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Batta I, Abrol A, Calhoun VD. Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data. J Neurosci Methods 2024; 406:110109. [PMID: 38494061 PMCID: PMC11100582 DOI: 10.1016/j.jneumeth.2024.110109] [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: 03/09/2023] [Revised: 02/12/2024] [Accepted: 03/12/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND For successful biomarker discovery, it is essential to develop computational frameworks that summarize high-dimensional neuroimaging data in terms of involved sub-systems of the brain, while also revealing underlying heterogeneous functional and structural changes covarying with specific cognitive and biological traits. However, unsupervised decompositions do not inculcate clinical assessment information, while supervised approaches extract only individual feature importance, thereby impeding qualitative interpretation at the level of subspaces. NEW METHOD We present a novel framework to extract robust multimodal brain subspaces associated with changes in a given cognitive or biological trait. Our approach involves active subspace learning on the gradients of a trained machine learning model followed by clustering to extract and summarize the most salient and consistent subspaces associated with the target variable. RESULTS Through a rigorous cross-validation procedure on an Alzheimer's disease (AD) dataset, our framework successfully extracts multimodal subspaces specific to a given clinical assessment (e.g., memory and other cognitive skills), and also retains predictive performance in standard machine learning algorithms. We also show that the salient active subspace directions occur consistently across randomly sub-sampled repetitions of the analysis. COMPARISON WITH EXISTING METHOD(S) Compared to existing unsupervised decompositions based on principle component analysis, the subspace components in our framework retain higher predictive information. CONCLUSIONS As an important step towards biomarker discovery, our framework not only uncovers AD-related brain regions in the associated brain subspaces, but also enables automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and proficiency in cognitive skills related to brain disorders like AD.
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Affiliation(s)
- Ishaan Batta
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA.
| | - Anees Abrol
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA
| | - Vince D Calhoun
- Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, USA; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA
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Bergamino M, Burke A, Sabbagh MN, Caselli RJ, Baxter LC, Stokes AM. Altered resting-state functional connectivity and dynamic network properties in cognitive impairment: an independent component and dominant-coactivation pattern analyses study. Front Aging Neurosci 2024; 16:1362613. [PMID: 38562990 PMCID: PMC10982426 DOI: 10.3389/fnagi.2024.1362613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Cognitive impairment (CI) due to Alzheimer's disease (AD) encompasses a decline in cognitive abilities and can significantly impact an individual's quality of life. Early detection and intervention are crucial in managing CI, both in the preclinical and prodromal stages of AD prior to dementia. Methods In this preliminary study, we investigated differences in resting-state functional connectivity and dynamic network properties between 23 individual with CI due to AD based on clinical assessment and 15 healthy controls (HC) using Independent Component Analysis (ICA) and Dominant-Coactivation Pattern (d-CAP) analysis. The cognitive status of the two groups was also compared, and correlations between cognitive scores and d-CAP switching probability were examined. Results Results showed comparable numbers of d-CAPs in the Default Mode Network (DMN), Executive Control Network (ECN), and Frontoparietal Network (FPN) between HC and CI groups. However, the Visual Network (VN) exhibited fewer d-CAPs in the CI group, suggesting altered dynamic properties of this network for the CI group. Additionally, ICA revealed significant connectivity differences for all networks. Spatial maps and effect size analyses indicated increased coactivation and more synchronized activity within the DMN in HC compared to CI. Furthermore, reduced switching probabilities were observed for the CI group in DMN, VN, and FPN networks, indicating less dynamic and flexible functional interactions. Discussion The findings highlight altered connectivity patterns within the DMN, VN, ECN, and FPN, suggesting the involvement of multiple functional networks in CI. Understanding these brain processes may contribute to developing targeted diagnostic and therapeutic strategies for CI due to AD.
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Affiliation(s)
- Maurizio Bergamino
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, OK, United States
| | - Anna Burke
- Division of Neurology, Barrow Neurological Institute, Phoenix, OK, United States
| | - Marwan N. Sabbagh
- Division of Neurology, Barrow Neurological Institute, Phoenix, OK, United States
| | - Richard J. Caselli
- Department of Neuropsychology, Mayo Clinic Arizona, Phoenix, AZ, United States
| | - Leslie C. Baxter
- Department of Neurology, Mayo Clinic Arizona, Phoenix, AZ, United States
| | - Ashley M. Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, OK, United States
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Li J, Tan C, Zhang L, Cai S, Shen Q, Liu Q, Wang M, Song C, Zhou F, Yuan J, Liu Y, Lan B, Liao H. Neural functional network of early Parkinson's disease based on independent component analysis. Cereb Cortex 2023; 33:11025-11035. [PMID: 37746803 DOI: 10.1093/cercor/bhad342] [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: 07/02/2023] [Revised: 08/08/2023] [Indexed: 09/26/2023] Open
Abstract
This work explored neural network changes in early Parkinson's disease: Resting-state functional magnetic resonance imaging was used to investigate functional alterations in different stages of Parkinson's disease (PD). Ninety-five PD patients (50 early/mild and 45 early/moderate) and 37 healthy controls (HCs) were included. Independent component analysis revealed significant differences in intra-network connectivity, specifically in the default mode network (DMN) and right frontoparietal network (RFPN), in both PD groups compared to HCs. Inter-network connectivity analysis showed reduced connectivity between the executive control network (ECN) and DMN, as well as ECN-left frontoparietal network (LFPN), in early/mild PD. Early/moderate PD exhibited decreased connectivity in ECN-LFPN, ECN-RFPN, ECN-DMN, and DMN-auditory network, along with increased connectivity in LFPN-cerebellar network. Correlations were found between ECN-DMN and ECN-LFPN connections with UPDRS-III scores in early/mild PD. These findings suggest that PD progression involves dysfunction in multiple intra- and inter-networks, particularly implicating the ECN, and a wider range of abnormal functional networks may mark the progression of the disease.
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Affiliation(s)
- Junli Li
- Department of Medical Imaging, Huizhou Central People's Hospital, Eling North Road, Huicheng District, Huizhou, Guangdong 516001, China
| | - Changlian Tan
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Lin Zhang
- Department of Radiology, Chengdu Fifth People's Hospital, Mashi Street, Wenjiang District, Chengdu, Sichuan 611130, China
| | - Sainan Cai
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Qin Shen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Qinru Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Min Wang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - ChenDie Song
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Fan Zhou
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Jiaying Yuan
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Yujing Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Bowen Lan
- Department of Medical Imaging, Huizhou Central People's Hospital, Eling North Road, Huicheng District, Huizhou, Guangdong 516001, China
| | - Haiyan Liao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
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Wu H, Song Y, Yang X, Chen S, Ge H, Yan Z, Qi W, Yuan Q, Liang X, Lin X, Chen J. Functional and structural alterations of dorsal attention network in preclinical and early-stage Alzheimer's disease. CNS Neurosci Ther 2023; 29:1512-1524. [PMID: 36942514 PMCID: PMC10173716 DOI: 10.1111/cns.14092] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 12/31/2022] [Accepted: 01/02/2023] [Indexed: 03/23/2023] Open
Abstract
OBJECTIVES Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are known as the preclinical and early stage of Alzheimer's disease (AD). The dorsal attention network (DAN) is mainly responsible for the "top-down" attention process. However, previous studies mainly focused on single functional modality and limited structure. This study aimed to investigate the multimodal alterations of DAN in SCD and aMCI to assess their diagnostic value in preclinical and early-stage AD. METHODS Resting-state functional magnetic resonance imaging (MRI) was carried out to measure the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC). Structural MRI was used to calculate the gray matter volume (GMV) and cortical thickness. Moreover, receiver-operating characteristic (ROC) analysis was used to distinguish these alterations in SCD and aMCI. RESULTS The SCD and aMCI groups showed both decreased ReHo in the right middle temporal gyrus (MTG) and decreased GMV compared to healthy controls (HCs). Especially in the SCD group, there were increased fALFF and increased ReHo in the left inferior occipital gyrus (IOG), decreased fALFF and increased FC in the left inferior parietal lobule (IPL), and reduced cortical thickness in the right inferior temporal gyrus (ITG). Furthermore, functional and structural alterations in the SCD and aMCI groups were closely related to episodic memory (EM), executive function (EF), and information processing speed (IPS). The combination of multiple indicators of DAN had a high accuracy in differentiating clinical stages. CONCLUSIONS Our current study demonstrated functional and structural alterations of DAN in SCD and aMCI, especially in the MTG, IPL, and SPL. Furthermore, cognitive performance was closely related to these significant alterations. Our study further suggested that the combined multiple indicators of DAN could be acted as the latent neuroimaging markers of preclinical and early-stage AD for their high diagnostic value.
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Affiliation(s)
- Huimin Wu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xinyi Yang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Zheng Yan
- Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- 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
| | - Qianqian Yuan
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xuhong Liang
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xingjian Lin
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Department of Radiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
- Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing, China
- Medical Imaging Center, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
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Rashidi-Ranjbar N, Rajji TK, Hawco C, Kumar S, Herrmann N, Mah L, Flint AJ, Fischer CE, Butters MA, Pollock BG, Dickie EW, Bowie CR, Soffer M, Mulsant BH, Voineskos AN. Association of functional connectivity of the executive control network or default mode network with cognitive impairment in older adults with remitted major depressive disorder or mild cognitive impairment. Neuropsychopharmacology 2023; 48:468-477. [PMID: 35410366 PMCID: PMC9852291 DOI: 10.1038/s41386-022-01308-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 02/13/2022] [Accepted: 03/09/2022] [Indexed: 02/02/2023]
Abstract
Major depressive disorder (MDD) is associated with an increased risk of developing dementia. The present study aimed to better understand this risk by comparing resting state functional connectivity (rsFC) in the executive control network (ECN) and the default mode network (DMN) in older adults with MDD or mild cognitive impairment (MCI). Additionally, we examined the association between rsFC in the ECN or DMN and cognitive impairment transdiagnostically. We assessed rsFC alterations in ECN and DMN in 383 participants from five groups at-risk for dementia-remitted MDD with normal cognition (MDD-NC), non-amnestic mild cognitive impairment (naMCI), remitted MDD + naMCI, amnestic MCI (aMCI), and remitted MDD + aMCI-and from healthy controls (HC) or individuals with Alzheimer's dementia (AD). Subject-specific whole-brain functional connectivity maps were generated for each network and group differences in rsFC were calculated. We hypothesized that alteration of rsFC in the ECN and DMN would be progressively larger among our seven groups, ranked from low to high according to their risk for dementia as HC, MDD-NC, naMCI, MDD + naMCI, aMCI, MDD + aMCI, and AD. We also regressed scores of six cognitive domains (executive functioning, processing speed, language, visuospatial memory, verbal memory, and working memory) on the ECN and DMN connectivity maps. We found a significant alteration in the rsFC of the ECN, with post hoc testing showing differences between the AD group and the HC, MDD-NC, or naMCI groups, but no significant alterations in rsFC of the DMN. Alterations in rsFC of the ECN and DMN were significantly associated with several cognitive domain scores transdiagnostically. Our findings suggest that a diagnosis of remitted MDD may not confer functional brain risk for dementia. However, given the association of rs-FC with cognitive performance (i.e., transdiagnostically), rs-FC may help in stratifying this risk among people with MDD and varying degrees of cognitive impairment.
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Affiliation(s)
- Neda Rashidi-Ranjbar
- Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Tarek K Rajji
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Toronto Dementia Research Alliance, University of Toronto, Toronto, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Sanjeev Kumar
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Nathan Herrmann
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Linda Mah
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Baycrest Health Sciences, Rotman Research Institute, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Alastair J Flint
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Centre for Mental Health, University Health Network, Toronto, ON, Canada
| | - Corinne E Fischer
- Keenan Research Centre for Biomedical Science, St. Michael's Hospital, Toronto, ON, Canada
| | - Meryl A Butters
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Bruce G Pollock
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Christopher R Bowie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Departments of Psychology and Psychiatry (CRB), Queen's University, Kingston, ON, Canada
| | - Matan Soffer
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Benoit H Mulsant
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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6
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Green ZD, Vidoni ED, Swerdlow RH, Burns JM, Morris JK, Honea RA. Increased Functional Connectivity of the Precuneus in Individuals with a Family History of Alzheimer's Disease. J Alzheimers Dis 2023; 91:559-571. [PMID: 36463439 PMCID: PMC9912732 DOI: 10.3233/jad-210326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
BACKGROUND First-degree relatives of individuals with late-onset Alzheimer's disease (AD) have increased risk for AD, with children of affected parents at an especially high risk. OBJECTIVE We aimed to investigate default mode network connectivity, medial temporal cortex volume, and cognition in cognitively healthy (CH) individuals with (FH+) and without (FH-) a family history of AD, alongside amnestic mild cognitive impairment (aMCI) and AD individuals, to determine the context and directionality of dysfunction in at-risk individuals. Our primary hypothesis was that there would be a linear decline (CH FH- > CH FH+ > aMCI > AD) within the risk groups on all measures of AD risk. METHODS We used MRI and fMRI to study cognitively healthy individuals (n = 28) with and without AD family history (FH+ and FH-, respectively), those with aMCI (n = 31) and early-stage AD (n = 25). We tested connectivity within the default mode network, as well as measures of volume and thickness within the medial temporal cortex and selected seed regions. RESULTS As expected, we identified decreased medial temporal cortex volumes in the aMCI and AD groups compared to cognitively healthy groups. We also observed patterns of connectivity across risk groups that suggest a nonlinear relationship of change, such that the FH+ group showed increased connectivity compared to the FH- and AD groups (CH FH+ > CH FH- > aMCI > AD). This pattern emerged primarily in connectivity between the precuneus and frontal regions. CONCLUSION These results add to a growing literature that suggests compensatory brain function in otherwise cognitively healthy individuals with a family history of AD.
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Affiliation(s)
- Zachary D. Green
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Eric D. Vidoni
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Russell H. Swerdlow
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Jeffrey M. Burns
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Jill K. Morris
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Robyn A. Honea
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA,Correspondence to: Robyn A. Honea, University of Kansas School of Medicine, Department of Neurology, University of Kansas Alzheimer’s Disease Research Center, 4350 Shawnee Mission Parkway, Fairway, KS, 66205, USA. Tel.: +1 913 588 5514; E-mail:
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Batta I, Abrol A, Calhoun VD, the Alzheimer’s Disease Neuroimaging Initiative. SVR-based Multimodal Active Subspace Analysis for the Brain using Neuroimaging Data.. [DOI: 10.1101/2022.07.28.501879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
ABSTRACTUnderstanding the patterns of changes in brain function and structure due to various disorders and diseases is of utmost importance. There have been numerous efforts toward successful biomarker discovery for complex brain disorders by evaluating neuroimaging datasets with novel analytical frameworks. However, due to the multi-faceted nature of the disorders involving a wide and overlapping range of symptoms as well as complex changes in structural and functional brain networks, it is increasingly important to devise computational frameworks that can consider the underlying patterns of heterogeneous changes with specific target assessments, at the same time producing a summarizing output from the high-dimensional neuroimaging data. While various machine learning approaches focus on diagnostic prediction, many learning frameworks analyze important features at the level of brain regions involved in prediction using supervised methods. Unsupervised learning methods have also been utilized to break down the neuroimaging features into lower dimensional components. However, most learning frameworks either do not consider the target assessment information while extracting brain subspaces, or can extract only higher dimensional importance associations as an ordered list of involved features, making manual interpretation at the level of subspaces difficult. We present a novel multimodal active subspace learning framework to understand various subspaces within the brain that are associated with changes in particular biological and cognitive traits. For a given cognitive or biological trait, our framework performs a decomposition of the feature importances to extract robust multimodal subspaces that define the most significant change in the given trait. Through a rigorous cross-validation procedure on an Alzheimer’s disease (AD) dataset, we show that our framework can extract subspaces covering both functional and structural modalities, which are specific to a given clinical assessment (like memory and other cognitive skills) and also retain predictive performance in standard machine learning algorithms. We show that our framework not only uncovers AD-related brain regions (e.g., hippocampus, entorhinal cortex) in the associated brain subspaces, but also enables an automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and cognitive skill proficiency related to brain disorders like AD.
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Xu X, Xu S, Han L, Yao X. Coupling analysis between functional and structural brain networks in Alzheimer's disease. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:8963-8974. [PMID: 35942744 DOI: 10.3934/mbe.2022416] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The coupling between functional and structural brain networks is difficult to clarify due to the complicated alterations in gray matter and white matter for the development of Alzheimer's disease (AD). A cohort of 112 participants [normal control group (NC, 62 cases), mild cognitive impairment group (MCI, 31 cases) and AD group (19 cases)], was recruited in our study. The brain networks of rsfMRI functional connectivity (rsfMRI-FC) and diffusion tensor imaging structural connectivity (DTI-SC) across the three groups were constructed, and their correlations were evaluated by Pearson's correlation analyses and multiple comparison with Bonferroni correction. Furthermore, the correlations between rsfMRI-SC/DTI-FC coupling and four neuropsychological scores of mini-mental state examination (MMSE), clinical dementia rating-sum of boxes (CDR-SB), functional activities questionnaire (FAQ) and montreal cognitive assessment (MoCA) were inferred by partial correlation analyses, respectively. The results demonstrated that there existed significant correlation between rsfMRI-FC and DTI-SC (p < 0.05), and the coupling of rsfMRI-FC/DTI-SC showed negative correlation with MMSE score (p < 0.05), positive correlations with CDR-SB and FAQ scores (p < 0.05), and no correlation with MoCA score (p > 0.05). It was concluded that there existed FC/SC coupling and varied network characteristics for rsfMRI and DTI, and this would provide the clues to understand the underlying mechanisms of cognitive deficits of AD.
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Affiliation(s)
- Xia Xu
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Song Xu
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Liting Han
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Xufeng Yao
- College of Medical Imaging, Jiading District Central Hospital affiliated Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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Zhang X, Guan Q, Li Y, Zhang J, Zhu W, Luo Y, Zhang H. Aberrant Cross-Tissue Functional Connectivity in Alzheimer’s Disease: Static, Dynamic, and Directional Properties. J Alzheimers Dis 2022; 88:273-290. [DOI: 10.3233/jad-215649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: BOLD signals in the gray matter (GM) and white matter (WM) are tightly coupled. However, our understanding of the cross-tissue functional network in Alzheimer’s disease (AD) is limited. Objective: We investigated the changes of cross-tissue functional connectivity (FC) metrics for the GM regions susceptible to AD damage. Methods: For each GM region in the default mode (DMN) and limbic networks, we obtained its low-order static FC with any WM region, and the high-order static FC between any two WM regions based on their FC pattern similarity with multiple GM regions. The dynamic and directional properties of cross-tissue FC were then acquired, specifically for the regional pairs whose low- or high-order static FCs showed significant differences between AD and normal control (NC). Moreover, these cross-tissue FC metrics were correlated with voxel-based GM volumes and MMSE in all participants. Results: Compared to NC, AD patients showed decreased low-order static FCs between the intra-hemispheric GM-WM pairs (right ITG-right fornix; left MoFG-left posterior corona radiata), and increased low-order static, dynamic, and directional FCs between the inter-hemispheric GM-WM pairs (right MTG-left superior/posterior corona radiata). The high-order static and directional FCs between the left cingulate bundle-left tapetum were increased in AD, based on their FCs with the GMs of DMN. Those decreased and increased cross-tissue FC metrics in AD had opposite correlations with memory-related GM volumes and MMSE (positive for the decreased and negative for the increased). Conclusion: Cross-tissue FC metrics showed opposite changes in AD, possibly as useful neuroimaging biomarkers to reflect neurodegenerative and compensatory mechanisms.
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Affiliation(s)
- Xingxing Zhang
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
| | - Qing Guan
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
- Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Yingjia Li
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
| | - Jianfeng Zhang
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
| | - Wanlin Zhu
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yuejia Luo
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
| | - Haobo Zhang
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Shenzhen University, Shenzhen, China
- Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China
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Boots A, Thomason ME, Espinoza-Heredia C, Pruitt PJ, Damoiseaux JS, Roseboom TJ, de Rooij SR. Sex-specific effects of prenatal undernutrition on resting-state functional connectivity in the human brain at age 68. Neurobiol Aging 2022; 112:129-138. [PMID: 35151035 PMCID: PMC9459445 DOI: 10.1016/j.neurobiolaging.2022.01.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 12/23/2021] [Accepted: 01/17/2022] [Indexed: 12/17/2022]
Abstract
Prenatal nutrition may significantly impact brain aging. Results from the Dutch Famine Birth Cohort indicated that prenatal undernutrition is negatively associated with cognition, brain volumes, perfusion and structural brain aging in late life, predominantly in men. This study investigates the association between prenatal undernutrition and late-life functional brain network connectivity. In an exploratory resting-state functional magnetic resonance imaging study of 112 participants from the Dutch Famine Birth Cohort, we investigated whether the within- and between-network functional connectivity of the default mode network, salience network and central executive network differ at age 68 in men (N = 49) and women (N = 63) either exposed or unexposed to undernutrition in early gestation. Additionally, we explored sex-specific effects. Compared to unexposed participants, exposed participants revealed multiple clusters of different functional connectivity within and between the three networks studied. Sex-specific analyses suggested a pattern of network desegregation fitting with brain aging in men and a more diffuse pattern of group differences in women. This study demonstrates that associations between prenatal undernutrition and brain network functional connectivity extend late into life.
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Affiliation(s)
- Amber Boots
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands; Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
| | - Moriah E Thomason
- Department of Child and Adolescent Psychiatry, New York University Langone Health, New York, NY, USA; Department of Population Health, New York University Langone Health, New York, NY, USA; Neuroscience Institute, New York University Langone Health, New York, NY, USA
| | - Claudia Espinoza-Heredia
- Department of Child and Adolescent Psychiatry, New York University Langone Health, New York, NY, USA
| | - Patrick J Pruitt
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
| | - Jessica S Damoiseaux
- Institute of Gerontology, Wayne State University, Detroit, MI, USA; Department of Psychology, Wayne State University, Detroit, MI, USA
| | - Tessa J Roseboom
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands; Amsterdam Public Health Research Institute, Amsterdam, The Netherlands; Department of Obstetrics and Gynaecology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Susanne R de Rooij
- Department of Epidemiology and Data Science, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands; Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
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11
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MRI biomarkers for Alzheimer's disease: the impact of functional connectivity in the default mode network and structural connectivity between lobes on diagnostic accuracy. Heliyon 2022; 8:e08901. [PMID: 35198768 PMCID: PMC8841367 DOI: 10.1016/j.heliyon.2022.e08901] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/09/2021] [Accepted: 01/31/2022] [Indexed: 11/23/2022] Open
Abstract
Background At present, clinical use of MRI in Alzheimer's disease (AD) is mostly focused on the assessment of brain atrophy, namely in the hippocampal region. Despite this, multiple biomarkers reflecting structural and functional brain connectivity changes have shown promising results in the assessment of AD. To help identify the most relevant ones that may stand a chance of being used in clinical practice, we compared multiple biomarker in terms of their value to discriminate AD from healthy controls and analyzed their age dependency. Methods 20 AD patients and 20 matched controls underwent MRI-scanning (3T GE), including T1-weighted, diffusion-MRI, and resting-state-fMRI (rsfMRI). Whole brain, white matter, gray matter, cortical gray matter and hippocampi volumes were measured using icobrain. rsfMRI between regions of the default-mode-network (DMN) was assessed using group independent-component-analysis. Median diffusivity and kurtosis were determined in gray and white-matter. DTI data was used to evaluate pairwise structural connectivity between lobar regions and the hippocampi. Logistic-Regression and Random-Forest models were trained to classify AD-status based on, respectively different isolated features and age, and feature-groups combined with age. Results Hippocampal features, features reflecting the functional connectivity between the medial-Pre-Frontal-Cortex (mPFC) and the posterior regions of the DMN, and structural interhemispheric frontal connectivity showed the strongest differences between AD-patients and controls. Structural interhemispheric parietal connectivity, structural connectivity between the parietal lobe and hippocampus in the right hemisphere, and mPFC-DMN-features, showed only an association with AD-status (p < 0.05) but not with age. Hippocampi volumes showed an association both with age and AD-status (p < 0.05). Smallest-hippocampus-volume was the most discriminative feature. The best performance (accuracy:0.74, sensitivity:0.74, specificity:0.74) was obtained with an RF-model combining the best feature from each feature-group (smallest hippocampus volume, WM volume, median GM MD, lTPJ-mPFC connectivity and structural interhemispheric frontal connectivity) and age. Conclusions Brain connectivity changes caused by AD are reflected in multiple MRI-biomarkers. Decline in both the functional DMN-connectivity and the parietal interhemispheric structural connectivity may assist sepparating healthy-aging driven changes from AD, complementing hippocampal volumes which are affected by both aging and AD.
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12
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Huang S, Zeng W, Shi Y. Internet-like brain hierarchical network model: Alzheimer's disease study as an example. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 211:106393. [PMID: 34551380 DOI: 10.1016/j.cmpb.2021.106393] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 08/30/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE The modular structure and hierarchy are important topological characteristics in real complex networks such as brain networks on temporal scale. However, there are few studies investigating the hierarchical structure at the spatial scale of brain networks, the application of which still remains to be further studied. METHODS In this study, a novel model of brain hierarchical network based on the hierarchical characteristic of Internet topology is proposed for the first time, which is called Internet-like brain hierarchical network (IBHN). In this model, the whole brain network is partitioned into multiple levels: brain wide area network (Brain-WAN), brain metropolitan network (Brain-MAN), and brain local area network (Brain-LAN). A Brain-MAN is formed by the interconnection of multiple Brain-LANs, and the interconnection of multiple Brain-MANs forms a Brain-WAN. A multivariate analysis method is employed to measure overall functional connectivity between two brain networks at the same network level rather than detecting the change of each node pair's functional connection. Furthermore, we demonstrate the utility of IBHN model with application to a practical case-control study involving 64 patients with Alzheimer's disease and 75 healthy controls. RESULTS The proposed model identified enhanced functional connectivity (P-value<0.05) at Brain-WAN level and reduced functional connectivity (P-value=0.004) at Brain-LAN level of Alzheimer's disease patients, which can be used as a multi-dimension functional reference for AD's diagnosis. CONCLUSIONS This study not only provides insight into AD pathophysiology, but also further proves the effectiveness of the proposed IBHN model. In addition, the IBHN model makes it possible to explore the brain's functional organization from multiple dimensions and offers a multi-level perspective for the research of complex brain network.
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Affiliation(s)
- Shaojun Huang
- College of Information Engineering, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China
| | - Weiming Zeng
- College of Information Engineering, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China.
| | - Yuhu Shi
- College of Information Engineering, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, Shanghai, 201306, China
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13
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Yuan Q, Qi W, Xue C, Ge H, Hu G, Chen S, Xu W, Song Y, Zhang X, Xiao C, Chen J. Convergent Functional Changes of Default Mode Network in Mild Cognitive Impairment Using Activation Likelihood Estimation. Front Aging Neurosci 2021; 13:708687. [PMID: 34675797 PMCID: PMC8525543 DOI: 10.3389/fnagi.2021.708687] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 08/30/2021] [Indexed: 12/13/2022] Open
Abstract
Background: Mild cognitive impairment (MCI) represents a transitional state between normal aging and dementia disorders, especially Alzheimer's disease (AD). The disruption of the default mode network (DMN) is often considered to be a potential biomarker for the progression from MCI to AD. The purpose of this study was to assess MRI-specific changes of DMN in MCI patients by elucidating the convergence of brain regions with abnormal DMN function. Methods: We systematically searched PubMed, Ovid, and Web of science for relevant articles. We identified neuroimaging studies by using amplitude of low frequency fluctuation /fractional amplitude of low frequency fluctuation (ALFF/fALFF), regional homogeneity (ReHo), and functional connectivity (FC) in MCI patients. Based on the activation likelihood estimation (ALE) algorithm, we carried out connectivity modeling of coordination-based meta-analysis and functional meta-analysis. Results: In total, this meta-analysis includes 39 articles on functional neuroimaging studies. Using computer software analysis, we discovered that DMN changes in patients with MCI mainly occur in bilateral inferior frontal lobe, right medial frontal lobe, left inferior parietal lobe, bilateral precuneus, bilateral temporal lobe, and parahippocampal gyrus (PHG). Conclusions: Herein, we confirmed the presence of DMN-specific damage in MCI, which is helpful in revealing pathology of MCI and further explore mechanisms of conversion from MCI to AD. Therefore, we provide a new specific target and direction for delaying conversion from MCI to AD.
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Affiliation(s)
- Qianqian Yuan
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Honglin Ge
- Department of Neurosurgery, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Department of Neurosurgery, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenwen Xu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - XuLian Zhang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chaoyong Xiao
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Department of Neurosurgery, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Department of Neurosurgery, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
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Wang S, Rao B, Chen L, Chen Z, Fang P, Miao G, Xu H, Liao W. Using Fractional Amplitude of Low-Frequency Fluctuations and Functional Connectivity in Patients With Post-stroke Cognitive Impairment for a Simulated Stimulation Program. Front Aging Neurosci 2021; 13:724267. [PMID: 34483891 PMCID: PMC8414996 DOI: 10.3389/fnagi.2021.724267] [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: 06/12/2021] [Accepted: 07/22/2021] [Indexed: 12/26/2022] Open
Abstract
Stroke causes alterations in local spontaneous neuronal activity and related networks functional connectivity. We hypothesized that these changes occur in patients with post-stroke cognitive impairment (PSCI). Fractional amplitude of low-frequency fluctuations (fALFF) was calculated in 36 patients with cognitive impairment, including 16 patients with hemorrhagic stroke (hPSCI group), 20 patients with ischemic stroke (iPSCI group). Twenty healthy volunteers closely matched to the patient groups with respect to age and gender were selected as the healthy control group (HC group). Regions with significant alteration were regarded as regions of interest (ROIs) using the one-way analysis of variance, and then the seed-based functional connectivity (FC) with other regions in the brain was analyzed. Pearson correlation analyses were performed to investigate the correlation between functional indexes and cognitive performance in patients with PSCI. Our results showed that fALFF values of bilateral posterior cingulate cortex (PCC)/precuneus and bilateral anterior cingulate cortex in the hPSCI group were lower than those in the HC group. Compared with the HC group, fALFF values were lower in the superior frontal gyrus and basal ganglia in the iPSCI group. Correlation analysis showed that the fALFF value of left PCC was positively correlated with MMSE scores and MoCA scores in hPSCI. Besides, the reduction of seed-based FC values was reported, especially in regions of the default-mode network (DMN) and the salience network (SN). Abnormalities of spontaneous brain activity and functional connectivity are observed in PSCI patients. The decreased fALFF and FC values in DMN of patients with hemorrhagic and SN of patients with ischemic stroke may be the pathological mechanism of cognitive impairment. Besides, we showed how to use fALFF values and functional connectivity maps to specify a target map on the cortical surface for repetitive transcranial magnetic stimulation (rTMS).
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Affiliation(s)
- Sirui Wang
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Bo Rao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Linglong Chen
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zhuo Chen
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Pinyan Fang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Guofu Miao
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Haibo Xu
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Weijing Liao
- Department of Rehabilitation Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
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15
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Amaefule CO, Dyrba M, Wolfsgruber S, Polcher A, Schneider A, Fliessbach K, Spottke A, Meiberth D, Preis L, Peters O, Incesoy EI, Spruth EJ, Priller J, Altenstein S, Bartels C, Wiltfang J, Janowitz D, Bürger K, Laske C, Munk M, Rudolph J, Glanz W, Dobisch L, Haynes JD, Dechent P, Ertl-Wagner B, Scheffler K, Kilimann I, Düzel E, Metzger CD, Wagner M, Jessen F, Teipel SJ. Association between composite scores of domain-specific cognitive functions and regional patterns of atrophy and functional connectivity in the Alzheimer's disease spectrum. NEUROIMAGE-CLINICAL 2020; 29:102533. [PMID: 33360018 PMCID: PMC7770965 DOI: 10.1016/j.nicl.2020.102533] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 11/24/2020] [Accepted: 12/12/2020] [Indexed: 02/06/2023]
Abstract
Composite scores provide reliable metrics of domain function in multicenter cohort. Visuo-spatial domain composite scores relate to anatomic changes in AD spectrum. Domain scores relate to network-specific resting-state connectivity in AD spectrum.
Background Cognitive decline has been found to be associated with gray matter atrophy and disruption of functional neural networks in Alzheimer’s disease (AD) in structural and functional imaging (fMRI) studies. Most previous studies have used single test scores of cognitive performance among monocentric cohorts. However, cognitive domain composite scores could be more reliable than single test scores due to the reduction of measurement error. Adopting a multicentric resting state fMRI (rs-fMRI) and cognitive domain approach, we provide a comprehensive description of the structural and functional correlates of the key cognitive domains of AD. Method We analyzed MRI, rs-fMRI and cognitive domain score data of 490 participants from an interim baseline release of the multicenter DELCODE study cohort, including 54 people with AD, 86 with Mild Cognitive Impairment (MCI), 175 with Subjective Cognitive Decline (SCD), and 175 Healthy Controls (HC) in the AD-spectrum. Resulting cognitive domain composite scores (executive, visuo-spatial, memory, working memory and language) from the DELCODE neuropsychological battery (DELCODE-NP), were previously derived using confirmatory factor analysis. Statistical analyses examined the differences between diagnostic groups, and the association of composite scores with regional atrophy and network-specific functional connectivity among the patient subgroup of SCD, MCI and AD. Result Cognitive performance, atrophy patterns and functional connectivity significantly differed between diagnostic groups in the AD-spectrum. Regional gray matter atrophy was positively associated with visuospatial and other cognitive impairments among the patient subgroup in the AD-spectrum. Except for the visual network, patterns of network-specific resting-state functional connectivity were positively associated with distinct cognitive impairments among the patient subgroup in the AD-spectrum. Conclusion Consistent associations between cognitive domain scores and both regional atrophy and network-specific functional connectivity (except for the visual network), support the utility of a multicentric and cognitive domain approach towards explicating the relationship between imaging markers and cognition in the AD-spectrum.
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Affiliation(s)
| | - Martin Dyrba
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Steffen Wolfsgruber
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital, Bonn, Germany
| | | | - Anja Schneider
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital, Bonn, Germany
| | - Klaus Fliessbach
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital, Bonn, Germany
| | - Annika Spottke
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurology, University Hospital Bonn, Bonn, Germany
| | - Dix Meiberth
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Psychiatry, University of Cologne, Cologne, Germany
| | - Lukas Preis
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Oliver Peters
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Enise I Incesoy
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Eike J Spruth
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Josef Priller
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Slawek Altenstein
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany; Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Claudia Bartels
- Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG), Goettingen, Germany
| | - Jens Wiltfang
- German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany; Department of Psychiatry and Psychotherapy, University Medical Center Goettingen (UMG), Goettingen, Germany; Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Daniel Janowitz
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilians University, Munich, Germany
| | - Katharina Bürger
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilians University, Munich, Germany
| | - Christoph Laske
- German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany; Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
| | - Matthias Munk
- Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, Germany
| | - Janna Rudolph
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Wenzel Glanz
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Laura Dobisch
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - John D Haynes
- Bernstein Center for Computational Neuroscience, Charité - Universitätsmedizin, Berlin, Germany
| | - Peter Dechent
- MR-Research in Neurology and Psychiatry, Georg-August-University Goettingen, Germany
| | - Birgit Ertl-Wagner
- Institute for Clinical Radiology, Ludwig Maximilians University, Munich, Germany
| | - Klaus Scheffler
- Department for Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Ingo Kilimann
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
| | - Emrah Düzel
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany
| | - Coraline D Metzger
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany; Institute of Cognitive Neurology and Dementia Research (IKND), Otto-von-Guericke University, Magdeburg, Germany; Department of Psychiatry and Psychotherapy, Otto-von-Guericke University, Magdeburg, Germany
| | - Michael Wagner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital, Bonn, Germany
| | - Frank Jessen
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany; Department of Psychiatry, University of Cologne, Cologne, Germany; Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Stefan J Teipel
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
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Resting state functional connectivity abnormalities and delayed recall performance in patients with amnestic mild cognitive impairment. Brain Imaging Behav 2020; 14:267-277. [PMID: 30421086 DOI: 10.1007/s11682-018-9974-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Amnestic Mild Cognitive Impairment (aMCI) represents the transition between healthy aging and Alzheimer's dementia (AD) wherein gradual impairment of cognitive abilities, especially memory sets in. Impairment in episodic memory, especially delayed recall, is a hallmark of AD and therefore, patients with aMCI with more severe impairment in episodic memory are considered to be at greater risk of imminent conversion to AD. Brain structural and functional abnormalities were examined by comparing gray matter volumes, white matter micro-structural integrity and resting state functional connectivity (rsFC), between patients with aMCI (n = 46) having lower vs. higher episodic memory delayed recall (EM-DR) performance scores, correcting the influences of age, sex, number of years of formal education and total brain volumes using voxel-based morphometry, whole-brain tract based spatial statistics and dual regression analysis respectively. 'Low' performers (n = 27) when compared to 'high' performers (n = 19) showed significantly increased rsFC in the dorsal attention network (DAN) and central executive network (CEN) in the absence of demonstrable gray matter volumetric or white matter micro-structural integrity differences at family-wise error (FWE) corrected (p < 0.05) significance threshold. Follow-up data available for 38 (low performers = 22; high performers = 16) of the above 46 subjects (82.60% follow-up rate) over a median follow-up period of 24.5 months revealed that 7 subjects (18.42%) had converted to dementia. These converted subjects included 5 of the 22 low performers (22.72%) and 2 of the 16 high performers (12.5%) within the follow-up sample (n = 38). The results of the study indicate that imminent conversion of aMCI to dementia is higher in low performers in comparison to high performers, which may be characterized by increased rsFC in task positive networks, viz., DAN and CEN, as opposed to gray or white matter structural changes. This finding, therefore, might be considered as a prognostic indicator of progression from aMCI to dementia.
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17
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Bai X, Zheng J, Zhang B, Luo Y. Cognitive Dysfunction and Neurophysiologic Mechanism of Breast Cancer Patients Undergoing Chemotherapy Based on Resting State Functional Magnetic Resonance Imaging. World Neurosurg 2020; 149:406-412. [PMID: 33096278 DOI: 10.1016/j.wneu.2020.10.066] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 10/11/2020] [Accepted: 10/12/2020] [Indexed: 01/28/2023]
Abstract
We studied chemotherapy-related cognitive impairment via resting state (RS)-functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) in 19 cases of patients with early breast cancer. White matter neuropsychological test treatment were carried out before and after chemotherapy, RS-fMRI, and DTI evaluation. In RS-fMRI with regional homogeneity (ReHo) reflects brain activity. In the DTI with fractional anisotropy (FA) reflect the integrity of the white matter. Determining the region of interest by image analysis, we calculated the neuropsychologic test score using the paired t-test and FA change ReHo values of regions of interest. Finally after the test treatment, in the chemotherapy group for pairing correlation analysis t-test scores change in meaningful inspection and change ReHo and FA. Chemotherapy after chemotherapy than before chemotherapy difference memory test and self-evaluation of cognitive (P < 0.05). ReHo value increases occurred in the right orbitofrontal region and the left dorsolateral prefrontal cortex. Declines in brain regions were the anterior inferior cerebellar lobe, cerebellar lobe, right middle temporal gyrus and the superior temporal gyrus, the lower right of the center area, and the central gyrus. This prospective study on resting state and RS-fMRI functional magnetic resonance DTI study DTI sequence combination chemotherapy for breast cancer-related cognitive disorders supports the "chemo brain" point of view. Chemotherapy can cause memory decline, accompanied by a partial area of the brain and white matter integrity in brain activity changes. Prompt clinical treatment RS-fMRI and DTI have potential applications in assessing chemotherapy-related cognitive impairment.
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Affiliation(s)
- Xiaoru Bai
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Jian Zheng
- Department of Medical Oncology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Bin Zhang
- Department of Oncology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yahong Luo
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China.
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Kelberman M, Keilholz S, Weinshenker D. What's That (Blue) Spot on my MRI? Multimodal Neuroimaging of the Locus Coeruleus in Neurodegenerative Disease. Front Neurosci 2020; 14:583421. [PMID: 33122996 PMCID: PMC7573566 DOI: 10.3389/fnins.2020.583421] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 09/16/2020] [Indexed: 01/04/2023] Open
Abstract
The locus coeruleus (LC) has long been underappreciated for its role in the pathophysiology of Alzheimer’s disease (AD), Parkinson’s disease (PD), and other neurodegenerative disorders. While AD and PD are distinct in clinical presentation, both are characterized by prodromal protein aggregation in the LC, late-stage degeneration of the LC, and comorbid conditions indicative of LC dysfunction. Many of these early studies were limited to post-mortem histological techniques due to the LC’s small size and location deep in the brainstem. Thus, there is a growing interest in utilizing in vivo imaging of the LC as a predictor of preclinical neurodegenerative processes and biomarker of disease progression. Simultaneously, neuroimaging in animal models of neurodegenerative disease holds promise for identifying early alterations to LC circuits, but has thus far been underutilized. While still in its infancy, a handful of studies have reported effects of single gene mutations and pathology on LC function in disease using various neuroimaging techniques. Furthermore, combining imaging and optogenetics or chemogenetics allows for interrogation of network connectivity in response to changes in LC activity. The purpose of this article is twofold: (1) to review what magnetic resonance imaging (MRI) and positron emission tomography (PET) have revealed about LC dysfunction in neurodegenerative disease and its potential as a biomarker in humans, and (2) to explore how animal models can be used to test hypotheses derived from clinical data and establish a mechanistic framework to inform LC-focused therapeutic interventions to alleviate symptoms and impede disease progression.
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Affiliation(s)
- Michael Kelberman
- Department of Human Genetics, Emory University, Atlanta, GA, United States
| | - Shella Keilholz
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - David Weinshenker
- Department of Human Genetics, Emory University, Atlanta, GA, United States
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Li X, Wang F, Liu X, Cao D, Cai L, Jiang X, Yang X, Yang T, Asakawa T. Changes in Brain Function Networks in Patients With Amnestic Mild Cognitive Impairment: A Resting-State fMRI Study. Front Neurol 2020; 11:554032. [PMID: 33101173 PMCID: PMC7554345 DOI: 10.3389/fneur.2020.554032] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 09/02/2020] [Indexed: 12/12/2022] Open
Abstract
Patients with amnestic mild cognitive impairment (aMCI) are at high risk of developing dementia. This study used resting-state functional magnetic resonance imaging (rs-fMRI) and an independent component analysis (ICA) approach to explore changes in functional connectivity (FC) in the default mode network (DMN), executive control network (ECN), and salience network (SN). Thirty patients with aMCI and 30 healthy controls (HCs) were enrolled. All the participants underwent an rs-fMRI scan. The brain FC in DMN, ECN, and SN was calculated using the ICA approach. We found that the FC of brain regions in DMN decreased significantly and that of brain regions in ECN increased, which was in accordance with the findings of previous studies on Alzheimer's disease (AD) and aMCI. We also found that the FC of brain regions in SN increased, which was different from the findings of previous studies on AD. The increase in FC in brain regions in SN might result from different pathophysiological states in AD and aMCI, indicating that a decrease in FC in SN does not occur in a person with aMCI. These results are consistent with those of previous studies using the voxel-mirrored homotopic connectivity approach and seed-based correlation analysis. We therefore considered that the decrease in FC in DMN and the increase in FC in ECN and SN might be peculiar patterns observed on the rs-fMRI of a person with aMCI. These findings may contribute to the development of imaging biomarkers for the diagnosis of aMCI.
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Affiliation(s)
- Xiaoling Li
- First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Feng Wang
- First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China
- Division of CT and MRI, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Xiaohui Liu
- Division of CT and MRI, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Danna Cao
- First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China
- Division of CT and MRI, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Lina Cai
- Division of CT and MRI, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Xiaoxu Jiang
- Division of CT and MRI, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Xu Yang
- Division of CT and MRI, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Tiansong Yang
- First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Tetsuya Asakawa
- Department of Neurosurgery, Hamamatsu University School of Medicine, Hamamatsu, Japan
- Research Base of Traditional Chinese Medicine Syndrome, Fujian University of Traditional Chinese Medicine, Fuzhou, China
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Subramanian S, Rajamanickam K, Prakash JS, Ramachandran M. Study on structural atrophy changes and functional connectivity measures in Alzheimer's disease. J Med Imaging (Bellingham) 2020; 7:016002. [PMID: 32118092 DOI: 10.1117/1.jmi.7.1.016002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 02/03/2020] [Indexed: 11/14/2022] Open
Abstract
Alzheimer's disease (AD) is characterized by the progressive accumulation of neurofibrillary tangles associated with amyloid plaques. We used 80 resting-state functional magnetic resonance imaging and 80 T 1 images acquired using MP-RAGE (magnetization-prepared rapid acquisition gradient echo) from Alzheimer's Disease Neuroimaging Initiative data to detect atrophy changes and functional connectivity patterns of the default mode networks (DMNs). The study subjects were classified into four groups (each with n = 20 ) based on their Mini-Mental State Examination (MMSE) score as follows: cognitively normal (CN), early mild cognitive impairment, late mild cognitive impairment, and AD. The resting-state functional connectivity of the DMN was examined between the groups using the CONN functional connectivity toolbox. Loss of gray matter in AD was observed. Atrophy measured by the volume of selected subcortical regions, using the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library's Integrated Registration and Segmentation Tool (FIRST), revealed significant volume loss in AD when compared to CN ( p < 0.05 ). DMNs were selected to assess functional connectivity. The negative connectivity of DMN increased in AD group compared to controls. Graph theory parameters, such as global and local efficiency, betweenness centrality, average path length, and cluster coefficient, were computed. Relatively higher correlation between MMSE and functional metrics ( r = 0.364 , p = 0.001 ) was observed as compared to atrophy measures ( r = 0.303 , p = 0.006 ). In addition, the receiver operating characteristic analysis showed large area under the curve ( A Z ) for functional parameters ( A Z > 0.9 ), compared to morphometric changes ( A Z < 0.8 ). In summary, it is observed that the functional connectivity measures may serve a better predictor in comparison to structural atrophy changes. We postulate that functional connectivity measures have the potential to evolve as a marker for the early detection of AD.
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Affiliation(s)
- Saraswathi Subramanian
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
| | - Karunanithi Rajamanickam
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
| | - Joy Sebastian Prakash
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
| | - Murugesan Ramachandran
- Chettinad Academy of Research and Education, Faculty of Allied Health Sciences, Kelambakkam, Chennai, Tamil Nadu, India
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Shaw TB, Bollmann S, Atcheson NT, Strike LT, Guo C, McMahon KL, Fripp J, Wright MJ, Salvado O, Barth M. Non-linear realignment improves hippocampus subfield segmentation reliability. Neuroimage 2019; 203:116206. [DOI: 10.1016/j.neuroimage.2019.116206] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 09/14/2019] [Accepted: 09/17/2019] [Indexed: 01/08/2023] Open
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Joshi H, Bharath S, Balachandar R, Sadanand S, Vishwakarma HV, Aiyappan S, Saini J, Kumar KJ, John JP, Varghese M. Differentiation of Early Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Healthy Elderly Samples Using Multimodal Neuroimaging Indices. Brain Connect 2019; 9:730-741. [DOI: 10.1089/brain.2019.0676] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Affiliation(s)
- Himanshu Joshi
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Multimodal Brain Image Analysis Laboratory (MBIAL), Neurobiology Research Center (NRC), National Institute of Mental Health and Neurosciences, Bangalore, India
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Srikala Bharath
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Rakesh Balachandar
- Multimodal Brain Image Analysis Laboratory (MBIAL), Neurobiology Research Center (NRC), National Institute of Mental Health and Neurosciences, Bangalore, India
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Shilpa Sadanand
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Harshita V. Vishwakarma
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Subramoniam Aiyappan
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Multimodal Brain Image Analysis Laboratory (MBIAL), Neurobiology Research Center (NRC), National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Keshav J. Kumar
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Department of Clinical Psychology, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - John P. John
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Multimodal Brain Image Analysis Laboratory (MBIAL), Neurobiology Research Center (NRC), National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Mathew Varghese
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
- Geriatric Clinic and Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
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Functional segregation of executive control network and frontoparietal network in Alzheimer's disease. Cortex 2019; 120:36-48. [PMID: 31228791 DOI: 10.1016/j.cortex.2019.04.026] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Revised: 03/21/2019] [Accepted: 04/10/2019] [Indexed: 12/12/2022]
Abstract
Functional connectivity pattern altered of default mode network (DMN) is gaining more attention as a potential noninvasive biomarker to diagnose incipient Alzheimer's disease. However, the changed functional connectivity except for DMN, the longitudinal changes in executive control network (ECN) and frontoparietal network (FPN) also has attracted wide interest. Moreover, AD-related functional connectivity abnormalities within the DMN are well replicated research, but the (increased/decreased and reduced) functional connectivity in ECN and FPN weren't receive adequate attention. To address the above issues, in this paper, we adopt sparse inverse covariance estimation (SICE) approach to investigate the changed functional connectivity of ECN and FPN on the ADNI2 dataset. Our experimental results indicate the left superior frontal gyrus (SFGmed.L) and left thalamus (THA.L) regions of ECN has shown increased functional connectivity, the left anterior cingulate (ACG.L) region of ECN has shown decreased functional connectivity. The Superior Parietal Gyrus (SPG) regions and left paracentral lobule (PCL.L) of FPN has shown increased functional connectivity, the left supramarginal gyrus (SMG.L) regions has shown decreased functional connectivity in AD patients. On the other hand, the ACG.L regions in ECN, SMG.L and left inferior parietal (IPL.L) in FPN have shown significantly reduced functional connectivity. These results demonstrate that increased/decreased functional connectivity and reduced functional connectivity not only within DMN, but also associated with ECN and FPN. It also suggest that AD is associated with the characteristics of large-scale functional networks, and these changed functional connectivity possibly as a potential noninvasive biomarker to diagnose incipient Alzheimer's disease.
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Early functional connectivity deficits and progressive microstructural alterations in the TgF344-AD rat model of Alzheimer’s Disease: A longitudinal MRI study. Neurobiol Dis 2019; 124:93-107. [DOI: 10.1016/j.nbd.2018.11.010] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 11/05/2018] [Accepted: 11/12/2018] [Indexed: 01/05/2023] Open
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Anckaerts C, Blockx I, Summer P, Michael J, Hamaide J, Kreutzer C, Boutin H, Couillard-Després S, Verhoye M, Van der Linden A. Early functional connectivity deficits and progressive microstructural alterations in the TgF344-AD rat model of Alzheimer’s Disease: A longitudinal MRI study. Neurobiol Dis 2019. [DOI: 10.1016/j.nbd.2018.11.010 and 21=21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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Lin SY, Lin CP, Hsieh TJ, Lin CF, Chen SH, Chao YP, Chen YS, Hsu CC, Kuo LW. Multiparametric graph theoretical analysis reveals altered structural and functional network topology in Alzheimer's disease. Neuroimage Clin 2019; 22:101680. [PMID: 30710870 PMCID: PMC6357901 DOI: 10.1016/j.nicl.2019.101680] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Revised: 12/03/2018] [Accepted: 01/20/2019] [Indexed: 01/08/2023]
Abstract
Alzheimer's disease (AD), an irreversible neurodegenerative disease, is the most common type of dementia in elderly people. This present study incorporated multiple structural and functional connectivity metrics into a graph theoretical analysis framework and investigated alterations in brain network topology in patients with mild cognitive impairment (MCI) and AD. By using this multiparametric analysis, we expected different connectivity metrics may reflect additional or complementary information regarding the topological changes in brain networks in MCI or AD. In our study, a total of 73 subjects participated in this study and underwent the magnetic resonance imaging scans. For the structural network, we compared commonly used connectivity metrics, including fractional anisotropy and normalized streamline count, with multiple diffusivity-based metrics. We compared Pearson correlation and covariance by investigating their sensitivities to functional network topology. Significant disruption of structural network topology in MCI and AD was found predominantly in regions within the limbic system, prefrontal and occipital regions, in addition to widespread alterations of local efficiency. At a global scale, our results showed that the disruption of the structural network was consistent across different edge definitions and global network metrics from the MCI to AD stages. Significant changes in connectivity and tract-specific diffusivity were also found in several limbic connections. Our findings suggest that tract-specific metrics (e.g., fractional anisotropy and diffusivity) provide more sensitive and interpretable measurements than does metrics based on streamline count. Besides, the use of inversed radial diffusivity provided additional information for understanding alterations in network topology caused by AD progression and its possible origins. Use of this proposed multiparametric network analysis framework may facilitate early MCI diagnosis and AD prevention.
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Affiliation(s)
- Shih-Yen Lin
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan; Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan
| | - Chen-Pei Lin
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Tsung-Jen Hsieh
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Chung-Fen Lin
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Sih-Huei Chen
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Yi-Ping Chao
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan; Department of Neurology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Yong-Sheng Chen
- Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan
| | - Chih-Cheng Hsu
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Li-Wei Kuo
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan; Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan.
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27
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The impact of localized grey matter damage on neighboring connectivity: posterior cortical atrophy and the visual network. Brain Imaging Behav 2018; 13:1292-1301. [DOI: 10.1007/s11682-018-9952-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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28
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Balachandar R, Bharath S, John JP, Joshi H, Sadanand S, Saini J, Kumar KJ, Varghese M. Resting-State Functional Connectivity Changes Associated with Visuospatial Cognitive Deficits in Patients with Mild Alzheimer Disease. Dement Geriatr Cogn Disord 2018; 43:229-236. [PMID: 28351035 DOI: 10.1159/000457118] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/17/2017] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND/AIMS Alzheimer disease (AD) is a neurodegenerative disorder characterized by progressive disconnection of various brain networks leading to neuropsychological impairment. Pathology in the visual association areas has been documented in presymptomatic AD and therefore we aimed at examining the relationship between brain connectivity and visuospatial (VS) cognitive deficits in early AD. METHODS Tests for VS working memory, episodic memory and construction were used to classify patients with AD (n = 48) as having severe VS deficits (n = 12, female = 4) or mild deficits (n = 11, female = 4). Resting-state functional magnetic resonance imaging and structural images were acquired as per the standard protocols. Between-group differences in resting-state functional connectivity (rsFC) were examined by dual regression analysis correcting for age, gender, and total brain volume. RESULTS Patients with AD having severe VS deficits exhibited significantly reduced rsFC in bilateral lingual gyri of the visual network compared to patients with mild VS deficits. CONCLUSION Reduced rsFC in the visual network in patients with more severe VS deficits may be a functional neuroimaging biomarker reflecting hypoconnectivity of the brain with progressive VS deficits during early AD.
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Affiliation(s)
- Rakesh Balachandar
- Department of Clinical Neuroscience, National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India
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Zimmermann J, Perry A, Breakspear M, Schirner M, Sachdev P, Wen W, Kochan NA, Mapstone M, Ritter P, McIntosh AR, Solodkin A. Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models. NEUROIMAGE-CLINICAL 2018; 19:240-251. [PMID: 30035018 PMCID: PMC6051478 DOI: 10.1016/j.nicl.2018.04.017] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Revised: 04/05/2018] [Accepted: 04/14/2018] [Indexed: 01/09/2023]
Abstract
Alzheimer's disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, “The Virtual Brain (TVB)”, based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes. Modeled local and global dynamics correlate with individual cognition in Alzheimer's. Proof of concept of The Virtual Brain to characterize individual dynamics Brain-behaviour relations depend on the network modeled (whole brain or limbic). Model parameters predict cognition better than metrics of neuroimaging data.
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Affiliation(s)
- J Zimmermann
- Baycrest Health Sciences, Rotman Research Institute, 3560 Bathurst St, Toronto, Ontario M6A 2E1, Canada.
| | - A Perry
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Program of Mental Health Research, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - M Breakspear
- Program of Mental Health Research, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia; Metro North Mental Health Service, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia
| | - M Schirner
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, Chariteplatz 1, Berlin 13353, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - P Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - W Wen
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - N A Kochan
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - M Mapstone
- UC Irvine Health School of Medicine, Irvine Hall, 1001 Health Sciences Road, Irvine, CA 92697-3950, USA
| | - P Ritter
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, Chariteplatz 1, Berlin 13353, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - A R McIntosh
- Baycrest Health Sciences, Rotman Research Institute, 3560 Bathurst St, Toronto, Ontario M6A 2E1, Canada
| | - A Solodkin
- UC Irvine Health School of Medicine, Irvine Hall, 1001 Health Sciences Road, Irvine, CA 92697-3950, USA
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Gao Y, Zheng J, Li Y, Guo D, Wang M, Cui X, Ye W. Decreased functional connectivity and structural deficit in alertness network with right-sided temporal lobe epilepsy. Medicine (Baltimore) 2018; 97:e0134. [PMID: 29620625 PMCID: PMC5902293 DOI: 10.1097/md.0000000000010134] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Patients with temporal lobe epilepsy (TLE) often suffer from alertness alterations. However, specific regions connected with alertness remain controversial, and whether these regions have structural impairment is also elusive. This study aimed to investigate the characteristics and neural mechanisms underlying the functions and structures of alertness network in patients with right-sided temporal lobe epilepsy (rTLE) by performing the attentional network test (ANT), resting-state functional magnetic resonance imaging (R-SfMRI), and diffusion tensor imaging (DTI).A total of 47 patients with rTLE and 34 healthy controls underwent ANT, R-SfMRI, and DTI scan. The seed-based functional connectivity (FC) method and deterministic tractography were used to analyze the data.Patients with rTLE had longer reaction times in the no-cue and double-cue conditions. However, no differences were noted in the alertness effect between the 2 groups. The patient group had lower FC compared with the control group in the right inferior parietal lobe (IPL), amygdala, and insula. Structural deficits were found in the right parahippocampal gyrus, superior temporal pole, insula, and amygdala in the patient group compared with the control group. Also significantly negative correlations were observed between abnormal fractional anisotropy (between the right insula and the superior temporal pole) and illness duration in the patients with rTLE.The findings of this study suggested abnormal intrinsic and phasic alertness, decreased FC, and structural deficits within the alerting network in the rTLE. This study provided new insights into the mechanisms of alertness alterations in rTLE.
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Affiliation(s)
| | | | | | | | | | | | - Wei Ye
- Department of Radiology, the First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China
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Syama A, Sen S, Kota LN, Viswanath B, Purushottam M, Varghese M, Jain S, Panicker MM, Mukherjee O. Mutation burden profile in familial Alzheimer's disease cases from India. Neurobiol Aging 2017; 64:158.e7-158.e13. [PMID: 29329714 DOI: 10.1016/j.neurobiolaging.2017.12.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2017] [Revised: 12/04/2017] [Accepted: 12/04/2017] [Indexed: 12/24/2022]
Abstract
This study attempts to identify coding risk variants in genes previously implicated in Alzheimer's disease (AD) pathways, through whole-exome sequencing of subjects (N = 17) with AD, with a positive family history of dementia (familial AD). We attempted to evaluate the mutation burden in genes encoding amyloid precursor protein metabolism and previously linked to risk of dementias. Novel variants were identified in genes involved in amyloid precursor protein metabolism such as PSEN1 (chr 14:73653575, W161C, tgg > tgT), PLAT (chr 8:42039530,G272R), and SORL1 (chr11:121414373,G601D). The mutation burden assessment of dementia-related genes for all 17 cases revealed 45 variants, which were either shared across subjects, or were present in just the 1 patient. The study shows that the clinical characteristics, and genetic correlates, obtained in this sample are broadly comparable to the other studies that have investigated familial forms of AD. Our study identifies rare deleterious genetic variations, in the coding region of genes involved in amyloid signaling, and other dementia-associated pathways.
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Affiliation(s)
- Adhikarla Syama
- Manav Rachna International Institute of Research and Studies (Deemed to be University), Faridabad, India
| | - Somdatta Sen
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | | | - Biju Viswanath
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Meera Purushottam
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Mathew Varghese
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | - Sanjeev Jain
- National Institute of Mental Health and Neuro Sciences, Bengaluru, India
| | | | - Odity Mukherjee
- Institute for Stem Cell Biology and Regenerative Medicine, Bengaluru, India.
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32
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Ma HR, Pan PL, Sheng LQ, Dai ZY, Wang GD, Luo R, Chen JH, Xiao PR, Zhong JG, Shi HC. Aberrant pattern of regional cerebral blood flow in Alzheimer's disease: a voxel-wise meta-analysis of arterial spin labeling MR imaging studies. Oncotarget 2017; 8:93196-93208. [PMID: 29190989 PMCID: PMC5696255 DOI: 10.18632/oncotarget.21475] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2017] [Accepted: 09/20/2017] [Indexed: 12/20/2022] Open
Abstract
Many studies have applied arterial spin labeling (ASL) to characterize cerebral perfusion patterns of Alzheimer's disease (AD). However, findings across studies are not conclusive. A quantitatively voxel-wise meta-analysis to pool the resting-state ASL studies that measure regional cerebral blood flow (rCBF) alterations in AD was conducted to identify the most consistent and replicable perfusion pattern using seed-based d mapping. The meta-analysis, including 17 ASL studies encompassing 327 AD patients and 357 healthy controls, demonstrated that decreased rCBF in AD patients relative to healthy controls were consistently identified in the bilateral posterior cingulate cortices (PCC)/precuneus, bilateral inferior parietal lobules (IPLs), and left dorsolateral prefrontal cortex. The meta-regression analysis showed that more severe cognitive impairment in the AD samples correlated with greater decreases of rCBF in the bilateral PCC and left IPL. This study characterizes an aberrant ASL-rCBF perfusion pattern of AD involving the posterior default mode network and executive network, which are implicated in its pathophysiology and hold promise for developing imaging biomarkers.
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Affiliation(s)
- Hai Rong Ma
- Department of Neurology, Traditional Chinese Medicine Hospital of Kunshan, Kunshan, PR China
| | - Ping Lei Pan
- Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
| | - Li Qin Sheng
- Department of Neurology, Traditional Chinese Medicine Hospital of Kunshan, Kunshan, PR China
| | - Zhen Yu Dai
- Department of Radiology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
| | - Gen Di Wang
- Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
| | - Rong Luo
- Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
| | - Jia Hui Chen
- Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
| | - Pei Rong Xiao
- Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
| | - Jian Guo Zhong
- Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
| | - Hai Cun Shi
- Department of Neurology, Affiliated Yancheng Hospital, School of Medicine, Southeast University, Yancheng, PR China
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Sun DM, Chen HF, Zuo QL, Su F, Bai F, Liu CF. Effect of PICALM rs3851179 polymorphism on the default mode network function in mild cognitive impairment. Behav Brain Res 2017; 331:225-232. [PMID: 28549650 DOI: 10.1016/j.bbr.2017.05.043] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 05/12/2017] [Accepted: 05/17/2017] [Indexed: 01/17/2023]
Abstract
Alterations in default mode network (DMN) functional connectivity (FC) might accompany the dysfunction of Alzheimer's disease (AD). Indeed, episodic memory impairment is a hallmark of AD, and mild cognitive impairment (MCI) has been associated with a high risk for AD. Phosphatidylinositol-binding clathrin assembly protein (PICALM) (rs3851179) has been associated with AD; in particular, the A allele may serve a protective role, while the G allele serves as a strong genetic risk factor. Therefore, the identification of genetic polymorphisms associated with the DMN is required in MCI subjects. In all, 32 MCI subjects and 32 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) and a genetic imaging approach. Subjects were divided into four groups according to the diagnosis (i.e., MCI and HCs) and the PICALM rs3851179 polymorphism (i.e., AA/AG genotype and GG genotype). The differences in FC within the DMN between the four subgroups were explored. Furthermore, we examined the relationship between our neuroimaging measures and cognitive performance. The regions associated with the genotype-by-disease interaction were in the left middle temporal gyrus (LMTG) and left middle frontal gyrus (LMFG). These changes in LMFG FC were generally manifested as an "inverse U-shaped curve", while a "U-shaped curve" was associated with the LMTG FC between these four subgroups (all P<0.05). Furthermore, higher FC within the LMFG was related to better episodic memory performance (i.e., AVLT 20min DR, rho=0.72, P=0.044) for the MCI subgroups with the GG genotype. The PICALM rs3851179 polymorphism significantly affects the DMN network in MCI. The LMFG and LMTG may be associated with opposite patterns. However, the altered LMFG FC in MCI patients with the GG genotype was more sensitive to episodic memory impairment, which is more likely to lead to a high risk of AD.
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Affiliation(s)
- Ding-Ming Sun
- Department of Neurology, Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China; Department of Neurology, Fourth Affiliated Yancheng Hospital of Nantong University, Yancheng 224001, China
| | - Hai-Feng Chen
- Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, and The Institute of Neuropsychiatry of Southeast University, Nanjing 210009, China
| | - Qi-Long Zuo
- Department of Neurology, Fourth Affiliated Yancheng Hospital of Nantong University, Yancheng 224001, China
| | - Fan Su
- Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, and The Institute of Neuropsychiatry of Southeast University, Nanjing 210009, China
| | - Feng Bai
- Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, and The Institute of Neuropsychiatry of Southeast University, Nanjing 210009, China
| | - Chun-Feng Liu
- Department of Neurology, Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou 215004, China.
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Badhwar A, Tam A, Dansereau C, Orban P, Hoffstaedter F, Bellec P. Resting-state network dysfunction in Alzheimer's disease: A systematic review and meta-analysis. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2017; 8:73-85. [PMID: 28560308 PMCID: PMC5436069 DOI: 10.1016/j.dadm.2017.03.007] [Citation(s) in RCA: 241] [Impact Index Per Article: 34.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Introduction We performed a systematic review and meta-analysis of the Alzheimer's disease (AD) literature to examine consistency of functional connectivity alterations in AD dementia and mild cognitive impairment, using resting-state functional magnetic resonance imaging. Methods Studies were screened using a standardized procedure. Multiresolution statistics were performed to assess the spatial consistency of findings across studies. Results Thirty-four studies were included (1363 participants, average 40 per study). Consistent alterations in connectivity were found in the default mode, salience, and limbic networks in patients with AD dementia, mild cognitive impairment, or in both groups. We also identified a strong tendency in the literature toward specific examination of the default mode network. Discussion Convergent evidence across the literature supports the use of resting-state connectivity as a biomarker of AD. The locations of consistent alterations suggest that highly connected hub regions in the brain might be an early target of AD.
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Affiliation(s)
- AmanPreet Badhwar
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
- Corresponding author. Tel.: +1-514-340-3540x3367; Fax: +1-514-340-2802.
| | - Angela Tam
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
- McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
| | - Christian Dansereau
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
| | - Pierre Orban
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
- Douglas Mental Health University Institute Research Centre, Montreal, Quebec, Canada
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Clinical Neuroscience and Medical Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Pierre Bellec
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montreal, Quebec, Canada
- Université de Montréal, Montreal, Quebec, Canada
- Corresponding author. Tel.: +1-514-340-3540x4782; Fax: +1-514-340-2802.
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Ruan Q, D'Onofrio G, Sancarlo D, Bao Z, Greco A, Yu Z. Potential neuroimaging biomarkers of pathologic brain changes in Mild Cognitive Impairment and Alzheimer's disease: a systematic review. BMC Geriatr 2016; 16:104. [PMID: 27184250 PMCID: PMC4869390 DOI: 10.1186/s12877-016-0281-7] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Accepted: 05/09/2016] [Indexed: 12/16/2022] Open
Abstract
Background Neuroimaging-biomarkers of Mild Cognitive Impairment (MCI) allow an early diagnosis in preclinical stages of Alzheimer’s disease (AD). The goal in this paper was to review of biomarkers for Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD), with emphasis on neuroimaging biomarkers. Methods A systematic review was conducted from existing literature that draws on markers and evidence for new measurement techniques of neuroimaging in AD, MCI and non-demented subjects. Selection criteria included: 1) age ≥ 60 years; 2) diagnosis of AD according to NIAAA criteria, 3) diagnosis of MCI according to NIAAA criteria with a confirmed progression to AD assessed by clinical follow-up, and 4) acceptable clinical measures of cognitive impairment, disability, quality of life, and global clinical assessments. Results Seventy-two articles were included in the review. With the development of new radioligands of neuroimaging, today it is possible to measure different aspects of AD neuropathology, early diagnosis of MCI and AD become probable from preclinical stage of AD to AD dementia and non-AD dementia. Conclusions The panel of noninvasive neuroimaging-biomarkers reviewed provides a set methods to measure brain structural and functional pathophysiological changes in vivo, which are closely associated with preclinical AD, MCI and non-AD dementia. The dynamic measures of these imaging biomarkers are used to predict the disease progression in the early stages and improve the assessment of therapeutic efficacy in these diseases in future clinical trials.
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Affiliation(s)
- Qingwei Ruan
- Shanghai Institute of Geriatrics and Gerontology, Shanghai Key Laboratory of Clinical Geriatrics, Department of Geriatrics, Huadong Hospital, and Research Center of Aging and Medicine, Shanghai Medical College, Fudan University, Shanghai, 200040, China
| | - Grazia D'Onofrio
- Geriatric Unit & Laboratory of Gerontology and Geriatrics, Department of Medical Sciences, IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Foggia, Italy.
| | - Daniele Sancarlo
- Geriatric Unit & Laboratory of Gerontology and Geriatrics, Department of Medical Sciences, IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Foggia, Italy
| | - Zhijun Bao
- Shanghai Institute of Geriatrics and Gerontology, Shanghai Key Laboratory of Clinical Geriatrics, Department of Geriatrics, Huadong Hospital, and Research Center of Aging and Medicine, Shanghai Medical College, Fudan University, Shanghai, 200040, China
| | - Antonio Greco
- Geriatric Unit & Laboratory of Gerontology and Geriatrics, Department of Medical Sciences, IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Foggia, Italy
| | - Zhuowei Yu
- Shanghai Institute of Geriatrics and Gerontology, Shanghai Key Laboratory of Clinical Geriatrics, Department of Geriatrics, Huadong Hospital, and Research Center of Aging and Medicine, Shanghai Medical College, Fudan University, Shanghai, 200040, China. .,Huadong Hospital, Shanghai Medical College, Fudan University, 221 West Yan An Road, Shanghai, 200040, P.R. China.
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Measuring Cortical Connectivity in Alzheimer's Disease as a Brain Neural Network Pathology: Toward Clinical Applications. J Int Neuropsychol Soc 2016; 22:138-63. [PMID: 26888613 DOI: 10.1017/s1355617715000995] [Citation(s) in RCA: 78] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVES The objective was to review the literature on diffusion tensor imaging as well as resting-state functional magnetic resonance imaging and electroencephalography (EEG) to unveil neuroanatomical and neurophysiological substrates of Alzheimer's disease (AD) as a brain neural network pathology affecting structural and functional cortical connectivity underlying human cognition. METHODS We reviewed papers registered in PubMed and other scientific repositories on the use of these techniques in amnesic mild cognitive impairment (MCI) and clinically mild AD dementia patients compared to cognitively intact elderly individuals (Controls). RESULTS Hundreds of peer-reviewed (cross-sectional and longitudinal) papers have shown in patients with MCI and mild AD compared to Controls (1) impairment of callosal (splenium), thalamic, and anterior-posterior white matter bundles; (2) reduced correlation of resting state blood oxygen level-dependent activity across several intrinsic brain circuits including default mode and attention-related networks; and (3) abnormal power and functional coupling of resting state cortical EEG rhythms. Clinical applications of these measures are still limited. CONCLUSIONS Structural and functional (in vivo) cortical connectivity measures represent a reliable marker of cerebral reserve capacity and should be used to predict and monitor the evolution of AD and its relative impact on cognitive domains in pre-clinical, prodromal, and dementia stages of AD.
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Joo SH, Lim HK, Lee CU. Three Large-Scale Functional Brain Networks from Resting-State Functional MRI in Subjects with Different Levels of Cognitive Impairment. Psychiatry Investig 2016; 13:1-7. [PMID: 26766941 PMCID: PMC4701672 DOI: 10.4306/pi.2016.13.1.1] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2015] [Revised: 10/19/2015] [Accepted: 10/19/2015] [Indexed: 11/29/2022] Open
Abstract
Normal aging and to a greater degree degenerative brain diseases such as Alzheimer's disease (AD), cause changes in the brain's structure and function. Degenerative changes in brain structure and decline in its function are associated with declines in cognitive ability. Early detection of AD is a key priority in dementia services and research. However, depending on the disease progression, neurodegenerative manifestations, such as cerebral atrophy, are detected late in course of AD. Functional changes in the brain may be an indirect indicator of trans-synaptic activity and they usually appear prior to structural changes in AD. Resting-state functional magnetic resonance imaging (RS-fMRI) has recently been highlighted as a new technique for interrogating intrinsic functional connectivity networks. Among the majority of RS-fMRI studies, the default mode network (DMN), salience network (SN), and central executive network (CEN) gained particular focus because alterations to their functional connectivity were observed in subjects who had AD, who had mild cognitive impairment (MCI), or who were at high risk for AD. Herein, we present a review of the current research on changes in functional connectivity, as measured by RS-fMRI. We focus on the DMN, SN, and CEN to describe RS-fMRI results from three groups: normal healthy aging, MCI and AD.
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Affiliation(s)
- Soo Hyun Joo
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hyun Kook Lim
- Department of Psychiatry, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea
| | - Chang Uk Lee
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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38
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Ouyang X, Chen K, Yao L, Hu B, Wu X, Ye Q, Guo X. Simultaneous changes in gray matter volume and white matter fractional anisotropy in Alzheimer's disease revealed by multimodal CCA and joint ICA. Neuroscience 2015; 301:553-62. [PMID: 26116521 DOI: 10.1016/j.neuroscience.2015.06.031] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Revised: 06/16/2015] [Accepted: 06/17/2015] [Indexed: 01/30/2023]
Abstract
The prominent morphometric alterations of Alzheimer's disease (AD) occur both in gray matter and in white matter. Multimodal fusion can examine joint information by combining multiple neuroimaging datasets to identify the covariant morphometric alterations in AD in greater detail. In the current study, we conducted a multimodal canonical correlation analysis and joint independent component analysis to identify the covariance patterns of the gray and white matter by fusing structural magnetic resonance imaging and diffusion tensor imaging data of 39 AD patients (23 males and 16 females, mean age: 74.91±8.13years) and 41 normal controls (NCs) (20 males and 21 females, mean age: 73.97±6.34years) derived from the Alzheimer's Disease Neuroimaging Initiative database. The results revealed 25 joint independent components (ICs), of which three joint ICs exhibited strong links between the gray matter volume and the white matter fractional anisotropy (FA) and significant differences between the AD and NC group. The joint IC maps revealed that the simultaneous changes in the gray matter and FA values primarily involved the following areas: (1) the temporal lobe/hippocampus-cingulum, (2) the frontal/cingulate gyrus-corpus callosum, and (3) the temporal/occipital/parietal lobe-corpus callosum/corona radiata. Our findings suggest that gray matter atrophy is associated with reduced white matter fiber integrity in AD and possibly expand the understanding of the neuropathological mechanisms in AD.
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Affiliation(s)
- X Ouyang
- College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - K Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA
| | - L Yao
- College of Information Science and Technology, Beijing Normal University, Beijing, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - B Hu
- College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - X Wu
- College of Information Science and Technology, Beijing Normal University, Beijing, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Q Ye
- College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - X Guo
- College of Information Science and Technology, Beijing Normal University, Beijing, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
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39
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Huang CC, Hsieh WJ, Lee PL, Peng LN, Liu LK, Lee WJ, Huang JK, Chen LK, Lin CP. Age-related changes in resting-state networks of a large sample size of healthy elderly. CNS Neurosci Ther 2015; 21:817-25. [PMID: 25864728 DOI: 10.1111/cns.12396] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2015] [Revised: 03/08/2015] [Accepted: 03/09/2015] [Indexed: 11/28/2022] Open
Abstract
AIMS Population aging is burdening the society globally, and the evaluation of functional networks is the key toward understanding cognitive changes in normal aging. However, the effect of age on default mode subnetworks has not been documented well, and age-related changes in many resting-state networks remain debatable. The purpose of this study was to propose more precise results for these issues using a large sample size. METHODS We used group-level meta-ICA analysis and dual regression approach for identifying resting-state networks from functional magnetic resonance imaging data of 430 healthy elderly participants. Partial correlation was used to observe age-related correlations within and between resting-state networks. RESULTS In the default mode network, only the ventral subnetwork negatively correlated with age. Age-related decrease in functional connectivity was also noted in the auditory, right frontoparietal, sensorimotor, and visual medial networks. Further, some age-related increases and decreases were observed for between-network correlations. CONCLUSION The results of this study suggest that only the ventral default mode subnetwork had age-related decline in functional connectivity and several reverse patterns of resting-state networks for network development. Understanding age-related network changes may provide solutions for the impact of population aging and diagnosis of neurodegenerative diseases.
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Affiliation(s)
- Chun-Chao Huang
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan.,Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan.,Department of Medicine, MacKay Medical College, Taipei, Taiwan
| | - Wen-Jin Hsieh
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Pei-Lin Lee
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan
| | - Li-Ning Peng
- Aging and Health Research Center, National Yang-Ming University, Taipei, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Li-Kuo Liu
- Aging and Health Research Center, National Yang-Ming University, Taipei, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Wei-Ju Lee
- Aging and Health Research Center, National Yang-Ming University, Taipei, Taiwan.,Department of Family Medicine, Taipei Veterans General Hospital Yuanshan Branch, Ilan, Taiwan
| | - Jon-Kway Huang
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan.,Department of Medicine, MacKay Medical College, Taipei, Taiwan
| | - Liang-Kung Chen
- Aging and Health Research Center, National Yang-Ming University, Taipei, Taiwan.,Center for Geriatrics and Gerontology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan.,Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan
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40
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Acosta-Cabronero J, Nestor PJ. Diffusion tensor imaging in Alzheimer's disease: insights into the limbic-diencephalic network and methodological considerations. Front Aging Neurosci 2014; 6:266. [PMID: 25324775 PMCID: PMC4183111 DOI: 10.3389/fnagi.2014.00266] [Citation(s) in RCA: 74] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2014] [Accepted: 09/15/2014] [Indexed: 11/25/2022] Open
Abstract
Glucose hypometabolism and gray matter atrophy are well known consequences of Alzheimer's disease (AD). Studies using these measures have shown that the earliest clinical stages, in which memory impairment is a relatively isolated feature, are associated with degeneration in an apparently remote group of areas—mesial temporal lobe (MTL), diencephalic structures such as anterior thalamus and mammillary bodies, and posterior cingulate. These sites are thought to be strongly anatomically inter-connected via a limbic-diencephalic network. Diffusion tensor imaging or DTI—an imaging technique capable of probing white matter tissue microstructure—has recently confirmed degeneration of the white matter connections of the limbic-diencephalic network in AD by way of an unbiased analysis strategy known as tract-based spatial statistics (TBSS). The present review contextualizes the relevance of these findings, in which the fornix is likely to play a fundamental role in linking MTL and diencephalon. An interesting by-product of this work has been in showing that alterations in diffusion behavior are complex in AD—while early studies tended to focus on fractional anisotropy, recent work has highlighted that this measure is not the most sensitive to early changes. Finally, this review will discuss in detail several technical aspects of DTI both in terms of image acquisition and TBSS analysis as both of these factors have important implications to ensure reliable observations are made that inform understanding of neurodegenerative diseases.
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Affiliation(s)
- Julio Acosta-Cabronero
- Brain Plasticity and Neurodegeneration Group, German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Germany
| | - Peter J Nestor
- Brain Plasticity and Neurodegeneration Group, German Center for Neurodegenerative Diseases (DZNE) Magdeburg, Germany
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