1
|
Zheng D, Ruan Y, Cao X, Guo W, Zhang X, Qi W, Yuan Q, Liang X, Zhang D, Huang Q, Xue C. Directed Functional Connectivity Changes of Triple Networks for Stable and Progressive Mild Cognitive Impairment. Neuroscience 2024; 545:47-58. [PMID: 38490330 DOI: 10.1016/j.neuroscience.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 03/17/2024]
Abstract
Mild cognitive impairment includes two distinct subtypes, namely progressive mild cognitive impairment and stable mild cognitive impairment. While alterations in extensive functional connectivity have been observed in both subtypes, limited attention has been given to directed functional connectivity. A triple network, composed of the central executive network, default mode network, and salience network, is considered to be the core cognitive network. We evaluated the alterations in directed functional connectivity within and between the triple network in progressive and stable mild cognitive impairment groups and investigated its role in predicting disease conversion. Resting-state functional magnetic resonance imaging was used to analyze directed functional connectivity within the triple networks. A correlation analysis was performed to investigate potential associations between altered directed functional connectivity within the triple networks and the neurocognitive performance of the participants. Our study revealed significant differences in directed functional connectivity within and between the triple network in the progressive and stable mild cognitive impairment groups. Altered directed functional connectivity within the triple network was involved in episodic memory and executive function. Thus, the directed functional connectivity of the triple network may be used as an imaging marker of mild cognitive impairment.
Collapse
Affiliation(s)
- Darui Zheng
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Yiming Ruan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xuan Cao
- Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, USA
| | - Wenxuan Guo
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xulian Zhang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Wenzhang Qi
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Qianqian Yuan
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Xuhong Liang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Da Zhang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China
| | - Qingling Huang
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
| | - Chen Xue
- Department of Radiology, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
| |
Collapse
|
2
|
Chen Z, Chen K, Li Y, Geng D, Li X, Liang X, Lu H, Ding S, Xiao Z, Ma X, Zheng L, Ding D, Zhao Q, Yang L. Structural, static, and dynamic functional MRI predictors for conversion from mild cognitive impairment to Alzheimer's disease: Inter-cohort validation of Shanghai Memory Study and ADNI. Hum Brain Mapp 2024; 45:e26529. [PMID: 37991144 PMCID: PMC10789213 DOI: 10.1002/hbm.26529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/06/2023] [Accepted: 10/23/2023] [Indexed: 11/23/2023] Open
Abstract
Mild cognitive impairment (MCI) is a critical prodromal stage of Alzheimer's disease (AD), and the mechanism underlying the conversion is not fully explored. Construction and inter-cohort validation of imaging biomarkers for predicting MCI conversion is of great challenge at present, due to lack of longitudinal cohorts and poor reproducibility of various study-specific imaging indices. We proposed a novel framework for inter-cohort MCI conversion prediction, involving comparison of structural, static, and dynamic functional brain features from structural magnetic resonance imaging (sMRI) and resting-state functional MRI (fMRI) between MCI converters (MCI_C) and non-converters (MCI_NC), and support vector machine for construction of prediction models. A total of 218 MCI patients with 3-year follow-up outcome were selected from two independent cohorts: Shanghai Memory Study cohort for internal cross-validation, and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort for external validation. In comparison with MCI_NC, MCI_C were mainly characterized by atrophy, regional hyperactivity and inter-network hypo-connectivity, and dynamic alterations characterized by regional and connectional instability, involving medial temporal lobe (MTL), posterior parietal cortex (PPC), and occipital cortex. All imaging-based prediction models achieved an area under the curve (AUC) > 0.7 in both cohorts, with the multi-modality MRI models as the best with excellent performances of AUC > 0.85. Notably, the combination of static and dynamic fMRI resulted in overall better performance as relative to static or dynamic fMRI solely, supporting the contribution of dynamic features. This inter-cohort validation study provides a new insight into the mechanisms of MCI conversion involving brain dynamics, and paves a way for clinical use of structural and functional MRI biomarkers in future.
Collapse
Affiliation(s)
- Zhihan Chen
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Academy for Engineering & TechnologyFudan UniversityShanghaiChina
| | - Keliang Chen
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Yuxin Li
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
| | - Daoying Geng
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Academy for Engineering & TechnologyFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
| | - Xiantao Li
- Department of Critical Care MedicineHuashan Hospital, Fudan UniversityShanghaiChina
| | - Xiaoniu Liang
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Huimeng Lu
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Saineng Ding
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Zhenxu Xiao
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Xiaoxi Ma
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Li Zheng
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Ding Ding
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
| | - Qianhua Zhao
- Department of Neurology, Huashan HospitalFudan UniversityShanghaiChina
- National Center for Neurological DisordersHuashan Hospital, Fudan UniversityShanghaiChina
- MOE Frontiers Center for Brain ScienceFudan UniversityShanghaiChina
- National Clinical Research Center for Aging and MedicineHuashan Hospital, Fudan UniversityShanghaiChina
| | - Liqin Yang
- Department of Radiology, Huashan HospitalFudan UniversityShanghaiChina
- Institute of Functional and Molecular Medical ImagingFudan UniversityShanghaiChina
| | | |
Collapse
|
3
|
Zhang X, Li Y, Guan Q, Dong D, Zhang J, Meng X, Chen F, Luo Y, Zhang H. Distance-dependent reconfiguration of hubs in Alzheimer's disease: a cross-tissue functional network study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.24.532772. [PMID: 36993290 PMCID: PMC10055319 DOI: 10.1101/2023.03.24.532772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
The hubs of the intra-grey matter (GM) network were sensitive to anatomical distance and susceptible to neuropathological damage. However, few studies examined the hubs of cross-tissue distance-dependent networks and their changes in Alzheimer's disease (AD). Using resting-state fMRI data of 30 AD patients and 37 normal older adults (NC), we constructed the cross-tissue networks based on functional connectivity (FC) between GM and white matter (WM) voxels. In the full-ranged and distance-dependent networks (characterized by gradually increased Euclidean distances between GM and WM voxels), their hubs were identified with weight degree metrics (frWD and ddWD). We compared these WD metrics between AD and NC; using the resultant abnormal WDs as the seeds, we performed seed-based FC analysis. With increasing distance, the GM hubs of distance-dependent networks moved from the medial to lateral cortices, and the WM hubs spread from the projection fibers to longitudinal fascicles. Abnormal ddWD metrics in AD were primarily located in the hubs of distance-dependent networks around 20-100mm. Decreased ddWDs were located in the left corona radiation (CR), which had decreased FCs with the executive network's GM regions in AD. Increased ddWDs were located in the posterior thalamic radiation (PTR) and the temporal-parietal-occipital junction (TPO), and their FCs were larger in AD. Increased ddWDs were shown in the sagittal striatum, which had larger FCs with the salience network's GM regions in AD. The reconfiguration of cross-tissue distance-dependent networks possibly reflected the disruption in the neural circuit of executive function and the compensatory changes in the neural circuits of visuospatial and social-emotional functions in AD.
Collapse
Affiliation(s)
- Xingxing Zhang
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Yingjia Li
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Qing Guan
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality of Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, 400715, China
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Jianfeng Zhang
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Xianghong Meng
- Department of Neurosurgery, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Fuyong Chen
- Department of Neurosurgery, Shenzhen Hospital of University of Hong Kong, Shenzhen, China
| | - Yuejia Luo
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
| | - Haobo Zhang
- Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
- School of Psychology, Shenzhen University, Shenzhen, China
| | | |
Collapse
|
4
|
Zeng Q, Li K, Luo X, Wang S, Xu X, Li Z, Zhang T, Liu X, Fu Y, Xu X, Wang C, Wang T, Zhou J, Liu Z, Chen Y, Huang P, Zhang M. Distinct Atrophy Pattern of Hippocampal Subfields in Patients with Progressive and Stable Mild Cognitive Impairment: A Longitudinal MRI Study. J Alzheimers Dis 2021; 79:237-247. [PMID: 33252076 DOI: 10.3233/jad-200775] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
BACKGROUND Predicting the prognosis of mild cognitive impairment (MCI) has outstanding clinical value, and the hippocampal volume is a reliable imaging biomarker of AD diagnosis. OBJECTIVE We aimed to longitudinally assess hippocampal sub-regional difference (volume and asymmetry) among progressive MCI (pMCI), stable MCI (sMCI) patients, and normal elderly. METHODS We identified 29 pMCI, 52 sMCI, and 102 normal controls (NC) from the ADNI database. All participants underwent neuropsychological assessment and 3T MRI scans three times. The time interval between consecutive MRI sessions was about 1 year. Volumes of hippocampal subfield were measured by Freesurfer. Based on the analysis of variance, repeated measures analyses, and receiver operating characteristic curves, we compared cross-sectional and longitudinal alteration sub-regional volume and asymmetry index. RESULTS Compared to NC, both MCI groups showed significant atrophy in all subfields. At baseline, pMCI have a smaller volume than sMCI in the bilateral subiculum, molecular layer (ML), the molecular and granule cell layers of the dentate gyrus, cornu ammonis 4, and right tail. Furthermore, repeated measures analyses revealed that pMCI patients showed a faster volume loss than sMCI in bilateral subiculum and ML. After controlling for age, gender, and education, most results remained unchanged. However, none of the hippocampal sub-regional volumes performed better than the whole hippocampus in ROC analyses, and no asymmetric difference between pMCI and sMCI was found. CONCLUSION The faster volume loss in subiculum and ML suggest a higher risk of disease progression in MCI patients. The hippocampal asymmetry may have smaller value in predicting the MCI prognosis.
Collapse
Affiliation(s)
- Qingze Zeng
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Kaicheng Li
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Xiao Luo
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Shuyue Wang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Xiaopei Xu
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Zheyu Li
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Tianyi Zhang
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Xiaocao Liu
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Yanv Fu
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Xiaojun Xu
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Chao Wang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Tao Wang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Jiong Zhou
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Zhirong Liu
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Yanxing Chen
- Department of Neurology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Peiyu Huang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | - Minming Zhang
- Department of Radiology, 2nd Affiliated Hospital of Zhejiang University School of Medicine, China
| | | |
Collapse
|
5
|
Emoto R, Kawaguchi A, Takahashi K, Matsui S. Effect-Size Estimation Using Semiparametric Hierarchical Mixture Models in Disease-Association Studies with Neuroimaging Data. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:7482403. [PMID: 33488762 PMCID: PMC7787870 DOI: 10.1155/2020/7482403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 10/08/2020] [Accepted: 11/27/2020] [Indexed: 11/20/2022]
Abstract
In disease-association studies using neuroimaging data, evaluating the biological or clinical significance of individual associations requires not only detection of disease-associated areas of the brain but also estimation of the magnitudes of the associations or effect sizes for individual brain areas. In this paper, we propose a model-based framework for voxel-based inferences under spatial dependency in neuroimaging data. Specifically, we employ hierarchical mixture models with a hidden Markov random field structure to incorporate the spatial dependency between voxels. A nonparametric specification is proposed for the effect size distribution to flexibly estimate the underlying effect size distribution. Simulation experiments demonstrate that compared with a naive estimation method, the proposed methods can substantially reduce the selection bias in the effect size estimates of the selected voxels with the greatest observed associations. An application to neuroimaging data from an Alzheimer's disease study is provided.
Collapse
Affiliation(s)
- Ryo Emoto
- Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya 466-0003, Japan
| | | | - Kunihiko Takahashi
- Medical and Dental Data Science Center, Tokyo Medical and Dental University, Tokyo 101-0062, Japan
| | - Shigeyuki Matsui
- Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya 466-0003, Japan
- Department of Data Science, The Institute of Statistical Mathematics, Tachikawa 190-8562, Japan
| |
Collapse
|
6
|
Tumati S, Marsman JBC, De Deyn PP, Martens S, Aleman A. Functional network topology associated with apathy in Alzheimer's disease. J Affect Disord 2020; 266:473-481. [PMID: 32056915 DOI: 10.1016/j.jad.2020.01.158] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 12/05/2019] [Accepted: 01/26/2020] [Indexed: 01/30/2023]
Abstract
BACKGROUND Apathy, a common neuropsychiatric (NPS) in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD), is associated with structural and metabolic brain changes. However, functional connectivity changes across the brain in association with apathy remain unclear. In this study, graph theoretical measures of integration and segregation from resting state functional connectivity in MCI and AD patients with low depression scores, and healthy controls. METHODS In MCI and AD patients with low depression scores, graph theoretical measures of integration and segregation were derived from resting state functional connectivity in patients, which were compared between those with apathy (NPS_A, n = 21) to those without NPS (NPS_None, n = 28) and those with NPS other than apathy (NPS_NA, n = 38). Additionally, the same measures were compared between AD patients and healthy controls (amyloid uptake below threshold levels). RESULTS Altered whole brain global efficiency and reduced local efficiency were found in NPS_A compared to NPS_None and NPS_NA. In similar contrasts, apathy was associated with increased participation coefficient in the frontoparietal and cingulo-opercular template-based networks. A study-specific network definition also showed similar results. In comparison, AD patients showed higher modularity compared to controls at the whole brain level and higher participation coefficient in the ventral attention network. LIMITATIONS The severity and dimensions of apathy were not assessed. CONCLUSIONS Loss of segregation in the frontoparietal and cingulo-opercular network, which are involved in the control of goal-directed behavior, was associated with apathy in MCI/AD. The results also suggest that network-level changes in AD patients may underlie specific NPS.
Collapse
Affiliation(s)
- Shankar Tumati
- NeuroImaging Center, University of Groningen, University Medical Center Groningen, the Netherlands.
| | - Jan-Bernard C Marsman
- NeuroImaging Center, University of Groningen, University Medical Center Groningen, the Netherlands
| | - Peter Paul De Deyn
- Department of Neurology, University of Groningen, University Medical Center Groningen, the Netherlands; Laboratory of Neurochemistry and Behaviour, Institute Born-Bunge, University of Antwerp, Belgium
| | - Sander Martens
- NeuroImaging Center, University of Groningen, University Medical Center Groningen, the Netherlands
| | - André Aleman
- NeuroImaging Center, University of Groningen, University Medical Center Groningen, the Netherlands; Department of Psychology, University of Groningen, the Netherlands
| | | |
Collapse
|
7
|
Schultz AP, Buckley RF, Hampton OL, Scott MR, Properzi MJ, Peña-Gómez C, Pruzin JJ, Yang HS, Johnson KA, Sperling RA, Chhatwal JP. Longitudinal degradation of the default/salience network axis in symptomatic individuals with elevated amyloid burden. Neuroimage Clin 2019; 26:102052. [PMID: 31711955 PMCID: PMC7229343 DOI: 10.1016/j.nicl.2019.102052] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 08/09/2019] [Accepted: 10/21/2019] [Indexed: 12/21/2022]
Abstract
Resting-state functional connectivity MRI (rs-fcMRI) is a non-invasive imaging technique that has come into increasing use to understand disrupted neural network function in neuropsychiatric disease. However, despite extensive study over the past 15 years, the development of rs-fcMRI as a biomarker has been impeded by a lack of reliable longitudinal rs-fcMRI measures. Here we focus on longitudinal change along the Alzheimer's disease (AD) trajectory and demonstrate the utility of Template Based Rotation (TBR) in detecting differential longitudinal rs-fcMRI change between higher and lower amyloid burden individuals with mildly impaired cognition. Specifically, we examine a small (N = 24), but densely sampled (~5 observations over ~3 years), cohort of symptomatic individuals with serial rs-fcMRI imaging and PiB-PET imaging for β-amyloid pathology. We observed longitudinal decline of the Default Mode and Salience network axis (DMN/SAL) among impaired individuals with high amyloid burden. No other networks showed differential change in high vs. low amyloid individuals over time. The standardized effect size of AD related DMN/SAL change is comparable to the standardized effect size of amyloid-related change on the mini-mental state exam (MMSE) and hippocampal volume (HV). Last, we show that the AD-related change in DMN/SAL connectivity is almost completely independent of change on MMSE or HV, suggesting that rs-fcMRI is sensitive to an aspect of AD progression that is not captured by these other measures. Together these analyses demonstrate that longitudinal rs-fcMRI using TBR can capture disease-relevant network disruption in a clinical population.
Collapse
Affiliation(s)
- Aaron P Schultz
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Rachel F Buckley
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Olivia L Hampton
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Matthew R Scott
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Michael J Properzi
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Cleofé Peña-Gómez
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, United States
| | - Jeremy J Pruzin
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States; Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Hyun-Sik Yang
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Keith A Johnson
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, United States
| | - Reisa A Sperling
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Jasmeer P Chhatwal
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
| |
Collapse
|
8
|
Xie Y, Liu T, Ai J, Chen D, Zhuo Y, Zhao G, He S, Wu J, Han Y, Yan T. Changes in Centrality Frequency of the Default Mode Network in Individuals With Subjective Cognitive Decline. Front Aging Neurosci 2019; 11:118. [PMID: 31281248 PMCID: PMC6595963 DOI: 10.3389/fnagi.2019.00118] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 05/03/2019] [Indexed: 12/31/2022] Open
Abstract
Despite subjective cognitive decline (SCD), a preclinical stage of Alzheimer's disease (AD), being widely studied in recent years, studies on centrality frequency in individuals with SCD are lacking. This study aimed to investigate the differences in centrality frequency between individuals with SCD and normal controls (NCs). Forty individuals with SCD and 53 well-matched NCs underwent a resting-state functional magnetic resonance imaging scan. We assessed individual dynamic functional connectivity using sliding window correlations. In each time window, brain regions with a high degree centrality were defined as hubs. Across the entire time window, the proportion of time that the hub appeared was characterized as centrality frequency. The centrality frequency correlated with cognitive performance differently in individuals with SCD and NCs. Our results revealed that in individuals with SCD, compared with NCs, correlations between centrality frequency of the anterior cortical regions and cognitive performance decreased (79.2% for NCs and 43.5% for individuals with SCD). In contrast, correlations between centrality frequency of the posterior cortical regions and cognitive performance increased in SCD individuals compared with NCs (20.8% for NCs and 56.5% for individuals with SCD). Moreover, the changes mainly focused on the anterior (93.3% for NCs and 45.5% for individuals with SCD) and posterior (6.7% for NCs and 54.5% for individuals with SCD) regions associated with the default mode network (DMN). In addition, we used absolute thresholds (correlation efficient r = 0.2, 0.25) and proportional thresholds (sparsity = 0.2, 0.25) to verify the results. Dynamic results are relative stable at absolute thresholds while static results are relative stable at proportional thresholds. Converging findings provide a new framework for the detection of the changes occurring in individuals with SCD via centrality frequency of the DMN.
Collapse
Affiliation(s)
- Yunyan Xie
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Tiantian Liu
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Jing Ai
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Duanduan Chen
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yiran Zhuo
- College of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Guanglei Zhao
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Shuai He
- Beijing Haidian Foreign Language Shiyan School, Beijing, China
| | - Jinglong Wu
- School of Mechatronical Engineering, Intelligent Robotics Institute, Beijing Institute of Technology, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Institute of Geriatrics, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China
| |
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
Avants BB, Hutchison RM, Mikulskis A, Salinas-Valenzuela C, Hargreaves R, Beaver J, Chiao P. Amyloid beta-positive subjects exhibit longitudinal network-specific reductions in spontaneous brain activity. Neurobiol Aging 2018; 74:191-201. [PMID: 30471630 DOI: 10.1016/j.neurobiolaging.2018.10.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 09/06/2018] [Accepted: 10/02/2018] [Indexed: 12/20/2022]
Abstract
Amyloid beta (Aβ) deposition and cognitive decline are key features of Alzheimer's disease. The relationship between Aβ status and changes in neuronal function over time, however, remains unclear. We evaluated the effect of baseline Aβ status on reference region spontaneous brain activity (SBA-rr) using resting-state functional magnetic resonance imaging and fluorodeoxyglucose positron emission tomography in patients with mild cognitive impairment. Patients (N = 62, [43 Aβ-positive]) from the Alzheimer's Disease Neuroimaging Initiative were divided into Aβ-positive and Aβ-negative groups via prespecified cerebrospinal fluid Aβ42 or 18F-florbetapir positron emission tomography standardized uptake value ratio cutoffs measured at baseline. We analyzed interaction of biomarker-confirmed Aβ status with SBA-rr change over a 2-year period using mixed-effects modeling. SBA-rr differences between Aβ-positive and Aβ-negative subjects increased significantly over time within subsystems of the default and visual networks. Changes exhibit an interaction with memory performance over time but were independent of glucose metabolism. Results reinforce the value of resting-state functional magnetic resonance imaging in evaluating Alzheimer''s disease progression and suggest spontaneous neuronal activity changes are concomitant with cognitive decline.
Collapse
Affiliation(s)
- Brian B Avants
- Biogen employee while completing work, 225 Binney Street, Cambridge, Massachusetts, 02142, USA.
| | | | - Alvydas Mikulskis
- Biogen employee while completing work, 225 Binney Street, Cambridge, Massachusetts, 02142, USA
| | | | | | - John Beaver
- Biogen, 225 Binney Street, Cambridge, Massachusetts, 02142, USA
| | - Ping Chiao
- Biogen, 225 Binney Street, Cambridge, Massachusetts, 02142, USA
| |
Collapse
|
11
|
Shi L, Miao X, Lou W, Liu K, Abrigo J, Wong A, Chu WCW, Wang D, Mok VCT. The Spatial Associations of Cerebral Blood Flow and Spontaneous Brain Activities with White Matter Hyperintensities-An Exploratory Study Using Multimodal Magnetic Resonance Imaging. Front Neurol 2017; 8:593. [PMID: 29170651 PMCID: PMC5684108 DOI: 10.3389/fneur.2017.00593] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Accepted: 10/23/2017] [Indexed: 12/12/2022] Open
Abstract
White matter hyperintensities (WMHs) have been reported to be correlated with functional brain changes, but the association of the specific WMHs distribution pattern with regional functional changes remains uncertain. The aim of this study is to explore the possible spatial correlation of WMH with changes in cerebral blood flow (CBF) and spontaneous brain activities in elderly using a novel approach. The WMHs, CBF, and spontaneous brain activities measured by intrinsic connectivity contrast (ICC), were quantified using multimodal magnetic resonance imaging for 69 elderly subjects. Such approach enables us to expand our search for newly identified correlated areas by drawing strengths of different modes and provides a means for triangulation as well as complementary insights. The results showed significant positive correlations between WMH volumes in the right superior corona radiata and CBF in the left supplementary motor area, as well as between WMH volumes in left anterior limb internal capsule and CBF in the right putamen. Significant correlations of regional WMH volumes and ICC were also detected between the right anterior corona radiata and the left cuneus, and the right superior occipital cortex, as well as between the right superior corona radiata and the left superior occipital cortex. These findings may suggest a regional compensatory functional enhancement accounting for the maintenance of cognitively normal status, which can be supported by the widely observed phenomenon that mild to moderate WMH load could have little effect on global cognitive performance.
Collapse
Affiliation(s)
- Lin Shi
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, Hong Kong.,Chow Yuk Ho Center of Innovative Technology for Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong.,Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Xinyuan Miao
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Wutao Lou
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Kai Liu
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Jill Abrigo
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Adrian Wong
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Winnie C W Chu
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Defeng Wang
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| | - Vincent C T Mok
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, Hong Kong.,Chow Yuk Ho Center of Innovative Technology for Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong.,Therese Pei Fong Chow Research Centre for Prevention of Dementia, The Chinese University of Hong Kong, Hong Kong, Hong Kong
| |
Collapse
|