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Chen TY, Zhu JD, Tsai SJ, Yang AC. Exploring morphological similarity and randomness in Alzheimer's disease using adjacent grey matter voxel-based structural analysis. Alzheimers Res Ther 2024; 16:88. [PMID: 38654366 PMCID: PMC11036786 DOI: 10.1186/s13195-024-01448-1] [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: 10/27/2023] [Accepted: 04/01/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Alzheimer's disease is characterized by large-scale structural changes in a specific pattern. Recent studies developed morphological similarity networks constructed by brain regions similar in structural features to represent brain structural organization. However, few studies have used local morphological properties to explore inter-regional structural similarity in Alzheimer's disease. METHODS Here, we sourced T1-weighted MRI images of 342 cognitively normal participants and 276 individuals with Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative database. The relationships of grey matter intensity between adjacent voxels were defined and converted to the structural pattern indices. We conducted the information-based similarity method to evaluate the structural similarity of structural pattern organization between brain regions. Besides, we examined the structural randomness on brain regions. Finally, the relationship between the structural randomness and cognitive performance of individuals with Alzheimer's disease was assessed by stepwise regression. RESULTS Compared to cognitively normal participants, individuals with Alzheimer's disease showed significant structural pattern changes in the bilateral posterior cingulate gyrus, hippocampus, and olfactory cortex. Additionally, individuals with Alzheimer's disease showed that the bilateral insula had decreased inter-regional structural similarity with frontal regions, while the bilateral hippocampus had increased inter-regional structural similarity with temporal and subcortical regions. For the structural randomness, we found significant decreases in the temporal and subcortical areas and significant increases in the occipital and frontal regions. The regression analysis showed that the structural randomness of five brain regions was correlated with the Mini-Mental State Examination scores of individuals with Alzheimer's disease. CONCLUSIONS Our study suggested that individuals with Alzheimer's disease alter micro-structural patterns and morphological similarity with the insula and hippocampus. Structural randomness of individuals with Alzheimer's disease changed in temporal, frontal, and occipital brain regions. Morphological similarity and randomness provide valuable insight into brain structural organization in Alzheimer's disease.
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Affiliation(s)
- Ting-Yu Chen
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jun-Ding Zhu
- Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Albert C Yang
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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Zheng C, Zhao W, Yang Z, Tang D, Feng M, Guo S. Resolving heterogeneity in Alzheimer's disease based on individualized structural covariance network. Prog Neuropsychopharmacol Biol Psychiatry 2024; 129:110873. [PMID: 37827426 DOI: 10.1016/j.pnpbp.2023.110873] [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: 04/15/2023] [Revised: 10/06/2023] [Accepted: 10/08/2023] [Indexed: 10/14/2023]
Abstract
The heterogeneity of Alzheimer's disease (AD) poses a challenge to precision medicine. We aimed to identify distinct subtypes of AD based on the individualized structural covariance network (IDSCN) analysis and to research the underlying neurobiology mechanisms. In this study, 187 patients with AD (age = 73.57 ± 6.00, 50% female) and 143 matched normal controls (age = 74.30 ± 7.80, 44% female) were recruited from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project database, and T1 images were acquired. We utilized the IDSCN analysis to generate individual-level altered structural covariance network and performed k-means clustering to subtype AD based on structural covariance network. Cognition, disease progression, morphological features, and gene expression profiles were further compared between subtypes, to characterize the heterogeneity in AD. Two distinct AD subtypes were identified in a reproducible manner, and we named the two subtypes as slow progression type (subtype 1, n = 104, age = 76.15 ± 6.44, 42% female) and rapid progression type (subtype 2, n = 83, age = 71.98 ± 8.72, 47% female), separately. Subtype 1 had better baseline visuospatial function than subtype 2 (p < 0.05), whereas subtype 2 had better baseline memory function than subtype 1 (p < 0.05). Subtype 2 showed worse progression in memory (p = 0.003), language (p = 0.003), visuospatial function (p = 0.020), and mental state (p = 0.038) than subtype 1. Subtype 1 often shared increased structural covariance network, mainly in the frontal lobe and temporal lobe regions, whereas subtype 2 often shared increased structural covariance network, mainly in occipital lobe regions and temporal lobe regions. Functional annotation further revealed that all differential structural covariance network between the two AD subtypes were mainly implicated in memory, learning, emotion, and cognition. Additionally, differences in gray matter volume (GMV) between AD subtypes were identified, and genes associated with GMV differences were found to be enriched in the terms potassium ion transport, synapse organization, and histone modification and the pathways viral infection, neurodegeneration-multiple diseases, and long-term depression. The two distinct AD subtypes were identified and characterized with neuroanatomy, cognitive trajectories, and gene expression profiles. These comprehensive results have implications for neurobiology mechanisms and precision medicine.
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Affiliation(s)
- Chuchu Zheng
- School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, China
| | - Wei Zhao
- School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, China
| | - Zeyu Yang
- School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, China
| | - Dier Tang
- School of Mathematics, Jilin University, Changchun 130015, China
| | - Muyi Feng
- School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, China
| | - Shuixia Guo
- School of Mathematics and Statistics, Hunan Normal University, Changsha 410006, China; Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha 410006, China.
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3
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Geng Z, Wu Y, Liu J, Zhan Y, Yan Y, Yang C, Pang X, Ji Y, Gao M, Zhou S, Wei L, Hu P, Wu X, Tian Y, Wang K. A Study on the Effect of Executive Control Network Functional Connection on the Therapeutic Efficacy of Repetitive Transcranial Magnetic Stimulation in Alzheimer's Disease. J Alzheimers Dis 2024; 99:1349-1359. [PMID: 38820018 DOI: 10.3233/jad-231449] [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] [Indexed: 06/02/2024]
Abstract
Background Alzheimer's disease (AD) is a neurodegenerative disease characterized by brain network dysfunction. Few studies have investigated whether the functional connections between executive control networks (ECN) and other brain regions can predict the therapeutic effect of repetitive transcranial magnetic stimulation (rTMS). Objective The purpose of this study is to examine the relationship between the functional connectivity (FC) within ECN networks and the efficacy of rTMS. Methods We recruited AD patients for rTMS treatment. We established an ECN using baseline period fMRI data and conducted an analysis of the ECN's FC throughout the brain. Concurrently, the support vector regression (SVR) method was employed to project post-rTMS cognitive scores, utilizing the connectional attributes of the ECN as predictive markers. Results The average age of the patients was 66.86±8.44 years, with 8 males and 13 females. Significant improvement on most cognitive measures. We use ECN connectivity and brain region functions in baseline patients as features for SVR model training and fitting. The SVR model could demonstrate significant predictability for changes in Montreal Cognitive Assessment scores among AD patients after rTMS treatment. The brain regions that contributed most to the prediction of the model (the top 10% of weights) were located in the medial temporal lobe, middle temporal gyrus, frontal lobe, parietal lobe and occipital lobe. Conclusions The stronger the antagonism between ECN and parieto-occipital lobe function, the better the prediction of cognitive improvement; the stronger the synergy between ECN and fronto-temporal lobe function, the better the prediction of cognitive improvement.
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Affiliation(s)
- Zhi Geng
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Yue Wu
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
- Department of Sleep Psychology, The Second Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Jiaqiu Liu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Yuqian Zhan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Yibing Yan
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Chaoyi Yang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Xuerui Pang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Yi Ji
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Manman Gao
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Shanshan Zhou
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Ling Wei
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Panpan Hu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, China
| | - Xingqi Wu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
| | - Yanghua Tian
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
- Department of Sleep Psychology, The Second Hospital of Anhui Medical University, Anhui Medical University, Hefei, China
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental Health, Hefei, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China
- Anhui Provincial Institute of Translational Medicine, Anhui Medical University, Hefei, China
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Li K, Luo X, Zeng Q, Liu X, Li J, Zhong S, Zhang X, Xu X, Wang S, Hong H, Jiaerken Y, Liu Z, Zhao S, Huang P, Zhang M, Chen Y. Gray matter structural covariance networks patterns associated with autopsy-confirmed LATE-NC compared to Alzheimer's disease pathology. Neurobiol Dis 2023; 189:106354. [PMID: 37977431 DOI: 10.1016/j.nbd.2023.106354] [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: 08/27/2023] [Revised: 11/14/2023] [Accepted: 11/14/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Cases with the limbic-predominant age-related TAR DNA-binding protein 43 (TDP-43) encephalopathy neuropathologic change (LATE-NC), Alzheimer's disease (AD), and mixed AD+TDP-43 pathology (AD+LATE-NC) share similar symptoms, which makes it a challenge for accurate diagnosis. Exploring the patterns of gray matter structural covariance networks (SCNs) in these three types may help to clarify the underlying mechanism and provide a basis for clinical interventions. METHODS We included ante-mortem MRI data of 10 LATE-NC, 39 AD, and 25 AD+LATE-NC from the ADNI autopsy sample. We used four regions of interest (left posterior cingulate cortex, right entorhinal cortex, frontoinsular and dorsolateral prefrontal cortex) to anchor the default mode network (DMN), salience network (SN), and executive control network (ECN). Finally, we assessed the SCN alternations using a multi-regression model-based linear-interaction analysis. RESULTS Cases with autopsy-confirmed LATE-NC and AD showed increased structural associations involving DMN, ECN, and SN. Cases with AD+LATE-NC showed increased structural association within DMN while decreased structural association between DMN and ECN. The volume of peak clusters showed significant associations with cognition and AD pathology. CONCLUSIONS This study showed different SCN patterns in the cases with LATE-NC, AD, and AD+LATE-NC, and indicated the network disconnection mechanism underlying these three neuropathological progressions. Further, SCN may serve as an effective biomarker to distinguish between different types of dementia.
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Affiliation(s)
- Kaicheng Li
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Luo
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jixuan Li
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Siyan Zhong
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xinyi Zhang
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaopei Xu
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Shuyue Wang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Hui Hong
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yerfan Jiaerken
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhirong Liu
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Shuai Zhao
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yanxing Chen
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
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5
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Tang Y, Cao M, Li Y, Lin Y, Wu X, Chen M. Altered structural covariance of locus coeruleus in individuals with significant memory concern and patients with mild cognitive impairment. Cereb Cortex 2023; 33:8523-8533. [PMID: 37130822 PMCID: PMC10321106 DOI: 10.1093/cercor/bhad137] [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: 11/19/2022] [Revised: 03/31/2023] [Accepted: 04/02/2023] [Indexed: 05/04/2023] Open
Abstract
The locus coeruleus (LC) is the site where tau accumulation is preferentially observed pathologically in Alzheimer's disease (AD) patients, but the changes in gray matter co-alteration patterns between the LC and the whole brain in the predementia phase of AD remain unclear. In this study, we estimated and compared the gray matter volume of the LC and its structural covariance (SC) with the whole brain among 161 normal healthy controls (HCs), 99 individuals with significant memory concern (SMC) and 131 patients with mild cognitive impairment (MCI). We found that SC decreased in MCI groups, which mainly involved the salience network and default mode network. These results imply that seeding from LC, the gray matter network disruption and disconnection appears early in the MCI group. The altered SC network seeding from the LC can serve as an imaging biomarker for discriminating the patients in the potential predementia phase of AD from the normal subjects.
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Affiliation(s)
- Yingmei Tang
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No.107 Yanjiang Road West, Guangzhou 510120, Guangdong, China
| | - Minghui Cao
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No.107 Yanjiang Road West, Guangzhou 510120, Guangdong, China
| | - Yunhua Li
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No.107 Yanjiang Road West, Guangzhou 510120, Guangdong, China
| | - Yuting Lin
- School of Psychology, Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, No.55 Zhongshan Avenue West, Guangzhou 510631, Guangdong, China
| | - Xiaoyan Wu
- School of Psychology, Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, No.55 Zhongshan Avenue West, Guangzhou 510631, Guangdong, China
| | - Meiwei Chen
- Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No.107 Yanjiang Road West, Guangzhou 510120, Guangdong, China
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Chen Z, Wu B, Li G, Zhou L, Zhang L, Liu J. MAPT rs17649553 T allele is associated with better verbal memory and higher small-world properties in Parkinson's disease. Neurobiol Aging 2023; 129:219-231. [PMID: 37413784 DOI: 10.1016/j.neurobiolaging.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/05/2023] [Accepted: 06/06/2023] [Indexed: 07/08/2023]
Abstract
Currently, over 90 genetic loci have been found to be associated with Parkinson's disease (PD) in genome-wide association studies, nevertheless, the effects of these genetic variants on the clinical features and brain structure of PD patients are largely unknown. This study investigated the effects of microtubule-associated protein tau (MAPT) rs17649553 (C>T), a genetic variant associated with reduced PD risk, on the clinical manifestations and brain networks of PD patients. We found MAPT rs17649553 T allele was associated with better verbal memory in PD patients. In addition, MAPT rs17649553 significantly shaped the topology of gray matter covariance network and white matter network. Both the network metrics in gray matter covariance network and white matter network were correlated with verbal memory, however, the mediation analysis showed that it was the small-world properties in white matter network that mediated the effects of MAPT rs17649553 on verbal memory. These results suggest that MAPT rs17649553 T allele is associated with higher small-world properties in structural network and better verbal memory in PD.
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Affiliation(s)
- Zhichun Chen
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Department of Neurology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Bin Wu
- Department of Neurology, Xuchang Central Hospital Affiliated with Henan University of Science and Technology, Henan, China
| | - Guanglu Li
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Liche Zhou
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Lina Zhang
- Department of Biostatistics, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jun Liu
- Department of Neurology and Institute of Neurology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
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Vermunt L, Sutphen C, Dicks E, de Leeuw DM, Allegri R, Berman SB, Cash DM, Chhatwal JP, Cruchaga C, Day G, Ewers M, Farlow M, Fox NC, Ghetti B, Graff-Radford N, Hassenstab J, Jucker M, Karch CM, Kuhle J, Laske C, Levin J, Masters CL, McDade E, Mori H, Morris JC, Perrin RJ, Preische O, Schofield PR, Suárez-Calvet M, Xiong C, Scheltens P, Teunissen CE, Visser PJ, Bateman RJ, Benzinger TLS, Fagan AM, Gordon BA, Tijms BM. Axonal damage and astrocytosis are biological correlates of grey matter network integrity loss: a cohort study in autosomal dominant Alzheimer disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.21.23287468. [PMID: 37016671 PMCID: PMC10071836 DOI: 10.1101/2023.03.21.23287468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
Brain development and maturation leads to grey matter networks that can be measured using magnetic resonance imaging. Network integrity is an indicator of information processing capacity which declines in neurodegenerative disorders such as Alzheimer disease (AD). The biological mechanisms causing this loss of network integrity remain unknown. Cerebrospinal fluid (CSF) protein biomarkers are available for studying diverse pathological mechanisms in humans and can provide insight into decline. We investigated the relationships between 10 CSF proteins and network integrity in mutation carriers (N=219) and noncarriers (N=136) of the Dominantly Inherited Alzheimer Network Observational study. Abnormalities in Aβ, Tau, synaptic (SNAP-25, neurogranin) and neuronal calcium-sensor protein (VILIP-1) preceded grey matter network disruptions by several years, while inflammation related (YKL-40) and axonal injury (NfL) abnormalities co-occurred and correlated with network integrity. This suggests that axonal loss and inflammation play a role in structural grey matter network changes. Key points Abnormal levels of fluid markers for neuronal damage and inflammatory processes in CSF are associated with grey matter network disruptions.The strongest association was with NfL, suggesting that axonal loss may contribute to disrupted network organization as observed in AD.Tracking biomarker trajectories over the disease course, changes in CSF biomarkers generally precede changes in brain networks by several years.
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Dang M, Yang C, Chen K, Lu P, Li H, Zhang Z. Hippocampus-centred grey matter covariance networks predict the development and reversion of mild cognitive impairment. Alzheimers Res Ther 2023; 15:27. [PMID: 36732782 PMCID: PMC9893696 DOI: 10.1186/s13195-023-01167-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 01/09/2023] [Indexed: 02/04/2023]
Abstract
BACKGROUND Mild cognitive impairment (MCI) has been thought of as the transitional stage between normal ageing and Alzheimer's disease, involving substantial changes in brain grey matter structures. As most previous studies have focused on single regions (e.g. the hippocampus) and their changes during MCI development and reversion, the relationship between grey matter covariance among distributed brain regions and clinical development and reversion of MCI remains unclear. METHODS With samples from two independent studies (155 from the Beijing Aging Brain Rejuvenation Initiative and 286 from the Alzheimer's Disease Neuroimaging Initiative), grey matter covariance of default, frontoparietal, and hippocampal networks were identified by seed-based partial least square analyses, and random forest models were applied to predict the progression from normal cognition to MCI (N-t-M) and the reversion from MCI to normal cognition (M-t-N). RESULTS With varying degrees, the grey matter covariance in the three networks could predict N-t-M progression (AUC = 0.692-0.792) and M-t-N reversion (AUC = 0.701-0.809). Further analyses indicated that the hippocampus has emerged as an important region in reversion prediction within all three brain networks, and even though the hippocampus itself could predict the clinical reversion of M-t-N, the grey matter covariance showed higher prediction accuracy for early progression of N-t-M. CONCLUSIONS Our findings are the first to report grey matter covariance changes in MCI development and reversion and highlight the necessity of including grey matter covariance changes along with hippocampal degeneration in the early detection of MCI and Alzheimer's disease.
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Affiliation(s)
- Mingxi Dang
- grid.20513.350000 0004 1789 9964State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, 100875 China
| | - Caishui Yang
- grid.20513.350000 0004 1789 9964State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, 100875 China ,grid.20513.350000 0004 1789 9964School of Systems Science, Beijing Normal University, Beijing, 100875 China
| | - Kewei Chen
- grid.418204.b0000 0004 0406 4925Banner Alzheimer’s Institute, Phoenix, AZ 85006 USA
| | - Peng Lu
- grid.20513.350000 0004 1789 9964State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, 100875 China
| | - He Li
- grid.410318.f0000 0004 0632 3409Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, 100700 China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China.
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9
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Xiao Y, Wang J, Huang K, Gao L, Yao S. Progressive structural and covariance connectivity abnormalities in patients with Alzheimer's disease. Front Aging Neurosci 2023; 14:1064667. [PMID: 36688148 PMCID: PMC9853893 DOI: 10.3389/fnagi.2022.1064667] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 12/13/2022] [Indexed: 01/07/2023] Open
Abstract
Background Alzheimer's disease (AD) is one of most prevalent neurodegenerative diseases worldwide and characterized by cognitive decline and brain structure atrophy. While studies have reported substantial grey matter atrophy related to progression of AD, it remains unclear about brain regions with progressive grey matter atrophy, covariance connectivity, and the associations with cognitive decline in AD patients. Objective This study aims to investigate the grey matter atrophy, structural covariance connectivity abnormalities, and the correlations between grey matter atrophy and cognitive decline during AD progression. Materials We analyzed neuroimaging data of healthy controls (HC, n = 45) and AD patients (n = 40) at baseline (AD-T1) and one-year follow-up (AD-T2) obtained from the Alzheimer's Disease Neuroimaging Initiative. We investigated AD-related progressive changes of grey matter volume, covariance connectivity, and the clinical relevance to further understand the pathological progression of AD. Results The results showed clear patterns of grey matter atrophy in inferior frontal gyrus, prefrontal cortex, lateral temporal gyrus, posterior cingulate cortex, insula, hippocampus, caudate, and thalamus in AD patients. There was significant atrophy in bilateral superior temporal gyrus (STG) and left caudate in AD patients over a one-year period, and the grey matter volume decrease in right STG and left caudate was correlated with cognitive decline. Additionally, we found reduced structural covariance connectivity between right STG and left caudate in AD patients. Using AD-related grey matter atrophy as features, there was high discrimination accuracy of AD patients from HC, and AD patients at different time points.
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Affiliation(s)
- Yaqiong Xiao
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
| | - Kaiyu Huang
- Center for Language and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Lei Gao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Shun Yao
- Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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10
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Ng KP, Qian X, Ng KK, Ji F, Rosa-Neto P, Gauthier S, Kandiah N, Zhou JH. Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer's disease continuum. eLife 2022; 11:e77745. [PMID: 36053063 PMCID: PMC9477498 DOI: 10.7554/elife.77745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 09/02/2022] [Indexed: 11/13/2022] Open
Abstract
Background Large-scale neuronal network breakdown underlies memory impairment in Alzheimer's disease (AD). However, the differential trajectories of the relationships between network organisation and memory across pathology and cognitive stages in AD remain elusive. We determined whether and how the influences of individual-level structural and metabolic covariance network integrity on memory varied with amyloid pathology across clinical stages without assuming a constant relationship. Methods Seven hundred and eight participants from the Alzheimer's Disease Neuroimaging Initiative were studied. Individual-level structural and metabolic covariance scores in higher-level cognitive and hippocampal networks were derived from magnetic resonance imaging and [18F] fluorodeoxyglucose positron emission tomography using seed-based partial least square analyses. The non-linear associations between network scores and memory across cognitive stages in each pathology group were examined using sparse varying coefficient modelling. Results We showed that the associations of memory with structural and metabolic networks in the hippocampal and default mode regions exhibited pathology-dependent differential trajectories across cognitive stages using sparse varying coefficient modelling. In amyloid pathology group, there was an early influence of hippocampal structural network deterioration on memory impairment in the preclinical stage, and a biphasic influence of the angular gyrus-seeded default mode metabolic network on memory in both preclinical and dementia stages. In non-amyloid pathology groups, in contrast, the trajectory of the hippocampus-memory association was opposite and weaker overall, while no metabolism covariance networks were related to memory. Key findings were replicated in a larger cohort of 1280 participants. Conclusions Our findings highlight potential windows of early intervention targeting network breakdown at the preclinical AD stage. Funding Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). We also acknowledge the funding support from the Duke NUS/Khoo Bridge Funding Award (KBrFA/2019-0020) and NMRC Open Fund Large Collaborative Grant (OFLCG09May0035).
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Affiliation(s)
- Kok Pin Ng
- Department of Neurology, National Neuroscience InstituteSingaporeSingapore
- Duke-NUS Medical SchoolSingaporeSingapore
- Lee Kong Chian School of Medicine, Nanyang Technological University SingaporeSingaporeSingapore
| | - Xing Qian
- Centre for Sleep and Cognition and Centre for Translational MR Research,Yong Loo Lin School of Medicine, National University of SingaporeSingaporeSingapore
| | - Kwun Kei Ng
- Centre for Sleep and Cognition and Centre for Translational MR Research,Yong Loo Lin School of Medicine, National University of SingaporeSingaporeSingapore
| | - Fang Ji
- Centre for Sleep and Cognition and Centre for Translational MR Research,Yong Loo Lin School of Medicine, National University of SingaporeSingaporeSingapore
| | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Alzheimer’s Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l’Ouest-de-l’Île-de-Montréal, and Departments of Neurology, Neurosurgery, Psychiatry, Pharmacology and Therapeutics, McGill UniversityMontrealCanada
- Montreal Neurological Institute, McGill UniversityMontrealCanada
| | - Serge Gauthier
- Department of Neurology & Neurosurgery, McGill UniversityMontrealCanada
| | - Nagaendran Kandiah
- Lee Kong Chian School of Medicine, Nanyang Technological University SingaporeSingaporeSingapore
| | - Juan Helen Zhou
- Centre for Sleep and Cognition and Centre for Translational MR Research,Yong Loo Lin School of Medicine, National University of SingaporeSingaporeSingapore
- Department of Electrical and Computer Engineering, National University of SingaporeSingaporeSingapore
- Integrative Sciences and Engineering Programme (ISEP), National University of SingaporeSingaporeSingapore
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11
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Gonuguntla V, Yang E, Guan Y, Koo B, Kim J. Brain signatures based on structural MRI: Classification for MCI, PMCI, and AD. Hum Brain Mapp 2022; 43:2845-2860. [PMID: 35289025 PMCID: PMC9120560 DOI: 10.1002/hbm.25820] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Revised: 01/23/2022] [Accepted: 02/08/2022] [Indexed: 12/05/2022] Open
Abstract
Structural MRI (sMRI) provides valuable information for understanding neurodegenerative illnesses such as Alzheimer's Disease (AD) since it detects the brain's cerebral atrophy. The development of brain networks utilizing single imaging data-sMRI is an understudied area that has the potential to provide a network neuroscientific viewpoint on the brain. In this paper, we proposed a framework for constructing a brain network utilizing sMRI data, followed by the extraction of signature networks and important regions of interest (ROIs). To construct a brain network using sMRI, nodes are defined as regions described by the brain atlas, and edge weights are determined using a distance measure called the Sorensen distance between probability distributions of gray matter tissue probability maps. The brain signatures identified are based on the changes in the networks of disease and control subjects. To validate the proposed methodology, we first identified the brain signatures and critical ROIs associated with mild cognitive impairment (MCI), progressive MCI (PMCI), and Alzheimer's disease (AD) with 60 reference subjects (15 each of control, MCI, PMCI, and AD). Then, 200 examination subjects (50 each of control, MCI, PMCI, and AD) were selected to evaluate the identified signature patterns. Results demonstrate that the proposed framework is capable of extracting brain signatures and has a number of potential applications in the disciplines of brain mapping, brain communication, and brain network-based applications.
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Affiliation(s)
| | - Ehwa Yang
- Medical Science Research InstituteSamsung Medical CenterSeoulSouth Korea
| | - Yi Guan
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Bang‐Bon Koo
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Jae‐Hun Kim
- Department of Radiology, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulSouth Korea
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12
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Structural covariance changes in major cortico-basal ganglia and thalamic networks in amyloid-positive patients with white matter hyperintensities. Neurobiol Aging 2022; 117:117-127. [DOI: 10.1016/j.neurobiolaging.2022.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 05/19/2022] [Accepted: 05/23/2022] [Indexed: 11/23/2022]
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13
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Zhao K, Zheng Q, Dyrba M, Rittman T, Li A, Che T, Chen P, Sun Y, Kang X, Li Q, Liu B, Liu Y, Li S. Regional Radiomics Similarity Networks Reveal Distinct Subtypes and Abnormality Patterns in Mild Cognitive Impairment. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2104538. [PMID: 35098696 PMCID: PMC9036024 DOI: 10.1002/advs.202104538] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 12/30/2021] [Indexed: 05/28/2023]
Abstract
Individuals with mild cognitive impairment (MCI) of different subtypes show distinct alterations in network patterns. The first aim of this study is to identify the subtypes of MCI by employing a regional radiomics similarity network (R2SN). The second aim is to characterize the abnormality patterns associated with the clinical manifestations of each subtype. An individual-level R2SN is constructed for N = 605 normal controls (NCs), N = 766 MCI patients, and N = 283 Alzheimer's disease (AD) patients. MCI patients' R2SN profiles are clustered into two subtypes using nonnegative matrix factorization. The patterns of brain alterations, gene expression, and the risk of cognitive decline in each subtype are evaluated. MCI patients are clustered into "similar to the pattern of NCs" (N-CI, N = 252) and "similar to the pattern of AD" (A-CI, N = 514) subgroups. Significant differences are observed between the subtypes with respect to the following: 1) clinical measures; 2) multimodal neuroimaging; 3) the proportion of progression to dementia (61.54% for A-CI and 21.77% for N-CI) within three years; 4) enriched genes for potassium-ion transport and synaptic transmission. Stratification into the two subtypes provides new insight for risk assessment and precise early intervention for MCI patients.
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Affiliation(s)
- Kun Zhao
- Beijing Advanced Innovation Centre for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100191China
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing100876China
| | - Qiang Zheng
- School of Computer and Control EngineeringYantai UniversityYantai264005China
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE)Rostock18147Germany
| | - Timothy Rittman
- Department of Clinical NeurosciencesUniversity of CambridgeCambridge Biomedical CampusCambridgeCB2 0SZUK
| | - Ang Li
- State Key Laboratory of Brain and Cognitive Science, Institute of BiophysicsChinese Academy of SciencesBeijing100101China
| | - Tongtong Che
- Beijing Advanced Innovation Centre for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100191China
| | - Pindong Chen
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing100049China
| | - Yuqing Sun
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing100049China
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
- School of Artificial IntelligenceUniversity of Chinese Academy of SciencesChinese Academy of SciencesBeijing100049China
| | - Qiongling Li
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijing100875China
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijing100875China
| | - Yong Liu
- School of Artificial IntelligenceBeijing University of Posts and TelecommunicationsBeijing100876China
- Brainnetome Center & National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing100190China
| | - Shuyu Li
- Beijing Advanced Innovation Centre for Biomedical EngineeringSchool of Biological Science and Medical EngineeringBeihang UniversityBeijing100191China
- State Key Laboratory of Cognition Neuroscience & LearningBeijing Normal UniversityBeijing100875China
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14
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de Flores R, Das SR, Xie L, Wisse LEM, Lyu X, Shah P, Yushkevich PA, Wolk DA. Medial Temporal Lobe Networks in Alzheimer's Disease: Structural and Molecular Vulnerabilities. J Neurosci 2022; 42:2131-2141. [PMID: 35086906 PMCID: PMC8916768 DOI: 10.1523/jneurosci.0949-21.2021] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 11/30/2021] [Accepted: 12/04/2021] [Indexed: 11/21/2022] Open
Abstract
The medial temporal lobe (MTL) is connected to the rest of the brain through two main networks: the anterior-temporal (AT) and the posterior-medial (PM) systems. Given the crucial role of the MTL and networks in the physiopathology of Alzheimer's disease (AD), the present study aimed at (1) investigating whether MTL atrophy propagates specifically within the AT and PM networks, and (2) evaluating the vulnerability of these networks to AD proteinopathies. To do that, we used neuroimaging data acquired in human male and female in three distinct cohorts: (1) resting-state functional MRI (rs-fMRI) from the aging brain cohort (ABC) to define the AT and PM networks (n = 68); (2) longitudinal structural MRI from Alzheimer's disease neuroimaging initiative (ADNI)GO/2 to highlight structural covariance patterns (n = 349); and (3) positron emission tomography (PET) data from ADNI3 to evaluate the networks' vulnerability to amyloid and tau (n = 186). Our results suggest that the atrophy of distinct MTL subregions propagates within the AT and PM networks in a dissociable manner. Brodmann area (BA)35 structurally covaried within the AT network while the parahippocampal cortex (PHC) covaried within the PM network. In addition, these networks are differentially associated with relative tau and amyloid burden, with higher tau levels in AT than in PM and higher amyloid levels in PM than in AT. Our results also suggest differences in the relative burden of tau species. The current results provide further support for the notion that two distinct MTL networks display differential alterations in the context of AD. These findings have important implications for disease spread and the cognitive manifestations of AD.SIGNIFICANCE STATEMENT The current study provides further support for the notion that two distinct medial temporal lobe (MTL) networks, i.e., anterior-temporal (AT) and the posterior-medial (PM), display differential alterations in the context of Alzheimer's disease (AD). Importantly, neurodegeneration appears to occur within these networks in a dissociable manner marked by their covariance patterns. In addition, the AT and PM networks are also differentially associated with relative tau and amyloid burden, and perhaps differences in the relative burden of tau species [e.g., neurofibriliary tangles (NFTs) vs tau in neuritic plaques]. These findings, in the context of a growing literature consistent with the present results, have important implications for disease spread and the cognitive manifestations of AD in light of the differential cognitive processes ascribed to them.
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Affiliation(s)
- Robin de Flores
- Department of Neurology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Université de Caen Normandie, Institut National de la Santé et de la Recherche Médicale Unité Mixte de Recherche Scientifique (UMRS) Unité 1237, Caen 14000, France
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Department of Radiology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
- Department of Diagnostic Radiology, Lund University, Lund 22185, Sweden
| | - Xueying Lyu
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Preya Shah
- Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia 19104, Pennsylvania
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia 19104, Pennsylvania
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15
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Li S, Bai R, Yang Y, Zhao R, Upreti B, Wang X, Liu S, Cheng Y, Xu J. Abnormal cortical thickness and structural covariance networks in systemic lupus erythematosus patients without major neuropsychiatric manifestations. Arthritis Res Ther 2022; 24:259. [PMID: 36443835 PMCID: PMC9703716 DOI: 10.1186/s13075-022-02954-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 11/11/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Non-neuropsychiatric systemic lupus erythematosus (non-NPSLE) has been confirmed to have subtle changes in brain structure before the appearance of obvious neuropsychiatric symptoms. Previous literature mainly focuses on brain structure loss in non-NPSLE; however, the results are heterogeneous, and the impact of structural changes on the topological structure of patients' brain networks remains to be determined. In this study, we combined neuroimaging and network analysis methods to evaluate the changes in cortical thickness and its structural covariance networks (SCNs) in patients with non-NPSLE. METHODS We compare the cortical thickness of non-NPSLE patients (N=108) and healthy controls (HCs, N=88) using both surface-based morphometry (SBM) and regions of interest (ROI) methods, respectively. After that, we analyzed the correlation between the abnormal cortical thickness results found in the ROI method and a series of clinical features. Finally, we constructed the SCNs of two groups using the regional cortical thickness and analyzed the abnormal SCNs of non-NPSLE. RESULTS By SBM method, we found that cortical thickness of 34 clusters in the non-NPSLE group was thinner than that in the HC group. ROI method based on Destrieux atlas showed that cortical thickness of 57 regions in the non-NPSLE group was thinner than that in the HC group and related to the course of disease, autoantibodies, the cumulative amount of immunosuppressive agents, and cognitive psychological scale. In the SCN analysis, the cortical thickness SCNs of the non-NPSLE group did not follow the small-world attribute at a few densities, and the global clustering coefficient appeared to increase. The area under the curve analysis showed that there were significant differences between the two groups in clustering coefficient, degree, betweenness, and local efficiency. There are a total of seven hubs for non-NPSLE, and five hubs in HCs, the two groups do not share a common hub distribution. CONCLUSION Extensive and obvious reduction in cortical thickness and abnormal topological organization of SCNs are observed in non-NPSLE patients. The observed abnormalities may not only be the realization of brain damage caused by the disease, but also the contribution of the compensatory changes within the nervous system.
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Affiliation(s)
- Shu Li
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Ru Bai
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yifan Yang
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Ruotong Zhao
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Bibhuti Upreti
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiangyu Wang
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shuang Liu
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China.
| | - Jian Xu
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China.
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16
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Yang Y, Cheng Y, Wang X, Upreti B, Cui R, Liu S, Shan B, Yu H, Luo C, Xu J. Gout Is Not Just Arthritis: Abnormal Cortical Thickness and Structural Covariance Networks in Gout. Front Neurol 2021; 12:662497. [PMID: 34603178 PMCID: PMC8481804 DOI: 10.3389/fneur.2021.662497] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 08/12/2021] [Indexed: 12/27/2022] Open
Abstract
Background: Hyperuricemia is the cause of gout. The antioxidant and neuroprotective effects of uric acid seem to benefit some patients with central nervous system injury. However, changes in the brain structure have not been discovered in patients with gout. Object: Clarify the changes in cortical thickness in patients with gout and the alteration of the structural covariance networks (SCNs) based on cortical thickness. Methods: We collected structural MRIs of 23 male gout patients and 23 age-matched healthy controls. After calculating and comparing the difference in cortical thickness between the two groups, we constructed and analyzed the cortical thickness covariance networks of the two groups, and we investigated for any changes in SCNs of gout patients. Results: Gout patients have thicker cortices in the left postcentral, left supramarginal, right medial temporal, and right medial orbitofrontal regions; and thinner cortices were found in the left insula, left superior frontal, right pericalcarine, and right precentral regions. In SCN analysis, between-group differences in global network measures showed that gout patients have a higher global efficiency. In regional network measures, more nodes in gout patients have increased centrality. In network hub analysis, we found that the transfer of the core hub area, rather than the change in number, may be the characteristic of the gout's cortical thickness covariance network. Conclusion: This is the first study on changes in brain cortical thickness and SCN based on graph theory in patients with gout. The present study found that, compared with healthy controls, gout patients show regional cortical thinning or thickening, and variation in the properties of the cortical thickness covariance network also changed. These alterations may be the combined effect of disease damage and physiological compensation. More research is needed to fully understand the complex underlying mechanisms of gout brain variation.
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Affiliation(s)
- Yifan Yang
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuqi Cheng
- Department of Psychiatry, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Xiangyu Wang
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Bibhuti Upreti
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Ruomei Cui
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Shuang Liu
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Baoci Shan
- Nuclear Analysis Technology Key Laboratory, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
| | - Hongjun Yu
- Magnetic Resonance Imaging Center, The First Hospital of Kunming, Kunming, China
| | - Chunrong Luo
- Magnetic Resonance Imaging Center, The First Hospital of Kunming, Kunming, China
| | - Jian Xu
- Department of Rheumatology and Immunology, First Affiliated Hospital of Kunming Medical University, Kunming, China
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17
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Fu Z, Zhao M, He Y, Wang X, Lu J, Li S, Li X, Kang G, Han Y, Li S. Divergent Connectivity Changes in Gray Matter Structural Covariance Networks in Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment, and Alzheimer's Disease. Front Aging Neurosci 2021; 13:686598. [PMID: 34483878 PMCID: PMC8415752 DOI: 10.3389/fnagi.2021.686598] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 07/19/2021] [Indexed: 01/18/2023] Open
Abstract
Alzheimer’s disease (AD) has a long preclinical stage that can last for decades prior to progressing toward amnestic mild cognitive impairment (aMCI) and/or dementia. Subjective cognitive decline (SCD) is characterized by self-experienced memory decline without any evidence of objective cognitive decline and is regarded as the later stage of preclinical AD. It has been reported that the changes in structural covariance patterns are affected by AD pathology in the patients with AD and aMCI within the specific large-scale brain networks. However, the changes in structural covariance patterns including normal control (NC), SCD, aMCI, and AD are still poorly understood. In this study, we recruited 42 NCs, 35 individuals with SCD, 43 patients with aMCI, and 41 patients with AD. Gray matter (GM) volumes were extracted from 10 readily identifiable regions of interest involved in high-order cognitive function and AD-related dysfunctional structures. The volume values were used to predict the regional densities in the whole brain by using voxel-based statistical and multiple linear regression models. Decreased structural covariance and weakened connectivity strength were observed in individuals with SCD compared with NCs. Structural covariance networks (SCNs) seeding from the default mode network (DMN), salience network, subfields of the hippocampus, and cholinergic basal forebrain showed increased structural covariance at the early stage of AD (referring to aMCI) and decreased structural covariance at the dementia stage (referring to AD). Moreover, the SCN seeding from the executive control network (ECN) showed a linearly increased extent of the structural covariance during the early and dementia stages. The results suggest that changes in structural covariance patterns as the order of NC-SCD-aMCI-AD are divergent and dynamic, and support the structural disconnection hypothesis in individuals with SCD.
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Affiliation(s)
- Zhenrong Fu
- School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Mingyan Zhao
- Department of Neurology, Tangshan Gongren Hospital, Tangshan, China.,Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Yirong He
- School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Xuetong Wang
- School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Jiadong Lu
- School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Shaoxian Li
- School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
| | - Xin Li
- School of Electrical Engineering, Yanshan University, Qinhuangdao, China.,Measurement Technology and Instrumentation Key Laboratory of Hebei Province, Qinhuangdao, China
| | - Guixia Kang
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.,Biomedical Engineering Institute, Hainan University, Haikou, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Shuyu Li
- School of Biological Science and Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
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18
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Li K, Fu Z, Qi S, Luo X, Zeng Q, Xu X, Huang P, Zhang M, Calhoun VD. Polygenic Hazard Score Associated Multimodal Brain Networks Along the Alzheimer's Disease Continuum. Front Aging Neurosci 2021; 13:725246. [PMID: 34539385 PMCID: PMC8446666 DOI: 10.3389/fnagi.2021.725246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/10/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Late-onset Alzheimer's disease (AD) is a polygenic neurodegenerative disease. Identifying the neuroimaging phenotypes behind the genetic predisposition of AD is critical to the understanding of AD pathogenesis. Two major questions which previous studies have led to are: (1) should the general "polygenic hazard score" (PHS) be a good choice to identify the individual genetic risk for AD; and (2) should researchers also include inter-modality relationships in the analyses considering these may provide complementary information about the AD etiology. METHODS We collected 88 healthy controls, 77 patients with mild cognitive impairment (MCI), and 22 AD patients to simulate the AD continuum included from the ADNI database. PHS-guided multimodal fusion was used to investigate the impact of PHS on multimodal brain networks in AD-continuum by maximizing both inter-modality association and reference-modality correlation. Fractional amplitude of low frequency fluctuations, gray matter (GM) volume, and amyloid standard uptake value ratios were included as neuroimaging features. Eventually, the changes in neuroimaging features along AD continuum were investigated, and relationships between cognitive performance and identified PHS associated multimodal components were established. RESULTS We found that PHS was associated with multimodal brain networks, which showed different functional and structural impairments under increased amyloid deposits. Notably, along with AD progression, functional impairment occurred before GM atrophy, amyloid deposition started from the MCI stage and progressively increased throughout the disease continuum. CONCLUSION PHS is associated with multi-facets of brain impairments along the AD continuum, including cognitive dysfunction, pathological deposition, which might underpin the AD pathogenesis.
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Affiliation(s)
- Kaicheng Li
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
| | - Shile Qi
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
| | - Xiao Luo
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaopei Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States
- Department of Psychology, Computer Science, Neuroscience Institute, and Physics, Georgia State University, Atlanta, GA, United States
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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19
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Qing Z, Chen F, Lu J, Lv P, Li W, Liang X, Wang M, Wang Z, Zhang X, Zhang B. Causal structural covariance network revealing atrophy progression in Alzheimer's disease continuum. Hum Brain Mapp 2021; 42:3950-3962. [PMID: 33978292 PMCID: PMC8288084 DOI: 10.1002/hbm.25531] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 04/10/2021] [Accepted: 04/26/2021] [Indexed: 01/24/2023] Open
Abstract
The structural covariance network (SCN) has provided a perspective on the large‐scale brain organization impairment in the Alzheimer's Disease (AD) continuum. However, the successive structural impairment across brain regions, which may underlie the disrupted SCN in the AD continuum, is not well understood. In the current study, we enrolled 446 subjects with AD, mild cognitive impairment (MCI) or normal aging (NA) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The SCN as well as a casual SCN (CaSCN) based on Granger causality analysis were applied to the T1‐weighted structural magnetic resonance images of the subjects. Compared with that of the NAs, the SCN was disrupted in the MCI and AD subjects, with the hippocampus and left middle temporal lobe being the most impaired nodes, which is in line with previous studies. In contrast, according to the 194 subjects with records on CSF amyloid and Tau, the CaSCN revealed that during AD progression, the CaSCN was enhanced. Specifically, the hippocampus, thalamus, and precuneus/posterior cingulate cortex (PCC) were identified as the core regions in which atrophy originated and could predict atrophy in other brain regions. Taken together, these findings provide a comprehensive view of brain atrophy in the AD continuum and the relationships among the brain atrophy in different regions, which may provide novel insight into the progression of AD.
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Affiliation(s)
- Zhao Qing
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.,Institute of Brain Science, Nanjing University, Nanjing, China
| | - Feng Chen
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Jiaming Lu
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Pin Lv
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Weiping Li
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xue Liang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Maoxue Wang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zhengge Wang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xin Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Bing Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.,Institute of Brain Science, Nanjing University, Nanjing, China
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20
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Chu T, Li J, Zhang Z, Gong P, Che K, Li Y, Zhang G, Mao N. Altered structural covariance of hippocampal subregions in patients with Alzheimer's disease. Behav Brain Res 2021; 409:113327. [PMID: 33930469 DOI: 10.1016/j.bbr.2021.113327] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 04/22/2021] [Accepted: 04/25/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND AND PURPOSE Different atrophy of hippocampus subregions is a valuable indicator of patients with Alzheimer's disease (AD). To explore the relationship among the hippocampal subregions of patients with AD, altered gray matter structural covariance of hippocampal subregions in patients with AD was studied. MATERIALS AND METHODS Participants were selected from the Open Access Series of Imaging Studies Database. Pearson correlations among the volume of the hippocampal subregions were generated as structural covariance network. Topological metrics for all selected sparsity ranges were calculated in the healthy controls (HCs) and patients with AD by using the GRETNA software package. Spearman correlation analysis was performed to statistically analyze the volume and Mini-mental State Examination (MMSE) scores of the hippocampal subregions of the patients with AD, with age and gender as interference covariates and corrected for false discovery rate (FDR) (p < 0.05). RESULTS The structural covariance network properties of the hippocampal subregions of patients with AD changed. The clustering coefficient (Cp) and network efficiency (Ne) decreased, characteristic path length (Lp) increased, and the hub nodes changed. The volumes of left parasubiculum, right granule cell layer of dentate gyrus (GC-DG), right molecular layer of the hippocampus (molecular_layer_HP), right Cornu Ammonis (CA) regions CA1 of the hippocampus proper, right fimbria and right CA4 were significantly correlated with the MMSE scores. CONCLUSIONS The structural covariance network of the hippocampal subregions of patients with AD was reorganized, and the transmission efficiency was weakened. This study explored the changes in these subregions from the network level, which may provide a new perspective and theoretical basis for the neurobiological mechanisms of patients with AD.
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Affiliation(s)
- Tongpeng Chu
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, 264000, PR China
| | - Jian Li
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, 264000, PR China
| | - Zhongsheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, 264000, PR China
| | - Peiyou Gong
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, 264000, PR China
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, 264000, PR China
| | - Yuna Li
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, 264000, PR China
| | - Gang Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, 264000, PR China.
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Affiliated Hospital of Qingdao University, Yantai, Shandong, 264000, PR China.
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21
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Wu Z, Peng Y, Hong M, Zhang Y. Gray Matter Deterioration Pattern During Alzheimer's Disease Progression: A Regions-of-Interest Based Surface Morphometry Study. Front Aging Neurosci 2021; 13:593898. [PMID: 33613265 PMCID: PMC7886803 DOI: 10.3389/fnagi.2021.593898] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 01/13/2021] [Indexed: 11/17/2022] Open
Abstract
Accurate detection of the regions of Alzheimer's disease (AD) lesions is critical for early intervention to effectively slow down the progression of the disease. Although gray matter volumetric abnormalities are commonly detected in patients with mild cognition impairment (MCI) and patients with AD, the gray matter surface-based deterioration pattern associated with the progression of the disease from MCI to AD stages is largely unknown. To identify group differences in gray matter surface morphometry, including cortical thickness, the gyrification index (GI), and the sulcus depth, 80 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were split into healthy controls (HCs; N = 20), early MCIs (EMCI; N = 20), late MCIs (LMCI; N = 20), and ADs (N = 20). Regions-of-interest (ROI)-based surface morphometry was subsequently studied and compared across the four stage groups to characterize the gray matter deterioration during AD progression. Co-alteration patterns (Spearman's correlation coefficient) across the whole brain were also examined. Results showed that patients with MCI and AD exhibited a significant reduction in cortical thickness (p < 0.001) mainly in the cingulate region (four subregions) and in the temporal (thirteen subregions), parietal (five subregions), and frontal (six subregions) lobes compared to HCs. The sulcus depth of the eight temporal, four frontal, four occipital, and eight parietal subregions were also significantly affected (p < 0.001) by the progression of AD. The GI was shown to be insensitive to AD progression (only three subregions were detected with a significant difference, p < 0.001). Moreover, Spearman's correlation analysis confirmed that the co-alteration pattern of the cortical thickness and sulcus depth indices is predominant during AD progression. The findings highlight the relevance between gray matter surface morphometry and the stages of AD, laying the foundation for in vivo tracking of AD progression. The co-alteration pattern of surface-based morphometry would improve the researchers' knowledge of the underlying pathologic mechanisms in AD.
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Affiliation(s)
- Zhanxiong Wu
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, China.,Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | - Yun Peng
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
| | - Ming Hong
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, China
| | - Yingchun Zhang
- Department of Biomedical Engineering, University of Houston, Houston, TX, United States
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22
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Carmon J, Heege J, Necus JH, Owen TW, Pipa G, Kaiser M, Taylor PN, Wang Y. Reliability and comparability of human brain structural covariance networks. Neuroimage 2020; 220:117104. [PMID: 32621973 DOI: 10.1016/j.neuroimage.2020.117104] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 05/01/2020] [Accepted: 06/25/2020] [Indexed: 12/11/2022] Open
Abstract
Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To our knowledge, little has been investigated in terms of the comparability of results between different data sets of healthy human subjects, as well as the reliability of results over the same subjects in different rescan sessions, image resolutions, or FreeSurfer versions. In terms of comparability, our results show substantial differences in the structural covariance matrix between data sets of age- and sex-matched healthy human adults. These differences persist after univariate site correction, they are exacerbated by low sample sizes, and they are most pronounced when using average cortical thickness as a morphological measure. Down-stream graph theoretic analyses further show statistically significant differences. In terms of reliability, substantial differences were also found when comparing repeated scan sessions of the same subjects, image resolutions, and even FreeSurfer versions of the same image. We could further estimate the relative measurement error and showed that it is largest when using cortical thickness as a morphological measure. Using simulated data, we argue that cortical thickness is least reliable because of larger relative measurement errors. Practically, we make the following recommendations (1) combining subjects across sites into one group should be avoided, particularly if sites differ in image resolutions, subject demographics, or preprocessing steps; (2) surface area and volume should be preferred as morphological measures over cortical thickness; (3) a large number of subjects (n≫30 for the Desikan-Killiany parcellation) should be used to estimate structural covariance; (4) measurement error should be assessed where repeated measurements are available; (5) if combining sites is critical, univariate (per ROI) site-correction is insufficient, but error covariance (between ROIs) should be explicitly measured and modelled.
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Affiliation(s)
- Jona Carmon
- Institute of Cognitive Science, Osnabrueck University, Osnabrueck, Germany
| | - Jil Heege
- Humboldt University Berlin, Berlin, Germany
| | - Joe H Necus
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Thomas W Owen
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | - Gordon Pipa
- Institute of Cognitive Science, Osnabrueck University, Osnabrueck, Germany
| | - Marcus Kaiser
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Peter N Taylor
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; Institute of Neurology, University College London, UK
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Institute of Neuroscience, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; Institute of Neurology, University College London, UK.
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23
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Wang YM, Yang ZY, Wang Y, Wang YY, Cai XL, Zhang RT, Hu HX, Cheung EFC, Chan RCK. Grey matter volume and structural covariance associated with schizotypy. Schizophr Res 2020; 224:88-94. [PMID: 33046333 DOI: 10.1016/j.schres.2020.09.021] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 06/16/2020] [Accepted: 09/24/2020] [Indexed: 02/09/2023]
Abstract
In this study, we applied brain grey matter volume and structural covariance methods on T1 weighted images to delineate potential structural brain changes in individuals with high schizotypy, who were defined as healthy individuals scoring in the top tenth percentile of the Schizotypal Personality Questionnaire (SPQ). Eighty-seven college students with high schizotypy and 122 controls were recruited in China. Differences in grey matter volume and volume covariance between the two groups, and correlations of grey matter volume with SPQ scores in the high schizotypy group were examined. We found that individuals with high schizotypy had decreased grey matter volume at the left medial superior frontal gyrus (medsFG) extending towards the superior frontal gyrus, decreased structural covariance within the right medsFG, between the right superior frontal gyrus (sFG), the right superior temporal gyrus and the right anterior insula; and increased structural covariance between the caudate and the right inferior temporal gyrus. Correlation analysis revealed that grey matter volume of the left middle temporal pole and the right sFG correlated positively with the SPQ total scores, volume of the bilateral cerebellum 9 sub-region correlated negatively with the SPQ cognitive-perceptual sub-scale scores, volume of the bilateral striatum correlated positively with the SPQ interpersonal sub-scale scores, and volume of the bilateral superior temporal pole correlated positively with the SPQ disorganization sub-scale scores in the high schizotypy group. These results highlight important grey matter structural changes in the medsFG in individuals with high schizotypy.
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Affiliation(s)
- Yong-Ming Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, PR China; Sino-Danish Center for Education and Research, Beijing 100190, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Zhou-Ya Yang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Yan-Yu Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China; Department of Psychology, Weifang Medical University, Shandong Province, PR China
| | - Xin-Lu Cai
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, PR China; Sino-Danish Center for Education and Research, Beijing 100190, PR China
| | - Rui-Ting Zhang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Hui-Xin Hu
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China
| | - Eric F C Cheung
- Castle Peak Hospital, Hong Kong Special Administrative Region
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, PR China; Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, PR China; Sino-Danish Center for Education and Research, Beijing 100190, PR China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, PR China.
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24
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Sigurdsson HP, Jackson SR, Jolley L, Mitchell E, Jackson GM. Alterations in cerebellar grey matter structure and covariance networks in young people with Tourette syndrome. Cortex 2020; 126:1-15. [DOI: 10.1016/j.cortex.2019.12.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 11/15/2019] [Accepted: 12/26/2019] [Indexed: 01/13/2023]
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25
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Tommasin S, De Giglio L, Ruggieri S, Petsas N, Giannì C, Pozzilli C, Pantano P. Multi-scale resting state functional reorganization in response to multiple sclerosis damage. Neuroradiology 2020; 62:693-704. [PMID: 32189024 DOI: 10.1007/s00234-020-02393-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 02/27/2020] [Indexed: 11/26/2022]
Abstract
PURPOSE In multiple sclerosis (MS), how brain functional changes relate to clinical conditions is still a matter of debate. The aim of this study was to investigate how functional connectivity (FC) reorganization at three different scales, ranging from local to whole brain, is related to tissue damage and disability. METHODS One-hundred-nineteen patients with MS were clinically evaluated with the Expanded Disability Status Scale and the Multiple Sclerosis Functional Composite. Patients and 42 healthy controls underwent a multimodal 3 T MRI, including resting-state functional MRI. RESULTS We identified 16 resting-state networks via independent component analysis and measured within-network, between-network, and whole-brain (global efficiency and degree centrality) FC. Within-network FC was higher in patients than in controls in default mode, frontoparietal, and executive-control networks, and corresponded to low clinical impairment (default mode network versus Expanded Disability Status Scale r = - 0.31, p < 0.01; right frontoparietal network versus Paced Auditory Serial Addition Test r = 0.33, p < 0.01). All measures of between-network and whole-brain FC, except default mode network global efficiency, were lower in patients than in controls, and corresponded to high disability (i.e., basal ganglia global efficiency versus Timed 25-Foot Walk r = - 0.25, p < 0.03; default mode global efficiency versus Expanded Disability Status Scale r = - 0.44, p < 0.001). Altered measures of within-network, between-network, and whole-brain FC were combined in functional indices that were linearly related to disease duration, Paced Auditory Serial Addition Test and lesion load and non-linearly related to Expanded Disability Status Scale. CONCLUSION We suggest that the combined evaluation of functional alterations occurring at different levels, from local to whole brain, could exhaustively describe neuroplastic changes in MS, while increased within-network FC likely represents adaptive compensatory processes, decreased between-network and whole-brain FC likely represent loss of functional network integration consequent to structural disruption.
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Affiliation(s)
- Silvia Tommasin
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy
| | - Laura De Giglio
- Medicine Department, Neurology Unit, San Filippo Neri Hospital, Via Giovanni Martinotti, 20, 00135, Rome, RM, Italy
| | - Serena Ruggieri
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy
| | - Nikolaos Petsas
- IRCCS Neuromed, (Pozzilli [IS], IT) Via Atinense, 18, 86077, Pozzilli, IS, Italy
| | - Costanza Giannì
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy
| | - Carlo Pozzilli
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy
- Sant'Andrea Hospital, MS Centre, Sapienza - University of Rome, Viale di Grottarossa 1035, 00189, Rome, Italy
| | - Patrizia Pantano
- Department of Human Neuroscience, Sapienza - University of Rome, Viale dell'Università 30, 00185, Rome, Italy.
- IRCCS Neuromed, (Pozzilli [IS], IT) Via Atinense, 18, 86077, Pozzilli, IS, Italy.
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26
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Wagner F, Duering M, Gesierich BG, Enzinger C, Ropele S, Dal-Bianco P, Mayer F, Schmidt R, Koini M. Gray Matter Covariance Networks as Classifiers and Predictors of Cognitive Function in Alzheimer's Disease. Front Psychiatry 2020; 11:360. [PMID: 32431629 PMCID: PMC7214682 DOI: 10.3389/fpsyt.2020.00360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 04/09/2020] [Indexed: 11/19/2022] Open
Abstract
The study of shared variation in gray matter morphology may define neurodegenerative diseases beyond what can be detected from the isolated assessment of regional brain volumes. We, therefore, aimed to (1) identify SCNs (structural covariance networks) that discriminate between Alzheimer's disease (AD) patients and healthy controls (HC), (2) investigate their diagnostic accuracy in comparison and above established markers, and (3) determine if they are associated with cognitive abilities. We applied a random forest algorithm to identify discriminating networks from a set of 20 SCNs. The algorithm was trained on a main sample of 104 AD patients and 104 age-matched HC and was then validated in an independent sample of 28 AD patients and 28 controls from another center. Only two of the 20 SCNs contributed significantly to the discrimination between AD and controls. These were a temporal and a secondary somatosensory SCN. Their diagnostic accuracy was 74% in the original cohort and 80% in the independent samples. The diagnostic accuracy of SCNs was comparable with that of conventional volumetric MRI markers including whole brain volume and hippocampal volume. SCN did not significantly increase diagnostic accuracy beyond that of conventional MRI markers. We found the temporal SCN to be associated with verbal memory at baseline. No other associations with cognitive functions were seen. SCNs failed to predict the course of cognitive decline over an average of 18 months. We conclude that SCNs have diagnostic potential, but the diagnostic information gain beyond conventional MRI markers is limited.
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Affiliation(s)
- Fabian Wagner
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | - Benno G Gesierich
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
| | | | - Stefan Ropele
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Peter Dal-Bianco
- Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Florian Mayer
- Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Reinhold Schmidt
- Department of Neurology, Medical University of Graz, Graz, Austria
| | - Marisa Koini
- Department of Neurology, Medical University of Graz, Graz, Austria
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27
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Li K, Luo X, Zeng Q, Jiaerken Y, Wang S, Xu X, Xu X, Xu J, Wang C, Zhou J, Huang P, Zhang M. Interactions between sleep disturbances and Alzheimer's disease on brain function: a preliminary study combining the static and dynamic functional MRI. Sci Rep 2019; 9:19064. [PMID: 31836777 PMCID: PMC6911090 DOI: 10.1038/s41598-019-55452-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 11/22/2019] [Indexed: 11/10/2022] Open
Abstract
Though sleep disturbance constitutes the risk factor for Alzheimer's disease (AD), the underlying mechanism is still unclear. This study aims to explore the interaction between sleep disturbances and AD on brain function. We included 192 normal controls, 111 mild cognitive impairment (MCI), and 30 AD patients, with either poor or normal sleep (PS, NS, respectively). To explore the strength and stability of brain activity, we used static amplitude of low-frequency fluctuation (sALFF) and dynamic ALFF (dALFF) variance. Further, we examined white matter hyperintensities (WMH) and amyloid PET deposition, representing the vascular risk factor and AD-related hallmark, respectively. We observed that sleep disturbance significantly interacted with disease severity, exposing distinct effects on sALFF and dALFF variance. Interestingly, PS groups showed the dALFF variance trajectory of initially increased, then decreased and finally increased along the AD spectrum, while showing the opposite trajectory of sALFF. Further correlation analysis showed that the WMH burden correlates with dALFF variance in PS groups. Conclusively, our study suggested that sleep disturbance interacts with AD severity, expressing as effects of compensatory in MCI and de-compensatory in AD, respectively. Further, vascular impairment might act as important pathogenesis underlying the interaction effect between sleep and AD.
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Affiliation(s)
- Kaicheng Li
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, China
| | - Xiao Luo
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, China
| | - Qingze Zeng
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, China
| | - Yerfan Jiaerken
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, China
| | - Shuyue Wang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, China
| | - Xiaopei Xu
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, China
| | - Xiaojun Xu
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, China
| | - Jingjing Xu
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, China
| | - Chao Wang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, China
| | - Jiong Zhou
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, China
| | - Peiyu Huang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, China.
| | - Minming Zhang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang, China.
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Luo X, Li K, Zeng Q, Huang P, Jiaerken Y, Wang S, Shen Z, Xu X, Xu J, Wang C, Kong L, Zhou J, Zhang M. Application of T1-/T2-Weighted Ratio Mapping to Elucidate Intracortical Demyelination Process in the Alzheimer's Disease Continuum. Front Neurosci 2019; 13:904. [PMID: 31551678 PMCID: PMC6748350 DOI: 10.3389/fnins.2019.00904] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 08/13/2019] [Indexed: 12/12/2022] Open
Abstract
Background The biological diagnosis criteria of the Alzheimer’s disease (AD) suggests that previous work may misclassify the cognitive impairment caused by other factors into AD. Consequently, re-assessing the imaging profile of AD continuum is needed. Considering the high vulnerability of cortical association fibers, we aimed to elucidate the cortical demyelination process in the AD continuum biologically defined. Methods According to the biological diagnosis criteria, we determined the positive amyloid status (A+) as cerebrospinal fluid (CSF) amyloid1–42 < 192 pg/ml, Florbetapir Positron emission tomography (PET) composite standardized uptake value ratio (SUVR) >1.11. Also, the positive Tau status (T+) was determined as p-Tau181 > 23 pg/ml. Based on the cognitive characterization, we further categorized 252 subjects into 27 cognitively unimpaired with normal AD biomarkers (A−T−, controls), 49 preclinical AD (A+T+), 113 AD with mild cognitive impairment (MCI) (A+T+), and 63 AD dementia (A+T+). We estimated the intracortical myelin content used the T1- and T2-weighted (T1W/T2W) ratio mapping. To investigate the sensitivity of the ratio mapping, we also utilized well-validated AD imaging biomarkers as the reference, including gray matter volume and Fludeoxyglucose PET (FDG-PET). Based on the general linear model, we conducted the voxel-wise two-sample T-tests between the controls and each group in the AD continuum. Results Compared to the controls, the results showed that the preclinical AD patients exhibited decreased T1W/T2W ratio value in the right inferior parietal lobule (IPL); as the disease progresses, the prodromal AD patients demonstrated lower ratio value in bilateral IPL, with hippocampus (HP) atrophy. Lastly, the AD dementia patients exhibited decreased ratio value in bilateral IPL and hippocampus; also, we observed the bilateral temporal cortices atrophy and widespread decreased metabolism in the AD dementia patients. After corrected with gray volume, the results remained mostly unchanged. Conclusion Our study implied the decreased right IPL T1W/T2W ratio might represent early AD-related demyelination in disease continuum. Additionally, we demonstrated that the T1W/T2W ratio mapping is an easy-to-implement and sensitive metric to assess the intracortical myelin content in AD, particularly in the early stage.
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Affiliation(s)
- Xiao Luo
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yeerfan Jiaerken
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Shuyue Wang
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Zhujing Shen
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jingjing Xu
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chao Wang
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Linlin Kong
- Department of Neurology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jiong Zhou
- Department of Neurology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
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