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Huang SM, Ong CT, Huang YC, Chen NH, Leung TK, Shen CY, Kuo LW. Resting-State Network Analysis Reveals Altered Functional Brain Connectivity in Essential Tremor. Brain Connect 2024; 14:382-390. [PMID: 38874971 DOI: 10.1089/brain.2024.0004] [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: 06/15/2024] Open
Abstract
Introduction: Essential tremor (ET) comprises motor and non-motor-related features, whereas the current neuro-pathogenetic basis is still insufficient to explain the etiologies of ET. Although cerebellum-associated circuits have been discovered, the large-scale cerebral network connectivity in ET remains unclear. This study aimed to characterize the ET in terms of functional connectivity as well as network. We hypothesized that the resting-state network (RSN) within cerebrum could be altered in patients with ET. Methods: Resting-state functional magnetic resonance imaging (fMRI) was used to evaluate the inter- and intra-network connectivity as well as the functional activity in ET and normal control. Correlation analysis was performed to explore the relationship between RSN metrics and tremor features. Results: Comparison of inter-network connectivity indicated a decreased connectivity between default mode network and ventral attention network in the ET group (p < 0.05). Differences in functional activity (assessed by amplitude of low-frequency fluctuation, ALFF) were found in several brain regions participating in various RSNs (p < 0.05). The ET group generally has higher degree centrality over normal control. Correlation analysis has revealed that tremor features are associated with inter-network connectivity (|r| = 0.135-0.506), ALFF (|r| = 0.313-0.766), and degree centrality (|r| = 0.523-0.710). Conclusion: Alterations in the cerebral network of ET were detected by using resting-state fMRI, demonstrating a potentially useful approach to explore the cerebral alterations in ET.
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Affiliation(s)
- Sheng-Min Huang
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Cheung-Ter Ong
- Department of Neurology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chia-Yi, Taiwan
| | - Yu-Ching Huang
- Department of Neurology, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
- Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan, Taiwan
| | - Nan-Hao Chen
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Ting-Kai Leung
- Department of Radiology, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
- Graduate Institute of Biomedical Materials and Tissue Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei, Taiwan
| | - Chun-Ying Shen
- Department of Radiology, Taoyuan General Hospital, Ministry of Health and Welfare, Taoyuan, Taiwan
| | - Li-Wei Kuo
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan
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Kang DW, Wang SM, Um YH, Kim S, Kim T, Kim D, Lee CU, Lim HK. Transcranial direct current stimulation and neuronal functional connectivity in MCI: role of individual factors associated to AD. Front Psychiatry 2024; 15:1428535. [PMID: 39224475 PMCID: PMC11366601 DOI: 10.3389/fpsyt.2024.1428535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Accepted: 07/11/2024] [Indexed: 09/04/2024] Open
Abstract
Background Alzheimer's disease (AD) encompasses a spectrum that may progress from mild cognitive impairment (MCI) to full dementia, characterized by amyloid-beta and tau accumulation. Transcranial direct current stimulation (tDCS) is being investigated as a therapeutic option, but its efficacy in relation to individual genetic and biological risk factors remains underexplored. Objective To evaluate the effects of a two-week anodal tDCS regimen on the left dorsolateral prefrontal cortex, focusing on functional connectivity changes in neural networks in MCI patients resulting from various possible underlying disorders, considering individual factors associated to AD such as amyloid-beta deposition, APOE ϵ4 allele, BDNF Val66Met polymorphism, and sex. Methods In a single-arm prospective study, 63 patients with MCI, including both amyloid-PET positive and negative cases, received 10 sessions of tDCS. We assessed intra- and inter-network functional connectivity (FC) using fMRI and analyzed interactions between tDCS effects and individual factors associated to AD. Results tDCS significantly enhanced intra-network FC within the Salience Network (SN) and inter-network FC between the Central Executive Network and SN, predominantly in APOE ϵ4 carriers. We also observed significant sex*tDCS interactions that benefited inter-network FC among females. Furthermore, the effects of multiple modifiers, particularly the interaction of the BDNF Val66Met polymorphism and sex, were evident, as demonstrated by increased intra-network FC of the SN in female Met non-carriers. Lastly, the effects of tDCS on FC did not differ between the group of 26 MCI patients with cerebral amyloid-beta deposition detected by flutemetamol PET and the group of 37 MCI patients without cerebral amyloid-beta deposition. Conclusions The study highlights the importance of precision medicine in tDCS applications for MCI, suggesting that individual genetic and biological profiles significantly influence therapeutic outcomes. Tailoring interventions based on these profiles may optimize treatment efficacy in early stages of AD.
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Affiliation(s)
- Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sunghwan Kim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - TaeYeong Kim
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
| | - Donghyeon Kim
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
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Katayama O, Stern Y, Habeck C, Coors A, Lee S, Harada K, Makino K, Tomida K, Morikawa M, Yamaguchi R, Nishijima C, Misu Y, Fujii K, Kodama T, Shimada H. Detection of neurophysiological markers of cognitive reserve: an EEG study. Front Aging Neurosci 2024; 16:1401818. [PMID: 39170899 PMCID: PMC11335520 DOI: 10.3389/fnagi.2024.1401818] [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: 03/16/2024] [Accepted: 07/26/2024] [Indexed: 08/23/2024] Open
Abstract
Background and objectives Cognitive reserve (CR) is a property of the brain that allows for better-than-expected cognitive performance relative to the degree of brain change over the course of life. However, neurophysiological markers of CR remain under-investigated. Electroencephalography (EEG) features may function as suitable neurophysiological markers of CR. To assess this, we investigated whether the dorsal attention network (DAN) and ventral attention network (VAN) activities, as measured during resting-state EEG, moderate the relationship between hippocampal volume and episodic memory. Methods Participants were recruited as part of the National Center for Geriatrics and Gerontology-Study of Geriatric Syndromes. Hippocampal volume was determined using magnetic MRI, and episodic memory was measured using word lists. After testing the effect of hippocampal volume on memory performance using multiple regression analysis, we evaluated the interactions between hippocampal volume and DAN and VAN network activities. We further used the Johnson-Neyman technique to quantify the moderating effects of DAN and VAN network activities on the relationship between hippocampal volume and word list memory, as well as to identify specific ranges of DAN and VAN network activity with significant hippocampal-memory association. Results A total of 449 participants were included in this study. Our analysis revealed significant moderation of DAN with a slope of β = -0.00012 (95% CI: -0.00024; -0.00001, p = 0.040), and VAN with a slope of β = 0.00014 (95% CI: 0.00001; 0.00026, p = 0.031). Further, we found that a larger hippocampal volume was associated with improved memory performance, and that this association became stronger as the DAN activity decreased until a limit of DAN activity of 944.9, after which the hippocampal volume was no longer significantly related to word-list memory performance. For the VAN, we found that a higher hippocampal volume was more strongly associated with better memory performance when VAN activity was higher. However, when VAN activity extended beyond -914.6, the hippocampal volume was no longer significantly associated with word-list memory. Discussion Our results suggest that attentional networks help to maintain memory performance in the face of age-related structural decline, meeting the criteria for the neural implementation of cognitive reserve.
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Affiliation(s)
- Osamu Katayama
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
- Japan Society for the Promotion of Science, Chiyoda-ku, Tokyo, Japan
- Department of Physical Therapy, Graduate School of Health Sciences, Kyoto Tachibana University, Oyake, Yamashina-ku, Kyoto, Japan
| | - Yaakov Stern
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Christian Habeck
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Annabell Coors
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, United States
| | - Sangyoon Lee
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Kenji Harada
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Keitaro Makino
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Kouki Tomida
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Masanori Morikawa
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Ryo Yamaguchi
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Chiharu Nishijima
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Yuka Misu
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Kazuya Fujii
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
| | - Takayuki Kodama
- Department of Physical Therapy, Graduate School of Health Sciences, Kyoto Tachibana University, Oyake, Yamashina-ku, Kyoto, Japan
| | - Hiroyuki Shimada
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Obu, Aichi, Japan
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Sheng J, Xin Y, Zhang Q, Yang Z, Wang L, Zhang Q, Wang B. Novel Alzheimer's disease subtypes based on functional brain connectivity in human connectome project. Sci Rep 2024; 14:14821. [PMID: 38937574 PMCID: PMC11211325 DOI: 10.1038/s41598-024-65846-z] [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: 12/28/2023] [Accepted: 06/25/2024] [Indexed: 06/29/2024] Open
Abstract
The pathogenesis of Alzheimer's disease (AD) remains unclear, but revealing individual differences in functional connectivity (FC) may provide insights and improve diagnostic precision. A hierarchical clustering-based autoencoder with functional connectivity was proposed to categorize 82 AD patients from the Alzheimer's Disease Neuroimaging Initiative. Compared to directly performing clustering, using an autoencoder to reduce the dimensionality of the matrix can effectively eliminate noise and redundant information in the data, extract key features, and optimize clustering performance. Subsequently, subtype differences in clinical and graph theoretical metrics were assessed. Results indicate a significant inter-subject heterogeneity in the degree of FC disruption among AD patients. We have identified two neurophysiological subtypes: subtype I exhibits widespread functional impairment across the entire brain, while subtype II shows mild impairment in the Limbic System region. What is worth noting is that we also observed significant differences between subtypes in terms of neurocognitive assessment scores associations with network functionality, and graph theory metrics. Our method can accurately identify different functional disruptions in subtypes of AD, facilitating personalized treatment and early diagnosis, ultimately improving patient outcomes.
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Affiliation(s)
- Jinhua Sheng
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China.
- Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, 310018, China.
| | - Yu Xin
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China
- Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, 310018, China
| | - Qiao Zhang
- Beijing Hospital, Beijing, 100730, China
- National Center of Gerontology, Beijing, 100730, China
- Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Ze Yang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China
- Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, 310018, China
| | - Luyun Wang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China
- Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, 310018, China
| | - Qian Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China
- Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, 310018, China
| | - Binbing Wang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China
- Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, 310018, China
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Dolci G, Cruciani F, Rahaman MA, Abrol A, Chen J, Fu Z, Galazzo IB, Menegaz G, Calhoun VD. An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease. ARXIV 2024:arXiv:2406.13292v1. [PMID: 38947922 PMCID: PMC11213156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Alzheimer's disease (AD) is the most prevalent form of dementia, affecting millions worldwide with a progressive decline in cognitive abilities. The AD continuum encompasses a prodormal stage known as Mild Cognitive Impairment (MCI), where patients may either progress to AD (MCIc) or remain stable (MCInc). Understanding the underlying mechanisms of AD requires complementary analysis derived from different data sources, leading to the development of multimodal deep learning models. In this study, we leveraged structural and functional Magnetic Resonance Imaging (sMRI/fMRI) to investigate the disease-induced grey matter and functional network connectivity changes. Moreover, considering AD's strong genetic component, we introduce Single Nucleotide Polymorphisms (SNPs) as a third channel. Given such diverse inputs, missing one or more modalities is a typical concern of multimodal methods. We hence propose a novel deep learning based classification framework where generative module employing Cycle Generative Adversarial Networks (cGAN) was adopted to impute missing data within the latent space. Additionally, we adopted an Explainable Artificial Intelligence (XAI) method, Integrated Gradients (IG), to extract input features relevance, enhancing our understanding of the learned representations. Two critical tasks were addressed: AD detection and MCI conversion prediction. Experimental results showed that our framework was able to reach the state-of-the-art in the classification of CN vs AD reaching an average test accuracy of 0.926 ± 0.02. For the MCInc vs MCIc task, we achieved an average prediction accuracy of 0.711 ± 0.01 using the pre-trained model for CN and AD. The interpretability analysis revealed that the classification performance was led by significant grey matter modulations in cortical and subcortical brain areas well known for their association with AD. Moreover, impairments in sensory-motor and visual resting state network connectivity along the disease continuum, as well as mutations in SNPs defining biological processes linked to amyloid-beta and cholesterol formation clearance and regulation, were identified as contributors to the achieved performance. Overall, our integrative deep learning approach shows promise for AD detection and MCI prediction, while shading light on important biological insights.
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Affiliation(s)
- Giorgio Dolci
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Federica Cruciani
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
| | - Md Abdur Rahaman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Anees Abrol
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Jiayu Chen
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | | | - Gloria Menegaz
- Department of Engineering for Innovation Medicine, University of Verona, Verona, Italy
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
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Chen Y, Liang L, Wei Y, Liu Y, Li X, Zhang Z, Li L, Deng D. Disrupted morphological brain network organization in subjective cognitive decline and mild cognitive impairment. Brain Imaging Behav 2024; 18:387-395. [PMID: 38147273 DOI: 10.1007/s11682-023-00839-6] [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] [Accepted: 12/01/2023] [Indexed: 12/27/2023]
Abstract
We aim to investigate the alterations in gray matter for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) from the perspective of the human connectome. High-resolution T1-weighted images were acquired from 54 patients with SCD, 95 patients with MCI, and 65 healthy controls (HC). Morphological brain networks (MBN) were constructed using similarities in the distribution of gray matter volumes between regions. The strength of morphological connections and topographic metrics derived from the graph-theoretical analysis were compared. Furthermore, we assessed the relationship between the observed morphological abnormalities and disease severity. According to the results, we found a significantly decreased morphological connection between the somatomotor network and ventral attention network in SCD compared to HC and MCI compared to SCD. The graph-theoretic analysis illustrated disruptions in the whole network organization, where the normalized shortest path increased and the global efficiency (Eg) decreased in MCI compared to SCD. In addition, Montreal Cognitive Assessment scores of SCD patients had a significantly negative correlation with Eg. The primary limitations of the present study include the cross-sectional design, no enrolled AD patients, no assessment of amyloidosis, and the need for more comprehensive neuropsychological tests. Our findings indicate the abnormalities of morphological networks at early stages in the AD continuum, which could be interpreted as compensatory changes to retain a normal level of cognitive function. The present study could provide new insight into the mechanism of AD.
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Affiliation(s)
- Yuxin Chen
- Medical College of Guangxi University, Guangxi University, Nanning, Guangxi, China
- Department of Radiology, the People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Lingyan Liang
- Department of Radiology, the People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Yichen Wei
- Department of Radiology, the People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Ying Liu
- Department of Radiology, the People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Xiaocheng Li
- Department of Radiology, the People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China
| | - Zhiguo Zhang
- School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China
- Peng Cheng Laboratory, Shenzhen, Guangdong, China
| | - Linling Li
- School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, Guangdong, China.
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China.
| | - Demao Deng
- Medical College of Guangxi University, Guangxi University, Nanning, Guangxi, China.
- Department of Radiology, the People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Science, Nanning, Guangxi, China.
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Kim J, Kim S, Um YH, Wang SM, Kim REY, Choe YS, Lee J, Kim D, Lim HK, Lee CU, Kang DW. Associations between Education Years and Resting-state Functional Connectivity Modulated by APOE ε4 Carrier Status in Cognitively Normal Older Adults. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE : THE OFFICIAL SCIENTIFIC JOURNAL OF THE KOREAN COLLEGE OF NEUROPSYCHOPHARMACOLOGY 2024; 22:169-181. [PMID: 38247423 PMCID: PMC10811405 DOI: 10.9758/cpn.23.1113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/13/2023] [Accepted: 09/26/2023] [Indexed: 01/23/2024]
Abstract
Objective : Cognitive reserve has emerged as a concept to explain the variable expression of clinical symptoms in the pathology of Alzheimer's disease (AD). The association between years of education, a proxy of cognitive reserve, and resting-state functional connectivity (rFC), a representative intermediate phenotype, has not been explored in the preclinical phase, considering risk factors for AD. We aimed to evaluate whether the relationship between years of education and rFC in cognitively preserved older adults differs depending on amyloid-beta deposition and APOE ε4 carrier status as effect modifiers. Methods : A total of 121 participants underwent functional magnetic resonance imaging, [18F] flutemetamol positron emission tomography-computed tomography, APOE genotyping, and a neuropsychological battery. Potential interactions between years of education and AD risk factors for rFC of AD-vulnerable neural networks were assessed with whole-brain voxel-wise analysis. Results : We found a significant education years-by-APOE ε4 carrier status interaction for the rFC from the seed region of the central executive (CEN) and dorsal attention networks. Moreover, there was a significant interaction of rFC between right superior occipital gyrus and the CEN seed region by APOE ε4 carrier status for memory performances and overall cognitive function. Conclusion : In preclinical APOE ε4 carriers, higher years of education were associated with higher rFC of the AD vulnerable network, but this contributed to lower cognitive function. These results contribute to a deeper understanding of the impact of cognitive reserve on sensitive functional intermediate phenotypic markers in the preclinical phase of AD.
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Affiliation(s)
- Jiwon Kim
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Sunghwan Kim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent’s Hospital, College of Medicine, The Catholic University of Korea, Suwon, Korea
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | | | | | - Jiyeon Lee
- Research Institute, NEUROPHET Inc., Seoul, Korea
| | | | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Research Institute, NEUROPHET Inc., Seoul, Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
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Yao W, Zhou H, Zhang X, Chen H, Bai F. Inflammation affects dynamic functional network connectivity pattern changes via plasma NFL in cognitive impairment patients. CNS Neurosci Ther 2024; 30:e14391. [PMID: 37545369 PMCID: PMC10848064 DOI: 10.1111/cns.14391] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/03/2023] [Accepted: 07/26/2023] [Indexed: 08/08/2023] Open
Abstract
BACKGROUND Plasma neurofilament light chain (NFL) is a biomarker of inflammation and neurodegenerative diseases such as Alzheimer's disease (AD). However, the underlying neural mechanisms by which NFL affects cognitive function remain unclear. In this study, we investigated the effects of inflammation on cognitive integrity in patients with cognitive impairment through the functional interaction of plasma NFL with large-scale brain networks. METHODS This study included 29 cognitively normal, 55 LowNFL patients, and 55 HighNFL patients. Group independent component analysis (ICA) was applied to the resting-state fMRI data, and 40 independent components (IC) were extracted for the whole brain. Next, the dynamic functional network connectivity (dFNC) of each subject was estimated using the sliding-window method and k-means clustering, and five dynamic functional states were identified. Finally, we applied mediation analysis to investigate the relationship between plasma NFL and dFNC indicators and cognitive scales. RESULTS The present study explored the dynamics of whole-brain FNC in controls and LowNFL and HighNFL patients and highlighted the temporal properties of dFNC states in relation to psychological scales. A potential mechanism for the association between dFNC indicators and NFL levels in cognitively impaired patients. CONCLUSIONS Our findings suggested the decreased ability of information processing and communication in the HighNFL group, which helps us to understand their abnormal cognitive functions clinically. Characteristic changes in the inflammation-coupled dynamic brain network may provide alternative biomarkers for the assessment of disease severity in cognitive impairment patients.
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Affiliation(s)
- Weina Yao
- Department of NeurologyZhongnan Hospital of Wuhan UniversityWuhanChina
- Geriatric Medicine CenterTaikang Xianlin Drum Tower Hospital Clinical College of Wuhan UniversityNanjingChina
| | - Huijuan Zhou
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western MedicineNanjing University of Chinese MedicineNanjingChina
| | - Xiao Zhang
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western MedicineNanjing University of Chinese MedicineNanjingChina
| | - Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western MedicineNanjing University of Chinese MedicineNanjingChina
| | - Feng Bai
- Geriatric Medicine CenterTaikang Xianlin Drum Tower Hospital Clinical College of Wuhan UniversityNanjingChina
- Geriatric Medicine CenterTaikang Xianlin Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing UniversityNanjingChina
- Department of NeurologyNanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing UniversityNanjingChina
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9
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Ersözlü E, Rauchmann BS. Analysis of Resting-State Functional Magnetic Resonance Imaging in Alzheimer's Disease. Methods Mol Biol 2024; 2785:89-104. [PMID: 38427190 DOI: 10.1007/978-1-0716-3774-6_7] [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: 03/02/2024]
Abstract
Alzheimer's disease (AD) has been characterized by widespread network disconnection among brain regions, widely overlapping with the hallmarks of the disease. Functional connectivity has been studied with an upward trend in the last two decades, predominantly in AD among other neuropsychiatric disorders, and presents a potential biomarker with various features that might provide unique contributions to foster our understanding of neural mechanisms of AD. The resting-state functional MRI (rs-fMRI) is usually used to measure the blood-oxygen-level-dependent signals that reflect the brain's functional connectivity. Nevertheless, the rs-fMRI is still underutilized, which might be due to the fairly complex acquisition and analytic methodology. In this chapter, we presented the common methods that have been applied in rs-fMRI literature, focusing on the studies on individuals in the continuum of AD. The key methodological aspects will be addressed that comprise acquiring, processing, and interpreting rs-fMRI data. More, we discussed the current and potential implications of rs-fMRI in AD.
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Affiliation(s)
- Ersin Ersözlü
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Department of Geriatric Psychiatry and Developmental Disorders, kbo-Isar-Amper-Klinikum Munich East, Academic Teaching Hospital of LMU Munich, Munich, Germany
| | - Boris-Stephan Rauchmann
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
- Department of Neuroradiology, University Hospital, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK
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10
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Zheng C, Zhao W, Yang Z, Guo S. Functional connectome hierarchy dysfunction in Alzheimer's disease and its relationship with cognition and gene expression profiling. J Neurosci Res 2024; 102:e25280. [PMID: 38284860 DOI: 10.1002/jnr.25280] [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: 08/23/2023] [Revised: 10/21/2023] [Accepted: 11/16/2023] [Indexed: 01/30/2024]
Abstract
Numerous researches have shown that the human brain organizes as a continuum axis crossing from sensory motor to transmodal cortex. Functional network alterations were commonly found in Alzheimer's disease (AD). Whether the hierarchy of AD brain networks has changed and how these changes related to gene expression profiling and cognition is unclear. Using resting-state functional magnetic resonance imaging data from 233 subjects (185 AD patients and 48 healthy controls), we studied the changes in the functional network gradients in AD. Moreover, we investigated the relationships between gradient alterations and cognition, and gene expression profiling, respectively. We found that the second gradient organizes as a continuum axis crossing from the sensory motor to the transmodal cortex. Compared to the healthy controls, the secondary gradient scores of the visual and somatomotor network (SOM) increased significantly in AD, and the secondary gradient scores of default mode and frontoparietal network decreased significantly in AD. The secondary gradient scores of SOM and salience network (SAL) significantly positively correlated with memory function in AD. The secondary gradient in SAL also significantly positively correlated with language function. The AD-related second gradient alterations were spatially associated with the gene expression and the relevant genes enriched in neurobiology-related pathways, specially expressed in various tissues, cell types, and developmental stages. These findings suggested the changes in the functional network gradients in AD and deepened our understanding of the correlation between macroscopic gradient structure and microscopic gene expression profiling in AD.
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Affiliation(s)
- Chuchu Zheng
- School of Mathematics and Statistics, Hunan Normal University, Changsha, China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, China
| | - Wei Zhao
- School of Mathematics and Statistics, Hunan Normal University, Changsha, China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, China
| | - Zeyu Yang
- School of Mathematics and Statistics, Hunan Normal University, Changsha, China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, China
| | - Shuixia Guo
- School of Mathematics and Statistics, Hunan Normal University, Changsha, China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, China
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11
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Katayama O, Stern Y, Habeck C, Lee S, Harada K, Makino K, Tomida K, Morikawa M, Yamaguchi R, Nishijima C, Misu Y, Fujii K, Kodama T, Shimada H. Neurophysiological markers in community-dwelling older adults with mild cognitive impairment: an EEG study. Alzheimers Res Ther 2023; 15:217. [PMID: 38102703 PMCID: PMC10722716 DOI: 10.1186/s13195-023-01368-6] [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: 09/26/2023] [Accepted: 12/05/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND Neurodegeneration and structural changes in the brain due to amyloid deposition have been observed even in individuals with mild cognitive impairment (MCI). EEG measurement is considered an effective tool because it is noninvasive, has few restrictions on the measurement environment, and is simple and easy to use. In this study, we investigated the neurophysiological characteristics of community-dwelling older adults with MCI using EEG. METHODS Demographic characteristics, cognitive function, physical function, resting-state MRI and electroencephalogram (rs-EEG), event-related potentials (ERPs) during Simon tasks, and task proportion of correct responses and reaction times (RTs) were obtained from 402 healthy controls (HC) and 47 MCI participants. We introduced exact low-resolution brain electromagnetic tomography-independent component analysis (eLORETA-ICA) to assess the rs-EEG network in community-dwelling older adults with MCI. RESULTS A lower proportion of correct responses to the Simon task and slower RTs were observed in the MCI group (p < 0.01). Despite no difference in brain volume between the HC and MCI groups, significant decreases in dorsal attention network (DAN) activity (p < 0.05) and N2 amplitude of ERP (p < 0.001) were observed in the MCI group. Moreover, DAN activity demonstrated a correlation with education (Rs = 0.32, p = 0.027), global cognitive function (Rs = 0.32, p = 0.030), and processing speed (Rs = 0.37, p = 0.010) in the MCI group. The discrimination accuracy for MCI with the addition of the eLORETA-ICA network ranged from 0.7817 to 0.7929, and the area under the curve ranged from 0.8492 to 0.8495. CONCLUSIONS The eLORETA-ICA approach of rs-EEG using noninvasive and relatively inexpensive EEG demonstrates specific changes in elders with MCI. It may provide a simple and valid assessment method with few restrictions on the measurement environment and may be useful for early detection of MCI in community-dwelling older adults.
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Affiliation(s)
- Osamu Katayama
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan.
- Japan Society for the Promotion of Science, Chiyoda-Ku, Tokyo, 102-0083, Japan.
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, 10032, USA.
- Department of Physical Therapy, Graduate School of Health Sciences, Kyoto Tachibana University, 34 Yamada-Cho, Oyake, Yamashina-Ku, Kyoto, 607-8175, Japan.
| | - Yaakov Stern
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, 10032, USA
| | - Christian Habeck
- Columbia University Vagelos College of Physicians and Surgeons, New York, NY, 10032, USA
| | - Sangyoon Lee
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Kenji Harada
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Keitaro Makino
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Kouki Tomida
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Masanori Morikawa
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Ryo Yamaguchi
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Chiharu Nishijima
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Yuka Misu
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Kazuya Fujii
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
| | - Takayuki Kodama
- Department of Physical Therapy, Graduate School of Health Sciences, Kyoto Tachibana University, 34 Yamada-Cho, Oyake, Yamashina-Ku, Kyoto, 607-8175, Japan
| | - Hiroyuki Shimada
- Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, 7-430 Morioka-Cho, Obu City, Aichi, 474-8511, Japan
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12
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Mousa D, Zayed N, Yassine IA. Correlation transfer function analysis as a biomarker for Alzheimer brain plasticity using longitudinal resting-state fMRI data. Sci Rep 2023; 13:21559. [PMID: 38057476 PMCID: PMC10700324 DOI: 10.1038/s41598-023-48693-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 11/29/2023] [Indexed: 12/08/2023] Open
Abstract
Neural plasticity is the ability of the brain to alter itself functionally and structurally as a result of its experience. However, longitudinal changes in functional connectivity of the brain are still unrevealed in Alzheimer's disease (AD). This study aims to discover the significant connections (SCs) between brain regions for AD stages longitudinally using correlation transfer function (CorrTF) as a new biomarker for the disease progression. The dataset consists of: 29 normal controls (NC), and 23, 24, and 23 for early, late mild cognitive impairments (EMCI, LMCI), and ADs, respectively, along three distant visits. The brain was divided into 116 regions using the automated anatomical labeling atlas, where the intensity time series is calculated, and the CorrTF connections are extracted for each region. Finally, the standard t-test and ANOVA test were employed to investigate the SCs for each subject's visit. No SCs, along three visits, were found For NC subjects. The most SCs were mainly directed from cerebellum in case of EMCI and LMCI. Furthermore, the hippocampus connectivity increased in LMCI compared to EMCI whereas missed in AD. Additionally, the patterns of longitudinal changes among the different AD stages compared to Pearson Correlation were similar, for SMC, VC, DMN, and Cereb networks, while differed for EAN and SN networks. Our findings define how brain changes over time, which could help detect functional changes linked to each AD stage and better understand the disease behavior.
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Affiliation(s)
- Doaa Mousa
- Computers and Systems Department, Electronics Research Institute, Cairo, Egypt.
| | - Nourhan Zayed
- Computers and Systems Department, Electronics Research Institute, Cairo, Egypt
- Mechanical Engineering Department, The British University in Egypt, Cairo, Egypt
| | - Inas A Yassine
- Systems and Biomedical Engineering Department, Cairo University, Giza, Egypt
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13
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Geng L, Feng Q, Wang X, Gao Y, Hao L, Qiu J. Connectome-based modeling reveals a resting-state functional network that mediates the relationship between social rejection and rumination. Front Psychol 2023; 14:1264221. [PMID: 37965648 PMCID: PMC10642796 DOI: 10.3389/fpsyg.2023.1264221] [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: 07/20/2023] [Accepted: 10/11/2023] [Indexed: 11/16/2023] Open
Abstract
Background Rumination impedes problem solving and is one of the most important factors in the onset and maintenance of multiple psychiatric disorders. The current study aims to investigate the impact of social rejection on rumination and explore the underlying neural mechanisms involved in this process. Methods We utilized psychological questionnaire and resting-state brain imaging data from a sample of 560 individuals. The predictive model for rumination scores was constructed using resting-state functional connectivity data through connectome-based predictive modeling. Additionally, a mediation analysis was conducted to investigate the mediating role of the prediction network in the relationship between social rejection and rumination. Results A positive correlation between social rejection and rumination was found. We obtained the prediction model of rumination and found that the strongest contributions came from the intra- and internetwork connectivity within the default mode network (DMN), dorsal attention network (DAN), frontoparietal control network (FPCN), and sensorimotor networks (SMN). Analysis of node strength revealed the significance of the supramarginal gyrus (SMG) and angular gyrus (AG) as key nodes in the prediction model. In addition, mediation analysis showed that the strength of the prediction network mediated the relationship between social rejection and rumination. Conclusion The findings highlight the crucial role of functional connections among the DMN, DAN, FPCN, and SMN in linking social rejection and rumination, particular in brain regions implicated in social cognition and emotion, namely the SMG and AG regions. These results enhance our understanding of the consequences of social rejection and provide insights for novel intervention strategies targeting rumination.
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Affiliation(s)
- Li Geng
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Qiuyang Feng
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xueyang Wang
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Yixin Gao
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Lei Hao
- College of Teacher Education, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
- Faculty of Psychology, Southwest University, Chongqing, China
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14
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Ruan Z, Gao L, Li S, Yu M, Rao B, Sun W, Zhou X, Li Y, Song X, Xu H. Functional abnormalities of the cerebellum in vascular mild cognitive impairment. Brain Imaging Behav 2023; 17:530-540. [PMID: 37433970 DOI: 10.1007/s11682-023-00783-5] [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] [Accepted: 05/05/2023] [Indexed: 07/13/2023]
Abstract
OBJECTIVES The alterations in cerebellar activity that occur in vascular mild cognitive impairment remain largely unexplored. This study aimed to investigate potential associations between abnormal cerebellar functional connectivity (FC) and changes in cognitive function by examining intracerebellar and cerebellar-cerebral FC. METHODS MRI data were collected from seventy-two patients with vascular mild cognitive impairment (VMCI), comprising 38 patients with small vessel mild cognitive impairment (SVMCI) and 34 with poststroke mild cognitive impairment (PSMCI), and from 43 demographically matched healthy controls (HCs). Changes in FC between subregions within the cerebellum and from each cerebellar subregion to the selected cerebral seed points in VMCI patients were calculated, and the association of these changes with cognitive function was examined. RESULTS Compared with HCs, we found that VMCI patients had 11 cerebellar subregions showing significant differences (mainly decreases) in FC with brain regions in the default-mode network (DMN), sensory-motor network (SMN), and frontoparietal network (FPN). In the intracerebellar FC analysis, 47 (8%) cerebellar connections had significant intergroup differences, mainly a reduced magnitude of FC in VMCI patients. In the correlation analysis, higher Montreal Cognitive Assessment (MoCA) scores were correlated with stronger intracerebellar FC (left crus II-right lobule VI, left crus II-right lobule VIIb) and cerebellar-cerebral FC (right lobule X-left precuneus, vermal lobule IX-right inferior parietal lobule) in both the SVMCI and PSMCI groups. CONCLUSION These findings suggest prominent intracerebellar and cerebellar-cerebral FC abnormalities in VMCI patients, contributing evidence for a possible role of the cerebellum in cognitive processes.
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Affiliation(s)
- Zhao Ruan
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City, Hubei Province, 430071, China
| | - Lei Gao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City, Hubei Province, 430071, China
| | - Sirui Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City, Hubei Province, 430071, China
| | - Minhua Yu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City, Hubei Province, 430071, China
| | - Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City, Hubei Province, 430071, China
| | - Wenbo Sun
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City, Hubei Province, 430071, China
| | - Xiaoli Zhou
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City, Hubei Province, 430071, China
| | - Yidan Li
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City, Hubei Province, 430071, China
| | - Xiaopeng Song
- Department of Biomedical Engineering, College of Engineering, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing, 100871, China.
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang District, Wuhan City, Hubei Province, 430071, China.
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15
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Chen X, Onur OA, Richter N, Fassbender R, Gramespacher H, Befahr Q, von Reutern B, Dillen K, Jacobs HIL, Kukolja J, Fink GR, Dronse J. Concordance of Intrinsic Brain Connectivity Measures Is Disrupted in Alzheimer's Disease. Brain Connect 2023; 13:344-355. [PMID: 34269605 DOI: 10.1089/brain.2020.0918] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Background: Recently, a new resting-state functional magnetic resonance imaging (rs-fMRI) measure to evaluate the concordance between different rs-fMRI metrics has been proposed and has not been investigated in Alzheimer's disease (AD). Methods: 3T rs-fMRI data were obtained from healthy young controls (YC, n = 26), healthy senior controls (SC, n = 29), and AD patients (n = 35). The fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were analyzed, followed by the calculation of their concordance using Kendall's W for each brain voxel across time. Group differences in the concordance were compared globally, within seven intrinsic brain networks, and on a voxel-by-voxel basis with covariates of age, sex, head motion, and gray matter volume. Results: The global concordance was lowest in AD among the three groups, with similar differences for the single metrics. When comparing AD to SC, reductions of concordance were detected in each of the investigated networks apart from the limbic network. For SC in comparison to YC, lower global concordance without any network-level difference was observed. Voxel-wise analyses revealed lower concordance in the right middle temporal gyrus in AD compared to SC and lower concordance in the left middle frontal gyrus in SC compared to YC. Lower fALFF were observed in the right angular gyrus in AD in comparison to SC, but ReHo and DC showed no group differences. Conclusions: The concordance of resting-state measures differentiates AD from healthy aging and may represent a novel imaging marker in AD.
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Affiliation(s)
- Xiangliang Chen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Oezguer A Onur
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nils Richter
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Ronja Fassbender
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Hannes Gramespacher
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Qumars Befahr
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Boris von Reutern
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Kim Dillen
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
| | - Heidi I L Jacobs
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry and Neuropsychology, Alzheimer Centre, Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Juraj Kukolja
- Department of Neurology and Clinical Neurophysiology, Helios University Hospital Wuppertal, Wuppertal, Germany
- Department of Neurology, Faculty of Health, Witten/Herdecke University, Witten, Germany
| | - Gereon R Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Julian Dronse
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
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16
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Yang H, Zhao X, Wang T, Zhou Z, Cheng Z, Zhao X, Cao Y. Hypoconnectivity within the cingulo-opercular network in patients with mild cognitive impairment in Chinese communities. Int J Geriatr Psychiatry 2023; 38:e5979. [PMID: 37548525 DOI: 10.1002/gps.5979] [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: 11/03/2022] [Accepted: 07/11/2023] [Indexed: 08/08/2023]
Abstract
INTRODUCTION At rest, the brain's higher cognitive systems engage in correlated activity patterns, forming networks. With mild cognitive impairment (MCI), it is essential to understand how functional connectivity within and between resting-state networks changes. This study used resting-state functional connectivity to identify significant differences within and between the cingulo-opercular network (CON) and default mode network (DMN). METHODS We assessed cognitive function in patients using the Chinese version of the Alzheimer's disease assessment scale-Cognitive subscale (ADAS-Cog). A group of MCI subjects (ages 60-83 years, n = 45) was compared to age-matched healthy controls (n = 70). Resting-state functional connectivity was used to determine functional connectivity strength within and between the CON and DMN. RESULTS Compared to healthy controls, the MCI group showed significantly lower functional connectivity within the CON (F = 10.76, df = 1, p = 0.001, FDR adjusted p = 0.003). Additionally, the MCI group displayed no distinct differences in functional connectivity within DMN (F = 0.162, df = 1, p = 0.688, FDR adjusted p = 0.688) and between CON and DMN (F = 2.270, df = 1, p = 0.135, FDR adjusted p = 0.262). Moreover, we found no correlation between ADAS-Cog and within- or between-connectivity metrics among subjects with MCI. CONCLUSIONS Our findings indicate that specific patterns of hypoconnectivity within CON circuitry may characterize MCI relative to healthy controls. This work improves our understanding of network dysfunction underlying MCI and could inform more targeted treatment.
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Affiliation(s)
- Huan Yang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | | | - Tenglong Wang
- Wuxi Mental Health Center, Nanjing Medical University, Wuxi, China
| | - Zhenhe Zhou
- Wuxi Mental Health Center, Nanjing Medical University, Wuxi, China
| | - Zaohuo Cheng
- Wuxi Mental Health Center, Nanjing Medical University, Wuxi, China
| | - Xingfu Zhao
- Wuxi Mental Health Center, Nanjing Medical University, Wuxi, China
| | - Yuping Cao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
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17
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Yang X, Wu H, Song Y, Chen S, Ge H, Yan Z, Yuan Q, Liang X, Lin X, Chen J. Functional MRI-specific alterations in frontoparietal network in mild cognitive impairment: an ALE meta-analysis. Front Aging Neurosci 2023; 15:1165908. [PMID: 37448688 PMCID: PMC10336325 DOI: 10.3389/fnagi.2023.1165908] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 05/16/2023] [Indexed: 07/15/2023] Open
Abstract
Background Mild cognitive impairment (MCI) depicts a transitory phase between healthy elderly and the onset of Alzheimer's disease (AD) with worsening cognitive impairment. Some functional MRI (fMRI) research indicated that the frontoparietal network (FPN) could be an essential part of the pathophysiological mechanism of MCI. However, damaged FPN regions were not consistently reported, especially their interactions with other brain networks. We assessed the fMRI-specific anomalies of the FPN in MCI by analyzing brain regions with functional alterations. Methods PubMed, Embase, and Web of Science were searched to screen neuroimaging studies exploring brain function alterations in the FPN in MCI using fMRI-related indexes, including the amplitude of low-frequency fluctuation, regional homogeneity, and functional connectivity. We integrated distinctive coordinates by activating likelihood estimation, visualizing abnormal functional regions, and concluding functional alterations of the FPN. Results We selected 29 studies and found specific changes in some brain regions of the FPN. These included the bilateral dorsolateral prefrontal cortex, insula, precuneus cortex, anterior cingulate cortex, inferior parietal lobule, middle temporal gyrus, superior frontal gyrus, and parahippocampal gyrus. Any abnormal alterations in these regions depicted interactions between the FPN and other networks. Conclusion The study demonstrates specific fMRI neuroimaging alterations in brain regions of the FPN in MCI patients. This could provide a new perspective on identifying early-stage patients with targeted treatment programs. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023432042, identifier: CRD42023432042.
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Affiliation(s)
- Xinyi Yang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Huimin Wu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zheng Yan
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Qianqian Yuan
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xuhong Liang
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xingjian Lin
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Department of Radiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
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18
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Li Y, An S, Zhou T, Su C, Zhang S, Li C, Jiang J, Mu Y, Yao N, Huang ZG. Triple-network analysis of Alzheimer's disease based on the energy landscape. Front Neurosci 2023; 17:1171549. [PMID: 37287802 PMCID: PMC10242117 DOI: 10.3389/fnins.2023.1171549] [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: 02/22/2023] [Accepted: 04/13/2023] [Indexed: 06/09/2023] Open
Abstract
Introduction Research on the brain activity during resting state has found that brain activation is centered around three networks, including the default mode network (DMN), the salient network (SN), and the central executive network (CEN), and switches between multiple modes. As a common disease in the elderly, Alzheimer's disease (AD) affects the state transitions of functional networks in the resting state. Methods Energy landscape, as a new method, can intuitively and quickly grasp the statistical distribution of system states and information related to state transition mechanisms. Therefore, this study mainly uses the energy landscape method to study the changes of the triple-network brain dynamics in AD patients in the resting state. Results AD brain activity patterns are in an abnormal state, and the dynamics of patients with AD tend to be unstable, with an unusually high flexibility in switching between states. Also , the subjects' dynamic features are correlated with clinical index. Discussion The atypical balance of large-scale brain systems in patients with AD is associated with abnormally active brain dynamics. Our study are helpful for further understanding the intrinsic dynamic characteristics and pathological mechanism of the resting-state brain in AD patients.
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Affiliation(s)
- Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Simeng An
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Tianlin Zhou
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Chunwang Su
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Siping Zhang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Chenxi Li
- Department of Military Medical Psychology, Air Force Medical University, Xi'an, Shaanxi, China
| | - Junjie Jiang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yunfeng Mu
- Department of Gynecological Oncology, Shaanxi Provincial Cancer Hospital, Xi'an, China
| | - Nan Yao
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- Department of Applied Physics, Xi'an University of Technology, Xi'an, China
| | - Zi-Gang Huang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi'an Jiaotong University, The Key Laboratory of Neuro-informatics and Rehabilitation Engineering of Ministry of Civil Affairs, Xi'an, Shaanxi, China
- Research Center for Brain-inspired Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China
- The State Key Laboratory of Congnitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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19
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Vorobyov V, Deev A, Chaprov K, Ustyugov AA, Lysikova E. Age-Related Modifications of Electroencephalogram Coherence in Mice Models of Alzheimer's Disease and Amyotrophic Lateral Sclerosis. Biomedicines 2023; 11:biomedicines11041151. [PMID: 37189768 DOI: 10.3390/biomedicines11041151] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 04/08/2023] [Accepted: 04/09/2023] [Indexed: 05/17/2023] Open
Abstract
Evident similarities in pathological features in aging and Alzheimer's disease (AD) raise the question of a role for natural age-related adaptive mechanisms in the prevention/elimination of disturbances in interrelations between different brain areas. In our previous electroencephalogram (EEG) studies on 5xFAD- and FUS-transgenic mice, as models of AD and amyotrophic lateral sclerosis (ALS), this suggestion was indirectly confirmed. In the current study, age-related changes in direct EEG synchrony/coherence between the brain structures were evaluated. METHODS In 5xFAD mice of 6-, 9-, 12-, and 18-month ages and their wild-type (WT5xFAD) littermates, we analyzed baseline EEG coherence between the cortex, hippocampus/putamen, ventral tegmental area, and substantia nigra. Additionally, EEG coherence between the cortex and putamen was analyzed in 2- and 5-month-old FUS mice. RESULTS In the 5xFAD mice, suppressed levels of inter-structural coherence vs. those in WT5xFAD littermates were observed at ages of 6, 9, and 12 months. In 18-month-old 5xFAD mice, only the hippocampus ventral tegmental area coherence was significantly reduced. In 2-month-old FUS vs. WTFUS mice, the cortex-putamen coherence suppression, dominated in the right hemisphere, was observed. In 5-month-old mice, EEG coherence was maximal in both groups. CONCLUSION Neurodegenerative pathologies are accompanied by the significant attenuation of intracerebral EEG coherence. Our data are supportive for the involvement of age-related adaptive mechanisms in intracerebral disturbances produced by neurodegeneration.
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Affiliation(s)
- Vasily Vorobyov
- School of Biosciences, Sir Martin Evans Building, Cardiff University, Museum Avenue, Cardiff CF10 3AX, UK
- Institute of Cell Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia
| | - Alexander Deev
- Institute of Theoretical and Experimental Biophysics, Russian Academy of Sciences, 142290 Pushchino, Russia
| | - Kirill Chaprov
- School of Biosciences, Sir Martin Evans Building, Cardiff University, Museum Avenue, Cardiff CF10 3AX, UK
- Institute of Physiologically Active Compounds, Russian Academy of Sciences, 142432 Chernogolovka, Russia
- Center of Pre-Clinical and Clinical Studies, Belgorod State National Research University, 308015 Belgorod, Russia
| | - Aleksey A Ustyugov
- Institute of Physiologically Active Compounds, Russian Academy of Sciences, 142432 Chernogolovka, Russia
| | - Ekaterina Lysikova
- School of Biosciences, Sir Martin Evans Building, Cardiff University, Museum Avenue, Cardiff CF10 3AX, UK
- Institute of Physiologically Active Compounds, Russian Academy of Sciences, 142432 Chernogolovka, Russia
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20
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Rashidi-Ranjbar N, Rajji TK, Hawco C, Kumar S, Herrmann N, Mah L, Flint AJ, Fischer CE, Butters MA, Pollock BG, Dickie EW, Bowie CR, Soffer M, Mulsant BH, Voineskos AN. Association of functional connectivity of the executive control network or default mode network with cognitive impairment in older adults with remitted major depressive disorder or mild cognitive impairment. Neuropsychopharmacology 2023; 48:468-477. [PMID: 35410366 PMCID: PMC9852291 DOI: 10.1038/s41386-022-01308-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 02/13/2022] [Accepted: 03/09/2022] [Indexed: 02/02/2023]
Abstract
Major depressive disorder (MDD) is associated with an increased risk of developing dementia. The present study aimed to better understand this risk by comparing resting state functional connectivity (rsFC) in the executive control network (ECN) and the default mode network (DMN) in older adults with MDD or mild cognitive impairment (MCI). Additionally, we examined the association between rsFC in the ECN or DMN and cognitive impairment transdiagnostically. We assessed rsFC alterations in ECN and DMN in 383 participants from five groups at-risk for dementia-remitted MDD with normal cognition (MDD-NC), non-amnestic mild cognitive impairment (naMCI), remitted MDD + naMCI, amnestic MCI (aMCI), and remitted MDD + aMCI-and from healthy controls (HC) or individuals with Alzheimer's dementia (AD). Subject-specific whole-brain functional connectivity maps were generated for each network and group differences in rsFC were calculated. We hypothesized that alteration of rsFC in the ECN and DMN would be progressively larger among our seven groups, ranked from low to high according to their risk for dementia as HC, MDD-NC, naMCI, MDD + naMCI, aMCI, MDD + aMCI, and AD. We also regressed scores of six cognitive domains (executive functioning, processing speed, language, visuospatial memory, verbal memory, and working memory) on the ECN and DMN connectivity maps. We found a significant alteration in the rsFC of the ECN, with post hoc testing showing differences between the AD group and the HC, MDD-NC, or naMCI groups, but no significant alterations in rsFC of the DMN. Alterations in rsFC of the ECN and DMN were significantly associated with several cognitive domain scores transdiagnostically. Our findings suggest that a diagnosis of remitted MDD may not confer functional brain risk for dementia. However, given the association of rs-FC with cognitive performance (i.e., transdiagnostically), rs-FC may help in stratifying this risk among people with MDD and varying degrees of cognitive impairment.
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Affiliation(s)
- Neda Rashidi-Ranjbar
- Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Tarek K Rajji
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Toronto Dementia Research Alliance, University of Toronto, Toronto, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Sanjeev Kumar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Nathan Herrmann
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Linda Mah
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Baycrest Health Sciences, Rotman Research Institute, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Alastair J Flint
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Centre for Mental Health, University Health Network, Toronto, ON, Canada
| | - Corinne E Fischer
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Meryl A Butters
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Bruce G Pollock
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Christopher R Bowie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Departments of Psychology and Psychiatry (CRB), Queen's University, Kingston, ON, Canada
| | - Matan Soffer
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Benoit H Mulsant
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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21
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Lopez S, Del Percio C, Lizio R, Noce G, Padovani A, Nobili F, Arnaldi D, Famà F, Moretti DV, Cagnin A, Koch G, Benussi A, Onofrj M, Borroni B, Soricelli A, Ferri R, Buttinelli C, Giubilei F, Güntekin B, Yener G, Stocchi F, Vacca L, Bonanni L, Babiloni C. Patients with Alzheimer's disease dementia show partially preserved parietal 'hubs' modeled from resting-state alpha electroencephalographic rhythms. Front Aging Neurosci 2023; 15:780014. [PMID: 36776437 PMCID: PMC9908964 DOI: 10.3389/fnagi.2023.780014] [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: 09/20/2021] [Accepted: 01/05/2023] [Indexed: 01/28/2023] Open
Abstract
Introduction Graph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. 'Degree' hubs reflect node centrality (the connection rate), while 'connector' hubs are those linked to several clusters of nodes (mainly long-range connections). Methods Here, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer's disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a 'network disease' and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8-12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2-40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of 'connector' hub were used. Results Convergent results showed that in both the Nold and ADD groups there were significant parietal 'degree' and 'connector' hubs derived from alpha rhythms. These hubs had a prominent outward 'directionality' in the two groups, but that 'directionality' was lower in ADD participants than in Nold participants. Discussion In conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward 'directionality' of partially preserved parietal 'degree' and 'connector' hubs derived from rsEEG alpha rhythms.
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Affiliation(s)
- Susanna Lopez
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | - Claudio Del Percio
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | - Roberta Lizio
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | | | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Flavio Nobili
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Dario Arnaldi
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Francesco Famà
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Davide V. Moretti
- Alzheimer’s Disease Rehabilitation Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Giacomo Koch
- Non-Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy
- Stroke Unit, Department of Neuroscience, Tor Vergata Policlinic, Rome, Italy
| | - Alberto Benussi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University “G. D’Annunzio” of Chieti-Pescara, Chieti, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy
- Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | | | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Türkiye
- Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Türkiye
| | - Görsev Yener
- Department of Neurology, Dokuz Eylül University Medical School, Izmir, Türkiye
- Faculty of Medicine, Izmir University of Economics, Izmir, Türkiye
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Roma, Rome, Italy
- Telematic University San Raffaele, Rome, Italy
| | - Laura Vacca
- Institute for Research and Medical Care, IRCCS San Raffaele Roma, Rome, Italy
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. D’Annunzio of Chieti-Pescara, Chieti, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
- San Raffaele of Cassino, Cassino, Italy
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22
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Hsu CL, Manor B, Iloputaife I, Oddsson LIE, Lipsitz L. Six month lower-leg mechanical tactile sensory stimulation alters functional network connectivity associated with improved gait in older adults with peripheral neuropathy – A pilot study. Front Aging Neurosci 2022; 14:1027242. [PMID: 36408098 PMCID: PMC9669982 DOI: 10.3389/fnagi.2022.1027242] [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: 08/24/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Foot sole somatosensory impairment associated with peripheral neuropathy (PN) is prevalent and a strong independent risk factor for gait disturbance and falls in older adults. Walkasins, a lower-limb sensory prosthesis, has been shown to improve gait and mobility in people with PN by providing afferent input related to foot sole pressure distributions via lower-leg mechanical tactile stimulation. Given that gait and mobility are regulated by sensorimotor and cognitive brain networks, it is plausible improvements in gait and mobility from wearing the Walkasins may be associated with elicited neuroplastic changes in the brain. As such, this study aimed to examine changes in brain network connectivity after 26 weeks of daily use of the prosthesis among individuals with diagnosed PN and balance problems. In this exploratory investigation, assessments of participant characteristics, Functional Gait Assessment (FGA), and resting-state functional magnetic resonance imaging were completed at study baseline and 26 weeks follow-up. We found that among those who have completed the study (N = 8; mean age 73.7 years) we observed a five-point improvement in FGA performance as well as significant changes in network connectivity over the 26 weeks that were correlated with improved FGA performance. Specifically, greater improvement in FGA score over 26 weeks was associated with increased connectivity within the Default Mode Network (DMN; p < 0.01), the Somatosensory Network (SMN; p < 0.01), and the Frontoparietal Network (FPN; p < 0.01). FGA improvement was also correlated with increased connectivity between the DMN and the FPN (p < 0.01), and decreased connectivity between the SMN and both the FPN (p < 0.01) and cerebellum (p < 0.01). These findings suggest that 26 weeks of daily use of the Walkasins device may provide beneficial neural modulatory changes in brain network connectivity via the sensory replacement stimulation that are relevant to gait improvements among older adults with PN.
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Affiliation(s)
- Chun Liang Hsu
- Hebrew SeniorLife, Hinda and Arthur Marcus Institute for Aging Research, Roslindale, MA, United States
- Harvard Medical School, Boston, MA, United States
- *Correspondence: Chun Liang Hsu,
| | - Brad Manor
- Hebrew SeniorLife, Hinda and Arthur Marcus Institute for Aging Research, Roslindale, MA, United States
- Harvard Medical School, Boston, MA, United States
- Division of Gerontology, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Ikechkwu Iloputaife
- Hebrew SeniorLife, Hinda and Arthur Marcus Institute for Aging Research, Roslindale, MA, United States
| | - Lars I. E. Oddsson
- Division of Rehabilitation Science, Department of Rehabilitation Medicine, Medical School, University of Minnesota, Minneapolis, MN, United States
- RxFunction Inc., Eden Prairie, MN, United States
| | - Lewis Lipsitz
- Hebrew SeniorLife, Hinda and Arthur Marcus Institute for Aging Research, Roslindale, MA, United States
- Harvard Medical School, Boston, MA, United States
- Division of Gerontology, Beth Israel Deaconess Medical Center, Boston, MA, United States
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23
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Crockett RA, Hsu CL, Dao E, Tam R, Eng JJ, Handy TC, Liu-Ambrose T. Weight for It: Resistance Training Mitigates White Matter Hyperintensity-Related Disruption to Functional Networks in Older Females. J Alzheimers Dis 2022; 90:553-563. [DOI: 10.3233/jad-220142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: White matter hyperintensities (WMH) are associated with impaired cognition and increased falls risk. Resistance training (RT) is a promising intervention to reduce WMH progression, improve executive functions, and reduce falls. However, the underlying neurobiological process by which RT improves executive functions and falls risk remain unclear. We hypothesized that: 1) RT reduces the level of WMH-related disruption to functional networks; and 2) reduced disruption to the sensorimotor and attention networks will be associated with improved executive function and reduced falls risk. Objective: Investigate the impact of 52 weeks of RT on WMH-related disruption to functional networks. Methods: Thirty-two older females (65–75 years) were included in this exploratory analysis of a 52-week randomized controlled trial. Participants received either twice-weekly RT or balance and tone training (control). We used lesion network mapping to assess changes in WMH-related disruption to the sensorimotor, dorsal attention, and ventral attention networks. Executive function was measured using the Stroop Colour-Word Test. Falls risk was assessed using the Physiological Profile Assessment (PPA) and the foam sway test. Results: RT significantly reduced the level of WMH-related disruption to the sensorimotor network (p = 0.005). Reduced disruption to the dorsal attention network was associated with improvements in Stroop performance (r = 0.527, p = 0.030). Reduced disruption to the ventral attention network was associated with reduced PPA score (r = 0.485, p = 0.049) Conclusion: RT may be a promising intervention to mitigate WMH-related disruption to the sensorimotor network. Additionally, reducing disruption to the dorsal and ventral attention networks may contribute to improved executive function and reduced falls risk respectively.
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Affiliation(s)
- Rachel A. Crockett
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada
- Aging, Mobility, and Cognitive Neuroscience Laboratory, University of British Columbia, Vancouver, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
| | - Chun Liang Hsu
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, USA
- Harvard Medical School, Harvard University, Boston, MA, USA
| | - Elizabeth Dao
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada
- Aging, Mobility, and Cognitive Neuroscience Laboratory, University of British Columbia, Vancouver, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
| | - Roger Tam
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
- Department of Radiology, University of British Columbia, Vancouver, Canada
- School of Biomedical Engineering, University of British Columbia, Vancouver, Canada
| | - Janice J. Eng
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada
- Rehabilitation Research Program, GF Strong Rehabilitation Centre, Vancouver Coastal Health Research Institute, Vancouver, Canada
| | - Todd C. Handy
- The Attentional Neuroscience Laboratory, University of British Columbia, Vancouver, Canada
- Department of Psychology, University of British Columbia, Vancouver, Canada
| | - Teresa Liu-Ambrose
- Department of Physical Therapy, University of British Columbia, Vancouver, Canada
- Aging, Mobility, and Cognitive Neuroscience Laboratory, University of British Columbia, Vancouver, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
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Malotaux V, Dricot L, Quenon L, Lhommel R, Ivanoiu A, Hanseeuw B. Default-mode network connectivity changes during the progression towards Alzheimer’s dementia: A longitudinal functional MRI study. Brain Connect 2022. [DOI: 10.1089/brain.2022.0008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Vincent Malotaux
- Université catholique de Louvain, Institute of Neuroscience, Avenue Emmanuel Mounier, 54, Brussels, Belgium, 1200
| | - Laurence Dricot
- Université catholique de Louvain, Institute of Neuroscience, Brussels, Belgium
| | - Lisa Quenon
- Université catholique de Louvain, Institute of Neuroscience, Brussels, Belgium
- University Hospital Saint-Luc, Neurology Department, Brussels, Belgium
| | - Renaud Lhommel
- University Hospital Saint-Luc, Nuclear Medicine Department, Brussels, Belgium
- Université catholique de Louvain, Institute of Neuroscience, Brussels, Belgium
| | - Adrian Ivanoiu
- Université catholique de Louvain, Institute of Neuroscience, Brussels, Belgium
- University Hospital Saint-Luc, Neurology Department, Brussels, Belgium
| | - Bernard Hanseeuw
- Université catholique de Louvain, Institute of Neuroscience, Brussels, Belgium
- University Hospital Saint-Luc, Neurology Department, Brussels, Belgium
- Harvard Medical School, Massachusetts General Hospital, Radiology Department, Boston, Massachusetts, United States
- Walloon Excellence in Lifesciences and Biotechnology, Wavre, Belgium
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Structural and functional connectivity abnormalities of the default mode network in patients with Alzheimer's disease and mild cognitive impairment within two independent datasets. Methods 2022; 205:29-38. [PMID: 35671900 DOI: 10.1016/j.ymeth.2022.06.001] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 05/29/2022] [Accepted: 06/03/2022] [Indexed: 12/14/2022] Open
Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia, and amnestic mild cognitive impairment (aMCI) has been defined as a transitional stage between normal aging and AD. Accumulating evidence has shown that altered functional connectivity (FC) and structural connectivity (SC) in the default mode network (DMN) is the prominent hallmarks of AD. However, the relationship between the changes in SC and FC of the DMN is not yet clear. In the present study, we derived the FC and SC matrices of the DMN with functional magnetic resonance imaging (fMRI) and diffusion-weighted imaging (DWI) data and further assessed FC and SC abnormalities within a discovery dataset of 120 participants (39 normal controls, 34 patients with aMCI and 47 patients with AD), as well as a replication dataset of 122 participants (43 normal controls, 37 patients with aMCI and 42 patients with AD). Disrupted SC and FC were found among DMN components (e.g., the posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), and hippocampus) in patients in the aMCI and AD groups in the discovery dataset; most of the disrupted connections were also identified in the replication dataset. More importantly, some SC and FC elements were significantly correlated with the cognitive ability of patients with aMCI and AD. In addition, we found structural-functional decoupling between the PCC and the right hippocampus in patients in the aMCI and AD groups. These findings of the alteration of DMN connectivity in neurodegenerative cohorts deepen our understanding of the pathophysiological mechanisms of AD.
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Kang DW, Wang SM, Um YH, Kim NY, Lee CU, Lim HK. Impact of APOE ε4 Carrier Status on Associations Between Subthreshold, Positive Amyloid-β Deposition, Brain Function, and Cognitive Performance in Cognitively Normal Older Adults: A Prospective Study. Front Aging Neurosci 2022; 14:871323. [PMID: 35677201 PMCID: PMC9168227 DOI: 10.3389/fnagi.2022.871323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/12/2022] [Indexed: 12/19/2022] Open
Abstract
BackgroundA growing body of evidence suggests a deteriorating effect of subthreshold amyloid-beta (Aβ) accumulation on cognition before the onset of clinical symptoms of Alzheimer's disease (AD). Despite the association between the Aβ-dependent pathway and the APOE ε4 allele, the impact of this allele on the progression from the subthreshold Aβ deposits to cognitive function impairment is unclear. Furthermore, the comparative analysis of positive Aβ accumulation in the preclinical phase is lacking.ObjectiveThis study aimed to explore the differential effect of the APOE ε4 carrier status on the association between Aβ deposition, resting-state brain function, and cognitive performance in cognitively normal (CN) older adults, depending on the Aβ burden status.MethodsOne hundred and eighty-two older CN adults underwent resting-state functional magnetic resonance imaging, [18F] flutemetamol (FMM) positron emission tomography, a neuropsychological battery, and APOE genotyping. We evaluated the resting-state brain function by measuring the local and remote functional connectivity (FC) and measured the remote FC in the default-mode network (DMN), central-executive network (CEN), and salience network (SN). In addition, the subjects were dichotomized into those with subthreshold and positive Aβ deposits using a neocortical standardized uptake value ratio with the cut-off value of 0.62, which was calculated with respect to the pons.ResultsThe present result showed that APOE ε4 carrier status moderated the relationship between Aβ deposition, local and remote resting-state brain function, and cognitive performance in each CN subthreshold and positive Aβ group. We observed the following: (i) the APOE ε4 carrier status-Aβ deposition and APOE ε4 carrier status-local FC interaction for the executive and memory function; (ii) the APOE ε4 carrier status-regional Aβ accumulation interaction for the local FC; and (iv) the APOE ε4 carrier status-local FC interaction for the remote inter-network FC between the DMN and CEN, contributing higher cognitive performance in the APOE ε4 carrier with higher inter-network FC. Finally, these results were modulated according to Aβ positivity.ConclusionThis study is the first attempt to thoroughly examine the influence of the APOE ε4 carrier status from the subthreshold to positive Aβ accumulation during the preclinical phase.
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Affiliation(s)
- Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Yoo Hyun Um
- Department of Psychiatry, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Nak-Young Kim
- Department of Psychiatry, Keyo Hospital, Uiwang, South Korea
| | - Chang Uk Lee
- Department 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
- *Correspondence: Hyun Kook Lim
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Liu L, Wang T, Du X, Zhang X, Xue C, Ma Y, Wang D. Concurrent Structural and Functional Patterns in Patients With Amnestic Mild Cognitive Impairment. Front Aging Neurosci 2022; 14:838161. [PMID: 35663572 PMCID: PMC9161636 DOI: 10.3389/fnagi.2022.838161] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 04/01/2022] [Indexed: 11/13/2022] Open
Abstract
Amnestic mild cognitive impairment (aMCI) is a clinical subtype of MCI, which is known to have a high risk of developing Alzheimer's disease (AD). Although neuroimaging studies have reported brain abnormalities in patients with aMCI, concurrent structural and functional patterns in patients with aMCI were still unclear. In this study, we combined voxel-based morphometry (VBM), amplitude of low-frequency fluctuations (ALFFs), regional homogeneity (Reho), and resting-state functional connectivity (RSFC) approaches to explore concurrent structural and functional alterations in patients with aMCI. We found that, compared with healthy controls (HCs), both ALFF and Reho were decreased in the right superior frontal gyrus (SFG_R) and right middle frontal gyrus (MFG_R) of patients with aMCI, and both gray matter volume (GMV) and Reho were decreased in the left inferior frontal gyrus (IFG_L) of patients with aMCI. Furthermore, we took these overlapping clusters from VBM, ALFF, and Reho analyses as seed regions to analyze RSFC. We found that, compared with HCs, patients with aMCI had decreased RSFC between SFG_R and the right temporal lobe (subgyral) (TL_R), the MFG_R seed and left superior temporal gyrus (STG_L), left inferior parietal lobule (IPL_L), and right anterior cingulate cortex (ACC_R), the IFG_L seed and left precentral gyrus (PRG_L), left cingulate gyrus (CG_L), and IPL_L. These findings highlighted shared imaging features in structural and functional magnetic resonance imaging (MRI), suggesting that SFG_R, MFG_R, and IFG_L may play a major role in the pathophysiology of aMCI, which might be useful to better understand the underlying neural mechanisms of aMCI and AD.
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Affiliation(s)
- Li Liu
- Affiliated Mental Health Center, Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tenglong Wang
- School of Humanities and Management, Graduate School of Wannan Medical College, Wuhu, China
| | - Xiangdong Du
- Department of Geriatric Psychiatry, Suzhou Mental Health Center, Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Xiaobin Zhang
- Department of Geriatric Psychiatry, Suzhou Mental Health Center, Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Chuang Xue
- Affiliated Mental Health Center, Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yu Ma
- Department of Geriatric Psychiatry, Suzhou Mental Health Center, Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Dong Wang
- Department of Geriatric Psychiatry, Suzhou Mental Health Center, Suzhou Guangji Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
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Qiu T, Xie F, Zeng Q, Shen Z, Du G, Xu X, Wang C, Li X, Luo X, Li K, Huang P, Zhang T, Zhang J, Dai S, Zhang M. Interactions between cigarette smoking and cognitive status on functional connectivity of the cortico-striatal circuits in individuals without dementia: A resting-state functional MRI study. CNS Neurosci Ther 2022; 28:1195-1204. [PMID: 35506354 PMCID: PMC9253779 DOI: 10.1111/cns.13852] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 04/12/2022] [Accepted: 04/16/2022] [Indexed: 11/27/2022] Open
Abstract
Aims Cigarette smoking is a modifiable risk factor for Alzheimer's disease (AD), and controlling risk factors may curb the progression of AD. However, the underlying neural mechanisms of the effects of smoking on cognition remain largely unclear. Therefore, we aimed to explore the interaction effects of smoking × cognitive status on cortico‐striatal circuits, which play a crucial role in addiction and cognition, in individuals without dementia. Methods We enrolled 304 cognitively normal (CN) non‐smokers, 44 CN smokers, 130 mild cognitive impairment (MCI) non‐smokers, and 33 MCI smokers. The mixed‐effect analysis was performed to explore the interaction effects between smoking and cognitive status (CN vs. MCI) based on functional connectivity (FC) of the striatal subregions (caudate, putamen, and nucleus accumbens [NAc]). Results The significant interaction effects of smoking × cognitive status on FC of the striatal subregions were detected in the left inferior parietal lobule (IPL), bilateral cuneus, and bilateral anterior cingulate cortex (ACC). Specifically, increased FC of right caudate to left IPL was found in CN smokers compared with non‐smokers. The MCI smokers showed decreased FC of right caudate to left IPL and of right putamen to bilateral cuneus and increased FC of bilateral NAc to bilateral ACC compared with CN smokers and MCI non‐smokers. Furthermore, a positive correlation between FC of the NAc to ACC with language and memory was detected in MCI smokers. Conclusions Cigarette smoking could affect the function of cortico‐striatal circuits in patients with MCI. Our findings suggest that quitting smoking in the prodromal stage of AD may have the potential to prevent disease progression.
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Affiliation(s)
- Tiantian Qiu
- Department of Radiology, Linyi People's Hospital, Linyi, China
| | - Fei Xie
- Department of Equipment and Medical Engineering, Linyi People's Hospital, Linyi, China
| | - Qingze Zeng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhujing Shen
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Guijin Du
- Department of Radiology, Linyi People's Hospital, Linyi, China
| | - Xiaopei Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Chao Wang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaodong Li
- Department of Radiology, Linyi People's Hospital, Linyi, China
| | - Xiao Luo
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Kaicheng Li
- 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
| | - Tianyi Zhang
- Department of Neurology, Tongde Hospital of Zhejiang Province, Hangzhou, China
| | - Jinling Zhang
- Cancer Center, Linyi People's Hospital, Linyi, China
| | - Shouping Dai
- Department of Radiology, Linyi People's Hospital, Linyi, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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Oyarzabal EA, Hsu LM, Das M, Chao THH, Zhou J, Song S, Zhang W, Smith KG, Sciolino NR, Evsyukova IY, Yuan H, Lee SH, Cui G, Jensen P, Shih YYI. Chemogenetic stimulation of tonic locus coeruleus activity strengthens the default mode network. SCIENCE ADVANCES 2022; 8:eabm9898. [PMID: 35486721 PMCID: PMC9054017 DOI: 10.1126/sciadv.abm9898] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 03/15/2022] [Indexed: 05/31/2023]
Abstract
The default mode network (DMN) of the brain is functionally associated with a wide range of behaviors. In this study, we used functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and spectral fiber photometry to investigate the selective neuromodulatory effect of norepinephrine (NE)-releasing noradrenergic neurons in the locus coeruleus (LC) on the mouse DMN. Chemogenetic-induced tonic LC activity decreased cerebral blood volume (CBV) and glucose uptake and increased synchronous low-frequency fMRI activity within the frontal cortices of the DMN. Fiber photometry results corroborated these findings, showing that LC-NE activation induced NE release, enhanced calcium-weighted neuronal spiking, and reduced CBV in the anterior cingulate cortex. These data suggest that LC-NE alters conventional coupling between neuronal activity and CBV in the frontal DMN. We also demonstrated that chemogenetic activation of LC-NE neurons strengthened functional connectivity within the frontal DMN, and this effect was causally mediated by reduced modulatory inputs from retrosplenial and hippocampal regions to the association cortices of the DMN.
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Affiliation(s)
- Esteban A. Oyarzabal
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
- Curriculum in Neurobiology, University of North Carolina, Chapel Hill, NC, USA
| | - Li-Ming Hsu
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Manasmita Das
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Tzu-Hao Harry Chao
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Jingheng Zhou
- In Vivo Neurobiology Group, Neurobiology Laboratory, NIEHS/NIH, Research Triangle Park, NC, USA
| | - Sheng Song
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Weiting Zhang
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Kathleen G. Smith
- Developmental Neurobiology Group, Neurobiology Laboratory, NIEHS/NIH, Research Triangle Park, NC, USA
| | - Natale R. Sciolino
- Developmental Neurobiology Group, Neurobiology Laboratory, NIEHS/NIH, Research Triangle Park, NC, USA
| | - Irina Y. Evsyukova
- Developmental Neurobiology Group, Neurobiology Laboratory, NIEHS/NIH, Research Triangle Park, NC, USA
| | - Hong Yuan
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Sung-Ho Lee
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Guohong Cui
- In Vivo Neurobiology Group, Neurobiology Laboratory, NIEHS/NIH, Research Triangle Park, NC, USA
| | - Patricia Jensen
- Developmental Neurobiology Group, Neurobiology Laboratory, NIEHS/NIH, Research Triangle Park, NC, USA
| | - Yen-Yu Ian Shih
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
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Gao F. Integrated Positron Emission Tomography/Magnetic Resonance Imaging in clinical diagnosis of Alzheimer's disease. Eur J Radiol 2021; 145:110017. [PMID: 34826792 DOI: 10.1016/j.ejrad.2021.110017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/30/2021] [Accepted: 10/31/2021] [Indexed: 12/01/2022]
Abstract
Alzheimer's disease (AD), a progressive neurodegenerative disease which seriously endangers the health of the aged, is the most common etiology of senile dementia. With the increasing progress of neuroimaging technology, more and more imaging methods have been applied to study Alzheimer's disease. The emergence of integrated PET/MRI (Positron Emission Tomography/Magnetic Resonance Imaging) is a major advance in multimodal molecular imaging with many advantages on the structure of resolution and contrast of image over computed tomography (CT), PET and MRI. PET/MRI is now used stepwise in neurodegenerative diseases, and also has broad prospect of application in the early diagnosis of AD. In this review, we emphatically introduce the imaging advances of AD including functional imaging and molecular imaging, the advantages of PET/MRI over other imaging methods and prospects of PET/MRI in AD clinical diagnosis, especially in early diagnosis, clinical assessment and prediction on AD.
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Affiliation(s)
- Feng Gao
- Key Laboratory for Experimental Teratology of the Ministry of Education and Center for Experimental Nuclear Medicine, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.
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31
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The potential roles of excitatory-inhibitory imbalances and the repressor element-1 silencing transcription factor in aging and aging-associated diseases. Mol Cell Neurosci 2021; 117:103683. [PMID: 34775008 DOI: 10.1016/j.mcn.2021.103683] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 11/02/2021] [Accepted: 11/06/2021] [Indexed: 12/28/2022] Open
Abstract
Disruptions to the central excitatory-inhibitory (E/I) balance are thought to be related to aging and underlie a host of neural pathologies, including Alzheimer's disease. Aging may induce an increase in excitatory signaling, causing an E/I imbalance, which has been linked to shorter lifespans in mice, flies, and worms. In humans, extended longevity correlates to greater repression of genes involved in excitatory neurotransmission. The repressor element-1 silencing transcription factor (REST) is a master regulator in neural cells and is believed to be upregulated with senescent stimuli, whereupon it counters hyperexcitability, insulin/insulin-like signaling pathway activity, oxidative stress, and neurodegeneration. This review examines the putative mechanisms that distort the E/I balance with aging and neurodegeneration, and the putative roles of REST in maintaining neuronal homeostasis.
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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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Wang P, Zhou B, Yao H, Xie S, Feng F, Zhang Z, Guo Y, An N, Zhou Y, Zhang X, Liu Y. Aberrant Hippocampal Functional Connectivity Is Associated with Fornix White Matter Integrity in Alzheimer's Disease and Mild Cognitive Impairment. J Alzheimers Dis 2021; 75:1153-1168. [PMID: 32390630 DOI: 10.3233/jad-200066] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common cause of dementia in older individuals, and amnestic mild cognitive impairment (aMCI) is currently considered the prodromal stage of AD. The hippocampus and fornix interact functionally and structurally, with the fornix being the major efferent white matter tract from the hippocampus. OBJECTIVE The main aim of this study was to examine the impairments present in subjects with AD or aMCI and the relationship of these impairments with the microstructure of the fornix and the functional connectivity (FC) and gray matter volume of the hippocampus. METHODS Forty-four AD, 34 aMCI, and 41 age- and gender-matched normal controls (NCs) underwent neuropsychological assessments and multimode MRI. We chose the bilateral hippocampi as the region of interest in which gray matter alterations and FC with the whole brain were assessed and the fornix body as the region of interest in which the microstructural integrity of the white matter was observed. We also evaluated the relationship among gray matter alterations, the abnormal FC of the hippocampus and the integrity of the fornix in AD/aMCIResults:Compared to the NC group, the AD and aMCI groups demonstrated decreased gray matter volume, reduced FC between the bilateral hippocampi and several brain regions in the default mode network and control network, and damaged integrity of the fornix body (decreased fractional anisotropy and increased diffusivity). We also found that left hippocampal FC with some regions, the integrity of the fornix body, and cognition ability were significantly correlated. Therefore, our findings suggest that damage to white matter integrity may partially explain the reduced resting-state FC of the hippocampus in AD and aMCI. CONCLUSION AD and aMCI are diseases of disconnectivity including not only functional but also structural disconnectivity. Damage to white matter integrity may partially explain the reduced resting-state FC in AD and aMCI. These findings have significant implications for diagnostics and modeling and provide insights for understanding the disconnection syndrome in AD.
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Affiliation(s)
- Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Nankai University, Tianjin, China.,Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Bo Zhou
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Hongxiang Yao
- Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Sangma Xie
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Feng Feng
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Zengqiang Zhang
- Hainan Hospital of Chinese PLA General Hospital, Sanya, China
| | - Yan'e Guo
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Ningyu An
- Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital, Nankai University, Tianjin, China
| | - Xi Zhang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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Xue C, Sun H, Yue Y, Wang S, Qi W, Hu G, Ge H, Yuan Q, Rao J, Tian L, Xiao C, Chen J. Structural and Functional Disruption of Salience Network in Distinguishing Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment. ACS Chem Neurosci 2021; 12:1384-1394. [PMID: 33825444 DOI: 10.1021/acschemneuro.1c00051] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Salience network (SN), playing a vital role in advanced cognitive function, is regarded to be impaired in subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI). The purpose of the study was to explore the importance of structural and functional features of SN in the diagnosis of SCD and aMCI. Structural and resting-state functional magnetic resonance imaging were collected from SCD, aMCI, and healthy control (HC). Cortex thickness, gray matter (GM) volume, spontaneous brain activity, functional connectivity (FC) within SN, and its relationship with cognitive function were analyzed. Moreover, the receiver operating characteristic analysis was performed to assess diagnostic efficacy of altered indictors for SCD and aMCI. Compared to HC, both SCD and aMCI showed decreased GM volume, decreased spontaneous brain activity, and increased FC within SN, while aMCI showed additional decreased cortex thickness. Furthermore, the altered FC in SCD and aMCI was significantly correlated with cognitive function. Particularly, the best-fitting classification models of SCD and aMCI were based on the combined multiple indicators. In conclusion, structure and function of SN were disrupted in SCD and aMCI, which involved in cognitive decline. The combined multiple indicators of SN provided powerful biomarkers for the diagnosis of SCD and aMCI.
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Affiliation(s)
- Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu210029, China
| | - Haiting Sun
- Department of Pediatrics, Xijing Hospital, the Fourth Milit ary Medical University (Air Force Medical University), Xi’an, Shaanxi 710032, China
| | - Yingying Yue
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Siyu Wang
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Wenzhang Qi
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu210029, China
| | - Guanjie Hu
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Honglin Ge
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Qianqian Yuan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu210029, China
| | - Jiang Rao
- Department of Rehabilitation, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Lei Tian
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu210029, China
| | - Chaoyong Xiao
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu210029, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, 210029, China
| | - Jiu Chen
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, Jiangsu, 210029, China
- Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
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El-Sappagh S, Alonso JM, Islam SMR, Sultan AM, Kwak KS. A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease. Sci Rep 2021; 11:2660. [PMID: 33514817 PMCID: PMC7846613 DOI: 10.1038/s41598-021-82098-3] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 12/29/2020] [Indexed: 01/30/2023] Open
Abstract
Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.
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Affiliation(s)
- Shaker El-Sappagh
- Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain.
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, 13518, Egypt.
| | - Jose M Alonso
- Centro Singular de Investigación en Tecnoloxías Intelixentes, Universidade de Santiago de Compostela, 15703, Santiago, Spain
| | - S M Riazul Islam
- Department of Computer Science and Engineering, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul, 05006, Korea
| | - Ahmad M Sultan
- Gastrointestinal Surgical Center, Faculty of Medicine, Mansoura University, Mansura, 35516, Egypt
| | - Kyung Sup Kwak
- Department of Information and Communication Engineering, Inha University, Incheon, 22212, South Korea.
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Sendi MSE, Zendehrouh E, Miller RL, Fu Z, Du Y, Liu J, Mormino EC, Salat DH, Calhoun VD. Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study. Front Neural Circuits 2021; 14:593263. [PMID: 33551754 PMCID: PMC7859281 DOI: 10.3389/fncir.2020.593263] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 12/15/2020] [Indexed: 12/24/2022] Open
Abstract
Background Alzheimer's disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart. Method We used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM. Results All states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks. Conclusion Our results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.
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Affiliation(s)
- Mohammad S. E. Sendi
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Elaheh Zendehrouh
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Robyn L. Miller
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Yuhui Du
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - Jingyu Liu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
| | - Elizabeth C. Mormino
- School of Medicine, Stanford University, Palo Alto, CA, United States
- Department of Neurology and Neurological Sciences, School of Medicine, Stanford University, Stanford, CA, United States
| | - David H. Salat
- Harvard Medical School, Cambridge, MA, United States
- Massachusetts General Hospital, Boston, MA, United States
| | - Vince D. Calhoun
- Wallace H. Coulter Department of Biomedical Engineering at Georgia Institute of Technology and Emory University, Atlanta, GA, United States
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
- Department of Computer Science, Georgia State University, Atlanta, GA, United States
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Yang T, Shen B, Wu A, Tang X, Chen W, Zhang Z, Chen B, Guo Z, Liu X. Abnormal Functional Connectivity of the Amygdala in Mild Cognitive Impairment Patients With Depression Symptoms Revealed by Resting-State fMRI. Front Psychiatry 2021; 12:533428. [PMID: 34335316 PMCID: PMC8319717 DOI: 10.3389/fpsyt.2021.533428] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 05/31/2021] [Indexed: 11/28/2022] Open
Abstract
Convergent evidence indicates that individuals with symptoms of depression exhibit altered functional connectivity (FC) of the amygdala, which is a key brain region in processing emotions. At present, the characteristics of amygdala functional circuits in patients with mild cognitive impairment (MCI) with and without depression are not clear. The current study examined the features of amygdala FC in patients with MCI with depression symptoms (D-MCI) using resting-state functional magnetic resonance imaging. We acquired resting-state functional magnetic resonance imaging data from 16 patients with D-MCI, 18 patients with MCI with no depression (nD-MCI), and 20 healthy controls (HCs) using a 3T scanner and compared the strength of amygdala FC between the three groups. Patients with D-MCI exhibited significant FC differences in the amygdala-medial prefrontal cortex and amygdala-sensorimotor networks. These results suggest that the dysfunction of the amygdala-medial prefrontal cortex network and the amygdala-sensorimotor network might be involved in the neural mechanism underlying depression in MCI.
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Affiliation(s)
- Ting Yang
- The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
| | - Bangli Shen
- The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
| | - Aiqin Wu
- The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
| | - Xinglu Tang
- The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
| | - Wei Chen
- Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | | | - Bo Chen
- Tongde Hospital of Zhejiang, Hangzhou, China
| | | | - Xiaozheng Liu
- The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
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Amaefule CO, Dyrba M, Wolfsgruber S, Polcher A, Schneider A, Fliessbach K, Spottke A, Meiberth D, Preis L, Peters O, Incesoy EI, Spruth EJ, Priller J, Altenstein S, Bartels C, Wiltfang J, Janowitz D, Bürger K, Laske C, Munk M, Rudolph J, Glanz W, Dobisch L, Haynes JD, Dechent P, Ertl-Wagner B, Scheffler K, Kilimann I, Düzel E, Metzger CD, Wagner M, Jessen F, Teipel SJ. Association between composite scores of domain-specific cognitive functions and regional patterns of atrophy and functional connectivity in the Alzheimer's disease spectrum. Neuroimage Clin 2020; 29:102533. [PMID: 33360018 PMCID: PMC7770965 DOI: 10.1016/j.nicl.2020.102533] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 11/24/2020] [Accepted: 12/12/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Cognitive decline has been found to be associated with gray matter atrophy and disruption of functional neural networks in Alzheimer's disease (AD) in structural and functional imaging (fMRI) studies. Most previous studies have used single test scores of cognitive performance among monocentric cohorts. However, cognitive domain composite scores could be more reliable than single test scores due to the reduction of measurement error. Adopting a multicentric resting state fMRI (rs-fMRI) and cognitive domain approach, we provide a comprehensive description of the structural and functional correlates of the key cognitive domains of AD. METHOD We analyzed MRI, rs-fMRI and cognitive domain score data of 490 participants from an interim baseline release of the multicenter DELCODE study cohort, including 54 people with AD, 86 with Mild Cognitive Impairment (MCI), 175 with Subjective Cognitive Decline (SCD), and 175 Healthy Controls (HC) in the AD-spectrum. Resulting cognitive domain composite scores (executive, visuo-spatial, memory, working memory and language) from the DELCODE neuropsychological battery (DELCODE-NP), were previously derived using confirmatory factor analysis. Statistical analyses examined the differences between diagnostic groups, and the association of composite scores with regional atrophy and network-specific functional connectivity among the patient subgroup of SCD, MCI and AD. RESULT Cognitive performance, atrophy patterns and functional connectivity significantly differed between diagnostic groups in the AD-spectrum. Regional gray matter atrophy was positively associated with visuospatial and other cognitive impairments among the patient subgroup in the AD-spectrum. Except for the visual network, patterns of network-specific resting-state functional connectivity were positively associated with distinct cognitive impairments among the patient subgroup in the AD-spectrum. CONCLUSION Consistent associations between cognitive domain scores and both regional atrophy and network-specific functional connectivity (except for the visual network), support the utility of a multicentric and cognitive domain approach towards explicating the relationship between imaging markers and cognition in the AD-spectrum.
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Affiliation(s)
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital, Bonn, Germany
| | | | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital, Bonn, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital, Bonn, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Dix Meiberth
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Lukas Preis
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Enise I Incesoy
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Eike J Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Claudia Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG), Goettingen, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany; Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG), Goettingen, Germany; Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilians University, Munich, Germany
| | - Katharina Bürger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilians University, Munich, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany; Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
| | - Matthias Munk
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
| | - Janna Rudolph
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - John D Haynes
- Bernstein Center for Computational Neuroscience, Charité - Universitätsmedizin, Berlin, Germany
| | - Peter Dechent
- MR-Research in Neurology and Psychiatry, Georg-August-University Goettingen, Germany
| | - Birgit Ertl-Wagner
- Institute for Clinical Radiology, Ludwig Maximilians University, Munich, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Coraline D Metzger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany; Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital, Bonn, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Psychiatry, University of Cologne, Cologne, Germany; Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
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Kumar D, Vipin A, Wong B, Ng KP, Kandiah N. Differential Effects of Confluent and Nonconfluent White Matter Hyperintensities on Functional Connectivity in Mild Cognitive Impairment. Brain Connect 2020; 10:547-554. [PMID: 33050714 DOI: 10.1089/brain.2020.0784] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Background: White matter hyperintensities (WMHs) indicate active small vessel disease. Emerging evidence suggests that confluent WMH (C-WMH) results in greater cognitive impairment compared with nonconfluent WMH (NC-WMH) visualized as punctate lesions. However, the mechanism linking C-WMH and early cognitive impairment is not clearly understood. Aims: To investigate the effects of C-WMH and NC-WMH on whole-brain functional connectivity (FC) across 138 regions of interest (ROIs) and cognition in 63 subjects with mild cognitive impairment (MCI). Methods: MCI subjects were classified as C-WMH or NC-WMH using the Staals criteria on the Fazekas WMH scale. Group-level ROI-to-ROI FC trends and differences based on WMH subtypes were computed using standard resting-state functional magnetic resonance imaging analysis. Global cognitive performance was measured with mini-mental state examination (MMSE). Results: Subjects with C-WMH exhibited increased inter-regional FC in the fronto-parietal, fronto-occipital, parieto-occipital, and temporo-parietal regions of the salience, dorsal-attention, default-mode, and visual networks compared with NC-WMH. Increased intra-regional FC was also observed within the frontal and parietal lobes in C-WMH. In addition to widespread increased FC in C-WMH, a few regions in the fronto-temporal and intra-frontal areas demonstrated reduced FC in C-WMH compared with NC-WMH. Analyses of cognitive correlates demonstrated increased parieto-occipital FC to be negatively associated with MMSE in the C-WMH. The increased parieto-occipital FC may be related to loss of higher order inhibitory control in the parieto-occipital regions induced by C-WMH or alternatively a compensatory mechanism to FC alterations induced by C-WMH. Conclusion: C-WMH in subjects with MCI is associated with widespread increase in intra- and inter-regional FC. These findings provide novel insights into divergent FC related to confluence of WMH in MCI. Impact statement White matter hyperintensities (WMHs) have been demonstrated to be a major risk factor for progressive cognitive decline. However, the relationship between structural and functional brain changes related to different types of WMH lesions as well as different stages of WMH progression remains unclear. In this study, we demonstrate that confluent WMH is significantly associated with divergent functional connectivity changes in patients with mild cognitive impairment (MCI). This finding may allow identification of MCI subjects who are adversely affected by WMH and thus provides a window of opportunity to introduce pharmacological and nonpharmacological interventional measures.
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Affiliation(s)
- Dilip Kumar
- National Neuroscience Institute, Neurology and Research Departments, Singapore, Singapore
| | - Ashwati Vipin
- National Neuroscience Institute, Neurology and Research Departments, Singapore, Singapore
| | - Benjamin Wong
- National Neuroscience Institute, Neurology and Research Departments, Singapore, Singapore
| | - Kok Pin Ng
- National Neuroscience Institute, Neurology and Research Departments, Singapore, Singapore.,Duke-NUS Medical School, Neuroscience Academic Clinical Programme, Singapore, Singapore
| | - Nagaendran Kandiah
- National Neuroscience Institute, Neurology and Research Departments, Singapore, Singapore.,Duke-NUS Medical School, Neuroscience Academic Clinical Programme, Singapore, Singapore.,NTU-Imperial Lee Kong Chian School of Medicine, Faculty, Singapore, Singapore
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Qi H, Hu Y, Lv Y, Wang P. Primarily Disrupted Default Subsystems Cause Impairments in Inter-system Interactions and a Higher Regulatory Burden in Alzheimer's Disease. Front Aging Neurosci 2020; 12:593648. [PMID: 33262699 PMCID: PMC7686542 DOI: 10.3389/fnagi.2020.593648] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 10/26/2020] [Indexed: 12/03/2022] Open
Abstract
Background: Intrinsically organized large-scale brain networks and their interactions support complex cognitive function. Investigations suggest that the default network (DN) is the earliest disrupted network and that the frontoparietal control network (FPCN) and dorsal attention network (DAN) are subsequently impaired in Alzheimer's disease (AD). These large-scale networks comprise different subsystems (DN: medial temporal lobe (MTL), dorsomedial prefrontal cortex (DM) subsystems and a Core; FPCN: FPCNA and FPCNB). Our previous research has indicated that different DN subsystems are not equally damaged in AD. However, changes in the patterns of interactions among these large-scale network subsystems and the underlying cause of the alterations in AD remain unclear. We hypothesized that disrupted DN subsystems cause specific impairments in inter-system interactions and a higher regulatory burden for the FPCNA. Method: To test this hypothesis, Granger causality analysis (GCA) was performed to explore effective functional connectivity (FC) pattern of these networks. The regional information flow strength (IFS) was calculated and compared across groups to explore changes in the subsystems and their inter-system interactions and the relationship between them. To investigate specific inter-system changes, we summed the inter-system IFS and performed correlation analyses of the bidirectional inter-system IFS, which was compared across groups. Additionally, correlation analyses of dynamic effective FC patterns were performed to reveal alterations in the temporal co-evolution of sets of inter-subsystem interactions. Furthermore, we used partial correlation analysis to quantify the FPCN's regulatory effects. Finally, we applied a support vector machine (SVM) linear classifier to probe which network most effectively discriminated patients from controls. Results: Compared with controls, AD patients showed a decreased intra-DN regional IFS, which was significantly related to the inter-network's IFS. The IFS between the DN subsystems and FPCN subsystems/DAN decreased. Critically, the correlation values of the decreased bidirectional IFS between the DN subsystems and FPCNA diminished. Additionally, the Core and DM play pivotal roles in disordered temporal co-evolution. Furthermore, the FPCNA showed enhanced regulation of the Core. Finally, the MTL subsystem and Core were effective at discriminating patients from controls. Conclusion: The predominantly disrupted DN subsystems caused impaired inter-system interactions and created a higher regulatory burden for the FPCNA.
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Affiliation(s)
- Huihui Qi
- Department of Medical Imaging, Tongji Hospital, Tongji University School of Medicine, Tongji University, Shanghai, China
| | - Yang Hu
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingru Lv
- Department of Imaging, Huashan Hospital, Fudan University, Shanghai, China
| | - Peijun Wang
- Department of Medical Imaging, Tongji Hospital Affiliated With Tongji University, Shanghai, China
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Soman SM, Raghavan S, Rajesh P, Mohanan N, Thomas B, Kesavadas C, Menon RN. Does resting state functional connectivity differ between mild cognitive impairment and early Alzheimer's dementia? J Neurol Sci 2020; 418:117093. [DOI: 10.1016/j.jns.2020.117093] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Revised: 06/27/2020] [Accepted: 08/10/2020] [Indexed: 10/23/2022]
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Li W, Wen W, Chen X, Ni B, Lin X, Fan W, The Alzheimer's Disease Neuroimaging Initiative. Functional Evolving Patterns of Cortical Networks in Progression of Alzheimer's Disease: A Graph-Based Resting-State fMRI Study. Neural Plast 2020; 2020:7839536. [PMID: 32684923 PMCID: PMC7341396 DOI: 10.1155/2020/7839536] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Accepted: 04/22/2020] [Indexed: 11/18/2022] Open
Abstract
AD is a common chronic progressive neurodegenerative disorder. However, the understanding of the dynamic longitudinal change of the brain in the progression of AD is still rough and sometimes conflicting. This paper analyzed the brain networks of healthy people and patients at different stages (EMCI, LMCI, and AD). The results showed that in global network properties, most differences only existed between healthy people and patients, and few were discovered between patients at different stages. However, nearly all subnetwork properties showed significant differences between patients at different stages. Moreover, the most interesting result was that we found two different functional evolving patterns of cortical networks in progression of AD, named 'temperature inversion' and "monotonous decline," but not the same monotonous decline trend as the external functional assessment observed in the course of disease progression. We suppose that those subnetworks, showing the same functional evolving pattern in AD progression, may have something the same in work mechanism in nature. And the subnetworks with 'temperature inversion' evolving pattern may play a special role in the development of AD.
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Affiliation(s)
- Wei Li
- The School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Wen Wen
- The School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xi Chen
- The School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China
| | - BingJie Ni
- The School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xuefeng Lin
- The School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Image Processing and Intelligent Control Key Laboratory of Education Ministry of China, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Wenliang Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
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Hakimdavoodi H, Amirmazlaghani M. Using autoregressive-dynamic conditional correlation model with residual analysis to extract dynamic functional connectivity. J Neural Eng 2020; 17:035008. [DOI: 10.1088/1741-2552/ab965b] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Dou X, Yao H, Feng F, Wang P, Zhou B, Jin D, Yang Z, Li J, Zhao C, Wang L, An N, Liu B, Zhang X, Liu Y. Characterizing white matter connectivity in Alzheimer's disease and mild cognitive impairment: An automated fiber quantification analysis with two independent datasets. Cortex 2020; 129:390-405. [PMID: 32574842 DOI: 10.1016/j.cortex.2020.03.032] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 12/13/2019] [Accepted: 03/31/2020] [Indexed: 12/28/2022]
Abstract
Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia. Diffusion tensor imaging (DTI) has been widely used to show structural integrity and delineate white matter (WM) degeneration in AD. The automated fiber quantification (AFQ) method is a fully automated approach that can rapidly and reliably identify major WM fiber tracts and evaluate WM properties. The main aim of this study was to assess WM integrity and abnormities in a cohort of patients with amnestic mild cognitive impairment (aMCI) and AD as well as normal controls (NCs). For this purpose, we first used AFQ to identify 20 major WM tracts and assessed WM integrity and abnormalities in a cohort of 120 subjects (39 NCs, 34 aMCI patients and 47 AD patients) in a discovery dataset and 122 subjects (43 NCs, 37 aMCI patients and 42 AD patients) in a replicated dataset. Pointwise differences along WM tracts were identified in the discovery dataset and simultaneously confirmed in the replicated dataset. Next, we investigated the utility of DTI measures along WM tracts as features to distinguish patients with AD from NCs via multilevel cross validation using a support vector machine. Correlation analysis revealed the identified microstructural WM alterations and classification output to be highly associated with cognitive ability in the patient groups, suggesting that they may be a robust biomarker of AD. This systematic study provides a pipeline to examine WM integrity and its potential clinical application in AD and may be useful for studying other neurological and psychiatric disorders.
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Affiliation(s)
- Xuejiao Dou
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China
| | - Hongxiang Yao
- Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Feng Feng
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, 300350, China; Department of Neurology, Nankai University Huanhu Hospital, Tianjin, China
| | - Bo Zhou
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Dan Jin
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China
| | - Zhengyi Yang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jin Li
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Cui Zhao
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Luning Wang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Ningyu An
- Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China
| | - Bing Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Xi Zhang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, China.
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
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Increased intrinsic default-mode network activity as a compensatory mechanism in aMCI: a resting-state functional connectivity MRI study. Aging (Albany NY) 2020; 12:5907-5919. [PMID: 32238610 PMCID: PMC7185142 DOI: 10.18632/aging.102986] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 03/24/2020] [Indexed: 11/25/2022]
Abstract
Numerous studies have investigated the differences in the mean functional connectivity (FC) strength between amnestic mild cognitive impairment (aMCI) patients and normal subjects using resting-state functional magnetic resonance imaging. However, whether the mean FC is increased, decreased or unchanged in aMCI patients compared to normal controls remains unclear. Two factors might lead to inconsistent results: the determination of regions of interest and the reliability of the FC. We explored differences in FC and the degree centrality (Dc) constructed by the bootstrap method, between and within networks (default-mode network (DN), frontoparietal control network (CN), dorsal attention network (AN)), and resulting from a hierarchical-clustering algorithm. The mean FC within the DN and CN was significantly increased (P < 0.05, uncorrected) in patients. Significant increases (P < 0.05, uncorrected) in the mean FC were found in patients between DN and CN and between DN and AN. Five pairs of FC (false discovery rate corrected) and the Dc of six regions (Bonferroni corrected) displayed a significant increase in patients. Lower cognitive ability was significantly associated with a greater increase in the Dc of the left superior temporal sulcus. Our results demonstrate that the early dysfunctions in aMCI disease are mainly compensatory impairments.
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Misiura MB, Howell JC, Wu J, Qiu D, Parker MW, Turner JA, Hu WT. Race modifies default mode connectivity in Alzheimer's disease. Transl Neurodegener 2020; 9:8. [PMID: 32099645 PMCID: PMC7029517 DOI: 10.1186/s40035-020-0186-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 02/04/2020] [Indexed: 12/11/2022] Open
Abstract
Background Older African Americans are more likely to develop Alzheimer's disease (AD) than older Caucasians, and this difference cannot be readily explained by cerebrovascular and socioeconomic factors alone. We previously showed that mild cognitive impairment and AD dementia were associated with attenuated increases in the cerebrospinal fluid (CSF) levels of total and phosphorylated tau in African Americans compared to Caucasians, even though there was no difference in beta-amyloid 1-42 level between the two races. Methods We extended our work by analyzing early functional magnetic resonance imaging (fMRI) biomarkers of the default mode network in older African Americans and Caucasians. We calculated connectivity between nodes of the regions belonging to the various default mode network subsystems and correlated these imaging biomarkers with non-imaging biomarkers implicated in AD (CSF amyloid, total tau, and cognitive performance). Results We found that race modifies the relationship between functional connectivity of default mode network subsystems and cognitive performance, tau, and amyloid levels. Conclusion These findings provide further support that race modifies the AD phenotypes downstream from cerebral amyloid deposition, and identifies key inter-subsystem connections for deep imaging and neuropathologic characterization.
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Affiliation(s)
- Maria B Misiura
- 1Department of Psychology, Georgia State University, Atlanta, GA USA.,2Departments of Neurology, Emory University, 615 Michael Street, Suite 505, Atlanta, GA 30322 USA
| | - J Christina Howell
- 2Departments of Neurology, Emory University, 615 Michael Street, Suite 505, Atlanta, GA 30322 USA
| | - Junjie Wu
- 3Departments of Radiology, Emory University, Atlanta, GA USA
| | - Deqiang Qiu
- 3Departments of Radiology, Emory University, Atlanta, GA USA
| | - Monica W Parker
- 2Departments of Neurology, Emory University, 615 Michael Street, Suite 505, Atlanta, GA 30322 USA
| | - Jessica A Turner
- 1Department of Psychology, Georgia State University, Atlanta, GA USA
| | - William T Hu
- 2Departments of Neurology, Emory University, 615 Michael Street, Suite 505, Atlanta, GA 30322 USA
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Taylor JL, Hambro BC, Strossman ND, Bhatt P, Hernandez B, Ashford JW, Cheng JJ, Iv M, Adamson MM, Lazzeroni LC, McNerney MW. The effects of repetitive transcranial magnetic stimulation in older adults with mild cognitive impairment: a protocol for a randomized, controlled three-arm trial. BMC Neurol 2019; 19:326. [PMID: 31842821 PMCID: PMC6912947 DOI: 10.1186/s12883-019-1552-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 12/03/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Mild Cognitive Impairment (MCI) carries a high risk of progression to Alzheimer's disease (AD) dementia. Previous clinical trials testing whether cholinesterase inhibitors can slow the rate of progression from MCI to AD dementia have yielded disappointing results. However, recent studies of the effects of repetitive transcranial magnetic stimulation (rTMS) in AD have demonstrated improvements in cognitive function. Because few rTMS trials have been conducted in MCI, we designed a trial to test the short-term efficacy of rTMS in MCI. Yet, in both MCI and AD, we know little about what site of stimulation would be ideal for improving cognitive function. Therefore, two cortical sites will be investigated in this trial: (1) the dorsolateral prefrontal cortex (DLPFC), which has been well studied for treatment of major depressive disorder; and (2) the lateral parietal cortex (LPC), a novel site with connectivity to AD-relevant limbic regions. METHODS/DESIGN In this single-site trial, we plan to enroll 99 participants with single or multi-domain amnestic MCI. We will randomize participants to one of three groups: (1) Active DLPFC rTMS; (2) Active LPC rTMS; and (3) Sham rTMS (evenly split between DLPFC and LPC locations). After completing 20 bilateral rTMS treatment sessions, participants will be followed for 6 months to test short-term efficacy and track durability of effects. The primary efficacy measure is the California Verbal Learning Test-II (CVLT-II), assessed 1 week after intervention. Secondary analyses will examine effects of rTMS on other cognitive measures, symptoms of depression, and brain function with respect to the site of stimulation. Finally, selected biomarkers will be analyzed to explore predictors of response and mechanisms of action. DISCUSSION The primary aim of this trial is to test the short-term efficacy of rTMS in MCI. Additionally, the project will provide information on the durability of cognitive effects and potentially distinct effects of stimulating DLPFC versus LPC regions. Future efforts would be directed toward better understanding therapeutic mechanisms and optimizing rTMS for treatment of MCI. Ultimately, if rTMS can be utilized to slow the rate of progression to AD dementia, this will be a significant advancement in the field. TRIAL REGISTRATION Clinical Trials NCT03331796. Registered 6 November 2017, https://clinicaltrials.gov/ct2/show/NCT03331796. All items from the World Health Organization Trial Registration Data Set are listed in Appendix A. PROTOCOL VERSION This report is based on version 1, approved by the DSMB on 30 November, 2017 and amended on 14 August, 2018 and 19 September, 2019.
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Affiliation(s)
- Joy L. Taylor
- US Department of Veterans Affairs (VA) Palo Alto Health Care System (151Y), Sierra-Pacific Mental Illness Research Education Clinical Center (MIRECC), 3801 Miranda Avenue, Palo Alto, CA 94304-1207 USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, School of Medicine, Stanford, CA USA
| | - Benjamin C. Hambro
- US Department of Veterans Affairs (VA) Palo Alto Health Care System (151Y), Sierra-Pacific Mental Illness Research Education Clinical Center (MIRECC), 3801 Miranda Avenue, Palo Alto, CA 94304-1207 USA
| | - Nicole D. Strossman
- US Department of Veterans Affairs (VA) Palo Alto Health Care System (151Y), Sierra-Pacific Mental Illness Research Education Clinical Center (MIRECC), 3801 Miranda Avenue, Palo Alto, CA 94304-1207 USA
| | - Priyanka Bhatt
- US Department of Veterans Affairs (VA) Palo Alto Health Care System (151Y), Sierra-Pacific Mental Illness Research Education Clinical Center (MIRECC), 3801 Miranda Avenue, Palo Alto, CA 94304-1207 USA
| | - Beatriz Hernandez
- US Department of Veterans Affairs (VA) Palo Alto Health Care System (151Y), Sierra-Pacific Mental Illness Research Education Clinical Center (MIRECC), 3801 Miranda Avenue, Palo Alto, CA 94304-1207 USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, School of Medicine, Stanford, CA USA
| | - J. Wesson Ashford
- Department of Psychiatry and Behavioral Sciences, Stanford University, School of Medicine, Stanford, CA USA
- War Related Illness and Injury Study Center (WRIISC), VA Palo Alto Health Care System, Palo Alto, CA USA
| | - Jauhtai Joseph Cheng
- US Department of Veterans Affairs (VA) Palo Alto Health Care System (151Y), Sierra-Pacific Mental Illness Research Education Clinical Center (MIRECC), 3801 Miranda Avenue, Palo Alto, CA 94304-1207 USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, School of Medicine, Stanford, CA USA
| | - Michael Iv
- Department of Radiology, Division of Neuroimaging and Neurointervention, Stanford University Medical Center, Stanford, CA USA
| | - Maheen M. Adamson
- Department of Psychiatry and Behavioral Sciences, Stanford University, School of Medicine, Stanford, CA USA
- Defense and Veterans Brain Injury Center and Polytrauma (DVBIC), VA Palo Alto Health Care System, Palo Alto, CA USA
| | - Laura C. Lazzeroni
- Department of Psychiatry and Behavioral Sciences, Stanford University, School of Medicine, Stanford, CA USA
| | - Margaret Windy McNerney
- US Department of Veterans Affairs (VA) Palo Alto Health Care System (151Y), Sierra-Pacific Mental Illness Research Education Clinical Center (MIRECC), 3801 Miranda Avenue, Palo Alto, CA 94304-1207 USA
- Department of Psychiatry and Behavioral Sciences, Stanford University, School of Medicine, Stanford, CA USA
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Bahrami M, Laurienti PJ, Simpson SL. Analysis of brain subnetworks within the context of their whole-brain networks. Hum Brain Mapp 2019; 40:5123-5141. [PMID: 31441167 DOI: 10.1002/hbm.24762] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 07/24/2019] [Accepted: 08/05/2019] [Indexed: 12/17/2022] Open
Abstract
Analyzing the structure and function of the brain from a network perspective has increased considerably over the past two decades, with regional subnetwork analyses becoming prominent in the recent literature. However, despite the fact that the brain, as a complex system of interacting subsystems (i.e., subnetworks), cannot be fully understood by analyzing its constituent parts as independent elements, most studies extract subnetworks from the whole and treat them as independent networks. This approach entails neglecting their interactions with other brain regions and precludes identifying potential compensatory mechanisms outside the analyzed subnetwork. In this study, using simulated and empirical data, we show that the analysis of brain subnetworks within the context of their whole-brain networks, that is, including their interactions with other brain regions, can yield different outcomes when compared to analyzing them as independent networks. We also provide a multivariate mixed-effects modeling framework that allows analyzing subnetworks within the context of their whole-brain networks, and show that it can better disentangle global (whole-brain) and local (subnetwork) differences when compared to standard t-test analyses. T-test analyses may produce misleading results in identifying complex global and local level differences. The provided multivariate model is an extension of a previously developed model for global, system-level hypotheses about the brain. The modified version detailed here provides the same utilities as the original model-quantifying the relationship between phenotypes and brain connectivity, comparing brain networks among groups, predicting brain connectivity from phenotypes, and simulating brain networks-but for local, subnetwork-level hypotheses.
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Affiliation(s)
- Mohsen Bahrami
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, North Carolina.,Department of Biomedical Engineering, Virginia Tech - Wake Forest School of Biomedical Engineering and Sciences, Winston-Salem, North Carolina
| | - Paul J Laurienti
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, North Carolina.,Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Sean L Simpson
- Laboratory for Complex Brain Networks, Wake Forest School of Medicine, Winston-Salem, North Carolina.,Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston Salem, North Carolina
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Joshi H, Bharath S, Balachandar R, Sadanand S, Vishwakarma HV, Aiyappan S, Saini J, Kumar KJ, John JP, Varghese M. Differentiation of Early Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Healthy Elderly Samples Using Multimodal Neuroimaging Indices. Brain Connect 2019; 9:730-741. [DOI: 10.1089/brain.2019.0676] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Affiliation(s)
- Himanshu Joshi
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Multimodal Brain Image Analysis Laboratory (MBIAL), Neurobiology Research Center (NRC), National Institute of Mental Health and Neurosciences, Bangalore, India
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Srikala Bharath
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Rakesh Balachandar
- Multimodal Brain Image Analysis Laboratory (MBIAL), Neurobiology Research Center (NRC), National Institute of Mental Health and Neurosciences, Bangalore, India
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Shilpa Sadanand
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Harshita V. Vishwakarma
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Subramoniam Aiyappan
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Multimodal Brain Image Analysis Laboratory (MBIAL), Neurobiology Research Center (NRC), National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Keshav J. Kumar
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Department of Clinical Psychology, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - John P. John
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Multimodal Brain Image Analysis Laboratory (MBIAL), Neurobiology Research Center (NRC), National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Mathew Varghese
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
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Wang J, Liu J, Wang Z, Sun P, Li K, Liang P. Dysfunctional interactions between the default mode network and the dorsal attention network in subtypes of amnestic mild cognitive impairment. Aging (Albany NY) 2019; 11:9147-9166. [PMID: 31645482 PMCID: PMC6834429 DOI: 10.18632/aging.102380] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 10/14/2019] [Indexed: 01/04/2023]
Abstract
An anticorrelated relationship in the spontaneous fluctuations between the default mode network (DMN) and dorsal attention network (DAN) is a robust feature of intrinsic brain organization in healthy individuals. Prior studies have reported a decreased anticorrelation between the DMN and the DAN in Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, it is unclear how this anticorrelation changes as MCI progresses to AD. We hypothesized that dysfunctional connectivity between the DMN and DAN may reflect the gradual decline from MCI to AD. To test this hypothesis, we investigated alterations in functional connectivity between the DMN and DAN in subtypes of amnestic MCI (aMCI) by comparing with the same functional pattern in healthy elderly individuals and patients with AD. We retrospectively collected brain imaging and neuropsychological data from 20 AD participants, 22 participants with multiple-domain aMCI (aMCI-m), 29 participants with single-domain aMCI (aMCI-s) and 23 sex-matched normal controls in this study. Resting-state functional connectivity analysis revealed that aMCI-s and aMCI-m groups demonstrated different magnitudes of increased anticorrelation between the DMN and DAN relative to the AD group. Furthermore, in aMCI-s, aMCI-m and AD participants, hypoconnectivity was found in specific regions within the DMN, including the precuneus and angular gyrus, and hyperconnectivity was found in areas outside the typical DMN networks, including the middle occipital gyrus, lingual gyrus and visual cortex, which indicated disease-related adaptations of brain networks. Our findings suggest that DMN-DAN anticorrelation may shed light on the understanding of the adaptations in brain function during the progression from MCI to AD and may serve as a potential biomarker to detect AD in the preclinical stage.
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Affiliation(s)
- Junkai Wang
- School of Psychology, Capital Normal University, Beijing Key Laboratory of Learning and Cognition, Beijing, China.,Department of Psychology, Tsinghua University, Beijing, China
| | - Jianghong Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zhiqun Wang
- Department of Radiology, Aerospace Center Hospital, Beijing, China
| | - Pei Sun
- Department of Psychology, Tsinghua University, Beijing, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
| | - Peipeng Liang
- School of Psychology, Capital Normal University, Beijing Key Laboratory of Learning and Cognition, Beijing, China
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