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Gao C, Bao S, Kim ME, Newlin NR, Kanakaraj P, Yao T, Rudravaram G, Huo Y, Moyer D, Schilling K, Kukull WA, Toga AW, Archer DB, Hohman TJ, Landman BA, Li Z. Field-of-view extension for brain diffusion MRI via deep generative models. J Med Imaging (Bellingham) 2024; 11:044008. [PMID: 39185475 PMCID: PMC11344266 DOI: 10.1117/1.jmi.11.4.044008] [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: 03/25/2024] [Revised: 08/01/2024] [Accepted: 08/01/2024] [Indexed: 08/27/2024] Open
Abstract
Purpose In brain diffusion magnetic resonance imaging (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field of view (FOV). We aim to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with a complete FOV can improve whole-brain tractography for corrupted data with an incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable brain dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data. Approach We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with an incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWIs) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWIs in the incomplete part of the FOV. Results For evaluating the imputed slices, on the Wisconsin Registry for Alzheimer's Prevention (WRAP) dataset, the proposed framework achievedPSNR b 0 = 22.397 ,SSIM b 0 = 0.905 ,PSNR b 1300 = 22.479 , andSSIM b 1300 = 0.893 ; on the National Alzheimer's Coordinating Center (NACC) dataset, it achievedPSNR b 0 = 21.304 ,SSIM b 0 = 0.892 ,PSNR b 1300 = 21.599 , andSSIM b 1300 = 0.877 . The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts ( p < 0.001 ) on both the WRAP and NACC datasets. Conclusions Results suggest that the proposed framework achieved sufficient imputation performance in brain dMRI data with an incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with an extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer's disease.
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Affiliation(s)
- Chenyu Gao
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Shunxing Bao
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Michael E. Kim
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Nancy R. Newlin
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Praitayini Kanakaraj
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Tianyuan Yao
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Gaurav Rudravaram
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Yuankai Huo
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Daniel Moyer
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Kurt Schilling
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
| | - Walter A. Kukull
- University of Washington, Department of Epidemiology, Seattle, Washington, United States
| | - Arthur W. Toga
- University of Southern California, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, Laboratory of Neuro Imaging, Los Angeles, California, United States
| | - Derek B. Archer
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, Tennessee, United States
| | - Timothy J. Hohman
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, Tennessee, United States
| | - Bennett A. Landman
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States
| | - Zhiyuan Li
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
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Peterson A, Sathe A, Zaras D, Yang Y, Durant A, Deters KD, Shashikumar N, Pechman KR, Kim ME, Gao C, Khairi NM, Li Z, Yao T, Huo Y, Dumitrescu L, Gifford KA, Wilson JE, Cambronero F, Risacher SL, Beason-Held LL, An Y, Arfanakis K, Erus G, Davatzikos C, Tosun D, Toga AW, Thompson PM, Mormino EC, Zhang P, Schilling K, Albert M, Kukull W, Biber SA, Landman BA, Johnson SC, Schneider J, Barnes LL, Bennett DA, Jefferson AL, Resnick SM, Saykin AJ, Hohman TJ, Archer DB. Sex, racial, and APOE-ε4 allele differences in longitudinal white matter microstructure in multiple cohorts of aging and Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.10.598357. [PMID: 38915636 PMCID: PMC11195046 DOI: 10.1101/2024.06.10.598357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
INTRODUCTION The effects of sex, race, and Apolipoprotein E (APOE) - Alzheimer's disease (AD) risk factors - on white matter integrity are not well characterized. METHODS Diffusion MRI data from nine well-established longitudinal cohorts of aging were free-water (FW)-corrected and harmonized. This dataset included 4,702 participants (age=73.06 ± 9.75) with 9,671 imaging sessions over time. FW and FW-corrected fractional anisotropy (FAFWcorr) were used to assess differences in white matter microstructure by sex, race, and APOE-ε4 carrier status. RESULTS Sex differences in FAFWcorr in association and projection tracts, racial differences in FAFWcorr in projection tracts, and APOE-ε4 differences in FW limbic and occipital transcallosal tracts were most pronounced. DISCUSSION There are prominent differences in white matter microstructure by sex, race, and APOE-ε4 carrier status. This work adds to our understanding of disparities in AD. Additional work to understand the etiology of these differences is warranted.
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Affiliation(s)
- Amalia Peterson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
| | - Aditi Sathe
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Dimitrios Zaras
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Yisu Yang
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Alaina Durant
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Kacie D. Deters
- Department of Integrative Biology and Physiology, University of California, Los Angeles
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Kimberly R. Pechman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Michael E. Kim
- Department of Computer Science, Vanderbilt University, Nashville, TN
| | - Chenyu Gao
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN
| | - Nazirah Mohd Khairi
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN
| | - Zhiyuan Li
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN
| | - Tianyuan Yao
- Department of Computer Science, Vanderbilt University, Nashville, TN
| | - Yuankai Huo
- Department of Computer Science, Vanderbilt University, Nashville, TN
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN
| | - Logan Dumitrescu
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN
| | - Katherine A. Gifford
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
| | - Jo Ellen Wilson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Center for Cognitive Medicine, Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
- Veteran‘s Affairs, Geriatric Research, Education and Clinical Center, Tennessee Valley Healthcare System
| | - Francis Cambronero
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
| | - Shannon L. Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN
| | - Lori L. Beason-Held
- Laboratory for Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Yang An
- Laboratory for Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Konstantinos Arfanakis
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL
- Department of Diagnostic Radiology, Rush University Medical Center, Chicago, IL
| | - Guray Erus
- Department of Radiology, University of Pennsylvania, Philadelphia, PA
| | | | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA
| | - Arthur W. Toga
- Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA
| | - Elizabeth C. Mormino
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA
| | - Panpan Zhang
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Kurt Schilling
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN2
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
| | | | | | | | - Marilyn Albert
- Department of Neurology, Johns Hopkins School of Medicine Baltimore, MD
| | - Walter Kukull
- National Alzheimer’s Coordinating Center, University of Washington, Seattle, WA
| | - Sarah A. Biber
- National Alzheimer’s Coordinating Center, University of Washington, Seattle, WA
| | - Bennett A. Landman
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
- Department of Computer Science, Vanderbilt University, Nashville, TN
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN2
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Disease Research Center, School of Medicine and Public Health, University of Wisconsin, Madison, WI
- Wisconsin Alzheimer’s Institute, School of Medicine and Public Health, University of Wisconsin, Madison, WI
| | - Julie Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL
| | - Lisa L. Barnes
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
- Department of Computer Science, Vanderbilt University, Nashville, TN
| | - Susan M. Resnick
- Laboratory for Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN
- Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN
| | - Derek B. Archer
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN
- Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN
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Poirier SE, Suskin NG, Khaw AV, Thiessen JD, Shoemaker JK, Anazodo UC. Probing Evidence of Cerebral White Matter Microstructural Disruptions in Ischemic Heart Disease Before and Following Cardiac Rehabilitation: A Diffusion Tensor MR Imaging Study. J Magn Reson Imaging 2024; 59:2137-2149. [PMID: 37589418 DOI: 10.1002/jmri.28964] [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: 05/05/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/18/2023] Open
Abstract
BACKGROUND Ischemic heart disease (IHD) is linked to brain white matter (WM) breakdown but how age or disease effects WM integrity, and whether it is reversible using cardiac rehabilitation (CR), remains unclear. PURPOSE To assess the effects of brain aging, cardiovascular disease, and CR on WM microstructure in brains of IHD patients following a cardiac event. STUDY TYPE Retrospective. POPULATION Thirty-five IHD patients (9 females; mean age = 59 ± 8 years), 21 age-matched healthy controls (10 females; mean age = 59 ± 8 years), and 25 younger controls (14 females; mean age = 26 ± 4 years). FIELD STRENGTH/SEQUENCE 3 T diffusion-weighted imaging with single-shot echo planar imaging acquired at 3 months and 9 months post-cardiac event. ASSESSMENT Tract-based spatial statistics (TBSS) and tractometry were used to compare fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in cerebral WM between: 1) older and younger controls to distinguish age-related from disease-related WM changes; 2) IHD patients at baseline (pre-CR) and age-matched controls to investigate if cardiovascular disease exacerbates age-related WM changes; and 3) IHD patients pre-CR and post-CR to investigate the neuroplastic effect of CR on WM microstructure. STATISTICAL TESTS Two-sample unpaired t-test (age: older vs. younger controls; IHD: IHD pre-CR vs. age-matched controls). One-sample paired t-test (CR: IHD pre- vs. post-CR). Statistical threshold: P < 0.05 (FWE-corrected). RESULTS TBSS and tractometry revealed widespread WM changes in older controls compared to younger controls while WM clusters of decreased FA in the fornix and increased MD in body of corpus callosum were observed in IHD patients pre-CR compared to age-matched controls. Robust WM improvements (increased FA, increased AD) were observed in IHD patients post-CR. DATA CONCLUSION In IHD, both brain aging and cardiovascular disease may contribute to WM disruptions. IHD-related WM disruptions may be favorably modified by CR. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Stefan E Poirier
- Lawson Imaging, Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Neville G Suskin
- Division of Cardiology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Alexander V Khaw
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Jonathan D Thiessen
- Lawson Imaging, Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Joel K Shoemaker
- School of Kinesiology, Western University, London, Ontario, Canada
| | - Udunna C Anazodo
- Lawson Imaging, Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
- Research Centre for Studies in Aging, McGill University, Montréal, Québec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
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Flaherty R, Sui YV, Masurkar AV, Betensky RA, Rusinek H, Lazar M. Diffusion imaging markers of accelerated aging of the lower cingulum in subjective cognitive decline. Front Neurol 2024; 15:1360273. [PMID: 38784911 PMCID: PMC11111894 DOI: 10.3389/fneur.2024.1360273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 04/29/2024] [Indexed: 05/25/2024] Open
Abstract
Introduction Alzheimer's Disease (AD) typically starts in the medial temporal lobe, then develops into a neurodegenerative cascade which spreads to other brain regions. People with subjective cognitive decline (SCD) are more likely to develop dementia, especially in the presence of amyloid pathology. Thus, we were interested in the white matter microstructure of the medial temporal lobe in SCD, specifically the lower cingulum bundle that leads into the hippocampus. Diffusion tensor imaging (DTI) has been shown to differentiate SCD participants who will progress to mild cognitive impairment from those who will not. However, the biology underlying these DTI metrics is unclear, and results in the medial temporal lobe have been inconsistent. Methods To better characterize the microstructure of this region, we applied DTI to cognitively normal participants in the Cam-CAN database over the age of 55 with cognitive testing and diffusion MRI available (N = 325, 127 SCD). Diffusion MRI was processed to generate regional and voxel-wise diffusion tensor values in bilateral lower cingulum white matter, while T1-weighted MRI was processed to generate regional volume and cortical thickness in the medial temporal lobe white matter, entorhinal cortex, temporal pole, and hippocampus. Results SCD participants had thinner cortex in bilateral entorhinal cortex and right temporal pole. No between-group differences were noted for any of the microstructural metrics of the lower cingulum. However, correlations with delayed story recall were significant for all diffusion microstructure metrics in the right lower cingulum in SCD, but not in controls, with a significant interaction effect. Additionally, the SCD group showed an accelerated aging effect in bilateral lower cingulum with MD, AxD, and RD. Discussion The diffusion profiles observed in both interaction effects are suggestive of a mixed neuroinflammatory and neurodegenerative pathology. Left entorhinal cortical thinning correlated with decreased FA and increased RD, suggestive of demyelination. However, right entorhinal cortical thinning also correlated with increased AxD, suggestive of a mixed pathology. This may reflect combined pathologies implicated in early AD. DTI was more sensitive than cortical thickness to the associations between SCD, memory, and age. The combined effects of mixed pathology may increase the sensitivity of DTI metrics to variations with age and cognition.
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Affiliation(s)
- Ryn Flaherty
- Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY, United States
| | - Yu Veronica Sui
- Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
| | - Arjun V. Masurkar
- Department of Neurology, New York University Grossman School of Medicine, New York, NY, United States
- Neuroscience Institute, New York University Grossman School of Medicine, New York, NY, United States
| | - Rebecca A. Betensky
- Department of Biostatistics, New York University School of Global Public Health, New York, NY, United States
| | - Henry Rusinek
- Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
- Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, United States
| | - Mariana Lazar
- Center for Advanced Imaging Innovation and Research, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States
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Wang X, Chen Q, Liu Y, Sun J, Li J, Zhao P, Cai L, Liu W, Yang Z, Wang Z, Lv H. Causal relationship between multiparameter brain MRI phenotypes and age: evidence from Mendelian randomization. Brain Commun 2024; 6:fcae077. [PMID: 38529357 PMCID: PMC10963122 DOI: 10.1093/braincomms/fcae077] [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: 10/02/2023] [Revised: 01/05/2024] [Accepted: 02/29/2024] [Indexed: 03/27/2024] Open
Abstract
To explore the causal relationship between age and brain health (cortical atrophy, white matter integrity, white matter hyperintensities and cerebral microbleeds in various brain regions) related multiparameter imaging features using two-sample Mendelian randomization. Age was determined as chronological age of the subject. Cortical volume, white matter micro-integrity, white matter hyperintensity volume and cerebral microbleeds of each brain region were included as phenotypes for brain health. Age and imaging of brain health related genetic data were analysed to determine the causal relationship using inverse-variance weighted model, validated by heterogeneity and horizontal pleiotropy variables. Age is causally related to increased volumes of white matter hyperintensities (β = 0.151). For white matter micro-integrity, fibres of the inferior cerebellar peduncle (axial diffusivity β = -0.128, orientation dispersion index β = 0.173), cerebral peduncle (axial diffusivity β = -0.136), superior fronto-occipital fasciculus (isotropic volume fraction β = 0.163) and fibres within the limbic system were causally deteriorated. We also detected decreased cortical thickness of multiple frontal and temporal regions (P < 0.05). Microbleeds were not related with aging (P > 0.05). Aging is a threat of brain health, leading to cortical atrophy mainly in the frontal lobes, as well as the white matter degeneration especially abnormal hyperintensity and deteriorated white matter integrity around the hippocampus.
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Affiliation(s)
- Xinghao Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Qian Chen
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Yawen Liu
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Jing Sun
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Jia Li
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Pengfei Zhao
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Linkun Cai
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Wenjuan Liu
- Department of Radiology, Aerospace Center Hospital, Beijing 100089, China
- Peking University Aerospace School of Clinical Medicine, Beijing 100089, China
| | - Zhenghan Yang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Han Lv
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
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Ofori E, Vaillancourt DE, Greig-Custo MT, Barker W, Hanson K, DeKosky ST, Garvan CS, Adjouadi M, Golde T, Loewenstein DA, Stecher C, Fowers R, Duara R. Free-water imaging reveals unique brain microstructural deficits in hispanic individuals with Dementia. Brain Imaging Behav 2024; 18:106-116. [PMID: 37903991 PMCID: PMC11157151 DOI: 10.1007/s11682-023-00819-w] [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] [Accepted: 10/22/2023] [Indexed: 11/01/2023]
Abstract
Prior evidence suggests that Hispanic and non-Hispanic individuals differ in potential risk factors for the development of dementia. Here we determine whether specific brain regions are associated with cognitive performance for either ethnicity along various stages of Alzheimer's disease. For this cross-sectional study, we examined 108 participants (61 Hispanic vs. 47 Non-Hispanic individuals) from the 1Florida Alzheimer's Disease Research Center (1Florida ADRC), who were evaluated at baseline with diffusion-weighted and T1-weighted imaging, and positron emission tomography (PET) amyloid imaging. We used FreeSurfer to segment 34 cortical regions of interest. Baseline Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used as measures of cognitive performance. Group analyses assessed free-water measures (FW) and volume. Statistically significant FW regions based on ethnicity x group interactions were used in a stepwise regression function to predict total MMSE and MoCA scores. Random forest models were used to identify the most predictive brain-based measures of a dementia diagnosis separately for Hispanic and non-Hispanic groups. Results indicated elevated FW values for the left inferior temporal gyrus, left middle temporal gyrus, left banks of the superior temporal sulcus, left supramarginal gyrus, right amygdala, and right entorhinal cortex in Hispanic AD subjects compared to non-Hispanic AD subjects. These alterations occurred in the absence of different volumes of these regions in the two AD groups. FW may be useful in detecting individual differences potentially reflective of varying etiology that can influence cognitive decline and identify MRI predictors of cognitive performance, particularly among Hispanics.
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Affiliation(s)
- Edward Ofori
- College of Health Solutions, Arizona State University, 425 N. 5th St Phoenix, Phoenix, AZ, 85004, USA.
| | - David E Vaillancourt
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, FL, USA
| | - Maria T Greig-Custo
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL, USA
| | - Warren Barker
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL, USA
| | - Kevin Hanson
- Clinical and Translational Science Institute, University of Florida, Gainesville, FL, USA
| | - Steven T DeKosky
- Emory Center for Neurodegenerative Disease, Departments of Pharmacology, Chemical Biology, & Neurology, Atlanta, GA, USA
| | - Cynthia S Garvan
- Department of Anesthesiology, University of Florida, Gainesville, FL, USA
| | - Malek Adjouadi
- Electrical and Computer Engineering, Florida International University, Miami, FL, USA
| | - Todd Golde
- Emory Center for Neurodegenerative Disease, Departments of Pharmacology, Chemical Biology, & Neurology, Atlanta, GA, USA
- Department of Psychiatry, Miller School of Medicine, Center for Cognitive Neuroscience and Aging University of Miami, Miami, FL, USA
| | - David A Loewenstein
- Department of Psychiatry, Miller School of Medicine, Center for Cognitive Neuroscience and Aging University of Miami, Miami, FL, USA
| | - Chad Stecher
- College of Health Solutions, Arizona State University, 425 N. 5th St Phoenix, Phoenix, AZ, 85004, USA
| | - Rylan Fowers
- College of Health Solutions, Arizona State University, 425 N. 5th St Phoenix, Phoenix, AZ, 85004, USA
| | - Ranjan Duara
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL, USA
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Munro CE, Boyle R, Chen X, Coughlan G, Gonzalez C, Jutten RJ, Martinez J, Orlovsky I, Robinson T, Weizenbaum E, Pluim CF, Quiroz YT, Gatchel JR, Vannini P, Amariglio R. Recent contributions to the field of subjective cognitive decline in aging: A literature review. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12475. [PMID: 37869044 PMCID: PMC10585124 DOI: 10.1002/dad2.12475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 06/23/2023] [Accepted: 08/04/2023] [Indexed: 10/24/2023]
Abstract
Subjective cognitive decline (SCD) is defined as self-experienced, persistent concerns of decline in cognitive capacity in the context of normal performance on objective cognitive measures. Although SCD was initially thought to represent the "worried well," these concerns can be linked to subtle brain changes prior to changes in objective cognitive performance and, therefore, in some individuals, SCD may represent the early stages of an underlying neurodegenerative disease process (e.g., Alzheimer's disease). The field of SCD research has expanded rapidly over the years, and this review aims to provide an update on new advances in, and contributions to, the field of SCD in key areas and themes identified by researchers in this field as particularly important and impactful. First, we highlight recent studies examining sociodemographic and genetic risk factors for SCD, including explorations of SCD across racial and ethnic minoritized groups, and examinations of sex and gender considerations. Next, we review new findings on relationships between SCD and in vivo markers of pathophysiology, utilizing neuroimaging and biofluid data, as well as associations between SCD and objective cognitive tests and neuropsychiatric measures. Finally, we summarize recent work on interventions for SCD and areas of future growth in the field of SCD.
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Affiliation(s)
| | - Rory Boyle
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Xi Chen
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Gillian Coughlan
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Christopher Gonzalez
- Department of PsychologyIllinois Institute of TechnologyChicagoIllinoisUSA
- Department of PsychiatryMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Roos J. Jutten
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jairo Martinez
- Department of PsychiatryMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of Psychological and Brain SciencesBoston UniversityBostonMassachusettsUSA
| | - Irina Orlovsky
- Department of Psychological and Brain SciencesUniversity of Massachusetts AmherstAmherstMassachusettsUSA
| | | | - Emma Weizenbaum
- Department of PsychiatryMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Celina F. Pluim
- Brigham and Women's HospitalBostonMassachusettsUSA
- Department of PsychiatryMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of Psychological and Brain SciencesBoston UniversityBostonMassachusettsUSA
| | - Yakeel T. Quiroz
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jennifer R. Gatchel
- Department of PsychiatryMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Patrizia Vannini
- Brigham and Women's HospitalBostonMassachusettsUSA
- Department of NeurologyMassachusetts General HospitalHarvard Medical SchoolBostonMassachusettsUSA
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8
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Archer DB, Schilling K, Shashikumar N, Jasodanand V, Moore EE, Pechman KR, Bilgel M, Beason‐Held LL, An Y, Shafer A, Ferrucci L, Risacher SL, Gifford KA, Landman BA, Jefferson AL, Saykin AJ, Resnick SM, Hohman TJ. Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12468. [PMID: 37780863 PMCID: PMC10540270 DOI: 10.1002/dad2.12468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/27/2023] [Accepted: 07/05/2023] [Indexed: 10/03/2023]
Abstract
Introduction It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. Methods Diffusion MRI data from several well-established longitudinal cohorts of aging (Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], Vanderbilt Memory & Aging Project [VMAP]) were free-water corrected and harmonized. This dataset included 1723 participants (age at baseline: 72.8 ± 8.87 years, 49.5% male) and 4605 imaging sessions (follow-up time: 2.97 ± 2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42 ± 1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. Results While we found a global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. Conclusions There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTS Longitudinal data were free-water corrected and harmonized.Global effects of white matter decline were seen in normal and abnormal aging.The free-water metric was most vulnerable to abnormal aging.Cingulum free-water was the most vulnerable to abnormal aging.
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Affiliation(s)
- Derek B. Archer
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Kurt Schilling
- Vanderbilt University Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Radiology & Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Varuna Jasodanand
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Elizabeth E. Moore
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Kimberly R. Pechman
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Murat Bilgel
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Lori L. Beason‐Held
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Yang An
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Andrea Shafer
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology BranchNational Institute on AgingBaltimoreMDUSA
| | - Shannon L. Risacher
- Indiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
| | - Katherine A. Gifford
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - Bennett A. Landman
- Vanderbilt University Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Radiology & Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Biomedical EngineeringVanderbilt UniversityNashvilleTennesseeUSA
- Department of Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Department of MedicineVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Andrew J. Saykin
- Indiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
| | - Susan M. Resnick
- Laboratory of Behavioral NeuroscienceNational Institute on AgingNational Institutes of HealthBaltimoreMarylandUSA
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
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9
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Archer DB, Schilling K, Shashikumar N, Jasodanand V, Moore EE, Pechman KR, Bilgel M, Beason-Held LL, An Y, Shafer A, Ferrucci L, Risacher SL, Gifford KA, Landman BA, Jefferson AL, Saykin AJ, Resnick SM, Hohman TJ. Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.17.541182. [PMID: 37292885 PMCID: PMC10245725 DOI: 10.1101/2023.05.17.541182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
INTRODUCTION It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. METHODS Diffusion MRI data from several well-established longitudinal cohorts of aging [Alzheimer's Neuroimaging Initiative (ADNI), Baltimore Longitudinal Study of Aging (BLSA), Vanderbilt Memory & Aging Project (VMAP)] was free-water corrected and harmonized. This dataset included 1,723 participants (age at baseline: 72.8±8.87 years, 49.5% male) and 4,605 imaging sessions (follow-up time: 2.97±2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42±1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. RESULTS While we found global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. CONCLUSIONS There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTS Longitudinal data was free-water corrected and harmonizedGlobal effects of white matter decline were seen in normal and abnormal agingThe free-water metric was most vulnerable to abnormal agingCingulum free-water was the most vulnerable to abnormal aging.
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Affiliation(s)
- Derek B Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kurt Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Varuna Jasodanand
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Elizabeth E Moore
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Kimberly R Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Andrea Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | | | - Shannon L Risacher
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Katherine A Gifford
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Andrew J Saykin
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indianapolis, IN, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
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10
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Chen Y, Wang Y, Song Z, Fan Y, Gao T, Tang X. Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review. Ageing Res Rev 2023; 87:101911. [PMID: 36931328 DOI: 10.1016/j.arr.2023.101911] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 03/01/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023]
Abstract
Alzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d-AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.
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Affiliation(s)
- Yu Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yifei Wang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Zeyu Song
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Tianxin Gao
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China; School of Life Science, Beijing Institute of Technology, Beijing 100081, China.
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11
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Gustavson DE, Archer DB, Elman JA, Puckett OK, Fennema-Notestine C, Panizzon MS, Shashikumar N, Hohman TJ, Jefferson AL, Eyler LT, McEvoy LK, Lyons MJ, Franz CE, Kremen WS. Associations among executive function Abilities, free Water, and white matter microstructure in early old age. Neuroimage Clin 2022; 37:103279. [PMID: 36493704 PMCID: PMC9731853 DOI: 10.1016/j.nicl.2022.103279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 10/26/2022] [Accepted: 11/30/2022] [Indexed: 12/04/2022]
Abstract
BACKGROUND Studies have investigated white matter microstructure in relation to late-life cognitive impairments, with fractional anisotropy (FA) and mean diffusivity (MD) measures thought to capture demyelination and axonal degradation. However, new post-processing methods allow isolation of free water (FW), which captures extracellular fluid contributions such as atrophy and neuroinflammation, from tissue components. FW also appears to be highly relevant to late-life cognitive impairment. Here, we evaluated whether executive functions are associated with FW, and FA and MD corrected for FW (FAFWcorr and MDFWcorr). METHOD We examined 489 non-demented men in the Vietnam Era Twin Study of Aging (VETSA) at mean age 68. Two latent factors capturing 'common executive function' and 'working-memory specific' processes were estimated based on 6 tasks. Analyses focused on 11 cortical white matter tracts across three metrics: FW, FAFWcorr, and MDFWcorr. RESULTS Better 'common executive function' was associated with lower FW across 9 of the 11 tracts. There were no significant associations with intracellular metrics after false discovery rate correction. Effects also appeared driven by individuals with MCI (13.7% of the sample). Working memory-specific tasks showed some associations with FAFWcorr, including the triangularis portion of the inferior frontal gyrus. There was no evidence that cognitive reserve (i.e., general cognitive ability assessed in early adulthood) moderated these associations between executive function and FW or FA. DISCUSSION Executive function abilities in early old age are associated primarily with extracellular fluid (FW) as opposed to white matter (FAFWcorr or MDFWcorr). Moderation analyses suggested cognitive reserve does not play a strong role in these associations, at least in this sample of non-demented men.
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Affiliation(s)
- Daniel E Gustavson
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Derek B Archer
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jeremy A Elman
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - Olivia K Puckett
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - Christine Fennema-Notestine
- Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Matthew S Panizzon
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Timothy J Hohman
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lisa T Eyler
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - Linda K McEvoy
- Department of Radiology, University of California San Diego, La Jolla, CA, USA; Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Michael J Lyons
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, USA
| | - Carol E Franz
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
| | - William S Kremen
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA; Center for Behavior Genetics of Aging, University of California San Diego, La Jolla, CA, USA
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Walsh MJM, Ofori E, Pagni BA, Chen K, Sullivan G, Braden BB. Preliminary findings of accelerated visual memory decline and baseline brain correlates in middle-age and older adults with autism: The case for hippocampal free-water. Front Aging Neurosci 2022; 14:1029166. [PMID: 36437999 PMCID: PMC9694823 DOI: 10.3389/fnagi.2022.1029166] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 10/13/2022] [Indexed: 11/13/2022] Open
Abstract
Research aimed at understanding cognitive and brain aging in adults with autism spectrum disorder (ASD) is growing, but critical longitudinal work is scant. Adults with ASD struggle with tasks involving visual memory compared with neurotypical adults (NT). This may be related to differences in size or integrity of the hippocampus and its' primary structural connectivity pathway, the fornix. The aim of this study was to describe preliminary findings of longitudinal aging trajectories in short- and long-term visual memory abilities in middle-age and older adults with ASD, compared with matched NT adults. We then evaluated baseline multi-modal imaging metrics of the hippocampal system, including the relatively novel metric of free-water, as potential correlates of longitudinal memory change in the ASD group. Middle-age and older adults with ASD (n = 25) and matched NT adults (n = 25) between the ages of 40 and 70 years were followed longitudinally at ~2-year intervals (range 2-5 years). Participants completed the Wechsler Memory Scale III Visual Reproduction task. Longitudinal mixed models were utilized to detect group differences in memory change with baseline age and sex as covariates. Hippocampal volume was measured via T1-weighted MRI images with FreeSurfer. Fornix fractional anisotropy and hippocampal and fornix free-water were measured from diffusion tensor imaging scans. Exploratory correlations were run between individual hippocampal system metrics and longitudinal slopes of visual memory change. There was a significant group by time interaction for long-term visual memory, such that middle-age and older adults with ASD declined faster than matched NT adults. There was no group by time interaction for short-term visual memory. Baseline hippocampal free-water was the only hippocampal system metric that correlated with long-term visual memory change in the ASD group. As one of the first longitudinal cognitive and brain aging studies in middle-age and older adults with ASD, our findings suggest vulnerabilities for accelerated long-term visual memory decline, compared to matched NT adults. Further, baseline hippocampal free-water may be a predictor of visual memory change in middle-age and older adults with ASD. These preliminary findings lay the groundwork for future prognostic applications of MRI for cognitive aging in middle-age and older adults with ASD.
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Affiliation(s)
- Melissa J. M. Walsh
- College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Edward Ofori
- College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Broc A. Pagni
- College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - Kewei Chen
- Banner Alzheimer’s Institute, Phoenix, AZ, United States
| | - Georgia Sullivan
- College of Health Solutions, Arizona State University, Tempe, AZ, United States
| | - B. Blair Braden
- College of Health Solutions, Arizona State University, Tempe, AZ, United States
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13
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López-Cáceres A, Cruz-Sanabria F, Mayorga P, Sanchez AI, Gonzalez-Nieves S, Ayala-Ramírez P, Zarante I, Matallana D. Association between risk polymorphisms for neurodegenerative diseases and cognition in colombian patients with frontotemporal dementia. Front Neurol 2022; 13:675301. [PMID: 36071893 PMCID: PMC9443520 DOI: 10.3389/fneur.2022.675301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Frontotemporal dementia (FTD) is a heterogeneous neurodegenerative disease of presenile onset. A better characterization of neurodegenerative disorders has been sought by using tools such as genome-wide association studies (GWAS), where associations between single nucleotide polymorphisms (SNPs) and cognitive profiles could constitute predictive biomarkers for these diseases. However, in FTD, associations between genotypes and cognitive phenotypes are yet to be explored. Here, we evaluate a possible relationship between genetic variants and some cognitive functions in an FTD population.MethodologyA total of 47 SNPs in genes associated with neurodegenerative diseases were evaluated using the Sequenom MassARRAY platform along with their possible relationship with performance in neuropsychological tests in 105 Colombian patients diagnosed with FTD.Results and discussionThe SNPs rs429358 (APOE), rs1768208 (MOBP), and rs1411478 (STX6), were identified as risk factors for having a low cognitive performance in inhibitory control and phonological verbal fluency. Although the significance level was not enough to reach the corrected alpha for multiple comparison correction, our exploratory data may constitute a starting point for future studies of these SNPs and their relationship with cognitive performance in patients with a probable diagnosis of FTD. Further studies with an expansion of the sample size and a long-term design could help to explore the predictive nature of the potential associations we identified.
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Affiliation(s)
- Andrea López-Cáceres
- Faculty of Medicine, Institute of Human Genetics, Pontificia Universidad Javeriana, Bogotá, Colombia
- Hospital Universitario Fundación Santa Fe de Bogotá, Bogotá, Colombia
- *Correspondence: Andrea López-Cáceres
| | - Francy Cruz-Sanabria
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
- Neuroscience Group, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Pilar Mayorga
- Mental Health Department, Hospital Universitario Fundación Santa Fe de Bogotá, Bogotá, Colombia
| | - Ana Isabel Sanchez
- Faculty of Health Sciences, Pontificia Universidad Javeriana, Cali, Colombia
- Imbanaco Medical Center, Cali, Colombia
| | | | - Paola Ayala-Ramírez
- Faculty of Medicine, Institute of Human Genetics, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Ignacio Zarante
- Faculty of Medicine, Institute of Human Genetics, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Diana Matallana
- Mental Health Department, Hospital Universitario Fundación Santa Fe de Bogotá, Bogotá, Colombia
- Department of Psychiatry, School of Medicine, Instituto de Envejecimiento, Pontificia Universidad Javeriana, Bogotá, Colombia
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