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Gao SL, Yue J, Li XL, Li A, Cao DN, Han SW, Wei ZY, Yang G, Zhang Q. Multimodal magnetic resonance imaging on brain network in amnestic mild cognitive impairment: A mini-review. Medicine (Baltimore) 2023; 102:e34994. [PMID: 37653770 PMCID: PMC10470781 DOI: 10.1097/md.0000000000034994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/02/2023] Open
Abstract
Amnestic mild cognitive impairment (aMCI) is a stage between normal aging and Alzheimer disease (AD) where individuals experience a noticeable decline in memory that is greater than what is expected with normal aging, but dose not meet the clinical criteria for AD. This stage is considered a transitional phase that puts individuals at a high risk for developing AD. It is crucial to intervene during this stage to reduce the changes of AD development. Recently, advanced multimodal magnetic resonance imaging techniques have been used to study the brain structure and functional networks in individuals with aMCI. Through the use of structural magnetic resonance imaging, diffusion tensor imaging, and functional magnetic resonance imaging, abnormalities in certain brain regions have been observed in individuals with aMCI. Specifically, the default mode network, salience network, and executive control network have been found to show abnormalities in both structure and function. This review aims to provide a comprehensive understanding of the brain structure and functional networks associated with aMCI. By analyzing the existing literature on multimodal magnetic resonance imaging and aMCI, this study seeks to uncover potential biomarkers and gain insight into the underlying pathogenesis of aMCI. This knowledge can then guide the development of future treatments and interventions to delay or prevent the progression of aMCI to AD.
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Affiliation(s)
- Sheng-Lan Gao
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Jinhuan Yue
- Shenzhen Frontiers in Chinese Medicine Research Co., Ltd., Shenzhen, China
| | - Xiao-Ling Li
- Division of CT and MRI, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Ang Li
- Sanofi-Aventis China Investment Co., Ltd, Beijing, China
| | - Dan-Na Cao
- Division of CT and MRI, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Sheng-Wang Han
- Third Ward of Rehabilitation Department, Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Ze-Yi Wei
- Graduate School of Heilongjiang University of Chinese Medicine, Harbin, China
| | - Guanhu Yang
- Department of Specialty Medicine, Ohio University, Athens, OH
| | - Qinhong Zhang
- Shenzhen Frontiers in Chinese Medicine Research Co., Ltd., Shenzhen, China
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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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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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Vettore M, De Marco M, Pallucca C, Bendini M, Gallucci M, Venneri A. White-Matter Hyperintensity Load and Differences in Resting-State Network Connectivity Based on Mild Cognitive Impairment Subtype. Front Aging Neurosci 2021; 13:737359. [PMID: 34690743 PMCID: PMC8529279 DOI: 10.3389/fnagi.2021.737359] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 09/09/2021] [Indexed: 11/13/2022] Open
Abstract
"Mild cognitive impairment" (MCI) is a diagnosis characterised by deficits in episodic memory (aMCI) or in other non-memory domains (naMCI). Although the definition of subtypes is helpful in clinical classification, it provides little insight on the variability of neurofunctional mechanisms (i.e., resting-state brain networks) at the basis of symptoms. In particular, it is unknown whether the presence of a high load of white-matter hyperintensities (WMHs) has a comparable effect on these functional networks in aMCI and naMCI patients. This question was addressed in a cohort of 123 MCI patients who had completed an MRI protocol inclusive of T1-weighted, fluid-attenuated inversion recovery (FLAIR) and resting-state fMRI sequences. T1-weighted and FLAIR images were processed with the Lesion Segmentation Toolbox to quantify whole-brain WMH volumes. The CONN toolbox was used to preprocess all fMRI images and to run an independent component analysis for the identification of four large-scale haemodynamic networks of cognitive relevance (i.e., default-mode, salience, left frontoparietal, and right frontoparietal networks) and one control network (i.e., visual network). Patients were classified based on MCI subtype (i.e., aMCI vs. naMCI) and WMH burden (i.e., low vs. high). Maps of large-scale networks were then modelled as a function of the MCI subtype-by-WMH burden interaction. Beyond the main effects of MCI subtype and WMH burden, a significant interaction was found in the salience and left frontoparietal networks. Having a low WMH burden was significantly more associated with stronger salience-network connectivity in aMCI (than in naMCI) in the right insula, and with stronger left frontoparietal-network connectivity in the right frontoinsular cortex. Vice versa, having a low WMH burden was significantly more associated with left-frontoparietal network connectivity in naMCI (than in aMCI) in the left mediotemporal lobe. The association between WMH burden and strength of connectivity of resting-state functional networks differs between aMCI and naMCI patients. Although exploratory in nature, these findings indicate that clinical profiles reflect mechanistic interactions that may play a central role in the definition of diagnostic and prognostic statuses.
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Affiliation(s)
- Martina Vettore
- Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom.,Department of General Psychology, University of Padua, Padua, Italy
| | - Matteo De Marco
- Department of Life Sciences, Brunel University London, Uxbridge, United Kingdom
| | - Claudia Pallucca
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.,Cognitive Impairment Center, Local Health Authority n.2 Marca Trevigiana, Treviso, Italy
| | - Matteo Bendini
- Unit of Neuroradiology, Treviso Regional Hospital, Treviso, Italy
| | - Maurizio Gallucci
- Cognitive Impairment Center, Local Health Authority n.2 Marca Trevigiana, Treviso, Italy
| | - Annalena Venneri
- Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom.,Department of Life Sciences, Brunel University London, Uxbridge, United Kingdom
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Yuan Q, Qi W, Xue C, Ge H, Hu G, Chen S, Xu W, Song Y, Zhang X, Xiao C, Chen J. Convergent Functional Changes of Default Mode Network in Mild Cognitive Impairment Using Activation Likelihood Estimation. Front Aging Neurosci 2021; 13:708687. [PMID: 34675797 PMCID: PMC8525543 DOI: 10.3389/fnagi.2021.708687] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 08/30/2021] [Indexed: 12/13/2022] Open
Abstract
Background: Mild cognitive impairment (MCI) represents a transitional state between normal aging and dementia disorders, especially Alzheimer's disease (AD). The disruption of the default mode network (DMN) is often considered to be a potential biomarker for the progression from MCI to AD. The purpose of this study was to assess MRI-specific changes of DMN in MCI patients by elucidating the convergence of brain regions with abnormal DMN function. Methods: We systematically searched PubMed, Ovid, and Web of science for relevant articles. We identified neuroimaging studies by using amplitude of low frequency fluctuation /fractional amplitude of low frequency fluctuation (ALFF/fALFF), regional homogeneity (ReHo), and functional connectivity (FC) in MCI patients. Based on the activation likelihood estimation (ALE) algorithm, we carried out connectivity modeling of coordination-based meta-analysis and functional meta-analysis. Results: In total, this meta-analysis includes 39 articles on functional neuroimaging studies. Using computer software analysis, we discovered that DMN changes in patients with MCI mainly occur in bilateral inferior frontal lobe, right medial frontal lobe, left inferior parietal lobe, bilateral precuneus, bilateral temporal lobe, and parahippocampal gyrus (PHG). Conclusions: Herein, we confirmed the presence of DMN-specific damage in MCI, which is helpful in revealing pathology of MCI and further explore mechanisms of conversion from MCI to AD. Therefore, we provide a new specific target and direction for delaying conversion from MCI to AD.
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Affiliation(s)
- Qianqian Yuan
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Department of Neurosurgery, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Department of Neurosurgery, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenwen Xu
- 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
| | - XuLian Zhang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chaoyong Xiao
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Department of Neurosurgery, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Department of Neurosurgery, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
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Song Y, Xu W, Chen S, Hu G, Ge H, Xue C, Qi W, Lin X, Chen J. Functional MRI-Specific Alterations in Salience Network in Mild Cognitive Impairment: An ALE Meta-Analysis. Front Aging Neurosci 2021; 13:695210. [PMID: 34381352 PMCID: PMC8350339 DOI: 10.3389/fnagi.2021.695210] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 06/01/2021] [Indexed: 01/03/2023] Open
Abstract
Background Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Amnestic MCI (aMCI) and non-amnestic MCI are the two subtypes of MCI with the former having a higher risk for progressing to Alzheimer's disease (AD). Compared with healthy elderly adults, individuals with MCI have specific functional alterations in the salience network (SN). However, no consistent results are documenting these changes. This meta-analysis aimed to investigate the specific functional alterations in the SN in MCI and aMCI. Methods: We systematically searched PubMed, Embase, and Web of Science for scientific neuroimaging literature based on three research methods, namely, functional connectivity (FC), regional homogeneity (ReHo), and the amplitude of low-frequency fluctuation or fractional amplitude of low-frequency fluctuation (ALFF/fALFF). Then, we conducted the coordinate-based meta-analysis by using the activation likelihood estimation algorithm. Results: In total, 30 functional neuroimaging studies were included. After extracting the data and analyzing it, we obtained specific changes in some brain regions in the SN including decreased ALFF/fALFF in the left superior temporal gyrus, the insula, the precentral gyrus, and the precuneus in MCI and aMCI; increased FC in the thalamus, the caudate, the superior temporal gyrus, the insula, and the cingulate gyrus in MCI; and decreased ReHo in the anterior cingulate gyrus in aMCI. In addition, as to FC, interactions of the SN with other networks including the default mode network and the executive control network were also observed mainly in the middle frontal gyrus and superior frontal gyrus in MCI and inferior frontal gyrus in aMCI. Conclusions: Specific functional alternations in the SN and interactions of the SN with other networks in MCI could be useful as potential imaging biomarkers for MCI or aMCI. Meanwhile, it provided a new insight in predicting the progression of health to MCI or aMCI and novel targets for proper intervention to delay the progression. Systematic Review Registration: [PROSPERO], identifier [No. CRD42020216259].
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Affiliation(s)
- Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenwen Xu
- 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
| | - Guanjie Hu
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- 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
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
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