024; 15:354. [PMID: 38191573 PMCID: PMC10774282 DOI: 10.1038/s41467-023-44271-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 12/06/2023] [Indexed: 01/10/2024] Open
Abstract
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.
Collapse
Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhao Wen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiong Chen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Biggs Alzheimer's Institute, University of Texas San Antonio Health Science Center, San Antonio, TX, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Paul Maruff
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, QLD, Australia
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel S Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| |
Collapse
|
67
|
He J, Antonyan L, Zhu H, Ardila K, Li Q, Enoma D, Zhang W, Liu A, Chekouo T, Cao B, MacDonald ME, Arnold PD, Long Q. A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders. Am J Hum Genet 2024; 111:48-69. [PMID: 38118447 PMCID: PMC10806749 DOI: 10.1016/j.ajhg.2023.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/04/2023] [Accepted: 11/16/2023] [Indexed: 12/22/2023] Open
Abstract
Brain imaging and genomics are critical tools enabling characterization of the genetic basis of brain disorders. However, imaging large cohorts is expensive and may be unavailable for legacy datasets used for genome-wide association studies (GWASs). Using an integrated feature selection/aggregation model, we developed an image-mediated association study (IMAS), which utilizes borrowed imaging/genomics data to conduct association mapping in legacy GWAS cohorts. By leveraging the UK Biobank image-derived phenotypes (IDPs), the IMAS discovered genetic bases underlying four neuropsychiatric disorders and verified them by analyzing annotations, pathways, and expression quantitative trait loci (eQTLs). A cerebellar-mediated mechanism was identified to be common to the four disorders. Simulations show that, if the goal is identifying genetic risk, our IMAS is more powerful than a hypothetical protocol in which the imaging results were available in the GWAS dataset. This implies the feasibility of reanalyzing legacy GWAS datasets without conducting additional imaging, yielding cost savings for integrated analysis of genetics and imaging.
Collapse
Affiliation(s)
- Jingni He
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Lilit Antonyan
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Harold Zhu
- Department of Biological Sciences, Faculty of Science, University of Calgary, Calgary, AB, Canada
| | - Karen Ardila
- Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada
| | - Qing Li
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - David Enoma
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | | | - Andy Liu
- Sir Winston Churchill High School, Calgary, AB, Canada; College of Letters and Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Thierry Chekouo
- Department of Mathematics and Statistics, Faculty of Science, University of Calgary, Calgary, AB, Canada; Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Bo Cao
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, Canada
| | - M Ethan MacDonald
- The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada; Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Paul D Arnold
- Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
| | - Quan Long
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Medical Genetics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada; Department of Mathematics and Statistics, Faculty of Science, University of Calgary, Calgary, AB, Canada.
| |
Collapse
|
68
|
Jameei H, Rakesh D, Zalesky A, Cairns MJ, Reay WR, Wray NR, Di Biase MA. Linking Polygenic Risk of Schizophrenia to Variation in Magnetic Resonance Imaging Brain Measures: A Comprehensive Systematic Review. Schizophr Bull 2024; 50:32-46. [PMID: 37354489 PMCID: PMC10754175 DOI: 10.1093/schbul/sbad087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/26/2023]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia is highly heritable, with a polygenic effect of many genes conferring risk. Evidence on whether cumulative risk also predicts alterations in brain morphology and function is inconsistent. This systematic review examined evidence for schizophrenia polygenic risk score (sczPRS) associations with commonly used magnetic resonance imaging (MRI) measures. We expected consistent evidence to emerge for significant sczPRS associations with variation in structure and function, specifically in frontal, temporal, and insula cortices that are commonly implicated in schizophrenia pathophysiology. STUDY DESIGN In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched MEDLINE, Embase, and PsycINFO for peer-reviewed studies published between January 2013 and March 2022. Studies were screened against predetermined criteria and National Institutes of Health (NIH) quality assessment tools. STUDY RESULTS In total, 57 studies of T1-weighted structural, diffusion, and functional MRI were included (age range = 9-80 years, Nrange = 64-76 644). We observed moderate, albeit preliminary, evidence for higher sczPRS predicting global reductions in cortical thickness and widespread variation in functional connectivity, and to a lesser extent, region-specific reductions in frontal and temporal volume and thickness. Conversely, sczPRS does not predict whole-brain surface area or gray/white matter volume. Limited evidence emerged for sczPRS associations with diffusion tensor measures of white matter microstructure in a large community sample and smaller cohorts of children and young adults. These findings were broadly consistent across community and clinical populations. CONCLUSIONS Our review supports the hypothesis that schizophrenia is a disorder of disrupted within and between-region brain connectivity, and points to specific whole-brain and regional MRI metrics that may provide useful intermediate phenotypes.
Collapse
Affiliation(s)
- Hadis Jameei
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Divyangana Rakesh
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, VIC, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, VIC, Australia
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
69
|
Sun YJ, Sahakian BJ, Langley C, Yang A, Jiang Y, Kang J, Zhao X, Li C, Cheng W, Feng J. Early-initiated childhood reading for pleasure: associations with better cognitive performance, mental well-being and brain structure in young adolescence. Psychol Med 2024; 54:359-373. [PMID: 37376848 DOI: 10.1017/s0033291723001381] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
BACKGROUND Childhood is a crucial neurodevelopmental period. We investigated whether childhood reading for pleasure (RfP) was related to young adolescent assessments of cognition, mental health, and brain structure. METHODS We conducted a cross-sectional and longitudinal study in a large-scale US national cohort (10 000 + young adolescents), using the well-established linear mixed model and structural equation methods for twin study, longitudinal and mediation analyses. A 2-sample Mendelian randomization (MR) analysis for potential causal inference was also performed. Important factors including socio-economic status were controlled. RESULTS Early-initiated long-standing childhood RfP (early RfP) was highly positively correlated with performance on cognitive tests and significantly negatively correlated with mental health problem scores of young adolescents. These participants with higher early RfP scores exhibited moderately larger total brain cortical areas and volumes, with increased regions including the temporal, frontal, insula, supramarginal; left angular, para-hippocampal; right middle-occipital, anterior-cingulate, orbital areas; and subcortical ventral-diencephalon and thalamus. These brain structures were significantly related to their cognitive and mental health scores, and displayed significant mediation effects. Early RfP was longitudinally associated with higher crystallized cognition and lower attention symptoms at follow-up. Approximately 12 h/week of youth regular RfP was cognitively optimal. We further observed a moderately significant heritability of early RfP, with considerable contribution from environments. MR analysis revealed beneficial causal associations of early RfP with adult cognitive performance and left superior temporal structure. CONCLUSIONS These findings, for the first time, revealed the important relationships of early RfP with subsequent brain and cognitive development and mental well-being.
Collapse
Affiliation(s)
- Yun-Jun Sun
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Barbara J Sahakian
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Christelle Langley
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK
| | - Anyi Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yuchao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Xingming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Chunhe Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| |
Collapse
|
70
|
Anderson JA, Rashidi-Ranjbar N, Nazeri A, Chad JA, Zhukovsky P, Mulsant BH, Herrmann N, Mah L, Flint AJ, Fischer CE, Pollock BG, Rajji TK, Voineskos AN. Age-Related Alterations in Gray Matter Microstructure in Older People With Remitted Major Depression at Risk for Dementia. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:374-384. [PMID: 38298786 PMCID: PMC10829634 DOI: 10.1016/j.bpsgos.2023.08.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 08/15/2023] [Accepted: 08/27/2023] [Indexed: 02/02/2024] Open
Abstract
Background Major depressive disorder (MDD) in late life is a risk factor for mild cognitive impairment (MCI) and Alzheimer's disease. However, studies of gray matter changes have produced varied estimates of which structures are implicated in MDD and dementia. Changes in gray matter volume and cortical thickness are macrostructural measures for the microstructural processes of free water accumulation and dendritic spine loss. Methods We conducted multishell diffusion imaging to assess gray matter microstructure in 244 older adults with remitted MDD (n = 44), MCI (n = 115), remitted MDD+MCI (n = 61), or without psychiatric disorders or cognitive impairment (healthy control participants; n = 24). We estimated measures related to neurite density, orientation dispersion, and free water (isotropic volume fraction) using a biophysically plausible model (neurite orientation dispersion and density imaging). Results Results showed that increasing age was correlated with an increase in isotropic volume fraction and a decrease in orientation dispersion index, which is consistent with neuropathology dendritic loss. In addition, this relationship between age and increased isotropic volume fraction was more disrupted in the MCI group than in the remitted MDD or healthy control groups. However, the association between age and orientation dispersion index was similar for all 3 groups. Conclusions The findings suggest that the neurite orientation dispersion and density imaging measures could be used to identify biological risk factors for Alzheimer's disease, signifying both conventional neurodegeneration observed with MCI and dendritic loss seen in MDD.
Collapse
Affiliation(s)
- John A.E. Anderson
- Department of Cognitive Science, Carleton University, Ottawa, Ontario, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Neda Rashidi-Ranjbar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science, St. Michael’s Hospital, Toronto, Ontario, Canada
| | - Arash Nazeri
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri (AN)
| | - Jordan A. Chad
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Baycrest Health Sciences, Toronto, Ontario, Canada
| | - Peter Zhukovsky
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Benoit H. Mulsant
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Nathan Herrmann
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Linda Mah
- Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Alastair J. Flint
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
| | - Corinne E. Fischer
- Keenan Research Centre for Biomedical Science, St. Michael’s Hospital, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Bruce G. Pollock
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Tarek K. Rajji
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Toronto Dementia Research Alliance, University of Toronto, Toronto, Ontario, Canada
| | - Aristotle N. Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science, St. Michael’s Hospital, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - PACt-MD Study Group
- Department of Cognitive Science, Carleton University, Ottawa, Ontario, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Keenan Research Centre for Biomedical Science, St. Michael’s Hospital, Toronto, Ontario, Canada
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri (AN)
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Baycrest Health Sciences, Toronto, Ontario, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Sunnybrook Health Sciences Centre, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
- Toronto Dementia Research Alliance, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
71
|
Wen J, Nasrallah IM, Abdulkadir A, Satterthwaite TD, Yang Z, Erus G, Robert-Fitzgerald T, Singh A, Sotiras A, Boquet-Pujadas A, Mamourian E, Doshi J, Cui Y, Srinivasan D, Skampardoni I, Chen J, Hwang G, Bergman M, Bao J, Veturi Y, Zhou Z, Yang S, Dazzan P, Kahn RS, Schnack HG, Zanetti MV, Meisenzahl E, Busatto GF, Crespo-Facorro B, Pantelis C, Wood SJ, Zhuo C, Shinohara RT, Gur RC, Gur RE, Koutsouleris N, Wolf DH, Saykin AJ, Ritchie MD, Shen L, Thompson PM, Colliot O, Wittfeld K, Grabe HJ, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Espeland M, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Resnick SM, Fan Y, Habes M, Wolk D, Shou H, Davatzikos C. Genomic loci influence patterns of structural covariance in the human brain. Proc Natl Acad Sci U S A 2023; 120:e2300842120. [PMID: 38127979 PMCID: PMC10756284 DOI: 10.1073/pnas.2300842120] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 10/31/2023] [Indexed: 12/23/2023] Open
Abstract
Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.
Collapse
Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science, Department of Neurology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ilya M. Nasrallah
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Ahmed Abdulkadir
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Theodore D. Satterthwaite
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zhijian Yang
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Guray Erus
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Timothy Robert-Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ashish Singh
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Aleix Boquet-Pujadas
- Biomedical Imaging Group, Department of Biomedical Engineering, École Polytechnique Fédérale de Lausanne, Lausanne1015, Switzerland
| | - Elizabeth Mamourian
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jimit Doshi
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Yuhan Cui
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Dhivya Srinivasan
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ioanna Skampardoni
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jiong Chen
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Gyujoon Hwang
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Mark Bergman
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Yogasudha Veturi
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zhen Zhou
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, LondonWC2R 2LS, United Kingdom
| | - Rene S. Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Hugo G. Schnack
- Department of Psychiatry, University Medical Center Utrecht, Utrecht 3584 CX Ut, Netherlands
| | - Marcus V. Zanetti
- Institute of Psychiatry, Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo05508-070, Brazil
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Heinrich Heine University, Düsseldorf40204, Germany
| | - Geraldo F. Busatto
- Institute of Psychiatry, Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo05508-070, Brazil
| | - Benedicto Crespo-Facorro
- Hospital Universitario Virgen del Rocio, School of Medicine, University of Sevilla,Sevilla41004, Spain
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Stephen J. Wood
- Orygen and the Centre for Youth Mental Health, Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Chuanjun Zhuo
- Key Laboratory of Real Tine Tracing of Brain Circuits in Psychiatry and Neurology, Department of Psychiatry, Tianjin Medical University, Tianjin300070, China
| | - Russell T. Shinohara
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich 80539, Germany
| | - Daniel H. Wolf
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Department of Radiology, Indiana University School of Medicine, Indianapolis, IN46202-3082
| | - Marylyn D. Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Paul M. Thompson
- Imaging Genetics Center, Department of Neurology, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
| | - Olivier Colliot
- Institut du Cerveau, Sorbonne Université, Paris75013, France
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, German Center for Neurodegenerative Diseases, University Medicine Greifswald, Greifswald17475, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, German Center for Neurodegenerative Diseases, University Medicine Greifswald, Greifswald17475, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Susan R. Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA98195
| | - Thomas R. Austin
- Department of Epidemiology, University of Washington, Seattle, WA98195
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Washington, MD20817
| | - Mark Espeland
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Divisions of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC27101
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC3010, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC3010, Australia
| | - Jurgen Fripp
- Health and Biosecurity, Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD4029, Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer's Institute, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI53792
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Department of Neurology, Washington University in St. Louis, St. Louis, MO63110
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD21205
| | - R. Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Yong Fan
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX78229
| | - David Wolk
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA19104
| | - Haochang Shou
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Christos Davatzikos
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| |
Collapse
|
72
|
Sigala RE, Lagou V, Shmeliov A, Atito S, Kouchaki S, Awais M, Prokopenko I, Mahdi A, Demirkan A. Machine Learning to Advance Human Genome-Wide Association Studies. Genes (Basel) 2023; 15:34. [PMID: 38254924 PMCID: PMC10815885 DOI: 10.3390/genes15010034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/19/2023] [Accepted: 12/22/2023] [Indexed: 01/24/2024] Open
Abstract
Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet, and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist's perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.
Collapse
Affiliation(s)
- Rafaella E. Sigala
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
| | - Vasiliki Lagou
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
| | - Aleksey Shmeliov
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
| | - Sara Atito
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, Surrey, UK
| | - Samaneh Kouchaki
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, Surrey, UK
| | - Muhammad Awais
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, Surrey, UK
| | - Inga Prokopenko
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
| | - Adam Mahdi
- Oxford Internet Institute, University of Oxford, Oxford OX1 3JS, Oxfordshire, UK;
| | - Ayse Demirkan
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
| |
Collapse
|
73
|
Panoyan MA, Shi Y, Abbatangelo CL, Adler N, Moo-Choy A, Parra EJ, Polimanti R, Hu P, Wendt FR. Exome-wide tandem repeats confer large effects on subcortical volumes in UK Biobank participants. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.11.23299818. [PMID: 38168307 PMCID: PMC10760277 DOI: 10.1101/2023.12.11.23299818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
The human subcortex is involved in memory and cognition. Structural and functional changes in subcortical regions is implicated in psychiatric conditions. We performed an association study of subcortical volumes using 15,941 tandem repeats (TRs) derived from whole exome sequencing (WES) data in 16,527 unrelated European ancestry participants. We identified 17 loci, most of which were associated with accumbens volume, and nine of which had fine-mapping probability supporting their causal effect on subcortical volume independent of surrounding variation. The most significant association involved NTN1 -[GCGG] N and increased accumbens volume (β=5.93, P=8.16x10 -9 ). Three exonic TRs had large effects on thalamus volume ( LAT2 -[CATC] N β=-949, P=3.84x10 -6 and SLC39A4 -[CAG] N β=-1599, P=2.42x10 -8 ) and pallidum volume ( MCM2 -[AGG] N β=-404.9, P=147x10 -7 ). These genetic effects were consistent measurements of per-repeat expansion/contraction effects on organism fitness. With 3-dimensional modeling, we reinforced these effects to show that the expanded and contracted LAT2 -[CATC] N repeat causes a frameshift mutation that prevents appropriate protein folding. These TRs also exhibited independent effects on several psychiatric symptoms, including LAT2 -[CATC] N and the tiredness/low energy symptom of depression (β=0.340, P=0.003). These findings link genetic variation to tractable biology in the brain and relevant psychiatric symptoms. We also chart one pathway for TR prioritization in future complex trait genetic studies.
Collapse
|
74
|
Liu M, Khasiyev F, Sariya S, Spagnolo‐Allende A, Sanchez DL, Andrews H, Yang Q, Beiser A, Qiao Y, Thomas EA, Romero JR, Rundek T, Brickman AM, Manly JJ, Elkind MSV, Seshadri S, Chen C, Hilal S, Wasserman BA, Tosto G, Fornage M, Gutierrez J. Chromosome 10q24.32 Variants Associate With Brain Arterial Diameters in Diverse Populations: A Genome-Wide Association Study. J Am Heart Assoc 2023; 12:e030935. [PMID: 38038215 PMCID: PMC10727334 DOI: 10.1161/jaha.123.030935] [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: 05/19/2023] [Accepted: 10/19/2023] [Indexed: 12/02/2023]
Abstract
BACKGROUND Brain arterial diameters (BADs) are novel imaging biomarkers of cerebrovascular disease, cognitive decline, and dementia. Traditional vascular risk factors have been associated with BADs, but whether there may be genetic determinants of BADs is unknown. METHODS AND RESULTS The authors studied 4150 participants from 6 geographically diverse population-based cohorts (40% European, 14% African, 22% Hispanic, 24% Asian ancestries). Brain arterial diameters for 13 segments were measured and averaged to obtain a global measure of BADs as well as the posterior and anterior circulations. A genome-wide association study revealed 14 variants at one locus associated with global BAD at genome-wide significance (P<5×10-8) (top single-nucleotide polymorphism, rs7921574; β=0.06 [P=1.54×10-8]). This locus mapped to an intron of CNNM2. A trans-ancestry genome-wide association study meta-analysis identified 2 more loci at NT5C2 (rs10748839; P=2.54×10-8) and AS3MT (rs10786721; P=4.97×10-8), associated with global BAD. In addition, 2 single-nucleotide polymorphisms colocalized with expression of CNNM2 (rs7897654; β=0.12 [P=6.17×10-7]) and AL356608.1 (rs10786719; β=-0.17 [P=6.60×10-6]) in brain tissue. For the posterior BAD, 2 variants at one locus mapped to an intron of TCF25 were identified (top single-nucleotide polymorphism, rs35994878; β=0.11 [P=2.94×10-8]). For the anterior BAD, one locus at ADAP1 was identified in trans-ancestry genome-wide association analysis (rs34217249; P=3.11×10-8). CONCLUSIONS The current study reveals 3 novel risk loci (CNNM2, NT5C2, and AS3MT) associated with BADs. These findings may help elucidate the mechanism by which BADs may influence cerebrovascular health.
Collapse
Affiliation(s)
- Minghua Liu
- Department of Neurology, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
| | - Farid Khasiyev
- Department of NeurologySaint Louis University School of MedicineSt. LouisMOUSA
| | - Sanjeev Sariya
- Department of Neurology, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
- The Gertrude H. Sergievsky Center, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
| | - Antonio Spagnolo‐Allende
- Department of Neurology, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
| | - Danurys L Sanchez
- Department of Neurology, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
- The Gertrude H. Sergievsky Center, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
| | - Howard Andrews
- Biostatistics Department, Mailman School of Public HealthColumbia UniversityNew YorkNYUSA
| | - Qiong Yang
- Department of Biostatistics, School of Public HealthBoston UniversityBostonMAUSA
| | - Alexa Beiser
- Department of Biostatistics, School of Public HealthBoston UniversityBostonMAUSA
| | - Ye Qiao
- Johns Hopkins University School of MedicineBaltimoreMDUSA
| | - Emy A Thomas
- Brown Foundation Institute of Molecular Medicine, McGovern Medical SchoolThe University of Texas Health Science Center at HoustonHoustonTXUSA
| | | | - Tatjana Rundek
- Department of NeurologyUniversity of Miami Miller School of MedicineMiamiFLUSA
- Department of Public Health SciencesUniversity of Miami Miller School of MedicineMiamiFLUSA
- Evelyn F. McKnight Brain InstituteUniversity of Miami Miller School of MedicineMiamiFLUSA
| | - Adam M Brickman
- Department of Neurology, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
- The Gertrude H. Sergievsky Center, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
| | - Jennifer J Manly
- Department of Neurology, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
- The Gertrude H. Sergievsky Center, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
| | - Mitchell SV Elkind
- Department of Neurology, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
- Department of Epidemiology, Mailman School of Public HealthColumbia UniversityNew YorkNYUSA
| | - Sudha Seshadri
- Department of NeurologyBoston University School of MedicineBostonMAUSA
- The Glenn Biggs Institute for Alzheimer’s and Neurodegenerative DiseasesUniversity of Texas Health Sciences CenterSan AntonioTXUSA
| | - Christopher Chen
- Memory Aging and Cognition Center, Department of Pharmacology, Yong Loo Lin School of MedicineNational University of SingaporeSingapore
| | - Saima Hilal
- Memory Aging and Cognition Center, Department of Pharmacology, Yong Loo Lin School of MedicineNational University of SingaporeSingapore
| | - Bruce A Wasserman
- Johns Hopkins University School of MedicineBaltimoreMDUSA
- University of Maryland School of MedicineBaltimoreMDUSA
| | - Giuseppe Tosto
- Department of Neurology, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
- The Gertrude H. Sergievsky Center, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical SchoolThe University of Texas Health Science Center at HoustonHoustonTXUSA
- Human Genetics Center, School of Public HealthThe University of Texas Health Science Center at HoustonHoustonTXUSA
| | - Jose Gutierrez
- Department of Neurology, Vagelos College of Physicians and SurgeonsColumbia UniversityNew YorkNYUSA
| |
Collapse
|
75
|
Zhu W, Liu F, Fu J, Qin W, Xue K, Tang J, Zhang Y, Yu C. Genes associated with spontaneous brain activity changes in clinically different patients with major depressive disorder: A transcription-neuroimaging association study. CNS Neurosci Ther 2023; 29:3913-3924. [PMID: 37311691 PMCID: PMC10651976 DOI: 10.1111/cns.14311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 05/19/2023] [Accepted: 06/02/2023] [Indexed: 06/15/2023] Open
Abstract
AIMS The amplitude of low-frequency fluctuations (ALFF) of resting-state functional MRI signals is a reliable neuroimaging measure of spontaneous brain activity. Inconsistent ALFF alterations have been reported in major depressive disorder (MDD) possibly due to clinical heterogeneity. This study was designed to investigate clinically sensitive and insensitive genes associated with ALFF alterations in MDD and the potential mechanisms. METHODS Transcription-neuroimaging association analyses of case-control ALFF differences from two independent neuroimaging datasets with gene expression data from Allen Human Brain Atlas were performed to identify the two gene sets. Various enrichment analyses were conducted to characterize their preference in biological functions, cell types, temporal stages, and shared effects with other psychiatric disorders. RESULTS Compared with controls, first-episode and drug-naïve patients showed more extensive ALFF alterations than patients with varied clinical features. We identified 903 clinically sensitive genes and 633 clinically insensitive genes, and the former was enriched for genes with down-regulated expression in the cerebral cortex of MDD patients. Despite shared functions of cell communication, signaling, and transport, clinically sensitive genes were enriched for cell differentiation and development whereas clinically insensitive genes were for ion transport and synaptic signaling. Clinically sensitive genes showed enrichment for microglia and macrophage from childhood to young adulthood in contrast to clinically insensitive genes for neurons before early infancy. Clinically sensitive genes (15.2%) were less likely correlated with ALFF alterations in schizophrenia than clinically insensitive genes (66.8%), and both were not relevant to bipolar disorder and adult attention deficit and hyperactivity disorder based on a third independent neuroimaging dataset. CONCLUSIONS Present results provide novel insights into the molecular mechanisms of spontaneous brain activity changes in clinically different patients with MDD.
Collapse
Affiliation(s)
- Wenshuang Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Jilian Fu
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Jie Tang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | | | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of SciencesShanghaiChina
| |
Collapse
|
76
|
Ma DR, Li SJ, Shi JJ, Liang YY, Hu ZW, Hao XY, Li MJ, Guo MN, Zuo CY, Yu WK, Mao CY, Tang MB, Zhang C, Xu YM, Wu J, Sun SL, Shi CH. Shared Genetic Architecture between Parkinson's Disease and Brain Structural Phenotypes. Mov Disord 2023; 38:2258-2268. [PMID: 37990409 DOI: 10.1002/mds.29598] [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: 05/10/2023] [Revised: 08/02/2023] [Accepted: 08/21/2023] [Indexed: 11/23/2023] Open
Abstract
BACKGROUND Patients with Parkinson's disease (PD) have consistently demonstrated brain structure abnormalities, indicating the presence of shared etiological and pathological processes between PD and brain structures; however, the genetic relationship remains poorly understood. OBJECTIVE The aim of this study was to investigate the extent of shared genetic architecture between PD and brain structural phenotypes (BSPs) and to identify shared genomic loci. METHODS We used the summary statistics from genome-wide association studies to conduct MiXeR and conditional/conjunctional false discovery rate analyses to investigate the shared genetic signatures between PD and BSPs. Subsequent expression quantitative trait loci mapping in the human brain and enrichment analyses were also performed. RESULTS MiXeR analysis identified genetic overlap between PD and various BSPs, including total cortical surface area, average cortical thickness, and specific brain volumetric structures. Further analysis using conditional false discovery rate (FDR) identified 21 novel PD risk loci on associations with BSPs at conditional FDR < 0.01, and the conjunctional FDR analysis demonstrated that PD shared several genomic loci with certain BSPs at conjunctional FDR < 0.05. Among the shared loci, 16 credible mapped genes showed high expression in the brain tissues and were primarily associated with immune function-related biological processes. CONCLUSIONS We confirmed the polygenic overlap with mixed directions of allelic effects between PD and BSPs and identified multiple shared genomic loci and risk genes, which are likely related to immune-related biological processes. These findings provide insight into the complex genetic architecture associated with PD. © 2023 International Parkinson and Movement Disorder Society.
Collapse
Affiliation(s)
- Dong-Rui Ma
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Shuang-Jie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Jing-Jing Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Yuan-Yuan Liang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Zheng-Wei Hu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Xiao-Yan Hao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Meng-Jie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Meng-Nan Guo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Chun-Yan Zuo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Wen-Kai Yu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Cheng-Yuan Mao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Mi-Bo Tang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Chan Zhang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Yu-Ming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| | - Jun Wu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| | - Shi-Lei Sun
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| | - Chang-He Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, China
| |
Collapse
|
77
|
Seidlitz J, Mallard TT, Vogel JW, Lee YH, Warrier V, Ball G, Hansson O, Hernandez LM, Mandal AS, Wagstyl K, Lombardo MV, Courchesne E, Glessner JT, Satterthwaite TD, Bethlehem RAI, Bernstock JD, Tasaki S, Ng B, Gaiteri C, Smoller JW, Ge T, Gur RE, Gandal MJ, Alexander-Bloch AF. The molecular genetic landscape of human brain size variation. Cell Rep 2023; 42:113439. [PMID: 37963017 DOI: 10.1016/j.celrep.2023.113439] [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: 11/09/2022] [Revised: 06/13/2023] [Accepted: 10/27/2023] [Indexed: 11/16/2023] Open
Abstract
Human brain size changes dynamically through early development, peaks in adolescence, and varies up to 2-fold among adults. However, the molecular genetic underpinnings of interindividual variation in brain size remain unknown. Here, we leveraged postmortem brain RNA sequencing and measurements of brain weight (BW) in 2,531 individuals across three independent datasets to identify 928 genome-wide significant associations with BW. Genes associated with higher or lower BW showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto functional and cellular axes of brain organization. Expression of BW genes was predictive of interspecies differences in brain size, and bioinformatic annotation revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses linked BW gene sets to neuroimaging measurements of brain size and brain-related clinical traits. Cumulatively, these results represent a major step toward delineating the molecular pathways underlying human brain size variation in health and disease.
Collapse
Affiliation(s)
- Jakob Seidlitz
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Younga H Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, UK; Department of Psychology, University of Cambridge, Cambridge CB2 1TN, UK
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Melbourne, VIC 3052, Australia
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö P663+Q9, Sweden; Memory Clinic, Skåne University Hospital, Malmö P663+Q9, Sweden
| | - Leanna M Hernandez
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Ayan S Mandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Michael V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
| | - Eric Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA 92093, USA; Autism Center of Excellence, University of California, San Diego, San Diego, CA 92093, USA
| | - Joseph T Glessner
- The Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | | | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard University, Boston, MA 02115, USA; Department of Neurosurgery, Boston Children's Hospital, Harvard University, Boston, MA 02115, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Bernard Ng
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Raquel E Gur
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael J Gandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
78
|
Madden DJ, Merenstein JL. Quantitative susceptibility mapping of brain iron in healthy aging and cognition. Neuroimage 2023; 282:120401. [PMID: 37802405 PMCID: PMC10797559 DOI: 10.1016/j.neuroimage.2023.120401] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/14/2023] [Accepted: 09/30/2023] [Indexed: 10/10/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging (MRI) technique that can assess the magnetic properties of cerebral iron in vivo. Although brain iron is necessary for basic neurobiological functions, excess iron content disrupts homeostasis, leads to oxidative stress, and ultimately contributes to neurodegenerative disease. However, some degree of elevated brain iron is present even among healthy older adults. To better understand the topographical pattern of iron accumulation and its relation to cognitive aging, we conducted an integrative review of 47 QSM studies of healthy aging, with a focus on five distinct themes. The first two themes focused on age-related increases in iron accumulation in deep gray matter nuclei versus the cortex. The overall level of iron is higher in deep gray matter nuclei than in cortical regions. Deep gray matter nuclei vary with regard to age-related effects, which are most prominent in the putamen, and age-related deposition of iron is also observed in frontal, temporal, and parietal cortical regions during healthy aging. The third theme focused on the behavioral relevance of iron content and indicated that higher iron in both deep gray matter and cortical regions was related to decline in fluid (speed-dependent) cognition. A handful of multimodal studies, reviewed in the fourth theme, suggest that iron interacts with imaging measures of brain function, white matter degradation, and the accumulation of neuropathologies. The final theme concerning modifiers of brain iron pointed to potential roles of cardiovascular, dietary, and genetic factors. Although QSM is a relatively recent tool for assessing cerebral iron accumulation, it has significant promise for contributing new insights into healthy neurocognitive aging.
Collapse
Affiliation(s)
- David J Madden
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC 27710, USA; Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, USA; Center for Cognitive Neuroscience, Duke University, Durham, NC 27708, USA.
| | - Jenna L Merenstein
- Brain Imaging and Analysis Center, Duke University Medical Center, Box 3918, Durham, NC 27710, USA
| |
Collapse
|
79
|
Tang D, Freudenberg J, Dahl A. Factorizing polygenic epistasis improves prediction and uncovers biological pathways in complex traits. Am J Hum Genet 2023; 110:1875-1887. [PMID: 37922884 PMCID: PMC10645564 DOI: 10.1016/j.ajhg.2023.10.002] [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: 06/05/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Epistasis is central in many domains of biology, but it has not yet been proven useful for understanding the etiology of complex traits. This is partly because complex-trait epistasis involves polygenic interactions that are poorly captured in current models. To address this gap, we developed a model called Epistasis Factor Analysis (EFA). EFA assumes that polygenic epistasis can be factorized into interactions between a few epistasis factors (EFs), which represent latent polygenic components of the observed complex trait. The statistical goals of EFA are to improve polygenic prediction and to increase power to detect epistasis, while the biological goal is to unravel genetic effects into more-homogeneous units. We mathematically characterize EFA and use simulations to show that EFA outperforms current epistasis models when its assumptions approximately hold. Applied to predicting yeast growth rates, EFA outperforms the additive model for several traits with large epistasis heritability and uniformly outperforms the standard epistasis model. We replicate these prediction improvements in a second dataset. We then apply EFA to four previously characterized traits in the UK Biobank and find statistically significant epistasis in all four, including two that are robust to scale transformation. Moreover, we find that the inferred EFs partly recover pre-defined biological pathways for two of the traits. Our results demonstrate that more realistic models can identify biologically and statistically meaningful epistasis in complex traits, indicating that epistasis has potential for precision medicine and characterizing the biology underlying GWAS results.
Collapse
Affiliation(s)
- David Tang
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA, USA.
| | - Jerome Freudenberg
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Andy Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
| |
Collapse
|
80
|
Bao J, Wen J, Wen Z, Yang S, Cui Y, Yang Z, Erus G, Saykin AJ, Long Q, Davatzikos C, Shen L. Brain-wide genome-wide colocalization study for integrating genetics, transcriptomics and brain morphometry in Alzheimer's disease. Neuroimage 2023; 280:120346. [PMID: 37634885 PMCID: PMC10552907 DOI: 10.1016/j.neuroimage.2023.120346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 06/19/2023] [Accepted: 08/22/2023] [Indexed: 08/29/2023] Open
Abstract
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. However, the AD mechanism has not yet been fully elucidated to date, hindering the development of effective therapies. In our work, we perform a brain imaging genomics study to link genetics, single-cell gene expression data, tissue-specific gene expression data, brain imaging-derived volumetric endophenotypes, and disease diagnosis to discover potential underlying neurobiological pathways for AD. To do so, we perform brain-wide genome-wide colocalization analyses to integrate multidimensional imaging genomic biobank data. Specifically, we use (1) the individual-level imputed genotyping data and magnetic resonance imaging (MRI) data from the UK Biobank, (2) the summary statistics of the genome-wide association study (GWAS) from multiple European ancestry cohorts, and (3) the tissue-specific cis-expression quantitative trait loci (cis-eQTL) summary statistics from the GTEx project. We apply a Bayes factor colocalization framework and mediation analysis to these multi-modal imaging genomic data. As a result, we derive the brain regional level GWAS summary statistics for 145 brain regions with 482,831 single nucleotide polymorphisms (SNPs) followed by posthoc functional annotations. Our analysis yields the discovery of a potential AD causal pathway from a systems biology perspective: the SNP chr10:124165615:G>A (rs6585827) mutation upregulates the expression of BTBD16 gene in oligodendrocytes, a specialized glial cells, in the brain cortex, leading to a reduced risk of volumetric loss in the entorhinal cortex, resulting in the protective effect on AD. We substantiate our findings with multiple evidence from existing imaging, genetic and genomic studies in AD literature. Our study connects genetics, molecular and cellular signatures, regional brain morphologic endophenotypes, and AD diagnosis, providing new insights into the mechanistic understanding of the disease. Our findings can provide valuable guidance for subsequent therapeutic target identification and drug discovery in AD.
Collapse
Affiliation(s)
- Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA; Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA 90292, USA
| | - Zixuan Wen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Yuhan Cui
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Zhijian Yang
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA.
| |
Collapse
|
81
|
Morey R, Zheng Y, Sun D, Garrett M, Gasperi M, Maihofer A, Baird CL, Grasby K, Huggins A, Haswell C, Thompson P, Medland S, Gustavson D, Panizzon M, Kremen W, Nievergelt C, Ashley-Koch A, Logue L. Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture. RESEARCH SQUARE 2023:rs.3.rs-3253035. [PMID: 37886496 PMCID: PMC10602057 DOI: 10.21203/rs.3.rs-3253035/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for the 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs. The multivariate GWASs of these GIBNs identified 74 genome-wide significant (GWS) loci (p<5×10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed with attention deficit hyperactivity disorder (ADHD), major depressive disorder (MDD), and insomnia, indicating genetic predisposition to a larger SA in the specific GIBN is associated with lower genetic risk of these disorders. CT GIBNs displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping the cortex into genetically informed networks.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Paul Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of the University of Southern California, Marina del Rey, California, USA
| | | | | | | | | | | | | | | |
Collapse
|
82
|
Lin S, Zhang H, Qi M, Cooper DN, Yang Y, Yang Y, Zhao H. Inferring the genetic relationship between brain imaging-derived phenotypes and risk of complex diseases by Mendelian randomization and genome-wide colocalization. Neuroimage 2023; 279:120325. [PMID: 37579999 DOI: 10.1016/j.neuroimage.2023.120325] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/09/2023] [Accepted: 08/11/2023] [Indexed: 08/16/2023] Open
Abstract
Observational studies consistently disclose brain imaging-derived phenotypes (IDPs) as critical markers for early diagnosis of both brain disorders and cardiovascular diseases. However, it remains unclear about the shared genetic landscape between brain IDPs and the risk of brain disorders and cardiovascular diseases, restricting the applications of potential diagnostic techniques through brain IDPs. Here, we reported genetic correlations and putative causal relationships between 921 brain IDPs, 20 brain disorders and six cardiovascular diseases by leveraging their large-scale genome-wide association study (GWAS) summary statistics. Applications of Mendelian randomization (MR) identified significant putative causal effects of multiple region-specific brain IDPs in relation to the increased risks for amyotrophic lateral sclerosis (ALS), major depressive disorder (MDD), autism spectrum disorder (ASD) and schizophrenia (SCZ). We also found brain IDPs specifically from temporal lobe as a putatively causal consequence of hypertension. The genome-wide colocalization analysis identified three genomic regions in which MDD, ASD and SCZ colocalized with the brain IDPs, and two novel SNPs to be associated with ASD, SCZ, and multiple brain IDPs. Furthermore, we identified a list of candidate genes involved in the shared genetics underlying pairs of brain IDPs and MDD, ASD, SCZ, ALS and hypertension. Our results provide novel insights into the genetic relationships between brain disorders and cardiovascular diseases and brain IDP, which may server as clues for using brain IDPs to predict risks of diseases.
Collapse
Affiliation(s)
- Siying Lin
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China
| | - Haoyang Zhang
- School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China
| | - Mengling Qi
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - David N Cooper
- Institute of Medical Genetics, School of Medicine, Cardiff University, Heath Park, Cardiff CF14 4XN, United Kingdom
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510006, China.
| | - Yuanhao Yang
- Mater Research Institute, The University of Queensland, Brisbane, QLD, Australia.
| | - Huiying Zhao
- Department of Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| |
Collapse
|
83
|
Barbu MC, Viejo-Romero M, Thng G, Adams MJ, Marwick K, Grant SG, McIntosh AM, Lawrie SM, Whalley HC. Pathway-Based Polygenic Risk Scores for Schizophrenia and Associations With Reported Psychotic-like Experiences and Neuroimaging Phenotypes in the UK Biobank. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2023; 3:814-823. [PMID: 37881537 PMCID: PMC10593950 DOI: 10.1016/j.bpsgos.2023.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 03/02/2023] [Accepted: 03/05/2023] [Indexed: 03/28/2023] Open
Abstract
Background Schizophrenia is a heritable psychiatric disorder with a polygenic architecture. Genome-wide association studies have reported that an increasing number of risk-associated variants and polygenic risk scores (PRSs) explain 17% of the variance in the disorder. Substantial heterogeneity exists in the effect of these variants, and aggregating them based on biologically relevant functions may provide mechanistic insight into the disorder. Methods Using the largest schizophrenia genome-wide association study conducted to date, we associated PRSs based on 5 gene sets previously found to contribute to schizophrenia pathophysiology-postsynaptic density of excitatory synapses, postsynaptic membrane, dendritic spine, axon, and histone H3-K4 methylation-along with respective whole-genome PRSs, with neuroimaging (n > 29,000) and reported psychotic-like experiences (n > 119,000) variables in healthy UK Biobank subjects. Results Several variables were significantly associated with the axon gene-set (psychotic-like communications, parahippocampal gyrus volume, fractional anisotropy thalamic radiations, and fractional anisotropy posterior thalamic radiations (β range -0.016 to 0.0916, false discovery rate-corrected p [pFDR] ≤ .05), postsynaptic density gene-set (psychotic-like experiences distress, global surface area, and cingulate lobe surface area [β range -0.014 to 0.0588, pFDR ≤ .05]), and histone gene set (entorhinal surface area: β = -0.016, pFDR = .035). From these, whole-genome PRSs were significantly associated with psychotic-like communications (β = 0.2218, pFDR = 1.34 × 10-7), distress (β = 0.1943, pFDR = 7.28 × 10-16), and fractional anisotropy thalamic radiations (β = -0.0143, pFDR = .036). Permutation analysis revealed that these associations were not due to chance. Conclusions Our results indicate that genetic variation in 3 gene sets relevant to schizophrenia may confer risk for the disorder through effects on previously implicated neuroimaging variables. Because associations were stronger overall for whole-genome PRSs, findings here highlight that selection of biologically relevant variants is not yet sufficient to address the heterogeneity of the disorder.
Collapse
Affiliation(s)
- Miruna C. Barbu
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| | - Maria Viejo-Romero
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| | - Gladi Thng
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| | - Mark J. Adams
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| | - Katie Marwick
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| | - Seth G.N. Grant
- Genes to Cognition Program, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, Scotland, United Kingdom
| | - Andrew M. McIntosh
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| | - Stephen M. Lawrie
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| | - Heather C. Whalley
- Division of Psychiatry, The University of Edinburgh, Royal Edinburgh Hospital, Edinburgh, Scotland, United Kingdom
| |
Collapse
|
84
|
Wen J, Zhao B, Yang Z, Erus G, Skampardoni I, Mamourian E, Cui Y, Hwang G, Bao J, Boquet-Pujadas A, Zhou Z, Veturi Y, Ritchie MD, Shou H, Thompson PM, Shen L, Toga AW, Davatzikos C. The Genetic Architecture of Multimodal Human Brain Age. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.13.536818. [PMID: 37333190 PMCID: PMC10274645 DOI: 10.1101/2023.04.13.536818] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N=31,557 European ancestry), white matter microstructure (WM-BAG, N=31,674), and functional connectivity (FC-BAG, N=32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5×10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5' untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96×10-2) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18×10-5). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.
Collapse
Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | | | - Zhen Zhou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yogasudha Veturi
- Department of Biobehavioral Health and Statistics, Penn State University, University Park, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, California
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| |
Collapse
|
85
|
Zhao B, Li T, Li Y, Fan Z, Xiong D, Wang X, Gao M, Smith SM, Zhu H. An atlas of trait associations with resting-state and task-evoked human brain functional organizations in the UK Biobank. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:1-23. [PMID: 38770197 PMCID: PMC11105703 DOI: 10.1162/imag_a_00015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Functional magnetic resonance imaging (fMRI) has been widely used to identify brain regions linked to critical functions, such as language and vision, and to detect tumors, strokes, brain injuries, and diseases. It is now known that large sample sizes are necessary for fMRI studies to detect small effect sizes and produce reproducible results. Here we report a systematic association analysis of 647 traits with imaging features extracted from resting-state and task-evoked fMRI data of more than 40,000 UK Biobank participants. We used a parcellation-based approach to generate 64,620 functional connectivity measures to reveal fine-grained details about cerebral cortex functional organizations. The difference between functional organizations at rest and during task was examined, and we have prioritized important brain regions and networks associated with a variety of human traits and clinical outcomes. For example, depression was most strongly associated with decreased connectivity in the somatomotor network. We have made our results publicly available and developed a browser framework to facilitate the exploration of brain function-trait association results (http://fmriatlas.org/).
Collapse
Affiliation(s)
- Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
- These authors contributed equally to this work
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- These authors contributed equally to this work
| | - Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Di Xiong
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mufeng Gao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Hongtu Zhu
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| |
Collapse
|
86
|
Yu G, Liu Z, Wu X, Becker B, Zhang K, Fan H, Peng S, Kuang N, Kang J, Dong G, Zhao XM, Schumann G, Feng J, Sahakian BJ, Robbins TW, Palaniyappan L, Zhang J. Common and disorder-specific cortical thickness alterations in internalizing, externalizing and thought disorders during early adolescence: an Adolescent Brain and Cognitive Development study. J Psychiatry Neurosci 2023; 48:E345-E356. [PMID: 37673436 PMCID: PMC10495167 DOI: 10.1503/jpn.220202] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 02/13/2023] [Accepted: 05/17/2023] [Indexed: 09/08/2023] Open
Abstract
BACKGROUND A growing body of neuroimaging studies has reported common neural abnormalities among mental disorders in adults. However, it is unclear whether the distinct disorder-specific mechanisms operate during adolescence despite the overlap among disorders. METHODS We studied a large cohort of more than 11 000 preadolescent (age 9-10 yr) children from the Adolescent Brain and Cognitive Development cohort. We adopted a regrouping approach to compare cortical thickness (CT) alterations and longitudinal changes between healthy controls (n = 4041) and externalizing (n = 1182), internalizing (n = 1959) and thought disorder (n = 347) groups. Genome-wide association study (GWAS) was performed on regional CT across 4468 unrelated European youth. RESULTS Youth with externalizing or internalizing disorders exhibited increased regional CT compared with controls. Externalizing (p = 8 × 10-4, Cohen d = 0.10) and internalizing disorders (p = 2 × 10-3, Cohen d = 0.08) shared thicker CT in the left pars opercularis. The somatosensory and the primary auditory cortex were uniquely affected in externalizing disorders, whereas the primary motor cortex and higher-order visual association areas were uniquely affected in internalizing disorders. Only youth with externalizing disorders showed decelerated cortical thinning from age 10-12 years. The GWAS found 59 genome-wide significant associated genetic variants across these regions. Cortical thickness in common regions was associated with glutamatergic neurons, while internalizing-specific regional CT was associated with astrocytes, oligodendrocyte progenitor cells and GABAergic neurons. LIMITATIONS The sample size of the GWAS was relatively small. CONCLUSION Our study provides strong evidence for the presence of specificity in CT, developmental trajectories and underlying genetic underpinnings among externalizing and internalizing disorders during early adolescence. Our results support the neurobiological validity of the regrouping approach that could supplement the use of a dimensional approach in future clinical practice.
Collapse
Affiliation(s)
- Gechang Yu
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Zhaowen Liu
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Xinran Wu
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Benjamin Becker
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Kai Zhang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Huaxin Fan
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Songjun Peng
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Nanyu Kuang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Jujiao Kang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Guiying Dong
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Xing-Ming Zhao
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Gunter Schumann
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Jianfeng Feng
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Barbara J Sahakian
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Trevor W Robbins
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Lena Palaniyappan
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| | - Jie Zhang
- From the Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China (Yu, Wu, Fan, Peng, Kuang, Kang, Dong, Zhao, Feng, Sahakian, Robbins, Zhang); the Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, China (Yu, Wu, Fan, Peng, Kuang, Feng, Zhang); the Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Mass., USA (Liu); the Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Mass., USA (Liu); the Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Mass., USA (Liu); the Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China (Becker); the School of Computer Science and Technology, East China Normal University, Shanghai, China (Zhang); the Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China (Kang); the MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China (Dong, Zhao); the Zhangjiang Fudan International Innovation Center, Shanghai, China (Zhao); the PONS Centre Shanghai, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China (Schumann); the PONS Centre Berlin, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany (Feng); the Shanghai Center for Mathematical Sciences, Shanghai, China (Feng); the Department of Computer Science, University of Warwick, Coventry, UK (Feng); the Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China (Feng); the Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China (Feng); the Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK (Sahakian); the Department of Psychology, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK (Robbins); the Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Que., Canada (Palaniyappan); the Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan); the Robarts Research Institute, Western University, London, Ont., Canada (Palaniyappan); the Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ont., Canada (Palaniyappan)
| |
Collapse
|
87
|
Warrier V, Stauffer EM, Huang QQ, Wigdor EM, Slob EAW, Seidlitz J, Ronan L, Valk SL, Mallard TT, Grotzinger AD, Romero-Garcia R, Baron-Cohen S, Geschwind DH, Lancaster MA, Murray GK, Gandal MJ, Alexander-Bloch A, Won H, Martin HC, Bullmore ET, Bethlehem RAI. Genetic insights into human cortical organization and development through genome-wide analyses of 2,347 neuroimaging phenotypes. Nat Genet 2023; 55:1483-1493. [PMID: 37592024 PMCID: PMC10600728 DOI: 10.1038/s41588-023-01475-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 07/13/2023] [Indexed: 08/19/2023]
Abstract
Our understanding of the genetics of the human cerebral cortex is limited both in terms of the diversity and the anatomical granularity of brain structural phenotypes. Here we conducted a genome-wide association meta-analysis of 13 structural and diffusion magnetic resonance imaging-derived cortical phenotypes, measured globally and at 180 bilaterally averaged regions in 36,663 individuals and identified 4,349 experiment-wide significant loci. These phenotypes include cortical thickness, surface area, gray matter volume, measures of folding, neurite density and water diffusion. We identified four genetic latent structures and causal relationships between surface area and some measures of cortical folding. These latent structures partly relate to different underlying gene expression trajectories during development and are enriched for different cell types. We also identified differential enrichment for neurodevelopmental and constrained genes and demonstrate that common genetic variants associated with cortical expansion are associated with cephalic disorders. Finally, we identified complex interphenotype and inter-regional genetic relationships among the 13 phenotypes, reflecting the developmental differences among them. Together, these analyses identify distinct genetic organizational principles of the cortex and their correlates with neurodevelopment.
Collapse
Affiliation(s)
- Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Department of Psychology, University of Cambridge, Cambridge, UK.
| | | | | | | | - Eric A W Slob
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Department of Applied Economics, Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, the Netherlands
- Erasmus University Rotterdam Institute for Behavior and Biology, Erasmus University Rotterdam, Rotterdam, the Netherlands
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Lisa Ronan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Sofie L Valk
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, FZ Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Andrew D Grotzinger
- Department of Psychology and Neuroscience, University of Colorado at Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Rafael Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Dpto. de Fisiología Médica y Biofísica, Seville, Spain
| | - Simon Baron-Cohen
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Daniel H Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Program in Neurogenetics, Department of Neurology, University of California, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Jane and TerrySemel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Institute of Precision Health, University of California, Los Angeles, CA, USA
| | - Madeline A Lancaster
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge, UK
| | - Graham K Murray
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Trust, Cambridge, UK
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Michael J Gandal
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - Aaron Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Hyejung Won
- Department of Genetics and the Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Trust, Cambridge, UK
| | - Richard A I Bethlehem
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Department of Psychology, University of Cambridge, Cambridge, UK.
| |
Collapse
|
88
|
Singhal P, Tan ALM, Drivas TG, Johnson KB, Ritchie MD, Beaulieu-Jones BK. Opportunities and challenges for biomarker discovery using electronic health record data. Trends Mol Med 2023; 29:765-776. [PMID: 37474378 PMCID: PMC10530198 DOI: 10.1016/j.molmed.2023.06.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/16/2023] [Accepted: 06/22/2023] [Indexed: 07/22/2023]
Abstract
Electronic health records (EHRs) have become increasingly relied upon as a source for biomedical research. One important research application of EHRs is the identification of biomarkers associated with specific patient states, especially within complex conditions. However, using EHRs for biomarker identification can be challenging because the EHR was not designed with research as the primary focus. Despite this challenge, the EHR offers huge potential for biomarker discovery research to transform our understanding of disease etiology and treatment and generate biological insights informing precision medicine initiatives. This review paper provides an in-depth analysis of how EHR data is currently used for phenotyping and identifying molecular biomarkers, current challenges and limitations, and strategies we can take to mitigate challenges going forward.
Collapse
Affiliation(s)
- P Singhal
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - A L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - T G Drivas
- Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - K B Johnson
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, USA
| | - M D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
| | | |
Collapse
|
89
|
Lin BD, Pries LK, van Os J, Luykx JJ, Rutten BPF, Guloksuz S. Adjusting for population stratification in polygenic risk score analyses: a guide for model specifications in the UK Biobank. J Hum Genet 2023; 68:653-656. [PMID: 37188914 DOI: 10.1038/s10038-023-01161-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 04/06/2023] [Accepted: 05/08/2023] [Indexed: 05/17/2023]
Abstract
The current study was conducted to provide a general guidance for model specifications in polygenic risk score (PRS) analyses of the UK Biobank, such as adjusting for covariates (i.e. age, sex, recruitment centers, and genetic batch) and the number of principal components (PCs) that need to be included. To cover behavioral, physical and mental health outcomes, we evaluated three continuous outcomes (BMI, smoking, drinking) and two binary outcomes (Major Depressive Disorder and educational attainment). We applied 3280 (656 per phenotype) different models including different sets of covariates. We evaluated these different model specifications by comparing regression parameters such as R2, coefficients, and P values, as well as ANOVA tests. Findings suggest that only up to three PCs appears to be sufficient for controlling population stratification for most outcomes, whereas including other covariates (particularly age and sex) appears to be more essential for model performance.
Collapse
Affiliation(s)
- Bochao Danae Lin
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Basic Medical Sciences, Henan University, Kaifeng, China
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Lotta-Katrin Pries
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Jim van Os
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Jurjen J Luykx
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- GGNet Mental Health, Warnsveld, The Netherlands
- Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Bart P F Rutten
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Sinan Guloksuz
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands.
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
| |
Collapse
|
90
|
Quarto T, Lella A, Di Carlo P, Rampino A, Paladini V, Papalino M, Romano R, Fazio L, Marvulli D, Popolizio T, Blasi G, Pergola G, Bertolino A. Heritability of amygdala reactivity to angry faces and its replicable association with the schizophrenia risk locus of miR-137. J Psychiatry Neurosci 2023; 48:E357-E366. [PMID: 37751917 PMCID: PMC10521919 DOI: 10.1503/jpn.230013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 07/11/2023] [Accepted: 07/30/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND Among healthy participants, the interindividual variability of brain response to facial emotions is associated with genetic variation, including common risk variants for schizophrenia, a heritable brain disorder characterized by anomalies in emotion processing. We aimed to identify genetic variants associated with heritable brain activity during processing of facial emotions among healthy participants and to explore the impact of these identified variants among patients with schizophrenia. METHODS We conducted a data-driven stepwise study including samples of healthy twins, unrelated healthy participants and patients with schizophrenia. Participants approached or avoided pictures of faces with negative emotional valence during functional magnetic resonance imaging (fMRI). RESULTS We investigated 3 samples of healthy participants - including 28 healthy twin pairs, 289 unrelated healthy participants (genome-wide association study [GWAS] discovery sample) and 90 unrelated healthy participants (replication sample) - and 1 sample of 48 patients with schizophrenia. Among healthy twins, we identified the amygdala as the brain region with the highest heritability during processing of angry faces (heritability estimate 0.54, p < 0.001). Subsequent GWAS in both discovery and replication samples of healthy non-twins indicated that amygdala activity was associated with a polymorphism in the miR-137 locus (rs1198575), a micro-RNA strongly involved in risk for schizophrenia. A significant effect in the same direction was found among patients with schizophrenia (p = 0.03). LIMITATIONS The limited sample size available for GWAS analyses may require further replication of results. CONCLUSION Our data-driven approach shows preliminary evidence that amygdala activity, as evaluated with our task, is heritable. Our genetic associations preliminarily suggest a role for miR-137 in brain activity during explicit processing of facial emotions among healthy participants and patients with schizophrenia, pointing to the amygdala as a brain region whose activity is related to miR-137.
Collapse
Affiliation(s)
- Tiziana Quarto
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Annalisa Lella
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Pasquale Di Carlo
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Antonio Rampino
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Vittoria Paladini
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Marco Papalino
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Raffaella Romano
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Leonardo Fazio
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Daniela Marvulli
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Teresa Popolizio
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Giuseppe Blasi
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Giulio Pergola
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Alessandro Bertolino
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| |
Collapse
|
91
|
Zhang Y, Lu Z, Sun Y, Zhang X, Li Q, Li M, Liao Y, Kang Z, Feng X, Zhao G, Sun J, Yang Y, Yan H, Zhang D, Yue W. Predictive role of pulvinar in social functional outcome of schizophrenia. Psychiatry Res 2023; 327:115419. [PMID: 37598626 DOI: 10.1016/j.psychres.2023.115419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/11/2023] [Accepted: 08/13/2023] [Indexed: 08/22/2023]
Abstract
Identifying objective biological subtypes that predict long-term functional outcomes is crucial for understanding neurobiological mechanisms and identifying potential targets. Using resting-state functional magnetic resonance imaging data from 178 patients and 70 controls, we explored social function patterns using latent profile analysis. Long-term outcomes were compared among the biological subtypes using K-means clustering. Partial least squares regression (PLSR) was used to identify gene expression profiles associated with alterations in activity by leveraging transcriptional data from the Allen Human Brain Atlas. In patients with more functional impairment, left medial pulvinar (PM) exhibited significantly lower regional homogeneity of brain activity (ReHo, [95% CI (0.06-0.27), P = 0.002), a finding validated in the independent cohort. Functional connectivity between PM and secondary visual cortex displayed a suggestive decrease. Patients belonging to "higher pulvinar ReHo - better information processing" demonstrated better long-term outcomes and acute treatment response [95% CI (11.2-34.4), P < 0.001]. The PLSR component of imaging-transcriptomic associations partly explained the ReHo differences among patients with varying levels of functional impairment. It revealed enrichment of genes in the synaptic signaling pathway. Pathological changes in the pulvinar may affect social functioning. Higher pulvinar ReHo and better information processing, two objective biomarkers, have a predictive value for better long-term functional outcomes.
Collapse
Affiliation(s)
- Yuyanan Zhang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Zhe Lu
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Yaoyao Sun
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Xiao Zhang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Qianqian Li
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Mingzhu Li
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Yundan Liao
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Zhewei Kang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Xiaoyang Feng
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Guorui Zhao
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Junyuan Sun
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Yang Yang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Hao Yan
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China
| | - Dai Zhang
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, Institute for Brain Research and Rehabilitation (IBRR), South China Normal University, Guangzhou 510631, China
| | - Weihua Yue
- NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University Sixth Hospital, Peking University Institute of Mental Health, Beijing 100191, China; Chinese Institute for Brain Research, Beijing 102206, China; PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China; Research Unit of Diagnosis and Treatment of Mood Cognitive Disorder (2018RU006), Chinese Academy of Medical Sciences, Beijing 100191, China.
| |
Collapse
|
92
|
Hébert HL, Pascal MM, Smith BH, Wynick D, Bennett DL. Big data, big consortia, and pain: UK Biobank, PAINSTORM, and DOLORisk. Pain Rep 2023; 8:e1086. [PMID: 38225956 PMCID: PMC10789453 DOI: 10.1097/pr9.0000000000001086] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 05/17/2023] [Accepted: 05/20/2023] [Indexed: 01/17/2024] Open
Abstract
Chronic pain (CP) is a common and often debilitating disorder that has major social and economic impacts. A subset of patients develop CP that significantly interferes with their activities of daily living and requires a high level of healthcare support. The challenge for treating physicians is in preventing the onset of refractory CP or effectively managing existing pain. To be able to do this, it is necessary to understand the risk factors, both genetic and environmental, for the onset of CP and response to treatment, as well as the pathogenesis of the disorder, which is highly heterogenous. However, studies of CP, particularly pain with neuropathic characteristics, have been hindered by a lack of consensus on phenotyping and data collection, making comparisons difficult. Furthermore, existing cohorts have suffered from small sample sizes meaning that analyses, especially genome-wide association studies, are insufficiently powered. The key to overcoming these issues is through the creation of large consortia such as DOLORisk and PAINSTORM and biorepositories, such as UK Biobank, where a common approach can be taken to CP phenotyping, which allows harmonisation across different cohorts and in turn increased study power. This review describes the approach that was used for studying neuropathic pain in DOLORisk and how this has informed current projects such as PAINSTORM, the rephenotyping of UK Biobank, and other endeavours. Moreover, an overview is provided of the outputs from these studies and the lessons learnt for future projects.
Collapse
Affiliation(s)
- Harry L. Hébert
- Chronic Pain Research Group, Division of Population Health and Genomics, Ninewells Hospital & Medical School, University of Dundee, Dundee, United Kingdom
| | - Mathilde M.V. Pascal
- Neural Injury Group, Nuffield Department of Clinical Neuroscience, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Blair H. Smith
- Chronic Pain Research Group, Division of Population Health and Genomics, Ninewells Hospital & Medical School, University of Dundee, Dundee, United Kingdom
| | - David Wynick
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - David L.H. Bennett
- Neural Injury Group, Nuffield Department of Clinical Neuroscience, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
93
|
Zeitoun T, El-Sohemy A. Using Mendelian Randomization to Study the Role of Iron in Health and Disease. Int J Mol Sci 2023; 24:13458. [PMID: 37686261 PMCID: PMC10487635 DOI: 10.3390/ijms241713458] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/09/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023] Open
Abstract
Iron has been shown to play a dual role in health and disease, with either a protective or harmful effect. Some of the contradictory findings from observational studies may be due to reverse causation, residual confounding, or small sample size. One approach that may overcome these limitations without the high cost of randomized control trials is the use of Mendelian randomization to examine the long-term role of iron in a variety of health outcomes. As there is emerging evidence employing Mendelian randomization as a method of assessing the role of micronutrients in health and disease, this narrative review will highlight recent Mendelian randomization findings examining the role of iron in cardiometabolic disorders, inflammation, neurological disorders, different cancers, and a number of other health-related outcomes.
Collapse
Affiliation(s)
| | - Ahmed El-Sohemy
- Department of Nutritional Sciences, Temerty Faculty of Medicine, University of Toronto, Medical Sciences Building, Room 5326A, 1 King’s College Circle, Toronto, ON M5S 1A8, Canada;
| |
Collapse
|
94
|
Wang H, Makowski C, Zhang Y, Qi A, Kaufmann T, Smeland OB, Fiecas M, Yang J, Visscher PM, Chen CH. Chromosomal inversion polymorphisms shape human brain morphology. Cell Rep 2023; 42:112896. [PMID: 37505983 PMCID: PMC10508191 DOI: 10.1016/j.celrep.2023.112896] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 06/27/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
The impact of chromosomal inversions on human brain morphology remains underexplored. We studied 35 common inversions classified from genotypes of 33,018 adults with European ancestry. The inversions at 2p22.3, 16p11.2, and 17q21.31 reach genome-wide significance, followed by 8p23.1 and 6p21.33, in their association with cortical and subcortical morphology. The 17q21.31, 8p23.1, and 16p11.2 regions comprise the LRRC37, OR7E, and NPIP duplicated gene families. We find the 17q21.31 MAPT inversion region, known for harboring neurological risk, to be the most salient locus among common variants for shaping and patterning the cortex. Overall, we observe the inverted orientations decreasing brain size, with the exception that the 2p22.3 inversion is associated with increased subcortical volume and the 8p23.1 inversion is associated with increased motor cortex. These significant inversions are in the genomic hotspots of neuropsychiatric loci. Our findings are generalizable to 3,472 children and demonstrate inversions as essential genetic variation to understand human brain phenotypes.
Collapse
Affiliation(s)
- Hao Wang
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Carolina Makowski
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Yanxiao Zhang
- Ludwig Institute for Cancer Research, La Jolla, CA 92093, USA; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Anna Qi
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA
| | - Tobias Kaufmann
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental Health, University of Tübingen, 72076 Tübingen, Germany; Norwegian Centre for Mental Disorders Research, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway
| | - Olav B Smeland
- Norwegian Centre for Mental Disorders Research, Oslo University Hospital and University of Oslo, 0450 Oslo, Norway
| | - Mark Fiecas
- Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN 55455, USA
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Chi-Hua Chen
- Center for Multimodal Imaging and Genetics, University of California San Diego, La Jolla, CA 92093, USA.
| |
Collapse
|
95
|
Yun T, Cosentino J, Behsaz B, McCaw ZR, Hill D, Luben R, Lai D, Bates J, Yang H, Schwantes-An TH, Zhou Y, Khawaja AP, Carroll A, Hobbs BD, Cho MH, McLean CY, Hormozdiari F. Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.28.23289285. [PMID: 37163049 PMCID: PMC10168505 DOI: 10.1101/2023.04.28.23289285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
High-dimensional clinical data are becoming more accessible in biobank-scale datasets. However, effectively utilizing high-dimensional clinical data for genetic discovery remains challenging. Here we introduce a general deep learning-based framework, REpresentation learning for Genetic discovery on Low-dimensional Embeddings (REGLE), for discovering associations between genetic variants and high-dimensional clinical data. REGLE uses convolutional variational autoencoders to compute a non-linear, low-dimensional, disentangled embedding of the data with highly heritable individual components. REGLE can incorporate expert-defined or clinical features and provides a framework to create accurate disease-specific polygenic risk scores (PRS) in datasets which have minimal expert phenotyping. We apply REGLE to both respiratory and circulatory systems: spirograms which measure lung function and photoplethysmograms (PPG) which measure blood volume changes. Genome-wide association studies on REGLE embeddings identify more genome-wide significant loci than existing methods and replicate known loci for both spirograms and PPG, demonstrating the generality of the framework. Furthermore, these embeddings are associated with overall survival. Finally, we construct a set of PRSs that improve predictive performance of asthma, chronic obstructive pulmonary disease, hypertension, and systolic blood pressure in multiple biobanks. Thus, REGLE embeddings can quantify clinically relevant features that are not currently captured in a standardized or automated way.
Collapse
Affiliation(s)
| | | | | | | | - Davin Hill
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 94304, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Robert Luben
- NIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
| | - Dongbing Lai
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - John Bates
- Verily Life Sciences, South San Francisco, CA 94080, USA
| | | | - Tae-Hwi Schwantes-An
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Division of Cardiology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | | | - Anthony P. Khawaja
- NIHR Biomedical Research Centre at Moorfields Eye Hospital & UCL Institute of Ophthalmology, London EC1V 9EL, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK
| | | | - Brian D. Hobbs
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Harvard Medical School, Boston, MA 02115, USA
| | | | | |
Collapse
|
96
|
Wen J, Skampardoni I, Tian YE, Yang Z, Cui Y, Erus G, Hwang G, Varol E, Boquet-Pujadas A, Chand GB, Nasrallah I, Satterthwaite T, Shou H, Shen L, Toga AW, Zaleskey A, Davatzikos C. Neuroimaging-AI Endophenotypes of Brain Diseases in the General Population: Towards a Dimensional System of Vulnerability. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.16.23294179. [PMID: 37662256 PMCID: PMC10473785 DOI: 10.1101/2023.08.16.23294179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Disease heterogeneity poses a significant challenge for precision diagnostics in both clinical and sub-clinical stages. Recent work leveraging artificial intelligence (AI) has offered promise to dissect this heterogeneity by identifying complex intermediate phenotypes - herein called dimensional neuroimaging endophenotypes (DNEs) - which subtype various neurologic and neuropsychiatric diseases. We investigate the presence of nine such DNEs derived from independent yet harmonized studies on Alzheimer's disease (AD1-2)1, autism spectrum disorder (ASD1-3)2, late-life depression (LLD1-2)3, and schizophrenia (SCZ1-2)4, in the general population of 39,178 participants in the UK Biobank study. Phenome-wide associations revealed prominent associations between the nine DNEs and phenotypes related to the brain and other human organ systems. This phenotypic landscape aligns with the SNP-phenotype genome-wide associations, revealing 31 genomic loci associated with the nine DNEs (Bonferroni corrected P-value < 5×10-8/9). The DNEs exhibited significant genetic correlations, colocalization, and causal relationships with multiple human organ systems and chronic diseases. A causal effect (odds ratio=1.25 [1.11, 1.40], P-value=8.72×1-4) was established from AD2, characterized by focal medial temporal lobe atrophy, to AD. The nine DNEs and their polygenic risk scores significantly improved the prediction accuracy for 14 systemic disease categories and mortality. These findings underscore the potential of the nine DNEs to identify individuals at a high risk of developing the four brain diseases during preclinical stages for precision diagnostics. All results are publicly available at: http://labs.loni.usc.edu/medicine/.
Collapse
Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Ioanna Skampardoni
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Ye Ella Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Gyujoon Hwang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Erdem Varol
- Department of Computer Science and Engineering, New York University, New York, USA
| | | | - Ganesh B. Chand
- Department of Radiology, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Ilya Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Theodore Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging (LONI), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Andrew Zaleskey
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for AI and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| |
Collapse
|
97
|
Geva R, Gusev A, Polyakov Y, Liram L, Rosolio O, Alexandru A, Genise N, Blatt M, Duchin Z, Waissengrin B, Mirelman D, Bukstein F, Blumenthal DT, Wolf I, Pelles-Avraham S, Schaffer T, Lavi LA, Micciancio D, Vaikuntanathan V, Badawi AA, Goldwasser S. Collaborative privacy-preserving analysis of oncological data using multiparty homomorphic encryption. Proc Natl Acad Sci U S A 2023; 120:e2304415120. [PMID: 37549296 PMCID: PMC10437415 DOI: 10.1073/pnas.2304415120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 06/09/2023] [Indexed: 08/09/2023] Open
Abstract
Real-world healthcare data sharing is instrumental in constructing broader-based and larger clinical datasets that may improve clinical decision-making research and outcomes. Stakeholders are frequently reluctant to share their data without guaranteed patient privacy, proper protection of their datasets, and control over the usage of their data. Fully homomorphic encryption (FHE) is a cryptographic capability that can address these issues by enabling computation on encrypted data without intermediate decryptions, so the analytics results are obtained without revealing the raw data. This work presents a toolset for collaborative privacy-preserving analysis of oncological data using multiparty FHE. Our toolset supports survival analysis, logistic regression training, and several common descriptive statistics. We demonstrate using oncological datasets that the toolset achieves high accuracy and practical performance, which scales well to larger datasets. As part of this work, we propose a cryptographic protocol for interactive bootstrapping in multiparty FHE, which is of independent interest. The toolset we develop is general-purpose and can be applied to other collaborative medical and healthcare application domains.
Collapse
Affiliation(s)
- Ravit Geva
- Tel Aviv Sorasky Medical Center, Tel Aviv64239, Israel
| | - Alexander Gusev
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA02215
| | | | - Lior Liram
- Duality Technologies, Inc., Hoboken, NJ07103
| | | | | | | | | | | | | | - Dan Mirelman
- Tel Aviv Sorasky Medical Center, Tel Aviv64239, Israel
| | | | | | - Ido Wolf
- Tel Aviv Sorasky Medical Center, Tel Aviv64239, Israel
| | | | - Tali Schaffer
- Tel Aviv Sorasky Medical Center, Tel Aviv64239, Israel
| | - Lee A. Lavi
- Tel Aviv Sorasky Medical Center, Tel Aviv64239, Israel
| | - Daniele Micciancio
- Duality Technologies, Inc., Hoboken, NJ07103
- University of California, San Diego, CA92093
| | - Vinod Vaikuntanathan
- Duality Technologies, Inc., Hoboken, NJ07103
- Massachusetts Institute of Technology, Cambridge, MA02139
| | | | - Shafi Goldwasser
- Duality Technologies, Inc., Hoboken, NJ07103
- Simons Institute for the Theory of Computing, University of California, Berkeley, CA94720
| |
Collapse
|
98
|
Shafee R, Moraczewski D, Liu S, Mallard T, Thomas A, Raznahan A. A sex-stratified analysis of the genetic architecture of human brain anatomy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.09.23293881. [PMID: 37609186 PMCID: PMC10441503 DOI: 10.1101/2023.08.09.23293881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Large biobanks have dramatically advanced our understanding of genetic influences on human brain anatomy. However, most studies have combined rather than compared males and females - despite theoretical grounds for potential sex differences. By systematically screening for sex differences in the common genetic architecture of > 1000 neuroanatomical phenotypes in the UK Biobank, we establish a general concordance between males and females in heritability estimates, genetic correlations and variant-level effects. Notable exceptions include: higher mean h 2 in females for regional volume and surface area phenotypes; between-sex genetic correlations that are significantly below 1 in the insula and parietal cortex; and, a male-specific effect common variant mapping to RBFOX1 - a gene linked to multiple male-biased neuropsychiatric disorders. This work suggests that common variant influences on human brain anatomy are largely consistent between males and females, with a few exceptions that will guide future research as biobanks continue to grow in size.
Collapse
|
99
|
Zhu H, Li T, Zhao B. Statistical Learning Methods for Neuroimaging Data Analysis with Applications. Annu Rev Biomed Data Sci 2023; 6:73-104. [PMID: 37127052 DOI: 10.1146/annurev-biodatasci-020722-100353] [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: 05/03/2023]
Abstract
The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.
Collapse
Affiliation(s)
- Hongtu Zhu
- Department of Biostatistics, Department of Statistics, Department of Genetics, and Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA;
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Tengfei Li
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| |
Collapse
|
100
|
Logue MW, Dasgupta S, Farrer LA. Genetics of Alzheimer's Disease in the African American Population. J Clin Med 2023; 12:5189. [PMID: 37629231 PMCID: PMC10455208 DOI: 10.3390/jcm12165189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/02/2023] [Accepted: 08/06/2023] [Indexed: 08/27/2023] Open
Abstract
Black/African American (AA) individuals have a higher risk of Alzheimer's disease (AD) than White non-Hispanic persons of European ancestry (EUR) for reasons that may include economic disparities, cardiovascular health, quality of education, and biases in the methods used to diagnose AD. AD is also heritable, and some of the differences in risk may be due to genetics. Many AD-associated variants have been identified by candidate gene studies, genome-wide association studies (GWAS), and genome-sequencing studies. However, most of these studies have been performed using EUR cohorts. In this paper, we review the genetics of AD and AD-related traits in AA individuals. Importantly, studies of genetic risk factors in AA cohorts can elucidate the molecular mechanisms underlying AD risk in AA and other populations. In fact, such studies are essential to enable reliable precision medicine approaches in persons with considerable African ancestry. Furthermore, genetic studies of AA cohorts allow exploration of the ways the impact of genes can vary by ancestry, culture, and economic and environmental disparities. They have yielded important gains in our knowledge of AD genetics, and increasing AA individual representation within genetic studies should remain a priority for inclusive genetic study design.
Collapse
Affiliation(s)
- Mark W. Logue
- National Center for PTSD, Behavioral Sciences Division, VA Boston Healthcare System, Boston, MA 02130, USA;
- Department of Psychiatry, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA;
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Shoumita Dasgupta
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA;
- Department of Medical Sciences and Education, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA
| | - Lindsay A. Farrer
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA;
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA
- Department of Ophthalmology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
- Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA
| |
Collapse
|