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Ma J, Lu JJ, Wu JJ, Xiang YT, Zheng MX, Hua XY, Xu JG. The moderating role of information processing speed in the relationship between brain remodeling and episodic memory in amnestic mild cognitive impairment. Alzheimers Dement 2024. [PMID: 39193657 DOI: 10.1002/alz.14130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 06/14/2024] [Accepted: 06/17/2024] [Indexed: 08/29/2024]
Abstract
INTRODUCTION The role of information processing speed (IPS) on relationships between episodic memory (EM) and central remodeling features in amnestic mild cognitive impairment (aMCI) was investigated. METHODS Neuropsychological evaluations and multimodal magnetic resonance imaging were performed on 48 patients diagnosed with aMCI and 50 healthy controls (HC). Moderation models explored the moderating effect of IPS on associations between EM and imaging features at single-region, connectivity, and network levels. RESULTS IPS significantly enhanced the positive correlations between recall and cortical thickness of left inferior temporal gyrus. IPS also notably amplified negative correlations between recognition and functional connectivity (FC) of left inferior parietal lobe and right occipital, as well as between recall/recognition and nodal clustering coefficient of left anterior cingulate cortex. DISCUSSION IPS functioned as a moderator of associations between recall and neuroimaging metrics at the "single region-connectivity-network" level, providing new insights for cognitive rehabilitation in aMCI patients. HIGHLIGHTS aMCI patients exhibited brain functional and structural remodeling alterations. IPS moderated relations between episodic memory and brain remodeling metrics. Therapy targeted at IPS can be considered for improving episodic memory in aMCI.
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Affiliation(s)
- Jie Ma
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Juan-Juan Lu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yun-Ting Xiang
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jian-Guang Xu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
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Markett S, Boeken OJ, Wudarczyk OA. Multimodal imaging investigation of structural rich club alterations in Alzheimer's disease and mild cognitive impairment: Amyloid deposition, structural atrophy, and functional activation differences. Eur J Neurosci 2024; 60:4169-4181. [PMID: 38779858 DOI: 10.1111/ejn.16384] [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: 09/13/2023] [Revised: 04/03/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024]
Abstract
Alzheimer's disease (AD) is characterized by significant cerebral dysfunction, including increased amyloid deposition, gray matter atrophy, and changes in brain function. The involvement of highly connected network hubs, known as the "rich club," in the pathology of the disease remains inconclusive despite previous research efforts. In this study, we aimed to systematically assess the link between the rich club and AD using a multimodal neuroimaging approach. We employed network analyses of diffusion magnetic resonance imaging (MRI), longitudinal assessments of gray matter atrophy, amyloid deposition measurements using positron emission tomography (PET) imaging, and meta-analytic data on functional activation differences. Our study focused on evaluating the role of both the structural brain network's core and extended rich club regions in individuals with mild cognitive impairment (MCI) and those diagnosed with AD. Our findings revealed that structural rich club regions exhibited accelerated gray matter atrophy and increased amyloid deposition in both MCI and AD. Importantly, these regions remained unaffected by altered functional activation patterns observed outside the core rich club regions. These results shed light on the connection between two major AD biomarkers and the rich club, providing valuable insights into AD as a potential disconnection syndrome.
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Affiliation(s)
| | - Ole J Boeken
- Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Neurology and Experimental Neurology, Charité-Universitätsmedizin, Berlin, Germany
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Li J, Yao C, Li Y, Liu X, Zhao Z, Shang Y, Yang J, Yao Z, Sheng Y, Hu B. Effects of second language acquisition on brain functional networks at different developmental stages. Brain Imaging Behav 2024; 18:808-818. [PMID: 38492128 DOI: 10.1007/s11682-024-00865-y] [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: 02/11/2024] [Indexed: 03/18/2024]
Abstract
Previous studies have shown that language acquisition influences both the structure and function of the brain. However, whether the acquisition of a second language at different periods of life alters functional network organization in different ways remains unclear. Here, functional magnetic resonance imaging data from 27 English-speaking monolingual controls and 52 Spanish-English bilingual individuals, including 22 early bilinguals who began learning a second language before the age of ten and 30 late bilinguals who started learning a second language at age fourteen or later, were collected from the OpenNeuro database. Topological metrics of resting-state functional networks, including small-world attributes, network efficiency, and rich- and diverse-club regions, that characterize functional integration and segregation of the networks were computed via a graph theoretical approach. The results showed obvious increases in network efficiency in early bilinguals and late bilinguals relative to the monolingual controls; for example, the global efficiency of late bilinguals and early bilinguals was improved relative to that of monolingual controls, and the local efficiency of early bilinguals occupied an intermediate position between that of late bilinguals and monolingual controls. Obvious increases in rich-club and diverse-club functional connectivity were observed in the bilinguals relative to the monolingual controls. Three network metrics were positively correlated with Spanish proficiency test scores. These findings demonstrated that early and late acquisition of a second language had different impacts on the functional networks of the brain.
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Affiliation(s)
- Jiajia Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China
| | - Chaofan Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China
| | - Yongchao Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China
| | - Xia Liu
- School of Computer Science, Qinghai Normal University, Xining, China
| | - Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China
| | - Yingying Shang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China
| | - Jing Yang
- Lanzhou University Second Hospital, Lanzhou, China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China.
| | - Yucen Sheng
- School of Foreign Languages, Lanzhou Jiaotong University, Lanzhou, China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu Province, China.
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University &, Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, China.
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Lohia K, Soans RS, Saxena R, Mahajan K, Gandhi TK. Distinct rich and diverse clubs regulate coarse and fine binocular disparity processing: Evidence from stereoscopic task-based fMRI. iScience 2024; 27:109831. [PMID: 38784010 PMCID: PMC11111836 DOI: 10.1016/j.isci.2024.109831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 03/07/2024] [Accepted: 04/24/2024] [Indexed: 05/25/2024] Open
Abstract
While cortical regions involved in processing binocular disparities have been studied extensively, little is known on how the human visual system adapts to changing disparity magnitudes. In this paper, we investigate causal mechanisms of coarse and fine binocular disparity processing using fMRI with a clinically validated, custom anaglyph-based stimulus. We make use of Granger causality and graph measures to reveal the existence of distinct rich and diverse clubs across different disparity magnitudes. We demonstrate that Middle Temporal area (MT) plays a specialized role with overlapping rich and diverse characteristics. Next, we show that subtle interhemispheric differences exist across various brain regions, despite an overall right hemisphere dominance. Finally, we pass the graph measures through the decision tree and found that the diverse clubs outperform rich clubs in decoding disparity magnitudes. Our study sets the stage for conducting further investigations on binocular disparity processing, particularly in the context of neuro-ophthalmic disorders with binocular impairments.
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Affiliation(s)
- Kritika Lohia
- Department of Electrical Engineering, Indian Institute of Technology – Delhi, New Delhi, India
| | - Rijul Saurabh Soans
- Department of Electrical Engineering, Indian Institute of Technology – Delhi, New Delhi, India
- Laboratory of Experimental Ophthalmology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Herbert Wertheim School of Optometry and Vision Science, University of California, Berkeley, Berkeley, CA, USA
| | - Rohit Saxena
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences, New Delhi, India
| | | | - Tapan K. Gandhi
- Department of Electrical Engineering, Indian Institute of Technology – Delhi, New Delhi, India
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Zheng D, Ruan Y, Cao X, Guo W, Zhang X, Qi W, Yuan Q, Liang X, Zhang D, Huang Q, Xue C. Directed Functional Connectivity Changes of Triple Networks for Stable and Progressive Mild Cognitive Impairment. Neuroscience 2024; 545:47-58. [PMID: 38490330 DOI: 10.1016/j.neuroscience.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 03/17/2024]
Abstract
Mild cognitive impairment includes two distinct subtypes, namely progressive mild cognitive impairment and stable mild cognitive impairment. While alterations in extensive functional connectivity have been observed in both subtypes, limited attention has been given to directed functional connectivity. A triple network, composed of the central executive network, default mode network, and salience network, is considered to be the core cognitive network. We evaluated the alterations in directed functional connectivity within and between the triple network in progressive and stable mild cognitive impairment groups and investigated its role in predicting disease conversion. Resting-state functional magnetic resonance imaging was used to analyze directed functional connectivity within the triple networks. A correlation analysis was performed to investigate potential associations between altered directed functional connectivity within the triple networks and the neurocognitive performance of the participants. Our study revealed significant differences in directed functional connectivity within and between the triple network in the progressive and stable mild cognitive impairment groups. Altered directed functional connectivity within the triple network was involved in episodic memory and executive function. Thus, the directed functional connectivity of the triple network may be used as an imaging marker of mild cognitive impairment.
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Affiliation(s)
- Darui Zheng
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Yiming Ruan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xuan Cao
- Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, USA
| | - Wenxuan Guo
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xulian Zhang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Wenzhang Qi
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Qianqian Yuan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xuhong Liang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Da Zhang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Qingling Huang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
| | - Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
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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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Ding H, Wang Z, Tang Y, Wang T, Qi M, Dou W, Qian L, Gao Y, Zhong Q, Yang X, Tian H, Zhang L, Zhu Y. Topological properties of individual gray matter morphological networks in identifying the preclinical stages of Alzheimer's disease: a preliminary study. Quant Imaging Med Surg 2023; 13:5258-5270. [PMID: 37581056 PMCID: PMC10423385 DOI: 10.21037/qims-22-1373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/08/2023] [Indexed: 08/16/2023]
Abstract
Background Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are preclinical stages of Alzheimer's disease (AD). Individual biomarkers are essential for evaluating altered neurological outcomes at both SCD and MCI stages for early diagnosis and intervention of AD. In this study, we aimed to investigate the relationships between topological properties of the individual brain morphological network and clinical cognitive performances among healthy controls (HCs) and patients with SCD or MCI. Methods The topological measurements of individual morphological networks were analyzed using graph theory, and inter-group differences of standard graph topology were correlated and regressed to scores of clinical cognitive functions. Results Compared with HCs, the topology of the individual morphological networks in SCD and MCI patients was significantly altered. At the global level, altered topology was characterized by lower global efficiency, shorter characteristics path length, and normalized characteristics path length [all P<0.05, false discovery rate (FDR) corrected]. In addition, at the regional level, SCD and MCI patients exhibited abnormal degree centrality in the caudate nucleus and nodal efficiency in the caudate nucleus, right insula, lenticular nucleus, and putamen (all P<0.05, FDR corrected). Conclusions The topological features of individual gray matter morphological networks may serve as biomarkers to improve disease prognosis and intervention in the early stages of AD, namely SCD and MCI. Moreover, these findings may further elucidate the relationships between brain morphological alterations and cognitive dysfunctions in SCD and MCI.
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Affiliation(s)
- Hongyuan Ding
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhihao Wang
- School of Biological Science & Medical Engineering, Southeast University, Nanjing, China
| | - Yin Tang
- Department of Medical Imaging, Jingjiang People’s Hospital, Jingjiang, China
| | - Tong Wang
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ming Qi
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | | | - Long Qian
- MR Research, GE Healthcare, Beijing, China
| | - Yaxin Gao
- Department of Rehabilitation, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China
- Gusu School, Nanjing Medical University, Suzhou, China
| | - Qian Zhong
- Department of Rehabilitation, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Xi Yang
- School of Rehabilitation Medicine, Nanjing Medical University, Nanjing, China
| | - Huifang Tian
- School of Rehabilitation Medicine, Nanjing Medical University, Nanjing, China
| | - Ling Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yi Zhu
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Chen Q, Chen F, Long C, Zhu Y, Jiang Y, Zhu Z, Lu J, Zhang X, Nedelska Z, Hort J, Zhang B. Spatial navigation is associated with subcortical alterations and progression risk in subjective cognitive decline. Alzheimers Res Ther 2023; 15:86. [PMID: 37098612 PMCID: PMC10127414 DOI: 10.1186/s13195-023-01233-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 04/18/2023] [Indexed: 04/27/2023]
Abstract
BACKGROUND Subjective cognitive decline (SCD) may serve as a symptomatic indicator for preclinical Alzheimer's disease; however, SCD is a heterogeneous entity regarding clinical progression. We aimed to investigate whether spatial navigation could reveal subcortical structural alterations and the risk of progression to objective cognitive impairment in SCD individuals. METHODS One hundred and eighty participants were enrolled: those with SCD (n = 80), normal controls (NCs, n = 77), and mild cognitive impairment (MCI, n = 23). SCD participants were further divided into the SCD-good (G-SCD, n = 40) group and the SCD-bad (B-SCD, n = 40) group according to their spatial navigation performance. Volumes of subcortical structures were calculated and compared among the four groups, including basal forebrain, thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens. Topological properties of the subcortical structural covariance network were also calculated. With an interval of 1.5 years ± 12 months of follow-up, the progression rate to MCI was compared between the G-SCD and B-SCD groups. RESULTS Volumes of the basal forebrain, the right hippocampus, and their respective subfields differed significantly among the four groups (p < 0.05, false discovery rate corrected). The B-SCD group showed lower volumes in the basal forebrain than the G-SCD group, especially in the Ch4p and Ch4a-i subfields. Furthermore, the structural covariance network of the basal forebrain and right hippocampal subfields showed that the B-SCD group had a larger Lambda than the G-SCD group, which suggested weakened network integration in the B-SCD group. At follow-up, the B-SCD group had a significantly higher conversion rate to MCI than the G-SCD group. CONCLUSION Compared to SCD participants with good spatial navigation performance, SCD participants with bad performance showed lower volumes in the basal forebrain, a reorganized structural covariance network of subcortical nuclei, and an increased risk of progression to MCI. Our findings indicated that spatial navigation may have great potential to identify SCD subjects at higher risk of clinical progression, which may contribute to making more precise clinical decisions for SCD individuals who seek medical help.
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Affiliation(s)
- Qian Chen
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, 210008, 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
| | - Futao Chen
- 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
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Cong Long
- 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
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yajing Zhu
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, 210008, 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
| | - Yaoxian Jiang
- 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
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zhengyang Zhu
- 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
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Jiaming Lu
- 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
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xin Zhang
- 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
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zuzana Nedelska
- Memory Clinic, Department of Neurology, 2nd Faculty of Medicine, Charles University, University Hospital Motol, Prague, Czechia
| | - Jakub Hort
- Memory Clinic, Department of Neurology, 2nd Faculty of Medicine, Charles University, University Hospital Motol, Prague, Czechia
| | - Bing Zhang
- Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, 210008, 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.
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.
- Jiangsu Key Laboratory of Molecular Medicine, Nanjing, China.
- Institute of Brain Science, Nanjing University, Nanjing, China.
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Chen Q, Chen F, Zhu Y, Long C, Lu J, Zhang X, Nedelska Z, Hort J, Chen J, Ma G, Zhang B. Reconfiguration of brain network dynamics underlying spatial deficits in subjective cognitive decline. Neurobiol Aging 2023; 127:82-93. [PMID: 37116409 DOI: 10.1016/j.neurobiolaging.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 02/12/2023] [Accepted: 03/06/2023] [Indexed: 03/12/2023]
Abstract
Brain dynamics and the associations with spatial navigation in individuals with subjective cognitive decline (SCD) remain unknown. In this study, a hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging data in a cohort of 80 SCD and 77 normal control (NC) participants. By HMM, 12 states with distinct brain activity were identified. The SCD group showed increased fractional occupancy in the states with less activated ventral default mode, posterior salience, and visuospatial networks, while decreased fractional occupancy in the state with general network activation. The SCD group also showed decreased probabilities of transition into and out of the state with general network activation, suggesting an inability to dynamically upregulate and downregulate brain network activity. Significant correlations between brain dynamics and spatial navigation were observed. The combined features of spatial navigation and brain dynamics showed an area under the curve of 0.854 in distinguishing between SCD and NC. The findings may provide exploratory evidence of the reconfiguration of brain network dynamics underlying spatial deficits in SCD.
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Zhang D, Liu S, Huang Y, Gao J, Liu W, Liu W, Ai K, Lei X, Zhang X. Altered Functional Connectivity Density in Type 2 Diabetes Mellitus with and without Mild Cognitive Impairment. Brain Sci 2023; 13:brainsci13010144. [PMID: 36672125 PMCID: PMC9856282 DOI: 10.3390/brainsci13010144] [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: 11/07/2022] [Revised: 01/06/2023] [Accepted: 01/11/2023] [Indexed: 01/18/2023] Open
Abstract
Although disturbed functional connectivity is known to be a factor influencing cognitive impairment, the neuropathological mechanisms underlying the cognitive impairment caused by type 2 diabetes mellitus (T2DM) remain unclear. To characterize the neural mechanisms underlying T2DM-related brain damage, we explored the altered functional architecture patterns in different cognitive states in T2DM patients. Thirty-seven T2DM patients with normal cognitive function (DMCN), 40 T2DM patients with mild cognitive impairment (MCI) (DMCI), and 40 healthy controls underwent neuropsychological assessments and resting-state functional MRI examinations. Functional connectivity density (FCD) analysis was performed, and the relationship between abnormal FCD and clinical/cognitive variables was assessed. The regions showing abnormal FCD in T2DM patients were mainly located in the temporal lobe and cerebellum, but the abnormal functional architecture was more extensive in DMCI patients. Moreover, in comparison with the DMCN group, DMCI patients showed reduced long-range FCD in the left superior temporal gyrus (STG), which was correlated with the Rey auditory verbal learning test score in all T2DM patients. Thus, DMCI patients show functional architecture abnormalities in more brain regions involved in higher-level cognitive function (executive function and auditory memory function), and the left STG may be involved in the neuropathology of auditory memory in T2DM patients. These findings provide some new insights into understanding the neural mechanisms underlying T2DM-related cognitive impairment.
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Affiliation(s)
- Dongsheng Zhang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
| | - Shasha Liu
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
| | - Yang Huang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
| | - Jie Gao
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
| | - Weirui Liu
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
| | - Wanting Liu
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
| | - Kai Ai
- Department of Clinical Science, Philips Healthcare, Xi’an 710000, China
| | - Xiaoyan Lei
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
| | - Xiaoling Zhang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an 710068, China
- Correspondence: ; Tel.: +86-13087581380
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11
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Mirza-Davies A, Foley S, Caseras X, Baker E, Holmans P, Escott-Price V, Jones DK, Harrison JR, Messaritaki E. The impact of genetic risk for Alzheimer's disease on the structural brain networks of young adults. Front Neurosci 2022; 16:987677. [PMID: 36532292 PMCID: PMC9748570 DOI: 10.3389/fnins.2022.987677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 11/09/2022] [Indexed: 12/02/2022] Open
Abstract
Introduction We investigated the structural brain networks of 562 young adults in relation to polygenic risk for Alzheimer's disease, using magnetic resonance imaging (MRI) and genotype data from the Avon Longitudinal Study of Parents and Children. Methods Diffusion MRI data were used to perform whole-brain tractography and generate structural brain networks for the whole-brain connectome, and for the default mode, limbic and visual subnetworks. The mean clustering coefficient, mean betweenness centrality, characteristic path length, global efficiency and mean nodal strength were calculated for these networks, for each participant. The connectivity of the rich-club, feeder and local connections was also calculated. Polygenic risk scores (PRS), estimating each participant's genetic risk, were calculated at genome-wide level and for nine specific disease pathways. Correlations were calculated between the PRS and (a) the graph theoretical metrics of the structural networks and (b) the rich-club, feeder and local connectivity of the whole-brain networks. Results In the visual subnetwork, the mean nodal strength was negatively correlated with the genome-wide PRS (r = -0.19, p = 1.4 × 10-3), the mean betweenness centrality was positively correlated with the plasma lipoprotein particle assembly PRS (r = 0.16, p = 5.5 × 10-3), and the mean clustering coefficient was negatively correlated with the tau-protein binding PRS (r = -0.16, p = 0.016). In the default mode network, the mean nodal strength was negatively correlated with the genome-wide PRS (r = -0.14, p = 0.044). The rich-club and feeder connectivities were negatively correlated with the genome-wide PRS (r = -0.16, p = 0.035; r = -0.15, p = 0.036). Discussion We identified small reductions in brain connectivity in young adults at risk of developing Alzheimer's disease in later life.
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Affiliation(s)
- Anastasia Mirza-Davies
- School of Medicine, University Hospital Wales, Cardiff University, Cardiff, United Kingdom
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Sonya Foley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Xavier Caseras
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Emily Baker
- UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Peter Holmans
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
| | - Valentina Escott-Price
- Division of Psychological Medicine and Clinical Neurosciences, MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, United Kingdom
- UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Judith R. Harrison
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- Institute for Translational and Clinical Research, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Eirini Messaritaki
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
- BRAIN Biomedical Research Unit, School of Medicine, Cardiff University, Cardiff, United Kingdom
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12
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Fathian A, Jamali Y, Raoufy MR. The trend of disruption in the functional brain network topology of Alzheimer's disease. Sci Rep 2022; 12:14998. [PMID: 36056059 PMCID: PMC9440254 DOI: 10.1038/s41598-022-18987-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 08/23/2022] [Indexed: 12/19/2022] Open
Abstract
Alzheimer's disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain's functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer's disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process.
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Affiliation(s)
- Alireza Fathian
- Biomathematics Laboratory, Department of Applied Mathematics, School of Mathematical Science, Tarbiat Modares University, Tehran, Iran
| | - Yousef Jamali
- Biomathematics Laboratory, Department of Applied Mathematics, School of Mathematical Science, Tarbiat Modares University, Tehran, Iran.
- Applied Systems Biology, Leibniz-Institute for Natural Product Research and Infection Biology - Hans-Knöll-Institute, Jena, Germany.
| | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
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13
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Cai C, Cao J, Yang C, Chen E. Diagnosis of Amnesic Mild Cognitive Impairment Using MGS-WBC and VGBN-LM Algorithms. Front Aging Neurosci 2022; 14:893250. [PMID: 35707699 PMCID: PMC9189381 DOI: 10.3389/fnagi.2022.893250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 04/20/2022] [Indexed: 11/13/2022] Open
Abstract
Computer-aided diagnosis (CAD) has undergone rapid development with the advent of advanced neuroimaging and machine learning methods. Nevertheless, how to extract discriminative features from the limited and high-dimensional data is not ideal, especially for amnesic mild cognitive impairment (aMCI) data based on resting-state functional magnetic resonance imaging (rs-fMRI). Furthermore, a robust and reliable system for aMCI detection is conducive to timely detecting and screening subjects at a high risk of Alzheimer's disease (AD). In this scenario, we first develop the mask generation strategy based on within-class and between-class criterion (MGS-WBC), which primarily aims at reducing data redundancy and excavating multiscale features of the brain. Concurrently, vector generation for brain networks based on Laplacian matrix (VGBN-LM) is presented to obtain the global features of the functional network. Finally, all multiscale features are fused to further improve the diagnostic performance of aMCI. Typical classifiers for small data learning, such as naive Bayesian (NB), linear discriminant analysis (LDA), logistic regression (LR), and support vector machines (SVMs), are adopted to evaluate the diagnostic performance of aMCI. This study helps to reveal discriminative neuroimaging features, and outperforms the state-of-the-art methods, providing new insights for the intelligent construction of CAD system of aMCI.
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Affiliation(s)
- Chunting Cai
- School of Informatics, Xiamen University, Xiamen, China
| | | | - Chenhui Yang
- School of Informatics, Xiamen University, Xiamen, China
| | - E. Chen
- Department of Neurology, Zhongshan Hospital Affiliated to Xiamen University, Xiamen, China
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14
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Zhou B, Wu X, Tang L, Li C. Dynamics of the Brain Functional Network Associated With Subjective Cognitive Decline and Its Relationship to Apolipoprotein E €4 Alleles. Front Aging Neurosci 2022; 14:806032. [PMID: 35356298 PMCID: PMC8959928 DOI: 10.3389/fnagi.2022.806032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 02/08/2022] [Indexed: 11/13/2022] Open
Abstract
The aim of our study was to explore the dynamic functional alterations in the brain in patients with subjective cognitive decline (SCD) and their relationship to apolipoprotein E (APOE) €4 alleles. In total, 95 SCD patients and 49 healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Then, the mean time series of 90 cortical or subcortical regions were extracted based on anatomical automatic labeling (AAL) atlas from the preprocessed rs-fMRI data. The static functional connectome (SFC) and dynamic functional connectome (DFC) were constructed and compared using graph theory methods and leading eigenvector dynamics analysis (LEiDA), respectively. The SCD group displayed a shorter lifetime (p = 0.003, false discovery rate corrected) and lower probability (p = 0.009, false discovery rate corrected) than the HC group in a characteristic dynamic functional network mainly involving the bilateral insular and temporal neocortex. No significant differences in the SFC were detected between the two groups. Moreover, the lower probability in the SCD group was found to be negatively correlated with the number of APOE ε4 alleles (r = −0.225, p = 0.041) in a partial correlation analysis with years of education as a covariate. Our results suggest that the DFC may be a more sensitive parameter than the SFC and can be used as a potential biomarker for the early detection of SCD.
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15
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Peng L, Feng J, Ma D, Xu X, Gao X. Rich-Club Organization Disturbances of the Individual Morphological Network in Subjective Cognitive Decline. Front Aging Neurosci 2022; 14:834145. [PMID: 35283748 PMCID: PMC8914315 DOI: 10.3389/fnagi.2022.834145] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 01/28/2022] [Indexed: 12/11/2022] Open
Abstract
Background Subjective cognitive decline (SCD) was considered to be the preclinical stage of Alzheimer's disease (AD). However, less is known about the altered rich-club organizations of the morphological networks in individuals with SCD. Methods This study included 53 individuals with SCD and 54 well-matched healthy controls (HC) from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. Individual-level brain morphological networks were constructed by estimating the Jensen-Shannon distance-based similarity in the distribution of regional gray matter volume. Rich-club properties were then detected, followed by statistical comparison. Results The characteristic rich-club organization of morphological networks (normalized rich-club coefficients > 1) was observed for both the SCD and HC groups under a range of thresholds. The SCD group showed a reduced normalized rich-club coefficient compared with the HC group. The SCD group exhibited the decreased strength and degree of rich-club connections than the HC group (strength: HC = 79.93, SCD = 74.37, p = 0.028; degree: HC = 85.28, SCD = 79.34, p = 0.027). Interestingly, the SCD group showed an increased strength of local connections than the HC group (strength: HC = 1982.16, SCD = 2003.38, p = 0.036). Conclusion Rich-club organization disturbances of morphological networks in individuals with SCD reveal a distinct pattern between the rich-club and peripheral regions. This altered rich-club organization pattern provides novel insights into the underlying mechanism of SCD and could be used to investigate prevention strategies at the preclinical stage of AD.
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Affiliation(s)
- Liling Peng
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Jing Feng
- The Fifth People’s Hospital of Jinan, Jinan, China
| | - Di Ma
- College of Information Science and Technology, Nanjing Forestry University, Nanjing, China
| | - Xiaowen Xu
- Department of Medical Imaging, School of Medicine, Tongji Hospital, Tongji University, Shanghai, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
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16
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Han H, Li X, Gan JQ, Yu H, Wang H. Biomarkers Derived from Alterations in Overlapping Community Structure of Resting-state Brain Functional Networks for Detecting Alzheimer's Disease. Neuroscience 2021; 484:38-52. [PMID: 34973385 DOI: 10.1016/j.neuroscience.2021.12.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 12/17/2021] [Accepted: 12/23/2021] [Indexed: 11/27/2022]
Abstract
Recent studies show that overlapping community structure is an important feature of the brain functional network. However, alterations in such overlapping community structure in Alzheimer's disease (AD) patients have not been examined yet. In this study, we investigate the overlapping community structure in AD by using resting-state functional magnetic resonance imaging (rs-fMRI) data. The collective sparse symmetric non-negative matrix factorization (cssNMF) is adopted to detect the overlapping community structure. Experimental results on 28 AD patients and 32 normal controls (NCs) from the ADNI2 dataset show that the two groups have remarkable differences in terms of the optimal number of communities, the hierarchy of communities detected at different scales, network functional segregation, and nodal functional diversity. In particular, the frontal-parietal and basal ganglia networks exhibit significant differences between the two groups. A machine learning framework proposed in this paper for AD detection achieved an accuracy of 76.7% when using the detected community strengths of the frontal-parietal and basal ganglia networks only as input features. These findings provide novel insights into the understanding of pathological changes in the brain functional network organization of AD and show the potential of the community structure-related features for AD detection.
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Affiliation(s)
- Hongfang Han
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China; Institute of Artificial Intelligence of Hefei Comprehensive National Science Center, Hefei 230094, Anhui, PR China
| | - Xuan Li
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China; School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK; Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425 Jülich, Germany
| | - John Q Gan
- School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
| | - Hua Yu
- Department of Cardiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230001, Anhui, PR China
| | - Haixian Wang
- Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 210096, Jiangsu, PR China; Institute of Artificial Intelligence of Hefei Comprehensive National Science Center, Hefei 230094, Anhui, PR China.
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17
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Liu Y, Li Z, Jiang X, Du W, Wang X, Sheng C, Jiang J, Han Y. Differences in Functional Brain Networks Between Subjective Cognitive Decline with and without Worry Groups: A Graph Theory Study from SILCODE. J Alzheimers Dis 2021; 84:1279-1289. [PMID: 34657889 DOI: 10.3233/jad-215156] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown. OBJECTIVE In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry. METHODS A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied. RESULTS Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p < 0.001). The above results appeared in both the whole brain and DMN. CONCLUSION There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline.
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Affiliation(s)
- Yi Liu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Zhuoyuan Li
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Xueyan Jiang
- Biomedical Engineering Institute, Hainan University, Haikou, China.,German Center for Neurodegenerative Disease, Clinical Research Group, Bonn, Germany
| | - Wenying Du
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Xiaoqi Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Can Sheng
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China.,Biomedical Engineering Institute, Hainan University, Haikou, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
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18
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Zhang D, Huang Y, Gao J, Lei Y, Ai K, Tang M, Yan X, Lei X, Yang Z, Shao Z, Zhang X. Altered Functional Topological Organization in Type-2 Diabetes Mellitus With and Without Microvascular Complications. Front Neurosci 2021; 15:726350. [PMID: 34630014 PMCID: PMC8493598 DOI: 10.3389/fnins.2021.726350] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 08/31/2021] [Indexed: 01/19/2023] Open
Abstract
Microvascular complications can accelerate cognitive impairment in patients with type 2 diabetes mellitus (T2DM) and have a high impact on their quality of life; however, the underlying mechanism is still unclear. The complex network in the human brain is the physiological basis for information processing and cognitive expression. Therefore, this study explored the relationship between the functional network topological properties and cognitive function in T2DM patients with and without microvascular complications (T2DM-C and T2DM-NC, respectively). Sixty-seven T2DM patients and 41 healthy controls (HCs) underwent resting-state functional MRI and neuropsychological assessment. Then, graph theoretical network analysis was performed to explore the global and nodal topological alterations in the functional whole brain networks of T2DM patients. Correlation analyses were performed to investigate the relationship between the altered topological parameters and cognitive/clinical variables. The T2DM-C group exhibited significantly higher local efficiency (Eloc), normalized cluster coefficient (γ), and small-world characteristics (σ) than the HCs. Patients with T2DM at different clinical stages (T2DM-C and T2DM-NC) showed varying degrees of abnormalities in node properties. In addition, compared with T2DM-NC patients, T2DM-C patients showed nodal properties disorders in the occipital visual network, cerebellum and middle temporal gyrus. The Eloc metrics were positively correlated with HbA1c level (P = 0.001, r = 0.515) and the NE values in the right paracentral lobule were negatively related with serum creatinine values (P = 0.001, r = −0.517) in T2DM-C patients. This study found that T2DM-C patients displayed more extensive changes at different network topology scales. The visual network and cerebellar may be the central vulnerable regions of T2DM-C patients.
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Affiliation(s)
- Dongsheng Zhang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Yang Huang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Jie Gao
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Yumeng Lei
- Department of Graduate, Xi'an Medical University, Xi'an, China
| | - Kai Ai
- Department of Clinical Science, Philips Healthcare, Xi'an, China
| | - Min Tang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Xuejiao Yan
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Xiaoyan Lei
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Zhen Yang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Zhirong Shao
- Department of Graduate, Xi'an Medical University, Xi'an, China
| | - Xiaoling Zhang
- Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China
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19
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Xue C, Qi W, Yuan Q, Hu G, Ge H, Rao J, Xiao C, Chen J. Disrupted Dynamic Functional Connectivity in Distinguishing Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment Based on the Triple-Network Model. Front Aging Neurosci 2021; 13:711009. [PMID: 34603006 PMCID: PMC8484524 DOI: 10.3389/fnagi.2021.711009] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 08/16/2021] [Indexed: 12/20/2022] Open
Abstract
Background: Subjective cognitive decline and amnestic mild cognitive impairment (aMCI) were widely thought to be preclinical AD spectrum disorders, characterized by aberrant functional connectivity (FC) within the triple networks of the default mode network (DMN), the salience network (SN), and the executive control network (ECN). Dynamic FC (DFC) analysis can capture temporal fluctuations in brain FC during the scan, which static FC analysis cannot. The purpose of the current study was to explore the changes in dynamic FC within the triple networks of the preclinical AD spectrum and further reveal their potential diagnostic value in diagnosing preclinical AD spectrum disorders. Methods: We collected resting-state functional magnetic resonance imaging data from 44 patients with subjective cognitive decline (SCD), 49 with aMCI, and 58 healthy controls (HCs). DFC analysis based on the sliding time-window correlation method was used to analyze DFC variability within the triple networks in the three groups. Then, correlation analysis was conducted to reveal the relationship between altered DFC variability within the triple networks and a decline in cognitive function. Furthermore, logistic regression analysis was used to assess the diagnostic accuracy of altered DFC variability within the triple networks in patients with SCD and aMCI. Results: Compared with the HC group, the groups with SCD and aMCI both showed altered DFC variability within the triple networks. DFC variability in the right middle temporal gyrus and left inferior frontal gyrus (IFG) within the ECN were significantly different between patients with SCD and aMCI. Moreover, the altered DFC variability in the left IFG within the ECN was obviously associated with a decline in episodic memory and executive function. The logistic regression analysis showed that multivariable analysis had high sensitivity and specificity for diagnosing SCD and aMCI. Conclusions: Subjective cognitive decline and aMCI showed varying degrees of change in DFC variability within the triple networks and altered DFC variability within the ECN involved episodic memory and executive function. More importantly, altered DFC variability and the triple-network model proved to be important biomarkers for diagnosing and identifying patients with preclinical AD spectrum disorders.
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Affiliation(s)
- 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
| | - Qianqian Yuan
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Jiang Rao
- Department of Rehabilitation, 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.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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20
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Zhang J, Kucyi A, Raya J, Nielsen AN, Nomi JS, Damoiseaux JS, Greene DJ, Horovitz SG, Uddin LQ, Whitfield-Gabrieli S. What have we really learned from functional connectivity in clinical populations? Neuroimage 2021; 242:118466. [PMID: 34389443 DOI: 10.1016/j.neuroimage.2021.118466] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/06/2021] [Accepted: 08/09/2021] [Indexed: 02/09/2023] Open
Abstract
Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts.
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Affiliation(s)
- Jiahe Zhang
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA.
| | - Aaron Kucyi
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
| | - Jovicarole Raya
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
| | - Ashley N Nielsen
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Jason S Nomi
- Department of Psychology, University of Miami, Miami, FL 33124, USA
| | - Jessica S Damoiseaux
- Institute of Gerontology and Department of Psychology, Wayne State University, Detroit, MI 48202, USA
| | - Deanna J Greene
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093, USA
| | | | - Lucina Q Uddin
- Department of Psychology, University of Miami, Miami, FL 33124, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, 125 Nightingale Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA
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21
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Xu W, Rao J, Song Y, Chen S, Xue C, Hu G, Lin X, Chen J. Altered Functional Connectivity of the Basal Nucleus of Meynert in Subjective Cognitive Impairment, Early Mild Cognitive Impairment, and Late Mild Cognitive Impairment. Front Aging Neurosci 2021; 13:671351. [PMID: 34248603 PMCID: PMC8267913 DOI: 10.3389/fnagi.2021.671351] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 05/11/2021] [Indexed: 01/10/2023] Open
Abstract
Background: The spectrum of early Alzheimer's disease (AD) is thought to include subjective cognitive impairment, early mild cognitive impairment (eMCI), and late mild cognitive impairment (lMCI). Choline dysfunction affects the early progression of AD, in which the basal nucleus of Meynert (BNM) is primarily responsible for cortical cholinergic innervation. The aims of this study were to determine the abnormal patterns of BNM-functional connectivity (BNM-FC) in the preclinical AD spectrum (SCD, eMCI, and lMCI) and further explore the relationships between these alterations and neuropsychological measures. Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) was used to investigate FC based on a seed mask (BNM mask) in 28 healthy controls (HC), 30 SCD, 24 eMCI, and 25 lMCI patients. Furthermore, the relationship between altered FC and neurocognitive performance was examined by a correlation analysis. The receiver operating characteristic (ROC) curve of abnormal BNM-FC was used to specifically determine the classification ability to differentiate the early AD disease spectrum relative to HC (SCD and HC, eMCI and HC, lMCI and HC) and pairs of groups in the AD disease spectrum (eMCI and SCD, lMCI and SCD, eMCI and lMCI). Results: Compared with HC, SCD patients showed increased FC in the bilateral SMA and decreased FC in the bilateral cerebellum and middle frontal gyrus (MFG), eMCI patients showed significantly decreased FC in the bilateral precuneus, and lMCI individuals showed decreased FC in the right lingual gyrus. Compared with the SCD group, the eMCI group showed decreased FC in the right superior frontal gyrus (SFG), while the lMCI group showed decreased FC in the left middle temporal gyrus (MTG). Compared with the eMCI group, the lMCI group showed decreased FC in the right hippocampus. Interestingly, abnormal FC was associated with certain cognitive domains and functions including episodic memory, executive function, information processing speed, and visuospatial function in the disease groups. BNM-FC of SFG in distinguishing eMCI from SCD; BNM-FC of MTG in distinguishing lMCI from SCD; BNM-FC of the MTG, hippocampus, and cerebellum in distinguishing SCD from HC; and BNM-FC of the hippocampus and MFG in distinguishing eMCI from lMCI have high sensitivity and specificity. Conclusions: The abnormal BNM-FC patterns can characterize the early disease spectrum of AD (SCD, eMCI, and lMCI) and are closely related to the cognitive domains. These new and reliable findings will provide a new perspective in identifying the early disease spectrum of AD and further strengthen the role of cholinergic theory in AD.
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Affiliation(s)
- Wenwen Xu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiang Rao
- Department of Rehabilitation, 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
| | - Chen Xue
- Department of Radiology, 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
| | - 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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