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Yueh-Hsin L, Dadario NB, Tang SJ, Crawford L, Tanglay O, Dow HK, Young I, Ahsan SA, Doyen S, Sughrue ME. Discernible interindividual patterns of global efficiency decline during theoretical brain surgery. Sci Rep 2024; 14:14573. [PMID: 38914649 PMCID: PMC11196730 DOI: 10.1038/s41598-024-64845-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 06/13/2024] [Indexed: 06/26/2024] Open
Abstract
The concept of functional localization within the brain and the associated risk of resecting these areas during removal of infiltrating tumors, such as diffuse gliomas, are well established in neurosurgery. Global efficiency (GE) is a graph theory concept that can be used to simulate connectome disruption following tumor resection. Structural connectivity graphs were created from diffusion tractography obtained from the brains of 80 healthy adults. These graphs were then used to simulate parcellation resection in every gross anatomical region of the cerebrum by identifying every possible combination of adjacent nodes in a graph and then measuring the drop in GE following nodal deletion. Progressive removal of brain parcellations led to patterns of GE decline that were reasonably predictable but had inter-subject differences. Additionally, as expected, there were deletion of some nodes that were worse than others. However, in each lobe examined in every subject, some deletion combinations were worse for GE than removing a greater number of nodes in a different region of the brain. Among certain patients, patterns of common nodes which exhibited worst GE upon removal were identified as "connectotypes". Given some evidence in the literature linking GE to certain aspects of neuro-cognitive abilities, investigating these connectotypes could potentially mitigate the impact of brain surgery on cognition.
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Affiliation(s)
- Lin Yueh-Hsin
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Suite 19, Level 7 Prince of Wales Private Hospital, Randwick, Sydney, NSW, 2031, Australia
| | - Nicholas B Dadario
- Robert Wood Johnson Medical School, Rutgers University, 125 Paterson St, New Brunswick, NJ, 08901, USA
| | - Si Jie Tang
- School of Medicine, 21772 University of California Davis Medical Center, 2315 Stockton Blvd., Sacramento, CA, 95817, USA
| | - Lewis Crawford
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW, 2000, Australia
| | - Onur Tanglay
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW, 2000, Australia
| | - Hsu-Kang Dow
- School of Computer Science and Engineering, University of New South Wales (UNSW), Building K17, Sydney, NSW, 2052, USA
| | - Isabella Young
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW, 2000, Australia
| | - Syed Ali Ahsan
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Suite 19, Level 7 Prince of Wales Private Hospital, Randwick, Sydney, NSW, 2031, Australia
| | - Stephane Doyen
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW, 2000, Australia
| | - Michael E Sughrue
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Suite 19, Level 7 Prince of Wales Private Hospital, Randwick, Sydney, NSW, 2031, Australia.
- Omniscient Neurotechnology, Level 10/580 George Street, Sydney, NSW, 2000, Australia.
- Centre for Minimally Invasive Neurosurgery, Prince of Wales Private Hospital, Suite 3, Level 7 Barker St, Randwick, NSW, 2031, USA.
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Yang R, Li J, Qin Y, Zhao L, Liu R, Yang F, Jiang G. A bibliometric analysis of cerebral microbleeds and cognitive impairment. Brain Cogn 2023; 169:105999. [PMID: 37262941 DOI: 10.1016/j.bandc.2023.105999] [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: 03/23/2023] [Revised: 05/14/2023] [Accepted: 05/16/2023] [Indexed: 06/03/2023]
Abstract
BACKGROUND AND OBJECTIVES Cerebral microbleeds (CMBs) are imaging markers for small cerebral vascular diseases, which can accumulate and impact the corresponding brain networks. CMBs can affect cognitive function, including executive function, information processing speed, and visuospatial memory. Bibliometrics is a scientific and innovative method that can analyze and visualize the scientific field quantitatively. In this study, we aimed to use bibliometric analysis to demonstrate the relationship and mechanisms between CMBs and cognitive impairment. Furthermore, we reviewed the relationship between CMBs and different cognitive disorders. The use of bibliometrics can help further clarify this relationship. METHODS We retrieved articles on CMBs and cognitive impairment from the Web of Science Core Collection. The keywords (such as stroke, dementia, and cerebral amyloid angiopathy), authors, countries, institutions and journals, in the field were visually analyzed using VOSviewer software and bibliometric websites. RESULTS This bibliometric analysis reveals the related trends of CMBs in the field of cognitive impairment. CMBs, along with other small vascular lesions, constitute the basis of cognitive impairment, and studying CMBs is essential to understand the mechanisms underlying cognitive impairment. CONCLUSION This bibliometric analysis reveals a strong link between CMBs and cognitive impairment-related diseases and that specific brain networks were affected by CMBs. This provides further insights into the possible mechanisms and causes of CMBs and cognitive impairment. The direct and indirect damage (such as oxidative stress and neuroinflammation) to the brain caused by CMBs, destruction of the frontal-subcortical circuits, elevated Cystatin C levels, and iron deposition are involved in the occurrence and development of cognitive impairment. CMBs may be a potential marker for detecting, quantifying, and predicting cognitive impairment.
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Affiliation(s)
- Rui Yang
- North Sichuan Medical College, Nanchong, Sichuan, China
| | - Jia Li
- North Sichuan Medical College, Nanchong, Sichuan, China
| | - Yaya Qin
- North Sichuan Medical College, Nanchong, Sichuan, China
| | - Li Zhao
- North Sichuan Medical College, Nanchong, Sichuan, China
| | - Rong Liu
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China
| | - Fanhui Yang
- Department of Nuclear Medicine, Affiliated Hospital of North Sichuan Medical College North Sichuan Medical College, Nanchong, Sichuan, China.
| | - Guohui Jiang
- Department of Neurology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
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Yang Z, Chen Y, Hou X, Xu Y, Bai F. Topologically convergent and divergent large scale complex networks among Alzheimer's disease spectrum patients: A systematic review. Heliyon 2023; 9:e15389. [PMID: 37101638 PMCID: PMC10123263 DOI: 10.1016/j.heliyon.2023.e15389] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/16/2023] [Accepted: 04/05/2023] [Indexed: 04/28/2023] Open
Abstract
Alzheimer's disease (AD) is associated with disruption at the level of a large-scale complex network. To explore the underlying mechanisms in the progression of AD, graph theory was used to quantitatively analyze the topological properties of structural and functional connections. Although an increasing number of studies have shown altered global and nodal network properties, little is known about the topologically convergent and divergent patterns between structural and functional networks among AD-spectrum patients. In this review, we summarized the topological patterns of the large-scale complex networks using multimodal neuroimaging graph theory analysis in AD spectrum patients. Convergent deficits in the connectivity characteristics were primarily in the default mode network (DMN) itself both in the structural and functional networks, while a divergent changes in the neighboring regions of the DMN were also observed between the patient groups. Together, the application of graph theory to large-scale complex brain networks provides quantitative insights into topological principles of brain network organization, which may lead to increasing attention in identifying the underlying neuroimaging pathological changes and predicting the progression of AD.
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Affiliation(s)
- Zhiyuan Yang
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Ya Chen
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing 210008, China
| | - Xinle Hou
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
- Department of Neurology, Nanjing Drum Tower Hospital, State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing 210008, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
- Geriatric Medicine Center, Affiliated Taikang Xianlin Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
- Correspondence to: 321 Zhongshan Road, Nanjing, 210008, China.
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Chu M, Jiang D, Liu L, Nie B, Rosa-Neto P, Chen K, Wu L. Clinical relevance of disrupted topological organization of anatomical connectivity in behavioral variant frontotemporal dementia. Neurobiol Aging 2023; 124:29-38. [PMID: 36724600 PMCID: PMC11102657 DOI: 10.1016/j.neurobiolaging.2023.01.004] [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: 07/26/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 01/13/2023]
Abstract
Graph theory is a novel approach used to examine the balance of brain connectomes. However, the clinical relevance of white matter (WM) connectome changes in the behavioral variant frontotemporal dementia (bvFTD) is not well understood. We aimed to investigate the clinical relevance of WM topological alterations in bvFTD. Thirty patients with probable bvFTD and 30 healthy controls underwent diffusion tensor imaging, structural MRI, and neuropsychological assessment. WM connectivity between 90 brain regions was calculated and the graph approach was applied to capture the individual characteristics of the anatomical network. Voxel-based morphometry and tract-based spatial statistics were used to present the gray matter atrophy and disrupted WM integrity. The topological organization was disrupted in patients with bvFTD both globally and locally. Compared to controls, bvFTD data showed a different pattern of hub region distributions. Notably, the nodal efficiency of the right superior orbital frontal gyrus was associated with apathy and disinhibition. Topological measures may be potential image markers for early diagnosis and disease severity monitoring of bvFTD.
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Affiliation(s)
- Min Chu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Deming Jiang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Li Liu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Binbin Nie
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Pedro Rosa-Neto
- McGill Centre for Studies in Aging, Alzheimer's Disease Research Unit, Montreal, Canada
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, AZ, USA; College of Medicine-Phoenix, University of Arizona, Tucson, AZ, USA; School of Mathematics and Statistics, Arizona State University, Tempe, AZ, USA; Arizona Alzheimer's Consortium, Phoenix, AZ, USA
| | - Liyong Wu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
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Sang F, Xu K, Chen Y. Brain Network Organization and Aging. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2023; 1419:99-108. [PMID: 37418209 DOI: 10.1007/978-981-99-1627-6_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
Despite recent substantial progress in neuroscience, the mechanisms and principles of the complex structure, functions, and the relationship between the brain and cognitive functions have not been fully understood. The modeling method of brain network can provide a new perspective for neuroscience research, and it is possible to provide new solutions to the related research problems. On this basis, the researchers define the concept of human brain connectome to highlight and emphasize the importance of network modeling methods in neuroscience. For example, using diffusion-weighted magnetic resonance imaging (dMRI) technology and fiber tractography methods, a white matter connection network of the whole brain can be constructed. From the perspective of brain function, functional magnetic resonance imaging (fMRI) data can build the brain functional connection network. A structural covariation modeling method is used to obtain a brain structure covariation network, and it appears to reflect developmental coordination or synchronized maturation between areas of the brain. In addition, network modeling and analysis methods can also be applied to other types of image data, such as positron emission tomography (PET), electroencephalogram (EEG), and magnetoencephalography (MEG). This chapter mainly reviews the research progress of researchers on brain structure, function, and other aspects at the network level in recent years.
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Affiliation(s)
- Feng Sang
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China
| | - Kai Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of Psychology, Beijing Normal University, Beijing, China.
- Beijing Aging Brain Rejuvenation Initiative (BABRI) Centre, Beijing Normal University, Beijing, China.
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Zhang HQ, Chau ACM, Shea YF, Chiu PKC, Bao YW, Cao P, Mak HKF. Disrupted Structural White Matter Network in Alzheimer's Disease Continuum, Vascular Dementia, and Mixed Dementia: A Diffusion Tensor Imaging Study. J Alzheimers Dis 2023; 94:1487-1502. [PMID: 37424470 DOI: 10.3233/jad-230341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
BACKGROUND Dementia presents a significant burden to patients and healthcare systems worldwide. Early and accurate diagnosis, as well as differential diagnosis of various types of dementia, are crucial for timely intervention and management. However, there is currently a lack of clinical tools for accurately distinguishing between these types. OBJECTIVE This study aimed to investigate the differences in the structural white matter (WM) network among different types of cognitive impairment/dementia using diffusion tensor imaging, and to explore the clinical relevance of the structural network. METHODS A total of 21 normal control, 13 subjective cognitive decline (SCD), 40 mild cognitive impairment (MCI), 22 Alzheimer's disease (AD), 13 mixed dementia (MixD), and 17 vascular dementia (VaD) participants were recruited. Graph theory was utilized to construct the brain network. RESULTS Our findings revealed a monotonic trend of disruption in the brain WM network (VaD > MixD > AD > MCI > SCD) in terms of decreased global efficiency, local efficiency, and average clustering coefficient, as well as increased characteristic path length. These network measurements were significantly associated with the clinical cognition index in each disease group separately. CONCLUSION These findings suggest that structural WM network measurements can be utilized to differentiate between different types of cognitive impairment/dementia, and these measurements can provide valuable cognition-related information.
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Affiliation(s)
- Hui-Qin Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Anson C M Chau
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Medical Radiation Science, Allied Health and Human Performance, University of South Australia, Adelaide, Australia
- Alliance for Research in Exercise, Nutrition, and Activity (ARENA), University of South Australia, Adelaide, Australia
| | - Yat-Fung Shea
- Division of Geriatrics, Department of Medicine, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Patrick Ka-Chun Chiu
- Division of Geriatrics, Department of Medicine, Queen Mary Hospital, Hong Kong, Hong Kong SAR, China
| | - Yi-Wen Bao
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Department of Medical Imaging Center, The Affiliated Huaian No.1 People's Hospital, Nanjing Medical University, Huai'an, China
| | - Peng Cao
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Henry Ka-Fung Mak
- Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Alzheimer's Disease Research Network, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
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7
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Wang D, Yao Q, Lin X, Hu J, Shi J. Disrupted topological properties of the structural brain network in patients with cerebellar infarction on different sides are associated with cognitive impairment. Front Neurol 2022; 13:982630. [PMID: 36203973 PMCID: PMC9530262 DOI: 10.3389/fneur.2022.982630] [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: 06/30/2022] [Accepted: 08/29/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To explore changes in the brain structural network in patients with cerebellar infarction on different sides and their correlations with changes in cognitive function. Methods Nineteen patients with acute left posterior cerebellar infarction and 18 patients with acute right posterior cerebellar infarction seen from July 2016 to September 2019 in the Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, were selected. A total of 27 healthy controls matched for sex, age, and years of education were recruited. The subjects underwent head diffusion magnetic resonance imaging examination and neuropsychological cognitive scale evaluation, and we analyzed changes in brain structural network properties in patients with cerebellar infarction and their correlation with changes in patients' cognitive function. Results The Mini-Mental Status Examination (MMSE), Montreal Cognitive Assessment (MOCA) and the Rey auditory verbal learning test (RAVLT) scores in the left and right cerebellar infarction groups were significantly lower than those in the healthy control group (p < 0.05). In addition, the digit span test (DST) scores were lower in the left cerebellar infarction group (p < 0.05); the trail-making test (TMT) times in the right cerebellar infarction group were significantly higher than those in the left cerebellar infarction group (p < 0.05). Meanwhile, the left and right cerebellar infarction groups had abnormal brain topological properties, including clustering coefficient, shortest path length, global efficiency, local efficiency and nodal efficiency. After unilateral cerebellar infarction, bilateral cerebral nodal efficiency was abnormal. Correlation analysis showed that there was a close correlation between decreased processing speed in patients with left cerebellar infarction and decreased efficiency of right cerebral nodes (p < 0.05), and there was a close relationship between executive dysfunction and decreased efficiency of left cerebral nodes in patients with right cerebellar infarction (p < 0.05). Conclusion Patients with cerebellar infarction have cognitive impairment. Unilateral cerebellar infarction can reduce the network efficiency of key regions in the bilateral cerebral hemispheres, and these abnormal changes are closely related to patient cognitive impairment. The results of this study provide evidence for understanding the underlying neural mechanisms of cerebellar cognitive impairment and suggest that brain topological network properties may be markers of cerebellar cognitive impairment.
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Affiliation(s)
- Duohao Wang
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Qun Yao
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xingjian Lin
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jun Hu
- Department of Radiology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jingping Shi
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Jingping Shi
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Zhang Y, Du X, Fu Y, Zhao Q, Wang Z, Qin W, Zhang Q. Effects of polygenic risk score of type 2 diabetes on the hippocampal topological property and episodic memory. Brain Imaging Behav 2022; 16:2506-2516. [PMID: 35904672 DOI: 10.1007/s11682-022-00706-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/10/2022] [Indexed: 11/02/2022]
Abstract
Type 2 diabetes is associated with a higher risk of dementia. The pathogenesis is complex and partly influenced by genetic factors. The hippocampus is the most vulnerable brain region in individuals with type 2 diabetes. However, whether the genetic risk of type 2 diabetes is associated with the hippocampus and episodic memory remains unclear. This study explored the influence of polygenic risk score (PRS) of type 2 diabetes on the white matter topological properties of the hippocampus among individuals with and without type 2 diabetes and its associations with episodic memory. This study included 103 individuals with type 2 diabetes and 114 well-matched individuals without type 2 diabetes. All the participants were genotyped, and a diffusion tensor imaging-based structural network was constructed. PRS was calculated based on a genome-wide association study of type 2 diabetes. The PRS-by-disease interactions on the bilateral hippocampal topological network properties were evaluated by analysis of covariance (ANCOVA). There were significant PRS-by-disease interaction effects on the nodal topological properties of the right hippocampus node. In the individuals with type 2 diabetes, the PRS was correlated with the right hippocampal nodal properties, and the nodal properties were correlated with the episodic memory. In addition, the right hippocampal nodal properties mediated the effect of PRS on episodic memory in individuals with type 2 diabetes. Our results suggested a gene-brain-cognition biological pathway, which might help understand the neural mechanism of the genetic risk of type 2 diabetes affects episodic memory in type 2 diabetes.
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Affiliation(s)
- Yang Zhang
- Department of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052, Tianjin, China
| | - Xin Du
- Department of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052, Tianjin, China
| | - Yumeng Fu
- Department of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052, Tianjin, China
| | - Qiuyue Zhao
- Department of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052, Tianjin, China
| | - Zirui Wang
- Department of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052, Tianjin, China
| | - Wen Qin
- Department of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052, Tianjin, China
| | - Quan Zhang
- Department of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, 300052, Tianjin, China. .,Department of Medical Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, 300052, Tianjin, China.
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Fu Z, Zhao M, He Y, Wang X, Li X, Kang G, Han Y, Li S. Aberrant topological organization and age-related differences in the human connectome in subjective cognitive decline by using regional morphology from magnetic resonance imaging. Brain Struct Funct 2022; 227:2015-2033. [PMID: 35579698 DOI: 10.1007/s00429-022-02488-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Accepted: 03/24/2022] [Indexed: 11/25/2022]
Abstract
Subjective cognitive decline (SCD) is characterized by self-experienced deficits in cognitive capacity with normal performance in objective cognitive tests. Previous structural covariance studies showed specific insights into understanding the structural alterations of the brain in neurodegenerative diseases. Moreover, in subjects with neurodegenerative diseases, accelerated brain degeneration with aging was shown. However, the age-related variations in coordinated topological patterns of morphological networks in individuals with SCD remain poorly understood. In this study, 77 individual morphological networks were constructed, including 42 normal controls (NCs) and 35 SCD individuals, from structural magnetic resonance imaging (sMRI). A stepwise linear regression model and partial correlation analysis were constructed to evaluate the differences in age-related alterations of the network properties in individuals with SCD compared with NCs. Compared with NC, the properties of integration and segregation in individuals with SCD were lower, and the aberrant metrics were negatively correlated with age in SCD. The rich-club connections persevered, but the paralimbic system connections were disrupted in individuals with SCD compared with NCs. In addition, age-related differences in nodal global efficiency are distributed mainly in prefrontal cortex regions. In conclusion, the age-related disruption of topological organizations in individuals with SCD may indicate that the degeneration of brain efficiency with aging was accelerated in individuals with SCD.
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Affiliation(s)
- Zhenrong Fu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Mingyan Zhao
- Department of Neurology, Tangshan Gongren Hospital, Tangshan, Hebei, China
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Yirong He
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Xuetong Wang
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Xin Li
- School of Electrical Engineering, Yanshan University, Qinhuangdao, China
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, China
| | - Guixia Kang
- School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
- Biomedical Engineering Institute, Hainan University, Haikou, China
- National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Shuyu Li
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China.
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10
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Feng G, Wang Y, Huang W, Chen H, Dai Z, Ma G, Li X, Zhang Z, Shu N. Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome. Hum Brain Mapp 2022; 43:3775-3791. [PMID: 35475571 PMCID: PMC9294303 DOI: 10.1002/hbm.25883] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/22/2022] [Accepted: 04/05/2022] [Indexed: 11/18/2022] Open
Abstract
An emerging trend is to use regression‐based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome‐based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects in Aging [HCP‐A]: 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic‐Net models are more accurate and robust in connectome‐based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field.
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Affiliation(s)
- Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Yiwen Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Haojie Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,BABRI Centre, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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Yang Z, Sheng X, Qin R, Chen H, Shao P, Xu H, Yao W, Zhao H, Xu Y, Bai F. Cognitive Improvement via Left Angular Gyrus-Navigated Repetitive Transcranial Magnetic Stimulation Inducing the Neuroplasticity of Thalamic System in Amnesic Mild Cognitive Impairment Patients. J Alzheimers Dis 2022; 86:537-551. [PMID: 35068464 DOI: 10.3233/jad-215390] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Stimulating superficial brain regions highly associated with the hippocampus by repetitive transcranial magnetic stimulation (rTMS) may improve memory of Alzheimer’s disease (AD) spectrum patients. Objective: We recruited 16 amnesic mild cognitive impairment (aMCI) and 6 AD patients in the study. All the patients were stimulated to the left angular gyrus, which was confirmed a strong link to the hippocampus through neuroimaging studies, by the neuro-navigated rTMS for four weeks. Methods: Automated fiber quantification using diffusion tensor imaging metrics and graph theory analysis on functional network were employed to detect the neuroplasticity of brain networks. Results: After neuro-navigated rTMS intervention, the episodic memory of aMCI patients and Montreal Cognitive Assessment score of two groups were significantly improved. Increased FA values of right anterior thalamic radiation among aMCI patients, while decreased functional network properties of thalamus subregions were observed, whereas similar changes not found in AD patients. It is worth noting that the improvement of cognition was associated with the neuroplasticity of thalamic system. Conclusion: We speculated that the rTMS intervention targeting left angular gyrus may be served as a strategy to improve cognitive impairment at the early stage of AD patients, supporting by the neuroplasticity of thalamic system.
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Affiliation(s)
- Zhiyuan Yang
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Xiaoning Sheng
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Haifeng Chen
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Pengfei Shao
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Hengheng Xu
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Weina Yao
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Hui Zhao
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital of The Affiliated Hospital of Nanjing University Medical School, and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neuropsychiatry Clinic Medical Center, Nanjing, China
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12
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Gao F. Integrated Positron Emission Tomography/Magnetic Resonance Imaging in clinical diagnosis of Alzheimer's disease. Eur J Radiol 2021; 145:110017. [PMID: 34826792 DOI: 10.1016/j.ejrad.2021.110017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/30/2021] [Accepted: 10/31/2021] [Indexed: 12/01/2022]
Abstract
Alzheimer's disease (AD), a progressive neurodegenerative disease which seriously endangers the health of the aged, is the most common etiology of senile dementia. With the increasing progress of neuroimaging technology, more and more imaging methods have been applied to study Alzheimer's disease. The emergence of integrated PET/MRI (Positron Emission Tomography/Magnetic Resonance Imaging) is a major advance in multimodal molecular imaging with many advantages on the structure of resolution and contrast of image over computed tomography (CT), PET and MRI. PET/MRI is now used stepwise in neurodegenerative diseases, and also has broad prospect of application in the early diagnosis of AD. In this review, we emphatically introduce the imaging advances of AD including functional imaging and molecular imaging, the advantages of PET/MRI over other imaging methods and prospects of PET/MRI in AD clinical diagnosis, especially in early diagnosis, clinical assessment and prediction on AD.
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Affiliation(s)
- Feng Gao
- Key Laboratory for Experimental Teratology of the Ministry of Education and Center for Experimental Nuclear Medicine, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.
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13
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Feng F, Huang W, Meng Q, Hao W, Yao H, Zhou B, Guo Y, Zhao C, An N, Wang L, Huang X, Zhang X, Shu N. Altered Volume and Structural Connectivity of the Hippocampus in Alzheimer's Disease and Amnestic Mild Cognitive Impairment. Front Aging Neurosci 2021; 13:705030. [PMID: 34675796 PMCID: PMC8524052 DOI: 10.3389/fnagi.2021.705030] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 09/10/2021] [Indexed: 01/08/2023] Open
Abstract
Background: Hippocampal atrophy is a characteristic of Alzheimer’s disease (AD). However, alterations in structural connectivity (number of connecting fibers) between the hippocampus and whole brain regions due to hippocampal atrophy remain largely unknown in AD and its prodromal stage, amnestic mild cognitive impairment (aMCI). Methods: We collected high-resolution structural MRI (sMRI) and diffusion tensor imaging (DTI) data from 36 AD patients, 30 aMCI patients, and 41 normal control (NC) subjects. First, the volume and structural connectivity of the bilateral hippocampi were compared among the three groups. Second, correlations between volume and structural connectivity in the ipsilateral hippocampus were further analyzed. Finally, classification ability by hippocampal volume, its structural connectivity, and their combination were evaluated. Results: Although the volume and structural connectivity of the bilateral hippocampi were decreased in patients with AD and aMCI, only hippocampal volume correlated with neuropsychological test scores. However, positive correlations between hippocampal volume and ipsilateral structural connectivity were displayed in patients with AD and aMCI. Furthermore, classification accuracy (ACC) was higher in AD vs. aMCI and aMCI vs. NC by the combination of hippocampal volume and structural connectivity than by a single parameter. The highest values of the area under the receiver operating characteristic (ROC) curve (AUC) in every two groups were all obtained by combining hippocampal volume and structural connectivity. Conclusions: Our results showed that the combination of hippocampal volume and structural connectivity (number of connecting fibers) is a new perspective for the discrimination of AD and aMCI.
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Affiliation(s)
- Feng Feng
- Department of Neurology, First Medical Center, Chinese PLA General Hospital, Beijing, China.,Department of Neurology, PLA Rocket Force Characteristic Medical Center, Beijing, China
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Qingqing Meng
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.,Health Care Office of the Service Bureau of Agency for Offices Administration of the Central Military Commission, Beijing, China
| | - Weijun Hao
- Department of Healthcare, Bureau of Guard, General Office of the Communist Party of China, Beijing, China
| | - Hongxiang Yao
- Department of Radiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Bo Zhou
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yan'e Guo
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Cui Zhao
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China.,Department of Geriatrics, Affiliated Hospital of Chengde Medical University, Chengde, China
| | - Ningyu An
- Department of Radiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Luning Wang
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Xusheng Huang
- Department of Neurology, First Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xi Zhang
- Department of Neurology, Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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14
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Chen Y, Zhou Z, Liang Y, Tan X, Li Y, Qin C, Feng Y, Ma X, Mo Z, Xia J, Zhang H, Qiu S, Shen D. Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high-order functional connectivity. Hum Brain Mapp 2021; 42:4671-4684. [PMID: 34213081 PMCID: PMC8410559 DOI: 10.1002/hbm.25575] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 06/03/2021] [Accepted: 06/08/2021] [Indexed: 12/12/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment and may progress to dementia. However, the brain functional mechanism of T2DM-related dementia is still less understood. Recent resting-state functional magnetic resonance imaging functional connectivity (FC) studies have proved its potential value in the study of T2DM with cognitive impairment (T2DM-CI). However, they mainly used a mass-univariate statistical analysis that was not suitable to reveal the altered FC "pattern" in T2DM-CI, due to lower sensitivity. In this study, we proposed to use high-order FC to reveal the abnormal connectomics pattern in T2DM-CI with a multivariate, machine learning-based strategy. We also investigated whether such patterns were different between T2DM-CI and T2DM without cognitive impairment (T2DM-noCI) to better understand T2DM-induced cognitive impairment, on 23 T2DM-CI and 27 T2DM-noCI patients, as well as 50 healthy controls (HCs). We first built the large-scale high-order brain networks based on temporal synchronization of the dynamic FC time series among multiple brain region pairs and then used this information to classify the T2DM-CI (as well as T2DM-noCI) from the matched HC based on support vector machine. Our model achieved an accuracy of 79.17% in T2DM-CI versus HC differentiation, but only 59.62% in T2DM-noCI versus HC classification. We found abnormal high-order FC patterns in T2DM-CI compared to HC, which was different from that in T2DM-noCI. Our study indicates that there could be widespread connectivity alterations underlying the T2DM-induced cognitive impairment. The results help to better understand the changes in the central neural system due to T2DM.
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Affiliation(s)
- Yuna Chen
- The First School of Clinical MedicineGuangzhou University of Chinese MedicineGuangzhouGuangdongChina
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Zhen Zhou
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Yi Liang
- Department of RadiologyThe First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouGuangdongChina
| | - Xin Tan
- Department of RadiologyThe First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouGuangdongChina
| | - Yifan Li
- The First School of Clinical MedicineGuangzhou University of Chinese MedicineGuangzhouGuangdongChina
| | - Chunhong Qin
- Department of RadiologyThe First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouGuangdongChina
| | - Yue Feng
- The First School of Clinical MedicineGuangzhou University of Chinese MedicineGuangzhouGuangdongChina
| | - Xiaomeng Ma
- The First School of Clinical MedicineGuangzhou University of Chinese MedicineGuangzhouGuangdongChina
| | - Zhanhao Mo
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
- Department of RadiologyChina‐Japan Union Hospital of Jilin UniversityChangchunJilinChina
| | - Jing Xia
- Institute of Brain‐Intelligence Technology, Zhangjiang LabShanghaiChina
| | - Han Zhang
- Institute of Brain‐Intelligence Technology, Zhangjiang LabShanghaiChina
| | - Shijun Qiu
- Department of RadiologyThe First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhouGuangdongChina
| | - Dinggang Shen
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
- Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
- Department of Artificial IntelligenceKorea UniversitySeoulRepublic of Korea
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15
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Xiong Y, Tian T, Fan Y, Yang S, Xiong X, Zhang Q, Zhu W. Diffusion Tensor Imaging Reveals Altered Topological Efficiency of Structural Networks in Type-2 Diabetes Patients With and Without Mild Cognitive Impairment. J Magn Reson Imaging 2021; 55:917-927. [PMID: 34382716 DOI: 10.1002/jmri.27884] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Some patients with type 2 diabetes mellitus (T2DM) progress towards mild cognitive impairment (MCI), while some patients can always maintain normal cognitive function. Network topologic alterations at global and nodal levels between T2DM individuals with and without cognitive impairment may underlie the difference. PURPOSE To investigate the topological alterations of the whole-brain white matter (WM) structural connectome in T2DM patients with and without MCI and characterize its relationship with disease severity. STUDY TYPE Cross-sectional and prospective study. SUBJECTS Forty-four (63.6% females) T2DM patients, 22 with mild cognitive impairment (DM-MCI) and 22 with normal cognition (DM-NC), and 34 (58.8% females) healthy controls (HC). FIELD STRENGTH/SEQUENCE 3 T/diffusion tensor imaging. ASSESSMENT Graph theoretical analysis was used to investigate the topological organization of the structural networks. The global topological properties and nodal efficiency were investigated and compared. Relationship between network metrics and clinical measurements was characterized. STATISTICAL TESTS Student's t-test, chi-square test, ANOVA, partial correlation analyses, and multiple comparisons correction. RESULTS The global topological organization of WM networks was significantly disrupted in T2DM patients with cognitive impairment (reduced global and local efficiency and increased shortest path length) but not in those with normal cognition, compared with controls. The DM-MCI group had significantly decreased network efficiency compared with the DM-NC group. Compared with controls, decreased nodal efficiency was detected in three regions in DM-NC group. More regions with decreased nodal efficiency were found in the DM-MCI group. Altered global network properties and nodal efficiency of some regions were correlated with diabetic duration, HbA1c levels, and cognitive assessment scores. DATA CONCLUSION The more disrupted WM connections and weaker organized network are found in DM-MCI patients relative to DM-NC patients and controls. Network analyses provide information for the neuropathology of cognitive decline in T2DM patients. Altered nodal efficiency may act as potential markers for early detection of T2DM-related MCI. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ying Xiong
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tian Tian
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Fan
- Beijing Intelligent Brain Cloud Inc., Beijing, China
| | - Shaolin Yang
- Department of Bioengineering, Psychiatry and Radiology, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Xiaoxiao Xiong
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiang Zhang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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16
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Huang W, Li X, Li X, Kang G, Han Y, Shu N. Combined Support Vector Machine Classifier and Brain Structural Network Features for the Individual Classification of Amnestic Mild Cognitive Impairment and Subjective Cognitive Decline Patients. Front Aging Neurosci 2021; 13:687927. [PMID: 34393757 PMCID: PMC8361326 DOI: 10.3389/fnagi.2021.687927] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/30/2021] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVE Individuals with subjective cognitive decline (SCD) or amnestic mild cognitive impairment (aMCI) represent important targets for the early detection and intervention of Alzheimer's disease (AD). In this study, we employed a multi-kernel support vector machine (SVM) to examine whether white matter (WM) structural networks can be used for screening SCD and aMCI. METHODS A total of 138 right-handed participants [51 normal controls (NC), 36 SCD, 51 aMCI] underwent MRI brain scans. For each participant, three types of WM networks with different edge weights were constructed with diffusion MRI data: fiber number-weighted networks, mean fractional anisotropy-weighted networks, and mean diffusivity (MD)-weighted networks. By employing a multiple-kernel SVM, we seek to integrate information from three weighted networks to improve classification performance. The accuracy of classification between each pair of groups was evaluated via leave-one-out cross-validation. RESULTS For the discrimination between SCD and NC, an area under the curve (AUC) value of 0.89 was obtained, with an accuracy of 83.9%. Further analysis revealed that the methods using three types of WM networks outperformed other methods using single WM network. Moreover, we found that most of discriminative features were from MD-weighted networks, which distributed among frontal lobes. Similar classification performance was also reported in the differentiation between subjects with aMCI and NCs (accuracy = 83.3%). Between SCD and aMCI, an AUC value of 0.72 was obtained, with an accuracy of 72.4%, sensitivity of 74.5% and specificity of 69.4%. The highest accuracy was achieved with features only selected from MD-weighted networks. CONCLUSION White matter structural network features help machine learning algorithms accurately identify individuals with SCD and aMCI from NCs. Our findings have significant implications for the development of potential brain imaging markers for the early detection of AD.
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Affiliation(s)
- Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Xuanyu Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Department of Neurology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Xin Li
- School of Electrical Engineering, Yanshan University, Qinhuangdao, China
- Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, China
| | - Guixia Kang
- Beijing University of Posts and Telecommunications, Beijing, 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
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
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17
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Zang J, Huang Y, Kong L, Lei B, Ke P, Li H, Zhou J, Xiong D, Li G, Chen J, Li X, Xiang Z, Ning Y, Wu F, Wu K. Effects of Brain Atlases and Machine Learning Methods on the Discrimination of Schizophrenia Patients: A Multimodal MRI Study. Front Neurosci 2021; 15:697168. [PMID: 34385901 PMCID: PMC8353157 DOI: 10.3389/fnins.2021.697168] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 07/07/2021] [Indexed: 11/24/2022] Open
Abstract
Recently, machine learning techniques have been widely applied in discriminative studies of schizophrenia (SZ) patients with multimodal magnetic resonance imaging (MRI); however, the effects of brain atlases and machine learning methods remain largely unknown. In this study, we collected MRI data for 61 first-episode SZ patients (FESZ), 79 chronic SZ patients (CSZ) and 205 normal controls (NC) and calculated 4 MRI measurements, including regional gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low-frequency fluctuation and degree centrality. We systematically analyzed the performance of two classifications (SZ vs NC; FESZ vs CSZ) based on the combinations of three brain atlases, five classifiers, two cross validation methods and 3 dimensionality reduction algorithms. Our results showed that the groupwise whole-brain atlas with 268 ROIs outperformed the other two brain atlases. In addition, the leave-one-out cross validation was the best cross validation method to select the best hyperparameter set, but the classification performances by different classifiers and dimensionality reduction algorithms were quite similar. Importantly, the contributions of input features to both classifications were higher with the GMV and ReHo features of brain regions in the prefrontal and temporal gyri. Furthermore, an ensemble learning method was performed to establish an integrated model, in which classification performance was improved. Taken together, these findings indicated the effects of these factors in constructing effective classifiers for psychiatric diseases and showed that the integrated model has the potential to improve the clinical diagnosis and treatment evaluation of SZ.
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Affiliation(s)
- Jinyu Zang
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Yuanyuan Huang
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
| | - Lingyin Kong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Bingye Lei
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Pengfei Ke
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Hehua Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
| | - Jing Zhou
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Dongsheng Xiong
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China
| | - Guixiang Li
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China.,National Engineering Research Center for Healthcare Devices, Guangzhou, China
| | - Jun Chen
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China.,National Engineering Research Center for Healthcare Devices, Guangzhou, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Zhiming Xiang
- Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China.,Department of Radiology, Panyu Central Hospital of Guangzhou, Guangzhou, China
| | - Yuping Ning
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
| | - Fengchun Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China
| | - Kai Wu
- Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, China.,Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China.,National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou, China.,The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou Huiai Hospital, Guangzhou, China.,Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou, China.,National Engineering Research Center for Healthcare Devices, Guangzhou, China.,Key Laboratory of Biomedical Engineering of Guangdong Province, South China University of Technology, Guangzhou, China.,Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
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18
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Tao W, Li H, Li X, Huang R, Shao W, Guan Q, Zhang Z. Progressive Brain Degeneration From Subjective Cognitive Decline to Amnestic Mild Cognitive Impairment: Evidence From Large-Scale Anatomical Connection Classification Analysis. Front Aging Neurosci 2021; 13:687530. [PMID: 34322011 PMCID: PMC8312851 DOI: 10.3389/fnagi.2021.687530] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 06/07/2021] [Indexed: 11/13/2022] Open
Abstract
People with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are both at high risk for Alzheimer’s disease (AD). Behaviorally, both SCD and aMCI have subjective reports of cognitive decline, but the latter suffers a more severe objective cognitive impairment than the former. However, it remains unclear how the brain develops from SCD to aMCI. In the current study, we aimed to investigate the topological characteristics of the white matter (WM) network that can successfully identify individuals with SCD or aMCI from healthy control (HC) and to describe the relationship of pathological changes between these two stages. To this end, three groups were recruited, including 22 SCD, 22 aMCI, and 22 healthy control (HC) subjects. We constructed WM network for each subject and compared large-scale topological organization between groups at both network and nodal levels. At the network level, the combined network indexes had the best performance in discriminating aMCI from HC. However, no indexes at the network level can significantly identify SCD from HC. These results suggested that aMCI but not SCD was associated with anatomical impairments at the network level. At the nodal level, we found that the short-path length can best differentiate between aMCI and HC subjects, whereas the global efficiency has the best performance in differentiating between SCD and HC subjects, suggesting that both SCD and aMCI had significant functional integration alteration compared to HC subjects. These results converged on the idea that the neural degeneration from SCD to aMCI follows a gradual process, from abnormalities at the nodal level to those at both nodal and network levels.
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Affiliation(s)
- Wuhai Tao
- Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Hehui Li
- Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Rong Huang
- Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China
| | - Wen Shao
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China
| | - Qing Guan
- Center for Brain Disorders and Cognitive Science, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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19
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Wright LM, De Marco M, Venneri A. A Graph Theory Approach to Clarifying Aging and Disease Related Changes in Cognitive Networks. Front Aging Neurosci 2021; 13:676618. [PMID: 34322008 PMCID: PMC8311855 DOI: 10.3389/fnagi.2021.676618] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 06/04/2021] [Indexed: 01/12/2023] Open
Abstract
In accordance with the physiological networks that underlie it, human cognition is characterized by both the segregation and interdependence of a number of cognitive domains. Cognition itself, therefore, can be conceptualized as a network of functions. A network approach to cognition has previously revealed topological differences in cognitive profiles between healthy and disease populations. The present study, therefore, used graph theory to determine variation in cognitive profiles across healthy aging and cognitive impairment. A comprehensive neuropsychological test battery was administered to 415 participants. This included three groups of healthy adults aged 18-39 (n = 75), 40-64 (n = 75), and 65 and over (n = 70) and three patient groups with either amnestic (n = 75) or non-amnestic (n = 60) mild cognitive impairment or Alzheimer's type dementia (n = 60). For each group, cognitive networks were created reflective of test-to-test covariance, in which nodes represented cognitive tests and edges reflected statistical inter-nodal significance (p < 0.05). Network metrics were derived using the Brain Connectivity Toolbox. Network-wide clustering, local efficiency and global efficiency of nodes showed linear differences across the stages of aging, being significantly higher among older adults when compared with younger groups. Among patients, these metrics were significantly higher again when compared with healthy older controls. Conversely, average betweenness centralities were highest in middle-aged participants and lower among older adults and patients. In particular, compared with controls, patients demonstrated a distinct lack of centrality in the domains of semantic processing and abstract reasoning. Network composition in the amnestic mild cognitive impairment group was similar to the network of Alzheimer's dementia patients. Using graph theoretical methods, this study demonstrates that the composition of cognitive networks may be measurably altered by the aging process and differentially impacted by pathological cognitive impairment. Network alterations characteristic of Alzheimer's disease in particular may occur early and be distinct from alterations associated with differing types of cognitive impairment. A shift in centrality between domains may be particularly relevant in identifying cognitive profiles indicative of underlying disease. Such techniques may contribute to the future development of more sophisticated diagnostic tools for neurodegenerative disease.
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Affiliation(s)
- Laura M Wright
- Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
| | - Matteo De Marco
- Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom
| | - Annalena Venneri
- Department of Neuroscience, University of Sheffield, Sheffield, United Kingdom.,Department of Life Sciences, Brunel University London, London, United Kingdom
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20
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Li W, Zhao H, Qing Z, Nedelska Z, Wu S, Lu J, Wu W, Yin Z, Hort J, Xu Y, Zhang B. Disrupted Network Topology Contributed to Spatial Navigation Impairment in Patients With Mild Cognitive Impairment. Front Aging Neurosci 2021; 13:630677. [PMID: 34149391 PMCID: PMC8210585 DOI: 10.3389/fnagi.2021.630677] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 04/08/2021] [Indexed: 12/26/2022] Open
Abstract
Impairment in spatial navigation (SN) and structural network topology is not limited to patients with Alzheimer’s disease (AD) dementia and can be detected earlier in patients with mild cognitive impairment (MCI). We recruited 32 MCI patients (65.91 ± 11.33 years old) and 28 normal cognition patients (NC; 69.68 ± 10.79 years old), all of whom underwent a computer-based battery of SN tests evaluating egocentric, allocentric, and mixed SN strategies and diffusion-weighted and T1-weighted Magnetic Resonance Imaging (MRI). To evaluate the topological features of the structural connectivity network, we calculated its measures such as the global efficiency, local efficiency, clustering coefficient, and shortest path length with GRETNA. We determined the correlation between SN accuracy and network topological properties. Compared to NC, MCI subjects demonstrated a lower egocentric navigation accuracy. Compared with NC, MCI subjects showed significantly decreased clustering coefficients in the left middle frontal gyrus, right rectus, right superior parietal gyrus, and right inferior parietal gyrus and decreased shortest path length in the left paracentral lobule. We observed significant positive correlations of the shortest path length in the left paracentral lobule with both the mixed allocentric–egocentric and the allocentric accuracy measured by the average total errors. A decreased clustering coefficient in the right inferior parietal gyrus was associated with a larger allocentric navigation error. White matter hyperintensities (WMH) did not affect the correlation between network properties and SN accuracy. This study demonstrated that structural connectivity network abnormalities, especially in the frontal and parietal gyri, are associated with a lower SN accuracy, independently of WMH, providing a new insight into the brain mechanisms associated with SN impairment in MCI.
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Affiliation(s)
- Weiping Li
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Hui Zhao
- Department of Neurology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zhao Qing
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zuzana Nedelska
- Department of Neurology, The Czech Brain Ageing Study, Memory Clinic, Second Faculty of Medicine-Charles University, University Hospital in Motol, Prague, Czechia.,International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czechia
| | - Sichu Wu
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Jiaming Lu
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Wenbo Wu
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Zhenyu Yin
- Department of Geriatrics, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Jakub Hort
- Department of Neurology, The Czech Brain Ageing Study, Memory Clinic, Second Faculty of Medicine-Charles University, University Hospital in Motol, Prague, Czechia.,International Clinical Research Center, St. Anne's University Hospital Brno, Brno, Czechia
| | - Yun Xu
- Department of Neurology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Bing Zhang
- Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
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21
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Yang C, Li X, Zhang J, Chen Y, Li H, Wei D, Lu P, Liang Y, Liu Z, Shu N, Wang F, Guan Q, Tao W, Wang Q, Jia J, Ai L, Cui R, Wang Y, Peng D, Zhang W, Chen K, Wang X, Zhao J, Wang Y, Dong Q, Wang J, Zhang Z. Early prevention of cognitive impairment in the community population: The Beijing Aging Brain Rejuvenation Initiative. Alzheimers Dement 2021; 17:1610-1618. [PMID: 33792187 DOI: 10.1002/alz.12326] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 01/01/2021] [Accepted: 02/04/2021] [Indexed: 12/23/2022]
Abstract
Facing considerable challenges associated with aging and dementia, China urgently needs an evidence-based health-care system for prevention and management of dementia. The Beijing Aging Brain Rejuvenation Initiative (BABRI) is a community-based cohort study initiated in 2008 that focuses on asymptomatic stages of dementia, aims to develop community-based prevention strategies for cognitive impairment, and provides a platform for scientific research and clinical trials. Thus far, BABRI has recruited 10,255 participants (aged 50 and over, 60.3% female), 2021 of whom have been followed up at least once at a 2- or 3-year interval. This article presents aims and study design of BABRI; summarizes preliminary behavioral and neuroimaging findings on mild cognitive impairment (MCI) and results of clinical trials on MCI; and discusses issues concerning early prevention in community, MCI diagnosis methods, and applications of database of aging and dementia. BABRI is proposed to build a systematic framework on brain health in old age.
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Affiliation(s)
- Caishui Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Junying Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing, China
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - He Li
- Institute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing, China
| | - Dongfeng Wei
- Institute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing, China
| | - Peng Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Ying Liang
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Zhen Liu
- National Institute on Drug Dependence, Peking University, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Fang Wang
- Dongcheng District Community Health Service Centre, Beijing, China
| | - Qing Guan
- School of Psychology and Society, Shenzhen University, Shenzhen, China
| | - Wuhai Tao
- School of Psychology and Society, Shenzhen University, Shenzhen, China
| | - Qingshan Wang
- Beijing Northern Hospital, China North Industries Group, Beijing, China
| | - Jianjun Jia
- Department of Geriatric Neurology, Chinese PLA General Hospital, Beijing, China
| | - Lin Ai
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ruixue Cui
- Department of Nuclear Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yanping Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing, China
| | - Dantao Peng
- Department of Neurology, China-Japan Friendship Hospital, Beijing, China
| | - Wei Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kewei Chen
- Banner Alzheimer's Institute, Phoenix, Arizona, USA
| | - Xiaomin Wang
- School of Basic Medicine, Capital Medical University, Beijing, China
| | - Jizong Zhao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yongyan Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Institute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing, China
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Jun Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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22
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Dong G, Yang L, Li CSR, Wang X, Zhang Y, Du W, Han Y, Tang X. Dynamic network connectivity predicts subjective cognitive decline: the Sino-Longitudinal Cognitive impairment and dementia study. Brain Imaging Behav 2021; 14:2692-2707. [PMID: 32361946 PMCID: PMC7606422 DOI: 10.1007/s11682-019-00220-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Subjective cognitive decline (SCD) is the preclinical stage of Alzheimer's disease (AD), the most common neurodegenerative disease in the elderly. We collected resting-state functional MRI data and applied novel graph-theoretical analyses to investigate the dynamic spatiotemporal cerebral connectivities in 63 individuals with SCD and 67 normal controls (NC). Temporal flexibility and spatiotemporal diversity were mapped to reflect dynamic time-varying functional interactions among the brain regions within and outside communities. Temporal flexibility indicates how frequently a brain region interacts with regions of other communities across time; spatiotemporal diversity describes how evenly a brain region interacts with regions belonging to other communities. SCD and NC differed in large-scale brain dynamics characterized by the two measures, which, with support vector machine, demonstrated higher classification accuracies than conventional static parameters and structural metrics. The findings characterize dynamic network dysfunction that may serve as a biomarker of the preclinical stage of AD.
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Affiliation(s)
- Guozhao Dong
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of technology, 5 South Zhongguancun Street, Beijing, 100081, China
| | - Liu Yang
- Department of Neurology, Xuanwu hospital of Capital Medical University, No.45 Street Changchun, District Xichen, Beijing, 100053, China
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Xiaoni Wang
- Department of Neurology, Xuanwu hospital of Capital Medical University, No.45 Street Changchun, District Xichen, Beijing, 100053, China
| | - Yihe Zhang
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of technology, 5 South Zhongguancun Street, Beijing, 100081, China
| | - Wenying Du
- Department of Neurology, Xuanwu hospital of Capital Medical University, No.45 Street Changchun, District Xichen, Beijing, 100053, China
| | - Ying Han
- Department of Neurology, Xuanwu hospital of Capital Medical University, No.45 Street Changchun, District Xichen, Beijing, 100053, China. .,National Clinical Research Center for Geriatric Disorders, Beijing, China.
| | - Xiaoying Tang
- Department of Biomedical Engineering, School of Life Science, Beijing Institute of technology, 5 South Zhongguancun Street, Beijing, 100081, China.
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23
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Lee S, Kim D, Youn H, Hyung WSW, Suh S, Kaiser M, Han CE, Jeong HG. Brain network analysis reveals that amyloidopathy affects comorbid cognitive dysfunction in older adults with depression. Sci Rep 2021; 11:4299. [PMID: 33619307 PMCID: PMC7900108 DOI: 10.1038/s41598-021-83739-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 02/03/2021] [Indexed: 12/24/2022] Open
Abstract
Late-life depression (LLD) may increase the risk of Alzheimer's dementia (AD). While amyloidopathy accelerates AD progression, its role in such patients has not yet been elucidated. We hypothesized that cerebral amyloidopathy distinctly affects the alteration of brain network topology and may be associated with distinct cognitive symptoms. We recruited 26 and 27 depressed mild cognitive impairment (MCI) patients with (LLD-MCI-A(+)) and without amyloid accumulation (LLD-MCI-A(-)), respectively, and 21 normal controls. We extracted structural brain networks using their diffusion-weighted images. We aimed to compare the distinct network deterioration in LLD-MCI with and without amyloid accumulation and the relationship with their distinct cognitive decline. Thus, we performed a group comparison of the network topological measures and investigated any correlations with neurocognitive testing scores. Topological features of brain networks were different according to the presence of amyloid accumulation. Disrupted network connectivity was highly associated with impaired recall and recognition in LLD-MCI-A(+) patients. Inattention and dysexecutive function were more influenced by the altered networks involved in fronto-limbic circuitry dysfunction in LLD-MCI-A(-) patients. Our results show that alterations in brain network topology may reflect different cognitive dysfunction depending on amyloid accumulation in depressed older adults with MCI.
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Affiliation(s)
- Suji Lee
- Department of Biomedical Sciences, Korea University Graduate School, Seoul, Republic of Korea
| | - Daegyeom Kim
- Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea
| | - HyunChul Youn
- Department of Psychiatry, Soonchunhyang University Bucheon Hospital, Bucheon, Republic of Korea
| | - Won Seok William Hyung
- Department of Psychiatry, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Sangil Suh
- Department of Radiology, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) Research Group, School of Computing, Newcastle University, Newcastle upon Tyne, NE4 5TG, UK
- Institute of Neuroscience, Newcastle University, The Henry Wellcome Building, Newcastle upon Tyne, NE2 4HH, UK
- Department of Functional Neurosurgery, School of Medicine, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, 200025, China
- Precision Imaging Beacon, School of Medicine, University of Nottingham, Nottingham, NG7 2UH, UK
| | - Cheol E Han
- Department of Electronics and Information Engineering, Korea University, Sejong, Republic of Korea.
- Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, Republic of Korea.
| | - Hyun-Ghang Jeong
- Department of Biomedical Sciences, Korea University Graduate School, Seoul, Republic of Korea.
- Department of Psychiatry, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea.
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24
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Shao W, He X, Li X, Tao W, Zhang J, Zhang S, Wang L, Qiao Y, Wang Y, Zhang Z, Peng D. Disrupted White Matter Networks from Subjective Memory Impairment to Amnestic Mild Cognitive Impairment. Curr Alzheimer Res 2021; 18:35-44. [PMID: 33761859 DOI: 10.2174/1567205018666210324115817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Revised: 12/13/2020] [Accepted: 03/08/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND AND OBJECTIVE Subjective memory impairment (SMI) is a preclinical stage prior to amnestic mild cognitive impairment (aMCI) along with the Alzheimer's disease (AD) continuum. We hypothesized that SMI patients had white matter (WM) network disruptions similar to those in aMCI patients. METHODS We used diffusion-tensor magnetic resonance imaging and graph theory to construct, analyze, and compare the WM networks among 20 normal controls (NC), 20 SMI patients, and 20 aMCI patients. RESULTS Compared with the NC group, the SMI group had significantly decreased global and local efficiency and an increased shortest path length. Moreover, similar to the aMCI group, the SMI group had lower nodal efficiency in regions located in the frontal and parietal lobes, limbic systems, and caudate nucleus compared to that of the NC group. CONCLUSION Similar to aMCI patient, SMI patients exhibited WM network disruptions, and detection of these disruptions could facilitate the early detection of SMI.
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Affiliation(s)
- Wen Shao
- Department of Neurology, China-Japan Friendship Hospital, Beijing 100029,China
| | - Xuwen He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875,China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875,China
| | - Wuhai Tao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875,China
| | - Junying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875,China
| | - Shujuan Zhang
- Department of Neurology, China-Japan Friendship Hospital, Beijing 100029,China
| | - Lei Wang
- Department of Neurology, China-Japan Friendship Hospital, Beijing 100029,China
| | - Yanan Qiao
- Department of Neurology, China-Japan Friendship Hospital, Beijing 100029,China
| | - Yu Wang
- Department of Neurology, China-Japan Friendship Hospital, Beijing 100029,China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875,China
| | - Dantao Peng
- Department of Neurology, China-Japan Friendship Hospital, Beijing 100029,China
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25
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The effects of cognitive training on the topological properties of brain structural network among community-dwelling older adults. J Clin Neurosci 2020; 83:77-82. [PMID: 33341367 DOI: 10.1016/j.jocn.2020.11.024] [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: 07/06/2020] [Revised: 09/27/2020] [Accepted: 11/23/2020] [Indexed: 11/21/2022]
Abstract
Increased number of neuroimaging studies has revealed association between age-related cognitive decline and alterations in the architecture of brain networks, while trials consistently confirmed benefits following cognitive training in the elderly. As a consequence, the present study aimed to investigate the potential moderating role of topological properties in brain structural network on training benefits. Among 32 community-dwelling older adults, 18 were randomly assigned to the training group to receive 24 sessions of multi-domain cognitive training (MDCT) over 12 weeks, and 14 to the control group. At baseline and 12-month follow-up, diffusion tensor imaging was acquired to construct the brain structural network, and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and the Visual Reasoning Test (VRT) were performed to assess cognitive functions. Compared with controls, participants received MDCT achieved significant larger gain in terms of delayed memory with a trend of better global cognitive function. In addition, Sigma coefficient of small-worldness were reduced in the MDCT group relative to the control group. Correlation between changes in Sigma and in delayed memory index were found among controls, however, not among older adults received MDCT. Our results demonstrated the modulating effects of cognitive training on the small-world architecture of brain structural network. And the present study suggested a trade-off mechanism underlying the benefits of cognitive training among aged people, where brain sacrificed its cost-effectiveness of network wiring for better cognitive functions.
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26
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Xue C, Sun H, Hu G, Qi W, Yue Y, Rao J, Yang W, Xiao C, Chen J. Disrupted Patterns of Rich-Club and Diverse-Club Organizations in Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment. Front Neurosci 2020; 14:575652. [PMID: 33177982 PMCID: PMC7593791 DOI: 10.3389/fnins.2020.575652] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 08/25/2020] [Indexed: 01/06/2023] Open
Abstract
Background Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) were considered to be a continuum of Alzheimer’s disease (AD) spectrum. The abnormal topological architecture and rich-club organization in the brain functional network can reveal the pathology of the AD spectrum. However, few studies have explored the disrupted patterns of diverse club organizations and the combination of rich- and diverse-club organizations in SCD and aMCI. Methods We collected resting-state functional magnetic resonance imaging data of 19 SCDs, 29 aMCIs, and 28 healthy controls (HCs) from the Alzheimer’s Disease Neuroimaging Initiative. Graph theory analysis was used to analyze the network metrics and rich- and diverse-club organizations simultaneously. Results Compared with HC, the aMCI group showed altered small-world and network efficiency, whereas the SCD group remained relatively stable. The aMCI group showed reduced rich-club connectivity compared with the HC. In addition, the aMCI group showed significantly increased feeder connectivity and decreased local connectivity of the diverse club compared with the SCD group. The overlapping nodes of the rich club and diverse club showed a significant difference in nodal efficiency and shortest path length (Lp) between groups. Notably, the Lp values of overlapping nodes in the SCD and aMCI groups were significantly associated with episodic memory. Conclusion The present study demonstrates that the network properties of SCD and aMCI have varying degrees of damage. The combination of the rich club and the diverse club can provide a novel insight into the pathological mechanism of the AD spectrum. The altered patterns in overlapping nodes might be potential biomarkers in the diagnosis of the AD spectrum.
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Affiliation(s)
- Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Haiting Sun
- Department of Pediatrics, Xijing Hospital, The Fourth Military Medical University (Air Force Medical University), Xi'an, China
| | - Guanjie Hu
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yingying Yue
- Department of Psychosomatics and Psychiatry, ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Jiang Rao
- Department of Rehabilitation, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenjie Yang
- 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, Fourth Clinical College of Nanjing Medical University, Nanjing, China
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27
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Li X, Wang Y, Wang W, Huang W, Chen K, Xu K, Zhang J, Chen Y, Li H, Wei D, Shu N, Zhang Z. Age-Related Decline in the Topological Efficiency of the Brain Structural Connectome and Cognitive Aging. Cereb Cortex 2020; 30:4651-4661. [PMID: 32219315 DOI: 10.1093/cercor/bhaa066] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 02/14/2020] [Accepted: 02/28/2020] [Indexed: 12/12/2022] Open
Abstract
Brain disconnection model has been proposed as a possible neural mechanism for cognitive aging. However, the relationship between structural connectivity degeneration and cognitive decline with normal aging remains unclear. In the present study, using diffusion MRI and tractography techniques, we report graph theory-based analyses of the brain structural connectome in a cross-sectional, community-based cohort of 633 cognitively healthy elderly individuals. Comprehensive neuropsychological assessment of the elderly subjects was performed. The association between age, brain structural connectome, and cognition across elderly individuals was examined. We found that the topological efficiency, modularity, and hub integration of the brain structural connectome exhibited a significant decline with normal aging, especially in the frontal, parietal, and superior temporal regions. Importantly, network efficiency was positively correlated with attention and executive function in elderly subjects and had a significant mediation effect on the age-related decline in these cognitive functions. Moreover, nodal efficiency of the brain structural connectome showed good performance for the prediction of attention and executive function in elderly individuals. Together, our findings revealed topological alterations of the brain structural connectome with normal aging, which provides possible structural substrates underlying cognitive aging and sensitive imaging markers for the individual prediction of cognitive functions in elderly subjects.
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Affiliation(s)
- Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
| | - Yezhou Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
| | - Wenxiao Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
| | - Kewei Chen
- BABRI Centre, Beijing Normal University, Beijing 100875, China
- Banner Alzheimer's Institute, Phoenix, AZ 85006, USA
| | - Kai Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
| | - Junying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
- Institute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing 10070, China
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
| | - He Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
- Institute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing 10070, China
| | - Dongfeng Wei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
- Institute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing 10070, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- BABRI Centre, Beijing Normal University, Beijing 100875, China
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Impaired brain network architecture in Cushing's disease based on graph theoretical analysis. Aging (Albany NY) 2020; 12:5168-5182. [PMID: 32208364 PMCID: PMC7138581 DOI: 10.18632/aging.102939] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 03/09/2020] [Indexed: 12/30/2022]
Abstract
To investigate the whole functional brain networks of active Cushing disease (CD) patients about topological parameters (small world and rich club et al.) and compared with healthy control (NC). Nineteen active CD patients and twenty-two healthy control subjects, matched in age, gender, and education, underwent resting-state fMRI. Graph theoretical analysis was used to calculate the functional brain network organizations for all participants, and those for active CD patients were compared for and NCs. Active CD patients revealed higher global efficiency, shortest path length and reduced cluster efficiency compared with healthy control. Additionally, small world organization was present in active CD patients but higher than healthy control. Moreover, rich club connections, feeder connections and local connections were significantly decreased in active CD patients. Functional network properties appeared to be disrupted in active CD patients compared with healthy control. Analyzing the changes that lead to abnormal network metrics will improve our understanding of the pathophysiological mechanisms underlying CD.
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Liu Y, Yang K, Hu X, Xiao C, Rao J, Li Z, Liu D, Zou Y, Chen J, Liu H. Altered Rich-Club Organization and Regional Topology Are Associated With Cognitive Decline in Patients With Frontal and Temporal Gliomas. Front Hum Neurosci 2020; 14:23. [PMID: 32153374 PMCID: PMC7047345 DOI: 10.3389/fnhum.2020.00023] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2019] [Accepted: 01/20/2020] [Indexed: 11/13/2022] Open
Abstract
Objectives Gliomas are widely considered to be related to the altered topological organization of functional networks before operations. Tumors are usually thought to cause multimodal cognitive impairments. The structure is thought to form the basics of function, and the aim of this study was to reveal the rich-club organization and topological patterns of white matter (WM) structural networks associated with cognitive impairments in patients with frontal and temporal gliomas. Methods Graph theory approaches were utilized to reveal the global and regional topological organization and rich-club organization of WM structural networks of 14 controls (CN), 13 frontal tumors (FTumor), and 18 temporal tumors (TTumor). Linear regression was used to assess the relationship between cognitive performances and altered topological parameters. Results When compared with CN, both FTumor and TTumor showed no alterations in small-world properties and global network efficiency, but instead showed altered local network efficiency. Second, FTumor and TTumor patients showed similar deficits in the nodal shortest path in the left rolandic operculum and degree centrality (DC) of the right dorsolateral and medial superior frontal gyrus (SFGmed). Third, compared to FTumor patients, TTumor patients showed a significantly higher DC in the right dorsolateral and SFGmed, a higher level of betweenness in the right SFGmed, and higher nodal efficiency in the left middle frontal gyrus and right SFGmed. Finally, rich-club organization was disrupted, with increased structural connectivity among rich-club nodes and reduced structural connectivity among peripheral nodes in FTumor and TTumor patients. Altered local efficiency in TTumor correlated with memory function, while altered local efficiency in FTumor correlated with the information processing speed. Conclusion Both FTumor and TTumor presented an intact global topology and altered regional topology related to cognitive impairment and may also share the convergent and divergent regional topological organization of WM structural networks. This suggested that a compensatory mechanism plays a key role in global topology formation in both FTumor and TTumor patients, and as such, development of a structural connectome for patients with brain tumors would be an invaluable medical resource and allow clinicians to make comprehensive preoperative planning.
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Affiliation(s)
- Yong Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Kun Yang
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Xinhua Hu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Chaoyong Xiao
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiang Rao
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Department of Rehabilitation Medicine, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zonghong Li
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Dongming Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yuanjie Zou
- Department of Neurosurgery, 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, Fourth Clinical College of Nanjing Medical University, Nanjing, China
| | - Hongyi Liu
- Department of Neurosurgery, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
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Sang L, Liu C, Wang L, Zhang J, Zhang Y, Li P, Qiao L, Li C, Qiu M. Disrupted Brain Structural Connectivity Network in Subcortical Ischemic Vascular Cognitive Impairment With No Dementia. Front Aging Neurosci 2020; 12:6. [PMID: 32063840 PMCID: PMC7000429 DOI: 10.3389/fnagi.2020.00006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 01/10/2020] [Indexed: 11/28/2022] Open
Abstract
The alteration of the functional topological organization in subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND) patients has been illuminated by previous neuroimaging studies. However, in regard to the changes in the structural connectivity of brain networks, little has been reported. In this study, a total of 27 subjects, consisting of 13 SIVCIND patients, and 14 normal controls, were recruited. Each of the structural connectivity networks was constructed by diffusion tensor tractography. Subsequently, graph theory, and network-based statistics (NBS) were employed to analyze the whole-brain mean factional anisotropy matrix. After removing the factor of age, gender, and duration of formal education, the clustering coefficients (Cp) and global efficiency (Eglob) were significantly decreased and the mean path length (Lp) was significantly increased in SIVCIND patients compared with normal controls. Using the combination of four network topological parameters as the classification feature, a classification accuracy of 78% was obtained by leave-one-out cross-validation for all subjects with a sensitivity of 69% and a specificity of 86%. Moreover, we also found decreased structural connections in the SIVCIND patients, which mainly concerned fronto-occipital, fronto-subcortical, and tempo-occipital connections (NBS corrected, p < 0.01). Additionally, significantly altered nodal centralities were found in several brain regions of the SIVCIND patients, mainly located in the prefrontal, subcortical, and temporal cortices. These results suggest that cognitive impairment in SIVCIND patients is associated with disrupted topological organization and provide structural evidence for developing reliable biomarkers related to cognitive decline in SIVCIND.
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Affiliation(s)
- Linqiong Sang
- Department of Medical Imaging, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Li Wang
- Department of Medical Imaging, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Jingna Zhang
- Department of Medical Imaging, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Ye Zhang
- Department of Medical Imaging, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Pengyue Li
- Department of Medical Imaging, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Liang Qiao
- Department of Medical Imaging, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Chuanming Li
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingguo Qiu
- Department of Medical Imaging, School of Biomedical Engineering, Third Military Medical University, Chongqing, China
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31
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Wang D, Yao Q, Yu M, Xiao C, Fan L, Lin X, Zhu D, Tian M, Shi J. Topological Disruption of Structural Brain Networks in Patients With Cognitive Impairment Following Cerebellar Infarction. Front Neurol 2019; 10:759. [PMID: 31379713 PMCID: PMC6659410 DOI: 10.3389/fneur.2019.00759] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Accepted: 07/01/2019] [Indexed: 11/13/2022] Open
Abstract
Cerebellar lesions can lead to a series of cognitive and emotional disorders by influencing cerebral activity via cerebro-cerebellar loops. To explore changes in cognitive function and structural brain networks in patients with posterior cerebellar infarction, we conducted the current study using diffusion-weighted MRI (32 cerebellar infarction patients, 29 controls). Moreover, a series of neuropsychological tests were used to assess the subject's cognitive performance. We found cognitive impairment following cerebellar infarction involving multiple cognitive domains, including memory, executive functions, visuospatial abilities, processing speed and language functions, and brain topological abnormalities, including changes in clustering coefficients, shortest path lengths, global efficiency, local efficiencies, betweenness centrality and nodal efficiencies. Our results indicated that measures of local efficiency, mainly in the precuneus, cingulate gyrus and frontal-temporal cortex, were significantly reduced with posterior cerebellar infarction. At the same time, The correlation analysis suggested thatthe abnormal alterations in the right PCG, bilateral DCG, right PCUN may play a core role in the cognitive impairment following cerebellar infarctions. The differences in topological features of the structural brain networks within the cerebro-cerebellar circuits may provide a new approach to explore the pathophysiological mechanisms of cognitive impairment following cerebellar infarction.
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Affiliation(s)
- Duohao Wang
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Qun Yao
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Miao Yu
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chaoyong Xiao
- Department of Radiology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Lin Fan
- Department of Neurology, Taizhou People's Hospital, Taizhou, China
| | - Xingjian Lin
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Donglin Zhu
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Minjie Tian
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jingping Shi
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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Deficits of visuospatial working memory and executive function in single- versus multiple-domain amnestic mild cognitive impairment: A combined ERP and sLORETA study. Clin Neurophysiol 2019; 130:739-751. [DOI: 10.1016/j.clinph.2019.01.025] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 01/22/2019] [Accepted: 01/29/2019] [Indexed: 02/07/2023]
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33
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van Montfort SJT, van Dellen E, Stam CJ, Ahmad AH, Mentink LJ, Kraan CW, Zalesky A, Slooter AJC. Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis. NEUROIMAGE-CLINICAL 2019; 23:101809. [PMID: 30981940 PMCID: PMC6461601 DOI: 10.1016/j.nicl.2019.101809] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 03/25/2019] [Accepted: 03/31/2019] [Indexed: 01/05/2023]
Abstract
Delirium is an acute neuropsychiatric syndrome characterized by altered levels of attention and awareness with cognitive deficits. It is most prevalent in elderly hospitalized patients and related to poor outcomes. Predisposing risk factors, such as older age, determine the baseline vulnerability for delirium, while precipitating factors, such as use of sedatives, trigger the syndrome. Risk factors are heterogeneous and the underlying biological mechanisms leading to vulnerability for delirium are poorly understood. We tested the hypothesis that delirium and its risk factors are associated with consistent brain network changes. We performed a systematic review and qualitative meta-analysis and included 126 brain network publications on delirium and its risk factors. Findings were evaluated after an assessment of methodological quality, providing N=99 studies of good or excellent quality on predisposing risk factors, N=10 on precipitation risk factors and N=7 on delirium. Delirium was consistently associated with functional network disruptions, including lower EEG connectivity strength and decreased fMRI network integration. Risk factors for delirium were associated with lower structural connectivity strength and less efficient structural network organization. Decreased connectivity strength and efficiency appear to characterize structural brain networks of patients at risk for delirium, possibly impairing the functional network, while functional network disintegration seems to be a final common pathway for the syndrome. Delirium is consistently associated with functional network impairments. Risk factors are associated with lower structural connectivity strength. Risk factors are associated with a less efficient structural network organization. Structural impairments make the functional network more vulnerable to deterioration. Functional network disintegration seems to be a final common pathway for delirium.
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Affiliation(s)
- S J T van Montfort
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
| | - E van Dellen
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Melbourne Neuropsychiatry Center, Department of Psychiatry, Level 3, Alan Gilbert Building, 161 Barry Street, Carlton South, 3053 Victoria, University of Melbourne and Melbourne Health, Australia
| | - C J Stam
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - A H Ahmad
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Psychology, Utrecht University, Heidelberglaan 1, 3584 CS Utrecht, The Netherlands
| | - L J Mentink
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - C W Kraan
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - A Zalesky
- Melbourne Neuropsychiatry Center, Department of Psychiatry, Level 3, Alan Gilbert Building, 161 Barry Street, Carlton South, 3053 Victoria, University of Melbourne and Melbourne Health, Australia
| | - A J C Slooter
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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Wang Y, Deng F, Jia Y, Wang J, Zhong S, Huang H, Chen L, Chen G, Hu H, Huang L, Huang R. Disrupted rich club organization and structural brain connectome in unmedicated bipolar disorder. Psychol Med 2019; 49:510-518. [PMID: 29734951 DOI: 10.1017/s0033291718001150] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
BACKGROUND Bipolar disorder (BD) has been associated with altered brain structural and functional connectivity. However, little is known regarding alterations of the structural brain connectome in BD. The present study aimed to use diffusion-tensor imaging (DTI) and graph theory approaches to investigate the rich club organization and white matter structural connectome in BD. METHODS Forty-two patients with unmedicated BD depression and 59 age-, sex- and handedness-matched healthy control participants underwent DTI. The whole-brain structural connectome was constructed by a deterministic fiber tracking approach. Graph theory analysis was used to examine the group-specific global and nodal topological properties, and rich club organizations, and then nonparametric permutation tests were used for group comparisons of network parameters. RESULTS Compared with healthy control participants, the patients with BD showed abnormal global properties, including increased characteristic path length, and decreased global efficiency and local efficiency. Locally, the patients with BD showed abnormal nodal parameters (nodal strength, nodal efficiency, and nodal betweenness) predominantly in the parietal, orbitofrontal, occipital, and cerebellar regions. Moreover, the patients with BD showed decreased rich club and feeder connectivity density. CONCLUSIONS Our results may reflect the disrupted white matter topological organization in the whole-brain, and abnormal regional connectivity supporting cognitive and affective functioning in depressed BD, which, in part, be due to impaired rich club connectivity.
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Affiliation(s)
- Ying Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University,Guangzhou 510630,China
| | - Feng Deng
- Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University,Guangzhou 510631,China
| | - Yanbin Jia
- Department of Psychiatry,First Affiliated Hospital of Jinan University,Guangzhou 510630,China
| | - Junjing Wang
- Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University,Guangzhou 510631,China
| | - Shuming Zhong
- Department of Psychiatry,First Affiliated Hospital of Jinan University,Guangzhou 510630,China
| | - Huiyuan Huang
- Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University,Guangzhou 510631,China
| | - Lixiang Chen
- Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University,Guangzhou 510631,China
| | - Guanmao Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University,Guangzhou 510630,China
| | - Huiqing Hu
- Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University,Guangzhou 510631,China
| | - Li Huang
- Medical Imaging Center, First Affiliated Hospital of Jinan University,Guangzhou 510630,China
| | - Ruiwang Huang
- Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University,Guangzhou 510631,China
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Zhang Y, Cao Y, Xie Y, Liu L, Qin W, Lu S, Zhang Q. Altered brain structural topological properties in type 2 diabetes mellitus patients without complications. J Diabetes 2019; 11:129-138. [PMID: 30039563 DOI: 10.1111/1753-0407.12826] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2018] [Revised: 06/19/2018] [Accepted: 07/13/2018] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is a risk factor for cognitive dysfunction, and white matter (WM) microstructural impairments play a critical role in T2DM-related cognitive decline. Disruptions to the WM have been detected in T2DM patients before clinical diagnosis of cognitive dysfunction. Herein, we investigated changes in brain structural topological properties and their correlation with behavior in T2DM patients without complications. METHODS Diffusion tensor imaging (DTI) structural network topological analysis was performed on T2DM patients and healthy controls. Intergroup differences in global and nodal parameters were analyzed, and correlations between the network parameters and behavioral performance were tested. RESULTS Type 2 diabetes mellitus patients exhibited preserved small-world properties, but altered nodal properties, including decreased efficiency in the right hippocampus, right amygdala, left pallidum, left postcentral gyrus, and right pole of the superior temporal gyrus, and increased degree in the right inferior frontal gyrus. Correlations were also found between the altered global and nodal parameters and behavioral performance. CONCLUSIONS The results verified the existence of WM structural network changes and the association between structural properties and cognitive state in T2DM patients before the occurrence of complications. Research of structural properties may contribute to our understanding of the intrinsic links between T2DM and cognition.
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Affiliation(s)
- Yang Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yujuan Cao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yingjie Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Linlin Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Shan Lu
- Department of Radiology, Tianjin Medical University Metabolic Diseases Hospital, Tianjin, China
| | - Quan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
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Sun Y, Bi Q, Wang X, Hu X, Li H, Li X, Ma T, Lu J, Chan P, Shu N, Han Y. Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome. Front Neurol 2019; 9:1178. [PMID: 30687226 PMCID: PMC6335339 DOI: 10.3389/fneur.2018.01178] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 12/20/2018] [Indexed: 12/12/2022] Open
Abstract
Background: Early prediction of disease progression in patients with amnestic mild cognitive impairment (aMCI) is important for early diagnosis and intervention of Alzheimer's disease (AD). Previous brain network studies have suggested topological disruptions of the brain connectome in aMCI patients. However, whether brain connectome markers at baseline can predict longitudinal conversion from aMCI to AD remains largely unknown. Methods: In this study, 52 patients with aMCI and 26 demographically matched healthy controls from a longitudinal cohort were evaluated. During 2 years of follow-up, 26 patients with aMCI were retrospectively classified as aMCI converters and 26 patients remained stable as aMCI non-converters based on whether they were subsequently diagnosed with AD. For each participant, diffusion tensor imaging at baseline and deterministic tractography were used to map the whole-brain white matter structural connectome. Graph theoretical analysis was applied to investigate the convergent and divergent connectivity patterns of structural connectome between aMCI converters and non-converters. Results: Disrupted topological organization of the brain structural connectome were identified in both aMCI converters and non-converters. More severe disruptions of structural connectivity in aMCI converters compared with non-converters were found, especially in the default-mode network regions and connections. Finally, a support vector machine-based classification demonstrated the good discriminative ability of structural connectivity in differentiating aMCI patients from controls with an accuracy of 98%, and in discriminating converters from non-converters with an accuracy of 81%. Conclusion: Our study provides potential structural connectome/connectivity-based biomarkers for predicting disease progression in aMCI, which is important for the early diagnosis of AD.
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Affiliation(s)
- Yu Sun
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Qiuhui Bi
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Xiaoni Wang
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Xiaochen Hu
- Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany
| | - Huijie Li
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.,CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China
| | - Xiaobo Li
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
| | - Ting Ma
- Department of Electronic and Information Engineering, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China
| | - Jie Lu
- Department of Radiology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Piu Chan
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China.,Beijing Institute of Geriatrics, XuanWu Hospital of Capital Medical University, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China.,Beijing Institute of Geriatrics, XuanWu Hospital of Capital Medical University, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
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Luo X, Jiaerken Y, Huang P, Xu XJ, Qiu T, Jia Y, Shen Z, Guan X, Zhou J, Zhang M. Alteration of regional homogeneity and white matter hyperintensities in amnestic mild cognitive impairment subtypes are related to cognition and CSF biomarkers. Brain Imaging Behav 2018; 12:188-200. [PMID: 28236166 DOI: 10.1007/s11682-017-9680-4] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Amnestic mild cognitive impairment can be further classified as single-domain aMCI (SD-aMCI) with isolated memory deficit, or multi-domain aMCI (MD-aMCI) if memory deficit is combined with impairment in other cognitive domains. Prior studies reported these clinical subtypes presumably differ in etiology. Thus, we aimed to explore the possible mechanisms between different aMCI subtypes by assessing alteration in brain activity and brain vasculature, and their relations with CSF AD biomarkers. 49 healthy controls, 32 SD-aMCI, and 32 MD-aMCI, who had undergone structural scans, resting-state functional MRI (rsfMRI) scans and neuropsychological evaluations, were identified. Regional homogeneity (ReHo) was employed to analyze regional synchronization. Periventricular white matter hyperintensities (PWMH) and deep WMH (DWMH) volume of each participant was quantitatively assessed. AD biomarkers from CSF were also measured. SD-aMCI showed decreased ReHo in medial temporal gyrus (MTG), and increased ReHo in lingual gyrus (LG) and superior temporal gyrus (STG) relative to controls. MD-aMCI showed decreased ReHo, mostly located in precuneus (PCu), LG and postcentral gyrus (PCG), relative to SD-aMCI and controls. As for microvascular disease, MD-aMCI patients had more PWMH burden than SD-aMCI and controls. Correlation analyses indicated mean ReHo in differenced regions were related with memory, language, and executive function in aMCI patients. However, no significant associations between PWMH and behavioral data were found. The Aβ level was related with the ReHo value of STG in SD-aMCI. MD-aMCI displayed different patterns of abnormal regional synchronization and more severe PWMH burden compared with SD-aMCI. Therefore aMCI is not a uniform disease entity, and MD-aMCI group may show more complicated pathologies than SD-aMCI group.
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Affiliation(s)
- Xiao Luo
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University, School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009, China
| | - Yerfan Jiaerken
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University, School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009, China
| | - Peiyu Huang
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University, School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009, China
| | - Xiao Jun Xu
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University, School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009, China
| | - Tiantian Qiu
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University, School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009, China
| | - Yunlu Jia
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University, School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009, China
| | - Zhujing Shen
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University, School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009, China
| | - Xiaojun Guan
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University, School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009, China
| | - Jiong Zhou
- Department of Neurology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University, School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009, China.
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Luo X, Li K, Zeng Q, Huang P, Jiaerken Y, Qiu T, Xu X, Zhou J, Xu J, Zhang M. Decreased Bilateral FDG-PET Uptake and Inter-Hemispheric Connectivity in Multi-Domain Amnestic Mild Cognitive Impairment Patients: A Preliminary Study. Front Aging Neurosci 2018; 10:161. [PMID: 29922150 PMCID: PMC5996941 DOI: 10.3389/fnagi.2018.00161] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 05/14/2018] [Indexed: 12/16/2022] Open
Abstract
Background: Amnestic mild cognitive impairment (aMCI) is a heterogeneous condition. Based on clinical symptoms, aMCI could be categorized into single-domain aMCI (SD-aMCI, only memory deficit) and multi-domain aMCI (MD-aMCI, one or more cognitive domain deficit). As core intrinsic functional architecture, inter-hemispheric connectivity maintains many cognitive abilities. However, few studies investigated whether SD-aMCI and MD-aMCI have different inter-hemispheric connectivity pattern. Methods: We evaluated inter-hemispheric connection pattern using fluorine-18 positron emission tomography - fluorodeoxyglucose (18F PET-FDG), resting-state functional MRI and structural T1 in 49 controls, 32 SD-aMCI, and 32 MD-aMCI patients. Specifically, we analyzed the 18F PET-FDG (intensity normalized by cerebellar vermis) in a voxel-wise manner. Then, we estimated inter-hemispheric functional and structural connectivity by calculating the voxel-mirrored homotopic connectivity (VMHC) and corpus callosum (CC) subregions volume. Further, we correlated inter-hemispheric indices with the behavioral score and pathological biomarkers. Results: We found that MD-aMCI exhibited more several inter-hemispheric connectivity damages than SD-aMCI. Specifically, MD-aMCI displayed hypometabolism in the bilateral middle temporal gyrus (MTG), inferior parietal lobe, and left precuneus (PCu) (p < 0.001, corrected). Correspondingly, MD-aMCI showed decreased VMHC in MTG, PCu, calcarine gyrus, and postcentral gyrus, as well as smaller mid-posterior CC than the SD-aMCI and controls (p < 0.05, corrected). Contrary to MD-aMCI, there were no neuroimaging indices with significant differences between SD-aMCI and controls, except reduced hypometabolism in bilateral MTG. Within aMCI patients, hypometabolism and reduced inter-hemispheric connectivity correlated with worse executive ability. Moreover, hypometabolism indices correlated to increased amyloid deposition. Conclusion: In conclusion, patients with MD-aMCI exhibited the more severe deficit in inter-hemispheric communication than SD-aMCI. This long-range connectivity deficit may contribute to cognitive profiles and potentially serve as a biomarker to estimate disease progression of aMCI patients.
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Affiliation(s)
- Xiao Luo
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Yeerfan Jiaerken
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Tiantian Qiu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jiong Zhou
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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Altered amygdala and hippocampus effective connectivity in mild cognitive impairment patients with depression: a resting-state functional MR imaging study with granger causality analysis. Oncotarget 2018; 8:25021-25031. [PMID: 28212570 PMCID: PMC5421906 DOI: 10.18632/oncotarget.15335] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 01/09/2017] [Indexed: 12/14/2022] Open
Abstract
Neuroimaging studies have demonstrated that the major depression disorder would increase the risk of dementia in the older with amnestic cognitive impairment. We used granger causality analysis algorithm to explore the amygdala- and hippocampus-based directional connectivity patterns in 12 patients with major depression disorder and amnestic cognitive impairment (mean age: 69.5 ± 10.3 years), 13 amnestic cognitive impairment patients (mean age: 72.7 ± 8.5 years) and 14 healthy controls (mean age: 64.7 ± 7.0 years). Compared with amnestic cognitive impairment patients and control groups respectively, the patients with both major depression disorder and amnestic cognitive impairment displayed increased effective connectivity from the right amygdala to the right lingual and calcarine gyrus, as well as to the bilateral supplementary motor areas. Meanwhile, the patients with both major depression disorder and amnestic cognitive impairment had enhanced effective connectivity from the left superior parietal gyrus, superior and middle occipital gyrus to the left hippocampus, the z values of which was also correlated with the scores of mini-mental state examination and auditory verbal learning test-immediate recall. Our findings indicated that the directional effective connectivity of right amygdala - occipital-parietal lobe – left hippocampus might be the pathway by which major depression disorder inhibited the brain activity in patients with amnestic cognitive impairment.
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Sang L, Chen L, Wang L, Zhang J, Zhang Y, Li P, Li C, Qiu M. Progressively Disrupted Brain Functional Connectivity Network in Subcortical Ischemic Vascular Cognitive Impairment Patients. Front Neurol 2018. [PMID: 29535678 PMCID: PMC5834750 DOI: 10.3389/fneur.2018.00094] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Cognitive impairment caused by subcortical ischemic vascular disease (SIVD) has been elucidated by many neuroimaging studies. However, little is known regarding the changes in brain functional connectivity networks in relation to the severity of cognitive impairment in SIVD. In the present study, 20 subcortical ischemic vascular cognitive impairment no dementia patients (SIVCIND) and 20 dementia patients (SIVaD) were enrolled; additionally, 19 normal controls were recruited. Each participant underwent a resting-state functional MRI scan. Whole-brain functional networks were analyzed with graph theory and network-based statistics (NBS) to study the functional organization of networks and find alterations in functional connectivity among brain regions. After adjustments for age, gender, and duration of formal education, there were significant group differences for two network functional organization indices, global efficiency and local efficiency, which decreased (NC > SIVCIND > SIVaD) as cognitive impairment worsened. Between-group differences in functional connectivity (NBS corrected, p < 0.01) mainly involved the orbitofrontal, parietal, and temporal cortices, as well as the basal ganglia. The brain connectivity network was progressively disrupted as cognitive impairment worsened, with an increased number of decreased connections between brain regions. We also observed more reductions in nodal efficiency in the prefrontal and temporal cortices for SIVaD than for SIVCIND. These findings indicated a progressively disrupted pattern of the brain functional connectivity network with increased cognitive impairment and showed promise for the development of reliable biomarkers of network metric changes related to cognitive impairment caused by SIVD.
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Affiliation(s)
- Linqiong Sang
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Lin Chen
- Department of Psychology, Third Military Medical University, Chongqing, China
| | - Li Wang
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Jingna Zhang
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Ye Zhang
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Pengyue Li
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
| | - Chuanming Li
- Department of Radiology, Southwest Hospital, Third Military Medical University, Chongqing, China
| | - Mingguo Qiu
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, Chongqing, China
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Andreotti J, Dierks T, Wahlund LO, Grieder M. Diverging Progression of Network Disruption and Atrophy in Alzheimer's Disease and Semantic Dementia. J Alzheimers Dis 2018; 55:981-993. [PMID: 27802229 PMCID: PMC5147505 DOI: 10.3233/jad-160571] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The progression of cognitive deficits in Alzheimer's disease and semantic dementia is accompanied by grey matter atrophy and white matter deterioration. The impact of neuronal loss on the structural network connectivity in these dementia subtypes is, however, not well understood. In order to gain a more refined knowledge of the topological organization of white matter alterations in dementia, we used a network-based approach to analyze the brain's structural connectivity network. Diffusion-weighted and anatomical MRI images of groups with eighteen Alzheimer's disease and six semantic dementia patients, as well as twenty-one healthy controls were recorded to reconstruct individual connectivity networks. Additionally, voxel-based morphometry, using grey and white matter volume, served to relate atrophy to altered structural connectivity. The analyses showed that Alzheimer's disease is characterized by decreased connectivity strength in various cortical regions. An overlap with grey matter loss was found only in the inferior frontal and superior temporal areas. In semantic dementia, significantly reduced network strength was found in the temporal lobes, which converged with grey and white matter atrophy. Therefore, this study demonstrated that the structural disconnection in early Alzheimer's disease goes beyond grey matter atrophy and is independent of white matter volume loss, an observation that was not found in semantic dementia.
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Affiliation(s)
- Jennifer Andreotti
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Thomas Dierks
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Lars-Olof Wahlund
- Karolinska Institute, Department of Neurobiology, Care Sciences and Society (NVS), Division of Clinical Geriatrics, Stockholm, Sweden
| | - Matthias Grieder
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
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42
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Abnormal organization of white matter networks in patients with subjective cognitive decline and mild cognitive impairment. Oncotarget 2018; 7:48953-48962. [PMID: 27418146 PMCID: PMC5226483 DOI: 10.18632/oncotarget.10601] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2015] [Accepted: 06/29/2016] [Indexed: 11/25/2022] Open
Abstract
Network analysis has been widely used in studying Alzheimer's disease (AD). However, how the white matter network changes in cognitive impaired patients with subjective cognitive decline (SCD) (a symptom emerging during early stage of AD) and amnestic mild cognitive impairment (aMCI) (a pre-dementia stage of AD) is still unclear. Here, structural networks were constructed respectively based on FA and FN for 36 normal controls, 21 SCD patients, and 33 aMCI patients by diffusion tensor imaging and graph theory. Significantly lower efficiency was found in aMCI patients than normal controls (NC). Though not significant, the values in those with SCD were intermediate between aMCI and NC. In addition, our results showed significantly altered betweenness centrality located in right precuneus, calcarine, putamen, and left anterior cingulate in aMCI patients. Furthermore, association was found between network metrics and cognitive impairment. Our study suggests that the structural network properties might be preserved in SCD stage and disrupted in aMCI stage, which may provide novel insights into pathological mechanisms of AD.
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43
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Muñoz-Moreno E, Tudela R, López-Gil X, Soria G. Early brain connectivity alterations and cognitive impairment in a rat model of Alzheimer's disease. Alzheimers Res Ther 2018; 10:16. [PMID: 29415770 PMCID: PMC5803915 DOI: 10.1186/s13195-018-0346-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 01/22/2018] [Indexed: 02/05/2023]
Abstract
BACKGROUND Animal models of Alzheimer's disease (AD) are essential to understanding the disease progression and to development of early biomarkers. Because AD has been described as a disconnection syndrome, magnetic resonance imaging (MRI)-based connectomics provides a highly translational approach to characterizing the disruption in connectivity associated with the disease. In this study, a transgenic rat model of AD (TgF344-AD) was analyzed to describe both cognitive performance and brain connectivity at an early stage (5 months of age) before a significant concentration of β-amyloid plaques is present. METHODS Cognitive abilities were assessed by a delayed nonmatch-to-sample (DNMS) task preceded by a training phase where the animals learned the task. The number of training sessions required to achieve a learning criterion was recorded and evaluated. After DNMS, MRI acquisition was performed, including diffusion-weighted MRI and resting-state functional MRI, which were processed to obtain the structural and functional connectomes, respectively. Global and regional graph metrics were computed to evaluate network organization in both transgenic and control rats. RESULTS The results pointed to a delay in learning the working memory-related task in the AD rats, which also completed a lower number of trials in the DNMS task. Regarding connectivity properties, less efficient organization of the structural brain networks of the transgenic rats with respect to controls was observed. Specific regional differences in connectivity were identified in both structural and functional networks. In addition, a strong correlation was observed between cognitive performance and brain networks, including whole-brain structural connectivity as well as functional and structural network metrics of regions related to memory and reward processes. CONCLUSIONS In this study, connectivity and neurocognitive impairments were identified in TgF344-AD rats at a very early stage of the disease when most of the pathological hallmarks have not yet been detected. Structural and functional network metrics of regions related to reward, memory, and sensory performance were strongly correlated with the cognitive outcome. The use of animal models is essential for the early identification of these alterations and can contribute to the development of early biomarkers of the disease based on MRI connectomics.
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Affiliation(s)
- Emma Muñoz-Moreno
- Experimental 7T MRI Unit, Institut d’Investigacions Biòmediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Raúl Tudela
- Consorcio Centro de Investigación Biomédica en Red (CIBER) de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Group of Biomedical Imaging, University of Barcelona, Barcelona, Spain
| | - Xavier López-Gil
- Experimental 7T MRI Unit, Institut d’Investigacions Biòmediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Guadalupe Soria
- Experimental 7T MRI Unit, Institut d’Investigacions Biòmediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
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44
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Zhang J, Liu Z, Li Z, Wang Y, Chen Y, Li X, Chen K, Shu N, Zhang Z. Disrupted White Matter Network and Cognitive Decline in Type 2 Diabetes Patients. J Alzheimers Dis 2018; 53:185-95. [PMID: 27163818 DOI: 10.3233/jad-160111] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Type 2 diabetes mellitus is accompanied by cognitive impairment and is associated with an increased risk of dementia. Damage to brain structures such as white matter network disruption may underlie this cognitive disturbance. In the present study, 886 non-diabetic and 163 type 2 diabetic participants completed a battery of neuropsychological tests. Among them, 38 diabetic patients and 34 non-diabetic participants that matched the patients for age/sex/education received a magnetic resonance imaging-based diffusion tensor imaging. Then we calculated the topological properties of the white matter network using a graph theoretical method to investigate network efficiency differences between groups. We found that type 2 diabetic patients had inferior performances compared to the non-diabetic controls, in several cognitive domains involving executive function, spatial processing, memory, and attention. We also found that diabetic patients exhibited a disrupted topological organization of the white matter network (including the global network properties, i.e., network strength, global efficiency, local efficiency and shortest path length, and the nodal efficiency of the right rolandic operculum) in the brain. Moreover, those global network properties and the nodal efficiency of the right rolandic operculum both had positive correlations with executive function in the patient group. The results suggest that type 2 diabetes mellitus leads to an alteration in the topological organization of the cortical white matter network and this alteration may account for the observed cognitive decline.
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Affiliation(s)
- Junying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China.,BABRI Centre, Beijing Normal University, Beijing, P. R. China
| | - Zhen Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China.,BABRI Centre, Beijing Normal University, Beijing, P. R. China
| | - Zixiao Li
- Tiantan Clinical Trial and Research Center for Stroke, Department of Neurology, Beijing TianTan Hospital, Capital Medical University, Beijing, P. R. China
| | - Yunxia Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China.,BABRI Centre, Beijing Normal University, Beijing, P. R. China
| | - Yaojing Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China.,BABRI Centre, Beijing Normal University, Beijing, P. R. China
| | - Xin Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China.,BABRI Centre, Beijing Normal University, Beijing, P. R. China
| | - Kewei Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China.,Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China.,BABRI Centre, Beijing Normal University, Beijing, P. R. China
| | - Zhanjun Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China.,BABRI Centre, Beijing Normal University, Beijing, P. R. China
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45
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Qian L, Zheng L, Shang Y, Zhang Y, Zhang Y. Intrinsic frequency specific brain networks for identification of MCI individuals using resting-state fMRI. Neurosci Lett 2017; 664:7-14. [PMID: 29107088 DOI: 10.1016/j.neulet.2017.10.052] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Revised: 10/16/2017] [Accepted: 10/25/2017] [Indexed: 12/14/2022]
Abstract
Numerous brain oscillations are well organized into several brain rhythms to support complex brain activities within distinct frequency bands. These rhythms temporally coexist in the same or different brain areas and may interact with each other with specific properties and physiological functions. However, the identification and evaluation of these various brain rhythms derived from BOLD-fMRI signals are obscure. To address this issue, we introduced a data-driven method named Complementary Ensemble Empirical Mode Decomposition (CEEMD) to automatically decompose the BOLD oscillations into several brain rhythms within distinct frequency bands. Thereafter, in order to evaluate the performance of CEEMD in the detection of subtle BOLD signals, a novel CEEMD-based high-dimensional pattern classification framework was proposed to accurately identify mild cognitive impairment individuals from the healthy controls. Our results showed CEEMD is a stable frequency decomposition method. Furthermore, CEEMD-based frequency specific topological profiles provided a classification accuracy of 93.33%, which was saliently higher than that of the conventional frequency separation based scheme. Importantly, our findings demonstrated that CEEMD could provide an effective means for brain oscillation separation, by which a more meaningful frequency bins could be used to detect the subtle changes embedded in the BOLD signals.
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Affiliation(s)
- Long Qian
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China; McGovern Institute for Brain Research, Peking University, Beijing, 100871, China
| | - Li Zheng
- Department of Biomedical Engineering, Peking University, Beijing, 100871, China
| | - Yuqing Shang
- Mechanobiology Institute, National University of Singapore, Singapore 117411, Singapore
| | - Yaoyu Zhang
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China
| | - Yi Zhang
- Center for Brain Imaging, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China.
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46
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Guillon J, Attal Y, Colliot O, La Corte V, Dubois B, Schwartz D, Chavez M, De Vico Fallani F. Loss of brain inter-frequency hubs in Alzheimer's disease. Sci Rep 2017; 7:10879. [PMID: 28883408 PMCID: PMC5589939 DOI: 10.1038/s41598-017-07846-w] [Citation(s) in RCA: 57] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Accepted: 06/29/2017] [Indexed: 01/05/2023] Open
Abstract
Alzheimer's disease (AD) causes alterations of brain network structure and function. The latter consists of connectivity changes between oscillatory processes at different frequency channels. We proposed a multi-layer network approach to analyze multiple-frequency brain networks inferred from magnetoencephalographic recordings during resting-states in AD subjects and age-matched controls. Main results showed that brain networks tend to facilitate information propagation across different frequencies, as measured by the multi-participation coefficient (MPC). However, regional connectivity in AD subjects was abnormally distributed across frequency bands as compared to controls, causing significant decreases of MPC. This effect was mainly localized in association areas and in the cingulate cortex, which acted, in the healthy group, as a true inter-frequency hub. MPC values significantly correlated with memory impairment of AD subjects, as measured by the total recall score. Most predictive regions belonged to components of the default-mode network that are typically affected by atrophy, metabolism disruption and amyloid-β deposition. We evaluated the diagnostic power of the MPC and we showed that it led to increased classification accuracy (78.39%) and sensitivity (91.11%). These findings shed new light on the brain functional alterations underlying AD and provide analytical tools for identifying multi-frequency neural mechanisms of brain diseases.
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Affiliation(s)
- J Guillon
- Inria Paris, Aramis project-team, 75013, Paris, France
- CNRS UMR-7225, Sorbonne Universites, UPMC Univ Paris 06, Inserm U-1127, Institut du cerveau et la moelle (ICM), Hopital Pitie-Salpetriere, 75013, Paris, France
| | - Y Attal
- MyBrain Technologies, Paris, France
| | - O Colliot
- Inria Paris, Aramis project-team, 75013, Paris, France
- CNRS UMR-7225, Sorbonne Universites, UPMC Univ Paris 06, Inserm U-1127, Institut du cerveau et la moelle (ICM), Hopital Pitie-Salpetriere, 75013, Paris, France
| | - V La Corte
- Institute of Psychology, University Paris Descartes, Sorbonne Paris Cite, France
- INSERM UMR 894, Center of Psychiatry and Neurosciences, Memory and Cognition Laboratory, Paris, France
| | - B Dubois
- Department of Neurology, Institut de la Memoire et de la Maladie dAlzheimer - IM2A, Paris, France
| | - D Schwartz
- CNRS UMR-7225, Sorbonne Universites, UPMC Univ Paris 06, Inserm U-1127, Institut du cerveau et la moelle (ICM), Hopital Pitie-Salpetriere, 75013, Paris, France
| | - M Chavez
- CNRS UMR-7225, Sorbonne Universites, UPMC Univ Paris 06, Inserm U-1127, Institut du cerveau et la moelle (ICM), Hopital Pitie-Salpetriere, 75013, Paris, France
| | - F De Vico Fallani
- Inria Paris, Aramis project-team, 75013, Paris, France.
- CNRS UMR-7225, Sorbonne Universites, UPMC Univ Paris 06, Inserm U-1127, Institut du cerveau et la moelle (ICM), Hopital Pitie-Salpetriere, 75013, Paris, France.
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Abstract
There is a long history linking traumatic brain injury (TBI) with the development of dementia. Despite significant reservations, such as recall bias or concluding causality for TBI, a summary of recent research points to several conclusions on the TBI-dementia relationship. 1) Increasing severity of a single moderate-to-severe TBI increases the risk of subsequent Alzheimer's disease (AD), the most common type of dementia. 2) Repetitive, often subconcussive, mild TBIs increases the risk for chronic traumatic encephalopathy (CTE), a degenerative neuropathology. 3) TBI may be a risk factor for other neurodegenerative disorders that can be associated with dementia. 4) TBI appears to lower the age of onset of TBI-related neurocognitive syndromes, potentially adding "TBI cognitive-behavioral features". The literature further indicates several specific risk factors for TBI-associated dementia: 5) any blast or blunt physical force to the head as long as there is violent head displacement; 6) decreased cognitive and/or neuronal reserve and the related variable of older age at TBI; and 7) the presence of apolipoprotein E ɛ4 alleles, a genetic risk factor for AD. Finally, there are neuropathological features relating TBI with neurocognitive syndromes: 8) acute TBI results in amyloid pathology and other neurodegenerative proteinopathies; 9) CTE shares features with neurodegenerative dementias; and 10) TBI results in white matter tract and neural network disruptions. Although further research is needed, these ten findings suggest that dose-dependent effects of violent head displacement in vulnerable brains predispose to dementia; among several potential mechanisms is the propagation of abnormal proteins along damaged white matter networks.
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Affiliation(s)
- Mario F Mendez
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA.,Department of Neurology, Neurobehavior Unit, V.A. Greater Los Angeles Healthcare System, Los Angeles, CA, USA
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48
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Shu N, Wang X, Bi Q, Zhao T, Han Y. Disrupted Topologic Efficiency of White Matter Structural Connectome in Individuals with Subjective Cognitive Decline. Radiology 2017; 286:229-238. [PMID: 28799862 DOI: 10.1148/radiol.2017162696] [Citation(s) in RCA: 88] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Purpose To determine whether individuals with subjective cognitive decline (SCD), which is defined by memory complaints with normal performance at objective neuropsychologic examinations, exhibit disruptions of white matter (WM) connectivity and topologic alterations of the brain structural connectome. Materials and Methods Diffusion-tensor magnetic resonance imaging and graph theory approaches were used to investigate the topologic organization of the brain structural connectome in 36 participants with SCD (21 women: mean age, 62.0 years ± 8.6 [standard deviation]; age range, 42-76 years; 15 men: mean age, 65.5 years ± 8.9; age range, 51-80 years) and 51 age-, sex-, and years of education-matched healthy control participants (33 women: mean age, 63.7 years ± 8.8; age range, 46-83 years; 18 men: mean age, 59.4 years ± 9.3; age range, 43-75 years). Individual WM networks were constructed for each participant, and the network properties between two groups were compared with a linear regression model. Results Graph theory analyses revealed that the participants with SCD had less global efficiency (P = .001) and local efficiency (P = .008) compared with the healthy control participants. Lower regional efficiency was mainly distributed in the bilateral prefrontal regions and left thalamus (P < .05, corrected). Furthermore, a disrupted subnetwork was observed that consisted of widespread anatomic connections (P < .05, corrected), which has the potential to discriminate individuals with SCD from control participants. Moreover, similar hub distributions and less connection strength between the hub regions (P = .023) were found in SCD. Importantly, diminished strength of the rich-club and local connections was correlated with the impaired memory performance in patients with SCD (rich-club connection: r = 0.43, P = .011; local connection: r = 0.36, P = .037). Conclusion This study demonstrated disrupted topologic efficiency of the brain's structural connectome in participants with SCD and provided potential connectome-based biomarkers for the early detection of cognitive impairment in elderly individuals. © RSNA, 2017 Online supplemental material is available for this article.
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Affiliation(s)
- Ni Shu
- From the State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research (N.S., Q.B., T.Z.), Center for Collaboration and Innovation in Brain and Learning Sciences (N.S., Q.B., T.Z.), and Beijing Key Laboratory of Brain Imaging and Connectomics (N.S., Q.B., T.Z.), Beijing Normal University, Beijing, P. R. China; Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, P. R. China (X.W., Y.H.); Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China (X.W., Y.H.); National Clinical Research Center for Geriatric Disorders, Beijing, P. R. China (Y.H.); and PKU Care Rehabilitation Hospital, Beijing, P. R. China (Y.H.)
| | - Xiaoni Wang
- From the State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research (N.S., Q.B., T.Z.), Center for Collaboration and Innovation in Brain and Learning Sciences (N.S., Q.B., T.Z.), and Beijing Key Laboratory of Brain Imaging and Connectomics (N.S., Q.B., T.Z.), Beijing Normal University, Beijing, P. R. China; Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, P. R. China (X.W., Y.H.); Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China (X.W., Y.H.); National Clinical Research Center for Geriatric Disorders, Beijing, P. R. China (Y.H.); and PKU Care Rehabilitation Hospital, Beijing, P. R. China (Y.H.)
| | - Qiuhui Bi
- From the State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research (N.S., Q.B., T.Z.), Center for Collaboration and Innovation in Brain and Learning Sciences (N.S., Q.B., T.Z.), and Beijing Key Laboratory of Brain Imaging and Connectomics (N.S., Q.B., T.Z.), Beijing Normal University, Beijing, P. R. China; Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, P. R. China (X.W., Y.H.); Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China (X.W., Y.H.); National Clinical Research Center for Geriatric Disorders, Beijing, P. R. China (Y.H.); and PKU Care Rehabilitation Hospital, Beijing, P. R. China (Y.H.)
| | - Tengda Zhao
- From the State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research (N.S., Q.B., T.Z.), Center for Collaboration and Innovation in Brain and Learning Sciences (N.S., Q.B., T.Z.), and Beijing Key Laboratory of Brain Imaging and Connectomics (N.S., Q.B., T.Z.), Beijing Normal University, Beijing, P. R. China; Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, P. R. China (X.W., Y.H.); Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China (X.W., Y.H.); National Clinical Research Center for Geriatric Disorders, Beijing, P. R. China (Y.H.); and PKU Care Rehabilitation Hospital, Beijing, P. R. China (Y.H.)
| | - Ying Han
- From the State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research (N.S., Q.B., T.Z.), Center for Collaboration and Innovation in Brain and Learning Sciences (N.S., Q.B., T.Z.), and Beijing Key Laboratory of Brain Imaging and Connectomics (N.S., Q.B., T.Z.), Beijing Normal University, Beijing, P. R. China; Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing 100053, P. R. China (X.W., Y.H.); Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China (X.W., Y.H.); National Clinical Research Center for Geriatric Disorders, Beijing, P. R. China (Y.H.); and PKU Care Rehabilitation Hospital, Beijing, P. R. China (Y.H.)
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Zhao T, Sheng C, Bi Q, Niu W, Shu N, Han Y. Age-related differences in the topological efficiency of the brain structural connectome in amnestic mild cognitive impairment. Neurobiol Aging 2017; 59:144-155. [PMID: 28882420 DOI: 10.1016/j.neurobiolaging.2017.08.005] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Revised: 08/01/2017] [Accepted: 08/02/2017] [Indexed: 01/07/2023]
Abstract
Amnestic mild cognitive impairment (aMCI) is accompanied by the accelerated cognitive decline and rapid brain degeneration with aging. However, the age-related alterations of the topological organization of the brain connectome in aMCI patients remained largely unknown. In this study, we constructed the brain structural connectome in 51 aMCI patients and 51 healthy controls by diffusion magnetic resonance imaging and deterministic tractography. The different age-related alteration patterns of the global and regional network metrics between aMCI patients and healthy controls were assessed by a linear regression model. Compared with healthy controls, significantly decreased global and local network efficiency in aMCI patients were found. When correlating network efficiency with age, we observed a significant decline in network efficiency with aging in the aMCI patients, while not in the healthy controls. The age-related decreases of nodal efficiency in aMCI patients were mainly distributed in the key regions of the default-mode network, such as precuneus, anterior cingulate gyrus, and parahippocampal gyrus. In addition, age-related decreases in the connection strength of the edges between peripheral nodes were observed in aMCI patients. Moreover, the decreased regional efficiency of the parahippocampal gyrus was correlated with impaired memory performances in patients. The present study suggests an age-related disruption of the topological organization of the brain structural connectome in aMCI patients, which may provide evidence for different neural mechanisms underlying aging in aMCI and may serve as a potential imaging marker for the early diagnosis of Alzheimer's disease.
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Affiliation(s)
- Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, P. R. China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, P. R. China
| | - Can Sheng
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, P. R. China; Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China
| | - Qiuhui Bi
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, P. R. China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, P. R. China
| | - Weili Niu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, P. R. China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, P. R. China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, P. R. China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, P. R. China.
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, P. R. China; Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China; National Clinical Research Center for Geriatric Disorders, Beijing, P. R. China; PKU Care Rehabilitation Hospital, Beijing, P. R. China.
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50
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Sheng C, Xia M, Yu H, Huang Y, Lu Y, Liu F, He Y, Han Y. Abnormal global functional network connectivity and its relationship to medial temporal atrophy in patients with amnestic mild cognitive impairment. PLoS One 2017. [PMID: 28650994 PMCID: PMC5484500 DOI: 10.1371/journal.pone.0179823] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Background Amnestic mild cognitive impairment (aMCI), which is recently considered as a high risk status for developing Alzheimer’s disease (AD), manifests with gray matter atrophy and increased focal functional activity in the medial temporal lobe (MTL). However, the abnormalities of whole-brain functional network connectivity in aMCI and its relationship to medial temporal atrophy (MTA) remain unknown. Methods In this study, thirty-six aMCI patients and thirty-five healthy controls (HCs) were recruited. Neuropsychological assessments and MTA visual rating scaling were carried out on all participants. Furthermore, whole brain functional network was constructed at voxel level, and functional connectivity strength (FCS) was computed as the sum of the connections for each node to capture its global integrity. General linear model was used to analyze the FCS values differences between aMCI and HCs. Then, the regions showing significant FCS differences were adopted as the imaging markers for discriminative analysis. Finally, the relationship between FCS values and clinical cognitive scores was correlated in patients with aMCI. Results Comparing to HCs, aMCI exhibited significant atrophy in the MTL, while higher FCS values within the bilateral MTL regions and orbitofrontal cortices. Notably, the right hippocampus had the highest classification power, with the area under receiver operating characteristics (ROC) curve (AUC) of 0.790 (confidence interval: 0.678, 0.901). Moreover, FCS values of the right hippocampus and the left temporal pole were positively correlated with the cognitive performance in aMCI. Conclusion This study demonstrated significantly structural atrophy and raised global functional integrity in the MTL, suggesting simultaneous disruption and compensation in prodromal AD. Increased intrinsic functional connectivity in the MTL may have the potential to discriminate subjects with tendency to develop AD.
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Affiliation(s)
- Can Sheng
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, P. R. China
- Department of Neurology, the First Hospital of Tsinghua University, Beijing, P. R. China
- Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, P. R. China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, P. R. China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China
| | - Haikuo Yu
- Department of Rehabilitation, XuanWu Hospital of Capital Medical University, Beijing, P. R. China
| | - Yue Huang
- School of Medical Sciences, Faculty of Medicine, UNSW Australia, New South Wales, Australia
| | - Yan Lu
- Department of Ophthalmology, XuanWu Hospital of Capital Medical University, Beijing, P. R. China
| | - Fang Liu
- Department of Neurology, the First Hospital of Tsinghua University, Beijing, P. R. China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, P. R. China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, P. R. China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, P. R. China
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, P. R. China
- Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, P. R. China
- Beijing Institute of Geriatrics, Beijing, P. R. China
- National Clinical Research Center for Geriatric Disorders, Beijing, P. R. China
- PKU Care Rehabilitation Hospital, Beijing, P. R. China
- * E-mail:
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