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Xu L, Ren C, Jing C, Wang G, Wei H, Kong M, Ba M. Predicting amyloid-PET and clinical conversion in apolipoprotein E ε3/ε3 non-demented individuals with multidimensional factors. Eur J Neurosci 2024; 60:3742-3758. [PMID: 38698692 DOI: 10.1111/ejn.16376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/14/2024] [Accepted: 04/16/2024] [Indexed: 05/05/2024]
Abstract
The apolipoprotein E (APOE) ε4 is a well-established risk factor of amyloid-β (Aβ) in Alzheimer's disease (AD). However, because of the high prevalence of APOE ε3, there may be a large number of people with APOE ε3/ε3 who are non-demented and have Aβ pathology. There are limited studies on assessing Aβ status and clinical conversion in the APOE ε3/ε3 non-demented population. Two hundred and ninety-three non-demented individuals with APOE ε3/ε3 from ADNI database were divided into Aβ-positron emission tomography (Aβ-PET) positivity (+) and Aβ-PET negativity (-) groups using cut-off value of >1.11. Stepwise regression searched for a single or multidimensional clinical variables for predicting Aβ-PET (+), and the receiver operating characteristic curve (ROC) assessed the accuracy of the predictive models. The Cox regression model explored the risk factors associated with clinical conversion to mild cognitive impairment (MCI) or AD. The results showed that the combination of sex, education, ventricle and white matter hyperintensity (WMH) volume can accurately predict Aβ-PET status in cognitively normal (CN), and the combination of everyday cognition study partner total (EcogSPTotal) score, age, plasma p-tau 181 and WMH can accurately predict Aβ-PET status in MCI individuals. EcogSPTotal score were independent predictors of clinical conversion to MCI or AD. The findings may provide a non-invasive and effective tool to improve the efficiency of screening Aβ-PET (+), accelerate and reduce costs of AD trial recruitment in future secondary prevention trials or help to select patients at high risk of disease progression in clinical trials.
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Affiliation(s)
- Lijuan Xu
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
| | - Chao Ren
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
| | - Chenxi Jing
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
| | - Gang Wang
- School of Ulsan Ship and Ocean College, Ludong University, Yantai, China
| | - Hongchun Wei
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
| | - Min Kong
- Department of Neurology, Yantaishan Hospital, Yantai City, Shandong, China
| | - Maowen Ba
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Shandong, China
- Yantai Regional Sub Center of National Center for Clinical Medical Research of Neurological Diseases, Shandong, China
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Li B, Shi K, Ren C, Kong M, Ba M. Detection of Tau-PET Positivity in Clinically Diagnosed Mild Cognitive Impairment with Multidimensional Features. J Alzheimers Dis 2023:JAD230180. [PMID: 37334600 DOI: 10.3233/jad-230180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
BACKGROUND The way to evaluate brain tau pathology in vivo is tau positron emission tomography (tau-PET) or cerebrospinal fluid (CSF) analysis. In the clinically diagnosed mild cognitive impairment (MCI), a significant proportion of tau-PET are negative. Interest in less expensive and convenient ways to detect tau pathology in Alzheimer's disease has increased due to the high cost of tau-PET and the invasiveness of lumbar puncture, which typically slows down the cost and enrollment of clinical trials. OBJECTIVE We aimed to investigate one simple and effective method in predicting tau-PET status in MCI individuals. METHODS The sample included 154 individuals which were dichotomized into tau-PET (+) and tau-PET (-) using a cut-off of >1.33. We used stepwise regression to select the unitary or combination of variables that best predicted tau-PET. The receiver operating characteristic curve was used to assess the accuracy of single and multiple clinical markers. RESULTS The combined performance of three variables [Alzheimer's Disease Assessment Scale-Cognitive Subscale 13 (ADAS-Cog13), Mini-Mental State Examination (MMSE), ADNI-Memory summary score (ADNI-MEM)] in neurocognitive measures demonstrated good predictive accuracy of tau-PET status [accuracy = 85.7%, area under the curve (AUC) = 0.879]. The combination of clinical markers model (APOEɛ4, neurocognitive measures and structural MRI imaging of middle temporal) had the best discriminative power (AUC = 0.946). CONCLUSION As a noninvasive test, the combination of APOEɛ4, neurocognitive measures and structural MRI imaging of middle temporal accurately predicts tau-PET status. The finding may provide a non-invasive, cost-effective tool for clinical application in predicting tau pathology among MCI individuals.
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Affiliation(s)
- Bingyu Li
- Department of Neurology, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Kening Shi
- Department of Neurology, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Chao Ren
- Department of Neurology, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Min Kong
- Department of Neurology, Yantaishan Hospital, Yantai, Shandong, China
| | - Maowen Ba
- Department of Neurology, Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
- Yantai Regional Sub Center of National Center for Clinical Medical Research of Neurological Diseases, Shandong, China
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Ku BS, Aberizk K, Addington J, Bearden CE, Cadenhead KS, Cannon TD, Carrión RE, Compton MT, Cornblatt BA, Druss BG, Mathalon DH, Perkins DO, Tsuang MT, Woods SW, Walker EF. The Association Between Neighborhood Poverty and Hippocampal Volume Among Individuals at Clinical High-Risk for Psychosis: The Moderating Role of Social Engagement. Schizophr Bull 2022; 48:1032-1042. [PMID: 35689540 PMCID: PMC9434451 DOI: 10.1093/schbul/sbac055] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Reductions in hippocampal volume (HV) have been associated with both prolonged exposure to stress and psychotic illness. This study sought to determine whether higher levels of neighborhood poverty would be associated with reduced HV among individuals at clinical high-risk for psychosis (CHR-P), and whether social engagement would moderate this association. This cross-sectional study included a sample of participants (N = 174, age-range = 12-33 years, 35.1% female) recruited for the second phase of the North American Prodrome Longitudinal Study. Generalized linear mixed models tested the association between neighborhood poverty and bilateral HV, as well as the moderating role of social engagement on this association. Higher levels of neighborhood poverty were associated with reduced left (β = -0.180, P = .016) and right HV (β = -0.185, P = .016). Social engagement significantly moderated the relation between neighborhood poverty and bilateral HV. In participants with lower levels of social engagement (n = 77), neighborhood poverty was associated with reduced left (β = -0.266, P = .006) and right HV (β = -0.316, P = .002). Among participants with higher levels of social engagement (n = 97), neighborhood poverty was not significantly associated with left (β = -0.010, P = .932) or right HV (β = 0.087, P = .473). In this study, social engagement moderated the inverse relation between neighborhood poverty and HV. These findings demonstrate the importance of including broader environmental influences and indices of social engagement when conceptualizing adversity and potential interventions for individuals at CHR-P.
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Affiliation(s)
- Benson S Ku
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GAUSA
| | | | - Jean Addington
- Department of Psychiatry, University of Calgary, Calgary, Alberta, Canada
| | - Carrie E Bearden
- Departments of Psychiatry and Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, USA
| | | | - Tyrone D Cannon
- Department of Psychiatry, Yale University, New Haven, CTUSA
- Department of Psychology, Yale University, New Haven, CTUSA
| | - Ricardo E Carrión
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Michael T Compton
- Department of Psychiatry, Columbia University Vagelos College of Physicians and Surgeons, and New York State Psychiatric Institute, New York, NY, USA
| | - Barbara A Cornblatt
- Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, USA
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Benjamin G Druss
- Department of Health Policy and Management, Rollins School of Public Health, Emory University, Atlanta, GAUSA
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, and San Francisco Veterans Affairs Medical Center, San Francisco, CAUSA
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Ming T Tsuang
- Department of Psychiatry, University of California, San Diego, CA, USA
| | - Scott W Woods
- Department of Psychiatry, Yale University, New Haven, CTUSA
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Amyloid and tau positive mild cognitive impairment: clinical and biomarker characteristics of dementia progression. Chin Med J (Engl) 2021; 134:1709-1719. [PMID: 34397597 PMCID: PMC8318651 DOI: 10.1097/cm9.0000000000001496] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Background: According to the amyloid, tau, neurodegeneration research framework classification, amyloid and tau positive (A+T+) mild cognitive impairment (MCI) individuals are defined as prodromal Alzheimer disease. This study was designed to compare the clinical and biomarker features between A+T+MCI individuals who progressed to progressive MCI (pMCI) and those who remained stable MCI (sMCI), and to identify relevant baseline clinical biomarker and features that could be used to predict progression to dementia within 2 years. Methods: We stratified 197 A+T+MCI individuals into pMCI (n = 64) and sMCI (n = 133) over 2 years. Demographics and cognitive assessment scores, cerebrospinal fluid (CSF), and neuroimaging biomarkers (18F-florbetapir positron emission tomography mean standardized uptake value ratios [SUVR] and structural magnetic resonance imaging [MRI]) were compared between pMCI and sMCI at baseline, 12- and 24-month follow-up. Logistic regression models then were used to evaluate clinical baseline and biomarker features that predicted dementia progression in A+T+MCI. Results: pMCI individuals had higher mean 18F-florbetapir SUVR, CSF total-tau (t-tau), and p-tau181P than those in sMCI individuals. pMCI individuals performed poorer in cognitive assessments, both global and domain specific (memory, executive, language, attention, and visuospatial skills) than sMCI. At baseline, there were significant differences in regions of interest of structural MRI between the two groups, including bilateral amygdala, hippocampus and entorhinal, bilateral inferior lateral ventricle, left superior and middle temporal, left posterior and caudal anterior cingulate (P < 0.05). Baseline CSF t-tau levels and cognitive scores of Montreal cognitive assessment, functional assessment questionnaire, and everyday cognition by the patient's study partner language domain could predict progression to dementia in A+T+MCI within 2 years. Conclusions: In future clinical trials, specific CSF and cognitive measures that predict dementia progression in A+T+MCI might be useful risk factors for assessing the risk of dementia progression.
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Zhao B, Ibrahim JG, Li Y, Li T, Wang Y, Shan Y, Zhu Z, Zhou F, Zhang J, Huang C, Liao H, Yang L, Thompson PM, Zhu H. Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies. Cereb Cortex 2020; 29:2904-2914. [PMID: 30010813 DOI: 10.1093/cercor/bhy157] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Revised: 06/11/2018] [Indexed: 12/20/2022] Open
Abstract
Brain genetics is an active research area. The degree to which genetic variants impact variations in brain structure and function remains largely unknown. We examined the heritability of regional brain volumes (P ~ 100) captured by single-nucleotide polymorphisms (SNPs) in UK Biobank (n ~ 9000). We found that regional brain volumes are highly heritable in this study population and common genetic variants can explain up to 80% of their variabilities (median heritability 34.8%). We observed omnigenic impact across the genome and examined the enrichment of SNPs in active chromatin regions. Principal components derived from regional volume data are also highly heritable, but the amount of variance in brain volume explained by the component did not seem to be related to its heritability. Heritability estimates vary substantially across large-scale functional networks, exhibit a symmetric pattern across left and right hemispheres, and are consistent in females and males (correlation = 0.638). We repeated the main analysis in Alzheimer's Disease Neuroimaging Initiative (n ~ 1100), Philadelphia Neurodevelopmental Cohort (n ~ 600), and Pediatric Imaging, Neurocognition, and Genetics (n ~ 500) datasets, which demonstrated that more stable estimates can be obtained from the UK Biobank.
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Affiliation(s)
- Bingxin Zhao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Joseph G Ibrahim
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tengfei Li
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yue Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ziliang Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Fan Zhou
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jingwen Zhang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Chao Huang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Huiling Liao
- Department of Statistics, Texas A&M University, College Station, TX, USA
| | - Liuqing Yang
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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6
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Zhang C, Kong M, Wei H, Zhang H, Ma G, Ba M. The effect of ApoE ε 4 on clinical and structural MRI markers in prodromal Alzheimer's disease. Quant Imaging Med Surg 2020; 10:464-474. [PMID: 32190571 PMCID: PMC7063277 DOI: 10.21037/qims.2020.01.14] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 01/15/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND Apolipoprotein E (ApoE) ε 4 has been identified as the strongest genetic risk factor for Alzheimer's disease (AD). However, the importance of ApoE ε 4 on clinical and biological heterogeneity of AD is still to be determined, particularly at the prodromal stage. Here, we evaluate the association of ApoE ε 4 with clinical cognition and neuroimaging regions in mild cognitive impairment (MCI) participants based on the AT (N) system, which is increasingly essential for developing a precise assessment of AD. METHODS We stratified 178 A+T+MCI participants (prodromal AD) into ApoE ε 4 (+) and ApoE ε 4 (-) according to ApoE genotype from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We determined Aβ-positivity (A+) by the standardized uptake values ratios (SUVR) means of florbetapir-PET-AV45 (the cut-off value of 1.1) and fibrillar tau-positivity (T+) by cerebrospinal fluid (CSF) phosphorylated-tau at threonine 181 position (p-Tau) (cut-off value of 23 pg/mL). We evaluated the effect of ApoE ε 4 status on cognitive conditions and brain atrophy from structural magnetic resonance imaging (MRI) scans. A multivariate analysis of variance was used to compare the differences of cognitive scores and brain atrophy from structural MRI regions of interest (ROIs) between both groups. Furthermore, we performed a linear regression model to assess the correlation between signature ROIs of structural MRI and cognitive scores in the prodromal AD participants. RESULTS ApoE ε 4 (+) prodromal AD participants had lower levels of CSF Aβ 1-42, higher levels of t-Tau, more memory and global cognitive impairment, and faster decline of global cognition, compared to ApoE ε 4 (-) prodromal AD. ApoE ε 4 (+) prodromal AD participants had a thinner cortical thickness of bilateral entorhinal, smaller subcortical volume of the left amygdala, bilateral hippocampus, and left ventral diencephalon (DC) relative to ApoE ε 4 (-) prodromal AD. Furthermore, the cortical thickness average of bilateral entorhinal was highly correlated with memory and global cognition. CONCLUSIONS ApoE ε 4 status in prodromal AD participants has an important effect on clinical cognitive domains. After ascertaining the ApoE ε 4 status, specific MRI regions can be correlated to the cognitive domain and will be helpful for precise assessment in prodromal AD.
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Affiliation(s)
- Chunhua Zhang
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China
| | - Min Kong
- Department of Neurology, Yantaishan Hospital, Yantai 264000, China
| | - Hongchun Wei
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China
| | - Hua Zhang
- Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
| | - Guozhao Ma
- Department of Neurology, East Hospital, Tongji University School of Medicine, Shanghai 200120, China
| | - Maowen Ba
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- Department of Neurology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China
- Department of Neurology, Yantaishan Hospital, Yantai 264000, China
- Department of Neurology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China
- Department of Neurology, East Hospital, Tongji University School of Medicine, Shanghai 200120, China
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Li X, Wu D, Cui Y, Liu B, Walter H, Schumann G, Li C, Jiang T. Reliable heritability estimation using sparse regularization in ultrahigh dimensional genome-wide association studies. BMC Bioinformatics 2019; 20:219. [PMID: 31039742 PMCID: PMC6492418 DOI: 10.1186/s12859-019-2792-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 04/02/2019] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Data from genome-wide association studies (GWASs) have been used to estimate the heritability of human complex traits in recent years. Existing methods are based on the linear mixed model, with the assumption that the genetic effects are random variables, which is opposite to the fixed effect assumption embedded in the framework of quantitative genetics theory. Moreover, heritability estimators provided by existing methods may have large standard errors, which calls for the development of reliable and accurate methods to estimate heritability. RESULTS In this paper, we first investigate the influences of the fixed and random effect assumption on heritability estimation, and prove that these two assumptions are equivalent under mild conditions in the theoretical aspect. Second, we propose a two-stage strategy by first performing sparse regularization via cross-validated elastic net, and then applying variance estimation methods to construct reliable heritability estimations. Results on both simulated data and real data show that our strategy achieves a considerable reduction in the standard error while reserving the accuracy. CONCLUSIONS The proposed strategy allows for a reliable and accurate heritability estimation using GWAS data. It shows the promising future that reliable estimations can still be obtained with even a relatively restricted sample size, and should be especially useful for large-scale heritability analyses in the genomics era.
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Affiliation(s)
- Xin Li
- School of Mathematical Sciences, Zhejiang University, 38 Zheda Road, Hangzhou, 310027 China
| | - Dongya Wu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing, 100049 China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
| | - Bing Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Chong Li
- School of Mathematical Sciences, Zhejiang University, 38 Zheda Road, Hangzhou, 310027 China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 95 East Zhongguancun Road, Beijing, 100190 China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, 4 Section 2 North Jianshe Road, Chengdu, 610054 China
- The Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072 Australia
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing, 100049 China
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Forsberg L, Sigurdsson S, Launer LJ, Gudnason V, Ullén F. Structural covariability hubs in old age. Neuroimage 2019; 189:307-315. [PMID: 30669008 DOI: 10.1016/j.neuroimage.2019.01.032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Revised: 12/14/2018] [Accepted: 01/12/2019] [Indexed: 02/07/2023] Open
Abstract
Studies have shown that inter-individual differences in grey matter, as measured by voxel-based morphometry, are coordinated between voxels. This has been done by studying covariance maps based on a limited number of seed regions. Here, we used GPU-based (Graphics Processing Unit) accelerated computing to calculate, for the first time, the aggregated map of the total structural topographical organisation in the brain on voxel level in a large sample of 960 healthy individuals in the age range 68-83 years. This map describes for each voxel the number of significant correlations with all other grey matter voxels in the brain. Voxels that correlate significantly with many other voxels are called hubs. A majority of these hubs were found in the basal ganglia, the thalamus, the brainstem, and the cerebellum; subcortical regions that have been preserved through vertebrate evolution, interact with large portions of the neocortex and play fundamental roles for the control of a wide range of behaviours. No significant difference in the level of covariability could be found with increasing age or between men and women in these hubs.
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Affiliation(s)
- Lars Forsberg
- The Icelandic Heart Association, IS-201, Kopavogur, Iceland; Department of Neuroscience, Karolinska Institutet, S-17177, Stockholm, Sweden.
| | | | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute of Aging, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Vilmundur Gudnason
- The Icelandic Heart Association, IS-201, Kopavogur, Iceland; The University of Iceland, IS-101, Reykjavik, Iceland
| | - Fredrik Ullén
- Department of Neuroscience, Karolinska Institutet, S-17177, Stockholm, Sweden
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9
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Wei H, Kong M, Zhang C, Guan L, Ba M. The structural MRI markers and cognitive decline in prodromal Alzheimer's disease: a 2-year longitudinal study. Quant Imaging Med Surg 2018; 8:1004-1019. [PMID: 30598878 DOI: 10.21037/qims.2018.10.08] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background Being clinically diagnosed with a mild cognitive impairment (MCI) due to Alzheimer's disease (AD) is widely studied. Yet, the clinical and structural neuroimaging characteristics for prodromal AD, which are defined as A+T+MCI based on the AT (N) system are still highly desirable. This study evaluates the differences of the cognitive assessments and structural magnetic resonance imaging (MRI) between the early MCI (EMCI) and late MCI (LMCI) participants based on the AT (N) system. The potential clinical value of the structural MRI as a predictor of cognitive decline during follow-up in prodromal AD is further investigated. Methods A total of 406 MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were chosen and dichotomized into EMCI and LMCI groups according to the Second Edition (Logical Memory II) Wechsler Memory Scale. Multiple markers' data was collected, including age, sex, years of education, ApoE4 status, cerebrospinal fluid (CSF) biomarkers, standardized uptake values ratios (SUVR) means of florbetapir-PET-AV45, cognitive measures, and structural MRI. We chose 197 A+T+MCI participants (prodromal AD) with positive biomarkers of Aβ plaques (labeled "A") and fibrillar tau (labeled "T"). We diagnosed Aβ plaques positive by the SUVR means of florbetapir-PET-AV45 (cut-off >1.1) and fibrillar tau positive by CSF phosphorylated-tau at threonine 181 (p-tau) (cut-off >23 pg/mL). The differences of cognitive assessments and regions of interest (ROIs) defined on the MRI template between EMCI and LMCI were compared. Furthermore, the potential clinical utility of the MRI as the predictor of cognitive decline in prodromal AD was evaluated by investigating the relationship between baseline MRI markers and cognition decline at the follow-up period, through a linear regression model. Results The LMCI participants had a significantly more amyloid burden and CSF levels of total t-tau than the EMCI participants. The LMCI participants scored a lower result than the EMCI group in the global cognition scales and subscales which included tests for memory, delayed recall memory, executive function, language, attention and visuospatial skills. The cognition levels declined faster in the LMCI participants during the 12- and 24-month follow-up. There were significant differences in ROIs on the structural MRI between the two groups, including a bilateral entorhinal, a bilateral hippocampus, a bilateral amygdala, a bilateral lateral ventricle and cingulate, a corpus callosum, and a left temporal. The thickness average of the left entorhinal, the left middle temporal, the left superior temporal, and the right isthmus cingulate was a main contributor to the decreased global cognition levels. The thickness average of the left superior temporal and bilateral entorhinal played a key role in the memory domain decline. The thickness average of the left middle temporal, and the right isthmus cingulate was significantly associated with an executive function decline. Conclusions Based on the AT (N) system, surely, both the EMCI and LMCI diagnoses presented significant differences in multiple cognition domains. Signature ROIs from the structural MRI tests had correlated a cognitive decline, and could act as one potential predictive marker.
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Affiliation(s)
- Hongchun Wei
- Department of Neurology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China
| | - Min Kong
- Department of Neurology, Yantaishan Hospital, Yantai 264000, China
| | - Chunhua Zhang
- Department of Neurology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China
| | - Lina Guan
- Department of Neurology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China
| | - Maowen Ba
- Department of Neurology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai 264000, China
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10
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Lee PH, Baker JT, Holmes AJ, Jahanshad N, Ge T, Jung JY, Cruz Y, Manoach DS, Hibar DP, Faskowitz J, McMahon KL, de Zubicaray GI, Martin NG, Wright MJ, Öngür D, Buckner R, Roffman J, Thompson PM, Smoller JW. Partitioning heritability analysis reveals a shared genetic basis of brain anatomy and schizophrenia. Mol Psychiatry 2016; 21:1680-1689. [PMID: 27725656 PMCID: PMC5144575 DOI: 10.1038/mp.2016.164] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 07/14/2016] [Accepted: 08/11/2016] [Indexed: 01/18/2023]
Abstract
Schizophrenia is a devastating neurodevelopmental disorder with a complex genetic etiology. Widespread cortical gray matter loss has been observed in patients and prodromal samples. However, it remains unresolved whether schizophrenia-associated cortical structure variations arise due to disease etiology or secondary to the illness. Here we address this question using a partitioning-based heritability analysis of genome-wide single-nucleotide polymorphism (SNP) and neuroimaging data from 1750 healthy individuals. We find that schizophrenia-associated genetic variants explain a significantly enriched proportion of trait heritability in eight brain phenotypes (false discovery rate=10%). In particular, intracranial volume and left superior frontal gyrus thickness exhibit significant and robust associations with schizophrenia genetic risk under varying SNP selection conditions. Cross-disorder comparison suggests that the neurogenetic architecture of schizophrenia-associated brain regions is, at least in part, shared with other psychiatric disorders. Our study highlights key neuroanatomical correlates of schizophrenia genetic risk in the general population. These may provide fundamental insights into the complex pathophysiology of the illness, and a potential link to neurocognitive deficits shaping the disorder.
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Affiliation(s)
- P H Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - J T Baker
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Schizophrenia and Bipolar Disorder Program, Psychotic Disorders Division, McLean Hospital, Belmont, MA, USA
| | - A J Holmes
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | - N Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - T Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
| | - J-Y Jung
- Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, CA, USA
| | - Y Cruz
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Harvard Graduate School of Education, Cambridge, MA, USA
| | - D S Manoach
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
| | - D P Hibar
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - J Faskowitz
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - K L McMahon
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - G I de Zubicaray
- Faculty of Health and Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - N G Martin
- Queensland Institute of Medical Research (QIMR) Berghofer, Brisbane, QLD, Australia
| | - M J Wright
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - D Öngür
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Schizophrenia and Bipolar Disorder Program, Psychotic Disorders Division, McLean Hospital, Belmont, MA, USA
| | - R Buckner
- Athinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - J Roffman
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Schizophrenia Clinical and Research Program, Massachusetts General Hospital, Boston, MA, USA
| | - P M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - J W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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11
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Kong D, Giovanello KS, Wang Y, Lin W, Lee E, Fan Y, Murali Doraiswamy P, Zhu H. Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP Data. J Alzheimers Dis 2016; 46:695-702. [PMID: 25869783 DOI: 10.3233/jad-150164] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The growing public threat of Alzheimer's disease (AD) has raised the urgency to discover and validate prognostic biomarkers in order to predicting time to onset of AD. It is anticipated that both whole genome single nucleotide polymorphism (SNP) data and high dimensional whole brain imaging data offer predictive values to identify subjects at risk for progressing to AD. The aim of this paper is to test whether both whole genome SNP data and whole brain imaging data offer predictive values to identify subjects at risk for progressing to AD. In 343 subjects with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI-1), we extracted high dimensional MR imaging (volumetric data on 93 brain regions plus a surface fluid registration based hippocampal subregion and surface data), and whole genome data (504,095 SNPs from GWAS), as well as routine neurocognitive and clinical data at baseline. MCI patients were then followed over 48 months, with 150 participants progressing to AD. Combining information from whole brain MR imaging and whole genome data was substantially superior to the standard model for predicting time to onset of AD in a 48-month national study of subjects at risk. Our findings demonstrate the promise of combined imaging-whole genome prognostic markers in people with mild memory impairment.
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Affiliation(s)
- Dehan Kong
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Kelly S Giovanello
- Department of Psychology, University of North Carolina, Chapel Hill, NC, USA.,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Yalin Wang
- School of Computing, Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Weili Lin
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.,Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
| | - Eunjee Lee
- Department of Statistics, University of North Carolina, Chapel Hill, NC, USA
| | - Yong Fan
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - P Murali Doraiswamy
- Departments of Psychiatry and Duke Institute for Brain Sciences, Duke University, Durham, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.,Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.,Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
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12
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Lee E, Zhu H, Kong D, Wang Y, Giovanello KS, Ibrahim JG. BFLCRM: A BAYESIAN FUNCTIONAL LINEAR COX REGRESSION MODEL FOR PREDICTING TIME TO CONVERSION TO ALZHEIMER'S DISEASE. Ann Appl Stat 2015; 9:2153-2178. [PMID: 26900412 DOI: 10.1214/15-aoas879] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer's disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog), and APOE status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM.
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Affiliation(s)
- Eunjee Lee
- Departments of Statistics and Operation Research, Biostatistics, and Psychology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Departments of Statistics and Operation Research, Biostatistics, and Psychology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dehan Kong
- Departments of Statistics and Operation Research, Biostatistics, and Psychology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering Arizona State University Tempe, AZ 85287-8809
| | - Kelly Sullivan Giovanello
- Departments of Statistics and Operation Research, Biostatistics, and Psychology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Joseph G Ibrahim
- Departments of Statistics and Operation Research, Biostatistics, and Psychology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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13
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Chen CH, Peng Q, Schork AJ, Lo MT, Fan CC, Wang Y, Desikan RS, Bettella F, Hagler DJ, Westlye LT, Kremen WS, Jernigan TL, Hellard SL, Steen VM, Espeseth T, Huentelman M, Håberg AK, Agartz I, Djurovic S, Andreassen OA, Schork N, Dale AM. Large-scale genomics unveil polygenic architecture of human cortical surface area. Nat Commun 2015; 6:7549. [PMID: 26189703 PMCID: PMC4518289 DOI: 10.1038/ncomms8549] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Accepted: 05/19/2015] [Indexed: 12/04/2022] Open
Abstract
Little is known about how genetic variation contributes to neuroanatomical variability, and whether particular genomic regions comprising genes or evolutionarily conserved elements are enriched for effects that influence brain morphology. Here, we examine brain imaging and single-nucleotide polymorphisms (SNPs) data from ∼2,700 individuals. We show that a substantial proportion of variation in cortical surface area is explained by additive effects of SNPs dispersed throughout the genome, with a larger heritable effect for visual and auditory sensory and insular cortices (h(2)∼0.45). Genome-wide SNPs collectively account for, on average, about half of twin heritability across cortical regions (N=466 twins). We find enriched genetic effects in or near genes. We also observe that SNPs in evolutionarily more conserved regions contributed significantly to the heritability of cortical surface area, particularly, for medial and temporal cortical regions. SNPs in less conserved regions contributed more to occipital and dorsolateral prefrontal cortices.
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Affiliation(s)
- Chi-Hua Chen
- Multimodal Imaging Laboratory, Department of Radiology, University of California San Diego, La Jolla, California 92037, USA
| | - Qian Peng
- Department of Human Biology, J. Craig Venter Institute, San Diego, California 92037, USA
- Department of Molecular and Cellular Neuroscience, The Scripps Research Institute, La Jolla, California 92037, USA
| | - Andrew J. Schork
- Multimodal Imaging Laboratory, Department of Radiology, University of California San Diego, La Jolla, California 92037, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, California 92093, USA
| | - Min-Tzu Lo
- Multimodal Imaging Laboratory, Department of Radiology, University of California San Diego, La Jolla, California 92037, USA
| | - Chun-Chieh Fan
- Multimodal Imaging Laboratory, Department of Radiology, University of California San Diego, La Jolla, California 92037, USA
- Department of Cognitive Science, University of California, San Diego, La Jolla, California 92093, USA
| | - Yunpeng Wang
- Multimodal Imaging Laboratory, Department of Radiology, University of California San Diego, La Jolla, California 92037, USA
- Department of Neurosciences, University of California, San Diego, La Jolla, California 92093, USA
- Norwegian Center for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, 0424 Oslo, Norway
| | - Rahul S. Desikan
- Multimodal Imaging Laboratory, Department of Radiology, University of California San Diego, La Jolla, California 92037, USA
| | - Francesco Bettella
- Norwegian Center for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, 0424 Oslo, Norway
| | - Donald J. Hagler
- Multimodal Imaging Laboratory, Department of Radiology, University of California San Diego, La Jolla, California 92037, USA
| | - Lars T. Westlye
- NORMENT, KG Jebsen Centre for Psychosis Research, Department of Psychology, University of Oslo, 0424 Oslo, Norway
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, 0317 Oslo, Norway
| | - William S. Kremen
- Department of Psychiatry, University of California, San Diego, La Jolla, California 92093, USA
- VA San Diego Center of Excellence for Stress and Mental Health, La Jolla, California 92037, USA
| | - Terry L. Jernigan
- Department of Cognitive Science, University of California, San Diego, La Jolla, California 92093, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, California 92093, USA
| | - Stephanie Le Hellard
- Dr E. Martens Research Group of Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, 5021 Bergen, Norway
- NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, 5021 Norway
| | - Vidar M. Steen
- Dr E. Martens Research Group of Biological Psychiatry, Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, 5021 Bergen, Norway
- NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, 5021 Norway
| | - Thomas Espeseth
- NORMENT, KG Jebsen Centre for Psychosis Research, Department of Psychology, University of Oslo, 0424 Oslo, Norway
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, 0317 Oslo, Norway
| | - Matt Huentelman
- Translational Genomics Research Institute, Phoenix, Arizona 85004, USA
| | - Asta K. Håberg
- Department of Neuroscience, Norwegian University of Science and Technology (NTNU), 7489 Trondheim, Norway
- Department of Medical Imaging, St. Olav's University Hospital, 7006 Trondheim, Norway
| | - Ingrid Agartz
- Norwegian Center for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, 0424 Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, 0319 Oslo, Norway
| | - Srdjan Djurovic
- NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, 5021 Norway
- Department of Medical Genetics, Oslo University Hospital, 0424 Oslo, Norway
| | - Ole A. Andreassen
- Norwegian Center for Mental Disorders Research (NORMENT), KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, 0424 Oslo, Norway
| | - Nicholas Schork
- Department of Human Biology, J. Craig Venter Institute, San Diego, California 92037, USA
| | - Anders M. Dale
- Multimodal Imaging Laboratory, Department of Radiology, University of California San Diego, La Jolla, California 92037, USA
- Department of Neurosciences, University of California, San Diego, La Jolla, California 92093, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, California 92093, USA
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14
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Cedarbaum J, Green RC, Harvey D, Jack CR, Jagust W, Luthman J, Morris JC, Petersen RC, Saykin AJ, Shaw L, Shen L, Schwarz A, Toga AW, Trojanowski JQ. 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimers Dement 2015; 11:e1-120. [PMID: 26073027 PMCID: PMC5469297 DOI: 10.1016/j.jalz.2014.11.001] [Citation(s) in RCA: 203] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 04/18/2013] [Indexed: 01/18/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Jesse Cedarbaum
- Neurology Early Clinical Development, Biogen Idec, Cambridge, MA, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Johan Luthman
- Neuroscience Clinical Development, Neuroscience & General Medicine Product Creation Unit, Eisai Inc., Philadelphia, PA, USA
| | - John C Morris
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Adam Schwarz
- Tailored Therapeutics, Eli Lilly and Company, Indianapolis, IN, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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15
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Strike LT, Couvy-Duchesne B, Hansell NK, Cuellar-Partida G, Medland SE, Wright MJ. Genetics and Brain Morphology. Neuropsychol Rev 2015; 25:63-96. [DOI: 10.1007/s11065-015-9281-1] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Accepted: 02/08/2015] [Indexed: 12/17/2022]
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16
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Hossini AM, Megges M, Prigione A, Lichtner B, Toliat MR, Wruck W, Schröter F, Nuernberg P, Kroll H, Makrantonaki E, Zouboulis CC, Zoubouliss CC, Adjaye J. Induced pluripotent stem cell-derived neuronal cells from a sporadic Alzheimer's disease donor as a model for investigating AD-associated gene regulatory networks. BMC Genomics 2015; 16:84. [PMID: 25765079 PMCID: PMC4344782 DOI: 10.1186/s12864-015-1262-5] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Accepted: 01/22/2015] [Indexed: 02/07/2023] Open
Abstract
Background Alzheimer’s disease (AD) is a complex, irreversible neurodegenerative disorder. At present there are neither reliable markers to diagnose AD at an early stage nor therapy. To investigate underlying disease mechanisms, induced pluripotent stem cells (iPSCs) allow the generation of patient-derived neuronal cells in a dish. Results In this study, employing iPS technology, we derived and characterized iPSCs from dermal fibroblasts of an 82-year-old female patient affected by sporadic AD. The AD-iPSCs were differentiated into neuronal cells, in order to generate disease-specific protein association networks modeling the molecular pathology on the transcriptome level of AD, to analyse the reflection of the disease phenotype in gene expression in AD-iPS neuronal cells, in particular in the ubiquitin-proteasome system (UPS), and to address expression of typical AD proteins. We detected the expression of p-tau and GSK3B, a physiological kinase of tau, in neuronal cells derived from AD-iPSCs. Treatment of neuronal cells differentiated from AD-iPSCs with an inhibitor of γ-secretase resulted in the down-regulation of p-tau. Transcriptome analysis of AD-iPS derived neuronal cells revealed significant changes in the expression of genes associated with AD and with the constitutive as well as the inducible subunits of the proteasome complex. The neuronal cells expressed numerous genes associated with sub-regions within the brain thus suggesting the usefulness of our in-vitro model. Moreover, an AD-related protein interaction network composed of APP and GSK3B among others could be generated using neuronal cells differentiated from two AD-iPS cell lines. Conclusions Our study demonstrates how an iPSC-based model system could represent (i) a tool to study the underlying molecular basis of sporadic AD, (ii) a platform for drug screening and toxicology studies which might unveil novel therapeutic avenues for this debilitating neuronal disorder. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1262-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Amir M Hossini
- Departments of Dermatology, Venereology, Allergology and Immunology, Dessau Medical Center, 06847, Dessau, Germany.
| | - Matthias Megges
- Institute for Stem Cell Research and Regenerative Medicine, Heinrich Heine University Duesseldorf, Moorenstr. 5, 40225, Duesseldorf, Germany. .,Molecular Embryology and Aging Group, Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany. .,Department of Biology, Chemistry and Pharmacy, Institute of Chemistry and Biochemistry, Freie Universität Berlin, Thielallee 63, 14195, Berlin, Germany.
| | - Alessandro Prigione
- Institute for Stem Cell Research and Regenerative Medicine, Heinrich Heine University Duesseldorf, Moorenstr. 5, 40225, Duesseldorf, Germany. .,Current address: Max Delbrueck Center for Molecular Medicine (MDC), Robert Roessle Str. 10, D-13125, Berlin, Germany.
| | - Bjoern Lichtner
- Institute for Stem Cell Research and Regenerative Medicine, Heinrich Heine University Duesseldorf, Moorenstr. 5, 40225, Duesseldorf, Germany.
| | - Mohammad R Toliat
- Cologne Center for Genomics (CCG), Institute for Genetics, University of Cologne, 50931, Cologne, Germany.
| | - Wasco Wruck
- Molecular Embryology and Aging Group, Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany.
| | - Friederike Schröter
- Molecular Embryology and Aging Group, Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany.
| | - Peter Nuernberg
- Cologne Center for Genomics (CCG), Institute for Genetics, University of Cologne, 50931, Cologne, Germany.
| | - Hartmut Kroll
- Institute for Transfusion Medicine Dessau, Red Cross Blood Transfusion Service NSTOB, 06847, Dessau, Germany.
| | - Eugenia Makrantonaki
- Departments of Dermatology, Venereology, Allergology and Immunology, Dessau Medical Center, 06847, Dessau, Germany. .,Geriatrics Research Group, Department of Geriatric Medicine, Charité Universitätsmedizin Berlin, Reinickendorfer Str. 61, 13447, Berlin, Germany.
| | | | - Christos C Zoubouliss
- Departments of Dermatology, Venereology, Allergology and Immunology, Dessau Medical Center, 06847, Dessau, Germany.
| | - James Adjaye
- Institute for Stem Cell Research and Regenerative Medicine, Heinrich Heine University Duesseldorf, Moorenstr. 5, 40225, Duesseldorf, Germany. .,Molecular Embryology and Aging Group, Department of Vertebrate Genomics, Max Planck Institute for Molecular Genetics, 14195, Berlin, Germany.
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17
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Han G, Sun J, Wang J, Bai Z, Song F, Lei H. Genomics in neurological disorders. GENOMICS PROTEOMICS & BIOINFORMATICS 2014; 12:156-63. [PMID: 25108264 PMCID: PMC4411357 DOI: 10.1016/j.gpb.2014.07.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2014] [Revised: 07/07/2014] [Accepted: 07/07/2014] [Indexed: 11/26/2022]
Abstract
Neurological disorders comprise a variety of complex diseases in the central nervous system, which can be roughly classified as neurodegenerative diseases and psychiatric disorders. The basic and translational research of neurological disorders has been hindered by the difficulty in accessing the pathological center (i.e., the brain) in live patients. The rapid advancement of sequencing and array technologies has made it possible to investigate the disease mechanism and biomarkers from a systems perspective. In this review, recent progresses in the discovery of novel risk genes, treatment targets and peripheral biomarkers employing genomic technologies will be discussed. Our major focus will be on two of the most heavily investigated neurological disorders, namely Alzheimer’s disease and autism spectrum disorder.
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Affiliation(s)
- Guangchun Han
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiya Sun
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiajia Wang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhouxian Bai
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fuhai Song
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongxing Lei
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing 100053, China.
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Ortiz A, Górriz JM, Ramírez J, Martinez-Murcia FJ. Automatic ROI selection in structural brain MRI using SOM 3D projection. PLoS One 2014; 9:e93851. [PMID: 24728041 PMCID: PMC3984096 DOI: 10.1371/journal.pone.0093851] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2013] [Accepted: 03/07/2014] [Indexed: 11/18/2022] Open
Abstract
This paper presents a method for selecting Regions of Interest (ROI) in brain Magnetic Resonance Imaging (MRI) for diagnostic purposes, using statistical learning and vector quantization techniques. The proposed method models the distribution of GM and WM tissues grouping the voxels belonging to each tissue in ROIs associated to a specific neurological disorder. Tissue distribution of normal and abnormal images is modelled by a Self-Organizing map (SOM), generating a set of representative prototypes, and the receptive field (RF) of each SOM prototype defines a ROI. Moreover, the proposed method computes the relative importance of each ROI by means of its discriminative power. The devised method has been assessed using 818 images from the Alzheimer's disease Neuroimaging Initiative (ADNI) which were previously segmented through Statistical Parametric Mapping (SPM). The proposed algorithm was used over these images to parcel ROIs associated to the Alzheimer's Disease (AD). Additionally, this method can be used to extract a reduced set of discriminative features for classification, since it compresses discriminative information contained in the brain. Voxels marked by ROIs which were computed using the proposed method, yield classification results up to 90% of accuracy for controls (CN) and Alzheimer's disease (AD) patients, and 84% of accuracy for Mild Cognitive Impairment (MCI) and AD patients.
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Affiliation(s)
- Andrés Ortiz
- Communications Engineering Department, Universidad de Málaga, Málaga, Spain
| | - Juan M. Górriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Javier Ramírez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
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Wu Z, Yang L, Wang Y. Applying Imaging Genetics to ADHD: the Promises and the Challenges. Mol Neurobiol 2014; 50:449-62. [DOI: 10.1007/s12035-014-8683-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2013] [Accepted: 03/13/2014] [Indexed: 12/31/2022]
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