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Li X, Pang H, Bu S, Zhao M, Wang J, Liu Y, Yu H, Fan G. Stage-dependent differential impact of network communication on cognitive function across the continuum of cognitive decline in Parkinson's disease. Neurobiol Dis 2024; 199:106578. [PMID: 38925316 DOI: 10.1016/j.nbd.2024.106578] [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: 04/02/2024] [Revised: 06/04/2024] [Accepted: 06/23/2024] [Indexed: 06/28/2024] Open
Abstract
OBJECTIVE Our objective was to explore the patterns of resting-state network (RSN) connectivity alterations and investigate how the influences of individual-level network connections on cognition varied across clinical stages without assuming a constant relationship. METHODS 108 PD patients with continuum of cognitive decline (PD-NC = 46, PD-MCI = 43, PDD = 19) and 34 healthy controls (HCs) underwent resting-state functional MRI and neuropsychological tests. Independent component analysis (ICA) and graph theory analyses (GTA) were employed to explore RSN connection changes. Additionally, stage-dependent differential impact of network communication on cognitive performance were examined using sparse varying coefficient modeling. RESULTS Compared to HCs, the dorsal attention network (DAN) and dorsal sensorimotor network (dSMN) were central networks with decreased connections in PD-NC and PD-MCI stage, while the lateral visual network (LVN) emerged as a central network in patients with dementia. Additionally, connectivity of the cerebellum network (CBN) increased in the PD-NC and PD-MCI stages. GTA demonstrated decreased nodal metrics for DAN and dSMN, coupled with an increase for CBN. Moreover, the degree centrality (DC) values of DAN and dSMN exhibited a stage-dependent differential impact on cognitive performance across the continuum of cognitive decline. CONCLUSION Our findings suggest that across the progression of cognitive impairment, the LVN gradually transitions into a core node with reduced connectivity, while the enhancement of connections in CBN diminishes. Furthermore, the non-linear relationship between the DC values of RSNs and cognitive decline indicates the potential for tailored interventions targeting specific stages.
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Affiliation(s)
- Xiaolu Li
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Huize Pang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Shuting Bu
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Mengwan Zhao
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Juzhou Wang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Yu Liu
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Hongmei Yu
- Department of Neurology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China
| | - Guoguang Fan
- Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning 110001, China.
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Al-Ezzi A, Arechavala RJ, Butler R, Nolty A, Kang JJ, Shimojo S, Wu DA, Fonteh AN, Kleinman MT, Kloner RA, Arakaki X. Disrupted brain functional connectivity as early signature in cognitively healthy individuals with pathological CSF amyloid/tau. Commun Biol 2024; 7:1037. [PMID: 39179782 PMCID: PMC11344156 DOI: 10.1038/s42003-024-06673-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 08/01/2024] [Indexed: 08/26/2024] Open
Abstract
Alterations in functional connectivity (FC) have been observed in individuals with Alzheimer's disease (AD) with elevated amyloid (Aβ) and tau. However, it is not yet known whether directed FC is already influenced by Aβ and tau load in cognitively healthy (CH) individuals. A 21-channel electroencephalogram (EEG) was used from 46 CHs classified based on cerebrospinal fluid (CSF) Aβ tau ratio: pathological (CH-PAT) or normal (CH-NAT). Directed FC was estimated with Partial Directed Coherence in frontal, temporal, parietal, central, and occipital regions. We also examined the correlations between directed FC and various functional metrics, including neuropsychology, cognitive reserve, MRI volumetrics, and heart rate variability between both groups. Compared to CH-NATs, the CH-PATs showed decreased FC from the temporal regions, indicating a loss of relative functional importance of the temporal regions. In addition, frontal regions showed enhanced FC in the CH-PATs compared to CH-NATs, suggesting neural compensation for the damage caused by the pathology. Moreover, CH-PATs showed greater FC in the frontal and occipital regions than CH-NATs. Our findings provide a useful and non-invasive method for EEG-based analysis to identify alterations in brain connectivity in CHs with a pathological versus normal CSF Aβ/tau.
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Affiliation(s)
- Abdulhakim Al-Ezzi
- Department of Neurosciences, Huntington Medical Research Institutes, Pasadena, CA, USA.
| | - Rebecca J Arechavala
- Department of Environmental and Occupational Health, Center for Occupational and Environmental Health (COEH), University of California, Irvine, CA, USA
| | - Ryan Butler
- Department of Neurosciences, Huntington Medical Research Institutes, Pasadena, CA, USA
| | - Anne Nolty
- Fuller Theological Seminary, Pasadena, CA, USA
| | | | - Shinsuke Shimojo
- The Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Daw-An Wu
- The Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Alfred N Fonteh
- Department of Neurosciences, Huntington Medical Research Institutes, Pasadena, CA, USA
| | - Michael T Kleinman
- Department of Environmental and Occupational Health, Center for Occupational and Environmental Health (COEH), University of California, Irvine, CA, USA
| | - Robert A Kloner
- Department of Neurosciences, Huntington Medical Research Institutes, Pasadena, CA, USA
- Department of Cardiovascular Research, Huntington Medical Research Institutes, Pasadena, CA, USA
| | - Xianghong Arakaki
- Department of Neurosciences, Huntington Medical Research Institutes, Pasadena, CA, USA.
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Filomena E, Picardi E, Tullo A, Pesole G, D’Erchia AM. Identification of deregulated lncRNAs in Alzheimer's disease: an integrated gene co-expression network analysis of hippocampus and fusiform gyrus RNA-seq datasets. Front Aging Neurosci 2024; 16:1437278. [PMID: 39086756 PMCID: PMC11288953 DOI: 10.3389/fnagi.2024.1437278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Accepted: 07/08/2024] [Indexed: 08/02/2024] Open
Abstract
Introduction The deregulation of lncRNAs expression has been associated with neuronal damage in Alzheimer's disease (AD), but how or whether they can influence its onset is still unknown. We investigated 2 RNA-seq datasets consisting, respectively, of the hippocampal and fusiform gyrus transcriptomic profile of AD patients, matched with non-demented controls. Methods We performed a differential expression analysis, a gene correlation network analysis (WGCNA) and a pathway enrichment analysis of two RNA-seq datasets. Results We found deregulated lncRNAs in common between hippocampus and fusiform gyrus and deregulated gene groups associated to functional pathways related to neurotransmission and memory consolidation. lncRNAs, co-expressed with known AD-related coding genes, were identified from the prioritized modules of both brain regions. Discussion We found common deregulated lncRNAs in the AD hippocampus and fusiform gyrus, that could be considered common signatures of AD pathogenesis, providing an important source of information for understanding the molecular changes of AD.
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Affiliation(s)
- Ermes Filomena
- Department of Biosciences, Biotechnology and Environment, University of Bari Aldo Moro, Bari, Italy
| | - Ernesto Picardi
- Department of Biosciences, Biotechnology and Environment, University of Bari Aldo Moro, Bari, Italy
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council, Bari, Italy
| | - Apollonia Tullo
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council, Bari, Italy
| | - Graziano Pesole
- Department of Biosciences, Biotechnology and Environment, University of Bari Aldo Moro, Bari, Italy
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council, Bari, Italy
| | - Anna Maria D’Erchia
- Department of Biosciences, Biotechnology and Environment, University of Bari Aldo Moro, Bari, Italy
- Institute of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council, Bari, Italy
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Volfart A, Rossion B. The neuropsychological evaluation of face identity recognition. Neuropsychologia 2024; 198:108865. [PMID: 38522782 DOI: 10.1016/j.neuropsychologia.2024.108865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 03/14/2024] [Accepted: 03/18/2024] [Indexed: 03/26/2024]
Abstract
Facial identity recognition (FIR) is arguably the ultimate form of recognition for the adult human brain. Even if the term prosopagnosia is reserved for exceptionally rare brain-damaged cases with a category-specific abrupt loss of FIR at adulthood, subjective and objective impairments or difficulties of FIR are common in the neuropsychological population. Here we provide a critical overview of the evaluation of FIR both for clinicians and researchers in neuropsychology. FIR impairments occur following many causes that should be identified objectively by both general and specific, behavioral and neural examinations. We refute the commonly used dissociation between perceptual and memory deficits/tests for FIR, since even a task involving the discrimination of unfamiliar face images presented side-by-side relies on cortical memories of faces in the right-lateralized ventral occipito-temporal cortex. Another frequently encountered confusion is between specific deficits of the FIR function and a more general impairment of semantic memory (of people), the latter being most often encountered following anterior temporal lobe damage. Many computerized tests aimed at evaluating FIR have appeared over the last two decades, as reviewed here. However, despite undeniable strengths, they often suffer from ecological limitations, difficulties of instruction, as well as a lack of consideration for processing speed and qualitative information. Taking into account these issues, a recently developed behavioral test with natural images manipulating face familiarity, stimulus inversion, and correct response times as a key variable appears promising. The measurement of electroencephalographic (EEG) activity in the frequency domain from fast periodic visual stimulation also appears as a particularly promising tool to complete and enhance the neuropsychological assessment of FIR.
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Affiliation(s)
- Angélique Volfart
- School of Psychology and Counselling, Faculty of Health, Queensland University of Technology, Australia.
| | - Bruno Rossion
- Centre for Biomedical Technologies, Queensland University of Technology, Australia; Université de Lorraine, CNRS, IMoPA, F-54000, Nancy, France.
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Yang R, Kong W, Liu K, Wen G, Yu Y. Exploring Imaging Genetic Markers of Alzheimer's Disease Based on a Novel Nonlinear Correlation Analysis Algorithm. J Mol Neurosci 2024; 74:35. [PMID: 38568443 DOI: 10.1007/s12031-024-02190-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/16/2024] [Indexed: 04/05/2024]
Abstract
Alzheimer's disease (AD) is an irreversible neurological disorder characterized by insidious onset. Identifying potential markers in its emergence and progression is crucial for early diagnosis and treatment. Imaging genetics typically merges genetic variables with multiple imaging parameters, employing various association analysis algorithms to investigate the links between pathological phenotypes and genetic variations, and to unearth molecular-level insights from brain images. However, most existing imaging genetics algorithms based on sparse learning assume a linear relationship between genetic factors and brain functions, limiting their ability to discern complex nonlinear correlation patterns and resulting in reduced accuracy. To address these issues, we propose a novel nonlinear imaging genetic association analysis method, Deep Self-Reconstruction-based Adaptive Sparse Multi-view Deep Generalized Canonical Correlation Analysis (DSR-AdaSMDGCCA). This approach facilitates joint learning of the nonlinear relationships between pathological phenotypes and genetic variations by integrating three different types of data: structural magnetic resonance imaging (sMRI), single-nucleotide polymorphism (SNP), and gene expression data. By incorporating nonlinear transformations in DGCCA, our model effectively uncovers nonlinear associations across multiple data types. Additionally, the DSR algorithm clusters samples with identical labels, incorporating label information into the nonlinear feature extraction process and thus enhancing the performance of association analysis. The application of the DSR-AdaSMDGCCA algorithm on real data sets identified several AD risk regions (such as the hippocampus, parahippocampus, and fusiform gyrus) and risk genes (including VSIG4, NEDD4L, and PINK1), achieving maximum classification accuracy with the fewest selected features compared to baseline algorithms. Molecular biology enrichment analysis revealed that the pathways enriched by these top genes are intimately linked to AD progression, affirming that our algorithm not only improves correlation analysis performance but also identifies biologically significant markers.
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Affiliation(s)
- Renbo Yang
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave, Shanghai, 201306, People's Republic of China
| | - Wei Kong
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave, Shanghai, 201306, People's Republic of China.
| | - Kun Liu
- College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave, Shanghai, 201306, People's Republic of China
| | - Gen Wen
- Department of Orthopedic Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
| | - Yaling Yu
- Department of Orthopedic Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
- Institute of Microsurgery on Extremities, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China
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Wang SM, Kang DW, Um YH, Kim S, Lee CU, Scheltens P, Lim HK. Plasma oligomer beta-amyloid is associated with disease severity and cerebral amyloid deposition in Alzheimer's disease spectrum. Alzheimers Res Ther 2024; 16:55. [PMID: 38468313 PMCID: PMC10926587 DOI: 10.1186/s13195-024-01400-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 01/26/2024] [Indexed: 03/13/2024]
Abstract
BACKGROUND Multimer detection system-oligomeric amyloid-β (MDS-OAβ) is a measure of plasma OAβ, which is associated with Alzheimer's disease (AD) pathology. However, the relationship between MDS-OAβ and disease severity of AD is not clear. We aimed to investigate MDS-OAβ levels in different stages of AD and analyze the association between MDS-OAβ and cerebral Aβ deposition, cognitive function, and cortical thickness in subjects within the AD continuum. METHODS In this cross-sectional study, we analyzed a total 126 participants who underwent plasma MDS-OAβ, structural magnetic resonance image of brain, and neurocognitive measures using Korean version of the Consortium to Establish a Registry for Alzheimer's Disease, and cerebral Aβ deposition or amyloid positron emission tomography (A-PET) assessed by [18F] flutemetamol PET. Subjects were divided into 4 groups: N = 39 for normal control (NC), N = 31 for A-PET-negative mild cognitive impairment (MCI) patients, N = 30 for A-PET-positive MCI patients, and N = 22 for AD dementia patients. The severity of cerebral Aβ deposition was expressed as standard uptake value ratio (SUVR). RESULTS Compared to the NC (0.803 ± 0.27), MDS-OAβ level was higher in the A-PET-negative MCI group (0.946 ± 0.137) and highest in the A-PET-positive MCI group (1.07 ± 0.17). MDS-OAβ level in the AD dementia group was higher than in the NC, but it fell to that of the A-PET-negative MCI group level (0.958 ± 0.103). There were negative associations between MDS-OAβ and cognitive function and both global and regional cerebral Aβ deposition (SUVR). Cortical thickness of the left fusiform gyrus showed a negative association with MDS-OAβ when we excluded the AD dementia group. CONCLUSIONS These findings suggest that MDS-OAβ is not only associated with neurocognitive staging, but also with cerebral Aβ burden in patients along the AD continuum.
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Affiliation(s)
- Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 10, 63-Ro, Yeongdeungpo-Gu, Seoul, 07345, South Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, South Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent Hospital, Suwon, Korea, College of Medicine, The Catholic University of Korea, Suwon, 16247, South Korea
| | - Sunghwan Kim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 10, 63-Ro, Yeongdeungpo-Gu, Seoul, 07345, South Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, South Korea
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Boelelaan 1118, Amsterdam, 1081, HZ, Netherlands
- EQT Life Sciences Partners, Amsterdam, 1071, DV, The Netherlands
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, 10, 63-Ro, Yeongdeungpo-Gu, Seoul, 07345, South Korea.
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Guo R, Tian X, Lin H, McKenna S, Li HD, Guo F, Liu J. Graph-Based Fusion of Imaging, Genetic and Clinical Data for Degenerative Disease Diagnosis. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:57-68. [PMID: 37991907 DOI: 10.1109/tcbb.2023.3335369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Abstract
Graph learning methods have achieved noteworthy performance in disease diagnosis due to their ability to represent unstructured information such as inter-subject relationships. While it has been shown that imaging, genetic and clinical data are crucial for degenerative disease diagnosis, existing methods rarely consider how best to use their relationships. How best to utilize information from imaging, genetic and clinical data remains a challenging problem. This study proposes a novel graph-based fusion (GBF) approach to meet this challenge. To extract effective imaging-genetic features, we propose an imaging-genetic fusion module which uses an attention mechanism to obtain modality-specific and joint representations within and between imaging and genetic data. Then, considering the effectiveness of clinical information for diagnosing degenerative diseases, we propose a multi-graph fusion module to further fuse imaging-genetic and clinical features, which adopts a learnable graph construction strategy and a graph ensemble method. Experimental results on two benchmarks for degenerative disease diagnosis (Alzheimers Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative) demonstrate its effectiveness compared to state-of-the-art graph-based methods. Our findings should help guide further development of graph-based models for dealing with imaging, genetic and clinical data.
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Biswas R, Sripada S. Causal functional connectivity in Alzheimer's disease computed from time series fMRI data. Front Comput Neurosci 2023; 17:1251301. [PMID: 38169714 PMCID: PMC10758424 DOI: 10.3389/fncom.2023.1251301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 11/28/2023] [Indexed: 01/05/2024] Open
Abstract
Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC) is more informative, providing a directed network representing causal relationships between brain regions. In this paper, we obtained the causal functional connectome for the whole brain from resting-state functional magnetic resonance imaging (rs-fMRI) recordings of subjects from three clinical groups: cognitively normal, mild cognitive impairment, and Alzheimer's disease. We applied the recently developed Time-aware PC (TPC) algorithm to infer the causal functional connectome for the whole brain. TPC supports model-free estimation of whole brain CFC based on directed graphical modeling in a time series setting. We compared the CFC outcome of TPC with that of other related approaches in the literature. Then, we used the CFC outcomes of TPC and performed an exploratory analysis of the difference in strengths of CFC edges between Alzheimer's and cognitively normal groups, based on edge-wise p-values obtained by Welch's t-test. The brain regions thus identified are found to be in agreement with literature on brain regions impacted by Alzheimer's disease, published by researchers from clinical/medical institutions.
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Affiliation(s)
- Rahul Biswas
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
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Yi F, Zhang Y, Yuan J, Liu Z, Zhai F, Hao A, Wu F, Somekh J, Peleg M, Zhu YC, Huang Z. Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysis. EClinicalMedicine 2023; 64:102247. [PMID: 37811490 PMCID: PMC10556591 DOI: 10.1016/j.eclinm.2023.102247] [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: 06/28/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 10/10/2023] Open
Abstract
Background Alzheimer's disease (AD) is a heterogeneously progressive neurodegeneration disorder with varied rates of deterioration, either between subjects or within different stages of a certain subject. Estimating the course of AD at early stages has treatment implications. We aimed to analyze disease progression to identify distinct patterns in AD trajectory. Methods We proposed a deep learning model to identify underlying patterns in the trajectory from cognitively normal (CN) to a state of mild cognitive impairment (MCI) to AD dementia, by jointly predicting time-to-conversion and clustering out distinct subgroups characterized by comprehensive features as well as varied progression rates. We designed and validated our model on the ADNI dataset (1370 participants). Prediction of time-to-conversion in AD trajectory was used to validate the expression of the identified patterns. Causality between patterns and time-to-conversion was further inferred using Mendelian randomization (MR) analysis. External validation was performed on the AIBL dataset (233 participants). Findings The proposed model clustered out patterns characterized by significantly different biomarkers and varied progression rates. The discovered patterns also showed a strong prediction ability, as indicated by hazard ratio (CN→MCI, HR = 3.51, p < 0.001; MCI→AD, HR = 8.11, p < 0.001), C-Index (CN→MCI, 0.618; MCI→AD, 0.718), and AUC (CN→MCI, 3 years 0.802, 5 years 0.876; MCI→AD, 3 years 0.914, 5 years 0.957). In the external validation cohort, our model demonstrated competitive performance on conversion time prediction (CN→MCI, C-Index = 0.693; MCI→AD, C-Index = 0.752). Moreover, suggestive associations between CN→MCI/MCI→AD patterns with four/three SNPs were mediated and MR analysis indicated a causal link between MCI→AD patterns and time-to-conversion in the first three years. Interpretation Our proposed model identifies biologically and clinically meaningful patterns from real-world data and provides promising performance on time-to-conversion prediction in AD trajectory, which could promote the understanding of disease progression, facilitate clinical trial design, and provide potential for decision-making. Funding The National Key Research and Development Program of China, the Key R&D Program of Zhejiang, and the National Nature Science Foundation of China.
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Affiliation(s)
- Fan Yi
- College of Computer Science and Technology, Zhejiang University, China
| | | | - Jing Yuan
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Ziyue Liu
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Feifei Zhai
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Ankai Hao
- College of Computer Science and Technology, Zhejiang University, China
| | - Fei Wu
- College of Computer Science and Technology, Zhejiang University, China
| | - Judith Somekh
- Department of Information Systems, University of Haifa, Haifa, Israel
| | - Mor Peleg
- Department of Information Systems, University of Haifa, Haifa, Israel
| | - Yi-Cheng Zhu
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Zhengxing Huang
- College of Computer Science and Technology, Zhejiang University, China
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Ribeiro-dos-Santos A, de Brito LM, de Araújo GS. The fusiform gyrus exhibits differential gene-gene co-expression in Alzheimer's disease. Front Aging Neurosci 2023; 15:1138336. [PMID: 37255536 PMCID: PMC10225579 DOI: 10.3389/fnagi.2023.1138336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/21/2023] [Indexed: 06/01/2023] Open
Abstract
Alzheimer's Disease (AD) is an irreversible neurodegenerative disease clinically characterized by the presence of β-amyloid plaques and tau deposits in various regions of the brain. However, the underlying factors that contribute to the development of AD remain unclear. Recently, the fusiform gyrus has been identified as a critical brain region associated with mild cognitive impairment, which may increase the risk of AD development. In our study, we performed gene co-expression and differential co-expression network analyses, as well as gene-expression-based prediction, using RNA-seq transcriptome data from post-mortem fusiform gyrus tissue samples collected from both cognitively healthy individuals and those with AD. We accessed differential co-expression networks in large cohorts such as ROSMAP, MSBB, and Mayo, and conducted over-representation analyses of gene pathways and gene ontology. Our results comprise four exclusive gene hubs in co-expression modules of Alzheimer's Disease, including FNDC3A, MED23, NRIP1, and PKN2. Further, we identified three genes with differential co-expressed links, namely FAM153B, CYP2C8, and CKMT1B. The differential co-expressed network showed moderate predictive performance for AD, with an area under the curve ranging from 0.71 to 0.76 (+/- 0.07). The over-representation analysis identified enrichment for Toll-Like Receptors Cascades and signaling pathways, such as G protein events, PIP2 hydrolysis and EPH-Epherin mechanism, in the fusiform gyrus. In conclusion, our findings shed new light on the molecular pathophysiology of AD by identifying new genes and biological pathways involved, emphasizing the crucial role of gene regulatory networks in the fusiform gyrus.
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Affiliation(s)
- Arthur Ribeiro-dos-Santos
- Programa de Pós-graduação em Genética e Biologia Molecular, Laboratório de Genética Humana e Médica, Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
| | - Leonardo Miranda de Brito
- Programa de Pós-graduação em Genética e Biologia Molecular, Laboratório de Genética Humana e Médica, Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
- Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil
| | - Gilderlanio Santana de Araújo
- Programa de Pós-graduação em Genética e Biologia Molecular, Laboratório de Genética Humana e Médica, Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil
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Hamza EA, Moustafa AA, Tindle R, Karki R, Nalla S, Hamid MS, El Haj M. Effect of APOE4 Allele and Gender on the Rate of Atrophy in the Hippocampus, Entorhinal Cortex, and Fusiform Gyrus in Alzheimer's Disease. Curr Alzheimer Res 2023; 19:CAR-EPUB-130079. [PMID: 36892120 DOI: 10.2174/1567205020666230309113749] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 02/21/2023] [Accepted: 02/25/2023] [Indexed: 03/10/2023]
Abstract
BACKGROUND The hippocampus, entorhinal cortex, and fusiform gyrus are brain areas that deteriorate during early-stage Alzheimer's disease (AD). The ApoE4 allele has been identified as a risk factor for AD development, is linked to an increase in the aggregation of amyloid ß (Aß) plaques in the brain, and is responsible for atrophy of the hippocampal area. However, to our knowledge, the rate of deterioration over time in individuals with AD, with or without the ApoE4 allele, has not been investigated. METHOD In this study, we, for the first time, analyze atrophy in these brain structures in AD patients with and without the ApoE4 using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. RESULTS It was found that the rate of decrease in the volume of these brain areas over 12 months was related to the presence of ApoE4. Further, we found that neural atrophy was not different for female and male patients, unlike prior studies, suggesting that the presence of ApoE4 is not linked to the gender difference in AD. CONCLUSION Our results confirm and extend previous findings, showing that the ApoE4 allele gradually impacts brain regions impacted by AD.
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Affiliation(s)
- Eid Abo Hamza
- Faculty of Education, Department of Mental Health, Tanta University, Egypt
- College of Education, Humanities & Social Sciences, Al Ain University, UAE
| | - Ahmed A Moustafa
- School of Psychology, Faculty of Society and Design, Bond University, Gold Coast, Queensland, Australia
- Department of Human Anatomy and Physiology, the Faculty of Health Sciences, University of Johannesburg, South Africa
| | - Richard Tindle
- Department of Psychology, University of the Sunshine Coast, Sunshine Coast, Queensland, Australia
| | - Rasu Karki
- Department of Psychology, Western Sydney University, Penrith, NSW, 2214, Australia
| | - Shahed Nalla
- Department of Human Anatomy and Physiology, the Faculty of Health Sciences, University of Johannesburg, South Africa
| | | | - Mohamad El Haj
- Laboratoire de Psychologie des Pays de la Loire (LPPL - EA 4638), Nantes Université, Univ. Angers., Nantes, F-44000, France
- Clinical Gerontology Department, CHU Nantes, Bd Jacques Monod,Nantes, F44093, France
- Institut Universitaire de France, Paris, France
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12
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Chen Z, Liu Y, Zhang Y, Li Q. Orthogonal latent space learning with feature weighting and graph learning for multimodal Alzheimer's disease diagnosis. Med Image Anal 2023; 84:102698. [PMID: 36462372 DOI: 10.1016/j.media.2022.102698] [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: 06/10/2022] [Revised: 10/18/2022] [Accepted: 11/17/2022] [Indexed: 11/23/2022]
Abstract
Recent studies have shown that multimodal neuroimaging data provide complementary information of the brain and latent space-based methods have achieved promising results in fusing multimodal data for Alzheimer's disease (AD) diagnosis. However, most existing methods treat all features equally and adopt nonorthogonal projections to learn the latent space, which cannot retain enough discriminative information in the latent space. Besides, they usually preserve the relationships among subjects in the latent space based on the similarity graph constructed on original features for performance boosting. However, the noises and redundant features significantly corrupt the graph. To address these limitations, we propose an Orthogonal Latent space learning with Feature weighting and Graph learning (OLFG) model for multimodal AD diagnosis. Specifically, we map multiple modalities into a common latent space by orthogonal constrained projection to capture the discriminative information for AD diagnosis. Then, a feature weighting matrix is utilized to sort the importance of features in AD diagnosis adaptively. Besides, we devise a regularization term with learned graph to preserve the local structure of the data in the latent space and integrate the graph construction into the learning processing for accurately encoding the relationships among samples. Instead of constructing a similarity graph for each modality, we learn a joint graph for multiple modalities to capture the correlations among modalities. Finally, the representations in the latent space are projected into the target space to perform AD diagnosis. An alternating optimization algorithm with proved convergence is developed to solve the optimization objective. Extensive experimental results show the effectiveness of the proposed method.
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Affiliation(s)
- Zhi Chen
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Yongguo Liu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
| | - Yun Zhang
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Qiaoqin Li
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
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13
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Li X, Zhou L, Zhang X, Jin Y, Zhao B, Zhang D, Xi C, Ruan J, Zhu Z, Jia JM. Proteins secreted by brain arteriolar smooth muscle cells are instructive for neural development. Mol Brain 2022; 15:97. [PMID: 36451193 PMCID: PMC9710182 DOI: 10.1186/s13041-022-00983-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022] Open
Abstract
Intercellular communication between vascular and nerve cells mediated by diffusible proteins has recently emerged as a critical intrinsic program for neural development. However, whether the vascular smooth muscle cell (VSMC) secretome regulates the connectivity of neural circuits remains unknown. Here, we show that conditioned medium from brain VSMC cultures enhances multiple neuronal functions, such as neuritogenesis, neuronal maturation, and survival, thereby improving circuit connectivity. However, protein denaturation by heating compromised these effects. Combined omics analyses of donor VSMC secretomes and recipient neuron transcriptomes revealed that overlapping pathways of extracellular matrix receptor signaling and adhesion molecule integrin binding mediate VSMC-dependent neuronal development. Furthermore, we found that human arterial VSMCs promote neuronal development in multiple ways, including expanding the time window for nascent neurite initiation, increasing neuronal density, and promoting synchronized firing, whereas human umbilical vein VSMCs lack this capability. These in vitro data indicate that brain arteriolar VSMCs may carry direct instructive information for neural development through intercellular communication in vivo.
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Affiliation(s)
- Xuzhao Li
- grid.8547.e0000 0001 0125 2443Fudan University, Shanghai, 200433 China ,grid.494629.40000 0004 8008 9315Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Laboratory of Neurovascular Biology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024 China
| | - Lili Zhou
- grid.494629.40000 0004 8008 9315Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Laboratory of Neurovascular Biology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024 China ,grid.13402.340000 0004 1759 700XZhejiang University School of Medicine, Hangzhou, 310058 China
| | - Xiaoxuan Zhang
- grid.8547.e0000 0001 0125 2443Fudan University, Shanghai, 200433 China ,grid.494629.40000 0004 8008 9315Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Laboratory of Neurovascular Biology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024 China
| | - Yuxiao Jin
- grid.8547.e0000 0001 0125 2443School of Life Sciences, Fudan University, Shanghai, 200438 China
| | - Bingrui Zhao
- grid.494629.40000 0004 8008 9315Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Laboratory of Neurovascular Biology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024 China
| | - Dongdong Zhang
- grid.494629.40000 0004 8008 9315Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Laboratory of Neurovascular Biology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024 China
| | - Chengjie Xi
- grid.40263.330000 0004 1936 9094Biotechnology Master’s Program, Brown University, Providence, USA
| | - Jiayu Ruan
- grid.494629.40000 0004 8008 9315Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Laboratory of Neurovascular Biology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024 China
| | - Zhu Zhu
- grid.494629.40000 0004 8008 9315Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Laboratory of Neurovascular Biology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024 China
| | - Jie-Min Jia
- grid.494629.40000 0004 8008 9315Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, 310024 China ,grid.494629.40000 0004 8008 9315Laboratory of Neurovascular Biology, Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, 310024 China
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14
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Li H, Wei M, Ye T, Liu Y, Qi D, Cheng X. Identification of the molecular subgroups in Alzheimer's disease by transcriptomic data. Front Neurol 2022; 13:901179. [PMID: 36204002 PMCID: PMC9530954 DOI: 10.3389/fneur.2022.901179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundAlzheimer's disease (AD) is a heterogeneous pathological disease with genetic background accompanied by aging. This inconsistency is present among molecular subtypes, which has led to diagnostic ambiguity and failure in drug development. We precisely distinguished patients of AD at the transcriptome level.MethodsWe collected 1,240 AD brain tissue samples collected from the GEO dataset. Consensus clustering was used to identify molecular subtypes, and the clinical characteristics were focused on. To reveal transcriptome differences among subgroups, we certificated specific upregulated genes and annotated the biological function. According to RANK METRIC SCORE in GSEA, TOP10 was defined as the hub gene. In addition, the systematic correlation between the hub gene and “A/T/N” was analyzed. Finally, we used external data sets to verify the diagnostic value of hub genes.ResultsWe identified three molecular subtypes of AD from 743 AD samples, among which subtypes I and III had high-risk factors, and subtype II had protective factors. All three subgroups had higher neuritis plaque density, and subgroups I and III had higher clinical dementia scores and neurofibrillary tangles than subgroup II. Our results confirmed a positive association between neurofibrillary tangles and dementia, but not neuritis plaques. Subgroup I genes clustered in viral infection, hypoxia injury, and angiogenesis. Subgroup II showed heterogeneity in synaptic pathology, and we found several essential beneficial synaptic proteins. Due to presenilin one amplification, Subgroup III was a risk subgroup suspected of familial AD, involving abnormal neurogenic signals, glial cell differentiation, and proliferation. Among the three subgroups, the highest combined diagnostic value of the hub genes were 0.95, 0.92, and 0.83, respectively, indicating that the hub genes had sound typing and diagnostic ability.ConclusionThe transcriptome classification of AD cases played out the pathological heterogeneity of different subgroups. It throws daylight on the personalized diagnosis and treatment of AD.
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Affiliation(s)
- He Li
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Meiqi Wei
- Institute of Chinese Medical Literature and Culture, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Tianyuan Ye
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Yiduan Liu
- School of Rehabilitation Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Dongmei Qi
- Experimental Center, Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Xiaorui Cheng
- Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China
- *Correspondence: Xiaorui Cheng
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Zhao X, Sui H, Yan C, Zhang M, Song H, Liu X, Yang J. Machine-Based Learning Shifting to Prediction Model of Deteriorative MCI Due to Alzheimer's Disease - A Two-Year Follow-Up Investigation. Curr Alzheimer Res 2022; 19:708-715. [PMID: 36278469 DOI: 10.2174/1567205020666221019122049] [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: 06/10/2022] [Revised: 08/26/2022] [Accepted: 08/26/2022] [Indexed: 01/27/2023]
Abstract
OBJECTIVE The aim of the present work was to investigate the features of the elderly population aged ≥65 yrs and with deteriorative mild cognitive impairment (MCI) due to Alzheimer's disease (AD) to establish a prediction model. METHODS A total of 105 patients aged ≥65 yrs and with MCI were followed up, with a collection of 357 features, which were derived from the demographic characteristics, hematological indicators (serum Aβ1-40, Aβ1-42, P-tau and MCP-1 levels, APOE gene), and multimodal brain Magnetic Resonance Imaging (MRI) imaging indicators of 116 brain regions (ADC, FA and CBF values). Cognitive function was followed up for 2 yrs. Based on the Python platform Anaconda, 105 patients were randomly divided into a training set (70%) and a test set (30%) by analyzing all features through a random forest algorithm, and a prediction model was established for the form of rapidly deteriorating MCI. RESULTS Of the 105 patients enrolled, 41 deteriorated, and 64 did not come within 2 yrs. Model 1 was established based on demographic characteristics, hematological indicators and multi-modal MRI image features, the accuracy of the training set being 100%, the accuracy of the test set 64%, sensitivity 50%, specificity 67%, and AUC 0.72. Model 2 was based on the first five features (APOE4 gene, FA value of left fusiform gyrus, FA value of left inferior temporal gyrus, FA value of left parahippocampal gyrus, ADC value of right calcarine fissure as surrounding cortex), the accuracy of the training set being 100%, the accuracy of the test set 85%, sensitivity 91%, specificity 80% and AUC 0.96. Model 3 was based on the first four features of Model 1, the accuracy of the training set is 100%, the accuracy of the test set 97%, sensitivity100%, specificity 95% and AUC 0.99. Model 4 was based on the first three characteristics of Model 1, the accuracy of the training set being 100%, the accuracy of the test set 94%, sensitivity 92%, specificity 94% and AUC 0.96. Model 5 was based on the hematological characteristics, the accuracy of the training set is 100%, the accuracy of the test set 91%, sensitivity 100%, specificity 88% and AUC 0.97. The models based on the demographic characteristics, imaging characteristics FA, CBF and ADC values had lower sensitivity and specificity. CONCLUSION Model 3, which has four important predictive characteristics, can predict the rapidly deteriorating MCI due to AD in the community.
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Affiliation(s)
- Xiaohui Zhao
- Department of Neurology, Shanghai Pudong New Area People's Hospital, Shanghai, People's Republic of China
| | - Haijing Sui
- Department of Radiology, Shanghai Pudong New Area People's Hospital, Shanghai, People's Republic of China
| | - Chengong Yan
- Department of Social Work, Shanghai Pudong New Area People's Hospital, Shanghai, People's Republic of China
| | - Min Zhang
- Department of Deep Learning and Artificial Intelligence, hcit.ai Co., Shanghai, People's Republic of China
| | - Haihan Song
- The Central Lab, Pudong New Area People's Hospital, Shanghai, China
| | - Xueyuan Liu
- Department of Social Work, Shanghai Pudong New Area People's Hospital, Shanghai, People's Republic of China
| | - Juan Yang
- Department of Neurology, Shanghai Pudong New Area People's Hospital, Shanghai, People's Republic of China
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16
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Zhao Y, Li L. Multimodal data integration via mediation analysis with high-dimensional exposures and mediators. Hum Brain Mapp 2022; 43:2519-2533. [PMID: 35129252 PMCID: PMC9057105 DOI: 10.1002/hbm.25800] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/06/2022] [Accepted: 01/23/2022] [Indexed: 12/28/2022] Open
Abstract
Motivated by an imaging proteomics study for Alzheimer's disease (AD), in this article, we propose a mediation analysis approach with high-dimensional exposures and high-dimensional mediators to integrate data collected from multiple platforms. The proposed method combines principal component analysis with penalized least squares estimation for a set of linear structural equation models. The former reduces the dimensionality and produces uncorrelated linear combinations of the exposure variables, whereas the latter achieves simultaneous path selection and effect estimation while allowing the mediators to be correlated. Applying the method to the AD data identifies numerous interesting protein peptides, brain regions, and protein-structure-memory paths, which are in accordance with and also supplement existing findings of AD research. Additional simulations further demonstrate the effective empirical performance of the method.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data ScienceIndiana University School of MedicineIndianapolisIndianaUSA
| | - Lexin Li
- Department of Biostatistics and EpidemiologyUniversity of California, BerkeleyBerkeleyCaliforniaUSA
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17
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Research on Frequent Itemset Mining of Imaging Genetics GWAS in Alzheimer’s Disease. Genes (Basel) 2022; 13:genes13020176. [PMID: 35205221 PMCID: PMC8871801 DOI: 10.3390/genes13020176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 01/11/2022] [Accepted: 01/16/2022] [Indexed: 12/04/2022] Open
Abstract
As an efficient method, genome-wide association study (GWAS) is used to identify the association between genetic variation and pathological phenotypes, and many significant genetic variations founded by GWAS are closely associated with human diseases. However, it is not enough to mine only a single marker effect variation on complex biological phenotypes. Mining highly correlated single nucleotide polymorphisms (SNP) is more meaningful for the study of Alzheimer's disease (AD). In this paper, we used two frequent pattern mining (FPM) framework, the FP-Growth and Eclat algorithms, to analyze the GWAS results of functional magnetic resonance imaging (fMRI) phenotypes. Moreover, we applied the definition of confidence to FP-Growth and Eclat to enhance the FPM framework. By calculating the conditional probability of identified SNPs, we obtained the corresponding association rules to provide support confidence between these important SNPs. The resulting SNPs showed close correlation with hippocampus, memory, and AD. The experimental results also demonstrate that our framework is effective in identifying SNPs and provide candidate SNPs for further research.
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Wang SM, Kim NY, Um YH, Kang DW, Na HR, Lee CU, Lim HK. Default mode network dissociation linking cerebral beta amyloid retention and depression in cognitively normal older adults. Neuropsychopharmacology 2021; 46:2180-2187. [PMID: 34158614 PMCID: PMC8505502 DOI: 10.1038/s41386-021-01072-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 06/12/2021] [Indexed: 11/09/2022]
Abstract
Cerebral beta amyloid (Aβ) deposition and late-life depression (LLD) are known to be associated with the trajectory of Alzheimer's disease (AD). However, their neurobiological link is not clear. Previous studies showed aberrant functional connectivity (FC) changes in the default mode network (DMN) in early Aβ deposition and LLD, but its mediating role has not been elucidated. This study was performed to investigate the distinctive association pattern of DMN FC linking LLD and Aβ retention in cognitively normal older adults. A total of 235 cognitively normal older adults with (n = 118) and without depression (n = 117) underwent resting-state functional magnetic resonance imaging and 18F-flutemetamol positron emission tomography to investigate the associations between Aβ burden, depression, and DMN FC. Independent component analysis showed increased anterior DMN FC and decreased posterior DMN FC in the depression group compared with the no depression group. Global cerebral Aβ retention was positively correlated with anterior and negatively correlated with posterior DMN FC. Anterior DMN FC was positively correlated with severity of depression, whereas posterior DMN FC was negatively correlated with cognitive function. In addition, the effects of global cerebral Aβ retention on severity of depression were mediated by subgenual anterior cingulate FC. Our results of anterior and posterior DMN FC dissociation pattern may be pivotal in linking cerebral Aβ pathology and LLD in the course of AD progression. Further longitudinal studies are needed to confirm the causal relationships between cerebral Aβ retention and LLD.
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Affiliation(s)
- Sheng-Min Wang
- grid.411947.e0000 0004 0470 4224Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Nak-Young Kim
- Department of Psychiatry, Geyo Hospital, Uiwang, South Korea
| | - Yoo Hyun Um
- grid.411947.e0000 0004 0470 4224Department of Psychiatry, St. Vincent Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Dong Woo Kang
- grid.411947.e0000 0004 0470 4224Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Hae-Ran Na
- grid.411947.e0000 0004 0470 4224Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Chang Uk Lee
- grid.411947.e0000 0004 0470 4224Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
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Chen L, Tang F, Gao H, Zhang X, Li X, Xiao D. CAPN3: A muscle‑specific calpain with an important role in the pathogenesis of diseases (Review). Int J Mol Med 2021; 48:203. [PMID: 34549305 PMCID: PMC8480384 DOI: 10.3892/ijmm.2021.5036] [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: 05/13/2021] [Accepted: 09/10/2021] [Indexed: 01/14/2023] Open
Abstract
Calpains are a family of Ca2+‑dependent cysteine proteases that participate in various cellular processes. Calpain 3 (CAPN3) is a classical calpain with unique N‑terminus and insertion sequence 1 and 2 domains that confer characteristics such as rapid autolysis, Ca2+‑independent activation and Na+ activation of the protease. CAPN3 is the only muscle‑specific calpain that has important roles in the promotion of calcium release from skeletal muscle fibers, calcium uptake of sarcoplasmic reticulum, muscle formation and muscle remodeling. Studies have indicated that recessive mutations in CAPN3 cause limb‑girdle muscular dystrophy (MD) type 2A and other types of MD; eosinophilic myositis, melanoma and epilepsy are also closely related to CAPN3. In the present review, the characteristics of CAPN3, its biological functions and roles in the pathogenesis of a number of disorders are discussed.
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Affiliation(s)
- Lin Chen
- Department of Emergency Medicine, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China
| | - Fajuan Tang
- Department of Emergency Medicine, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China
| | - Hu Gao
- Department of Emergency Medicine, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China
| | - Xiaoyan Zhang
- Department of Emergency Medicine, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China
| | - Xihong Li
- Department of Emergency Medicine, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China
| | - Dongqiong Xiao
- Department of Emergency Medicine, West China Second University Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China
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