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Liu H, Jing J, Jiang J, Wen W, Zhu W, Li Z, Pan Y, Cai X, Liu C, Zhou Y, Meng X, Wang Y, Li H, Jiang Y, Zheng H, Wang S, Niu H, Kochan N, Brodaty H, Wei T, Sachdev PS, Fan Y, Liu T, Wang Y. Exploring the link between brain topological resilience and cognitive performance in the context of aging and vascular risk factors: A cross-ethnicity population-based study. Sci Bull (Beijing) 2024; 69:2735-2744. [PMID: 38664095 DOI: 10.1016/j.scib.2024.04.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 02/08/2024] [Accepted: 04/07/2024] [Indexed: 09/09/2024]
Abstract
Brain aging is typically associated with a significant decline in cognitive performance. Vascular risk factors (VRF) and subsequent atherosclerosis (AS) play a major role in this process. Brain resilience reflects the brain's ability to withstand external perturbations, but the relationship of brain resilience with cognition during the aging process remains unclear. Here, we investigated how brain topological resilience (BTR) is associated with cognitive performance in the face of aging and vascular risk factors. We used data from two cross-ethnicity community cohorts, PolyvasculaR Evaluation for Cognitive Impairment and Vascular Events (PRECISE, n = 2220) and Sydney Memory and Ageing Study (MAS, n = 246). We conducted an attack simulation on brain structural networks based on k-shell decomposition and node degree centrality. BTR was defined based on changes in the size of the largest subgroup of the network during the simulation process. Subsequently, we explored the negative correlations of BTR with age, VRF, and AS, and its positive correlation with cognitive performance. Furthermore, using structural equation modeling (SEM), we constructed path models to analyze the directional dependencies among these variables, demonstrating that aging, AS, and VRF affect cognition by disrupting BTR. Our results also indicated the specificity of this metric, independent of brain volume. Overall, these findings underscore the supportive role of BTR on cognition during aging and highlight its potential application as an imaging marker for objective assessment of brain cognitive performance.
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Affiliation(s)
- Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Jing Jing
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
| | - Jiyang Jiang
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Wei Wen
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Wanlin Zhu
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Zixiao Li
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Yuesong Pan
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Xueli Cai
- Department of Neurology, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Chang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Yijun Zhou
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Xia Meng
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Yilong Wang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Hao Li
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
| | - Yong Jiang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Huaguang Zheng
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Suying Wang
- Cerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Nicole Kochan
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Henry Brodaty
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Tiemin Wei
- Department of Cardiology, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Perminder S Sachdev
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Yubo Fan
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China.
| | - Yongjun Wang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
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Wang X, Zhou H, Yan CQ, Shi GX, Zhou P, Huo JW, Yang JW, Zhang YN, Wang L, Cao Y, Liu CZ. Cognitive and Hippocampal Changes in Older Adults With Subjective Cognitive Decline After Acupuncture Intervention. Am J Geriatr Psychiatry 2024; 32:1014-1027. [PMID: 38521736 DOI: 10.1016/j.jagp.2024.03.001] [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: 01/21/2024] [Revised: 03/01/2024] [Accepted: 03/02/2024] [Indexed: 03/25/2024]
Abstract
OBJECTIVE Converging evidence indicates that subjective cognitive decline (SCD) could be an early indicator of dementia. The hippocampus is the earliest affected region during the progression of cognitive impairment. However, little is known about whether and how acupuncture change the hippocampal structure and function of SCD individuals. METHODS Here, we used multi-modal MRI to reveal the mechanism of acupuncture in treating SCD. Seventy-two older participants were randomized into acupuncture or sham acupuncture group and treated for 12 weeks. RESULTS At the end of the intervention, compared to sham acupuncture, participants with acupuncture treatment showed improvement in composite Z score from multi-domain neuropsychological tests, as well as increased hippocampal volume and functional connectivity. Moreover, the greater white matter integrity of the fornix, which is the major output tract of the hippocampus, was shown in the acupuncture group. CONCLUSION These findings suggest that acupuncture may improve the cognitive function of SCD individuals, and increase hippocampal volume on the regional level and enhance the structural and functional connectivity of hippocampus on the connective level.
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Affiliation(s)
- Xu Wang
- International Acupuncture and Moxibustion Innovation Institute, School of Acupuncture-Moxibustion and Tuina (XW, HZ, C-QY, G-XS, PZ, J-WY, LW, YC, C-ZL), Beijing University of Chinese Medicine, Beijing, China; School of Life Sciences (XW), Beijing University of Chinese Medicine, Beijing, China
| | - Hang Zhou
- International Acupuncture and Moxibustion Innovation Institute, School of Acupuncture-Moxibustion and Tuina (XW, HZ, C-QY, G-XS, PZ, J-WY, LW, YC, C-ZL), Beijing University of Chinese Medicine, Beijing, China
| | - Chao-Qun Yan
- International Acupuncture and Moxibustion Innovation Institute, School of Acupuncture-Moxibustion and Tuina (XW, HZ, C-QY, G-XS, PZ, J-WY, LW, YC, C-ZL), Beijing University of Chinese Medicine, Beijing, China
| | - Guang-Xia Shi
- International Acupuncture and Moxibustion Innovation Institute, School of Acupuncture-Moxibustion and Tuina (XW, HZ, C-QY, G-XS, PZ, J-WY, LW, YC, C-ZL), Beijing University of Chinese Medicine, Beijing, China
| | - Ping Zhou
- International Acupuncture and Moxibustion Innovation Institute, School of Acupuncture-Moxibustion and Tuina (XW, HZ, C-QY, G-XS, PZ, J-WY, LW, YC, C-ZL), Beijing University of Chinese Medicine, Beijing, China
| | - Jian-Wei Huo
- Department of Radiology (J-WH, Y-NZ), Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Dongcheng District, Beijing, China
| | - Jing-Wen Yang
- International Acupuncture and Moxibustion Innovation Institute, School of Acupuncture-Moxibustion and Tuina (XW, HZ, C-QY, G-XS, PZ, J-WY, LW, YC, C-ZL), Beijing University of Chinese Medicine, Beijing, China
| | - Ya-Nan Zhang
- Department of Radiology (J-WH, Y-NZ), Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Dongcheng District, Beijing, China
| | - Lu Wang
- International Acupuncture and Moxibustion Innovation Institute, School of Acupuncture-Moxibustion and Tuina (XW, HZ, C-QY, G-XS, PZ, J-WY, LW, YC, C-ZL), Beijing University of Chinese Medicine, Beijing, China
| | - Yan Cao
- International Acupuncture and Moxibustion Innovation Institute, School of Acupuncture-Moxibustion and Tuina (XW, HZ, C-QY, G-XS, PZ, J-WY, LW, YC, C-ZL), Beijing University of Chinese Medicine, Beijing, China
| | - Cun-Zhi Liu
- International Acupuncture and Moxibustion Innovation Institute, School of Acupuncture-Moxibustion and Tuina (XW, HZ, C-QY, G-XS, PZ, J-WY, LW, YC, C-ZL), Beijing University of Chinese Medicine, Beijing, China.
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Wang X, Huang CC, Tsai SJ, Lin CP, Cai Q. The aging trajectories of brain functional hierarchy and its impact on cognition across the adult lifespan. Front Aging Neurosci 2024; 16:1331574. [PMID: 38313436 PMCID: PMC10837851 DOI: 10.3389/fnagi.2024.1331574] [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: 11/01/2023] [Accepted: 01/09/2024] [Indexed: 02/06/2024] Open
Abstract
Introduction The hierarchical network architecture of the human brain, pivotal to cognition and behavior, can be explored via gradient analysis using restingstate functional MRI data. Although it has been employed to understand brain development and disorders, the impact of aging on this hierarchical architecture and its link to cognitive decline remains elusive. Methods This study utilized resting-state functional MRI data from 350 healthy adults (aged 20-85) to investigate the functional hierarchical network using connectome gradient analysis with a cross-age sliding window approach. Gradient-related metrics were estimated and correlated with age to evaluate trajectory of gradient changes across lifespan. Results The principal gradient (unimodal-to-transmodal) demonstrated a significant non-linear relationship with age, whereas the secondary gradient (visual-to-somatomotor) showed a simple linear decreasing pattern. Among the principal gradient, significant age-related changes were observed in the somatomotor, dorsal attention, limbic and default mode networks. The changes in the gradient scores of both the somatomotor and frontal-parietal networks were associated with greater working memory and visuospatial ability. Gender differences were found in global gradient metrics and gradient scores of somatomotor and default mode networks in the principal gradient, with no interaction with age effect. Discussion Our study delves into the aging trajectories of functional connectome gradient and its cognitive impact across the adult lifespan, providing insights for future research into the biological underpinnings of brain function and pathological models of atypical aging processes.
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Affiliation(s)
- Xiao Wang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
- NYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai, Shanghai, China
| | - Shih-Jen Tsai
- Brain Research Center, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Ching-Po Lin
- Brain Research Center, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Institute of Neuroscience, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
| | - Qing Cai
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), Institute of Brain and Education Innovation, School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai, China
- NYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai, Shanghai, China
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Ping L, Sun S, Zhou C, Que J, You Z, Xu X, Cheng Y. Altered topology of individual brain structural covariance networks in major depressive disorder. Psychol Med 2023:1-12. [PMID: 37427670 DOI: 10.1017/s003329172300168x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
BACKGROUND The neurobiological pathogenesis of major depression disorder (MDD) remains largely controversial. Previous literatures with limited sample size utilizing group-level structural covariance networks (SCN) commonly generated mixed findings regarding the topology of brain networks. METHODS We analyzed T1 images from a high-powered multisite sample including 1173 patients with MDD and 1019 healthy controls (HCs). We used regional gray matter volume to construct individual SCN by utilizing a novel approach based on the interregional effect size difference. We further investigated MDD-related structural connectivity alterations using topological metrics. RESULTS Compared to HCs, the MDD patients showed a shift toward randomization characterized by increased integration. Further subgroup analysis of patients in different stages revealed this randomization pattern was also observed in patients with recurrent MDD, while the first-episode drug naïve patients exhibited decreased segregation. Altered nodal properties in several brain regions which have a key role in both emotion regulation and executive control were also found in MDD patients compared with HCs. The abnormalities in inferior temporal gyrus were not influenced by any specific site. Moreover, antidepressants increased nodal efficiency in the anterior ventromedial prefrontal cortex. CONCLUSIONS The MDD patients at different stages exhibit distinct patterns of randomization in their brain networks, with increased integration during illness progression. These findings provide valuable insights into the disruption in structural brain networks that occurs in patients with MDD and might be useful to guide future therapeutic interventions.
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Affiliation(s)
- Liangliang Ping
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Shan Sun
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Cong Zhou
- School of Mental Health, Jining Medical University, Jining, China
| | - Jianyu Que
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Zhiyi You
- Department of Psychiatry, Xiamen Xianyue Hospital, Xiamen, China
| | - Xiufeng Xu
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yuqi Cheng
- Department of Psychiatry, The First Affiliated Hospital of Kunming Medical University, Kunming, China
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Sun S, Liu D, Zhou Y, Yang G, Cui LB, Xu X, Guo Y, Sun T, Jiang J, Li N, Wang Y, Li S, Wang X, Fan L, Cao F. Longitudinal real world correlation study of blood pressure and novel features of cerebral magnetic resonance angiography by artificial intelligence analysis on elderly cognitive impairment. Front Aging Neurosci 2023; 15:1121152. [PMID: 36819723 PMCID: PMC9935573 DOI: 10.3389/fnagi.2023.1121152] [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: 12/13/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
Objective This study aims to investigate novel clinical risk factors for cognitive impairment (CI) in elderly. Methods A total of 3221 patients (259 patients with CI and 2,962 subjects without CI) were recruited into this nested case-control study who underwent cerebral magnetic resonance angiography (MRA) from 2007 to 2021. All of the clinical data with MRA imaging were recorded followed by standardization processing blindly. The maximum stenosis score of the posterior circulatory artery, including the basilar artery, and bilateral posterior cerebral artery (PCA), was calculated by the cerebral MRA automatic quantitative analysis method. Logistic regression (LR) analysis was used to evaluate the relationship between risk factors and CI. Four machine learning approaches, including LR, decision tree (DT), random forest (RF), and support vector machine (SVM), employing 5-fold cross-validation were used to establish CI predictive models. Results After matching with age and gender, 208 CI patients and 208 control subjects were finalized the follow-up (3.46 ± 3.19 years) with mean age at 84.47 ± 6.50 years old. Pulse pressure (PP) in first tertile (<58 mmHg) (OR 0.588, 95% confidence interval (CI): 0.362-0.955) was associated with a decreased risk for CI, and ≥50% stenosis of the left PCA (OR 2.854, 95% CI: 1.387-5.872) was associated with an increased risk for CI after adjusting for body mass index, myocardial infarction, and stroke history. Based on the means of various blood pressure (BP) parameters, the performance of the LR, DT, RF and SVM models accurately predicted CI (AUC 0.740, 0.786, 0.762, and 0.753, respectively) after adding the stenosis score of posterior circulatory artery. Conclusion Elderly with low pulse differential pressure may have lower risk for cognitive impairment. The hybrid model combined with the stenosis score of posterior circulatory artery, clinical indicators, and the means of various BP parameters can effectively predict the risk of CI in elderly individuals.
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Affiliation(s)
- Shasha Sun
- Department of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Dongyue Liu
- Department of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yanfeng Zhou
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China,Laboratory of Computational Biology and Machine Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Ge Yang
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China,Laboratory of Computational Biology and Machine Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Long-Biao Cui
- Department of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Xian Xu
- Department of Radiology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yuanhao Guo
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China,Laboratory of Computational Biology and Machine Intelligence, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Ting Sun
- Department of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Jiacheng Jiang
- Department of Radiology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Na Li
- Nankai University School of Medicine, Nankai University, Tianjin, China
| | - Yabin Wang
- Department of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Sulei Li
- Department of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Xinjiang Wang
- Department of Radiology, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Li Fan
- Department of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China,Li Fan,
| | - Feng Cao
- Department of Cardiology, Chinese PLA Medical School, The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China,*Correspondence: Feng Cao,
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