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Reese M, Wong MK, Cheong V, Ha CI, Cooter Wright M, Browndyke J, Moretti E, Devinney MJ, Habib AS, Moul JW, Shaw LM, Waligorska T, Whitson HE, Cohen HJ, Welsh-Bohmer KA, Plassman BL, Mathew JP, Berger M. Cognitive and Cerebrospinal Fluid Alzheimer's Disease-related Biomarker Trajectories in Older Surgical Patients and Matched Nonsurgical Controls. Anesthesiology 2024; 140:963-978. [PMID: 38324729 PMCID: PMC11003848 DOI: 10.1097/aln.0000000000004924] [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] [Indexed: 02/09/2024]
Abstract
BACKGROUND Anesthesia and/or surgery accelerate Alzheimer's disease pathology and cause memory deficits in animal models, yet there is a lack of prospective data comparing cerebrospinal fluid (CSF) Alzheimer's disease-related biomarker and cognitive trajectories in older adults who underwent surgery versus those who have not. Thus, the objective here was to better understand whether anesthesia and/or surgery contribute to cognitive decline or an acceleration of Alzheimer's disease-related pathology in older adults. METHODS The authors enrolled 140 patients 60 yr or older undergoing major nonneurologic surgery and 51 nonsurgical controls via strata-based matching on age, sex, and years of education. CSF amyloid β (Aβ) 42, tau, and p-tau-181p levels and cognitive function were measured before and after surgery, and at the same time intervals in controls. RESULTS The groups were well matched on 25 of 31 baseline characteristics. There was no effect of group or interaction of group by time for baseline to 24-hr or 6-week postoperative changes in CSF Aβ, tau, or p-tau levels, or tau/Aβ or p-tau/Aβ ratios (Bonferroni P > 0.05 for all) and no difference between groups in these CSF markers at 1 yr (P > 0.05 for all). Nonsurgical controls did not differ from surgical patients in baseline cognition (mean difference, 0.19 [95% CI, -0.06 to 0.43]; P = 0.132), yet had greater cognitive decline than the surgical patients 1 yr later (β, -0.31 [95% CI, -0.45 to -0.17]; P < 0.001) even when controlling for baseline differences between groups. However, there was no difference between nonsurgical and surgical groups in 1-yr postoperative cognitive change in models that used imputation or inverse probability weighting for cognitive data to account for loss to follow up. CONCLUSIONS During a 1-yr time period, as compared to matched nonsurgical controls, the study found no evidence that older patients who underwent anesthesia and noncardiac, nonneurologic surgery had accelerated CSF Alzheimer's disease-related biomarker (tau, p-tau, and Aβ) changes or greater cognitive decline. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Melody Reese
- Duke University Medical Center (DUMC), Department of Anesthesiology, Durham, NC, USA
- DUMC, Center for the Study of Aging and Human Development, Durham, NC, USA
| | - Megan K. Wong
- Duke University Medical Center (DUMC), Department of Anesthesiology, Durham, NC, USA
| | - Vanessa Cheong
- Duke University Medical Center (DUMC), Department of Anesthesiology, Durham, NC, USA
- Duke University-National University of Singapore Medical School, Singapore
| | - Christine I. Ha
- Duke University Medical Center (DUMC), Department of Anesthesiology, Durham, NC, USA
| | - Mary Cooter Wright
- Duke University Medical Center (DUMC), Department of Anesthesiology, Durham, NC, USA
| | - Jeffrey Browndyke
- DUMC, Department of Psychiatry and Behavioral Medicine, Durham, NC, USA
| | - Eugene Moretti
- Duke University Medical Center (DUMC), Department of Anesthesiology, Durham, NC, USA
| | - Michael J. Devinney
- Duke University Medical Center (DUMC), Department of Anesthesiology, Durham, NC, USA
| | - Ashraf S. Habib
- Duke University Medical Center (DUMC), Department of Anesthesiology, Durham, NC, USA
| | - Judd W. Moul
- Duke University Medical Center (DUMC), Department of Anesthesiology, Durham, NC, USA
- DUMC, Department of Surgery, Durham, NC, USA
| | - Leslie M. Shaw
- Perelman School of Medicine University of Pennsylvania, Department of Pathology and Laboratory Medicine, Philadelphia, PA, USA
| | - Teresa Waligorska
- Perelman School of Medicine University of Pennsylvania, Department of Pathology and Laboratory Medicine, Philadelphia, PA, USA
| | - Heather E. Whitson
- DUMC, Center for the Study of Aging and Human Development, Durham, NC, USA
- DUMC, Department of Medicine, Durham, NC, USA
- DUMC, Duke/UNC Alzheimer’s Disease Research Center, Durham, NC, USA
| | - Harvey J. Cohen
- DUMC, Center for the Study of Aging and Human Development, Durham, NC, USA
- DUMC, Department of Medicine, Durham, NC, USA
- DUMC, Duke/UNC Alzheimer’s Disease Research Center, Durham, NC, USA
| | - Kathleen A. Welsh-Bohmer
- DUMC, Department of Psychiatry and Behavioral Medicine, Durham, NC, USA
- DUMC, Duke/UNC Alzheimer’s Disease Research Center, Durham, NC, USA
| | - Brenda L. Plassman
- DUMC, Department of Psychiatry and Behavioral Medicine, Durham, NC, USA
- DUMC, Duke/UNC Alzheimer’s Disease Research Center, Durham, NC, USA
| | - Joseph P. Mathew
- Duke University Medical Center (DUMC), Department of Anesthesiology, Durham, NC, USA
| | - Miles Berger
- Duke University Medical Center (DUMC), Department of Anesthesiology, Durham, NC, USA
- DUMC, Center for the Study of Aging and Human Development, Durham, NC, USA
- DUMC, Duke/UNC Alzheimer’s Disease Research Center, Durham, NC, USA
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Marawi T, Zhukovsky P, Rashidi-Ranjbar N, Bowie CR, Brooks H, Fischer CE, Flint AJ, Herrmann N, Mah L, Pollock BG, Rajji TK, Tartaglia MC, Voineskos AN, Mulsant BH. Brain-Cognition Associations in Older Patients With Remitted Major Depressive Disorder or Mild Cognitive Impairment: A Multivariate Analysis of Gray and White Matter Integrity. Biol Psychiatry 2023; 94:913-923. [PMID: 37271418 DOI: 10.1016/j.biopsych.2023.05.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 05/10/2023] [Accepted: 05/24/2023] [Indexed: 06/06/2023]
Abstract
BACKGROUND Almost half of older patients with major depressive disorder (MDD) present with cognitive impairment, and one-third meet diagnostic criteria for mild cognitive impairment (MCI). However, mechanisms linking MDD and MCI remain unclear. We investigated multivariate associations between brain structural alterations and cognition in 3 groups of older patients at risk for dementia, remitted MDD (rMDD), MCI, and rMDD+MCI, as well as cognitively healthy nondepressed control participants. METHODS We analyzed magnetic resonance imaging data and cognitive domain scores in participants from the PACt-MD (Prevention of Alzheimer's Disease With Cognitive Remediation Plus Transcranial Direct Current Stimulation in Mild Cognitive Impairment and Depression) study. Following quality control, we measured cortical thickness and subcortical volumes of selected regions from 283 T1-weighted scans and fractional anisotropy of white matter tracts from 226 diffusion-weighted scans. We assessed brain-cognition associations using partial least squares regressions in the whole sample and in each subgroup. RESULTS In the entire sample, atrophy in the medial temporal lobe and subregions of the motor and prefrontal cortex was associated with deficits in verbal and visuospatial memory, language skills, and, to a lesser extent, processing speed (p < .0001; multivariate r = 0.30, 0.34, 0.26, and 0.18, respectively). Widespread reduced white matter integrity was associated with deficits in executive functioning, working memory, and processing speed (p = .008; multivariate r = 0.21, 0.26, 0.35, respectively). Overall, associations remained significant in the MCI and rMDD+MCI groups, but not the rMDD or healthy control groups. CONCLUSIONS We confirm findings of brain-cognition associations previously reported in MCI and extend them to rMDD+MCI, but similar associations in rMDD are not supported. Early-onset and treated MDD might not contribute to structural alterations associated with cognitive impairment.
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Affiliation(s)
- Tulip Marawi
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Peter Zhukovsky
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Neda Rashidi-Ranjbar
- Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Christopher R Bowie
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychology, Queen's University, Kingston, Ontario, Canada; Department of Psychiatry, Queen's University, Kingston, Ontario, Canada
| | - Heather Brooks
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Corinne E Fischer
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Alastair J Flint
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Centre for Mental Health, University Health Network, Toronto, Ontario, Canada
| | - Nathan Herrmann
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Sunnybrook Health Sciences Centre, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Linda Mah
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Baycrest Health Services, Rotman Research Institute, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Bruce G Pollock
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Tarek K Rajji
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Toronto Dementia Research Alliance, University of Toronto, Toronto, Ontario, Canada
| | - Maria Carmela Tartaglia
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada
| | - Aristotle N Voineskos
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Benoit H Mulsant
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada; Toronto Dementia Research Alliance, University of Toronto, Toronto, Ontario, Canada.
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Zhao J, Jing B, Liu J, Chen F, Wu Y, Li H. Probing bundle-wise abnormalities in patients infected with human immunodeficiency virus using fixel-based analysis: new insights into neurocognitive impairments. Chin Med J (Engl) 2023; 136:2178-2186. [PMID: 37605986 PMCID: PMC10508508 DOI: 10.1097/cm9.0000000000002829] [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: 12/05/2022] [Indexed: 08/23/2023] Open
Abstract
BACKGROUND Changes in white matter (WM) underlie the neurocognitive damages induced by a human immunodeficiency virus (HIV) infection. This study aimed to examine using a bundle-associated fixel-based analysis (FBA) pipeline for investigating the microstructural and macrostructural alterations in the WM of the brain of HIV patients. METHODS This study collected 93 HIV infected patients and 45 age/education/handedness matched healthy controls (HCs) at the Beijing Youan Hospital between January 1, 2016 and December 30, 2016.All HIV patients underwent neurocognitive evaluation and laboratory testing followed by magnetic resonance imaging (MRI) scanning. In order to detect the bundle-wise WM abnormalities accurately, a specific WM bundle template with 56 tracts of interest was firstly generated by an automated fiber clustering method using a subset of subjects. Fixel-based analysis was used to investigate bundle-wise differences between HIV patients and HCs in three perspectives: fiber density (FD), fiber cross-section (FC), and fiber density and cross-section (FDC). The between-group differences were detected by a two-sample t -test with the false discovery rate (FDR) correction ( P <0.05). Furthermore, the covarying relationship in FD, FC and FDC between any pair of bundles was also accessed by the constructed covariance networks, which was subsequently compared between HIV and HCs via permutation t -tests. The correlations between abnormal WM metrics and the cognitive functions of HIV patients were explored via partial correlation analysis after controlling age and gender. RESULTS Among FD, FC and FDC, FD was the only metric that showed significant bundle-wise alterations in HIV patients compared to HCs. Increased FD values were observed in the bilateral fronto pontine tract, corona radiata frontal, left arcuate fasciculus, left corona radiata parietal, left superior longitudinal fasciculus III, and right superficial frontal parietal (SFP) (all FDR P <0.05). In bundle-wise covariance network, HIV patients displayed decreased FD and increased FC covarying patterns in comparison to HC ( P <0.05) , especially between associated pathways. Finally, the FCs of several tracts exhibited a significant correlation with language and attention-related functions. CONCLUSIONS Our study demonstrated the utility of FBA on detecting the WM alterations related to HIV infection. The bundle-wise FBA method provides a new perspective for investigating HIV-induced microstructural and macrostructural WM-related changes, which may help to understand cognitive dysfunction in HIV patients thoroughly.
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Affiliation(s)
- Jing Zhao
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100069, China
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application,School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Jiaojiao Liu
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
| | - Feng Chen
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
| | - Ye Wu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China
| | - Hongjun Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100069, China
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
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Zhou Y, Wei L, Gao S, Wang J, Hu Z. Characterization of diffusion magnetic resonance imaging revealing relationships between white matter disconnection and behavioral disturbances in mild cognitive impairment: a systematic review. Front Neurosci 2023; 17:1209378. [PMID: 37360170 PMCID: PMC10285107 DOI: 10.3389/fnins.2023.1209378] [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: 04/20/2023] [Accepted: 05/23/2023] [Indexed: 06/28/2023] Open
Abstract
White matter disconnection is the primary cause of cognition and affection abnormality in mild cognitive impairment (MCI). Adequate understanding of behavioral disturbances, such as cognition and affection abnormality in MCI, can help to intervene and slow down the progression of Alzheimer's disease (AD) promptly. Diffusion MRI is a non-invasive and effective technique for studying white matter microstructure. This review searched the relevant papers published from 2010 to 2022. Sixty-nine studies using diffusion MRI for white matter disconnections associated with behavioral disturbances in MCI were screened. Fibers connected to the hippocampus and temporal lobe were associated with cognition decline in MCI. Fibers connected to the thalamus were associated with both cognition and affection abnormality. This review summarized the correspondence between white matter disconnections and behavioral disturbances such as cognition and affection, which provides a theoretical basis for the future diagnosis and treatment of AD.
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Affiliation(s)
- Yu Zhou
- College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
| | - Lan Wei
- Business School, The University of Sydney, Sydney, NSW, Australia
| | - Song Gao
- College of Agricultural Equipment Engineering, Henan University of Science and Technology, Luoyang, China
| | - Jun Wang
- School of Information Engineering, Henan University of Science and Technology, Luoyang, China
| | - Zhigang Hu
- College of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China
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Murdy TJ, Dunn AR, Singh S, Telpoukhovskaia MA, Zhang S, White JK, Kahn I, Febo M, Kaczorowski CC. Leveraging genetic diversity in mice to inform individual differences in brain microstructure and memory. Front Behav Neurosci 2023; 16:1033975. [PMID: 36703722 PMCID: PMC9871587 DOI: 10.3389/fnbeh.2022.1033975] [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: 09/01/2022] [Accepted: 12/08/2022] [Indexed: 01/11/2023] Open
Abstract
In human Alzheimer's disease (AD) patients and AD mouse models, both differential pre-disease brain features and differential disease-associated memory decline are observed, suggesting that certain neurological features may protect against AD-related cognitive decline. The combination of these features is known as brain reserve, and understanding the genetic underpinnings of brain reserve may advance AD treatment in genetically diverse human populations. One potential source of brain reserve is brain microstructure, which is genetically influenced and can be measured with diffusion MRI (dMRI). To investigate variation of dMRI metrics in pre-disease-onset, genetically diverse AD mouse models, we utilized a population of genetically distinct AD mice produced by crossing the 5XFAD transgenic mouse model of AD to 3 inbred strains (C57BL/6J, DBA/2J, FVB/NJ) and two wild-derived strains (CAST/EiJ, WSB/EiJ). At 3 months of age, these mice underwent diffusion magnetic resonance imaging (dMRI) to probe neural microanatomy in 83 regions of interest (ROIs). At 5 months of age, these mice underwent contextual fear conditioning (CFC). Strain had a significant effect on dMRI measures in most ROIs tested, while far fewer effects of sex, sex*strain interactions, or strain*sex*5XFAD genotype interactions were observed. A main effect of 5XFAD genotype was observed in only 1 ROI, suggesting that the 5XFAD transgene does not strongly disrupt neural development or microstructure of mice in early adulthood. Strain also explained the most variance in mouse baseline motor activity and long-term fear memory. Additionally, significant effects of sex and strain*sex interaction were observed on baseline motor activity, and significant strain*sex and sex*5XFAD genotype interactions were observed on long-term memory. We are the first to study the genetic influences of brain microanatomy in genetically diverse AD mice. Thus, we demonstrated that strain is the primary factor influencing brain microstructure in young adult AD mice and that neural development and early adult microstructure are not strongly altered by the 5XFAD transgene. We also demonstrated that strain, sex, and 5XFAD genotype interact to influence memory in genetically diverse adult mice. Our results support the usefulness of the 5XFAD mouse model and convey strong relationships between natural genetic variation, brain microstructure, and memory.
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Affiliation(s)
| | - Amy R. Dunn
- The Jackson Laboratory, Bar Harbor, ME, United States
| | - Surjeet Singh
- The Jackson Laboratory, Bar Harbor, ME, United States
| | | | | | | | - Itamar Kahn
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Marcelo Febo
- Department of Neuroscience, University of Florida College of Medicine, Gainesville, FL, United States
| | - Catherine C. Kaczorowski
- The Jackson Laboratory, Bar Harbor, ME, United States,*Correspondence: Catherine C. Kaczorowski,
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Saboo KV, Hu C, Varatharajah Y, Przybelski SA, Reid RI, Schwarz CG, Graff-Radford J, Knopman DS, Machulda MM, Mielke MM, Petersen RC, Arnold PM, Worrell GA, Jones DT, Jack Jr CR, Iyer RK, Vemuri P. Deep learning identifies brain structures that predict cognition and explain heterogeneity in cognitive aging. Neuroimage 2022; 251:119020. [PMID: 35196565 PMCID: PMC9045384 DOI: 10.1016/j.neuroimage.2022.119020] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Revised: 01/20/2022] [Accepted: 02/17/2022] [Indexed: 12/02/2022] Open
Abstract
Specific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heterogeneity in cognitive aging. Identification of these structures is crucial for screening of older adults at risk of cognitive decline. Using deep learning models augmented with a model-interpretation technique on data from 1432 Mayo Clinic Study of Aging participants, we identified a subset of brain structures that were most predictive of individualized cognitive trajectories and indicative of cognitively resilient vs. vulnerable individuals. Specifically, these structures explained why some participants were resilient to the deleterious effects of elevated brain amyloid and poor vascular health. Of these, medial temporal lobe and fornix, reflective of age and pathology-related degeneration, and corpus callosum, reflective of inter-hemispheric disconnection, accounted for 60% of the heterogeneity explained by the most predictive structures. Our results are valuable for identifying cognitively vulnerable individuals and for developing interventions for cognitive decline.
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Zhou Y, Si X, Chen Y, Chao Y, Lin CP, Li S, Zhang X, Ming D, Li Q. Hippocampus- and Thalamus-Related Fiber-Specific White Matter Reductions in Mild Cognitive Impairment. Cereb Cortex 2021; 32:3159-3174. [PMID: 34891164 DOI: 10.1093/cercor/bhab407] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/04/2021] [Accepted: 10/20/2021] [Indexed: 11/13/2022] Open
Abstract
Early diagnosis of mild cognitive impairment (MCI) fascinates screening high-risk Alzheimer's disease (AD). White matter is found to degenerate earlier than gray matter and functional connectivity during MCI. Although studies reveal white matter degenerates in the limbic system for MCI, how other white matter degenerates during MCI remains unclear. In our method, regions of interest with a high level of resting-state functional connectivity with hippocampus were selected as seeds to track fibers based on diffusion tensor imaging (DTI). In this way, hippocampus-temporal and thalamus-related fibers were selected, and each fiber's DTI parameters were extracted. Then, statistical analysis, machine learning classification, and Pearson's correlations with behavior scores were performed between MCI and normal control (NC) groups. Results show that: 1) the mean diffusivity of hippocampus-temporal and thalamus-related fibers are significantly higher in MCI and could be used to classify 2 groups effectively. 2) Compared with normal fibers, the degenerated fibers detected by the DTI indexes, especially for hippocampus-temporal fibers, have shown significantly higher correlations with cognitive scores. 3) Compared with the hippocampus-temporal fibers, thalamus-related fibers have shown significantly higher correlations with depression scores within MCI. Our results provide novel biomarkers for the early diagnoses of AD.
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Affiliation(s)
- Yu Zhou
- School of Microelectronics, Tianjin University, Tianjin 300072, China
| | - Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China.,Institute of Applied Psychology, Tianjin University, Tianjin 300350, China
| | - Yuanyuan Chen
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Yiping Chao
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan 33302, Taiwan.,Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan 33302, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience Hsinchu City, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
| | - Sicheng Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Xingjian Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin 300072, China
| | - Qiang Li
- School of Microelectronics, Tianjin University, Tianjin 300072, China
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Frau-Pascual A, Augustinack J, Varadarajan D, Yendiki A, Salat DH, Fischl B, Aganj I. Conductance-Based Structural Brain Connectivity in Aging and Dementia. Brain Connect 2021; 11:566-583. [PMID: 34042511 PMCID: PMC8558081 DOI: 10.1089/brain.2020.0903] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Background: Structural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer's disease (AD) progression. Methods: In this work, we used our recently proposed structural connectivity quantification measure derived from diffusion magnetic resonance imaging, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the second phase of Alzheimer's Disease Neuroimaging Initiative and third release in the Open Access Series of Imaging Studies data sets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these data sets to the Human Connectome Project data set, as a reference, and eventually validated externally on two cohorts of the European DTI Study in Dementia database. Results: Our analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among subcortical regions, and cingulate and temporal cortices. Discussion: Results indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anticorrelation in structural connections. Impact statement This work presents a methodology and a set of analyses that open new possibilities in the study of healthy and pathological aging. The methodology used here is sensitive to direct and indirect pathways in deriving brain connectivity measures from diffusion-weighted magnetic resonance imaging, and therefore provides information that many state-of-the-art methods do not account for. As a result, this technique may provide the research community with ways to detect subtle effects of healthy aging and Alzheimer's disease.
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Affiliation(s)
- Aina Frau-Pascual
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Divya Varadarajan
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - David H. Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Iman Aganj
- Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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Detection of functional and structural brain alterations in female schizophrenia using elastic net logistic regression. Brain Imaging Behav 2021; 16:281-290. [PMID: 34313906 PMCID: PMC8825615 DOI: 10.1007/s11682-021-00501-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/05/2021] [Indexed: 12/19/2022]
Abstract
Neuroimaging technique is a powerful tool to characterize the abnormality of brain networks in schizophrenia. However, the neurophysiological substrate of schizophrenia is still unclear. Here we investigated the patterns of brain functional and structural changes in female patients with schizophrenia using elastic net logistic regression analysis of resting-state functional magnetic resonance imaging data. Data from 52 participants (25 female schizophrenia patients and 27 healthy controls) were obtained. Using an elastic net penalty, the brain regions most relevant to schizophrenia pathology were defined in the models using the amplitude of low-frequency fluctuations (ALFF) and gray matter, respectively. The receiver operating characteristic analysis showed reliable classification accuracy with 85.7% in ALFF analysis, and 77.1% in gray matter analysis. Notably, our results showed eight common regions between the ALFF and gray matter analyses, including the Frontal-Inf-Orb-R, Rolandic-Oper-R, Olfactory-R, Angular-L, Precuneus-L, Precuenus-R, Heschl-L, and Temporal-Pole-Mid-R. In addition, the severity of symptoms was found positively associated with the ALFF within the Rolandic-Oper-R and Frontal-Inf-Orb-R. Our findings indicated that elastic net logistic regression could be a useful tool to identify the characteristics of schizophrenia -related brain deterioration, which provides novel insights into schizophrenia diagnosis and prediction.
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Alm KH, Bakker A. Relationships Between Diffusion Tensor Imaging and Cerebrospinal Fluid Metrics in Early Stages of the Alzheimer's Disease Continuum. J Alzheimers Dis 2020; 70:965-981. [PMID: 31306117 DOI: 10.3233/jad-181210] [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] [Indexed: 01/09/2023]
Abstract
Recently, the field of Alzheimer's disease (AD) research has adopted a new framework that places the progression of AD along a continuum consisting of a preclinical stage, followed by conversion to mild cognitive impairment, and ultimately dementia. Important neuropathological changes occur in the preclinical phase, necessitating the identification of metrics that can detect such early changes. While cerebrospinal fluid (CSF) measures of amyloid and tau are generally accepted as biomarkers of AD pathology, neuroimaging measures used to index white matter alterations throughout the brain remain less widely endorsed as candidate biomarkers. To explore the relationship between white matter alterations and AD pathology, we review the literature on multimodal studies that assessed both CSF markers and white matter indices, derived from diffusion tensor imaging (DTI) methods, across cohorts primarily in the early phases of AD. Our review indicates that abnormal CSF measures of Aβ42 and tau are associated with widespread alterations in white matter microstructure throughout the brain. Furthermore, white matter variability is related to individual differences in behavior and can aid in tracking longitudinal changes in cognition. Our review advocates for the utilization of DTI metrics in investigations of early AD and suggests that the combined use of DTI and CSF markers may better explain individual differences in cognition and disease progression. However, further research is needed to resolve certain mixed findings.
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Affiliation(s)
- Kylie H Alm
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Arnold Bakker
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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11
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Nicolas R, Hiba B, Dilharreguy B, Barse E, Baillet M, Edde M, Pelletier A, Periot O, Helmer C, Allard M, Dartigues JF, Amieva H, Pérès K, Fernandez P, Catheline G. Changes Over Time of Diffusion MRI in the White Matter of Aging Brain, a Good Predictor of Verbal Recall. Front Aging Neurosci 2020; 12:218. [PMID: 32922282 PMCID: PMC7456903 DOI: 10.3389/fnagi.2020.00218] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Accepted: 06/19/2020] [Indexed: 11/13/2022] Open
Abstract
Objective: Extensive research using water-diffusion MRI reported age-related modifications of cerebral White Matter (WM). Moreover, water-diffusion parameter modifications have been frequently associated with cognitive performances in the elderly sample, reinforcing the idea of aging inducing microstructural disconnection of the brain which in turn impacts cognition. However, only few studies really assessed over-time modifications of these parameters and their relationship with episodic memory outcome of elderly. Materials and Methods: One-hundred and thirty elderly subjects without dementia (74.1 ± 4.1 years; 47% female) were included in this study. Diffusion tensor imaging (DTI) was performed at two-time points (3.49 ± 0.68 years apart), allowing the assessment of changes in water-diffusion parameters over time using a specific longitudinal pipeline. White matter hyperintensity (WMH) burden and gray matter (GM) atrophy were also measured on FLAIR and T1-weighted sequences collected during these two MRI sessions. Free and cued verbal recall scores assessed at the last follow-up of the cohort were used as episodic memory outcome. Changes in water-diffusion parameters over time were included in serial linear regression models to predict retrieval or storage ability of elderly. Results: GM atrophy and an increase in mean diffusivity (MD) and WMH load between the two-time points were observed. The increase in MD was significantly correlated with WMH load and the different memory scores. In models accounting for the baseline cognitive score, GM atrophy, or WMH load, MD changes still significantly predict free verbal recall, and not total verbal recall, suggesting the specific association with the retrieval deficit in healthy aging. Conclusion: In elderly, microstructural WM changes are good predictors of lower free verbal recall performances. Moreover, this contribution is not only driven by WMH load increase. This last observation is in line with studies reporting early water-diffusion modification in WM tissue during aging, resulting lately in the appearance of WMH on conventional MRI.
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Affiliation(s)
- Renaud Nicolas
- Université de Bordeaux, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,CNRS, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France
| | - Bassem Hiba
- Université de Bordeaux, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,CNRS, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France
| | - Bixente Dilharreguy
- Université de Bordeaux, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,CNRS, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France
| | - Elodie Barse
- Université de Bordeaux, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,CNRS, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,Laboratoire Neuroimagerie et vie quotidienne, EPHE-PSL University, Bordeaux, France
| | - Marion Baillet
- Université de Bordeaux, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,CNRS, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,Laboratoire Neuroimagerie et vie quotidienne, EPHE-PSL University, Bordeaux, France
| | - Manon Edde
- Université de Bordeaux, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,CNRS, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,Laboratoire Neuroimagerie et vie quotidienne, EPHE-PSL University, Bordeaux, France
| | - Amandine Pelletier
- Université de Bordeaux, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,CNRS, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,Laboratoire Neuroimagerie et vie quotidienne, EPHE-PSL University, Bordeaux, France
| | - Olivier Periot
- Université de Bordeaux, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,CNRS, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France
| | - Catherine Helmer
- Université de Bordeaux, ISPED, Centre INSERM U1219-Bordeaux Population Health Research Center, Bordeaux, France.,INSERM, ISPED, Centre INSERM U1219-Bordeaux Population Heath Research Center, Bordeaux, France
| | - Michele Allard
- Université de Bordeaux, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,CNRS, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,Service de Médecine Nucléaire, CHU de Bordeaux, Bordeaux, France
| | - Jean-François Dartigues
- Université de Bordeaux, ISPED, Centre INSERM U1219-Bordeaux Population Health Research Center, Bordeaux, France.,INSERM, ISPED, Centre INSERM U1219-Bordeaux Population Heath Research Center, Bordeaux, France.,CMRR, CHU de Bordeaux, Bordeaux, France
| | - Hélène Amieva
- Université de Bordeaux, ISPED, Centre INSERM U1219-Bordeaux Population Health Research Center, Bordeaux, France.,INSERM, ISPED, Centre INSERM U1219-Bordeaux Population Heath Research Center, Bordeaux, France
| | - Karine Pérès
- Université de Bordeaux, ISPED, Centre INSERM U1219-Bordeaux Population Health Research Center, Bordeaux, France.,INSERM, ISPED, Centre INSERM U1219-Bordeaux Population Heath Research Center, Bordeaux, France
| | - Philippe Fernandez
- Université de Bordeaux, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,CNRS, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,Service de Médecine Nucléaire, CHU de Bordeaux, Bordeaux, France
| | - Gwénaëlle Catheline
- Université de Bordeaux, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,CNRS, INCIA, UMR 5287-équipe NeuroImagerie et Cognition Humaine, Bordeaux, France.,Laboratoire Neuroimagerie et vie quotidienne, EPHE-PSL University, Bordeaux, France
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12
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Investigating microstructural variation in the human hippocampus using non-negative matrix factorization. Neuroimage 2020; 207:116348. [DOI: 10.1016/j.neuroimage.2019.116348] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 11/05/2019] [Accepted: 11/08/2019] [Indexed: 11/23/2022] Open
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13
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Rabin JS, Perea RD, Buckley RF, Neal TE, Buckner RL, Johnson KA, Sperling RA, Hedden T. Global White Matter Diffusion Characteristics Predict Longitudinal Cognitive Change Independently of Amyloid Status in Clinically Normal Older Adults. Cereb Cortex 2019; 29:1251-1262. [PMID: 29425267 PMCID: PMC6499008 DOI: 10.1093/cercor/bhy031] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 01/08/2018] [Indexed: 02/07/2023] Open
Abstract
White matter degradation has been proposed as one possible explanation for age-related cognitive decline. In the present study, we examined 2 main questions: 1) Do diffusion characteristics predict longitudinal change in cognition independently or synergistically with amyloid status? 2) Are the effects of diffusion characteristics on longitudinal cognitive change tract-specific or global in nature? Cognitive domains of executive function, episodic memory, and processing speed were measured annually (mean follow-up = 3.93 ± 1.25 years). Diffusion tensor imaging and Pittsburgh Compound-B positron emission tomography were performed at baseline in 265 clinically normal older adults (aged 63-90). Tract-specific diffusion was measured as the mean fractional anisotropy (FA) for 9 major white matter tracts. Global diffusion was measured as the mean FA across the 9 white matter tracts. Linear mixed models demonstrated independent, rather than synergistic, effects of global FA and amyloid status on cognitive decline. After controlling for amyloid status, lower global FA was associated with worse longitudinal performance in episodic memory and processing speed, but not executive function. After accounting for global FA, none of the individual tracts predicted a significant change in cognitive performance. These findings suggest that global, rather than tract-specific, diffusion characteristics predict longitudinal cognitive decline independently of amyloid status.
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Affiliation(s)
- Jennifer S Rabin
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rodrigo D Perea
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rachel F Buckley
- Florey Institutes of Neuroscience and Mental Health, Melbourne and Melbourne School of Psychological Science, University of Melbourne, Melbourne, Australia
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Taylor E Neal
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Randy L Buckner
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Keith A Johnson
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Boston, MA, USA
- Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Boston, MA, USA
| | - Trey Hedden
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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14
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Sui X, Rajapakse JC. Profiling heterogeneity of Alzheimer's disease using white-matter impairment factors. NEUROIMAGE-CLINICAL 2018; 20:1222-1232. [PMID: 30412925 PMCID: PMC6226553 DOI: 10.1016/j.nicl.2018.10.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 09/28/2018] [Accepted: 10/23/2018] [Indexed: 01/09/2023]
Abstract
The clinical presentation of Alzheimer's disease (AD) is not unitary as heterogeneity exists in the disease's clinical and anatomical characteristics. MRI studies have revealed that heterogeneous gray matter atrophy patterns are associated with specific traits of cognitive decline. Although white matter (WM) impairment also contributes to AD pathology, its heterogeneity remains unclear. The Latent Dirichlet Allocation (LDA) method is a suitable framework to study heterogeneity and allows to identify latent impairment factors of AD instead of simply mapping an overall disease effect. By exploring whole brain WM skeleton images by using LDA, three latent factors were revealed in AD: a temporal-frontal impairment factor (temporal and frontal lobes, especially hippocampus and para-hippocampus), a parietal factor (parietal lobe, especially precuneus), and a long fibre bundle factor (corpus callosum and superior longitudinal fasciculus). As revealed by longitudinal analysis, the latent factors have distinct impact on cognitive decline: for executive function (EF), the temporal-frontal factor was more strongly associated with baseline EF compared with the parietal factor, while the long-fibre bundle factor was most associated with decline rate of EF; for memory, the three factors showed almost equal effect on the baseline memory and decline rate. For each participant, LDA estimates his/her composition profile of latent impairment factors, which indicates disease subtype. We also found that the APOE genotype affects the AD subtype. Specifically, APOE ε4 was more associated with the long fibre bundle factor and APOE ε2 was more associated with temporal-frontal factor. By investigating heterogeneity and subtypes of AD through white matter impairment factors, our study could facilitate precision medicine. LDA revealed three latent white matter impairment factors in Alzheimer’s disease. Latent factors associate with executive function and memory decline differently. Individual factor composition indicates disease subtype. The APOE genotype is associated with the factor composition.
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Affiliation(s)
- Xiuchao Sui
- School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore
| | - Jagath C Rajapakse
- School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore.
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- School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore
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15
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Cross-sectional variations of white and grey matter in older hypertensive patients with subjective memory complaints. NEUROIMAGE-CLINICAL 2017; 17:804-810. [PMID: 29276677 PMCID: PMC5738235 DOI: 10.1016/j.nicl.2017.12.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Revised: 11/23/2017] [Accepted: 12/16/2017] [Indexed: 11/24/2022]
Abstract
Mild cognitive impairment and Alzheimer's dementia involve a grey matter disease, quantifiable by 18F-Fluorodeoxyglucose positron emission tomography (FDG-PET), but also white matter damage, evidenced by diffusion tensor magnetic resonance imaging (DTI), which may play an additional pathogenic role. This study aimed to determine whether such DTI and PET variations are also interrelated in a high-risk population of older hypertensive patients with only subjective memory complaints (SMC). Sixty older hypertensive patients (75 ± 5 years) with SMC were referred to DTI and FDG-PET brain imaging, executive and memory tests, as well as peripheral and central blood pressure (BP) measurements. Mean apparent diffusion coefficient (ADCmean) was determined in overall white matter and correlated with the grey matter distribution of the metabolic rate of glucose (CMRGlc) using whole-brain voxel-based analyses of FDG-PET images. ADCmean was variable between individuals, ranging from 0.82 to 1.01.10− 3 mm2 sec− 1, and mainly in relation with CMRGlc of areas involved in Alzheimer's disease such as internal temporal areas, posterior associative junctions, posterior cingulum but also insulo-opercular areas (global correlation coefficient: − 0.577, p < 0.001). Both the ADCmean and CMRGlc of the interrelated grey matter areas were additionally and concordantly linked to the results of executive and memory tests and to systolic central BP (all p < 0.05). Altogether, our findings show that cross-sectional variations in overall white brain matter are linked to the metabolism of Alzheimer-like cortical areas and to cognitive performance in older hypertensive patients with only subjective memory complaints. Additional relationships with central BP strengthen the hypothesis of a contributing pathogenic role of hypertension. Changes in grey and white matter are interrelated in elderly hypertensive patients. They can be documented in the absence of any objective memory complaint. These interrelationships are associated with cognitive test results. The interrelated grey matter areas are likely to define an Alzheimer-like pattern. These interrelationships are linked to the level of central blood pressure.
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16
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Yu J, Lam CLM, Lee TMC. White matter microstructural abnormalities in amnestic mild cognitive impairment: A meta-analysis of whole-brain and ROI-based studies. Neurosci Biobehav Rev 2017; 83:405-416. [PMID: 29092777 DOI: 10.1016/j.neubiorev.2017.10.026] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 10/24/2017] [Accepted: 10/27/2017] [Indexed: 12/29/2022]
Abstract
Studies that examined white matter (WM) alterations in amnestic mild cognitive impairment (aMCI) abound. This timely meta-analysis aims to synthesize the results of these studies. Seventy-seven studies (totalNaMCI=1844) were included. Fourteen region-of-interest-based (ROI-based) (k≥8;NaMCI≥284 per ROI) and two activation likelihood estimation (ALE) meta-analyses (fractional anisotropy [FA]: k=15;NaMCI=463; mean diffusivity [MD]: k=8;NaMCI=193) were carried out. Among the many significant ROI-related findings, reliable FA and MD alterations in the fornix, uncinate fasciculus, and parahippocampal cingulum were observed in aMCI. Larger effects were observed in MD relative to FA. The ALE meta-analysis revealed a significant FA decrease among aMCI subjects in the posterior corona radiata. These results provide robust evidence of the presence of WM abnormalities in aMCI. Our findings also highlight the importance of carrying out both ROI-based and whole-brain-based research to obtain a complete picture of WM microstructural alterations associated with the condition..
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Affiliation(s)
- Junhong Yu
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong; Laboratory of Neuropsychology, The University of Hong Kong, Hong Kong
| | - Charlene L M Lam
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong; Laboratory of Neuropsychology, The University of Hong Kong, Hong Kong
| | - Tatia M C Lee
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong; Laboratory of Neuropsychology, The University of Hong Kong, Hong Kong; Institute of Clinical Neuropsychology, The University of Hong Kong, Hong Kong.
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