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Heywood A, Stocks J, Schneider JA, Arfanakis K, Bennett DA, Beg MF, Wang L. In vivo effect of LATE-NC on integrity of white matter connections to the hippocampus. Alzheimers Dement 2024. [PMID: 38877688 DOI: 10.1002/alz.13808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/20/2024] [Accepted: 02/25/2024] [Indexed: 06/16/2024]
Abstract
INTRODUCTION TAR DNA-binding protein 43 (TDP-43) is a highly prevalent proteinopathy that is involved in neurodegenerative processes, including axonal damage. To date, no ante mortem biomarkers exist for TDP-43, and few studies have directly assessed its impact on neuroimaging measures utilizing pathologic quantification. METHODS Ante mortem diffusion-weighted images were obtained from community-dwelling older adults. Regression models calculated the relationship between post mortem TDP-43 burden and ante mortem fractional anisotropy (FA) within each voxel in connection with the hippocampus, controlling for coexisting Alzheimer's disease and demographics. RESULTS Results revealed a significant negative relationship (false discovery rate [FDR] corrected p < .05) between post mortem TDP-43 and ante mortem FA in one cluster within the left medial temporal lobe connecting to the parahippocampal cortex, entorhinal cortex, and cingulate, aligning with the ventral subdivision of the cingulum. FA within this cluster was associated with cognition. DISCUSSION Greater TDP-43 burden is associated with lower FA within the limbic system, which may contribute to impairment in learning and memory. HIGHLIGHTS Post mortem TDP-43 pathological burden is associated with reduced ante mortem fractional anisotropy. Reduced FA located in the parahippocampal portion of the cingulum. FA in this area was associated with reduced episodic and semantic memory. FA in this area was associated with increased inward hippocampal surface deformation.
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Affiliation(s)
- Ashley Heywood
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Jane Stocks
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA
- Department of Pathology, Rush University Medical Center, Chicago, Illinois, USA
| | - Konstantinos Arfanakis
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Suite, Chicago, Illinois, USA
- Department of Diagnostic Radiology, Rush University Medical Center, Chicago, Illinois, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA
| | - Mirza Faisal Beg
- Simon Fraser University, School of Engineering Science, 8888 University Drive, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Lei Wang
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Department of Psychiatry and Behavioral Health, Ohio State University College of Medicine, Columbus, Ohio, USA
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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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Sakaie K, Koenig K, Lerner A, Appleby B, Ogrocki P, Pillai JA, Rao S, Leverenz JB, Lowe MJ. Multi-shell diffusion MRI of the fornix as a biomarker for cognition in Alzheimer's disease. Magn Reson Imaging 2024; 109:221-226. [PMID: 38521367 DOI: 10.1016/j.mri.2024.03.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 03/05/2024] [Accepted: 03/19/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND AND PURPOSE A substantial fraction of those who had Alzheimer's Disease (AD) pathology on autopsy did not have dementia in life. While biomarkers for AD pathology are well-developed, biomarkers specific to cognitive domains affected by early AD are lagging. Diffusion MRI (dMRI) of the fornix is a candidate biomarker for early AD-related cognitive changes but is susceptible to bias due to partial volume averaging (PVA) with cerebrospinal fluid. The purpose of this work is to leverage multi-shell dMRI to correct for PVA and to evaluate PVA-corrected dMRI measures in fornix as a biomarker for cognition in AD. METHODS Thirty-three participants in the Cleveland Alzheimer's Disease Research Center (CADRC) (19 with normal cognition (NC), 10 with mild cognitive impairment (MCI), 4 with dementia due to AD) were enrolled in this study. Multi-shell dMRI was acquired, and voxelwise fits were performed with two models: 1) diffusion tensor imaging (DTI) that was corrected for PVA and 2) neurite orientation dispersion and density imaging (NODDI). Values of tissue integrity in fornix were correlated with neuropsychological scores taken from the Uniform Data Set (UDS), including the UDS Global Composite 5 score (UDSGC5). RESULTS Statistically significant correlations were found between the UDSGC5 and PVA-corrected measure of mean diffusivity (MDc, r = -0.35, p < 0.05) from DTI and the intracelluar volume fraction (ficvf, r = 0.37, p < 0.04) from NODDI. A sensitivity analysis showed that the relationship to MDc was driven by episodic memory, which is often affected early in AD, and language. CONCLUSION This cross-sectional study suggests that multi-shell dMRI of the fornix that has been corrected for PVA is a potential biomarker for early cognitive domain changes in AD. A longitudinal study will be necessary to determine if the imaging measure can predict cognitive decline.
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Affiliation(s)
- Ken Sakaie
- Imaging Institute, The Cleveland Clinic, 9500 Euclid Ave, Mail code U-15, Cleveland, OH 44195, USA.
| | - Katherine Koenig
- Imaging Institute, The Cleveland Clinic, 9500 Euclid Ave, Mail code U-15, Cleveland, OH 44195, USA
| | - Alan Lerner
- Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Brian Appleby
- Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Paula Ogrocki
- Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Jagan A Pillai
- Lou Ruvo Center for Brain Health, The Cleveland Clinic, 9500 Euclid Ave, Mail code U-10, Cleveland, OH 44195, USA
| | - Stephen Rao
- Lou Ruvo Center for Brain Health, The Cleveland Clinic, 9500 Euclid Ave, Mail code U-10, Cleveland, OH 44195, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, The Cleveland Clinic, 9500 Euclid Ave, Mail code U-10, Cleveland, OH 44195, USA
| | - Mark J Lowe
- Imaging Institute, The Cleveland Clinic, 9500 Euclid Ave, Mail code U-15, Cleveland, OH 44195, USA
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Mohammadi H, Ariaei A, Ghobadi Z, Gorgich EAC, Rustamzadeh A. Which neuroimaging and fluid biomarkers method is better in theranostic of Alzheimer's disease? An umbrella review. IBRO Neurosci Rep 2024; 16:403-417. [PMID: 38497046 PMCID: PMC10940808 DOI: 10.1016/j.ibneur.2024.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/24/2024] [Indexed: 03/19/2024] Open
Abstract
Biomarkers are measured to evaluate physiological and pathological processes as well as responses to a therapeutic intervention. Biomarkers can be classified as diagnostic, prognostic, predictor, clinical, and therapeutic. In Alzheimer's disease (AD), multiple biomarkers have been reported so far. Nevertheless, finding a specific biomarker in AD remains a major challenge. Three databases, including PubMed, Web of Science, and Scopus were selected with the keywords of Alzheimer's disease, neuroimaging, biomarker, and blood. The results were finalized with 49 potential CSF/blood and 35 neuroimaging biomarkers. To distinguish normal from AD patients, amyloid-beta42 (Aβ42), plasma glial fibrillary acidic protein (GFAP), and neurofilament light (NFL) as potential biomarkers in cerebrospinal fluid (CSF) as well as the serum could be detected. Nevertheless, most of the biomarkers fairly change in the CSF during AD, listed as kallikrein 6, virus-like particles (VLP-1), galectin-3 (Gal-3), and synaptotagmin-1 (Syt-1). From the neuroimaging aspect, atrophy is an accepted biomarker for the neuropathologic progression of AD. In addition, Magnetic resonance spectroscopy (MRS), diffusion weighted imaging (DWI), diffusion tensor imaging (DTI), tractography (DTT), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI), can be used to detect AD. Using neuroimaging and CSF/blood biomarkers, in combination with artificial intelligence, it is possible to obtain information on prognosis and follow-up on the different stages of AD. Hence physicians could select the suitable therapy to attenuate disease symptoms and follow up on the efficiency of the prescribed drug.
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Affiliation(s)
- Hossein Mohammadi
- Department of Bioimaging, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences (MUI), Isfahan, Islamic Republic of Iran
| | - Armin Ariaei
- Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Islamic Republic of Iran
| | - Zahra Ghobadi
- Advanced Medical Imaging Ward, Pars Darman Medical Imaging Center, Karaj, Islamic Republic of Iran
| | - Enam Alhagh Charkhat Gorgich
- Department of Anatomy, School of Medicine, Iranshahr University of Medical Sciences, Iranshahr, Islamic Republic of Iran
| | - Auob Rustamzadeh
- Cellular and Molecular Research Center, Research Institute for Non-communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran
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Feng Y, Chandio BQ, Villalon-Reina JE, Thomopoulos SI, Nir TM, Benavidez S, Laltoo E, Chattopadhyay T, Joshi H, Venkatasubramanian G, John JP, Jahanshad N, Reid RI, Jack CR, Weiner MW, Thompson PM. Microstructural Mapping of Neural Pathways in Alzheimer's Disease using Macrostructure-Informed Normative Tractometry. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.25.591183. [PMID: 38712293 PMCID: PMC11071453 DOI: 10.1101/2024.04.25.591183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Introduction Diffusion MRI is sensitive to the microstructural properties of brain tissues, and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest, without considering the underlying fiber geometry. Methods Here, we propose a novel Macrostructure-Informed Normative Tractometry (MINT) framework, to investigate how white matter microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure. Results In two multi-site cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics' sensitivity to dementia. Discussion We show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brain's neural pathways.
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Affiliation(s)
- Yixue Feng
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Bramsh Q. Chandio
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Julio E. Villalon-Reina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Sophia I. Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Talia M. Nir
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Sebastian Benavidez
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Tamoghna Chattopadhyay
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Himanshu Joshi
- Multimodal Brain Image Analysis Laboratory National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - Ganesan Venkatasubramanian
- Translational Psychiatry Laboratory, National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - John P. John
- Multimodal Brain Image Analysis Laboratory National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
| | - Robert I. Reid
- Department of Information Technology, Mayo Clinic and Foundation, Rochester, MN, United States
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, United States
| | - Clifford R. Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, MN, United States
| | - Michael W. Weiner
- Department of Radiology and Biomedical Imaging, UCSF School of Medicine, San Francisco, CA, United States
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States
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Müller HP, Kassubek J. Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing. Front Hum Neurosci 2024; 18:1378896. [PMID: 38628970 PMCID: PMC11018884 DOI: 10.3389/fnhum.2024.1378896] [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: 01/30/2024] [Accepted: 02/26/2024] [Indexed: 04/19/2024] Open
Abstract
Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.
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Heise V, Offer A, Whiteley W, Mackay CE, Armitage JM, Parish S. A comprehensive analysis of APOE genotype effects on human brain structure in the UK Biobank. Transl Psychiatry 2024; 14:143. [PMID: 38472178 PMCID: PMC10933274 DOI: 10.1038/s41398-024-02848-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 02/14/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
Alzheimer's disease (AD) risk is increased in carriers of the apolipoprotein E (APOE) ε4 allele and decreased in ε2 allele carriers compared with the ε3ε3 genotype. The aim of this study was to determine whether: the APOE genotype affects brain grey (GM) or white matter (WM) structure; and if differences exist, the age when they become apparent and whether there are differential effects by sex. We used cross-sectional magnetic resonance imaging data from ~43,000 (28,494 after pre-processing) white British cognitively healthy participants (7,446 APOE ε4 carriers) aged 45-80 years from the UK Biobank cohort and investigated image-derived phenotypes (IDPs). We observed no statistically significant effects of APOE genotype on GM structure volumes or median T2* in subcortical structures, a measure related to iron content. The volume of white matter hyperintensities differed significantly between APOE genotype groups with higher volumes in APOE ε4ε4 (effect size 0.14 standard deviations [SD]) and ε3ε4 carriers (effect size 0.04 SD) but no differences in ε2 carriers compared with ε3ε3 carriers. WM integrity measures in the dorsal (mean diffusivity [MD]) and ventral cingulum (MD and intracellular volume fraction), posterior thalamic radiation (MD and isotropic volume fraction) and sagittal stratum (MD) indicated lower integrity in APOE ε4ε4 carriers (effect sizes around 0.2-0.3 SD) and ε3ε4 (effect sizes around 0.05 SD) carriers but no differences in ε2 carriers compared with the APOE ε3ε3 genotype. Effects did not differ between men and women. APOE ε4 homozygotes had lower WM integrity specifically at older ages with a steeper decline of WM integrity from the age of 60 that corresponds to around 5 years greater "brain age". APOE genotype affects various white matters measures, which might be indicative of preclinical AD processes. This hypothesis can be assessed in future when clinical outcomes become available.
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Affiliation(s)
- Verena Heise
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Alison Offer
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - William Whiteley
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Clare E Mackay
- Department of Psychiatry, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK
| | - Jane M Armitage
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Sarah Parish
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
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9
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Zhu AH, Nir TM, Javid S, Villalon-Reina JE, Rodrigue AL, Strike LT, de Zubicaray GI, McMahon KL, Wright MJ, Medland SE, Blangero J, Glahn DC, Kochunov P, Håberg AK, Thompson PM, Jahanshad N. Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.22.581646. [PMID: 38463962 PMCID: PMC10925090 DOI: 10.1101/2024.02.22.581646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan, complementing the process of gray matter development and degeneration. Here, we create normative lifespan reference curves for global and regional WM microstructure by harmonizing diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three harmonization methods on regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with known WM maturation peaks. Lifespan FA reference curves were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are publicly available as eHarmonize (https://github.com/ahzhu/eharmonize).
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Affiliation(s)
- Alyssa H Zhu
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Talia M Nir
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Shayan Javid
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Julio E Villalon-Reina
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
| | - Amanda L Rodrigue
- Department of Psychiatry and Behavioral Science, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lachlan T Strike
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Queensland University of Technology, Brisbane, QLD, Australia
| | | | - Katie L McMahon
- Queensland University of Technology, Brisbane, QLD, Australia
| | - Margaret J Wright
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- Queensland University of Technology, Brisbane, QLD, Australia
- School of Psychology, `, Brisbane, QLD, Australia
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - David C Glahn
- Department of Psychiatry and Behavioral Science, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Peter Kochunov
- Faillace Department of Psychiatry and Behavioral Sciences at McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of MiDtT National Research Center, St. Olav's Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Paul M Thompson
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Marina del Rey, CA, USA
- Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA, USA
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10
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Jang WH, Lee SH. Diffusion tensor imaging of the brain in children with sensory processing disorder: A review. J Neuroimaging 2024; 34:167-178. [PMID: 38183169 DOI: 10.1111/jon.13186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 12/20/2023] [Accepted: 12/20/2023] [Indexed: 01/07/2024] Open
Abstract
Sensory processing disorder (SPD) is a clinical condition characterized by difficulties in the neurological processes of registering, discriminating, organizing, and responding to various sensory sensations. This study aimed to review the association between impaired white matter (WM) tract structure and neurofunctional deficits in children with SPD using diffusion tensor imaging (DTI). A comprehensive literature search was conducted using the online databases Google Scholar and PubMed (from 2010 to July 2023), resulting in the selection of nine relevant studies. Findings revealed that the splenium of the corpus callosum (SCC), superior longitudinal fasciculus (SLF), posterior corona radiata (PCR), and posterior thalamic radiation (PTR) exhibited reduced microstructural integrity, strongly associated with SPD. Specifically, auditory over-responsivity, a subtype of SPD, was linked to impaired integrity of the PCR, PTR, anterior corona radiata, and SLF. Tactile over-responsivity (TOR) was correlated with markers of decreased integrity in the SCC, superior corona radiata, and left PTR. Among the DTI parameters, decreased fractional anisotropy (FA) emerged as the most reliable factor for identifying SPD, followed by increased radial diffusivity (RD) and mean diffusivity (MD). Notably, significant correlations were observed between with auditory over-responsivity and TOR with the DTI parameters (positive for FA and negative for RD and MD). Overall, this review confirms the impaired integrity of specific WM tracts in children with SPD and establishes correlations between DTI parameters and neurobehavioral deficits associated with the disorder. The insights gained from this review contribute to a better understanding of SPD and hold clinical implications for its diagnosis and treatment.
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Affiliation(s)
- Woo-Hyuk Jang
- Department of Occupational Therapy, College of Health Science, Kangwon National University, Samcheok-si, Republic of Korea
| | - Seon-Hee Lee
- Department of Occupational Therapy, College of Health Science, Kangwon National University, Samcheok-si, Republic of Korea
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11
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Chen Y, Lu P, Wu S, Yang J, Liu W, Zhang Z, Xu Q. CD163-Mediated Small-Vessel Injury in Alzheimer's Disease: An Exploration from Neuroimaging to Transcriptomics. Int J Mol Sci 2024; 25:2293. [PMID: 38396970 PMCID: PMC10888773 DOI: 10.3390/ijms25042293] [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: 01/10/2024] [Revised: 02/10/2024] [Accepted: 02/11/2024] [Indexed: 02/25/2024] Open
Abstract
Patients with Alzheimer's disease (AD) often present with imaging features indicative of small-vessel injury, among which, white-matter hyperintensities (WMHs) are the most prevalent. However, the underlying mechanism of the association between AD and small-vessel injury is still obscure. The aim of this study is to investigate the mechanism of small-vessel injury in AD. Differential gene expression analyses were conducted to identify the genes related to WMHs separately in mild cognitive impairment (MCI) and cognitively normal (CN) subjects from the ADNI database. The WMH-related genes identified in patients with MCI were considered to be associated with small-vessel injury in early AD. Functional enrichment analyses and a protein-protein interaction (PPI) network were performed to explore the pathway and hub genes related to the mechanism of small-vessel injury in MCI. Subsequently, the Boruta algorithm and support vector machine recursive feature elimination (SVM-RFE) algorithm were performed to identify feature-selection genes. Finally, the mechanism of small-vessel injury was analyzed in MCI from the immunological perspectives; the relationship of feature-selection genes with various immune cells and neuroimaging indices were also explored. Furthermore, 5×FAD mice were used to demonstrate the genes related to small-vessel injury. The results of the logistic regression analyses suggested that WMHs significantly contributed to MCI, the early stage of AD. A total of 276 genes were determined as WMH-related genes in patients with MCI, while 203 WMH-related genes were obtained in CN patients. Among them, only 15 genes overlapped and were thus identified as the crosstalk genes. By employing the Boruta and SVM-RFE algorithms, CD163, ALDH3B1, MIR22HG, DTX2, FOLR2, ALDH2, and ZNF23 were recognized as the feature-selection genes linked to small-vessel injury in MCI. After considering the results from the PPI network, CD163 was finally determined as the critical WMH-related gene in MCI. The expression of CD163 was correlated with fractional anisotropy (FA) values in regions that are vulnerable to small-vessel injury in AD. The immunostaining and RT-qPCR results from the verifying experiments demonstrated that the indicators of small-vessel injury presented in the cortical tissue of 5×FAD mice and related to the upregulation of CD163 expression. CD163 may be the most pivotal candidates related to small-vessel injury in early AD.
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Affiliation(s)
- Yuewei Chen
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
- Department of Neurology, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
- Renji-UNSW CHeBA (Centre for Healthy Brain Ageing of University of New South Wales) Neurocognitive Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Peiwen Lu
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
- Renji-UNSW CHeBA (Centre for Healthy Brain Ageing of University of New South Wales) Neurocognitive Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Shengju Wu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jie Yang
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
- Department of Neurology, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
- Renji-UNSW CHeBA (Centre for Healthy Brain Ageing of University of New South Wales) Neurocognitive Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Wanwan Liu
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
| | - Zhijun Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Qun Xu
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
- Department of Neurology, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
- Renji-UNSW CHeBA (Centre for Healthy Brain Ageing of University of New South Wales) Neurocognitive Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
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12
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Morrissey ZD, Gao J, Shetti A, Li W, Zhan L, Li W, Fortel I, Saido T, Saito T, Ajilore O, Cologna SM, Lazarov O, Leow AD. Temporal Alterations in White Matter in An App Knock-In Mouse Model of Alzheimer's Disease. eNeuro 2024; 11:ENEURO.0496-23.2024. [PMID: 38290851 PMCID: PMC10897532 DOI: 10.1523/eneuro.0496-23.2024] [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: 11/27/2023] [Revised: 01/05/2024] [Accepted: 01/17/2024] [Indexed: 02/01/2024] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia and results in neurodegeneration and cognitive impairment. White matter (WM) is affected in AD and has implications for neural circuitry and cognitive function. The trajectory of these changes across age, however, is still not well understood, especially at earlier stages in life. To address this, we used the AppNL-G-F/NL-G-F knock-in (APPKI) mouse model that harbors a single copy knock-in of the human amyloid precursor protein (APP) gene with three familial AD mutations. We performed in vivo diffusion tensor imaging (DTI) to study how the structural properties of the brain change across age in the context of AD. In late age APPKI mice, we observed reduced fractional anisotropy (FA), a proxy of WM integrity, in multiple brain regions, including the hippocampus, anterior commissure (AC), neocortex, and hypothalamus. At the cellular level, we observed greater numbers of oligodendrocytes in middle age (prior to observations in DTI) in both the AC, a major interhemispheric WM tract, and the hippocampus, which is involved in memory and heavily affected in AD, prior to observations in DTI. Proteomics analysis of the hippocampus also revealed altered expression of oligodendrocyte-related proteins with age and in APPKI mice. Together, these results help to improve our understanding of the development of AD pathology with age, and imply that middle age may be an important temporal window for potential therapeutic intervention.
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Affiliation(s)
- Zachery D Morrissey
- Graduate Program in Neuroscience, University of Illinois Chicago, Chicago, Illinois 60612
- Department of Psychiatry, University of Illinois Chicago, Chicago, Illinois 60612
- Department of Anatomy & Cell Biology, University of Illinois Chicago, Chicago, Illinois 60612
| | - Jin Gao
- Department of Electrical & Computer Engineering, University of Illinois Chicago, Chicago, Illinois 60607
- Preclinical Imaging Core, University of Illinois Chicago, Chicago, Illinois 60612
| | - Aashutosh Shetti
- Department of Anatomy & Cell Biology, University of Illinois Chicago, Chicago, Illinois 60612
| | - Wenping Li
- Department of Chemistry, University of Illinois Chicago, Chicago, Illinois 60607
| | - Liang Zhan
- Department of Electrical & Computer Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15261
| | - Weiguo Li
- Preclinical Imaging Core, University of Illinois Chicago, Chicago, Illinois 60612
- Department of Bioengineering, University of Illinois Chicago, Chicago, Illinois 60607
- Department of Radiology, Northwestern University, Chicago, Illinois 60611
| | - Igor Fortel
- Department of Bioengineering, University of Illinois Chicago, Chicago, Illinois 60607
| | - Takaomi Saido
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Wako 351-0198, Japan
| | - Takashi Saito
- Department of Neurocognitive Science, Institute of Brain Science, Nagoya City University, Nagoya 467-8601, Japan
| | - Olusola Ajilore
- Department of Psychiatry, University of Illinois Chicago, Chicago, Illinois 60612
| | - Stephanie M Cologna
- Department of Chemistry, University of Illinois Chicago, Chicago, Illinois 60607
| | - Orly Lazarov
- Department of Anatomy & Cell Biology, University of Illinois Chicago, Chicago, Illinois 60612
| | - Alex D Leow
- Department of Psychiatry, University of Illinois Chicago, Chicago, Illinois 60612
- Department of Bioengineering, University of Illinois Chicago, Chicago, Illinois 60607
- Department of Computer Science, University of Illinois Chicago, Chicago, Illinois 60607
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13
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Tu MC, Huang SM, Hsu YH, Yang JJ, Lin CY, Kuo LW. Joint diffusional kurtosis magnetic resonance imaging analysis of white matter and the thalamus to identify subcortical ischemic vascular disease. Sci Rep 2024; 14:2570. [PMID: 38297073 PMCID: PMC10830492 DOI: 10.1038/s41598-024-52910-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/25/2024] [Indexed: 02/02/2024] Open
Abstract
Identifying subcortical ischemic vascular disease (SIVD) in older adults is important but challenging. Growing evidence suggests that diffusional kurtosis imaging (DKI) can detect SIVD-relevant microstructural pathology, and a systematic assessment of the discriminant power of DKI metrics in various brain tissue microstructures is urgently needed. Therefore, the current study aimed to explore the value of DKI and diffusion tensor imaging (DTI) metrics in detecting early-stage SIVD by combining multiple diffusion metrics, analysis strategies, and clinical-radiological constraints. This prospective study compared DKI with diffusivity and macroscopic imaging evaluations across the aging spectrum including SIVD, Alzheimer's disease (AD), and cognitively normal (NC) groups. Using a white matter atlas and segregated thalamus analysis with considerations of the pre-identified macroscopic pathology, the most effective diffusion metrics were selected and then examined using multiple clinical-radiological constraints in a two-group or three-group paradigm. A total of 122 participants (mean age, 74.6 ± 7.38 years, 72 women) including 42 with SIVD, 50 with AD, and 30 NC were evaluated. Fractional anisotropy, mean kurtosis, and radial kurtosis were critical metrics in detecting early-stage SIVD. The optimal selection of diffusion metrics showed 84.4-100% correct classification of the results in a three-group paradigm, with an area under the curve of .909-.987 in a two-group paradigm related to SIVD detection (all P < .001). We therefore concluded that greatly resilient to the effect of pre-identified macroscopic pathology, the combination of DKI/DTI metrics showed preferable performance in identifying early-stage SIVD among adults across the aging spectrum.
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Affiliation(s)
- Min-Chien Tu
- Department of Neurology, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan
- Department of Neurology, School of Medicine, Tzu Chi University, Hualien, Taiwan
| | - Sheng-Min Huang
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Yen-Hsuan Hsu
- Department of Psychology, National Chung Cheng University, Chiayi, Taiwan
- Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, Chiayi, Taiwan
| | - Jir-Jei Yang
- Department of Radiology, Taichung Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Taichung, Taiwan
| | | | - Li-Wei Kuo
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan.
- Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan.
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14
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Lee EY, Kim J, Prado-Rico JM, Du G, Lewis MM, Kong L, Yanosky JD, Eslinger P, Kim BG, Hong YS, Mailman RB, Huang X. Effects of mixed metal exposures on MRI diffusion features in the medial temporal lobe. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.07.18.23292828. [PMID: 37503124 PMCID: PMC10371112 DOI: 10.1101/2023.07.18.23292828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Background Environmental exposure to metal mixtures is common and may be associated with increased risk for neurodegenerative disorders including Alzheimer's disease. Objective This study examined associations of mixed metal exposures with medial temporal lobe (MTL) MRI structural metrics and neuropsychological performance. Methods Metal exposure history, whole blood metal, and neuropsychological tests were obtained from subjects with/without a history of mixed metal exposure from welding fumes (42 exposed subjects; 31 controls). MTL structures (hippocampus, entorhinal and parahippocampal cortices) were assessed by morphologic (volume, cortical thickness) and diffusion tensor imaging [mean (MD), axial (AD), radial diffusivity (RD), and fractional anisotropy (FA)] metrics. In exposed subjects, correlation, multiple linear, Bayesian kernel machine regression, and mediation analyses were employed to examine effects of single- or mixed-metal predictor(s) and their interactions on MTL structural and neuropsychological metrics; and on the path from metal exposure to neuropsychological consequences. Results Compared to controls, exposed subjects had higher blood Cu, Fe, K, Mn, Pb, Se, and Zn levels (p's<0.026) and poorer performance in processing/psychomotor speed, executive, and visuospatial domains (p's<0.046). Exposed subjects displayed higher MD, AD, and RD in all MTL ROIs (p's<0.040) and lower FA in entorhinal and parahippocampal cortices (p's<0.033), but not morphological differences. Long-term mixed-metal exposure history indirectly predicted lower processing speed performance via lower parahippocampal FA (p=0.023). Higher whole blood Mn and Cu predicted higher entorhinal diffusivity (p's<0.043) and lower Delayed Story Recall performance (p=0.007) without overall metal mixture or interaction effects. Discussion Mixed metal exposure predicted MTL structural and neuropsychological features that are similar to Alzheimer's disease at-risk populations. These data warrant follow-up as they may illuminate the path for environmental exposure to Alzheimer's disease-related health outcomes.
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Affiliation(s)
- Eun-Young Lee
- Department of Health Care and Science, Dong-A University, Busan, South-Korea
| | - Juhee Kim
- Department of Health Care and Science, Dong-A University, Busan, South-Korea
| | - Janina Manzieri Prado-Rico
- Department of Neurology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
| | - Guangwei Du
- Department of Neurology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
| | - Mechelle M. Lewis
- Department of Neurology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
- Department of Pharmacology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
| | - Lan Kong
- Department of Public Health Sciences, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
| | - Jeff D. Yanosky
- Department of Public Health Sciences, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
| | - Paul Eslinger
- Department of Neurology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
| | - Byoung-Gwon Kim
- Department of Preventive Medicine, College of Medicine, Dong-A University, Busan, South Korea
| | - Young-Seoub Hong
- Department of Preventive Medicine, College of Medicine, Dong-A University, Busan, South Korea
| | - Richard B. Mailman
- Department of Neurology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
- Department of Pharmacology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
| | - Xuemei Huang
- Department of Neurology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
- Department of Pharmacology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
- Department of Radiology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
- Department of Neurosurgery, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
- Department of Kinesiology, Pennsylvania State University-Milton S. Hershey Medical Center, Hershey PA 17033, USA
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15
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Ruiz-Rizzo AL, Finke K, Archila-Meléndez ME. Diffusion Tensor Imaging in Alzheimer's Studies. Methods Mol Biol 2024; 2785:105-113. [PMID: 38427191 DOI: 10.1007/978-1-0716-3774-6_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
In this chapter, we describe the use of quantitative metrics of white matter obtained from the diffusion tensor model based on diffusion-weighted imaging in Alzheimer's disease (AD). Our description synthesizes insights not only from patient populations with AD dementia but also from participants at risk for AD dementia (e.g., amnestic mild cognitive impairment, subjective cognitive decline, or familial AD mutation carriers). A reference to studies examining correlations with behavioral variables is also included. Our main message is to caution against the overinterpretation of diffusion metrics and to favor analyses that focus on regions of interest or major white matter tracts for biomarker studies in AD.
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Affiliation(s)
| | - Kathrin Finke
- Department of Neurology, Jena University Hospital, Jena, Germany
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16
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Quattrini G, Pini L, Boscolo Galazzo I, Jelescu IO, Jovicich J, Manenti R, Frisoni GB, Marizzoni M, Pizzini FB, Pievani M. Microstructural alterations in the locus coeruleus-entorhinal cortex pathway in Alzheimer's disease and frontotemporal dementia. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12513. [PMID: 38213948 PMCID: PMC10781651 DOI: 10.1002/dad2.12513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/04/2023] [Accepted: 11/20/2023] [Indexed: 01/13/2024]
Abstract
INTRODUCTION We investigated in vivo the microstructural integrity of the pathway connecting the locus coeruleus to the transentorhinal cortex (LC-TEC) in patients with Alzheimer's disease (AD) and frontotemporal dementia (FTD). METHODS Diffusion-weighted MRI scans were collected for 21 AD, 20 behavioral variants of FTD (bvFTD), and 20 controls. Fractional anisotropy (FA), mean, axial, and radial diffusivities (MD, AxD, RD) were computed in the LC-TEC pathway using a normative atlas. Atrophy was assessed using cortical thickness and correlated with microstructural measures. RESULTS We found (i) higher RD in AD than controls; (ii) higher MD, RD, and AxD, and lower FA in bvFTD than controls and AD; and (iii) a negative association between LC-TEC MD, RD, and AxD, and entorhinal cortex (EC) thickness in bvFTD (all p < 0.050). DISCUSSION LC-TEC microstructural alterations are more pronounced in bvFTD than AD, possibly reflecting neurodegeneration secondary to EC atrophy. Highlights Microstructural integrity of LC-TEC pathway is understudied in AD and bvFTD.LC-TEC microstructural alterations are present in both AD and bvFTD.Greater LC-TEC microstructural alterations in bvFTD than AD.LC-TEC microstructural alterations in bvFTD are associated to EC neurodegeneration.
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Affiliation(s)
- Giulia Quattrini
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE)IRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
- Department of Molecular and Translational MedicineUniversity of BresciaBresciaItaly
| | - Lorenzo Pini
- Padova Neuroscience CenterUniversity of PadovaPadovaItaly
| | | | - Ileana O. Jelescu
- Department of RadiologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Jorge Jovicich
- Center of Mind/Brain SciencesUniversity of TrentoRoveretoItaly
| | - Rosa Manenti
- Neuropsychology UnitIRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
| | - Giovanni B. Frisoni
- Memory Center and LANVIE ‐ Laboratory of Neuroimaging of AgingUniversity Hospitals and University of GenevaGenevaSwitzerland
| | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE)IRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
- Laboratory of Biological PsychiatryIRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
| | - Francesca B. Pizzini
- Department of Engineering for Innovation MedicineUniversity of VeronaVeronaItaly
| | - Michela Pievani
- Laboratory of Alzheimer's Neuroimaging and Epidemiology (LANE)IRCCS Istituto Centro San Giovanni di Dio FatebenefratelliBresciaItaly
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17
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Cui Y, Liu C, Wang Y, Xie H. Multimodal magnetic resonance scans of patients with mild cognitive impairment. Dement Neuropsychol 2023; 17:e20230017. [PMID: 38111592 PMCID: PMC10727029 DOI: 10.1590/1980-5764-dn-2023-0017] [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: 03/14/2023] [Revised: 09/04/2023] [Accepted: 10/20/2023] [Indexed: 12/20/2023] Open
Abstract
The advancement of neuroimaging technology offers a pivotal reference for the early detection of mild cognitive impairment (MCI), a significant area of focus in contemporary cognitive function research. Structural MRI scans present visual and quantitative manifestations of alterations in brain tissue, whereas functional MRI scans depict the metabolic and functional state of brain tissues from diverse perspectives. As various magnetic resonance techniques possess both strengths and constraints, this review examines the methodologies and outcomes of multimodal magnetic resonance technology in MCI diagnosis, laying the groundwork for subsequent diagnostic and therapeutic interventions for MCI.
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Affiliation(s)
- Yu Cui
- Shandong First Medical University, The Second Affiliated Hospital, Department of Neurosurgery, Tai’an, Shandong, China
| | - Chenglong Liu
- Shandong First Medical University, The Second Affiliated Hospital, Department of Radiology, Tai’an, Shandong, China
| | - Ying Wang
- Shandong First Medical University, Department of Scientific Research, Ji’nan, Shandong, China
| | - Hongyan Xie
- Shandong First Medical University, The Second Affiliated Hospital, Department of Neurology, Tai’an, Shandong, China
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18
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Jain PR, Yates M, de Celis CR, Drineas P, Jahanshad N, Thompson P, Paschou P. Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes. Neuroimage 2023; 284:120466. [PMID: 37995919 DOI: 10.1016/j.neuroimage.2023.120466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 10/17/2023] [Accepted: 11/20/2023] [Indexed: 11/25/2023] Open
Abstract
Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes, with nine proteins and five metabolites replicated using independent exposure data. We found causal associations between accumbens volume and plasma protease c1 inhibitor as well as strong association between putamen volume and Agouti signaling protein. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.
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Affiliation(s)
- Pritesh R Jain
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States
| | - Madison Yates
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States
| | - Carlos Rubin de Celis
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States
| | - Petros Drineas
- Department of Computer Science, Purdue University, United States
| | - Neda Jahanshad
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of South California, United States
| | - Paul Thompson
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of South California, United States
| | - Peristera Paschou
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States.
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19
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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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20
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Shrestha S, Zhu X, Sullivan KJ, Blackshear C, Deal JA, Sharrett AR, Kamath V, Schneider ALC, Jack CR, Huang J, Palta P, Reid RI, Knopman DS, Gottesman RF, Chen H, Windham BG, Griswold ME, Mosley TH. Association of Olfaction and Microstructural Integrity of Brain Tissue in Community-Dwelling Adults: Atherosclerosis Risk in Communities Neurocognitive Study. Neurology 2023; 101:e1328-e1340. [PMID: 37541841 PMCID: PMC10558165 DOI: 10.1212/wnl.0000000000207636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/30/2023] [Indexed: 08/06/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Research on olfaction and brain neuropathology may help understand brain regions associated with normal olfaction and dementia pathophysiology. To identify early regional brain structures affected in poor olfaction, we examined cross-sectional associations of microstructural integrity of the brain with olfaction in the Atherosclerosis Risk in Communities Neurocognitive Study. METHODS Participants were selected from a prospective cohort study of community-dwelling adults; selection criteria included the following: evidence of cognitive impairment, participation in a previous MRI study, and a random sample of cognitively normal participants. Microstructural integrity was measured by 2 diffusion tensor imaging (DTI) measures, fractional anisotropy (FA) and mean diffusivity (MD), and olfaction by a 12-item odor identification test at the same visit. Higher FA and MD values indicate better and worse microstructural integrity, respectively, and higher odor identification scores indicate better olfaction. We used brain region-specific linear regression models to examine associations between DTI measures and olfaction, adjusting for potential confounders. RESULTS Among 1,418 participants (mean age 76 ± 5 years, 41% male, 21% Black race, 59% with normal cognition), the mean olfaction score was 9 ± 2.3. Relevant to olfaction, higher MD in the medial temporal lobe (MTL) regions, namely the hippocampus (β -0.79 [95% CI -0.94 to -0.65] units lower olfaction score per 1 SD higher MD), amygdala, entorhinal area, and some white matter (WM) tracts connecting to these regions, was associated with olfaction. We also observed associations with MD and WM FA in multiple atlas regions that were not previously implicated in olfaction. The associations between MD and olfaction were particularly stronger in the MTL regions among individuals with mild cognitive impairment (MCI) compared with those with normal cognition (e.g., βhippocampus -0.75 [95% CI -1.02 to -0.49] and -0.44 [95% CI -0.63 to -0.26] for MCI and normal cognition, respectively, p interaction = 0.004). DISCUSSION Neuronal microstructural integrity in multiple brain regions, particularly the MTL (the regions known to be affected in early Alzheimer disease), is associated with odor identification ability. Differential associations in the MTL regions among cognitively normal individuals compared with those with MCI may reflect the earlier vs later effects of the dementia pathogenesis. It is likely that some of the associated regions may not have any functional relevance to olfaction.
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Affiliation(s)
- Srishti Shrestha
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing.
| | - Xiaoqian Zhu
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Kevin J Sullivan
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Chad Blackshear
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Jennifer A Deal
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - A Richey Sharrett
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Vidyulata Kamath
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Andrea L C Schneider
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Clifford R Jack
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Juebin Huang
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Priya Palta
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Robert I Reid
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - David S Knopman
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Rebecca F Gottesman
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Honglei Chen
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - B Gwen Windham
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Michael E Griswold
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
| | - Thomas H Mosley
- From the The Memory Impairment and Neurodegenerative Dementia (MIND) Center (S.S., X.Z., K.J.S., C.B., J.H., B.G.W., M.E.G., T.H.M.), University of Mississippi Medical Center, Jackson; Department of Epidemiology (J.A.D., A.R.S.), Johns Hopkins University Bloomberg School of Public Health; Department of Psychiatry and Behavioral Sciences (V.K.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Neurology (A.L.C.S.), and Department of Biostatistics, Epidemiology, and Informatics (A.L.C.S.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Department of Radiology (C.R.J., R.I.R.), Mayo Clinic, Rochester, MN; Department of Neurology (J.H.), University of Mississippi Medical Center, Jackson; Department of Neurology (P.P.), University of North Carolina at Chapel Hill; Department of Neurology (D.S.K.), Mayo Clinic, Rochester, MN; Stroke Branch (R.F.G.), National Institute of Neurological Disorders and Stroke Intramural Research Program, Bethesda, MD; and Department of Epidemiology and Biostatistics (H.C.), Michigan State University, East Lansing
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21
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Ng YL, Tan CS, Egle M, Gyanwali B, Tozer DJ, Markus HS, Chen C, Hilal S. The association of diffusion tensor MRI measures of normal appearing white matter and cognition. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2023; 5:100174. [PMID: 37457665 PMCID: PMC10344700 DOI: 10.1016/j.cccb.2023.100174] [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: 01/09/2023] [Revised: 05/29/2023] [Accepted: 07/01/2023] [Indexed: 07/18/2023]
Abstract
Objective Median and peak height of fractional anisotropy (FA) and mean diffusivity (MD) are diffusion tensor imaging (DTI) markers used to quantify white matter microstructure changes. We examine the association of DTI histogram-derived measures in global normal appearing white matter (NAWM) and cognitive decline in patients with normal cognition and cognitive impairment no dementia from a memory clinic in Singapore. Methods A total of 252 patients (mean age: 71.1 ± 7.6 years, 53.2% women) were included. All patients underwent clinical assessments, a brain MRI scan at baseline, and neuropsychological assessments annually for 2 years. DTI scans were processed to obtain MD and FA histogram-derived measures. The National Institute of Neurological Disorders and Stroke and the Canadian Stroke Network harmonization neuropsychological battery were used to assess cognitive function. Linear regression models with generalised estimating equation (GEE) and logistic regression models were used to examine the association between DTI histogram measures and cognitive decline. Results When compared to baseline, MD and FA measures at Year 2 were associated with an accelerated worsening in global cognition (all p for interaction <0.001; Year 0 vs 2, MD median: -0.29 (95%CI: -0.49, -0.09) vs -0.45 (95%CI: -0.65,-0.25); MD peak height: 0.22 (95%CI: 0.07, 0.37) vs 0.37 (95%CI: 0.21, 0.53); FA median: 0.11 (95%CI: -0.05, 0.26) vs 0.22 (95%CI: 0.07, 0.37); FA peak height: -0.14 (95%CI: -0.28, 0.00) vs -0.24 (95%CI: -0.38, -0.10);). Similar findings were observed for executive function and visuomotor speed while only MD measures predicted worsening in memory domain. Interpretation This study shows that DTI histogram measures are associated with accelerated cognitive decline suggesting the utility of DTI as a pre-clinical marker in predicting the worsening of cognition in clinical trials.
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Affiliation(s)
- Yi Lin Ng
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Chuen Seng Tan
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Marco Egle
- Department of Clinical Neurosciences, Stroke Research Group, University of Cambridge, Cambridge, United Kingdom
| | - Bibek Gyanwali
- Memory Aging and Cognition Centre, National University Health System, Singapore
| | - Daniel J. Tozer
- Department of Clinical Neurosciences, Stroke Research Group, University of Cambridge, Cambridge, United Kingdom
| | - Hugh S. Markus
- Department of Clinical Neurosciences, Stroke Research Group, University of Cambridge, Cambridge, United Kingdom
| | - Christopher Chen
- Memory Aging and Cognition Centre, National University Health System, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Saima Hilal
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
- Memory Aging and Cognition Centre, National University Health System, Singapore
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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22
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Konwar S, Manca R, De Marco M, Soininen H, Venneri A. The effect of physical activity on white matter integrity in aging and prodromal to mild Alzheimer's disease with vascular comorbidity. Front Aging Neurosci 2023; 15:1096798. [PMID: 37520122 PMCID: PMC10382177 DOI: 10.3389/fnagi.2023.1096798] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 05/02/2023] [Indexed: 08/01/2023] Open
Abstract
Background Physical activity is a modifiable lifestyle factor that has been previously associated with reduced vascular burden and reduced risk of dementia. Objectives This study tested whether physical activity (i.e., being inactive vs. active) contributed to preservation of white matter microstructure in healthy aging controls and patients in prodromal to mild Alzheimer's disease with low/high vascular burden. Materials A total of 213 participants were recruited from memory clinics. They were classified as being either physically active (n = 113) or inactive (n = 100) based on the Cardiovascular Risk Factors, Aging and Dementia (CAIDE) questionnaire. Diffusion-weighted images were acquired for all participants and pre-processed based on a standard protocol. Methods A factorial design using voxel-wise tract-based spatial statistics (TBSS) was adopted, with 5,000 permutations and threshold-free cluster enhancement (TFCE), to identify significant clusters for fractional anisotropy (FA), axial diffusivity (AxD), mean diffusivity (MD), and radial diffusivity (RD). Results Clusters of higher FA and lower AxD, MD, and RD values were found for physically active compared with inactive participants that were widespread covering mainly association and projection tracts but also some commissural tracts. A three-way Group × Physical Activity × Vascular Burden interaction effect was found for FA mostly in a variety of projection tracts with a right predominance, and some commissural and association tracts. Post hoc analyses revealed higher FA in patients with high vascular burden who were physically active compared with those patients with high vascular burden who were inactive mainly in projection and association/limbic tracts with a right predominance. Additionally, higher FA was observed in physically active patients with high vascular burden as compared with physically inactive controls with high vascular burden, mainly in bilateral projection fibers and cerebellar regions. Conclusion Voxel-wise TBSS analysis revealed better preservation of white matter microstructure that was prominent in the high-risk group such as the patients with high vascular burden, specifically those who were physically active. The beneficial effects of physical activity on white matter microstructure were not observed in the controls.
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Affiliation(s)
- Srijan Konwar
- Department of Life Sciences, Brunel University London, Uxbridge, United Kingdom
| | - Riccardo Manca
- Department of Life Sciences, Brunel University London, Uxbridge, United Kingdom
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Matteo De Marco
- Department of Life Sciences, Brunel University London, Uxbridge, United Kingdom
| | - Hilkka Soininen
- Department of Neurology, University of Eastern Finland, Kuopio, Finland
| | - Annalena Venneri
- Department of Life Sciences, Brunel University London, Uxbridge, United Kingdom
- Department of Medicine and Surgery, University of Parma, Parma, Italy
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23
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He XY, Wu BS, Kuo K, Zhang W, Ma Q, Xiang ST, Li YZ, Wang ZY, Dong Q, Feng JF, Cheng W, Yu JT. Association between polygenic risk for Alzheimer's disease and brain structure in children and adults. Alzheimers Res Ther 2023; 15:109. [PMID: 37312172 DOI: 10.1186/s13195-023-01256-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 06/01/2023] [Indexed: 06/15/2023]
Abstract
BACKGROUND The correlations between genetic risk for Alzheimer's disease (AD) with comprehensive brain regions at a regional scale are still not well understood. We aim to explore whether these associations vary across different age stages. METHODS This study used large existing genome-wide association datasets to calculate polygenic risk score (PRS) for AD in two populations from the UK Biobank (N ~ 23 000) and Adolescent Brain Cognitive Development Study (N ~ 4660) who had multimodal macrostructural and microstructural magnetic resonance imaging (MRI) metrics. We used linear mixed-effect models to assess the strength of the association between AD PRS and multiple MRI metrics of regional brain structures at different stages of life. RESULTS Compared to those with lower PRSs, adolescents with higher PRSs had thinner cortex in the caudal anterior cingulate and supramarginal. In the middle-aged and elderly population, AD PRS had correlations with regional structure shrink primarily located in the cingulate, prefrontal cortex, hippocampus, thalamus, amygdala, and striatum, whereas the brain expansion was concentrated near the occipital lobe. Furthermore, both adults and adolescents with higher PRSs exhibited widespread white matter microstructural changes, indicated by decreased fractional anisotropy (FA) or increased mean diffusivity (MD). CONCLUSIONS In conclusion, our results suggest genetic loading for AD may influence brain structures in a highly dynamic manner, with dramatically different patterns at different ages. This age-specific change is consistent with the classical pattern of brain impairment observed in AD patients.
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Affiliation(s)
- Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Kevin Kuo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Qing Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Shi-Tong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Yu-Zhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Zi-Yi Wang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China
- ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China.
- ISTBI-ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, National Center for Neurological Disorders, Fudan University, Shanghai, 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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Demir A, Rosas HD. Exploring interhemispheric connectivity using the directional tract density patterns of the corpus callosum. NEUROIMAGE. REPORTS 2023; 3:100174. [PMID: 37388455 PMCID: PMC10310067 DOI: 10.1016/j.ynirp.2023.100174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
The corpus callosum (CC) is one of the most important interhemispheric white matter tracts that connects interrelated regions of the cerebral cortex. Its disruption has been investigated in previous studies and has been found to play an important role in several neurodegenerative disorders. Currently available methods to assess the interhemispheric connectivity of the CC have several limitations: i) they require the a priori identification of specific cortical regions as targets or seeds, ii) they are limited by the characterization of only small components of the structure, primarily voxels that constitute the mid-sagittal slice, and iii) they use global measures of microstructural integrity, which provide only limited characterization. In order to address some of these limitations, we developed a novel method that enables the characterization of white matter tracts covering the structure of CC, from the mid-sagittal plane to corresponding regions of cortex, using directional tract density patterns (dTDPs). We demonstrate that different regions of CC have distinctive dTDPs that reflect a unique regional topology. We conducted a pilot study using this approach to evaluate two different datasets collected from healthy subjects, and we demonstrate that this method is reliable, reproducible, and independent of diffusion acquisition parameters, suggesting its potential applicability to clinical applications.
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Affiliation(s)
- Ali Demir
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - H. Diana Rosas
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
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26
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Tian X, Liu Y, Wang L, Zeng X, Huang Y, Wang Z. An extensible hierarchical graph convolutional network for early Alzheimer's disease identification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 238:107597. [PMID: 37216716 DOI: 10.1016/j.cmpb.2023.107597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 04/30/2023] [Accepted: 05/10/2023] [Indexed: 05/24/2023]
Abstract
BACKGROUND AND OBJECTIVE For early identification of Alzheimer's disease (AD) based on multi-modal magnetic resonance imaging (MRI) data, it is important to make comprehensive use of image features and non-image information to analyze the gray matter atrophy and the structural/functional connectivity abnormalities for different courses of AD. METHODS In this study, we propose an extensible hierarchical graph convolutional network (EH-GCN) for early AD identification. Based on the extracted image features from multi-modal MRI data using the presented multi-branch residual network (ResNet), the brain regions-of-interests (ROIs) based GCN is built to extract structural and functional connectivity features between different ROIs of the brain. In order to further improve the performance of AD identification, an optimized spatial GCN is proposed as convolution operator in the population-based GCN to avoid rebuilding the graph network and take advantage of relationships between subjects. Finally, the proposed EH-GCN is built by embedding the image features and internal brain connectivity features into the spatial population-based GCN, which provides an extensible way to improve early AD identification performance by adding imaging features and non-image information from multi-modal data. RESULTS Experiments are performed on two datasets, which illustrate the effectiveness of the extracted structural/functional connectivity features and the high computational efficiency of the proposed method. The classification accuracy of AD vs NC, AD vs MCI and MCI vs NC classification tasks reaches 88.71%, 82.71% and 79.68% respectively. The extracted connectivity features between ROIs indicate that functional abnormalities are earlier than gray matter atrophy and abnormalities of structural connections, which is consistent with the clinical manifestations. The proposed method allows for the addition of other modal image features and non-image information from multi-modal data to continuously improve the performance of clinical data analysis. CONCLUSIONS The proposed method can help us comprehensively analyze the role of gray matter atrophy, the damage of white matter nerve fiber tracts and the degradation of functional connectivity for different courses of AD, which could be useful for further extraction of clinical biomarkers for early AD identification.
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Affiliation(s)
- Xu Tian
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Yan Liu
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China.
| | - Ling Wang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiangzhu Zeng
- Department of Radiology, Peking University Third Hospital, Beijing, China.
| | - Yulang Huang
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Zeng Wang
- Department of Radiology, Peking University Third Hospital, Beijing, China
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27
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Bingham KS, Calarco N, Dickie EW, Alexopoulos GS, Butters MA, Meyers BS, Marino P, Neufeld NH, Rothschild AJ, Whyte EM, Mulsant BH, Flint AJ, Voineskos AN. The relationship of white matter microstructure with psychomotor disturbance and relapse in remitted psychotic depression. J Affect Disord 2023; 334:317-324. [PMID: 37149056 DOI: 10.1016/j.jad.2023.04.136] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 04/06/2023] [Accepted: 04/29/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND Psychomotor disturbance is common in psychotic depression and is associated with relapse. In this analysis, we examined whether white matter microstructure is associated with relapse probability in psychotic depression and, if so, whether white matter microstructure accounts for the association between psychomotor disturbance and relapse. METHODS We used tractography to characterize diffusion-weighted MRI data in 80 participants enrolled in a randomized clinical trial that compared efficacy and tolerability of sertraline plus olanzapine with sertraline plus placebo in the continuation treatment of remitted psychotic depression. Cox proportional hazard models tested the relationships between psychomotor disturbance (processing speed and CORE score) at baseline, white matter microstructure (fractional anisotropy [FA] and mean diffusivity [MD]) in 15 selected tracts at baseline, and relapse probability. RESULTS CORE was significantly associated with relapse. Higher mean MD was significantly associated with relapse in the each of the following tracts: corpus callosum, left striato-frontal, left thalamo-frontal, and right thalamo-frontal. CORE and MD were each associated with relapse in the final models. LIMITATIONS As a secondary analysis with a small sample size, this study was not powered for its aims, and is vulnerable to types I and II statistical errors. Further, the sample size was not sufficient to test the interaction of the independent variables and randomized treatment group with relapse probability. CONCLUSIONS While both psychomotor disturbance and MD were associated with psychotic depression relapse, MD did not account for the relationship between psychomotor disturbance and relapse. The mechanism by which of psychomotor disturbance increases the risk of relapse requires further investigation. CLINICAL TRIAL REGISTRATION Study of the Pharmacotherapy of Psychotic Depression II (STOP-PD II); NCT01427608. URL: https://clinicaltrials.gov/ct2/show/NCT01427608.
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Affiliation(s)
- Kathleen S Bingham
- Centre for Mental Health, University Health Network, Toronto, Canada; Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada.
| | - Navona Calarco
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Erin W Dickie
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - George S Alexopoulos
- Department of Psychiatry, Weill Cornell Medicine of Cornell University and New York Presbyterian Hospital, Westchester Division, New York, USA
| | - Meryl A Butters
- Department of Psychiatry, University of Pittsburgh School of Medicine and UPMC Western Psychiatric Hospital, Pittsburgh, USA
| | - Barnett S Meyers
- Department of Psychiatry, Weill Cornell Medicine of Cornell University and New York Presbyterian Hospital, Westchester Division, New York, USA
| | - Patricia Marino
- Department of Psychiatry, Weill Cornell Medicine of Cornell University and New York Presbyterian Hospital, Westchester Division, New York, USA
| | - Nicholas H Neufeld
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Anthony J Rothschild
- University of Massachusetts Chan Medical School and UMass Memorial Health Care, Worcester, USA
| | - Ellen M Whyte
- Department of Psychiatry, University of Pittsburgh School of Medicine and UPMC Western Psychiatric Hospital, Pittsburgh, USA
| | - Benoit H Mulsant
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Alastair J Flint
- Centre for Mental Health, University Health Network, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
| | - Aristotle N Voineskos
- Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Canada
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28
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Jain P, Yates M, de Celis CR, Drineas P, Jahanshad N, Thompson P, Paschou P. Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.30.23287968. [PMID: 37066330 PMCID: PMC10104218 DOI: 10.1101/2023.03.30.23287968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2,994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes. We found causal associations between amygdala volume and granzyme A as well as association between accumbens volume and plasma protease c1 inhibitor. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.
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Affiliation(s)
- Pritesh Jain
- Department of Biological Sciences, Purdue University
| | - Madison Yates
- Department of Biological Sciences, Purdue University
| | | | | | - Neda Jahanshad
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of South California
| | - Paul Thompson
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of South California
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29
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Yang Y, Schilling K, Shashikumar N, Jasodanand V, Moore EE, Pechman KR, Bilgel M, Beason‐Held LL, An Y, Shafer A, Risacher SL, Landman BA, Jefferson AL, Saykin AJ, Resnick SM, Hohman TJ, Archer DB. White matter microstructural metrics are sensitively associated with clinical staging in Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12425. [PMID: 37213219 PMCID: PMC10192723 DOI: 10.1002/dad2.12425] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 03/06/2023] [Accepted: 03/12/2023] [Indexed: 05/23/2023]
Abstract
Introduction White matter microstructure may be abnormal along the Alzheimer's disease (AD) continuum. Methods Diffusion magnetic resonance imaging (dMRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 627), Baltimore Longitudinal Study of Aging (BLSA, n = 684), and Vanderbilt Memory & Aging Project (VMAP, n = 296) cohorts were free-water (FW) corrected and conventional, and FW-corrected microstructural metrics were quantified within 48 white matter tracts. Microstructural values were subsequently harmonized using the Longitudinal ComBat technique and inputted as independent variables to predict diagnosis (cognitively unimpaired [CU], mild cognitive impairment [MCI], AD). Models were adjusted for age, sex, race/ethnicity, education, apolipoprotein E (APOE) ε4 carrier status, and APOE ε2 carrier status. Results Conventional dMRI metrics were associated globally with diagnostic status; following FW correction, the FW metric itself exhibited global associations with diagnostic status, but intracellular metric associations were diminished. Discussion White matter microstructure is altered along the AD continuum. FW correction may provide further understanding of the white matter neurodegenerative process in AD. Highlights Longitudinal ComBat successfully harmonized large-scale diffusion magnetic resonance imaging (dMRI) metrics.Conventional dMRI metrics were globally sensitive to diagnostic status.Free-water (FW) correction mitigated intracellular associations with diagnostic status.The FW metric itself was globally sensitive to diagnostic status. Multivariate conventional and FW-corrected models may provide complementary information.
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Affiliation(s)
- Yisu Yang
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
| | - 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
| | - Shannon L. Risacher
- Indiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer's Disease Research CenterIndianapolisIndianaUSA
| | - Bennett A. Landman
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- 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
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
- 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
| | - Derek B. Archer
- Vanderbilt Memory and Alzheimer's CenterVanderbilt University School of MedicineNashvilleTennesseeUSA
- Vanderbilt Genetics InstituteVanderbilt University Medical CenterNashvilleTennesseeUSA
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30
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Liu Q, Zhang X. Multimodality neuroimaging in vascular mild cognitive impairment: A narrative review of current evidence. Front Aging Neurosci 2023; 15:1073039. [PMID: 37009448 PMCID: PMC10050753 DOI: 10.3389/fnagi.2023.1073039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 02/24/2023] [Indexed: 03/17/2023] Open
Abstract
The vascular mild cognitive impairment (VaMCI) is generally accepted as the premonition stage of vascular dementia (VaD). However, most studies are focused mainly on VaD as a diagnosis in patients, thus neglecting the VaMCI stage. VaMCI stage, though, is easily diagnosed by vascular injuries and represents a high-risk period for the future decline of patients’ cognitive functions. The existing studies in China and abroad have found that magnetic resonance imaging technology can provide imaging markers related to the occurrence and development of VaMCI, which is an important tool for detecting the changes in microstructure and function of VaMCI patients. Nevertheless, most of the existing studies evaluate the information of a single modal image. Due to the different imaging principles, the data provided by a single modal image are limited. In contrast, multi-modal magnetic resonance imaging research can provide multiple comprehensive data such as tissue anatomy and function. Here, a narrative review of published articles on multimodality neuroimaging in VaMCI diagnosis was conducted,and the utilization of certain neuroimaging bio-markers in clinical applications was narrated. These markers include evaluation of vascular dysfunction before tissue damages and quantification of the extent of network connectivity disruption. We further provide recommendations for early detection, progress, prompt treatment response of VaMCI, as well as optimization of the personalized treatment plan.
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Affiliation(s)
- Qiuping Liu
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
- Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xuezhu Zhang
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
- *Correspondence: Xuezhu Zhang,
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31
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Haddad SMH, Pieruccini-Faria F, Montero-Odasso M, Bartha R. Localized White Matter Tract Integrity Measured by Diffusion Tensor Imaging Is Altered in People with Mild Cognitive Impairment and Associated with Dual-Task and Single-Task Gait Speed. J Alzheimers Dis 2023; 92:1367-1384. [PMID: 36911933 DOI: 10.3233/jad-220476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
BACKGROUND Altered white matter (WM) tract integrity may contribute to mild cognitive impairment (MCI) and gait abnormalities. OBJECTIVE The purpose of this study was to determine whether diffusion tensor imaging (DTI) metrics were altered in specific portions of WM tracts in people with MCI and to determine whether gait speed variations were associated with the specific DTI metric changes. METHODS DTI was acquired in 44 people with MCI and 40 cognitively normal elderly controls (CNCs). Fractional anisotropy (FA) and radial diffusivity (RD) were measured along 18 major brain WM tracts using probabilistic tractography. The average FA and RD along the tracts were compared between the groups using MANCOVA and post-hoc tests. The tracts with FA or RD differences between the groups were examined using an along-tract exploratory analysis to identify locations that differed between the groups. Associations between FA and RD in whole tracts and in the segments of the tracts that differed between the groups and usual/dual-task gait velocities and gross cognition were examined. RESULTS Lower FA and higher RD was observed in right cingulum-cingulate gyrus endings (rh.ccg) of the MCI group compared to the CNC group. These changes were localized to the posterior portions of the rh.ccg and correlated with gait velocities. CONCLUSION Lower FA and higher RD in the posterior portion of the rh.ccg adjacent to the posterior cingulate suggests decreased microstructural integrity in the MCI group. The correlation of these metrics with gait velocities suggests an important role for this tract in maintaining normal cognitive-motor function.
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Affiliation(s)
- Seyyed M H Haddad
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada
| | - Frederico Pieruccini-Faria
- Department of Medicine, Division of Geriatric Medicine, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada.,Gait and Brain Lab, Parkwood Institute, Lawson Health Research Institute, London, Canada
| | - Manuel Montero-Odasso
- Department of Medicine, Division of Geriatric Medicine, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada.,Gait and Brain Lab, Parkwood Institute, Lawson Health Research Institute, London, Canada.,Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, Canada
| | - Robert Bartha
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, University of Western Ontario, London, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, University of Western Ontario, London, Canada
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32
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Takeuchi H, Kawashima R. Effects of television viewing on brain structures and risk of dementia in the elderly: Longitudinal analyses. Front Neurosci 2023; 17:984919. [PMID: 36968501 PMCID: PMC10030518 DOI: 10.3389/fnins.2023.984919] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 02/16/2023] [Indexed: 03/10/2023] Open
Abstract
IntroductionTV viewing in the elderly and in children is associated with subsequent greater decline of various cognitive functions including verbal working memory, but results of its association with subsequent risk of dementia were divided.MethodsIn this longitudinal cohort study of UK Biobank, we investigated the associations of TV viewing length with subsequent risk of dementia and longitudinal changes of brain structural measures after corrections of a wide range of potential confounders.ResultsOur results showed longer TV viewing was associated with increased risk of subsequent onset of dementia, as well as subsequent greater decline in intracellular volume fraction (ICVF) in the extensive areas of right lateral temporal cortex and the right medial temporal cortex, in the area around the left middle and inferior temporal cortex as well as the left fusiform gyrus, and the area adjacent to the left inferior frontal gyrus, and left insula.DiscussionThese results may suggest prolonged TV viewing was associated with decline in density of neurites (axon, dendrites) in areas particularly implicated in language, communication, and memory, which are altered in dementia.
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Affiliation(s)
- Hikaru Takeuchi
- Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- *Correspondence: Hikaru Takeuchi,
| | - Ryuta Kawashima
- Division of Developmental Cognitive Neuroscience, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
- Smart Aging Research Center, Tohoku University, Sendai, Japan
- Department of Advanced Brain Science, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
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33
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Raunig DL, Pennello GA, Delfino JG, Buckler AJ, Hall TJ, Guimaraes AR, Wang X, Huang EP, Barnhart HX, deSouza N, Obuchowski N. Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap. Acad Radiol 2023; 30:159-182. [PMID: 36464548 PMCID: PMC9825667 DOI: 10.1016/j.acra.2022.10.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 10/24/2022] [Accepted: 10/29/2022] [Indexed: 12/02/2022]
Abstract
Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.
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Affiliation(s)
- David L Raunig
- Department of Statistical and Quantitative Sciences, Data Science Institute, Takeda Pharmaceuticals, Cambridge, Massachusetts.
| | - Gene A Pennello
- Center for Devices and Radiological Health, US Food and Drug Administration Division of Imaging, Diagnostic and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | - Jana G Delfino
- Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland
| | | | - Timothy J Hall
- Department of Medical Physics, University of Wisconsin, Madison, Wisconsin
| | - Alexander R Guimaraes
- Department of Diagnostic Radiology, Oregon Health & Sciences University, Portland, Oregon
| | - Xiaofeng Wang
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland, Ohio
| | - Erich P Huang
- Biometric Research Program, Division of Cancer Treatment and Diagnosis - National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Huiman X Barnhart
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - Nandita deSouza
- Division of Radiotherapy and Imaging, the Insitute of Cancer Research and Royal Marsden NHS Foundation Trust, London, United Kingdom
| | - Nancy Obuchowski
- Department of Quantitative Health Sciences, Lerner Research Institute Cleveland Clinic Foundation, Cleveland, Ohio
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Ottoy J, Ozzoude M, Zukotynski K, Kang MS, Adamo S, Scott C, Ramirez J, Swardfager W, Lam B, Bhan A, Mojiri P, Kiss A, Strother S, Bocti C, Borrie M, Chertkow H, Frayne R, Hsiung R, Laforce RJ, Noseworthy MD, Prato FS, Sahlas DJ, Smith EE, Kuo PH, Chad JA, Pasternak O, Sossi V, Thiel A, Soucy JP, Tardif JC, Black SE, Goubran M. Amyloid-PET of the white matter: Relationship to free water, fiber integrity, and cognition in patients with dementia and small vessel disease. J Cereb Blood Flow Metab 2023; 43:921-936. [PMID: 36695071 DOI: 10.1177/0271678x231152001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
White matter (WM) injury is frequently observed along with dementia. Positron emission tomography with amyloid-ligands (Aβ-PET) recently gained interest for detecting WM injury. Yet, little is understood about the origin of the altered Aβ-PET signal in WM regions. Here, we investigated the relative contributions of diffusion MRI-based microstructural alterations, including free water and tissue-specific properties, to Aβ-PET in WM and to cognition. We included a unique cohort of 115 participants covering the spectrum of low-to-severe white matter hyperintensity (WMH) burden and cognitively normal to dementia. We applied a bi-tensor diffusion-MRI model that differentiates between (i) the extracellular WM compartment (represented via free water), and (ii) the fiber-specific compartment (via free water-adjusted fractional anisotropy [FA]). We observed that, in regions of WMH, a decrease in Aβ-PET related most closely to higher free water and higher WMH volume. In contrast, in normal-appearing WM, an increase in Aβ-PET related more closely to higher cortical Aβ (together with lower free water-adjusted FA). In relation to cognitive impairment, we observed a closer relationship with higher free water than with either free water-adjusted FA or WM PET. Our findings support free water and Aβ-PET as markers of WM abnormalities in patients with mixed dementia, and contribute to a better understanding of processes giving rise to the WM PET signal.
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Affiliation(s)
- Julie Ottoy
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Miracle Ozzoude
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Katherine Zukotynski
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Departments of Medicine and Radiology, McMaster University, Hamilton, ON, Canada.,Department of Medical Imaging, Schulich School of Medicine & Dentistry, Western University, London, ON, Canada.,Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Min Su Kang
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sabrina Adamo
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Christopher Scott
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Joel Ramirez
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Walter Swardfager
- Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada
| | - Benjamin Lam
- Department of Medicine (Division of Neurology), Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Aparna Bhan
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Parisa Mojiri
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Alex Kiss
- Department of Research Design and Biostatistics, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Stephen Strother
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,The Rotman Research Institute Baycrest, University of Toronto, Toronto, ON, Canada
| | - Christian Bocti
- Service de Neurologie, Département de Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Michael Borrie
- Lawson Health Research Institute, Western University, London, ON, Canada
| | - Howard Chertkow
- Jewish General Hospital and Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Richard Frayne
- Departments of Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Robin Hsiung
- Physics and Astronomy Department and DM Center for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Robert Jr Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, Université Laval, Québec, QC, Canada
| | - Michael D Noseworthy
- Departments of Medicine and Radiology, McMaster University, Hamilton, ON, Canada.,Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada
| | - Frank S Prato
- Lawson Health Research Institute, Western University, London, ON, Canada
| | | | - Eric E Smith
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Phillip H Kuo
- Department of Medical Imaging, Medicine, and Biomedical Engineering, University of Arizona, Tucson, AZ, USA
| | - Jordan A Chad
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,The Rotman Research Institute Baycrest, University of Toronto, Toronto, ON, Canada
| | - Ofer Pasternak
- Departments of Psychiatry and Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Vesna Sossi
- Physics and Astronomy Department and DM Center for Brain Health, University of British Columbia, Vancouver, BC, Canada
| | - Alexander Thiel
- Jewish General Hospital and Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jean-Paul Soucy
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | | | - Sandra E Black
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medicine (Division of Neurology), Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Research Program, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Maged Goubran
- LC Campbell Cognitive Neurology Unit, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Physical Sciences Platform, Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
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35
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Loftus JR, Puri S, Meyers SP. Multimodality imaging of neurodegenerative disorders with a focus on multiparametric magnetic resonance and molecular imaging. Insights Imaging 2023; 14:8. [PMID: 36645560 PMCID: PMC9842851 DOI: 10.1186/s13244-022-01358-6] [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: 08/17/2022] [Accepted: 12/13/2022] [Indexed: 01/17/2023] Open
Abstract
Neurodegenerative diseases afflict a large number of persons worldwide, with the prevalence and incidence of dementia rapidly increasing. Despite their prevalence, clinical diagnosis of dementia syndromes remains imperfect with limited specificity. Conventional structural-based imaging techniques also lack the accuracy necessary for confident diagnosis. Multiparametric magnetic resonance imaging and molecular imaging provide the promise of improving specificity and sensitivity in the diagnosis of neurodegenerative disease as well as therapeutic monitoring of monoclonal antibody therapy. This educational review will briefly focus on the epidemiology, clinical presentation, and pathologic findings of common and uncommon neurodegenerative diseases. Imaging features of each disease spanning from conventional magnetic resonance sequences to advanced multiparametric methods such as resting-state functional magnetic resonance imaging and arterial spin labeling imaging will be described in detail. Additionally, the review will explore the findings of each diagnosis on molecular imaging including single-photon emission computed tomography and positron emission tomography with a variety of clinically used and experimental radiotracers. The literature and clinical cases provided demonstrate the power of advanced magnetic resonance imaging and molecular techniques in the diagnosis of neurodegenerative diseases and areas of future and ongoing research. With the advent of combined positron emission tomography/magnetic resonance imaging scanners, hybrid protocols utilizing both techniques are an attractive option for improving the evaluation of neurodegenerative diseases.
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Affiliation(s)
- James Ryan Loftus
- grid.412750.50000 0004 1936 9166Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642 USA
| | - Savita Puri
- grid.412750.50000 0004 1936 9166Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642 USA
| | - Steven P. Meyers
- grid.412750.50000 0004 1936 9166Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY 14642 USA
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36
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Liu S, Liu Y, Xu X, Chen R, Liang D, Jin Q, Liu H, Chen G, Zhu Y. Accelerated cardiac diffusion tensor imaging using deep neural network. Phys Med Biol 2023; 68. [PMID: 36595239 DOI: 10.1088/1361-6560/acaa86] [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: 05/23/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022]
Abstract
Cardiac diffusion tensor imaging (DTI) is a noninvasive method for measuring the microstructure of the myocardium. However, its long scan time significantly hinders its wide application. In this study, we developed a deep learning framework to obtain high-quality DTI parameter maps from six diffusion-weighted images (DWIs) by combining deep-learning-based image generation and tensor fitting, and named the new framework FG-Net. In contrast to frameworks explored in previous deep-learning-based fast DTI studies, FG-Net generates inter-directional DWIs from six input DWIs to supplement the loss information and improve estimation accuracy for DTI parameters. FG-Net was evaluated using two datasets ofex vivohuman hearts. The results showed that FG-Net can generate fractional anisotropy, mean diffusivity maps, and helix angle maps from only six raw DWIs, with a quantification error of less than 5%. FG-Net outperformed conventional tensor fitting and black-box network fitting in both qualitative and quantitative metrics. We also demonstrated that the proposed FG-Net can achieve highly accurate fractional anisotropy and helix angle maps in DWIs with differentb-values.
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Affiliation(s)
- Shaonan Liu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China.,Department of Computer Science, Inner Mongolia University, Hohhot, People's Republic of China
| | - Yuanyuan Liu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Xi Xu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Rui Chen
- Department of Radiology, Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China
| | - Dong Liang
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China
| | - Qiyu Jin
- Department of Mathematical Science, Inner Mongolia University, Hohhot, People's Republic of China
| | - Hui Liu
- Department of Radiology, Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences, Guangzhou, People's Republic of China
| | - Guoqing Chen
- Department of Mathematical Science, Inner Mongolia University, Hohhot, People's Republic of China
| | - Yanjie Zhu
- Paul C. Lauterbur Research Centre for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, People's Republic of China.,National Center for Applied Mathematics Shenzhen, Shenzhen, Guangdong, People's Republic of China
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Shih NC, Kurniawan ND, Cabeen RP, Korobkova L, Wong E, Chui HC, Clark KA, Miller CA, Hawes D, Jones KT, Sepehrband F. Microstructural mapping of dentate gyrus pathology in Alzheimer's disease: A 16.4 Tesla MRI study. Neuroimage Clin 2023; 37:103318. [PMID: 36630864 PMCID: PMC9841366 DOI: 10.1016/j.nicl.2023.103318] [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: 08/10/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/07/2023]
Abstract
The dentate gyrus (DG) is an integral portion of the hippocampal formation, and it is composed of three layers. Quantitative magnetic resonance (MR) imaging has the capability to map brain tissue microstructural properties which can be exploited to investigate neurodegeneration in Alzheimer's disease (AD). However, assessing subtle pathological changes within layers requires high resolution imaging and histological validation. In this study, we utilized a 16.4 Tesla scanner to acquire ex vivo multi-parameter quantitative MRI measures in human specimens across the layers of the DG. Using quantitative diffusion tensor imaging (DTI) and multi-parameter MR measurements acquired from AD (N = 4) and cognitively normal control (N = 6) tissues, we performed correlation analyses with histological measurements. Here, we found that quantitative MRI measures were significantly correlated with neurofilament and phosphorylated Tau density, suggesting sensitivity to layer-specific changes in the DG of AD tissues.
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Affiliation(s)
- Nien-Chu Shih
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Nyoman D Kurniawan
- Center for Advanced Imaging, The University of Queensland, Brisbane 4072, Australia
| | - Ryan P Cabeen
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Laura Korobkova
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90089. USA
| | - Ellen Wong
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Department of Neurology, Rancho Los Amigos National Rehabilitation Center, Downey, CA 90242, USA
| | - Helena C Chui
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Kristi A Clark
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Carol A Miller
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Debra Hawes
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Department of Pathology and Laboratory Medicine, Children's Hospital of Los Angeles, Los Angeles, CA 90033, USA
| | - Kymry T Jones
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
| | - Farshid Sepehrband
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
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Subramanyam Rallabandi V, Seetharaman K. Classification of cognitively normal controls, mild cognitive impairment and Alzheimer’s disease using transfer learning approach. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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39
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Mendez Colmenares A, Hefner MB, Calhoun VD, Salerno EA, Fanning J, Gothe NP, McAuley E, Kramer AF, Burzynska AZ. Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter. Front Neurol 2023; 14:1094313. [PMID: 37139071 PMCID: PMC10149813 DOI: 10.3389/fneur.2023.1094313] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 03/24/2023] [Indexed: 05/05/2023] Open
Abstract
In the past 20 years, white matter (WM) microstructure has been studied predominantly using diffusion tensor imaging (DTI). Decreases in fractional anisotropy (FA) and increases in mean (MD) and radial diffusivity (RD) have been consistently reported in healthy aging and neurodegenerative diseases. To date, DTI parameters have been studied individually (e.g., only FA) and separately (i.e., without using the joint information across them). This approach gives limited insights into WM pathology, increases the number of multiple comparisons, and yields inconsistent correlations with cognition. To take full advantage of the information in a DTI dataset, we present the first application of symmetric fusion to study healthy aging WM. This data-driven approach allows simultaneous examination of age differences in all four DTI parameters. We used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) in cognitively healthy adults (age 20-33, n = 51 and age 60-79, n = 170). Four-way mCCA + jICA yielded one high-stability modality-shared component with co-variant patterns of age differences in RD and AD in the corpus callosum, internal capsule, and prefrontal WM. The mixing coefficients (or loading parameters) showed correlations with processing speed and fluid abilities that were not detected by unimodal analyses. In sum, mCCA + jICA allows data-driven identification of cognitively relevant multimodal components within the WM. The presented method should be further extended to clinical samples and other MR techniques (e.g., myelin water imaging) to test the potential of mCCA+jICA to discriminate between different WM disease etiologies and improve the diagnostic classification of WM diseases.
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Affiliation(s)
- Andrea Mendez Colmenares
- BRAiN Laboratory, Department of Human Development and Family Studies, Colorado State University, Fort Collins, CO, United States
- Molecular, Cellular and Integrative Neurosciences, Colorado State University, Fort Collins, CO, United States
| | - Michelle B. Hefner
- BRAiN Laboratory, Department of Human Development and Family Studies, Colorado State University, Fort Collins, CO, United States
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, United States
| | - Elizabeth A. Salerno
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine in St. Louis, St. Louis, MO, United States
| | - Jason Fanning
- Department of Health and Exercise Sciences, Wake Forest University, Winston-Salem, NC, United States
| | - Neha P. Gothe
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Edward McAuley
- Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Arthur F. Kramer
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- Department of Psychology, Northeastern University, Boston, MA, United States
| | - Agnieszka Z. Burzynska
- BRAiN Laboratory, Department of Human Development and Family Studies, Colorado State University, Fort Collins, CO, United States
- Molecular, Cellular and Integrative Neurosciences, Colorado State University, Fort Collins, CO, United States
- Department of Human Development and Family Studies, Colorado State University, Fort Collins, CO, United States
- *Correspondence: Agnieszka Z. Burzynska,
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40
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Giraldo DL, Smith RE, Struyfs H, Niemantsverdriet E, De Roeck E, Bjerke M, Engelborghs S, Romero E, Sijbers J, Jeurissen B. Investigating Tissue-Specific Abnormalities in Alzheimer's Disease with Multi-Shell Diffusion MRI. J Alzheimers Dis 2022; 90:1771-1791. [PMID: 36336929 PMCID: PMC9789487 DOI: 10.3233/jad-220551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Most studies using diffusion-weighted MRI (DW-MRI) in Alzheimer's disease (AD) have focused their analyses on white matter (WM) microstructural changes using the diffusion (kurtosis) tensor model. Although recent works have addressed some limitations of the tensor model, such as the representation of crossing fibers and partial volume effects with cerebrospinal fluid (CSF), the focus remains in modeling and analyzing the WM. OBJECTIVE In this work, we present a brain analysis approach for DW-MRI that disentangles multiple tissue compartments as well as micro- and macroscopic effects to investigate differences between groups of subjects in the AD continuum and controls. METHODS By means of the multi-tissue constrained spherical deconvolution of multi-shell DW-MRI, underlying brain tissue is modeled with a WM fiber orientation distribution function along with the contributions of gray matter (GM) and CSF to the diffusion signal. From this multi-tissue model, a set of measures capturing tissue diffusivity properties and morphology are extracted. Group differences were interrogated following fixel-, voxel-, and tensor-based morphometry approaches while including strong FWE control across multiple comparisons. RESULTS Abnormalities related to AD stages were detected in WM tracts including the splenium, cingulum, longitudinal fasciculi, and corticospinal tract. Changes in tissue composition were identified, particularly in the medial temporal lobe and superior longitudinal fasciculus. CONCLUSION This analysis framework constitutes a comprehensive approach allowing simultaneous macro and microscopic assessment of WM, GM, and CSF, from a single DW-MRI dataset.
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Affiliation(s)
- Diana L. Giraldo
- Computer Imaging and Medical Applications Laboratory - Cim@Lab, Universidad Nacional de Colombia, Bogotá, Colombia,imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium,μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Robert E. Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia,The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Hanne Struyfs
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Ellen De Roeck
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Maria Bjerke
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium,Laboratory of Neurochemistry, Department of Clinical Chemistry, and Center for Neurosciences (C4N), Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium,Department of Neurology, and Center for Neurosciences (C4N), Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Eduardo Romero
- Computer Imaging and Medical Applications Laboratory - Cim@Lab, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Jan Sijbers
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium,μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium,μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium,Lab for Equilibrium Investigations and Aerospace, Department of Physics, University of Antwerp, Antwerp, Belgium,Correspondence to: Ben Jeurissen, PhD, imec - Vision Lab, Department of Physics, University of Antwerp (CDE), Universiteitsplein 1, Building N, 2610 Antwerp, Belgium. Tel.: +32 3 265 24 77; E-mail:
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David S, Brown LL, Heemskerk AM, Aron E, Leemans A, Aron A. Sensory processing sensitivity and axonal microarchitecture: identifying brain structural characteristics for behavior. Brain Struct Funct 2022; 227:2769-2785. [PMID: 36151482 PMCID: PMC9618477 DOI: 10.1007/s00429-022-02571-1] [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: 11/25/2021] [Accepted: 09/08/2022] [Indexed: 11/25/2022]
Abstract
Previous research using functional MRI identified brain regions associated with sensory processing sensitivity (SPS), a proposed normal phenotype trait. To further validate SPS, to characterize it anatomically, and to test the usefulness in psychology of methodologies that assess axonal properties, the present study correlated SPS proxy questionnaire scores (adjusted for neuroticism) with diffusion tensor imaging (DTI) measures. Participants (n = 408) from the Human Connectome Project were studied. Voxelwise analysis showed that mean- and radial diffusivity correlated positively with SPS scores in the right and left subcallosal and anterior-ventral cingulum bundle, and the right forceps minor of the corpus callosum, all frontal cortex areas generally underlying emotion, motivation, and cognition. Further analyses showed correlations throughout medial frontal cortical regions in the right and left ventromedial prefrontal cortex, including the superior longitudinal fasciculus, inferior fronto-occipital fasciculus, uncinate, and arcuate fasciculus. Fractional anisotropy was negatively correlated with SPS scores in white matter (WM) of the right premotor/motor/somatosensory/supramarginal gyrus regions. Region of interest (ROI) analysis showed small effect sizes (- 0.165 to 0.148) in WM of the precuneus and inferior frontal gyrus. Other ROI effects were found in the dorsal-, ventral visual pathways and primary auditory cortex. The results reveal that in a large group of participants, axonal microarchitectural differences can be identified with SPS traits that are subtle and in the range of typical behavior. The results suggest that the heightened sensory processing in people who show that SPS may be influenced by the microstructure of WM in specific cortical regions. Although previous fMRI studies had identified most of these areas, the DTI results put a new focus on brain areas related to attention and cognitive flexibility, empathy, emotion, and first levels of sensory processing, as in primary auditory cortex. Psychological trait characterization may benefit from DTI methodology by identifying influential brain systems for traits.
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Affiliation(s)
- Szabolcs David
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Lucy L Brown
- Department of Neurology, Einstein College of Medicine, Bronx, NY, USA
| | - Anneriet M Heemskerk
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Elaine Aron
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Arthur Aron
- Department of Psychology, Stony Brook University, Stony Brook, NY, USA
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42
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Dong A, Zhang G, Liu J, Wei Z. Latent feature representation learning for Alzheimer's disease classification. Comput Biol Med 2022; 150:106116. [PMID: 36215848 DOI: 10.1016/j.compbiomed.2022.106116] [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: 04/25/2022] [Revised: 08/18/2022] [Accepted: 09/17/2022] [Indexed: 11/03/2022]
Abstract
Early detection and treatment of Alzheimer's Disease (AD) are significant. Recently, multi-modality imaging data have promoted the development of the automatic diagnosis of AD. This paper proposes a method based on latent feature fusion to make full use of multi-modality image data information. Specifically, we learn a specific projection matrix for each modality by introducing a binary label matrix and local geometry constraints and then project the original features of each modality into a low-dimensional target space. In this space, we fuse latent feature representations of different modalities for AD classification. The experimental results on Alzheimer's Disease Neuroimaging Initiative database demonstrate the proposed methods effectiveness in classifying AD.
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Affiliation(s)
- Aimei Dong
- Faculty of Computer Science and Technology,Qilu University of Technology(Shandong Academy of Sciences),Jinan, 250353, China.
| | - Guodong Zhang
- Faculty of Computer Science and Technology,Qilu University of Technology(Shandong Academy of Sciences),Jinan, 250353, China.
| | - Jian Liu
- Faculty of Computer Science and Technology,Qilu University of Technology(Shandong Academy of Sciences),Jinan, 250353, China.
| | - Zhonghe Wei
- Faculty of Computer Science and Technology,Qilu University of Technology(Shandong Academy of Sciences),Jinan, 250353, China.
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43
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Xiao D, Wang K, Theriault L, Charbel E. White matter integrity and key structures affected in Alzheimer's disease characterized by diffusion tensor imaging. Eur J Neurosci 2022; 56:5319-5331. [PMID: 36048971 DOI: 10.1111/ejn.15815] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 08/13/2022] [Accepted: 08/22/2022] [Indexed: 12/14/2022]
Abstract
White matter (WM) degeneration is suggested to predict the early signs of Alzheimer's disease (AD). The exact structural regions of brain circuitry involved are not known. This study aims to examine the associations between WM tract integrity, represented by the diffusion tensor imaging (DTI) measures, and AD diagnosis and to denote the key substrates in predicting AD. It included DTI measures of mean diffusivity (MD), fractional anisotropy, radial diffusivity and axial diffusivity of 18 main WM tracts in 84 non-Hispanic white participants from the Alzheimer's Disease Neuroimaging Initiative dataset. The multivariable general linear model was used to examine the association of AD diagnosis with each DTI measure adjusting for age, gender and education. The corpus callosum, fornix, cingulum hippocampus, uncinate fasciculus, sagittal striatum, left posterior thalamic radiation and fornix-stria terminalis showed significant increases in MD, radial and axial diffusivity, whereas the splenium of corpus callosum and the fornix showed significant decreases in fractional anisotropy among AD patients. Variable cluster analysis identified that hippocampus volume, mini-mental state examination (MMSE), cingulate gyrus/hippocampus, inferior fronto-occipital fasciculus and uncinate fasciculus are highly correlated in one cluster with MD measures. In conclusion, there were significant differences in DTI measures between the brain WM of AD patients and controls. Age is the risk factor associated with AD, not gender or education. Right cingulum gyrus and right uncinate fasciculus are particularly affected, correlating well with a cognitive test MMSE and MD measures for dementia in AD patients and could be a region of focus for AD staging.
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Affiliation(s)
- Danqing Xiao
- Department of STEM, School of Arts and Sciences, Regis College, Weston, Massachusetts, USA.,Neuroimaging Center, McLean Hospital, Belmont, Massachusetts, USA
| | - Kesheng Wang
- Department of Family and Community Health, School of Nursing, Health Sciences Center, West Virginia University, Morgantown, West Virginia, USA
| | - Luke Theriault
- Department of STEM, School of Arts and Sciences, Regis College, Weston, Massachusetts, USA.,School of Medicine, St. George's University, Saint George's, Grenada
| | - Elhelou Charbel
- Department of STEM, School of Arts and Sciences, Regis College, Weston, Massachusetts, USA
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Maharjan S, Tsai AP, Lin PB, Ingraham C, Jewett MR, Landreth GE, Oblak AL, Wang N. Age-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging. Front Neurosci 2022; 16:964654. [PMID: 36061588 PMCID: PMC9428354 DOI: 10.3389/fnins.2022.964654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 07/18/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose To evaluate the age-dependent microstructure changes in 5xFAD mice using high-resolution diffusion tensor imaging (DTI). Methods The 5xFAD mice at 4, 7.5, and 12 months and the wild-type controls at 4 months were scanned at 9.4T using a 3D echo-planar imaging (EPI) pulse sequence with the isotropic spatial resolution of 100 μm. The b-value was 3000 s/mm2 for all the diffusion MRI scans. The samples were also acquired with a gradient echo pulse sequence at 50 μm isotropic resolution. The microstructure changes were quantified with DTI metrics, including fractional anisotropy (FA) and mean diffusivity (MD). The conventional histology was performed to validate with MRI findings. Results The FA values (p = 0.028) showed significant differences in the cortex between wild-type (WT) and 5xFAD mice at 4 months, while hippocampus, anterior commissure, corpus callosum, and fornix showed no significant differences for either FA and MD. FA values of 5xFAD mice gradually decreased in cortex (0.140 ± 0.007 at 4 months, 0.132 ± 0.008 at 7.5 months, 0.126 ± 0.013 at 12 months) and fornix (0.140 ± 0.007 at 4 months, 0.132 ± 0.008 at 7.5 months, 0.126 ± 0.013 at 12 months) with aging. Both FA (p = 0.029) and MD (p = 0.037) demonstrated significant differences in corpus callosum between 4 and 12 months age old. FA and MD were not significantly different in the hippocampus or anterior commissure. The age-dependent microstructure alterations were better captured by FA when compared to MD. Conclusion FA showed higher sensitivity to monitor amyloid deposition in 5xFAD mice. DTI may be utilized as a sensitive biomarker to monitor beta-amyloid progression for preclinical studies.
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Affiliation(s)
- Surendra Maharjan
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, United States
| | - Andy P. Tsai
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, United States
| | - Peter B. Lin
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, United States
| | - Cynthia Ingraham
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, United States
| | - Megan R. Jewett
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, United States
| | - Gary E. Landreth
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, United States
- Department of Anatomy, Cell Biology and Physiology, Indiana University, Indianapolis, IN, United States
| | - Adrian L. Oblak
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, United States
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, United States
| | - Nian Wang
- Department of Radiology and Imaging Sciences, Indiana University, Indianapolis, IN, United States
- Stark Neurosciences Research Institute, Indiana University, Indianapolis, IN, United States
- *Correspondence: Nian Wang,
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Li C, Li Y, Wu J, Wu M, Peng F, Chao Q. Triple Network Model-Based Analysis on Abnormal Core Brain Functional Network Dynamics in Different Stage of Amnestic Mild Cognitive Impairment. J Alzheimers Dis 2022; 89:519-533. [DOI: 10.3233/jad-220282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Amnestic mild cognitive impairment (aMCI) is considered to be a transitional stage of Alzheimer’s disease (AD) because it has the same clinical symptoms as AD but with lower severity. Studies have confirmed that patients with aMCI are more likely to develop to AD. Although studies on resting state functional connectivity have revealed the abnormal organization of brain networks, the dynamic changes of the functional connectivity across the scans have been ignored. Objective: Dynamic functional connectivity is a novel method to reveal the temporal variation of brain networks. This paper aimed to investigate the dynamic characteristics of brain functional connectivity in the early and late phases of aMCI. Methods: Based on the “triple network” model, we used the sliding time window approach to construct dynamical functional networks and then analyzed the dynamic characteristics of the functional connectivity across the entire scan. Results: The results showed that patients with aMCI had longer dwell times in weaker network connection than in the strong network. The transitions between different states become more frequent, and the stability of the patient’s brain core network deteriorates. This study also found the correlation between the altered dynamic properties of the core functional networks and the patient’s clinical Mini-Mental State Examination assessment scale sores. Conclusion: This study revealed that the characteristics of dynamic functional networks constructed by the core cognitive networks varied in distinct ways at different stages of aMCI, which could provide a new idea for exploring the neuro-mechanisms of neurological disorders.
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Affiliation(s)
- Chenxi Li
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, Shaanxi, China
| | - Youjun Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, P. R. China
- National Engineering Research Center for Healthcare Devices. Guangzhou, Guangdong, P.R. China
- The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs, Xi’an, Shaanxi, P. R. China
| | - Jianqian Wu
- School of Public Policy and Adiminstration, Xi’an Jiaotong University, Xi’an, Shaanxi, P. R. China
| | - Min Wu
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Health and Rehabilitation Science, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, Shaanxi, P. R. China
| | - Fang Peng
- Department of Military Medical Psychology, Air Force Medical University, Xi’an, Shaanxi, China
| | - Qiuling Chao
- School of Public Policy and Adiminstration, Xi’an Jiaotong University, Xi’an, Shaanxi, P. R. China
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Tang Q, Tu W, Kong L. Estimation for partial functional partially linear additive model. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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47
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Saini F, Dell’Acqua F, Strydom A. Structural Connectivity in Down Syndrome and Alzheimer's Disease. Front Neurosci 2022; 16:908413. [PMID: 35937882 PMCID: PMC9354601 DOI: 10.3389/fnins.2022.908413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 06/21/2022] [Indexed: 12/02/2022] Open
Abstract
Down syndrome (DS) arises from the triplication of chromosome 21, which leads to an atypical neurodevelopment and the overproduction of the amyloid precursor protein, predisposing to early Alzheimer's disease (AD). Not surprisingly, trisomy 21 is widely considered a model to study predementia stages of AD. After decades, in which neural loss was the main focus, research in AD is now moving toward understanding the neurodegenerative aspects affecting white matter. Motivated by the development of magnetic resonance imaging (MRI)-based diffusion techniques, this shift in focus has led to several exploratory studies on both young and older individuals with DS. In this review, we synthesise the initial efforts made by researchers in characterising in-vivo structural connectivity in DS, together with the AD footprint on top of such pre-existing connectivity related to atypical brain development. The white matter structures found to be affected in DS are the corpus callosum and all the main long-association fibres, namely the inferior fronto-occipital fasciculus, the inferior and superior longitudinal fasciculus, the uncinate fasciculus and the cingulum bundle. Furthermore, the cingulum bundle and the corpus callosum appear to be particularly sensitive to early AD changes in this population. Findings are discussed in terms of their functional significance, alongside methodological considerations and implications for future research.
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Affiliation(s)
- Fedal Saini
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Flavio Dell’Acqua
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Andre Strydom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
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Keong NC, Lock C, Soon S, Hernowo AT, Czosnyka Z, Czosnyka M, Pickard JD, Narayanan V. Diffusion Tensor Imaging Profiles Can Distinguish Diffusivity and Neural Properties of White Matter Injury in Hydrocephalus vs. Non-hydrocephalus Using a Strategy of a Periodic Table of DTI Elements. Front Neurol 2022; 13:868026. [PMID: 35873785 PMCID: PMC9296826 DOI: 10.3389/fneur.2022.868026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 04/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background:The aim of this study was to create a simplistic taxonomy to improve transparency and consistency in, and reduce complexity of, interpreting diffusion tensor imaging (DTI) profiles in white matter disruption. Using a novel strategy of a periodic table of DTI elements, we examined if DTI profiles could demonstrate neural properties of disruption sufficient to characterize white matter changes specific for hydrocephalus vs. non-hydrocephalus, and to distinguish between cohorts of neural injury by their differing potential for reversibility.MethodsDTI datasets from three clinical cohorts representing pathological milestones from reversible to irreversible brain injury were compared to those of healthy controls at baseline, over time and with interventions. The final dataset comprised patients vs. controls in the following groupings: mild traumatic brain injury (mTBI), n = 24 vs. 27, normal pressure hydrocephalus (NPH), n = 16 vs. 9 and Alzheimer's disease (AD), n = 27 vs. 47. We generated DTI profiles from fractional anisotropy (FA) and mean, axial and radial diffusivity measures (MD, L1 and L2 and 3 respectively), and constructed an algorithm to map changes consistently to a periodic table of elements, which fully described their diffusivity and neural properties.ResultsMapping tissue signatures to a periodic table of DTI elements rapidly characterized cohorts by their differing patterns of injury. At baseline, patients with mTBI displayed the most preserved tracts. In NPH, the magnitude of changes was dependent on “familial” DTI neuroanatomy, i.e., potential for neural distortion from risk of ventriculomegaly. With time, patients with Alzheimer's disease were significantly different to controls across multiple measures. By contrast, patients with mTBI showed both loss of integrity and pathophysiological processes of neural repair. In NPH, some patterns of injury, such as “stretch/compression” and “compression” were more reversible following intervention than others; these neural profile properties suggested “microstructural resilience” to injury.ConclusionUsing the novel strategy of a periodic table of DTI elements, our study has demonstrated it is possible to distinguish between different cohorts along the spectrum of brain injury by describing neural profile properties of white matter disruption. Further work to contribute datasets of disease toward this proposed taxonomic framework would enhance the translatability of DTI profiles to the clinical-research interface.
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Affiliation(s)
- Nicole C. Keong
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- *Correspondence: Nicole C. Keong
| | - Christine Lock
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Shereen Soon
- Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - Aditya Tri Hernowo
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Zofia Czosnyka
- Neurosurgical Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Marek Czosnyka
- Neurosurgical Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - John D. Pickard
- Neurosurgical Division, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Vairavan Narayanan
- Department of Surgery, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
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Berente DB, Zsuffa J, Werber T, Kiss M, Drotos A, Kamondi A, Csukly G, Horvath AA. Alteration of Visuospatial System as an Early Marker of Cognitive Decline: A Double-Center Neuroimaging Study. Front Aging Neurosci 2022; 14:854368. [PMID: 35754966 PMCID: PMC9226394 DOI: 10.3389/fnagi.2022.854368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 04/27/2022] [Indexed: 11/30/2022] Open
Abstract
Amnestic-type mild cognitive impairment (a-MCI) represents the prodromal phase of Alzheimer's disease associated with a high conversion rate to dementia and serves as a potential golden period for interventions. In our study, we analyzed the role of visuospatial (VS) functions and networks in the recognition of a-MCI. We examined 78 participants (32 patients and 46 controls) in a double-center arrangement using neuropsychology, structural, and resting-state functional MRI. We found that imaging of the lateral temporal areas showed strong discriminating power since in patients only the temporal pole (F = 5.26, p = 0.034) and superior temporal gyrus (F = 8.04, p < 0.001) showed reduced cortical thickness. We demonstrated significant differences between controls and patients in various neuropsychological results; however, analysis of cognitive subdomains revealed that the largest difference was presented in VS skills (F = 8.32, p < 0.001). Functional connectivity analysis of VS network showed that patients had weaker connectivity between the left and right frontotemporal areas, while stronger local connectivity was presented between the left frontotemporal structures (FWE corrected p < 0.05). Our results highlight the remarkable potential of examining the VS system in the early detection of cognitive decline. Since resting-state setting of functional MRI simplifies the possible automatization of data analysis, detection of VS system alterations might provide a non-invasive biomarker of a-MCI.
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Affiliation(s)
| | - Janos Zsuffa
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary.,Department of Family Medicine, Semmelweis University, Budapest, Hungary
| | - Tom Werber
- Faculty of Medicine, Semmelweis University, Budapest, Hungary
| | - Mate Kiss
- Siemens Healthcare, Budapest, Hungary
| | - Anita Drotos
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary
| | - Anita Kamondi
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary.,Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Gabor Csukly
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary.,Research Group of Clinical Neuroscience and Neuroimaging, Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Andras Attila Horvath
- Neurocognitive Research Center, National Institute of Mental Health, Neurology and Neurosurgery, Budapest, Hungary.,Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary
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Nishioka C, Liang HF, Ong S, Sun SW. Axonal transport impairment and its relationship with diffusion tensor imaging metrics of a murine model of p301L tau induced tauopathy. Neuroscience 2022; 498:144-154. [PMID: 35753531 DOI: 10.1016/j.neuroscience.2022.06.025] [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: 05/05/2022] [Accepted: 06/15/2022] [Indexed: 11/15/2022]
Abstract
Diffusion Tensor Imaging (DTI) and Manganese Enhanced MRI (MEMRI) are noninvasive tools to characterize neural fiber microstructure and axonal transport. A combination of both may provide novel insights into the progress of neurodegeneration. To investigate the relationship of DTI and MEMRI in white matter of tauopathy, twelve optic nerves of 11-month-old p301L tau mice were imaged and finished with postmortem immunohistochemistry. MEMRI was used to quantify Mn2+ accumulation rates in the optic nerve (ON, termed ONAR) and the Superior Colliculus (SC, termed SCAR), the primary terminal site of ON in mice. We found that both ONAR and SCAR revealed a significant linear correlation with mean diffusion (mD) and radial diffusion (rD) but not with other DTI quantities. Immunohistochemistry findings showed that ONAR, mD, and rD are significantly correlated with the myelin content (Myelin Basic Protein, p < 0.05) but not with the axonal density (SMI-31), tubulin density, or tau aggregates (AT8 staining). In summary, slower axonal transport appeared to have less myelinated axons and thinner remaining axons, associated with reduced rD and mD of in vivo DTI. A combination of in vivo MEMRI and DTI can provide critical information to delineate the progress of white matter deficits in neurodegenerative diseases.
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Affiliation(s)
- Christopher Nishioka
- Basic Sciences, School of Medicine, Loma Linda University, Loma Linda, CA, United States; Neuroscience Graduate Program, University of California, Riverside, CA, United States
| | - Hsiao-Fang Liang
- Basic Sciences, School of Medicine, Loma Linda University, Loma Linda, CA, United States
| | - Stephen Ong
- Basic Sciences, School of Medicine, Loma Linda University, Loma Linda, CA, United States; Robert Wood Johnson Barnabas Health (RWJBH) and Rutgers University, United States
| | - Shu-Wei Sun
- Basic Sciences, School of Medicine, Loma Linda University, Loma Linda, CA, United States; Neuroscience Graduate Program, University of California, Riverside, CA, United States.
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