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Tian C, Schrack JA, Agrawal Y, An Y, Cai Y, Wang H, Gross AL, Tian Q, Simonsick EM, Ferrucci L, Resnick SM, Wanigatunga AA. Cross-sectional associations between multisensory impairment and brain volumes in older adults: Baltimore Longitudinal Study of Aging. Sci Rep 2024; 14:9339. [PMID: 38653745 DOI: 10.1038/s41598-024-59965-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 04/17/2024] [Indexed: 04/25/2024] Open
Abstract
Sensory impairment and brain atrophy is common among older adults, increasing the risk of dementia. Yet, the degree to which multiple co-occurring sensory impairments (MSI across vision, proprioception, vestibular function, olfactory, and hearing) are associated with brain morphometry remain unexplored. Data were from 208 cognitively unimpaired participants (mean age 72 ± 10 years; 59% women) enrolled in the Baltimore Longitudinal Study of Aging. Multiple linear regression models were used to estimate cross-sectional associations between MSI and regional brain imaging volumes. For each additional sensory impairment, there were associated lower orbitofrontal gyrus and entorhinal cortex volumes but higher caudate and putamen volumes. Participants with MSI had lower mean volumes in the superior frontal gyrus, orbitofrontal gyrus, superior parietal lobe, and precuneus compared to participants with < 2 impairments. While MSI was largely associated with lower brain volumes, our results suggest the possibility that MSI was associated with higher basal ganglia volumes. Longitudinal analyses are needed to evaluate the temporality and directionality of these associations.
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Affiliation(s)
- Chenxin Tian
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jennifer A Schrack
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center on Aging and Health, Johns Hopkins University, 2024 E. Monument Street, Suite 2-700, Rm 2-726, Baltimore, MD, 21205, USA
| | - Yuri Agrawal
- Department of Otolaryngology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Yang An
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - Yurun Cai
- Department of Health and Community Systems, University of Pittsburgh School of Nursing, Pittsburgh, PA, USA
| | - Hang Wang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center on Aging and Health, Johns Hopkins University, 2024 E. Monument Street, Suite 2-700, Rm 2-726, Baltimore, MD, 21205, USA
| | - Alden L Gross
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center on Aging and Health, Johns Hopkins University, 2024 E. Monument Street, Suite 2-700, Rm 2-726, Baltimore, MD, 21205, USA
| | - Qu Tian
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - Eleanor M Simonsick
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - Luigi Ferrucci
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - Susan M Resnick
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - Amal A Wanigatunga
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Center on Aging and Health, Johns Hopkins University, 2024 E. Monument Street, Suite 2-700, Rm 2-726, Baltimore, MD, 21205, USA.
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Petkus AJ, Wang X, Younan D, Salminen LE, Resnick SM, Rapp SR, Espeland MA, Gatz M, Widaman KF, Casanova R, Chui H, Barnard RT, Gaussoin SA, Goveas JS, Hayden KM, Henderson VW, Sachs BC, Saldana S, Shadyab AH, Shumaker SA, Chen JC. 20-year depressive symptoms, dementia, and structural neuropathology in older women. Alzheimers Dement 2024. [PMID: 38591250 DOI: 10.1002/alz.13781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 01/03/2024] [Accepted: 01/24/2024] [Indexed: 04/10/2024]
Abstract
INTRODUCTION The course of depressive symptoms and dementia risk is unclear, as are potential structural neuropathological common causes. METHODS Utilizing joint latent class mixture models, we identified longitudinal trajectories of annually assessed depressive symptoms and dementia risk over 21 years in 957 older women (baseline age 72.7 years old) from the Women's Health Initiative Memory Study. In a subsample of 569 women who underwent structural magnetic resonance imaging, we examined whether estimates of cerebrovascular disease and Alzheimer's disease (AD)-related neurodegeneration were associated with identified trajectories. RESULTS Five trajectories of depressive symptoms and dementia risk were identified. Compared to women with minimal symptoms, women who reported mild and stable and emerging depressive symptoms were at the highest risk of developing dementia and had more cerebrovascular disease and AD-related neurodegeneration. DISCUSSION There are heterogeneous profiles of depressive symptoms and dementia risk. Common neuropathological factors may contribute to both depression and dementia. Highlights The progression of depressive symptoms and concurrent dementia risk is heterogeneous. Emerging depressive symptoms may be a prodromal symptom of dementia. Cerebrovascular disease and AD are potentially shared neuropathological factors.
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Affiliation(s)
- Andrew J Petkus
- Department of Neurology, University of Southern California, Los Angeles, California, USA
| | - Xinhui Wang
- Department of Neurology, University of Southern California, Los Angeles, California, USA
| | - Diana Younan
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA
| | - Lauren E Salminen
- Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Marina del Rey, California, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, USA
| | - Stephen R Rapp
- Department of Neurology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Mark A Espeland
- Department of Biostatistics and Data Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Margaret Gatz
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, USA
| | - Keith F Widaman
- Graduate School of Education, University of California, Riverside, Riverside, California, USA
| | - Ramon Casanova
- Department of Biostatistics and Data Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Helena Chui
- Department of Neurology, University of Southern California, Los Angeles, California, USA
| | - Ryan T Barnard
- Department of Biostatistics and Data Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Sarah A Gaussoin
- Department of Biostatistics and Data Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Joseph S Goveas
- Department of Psychiatry and Behavioral Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Kathleen M Hayden
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Victor W Henderson
- Departments of Epidemiology and Population Health and of Neurology and Neurological Sciences, Stanford University, Palo Alto, California, USA
| | - Bonnie C Sachs
- Department of Neurology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Santiago Saldana
- Department of Biostatistics and Data Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Aladdin H Shadyab
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California, USA
| | - Sally A Shumaker
- Department of Neurology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Jiu-Chiuan Chen
- Department of Neurology, University of Southern California, Los Angeles, California, USA
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA
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Tian Q, Greig EE, Walker KA, Fishbein KW, Spencer RG, Resnick SM, Ferrucci L. Plasma metabolomic markers underlying skeletal muscle mitochondrial function relationships with cognition and motor function. Age Ageing 2024; 53:afae079. [PMID: 38615247 DOI: 10.1093/ageing/afae079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Indexed: 04/15/2024] Open
Abstract
BACKGROUND Lower skeletal muscle mitochondrial function is associated with future cognitive impairment and mobility decline, but the biological underpinnings for these associations are unclear. We examined metabolomic markers underlying skeletal muscle mitochondrial function, cognition and motor function. METHODS We analysed data from 560 participants from the Baltimore Longitudinal Study of Aging (mean age: 68.4 years, 56% women, 28% Black) who had data on skeletal muscle oxidative capacity (post-exercise recovery rate of phosphocreatine, kPCr) via 31P magnetic resonance spectroscopy and targeted plasma metabolomics using LASSO model. We then examined which kPCr-related markers were also associated with cognition and motor function in a larger sample (n = 918, mean age: 69.4, 55% women, 27% Black). RESULTS The LASSO model revealed 24 metabolites significantly predicting kPCr, with the top 5 being asymmetric dimethylarginine, lactic acid, lysophosphatidylcholine a C18:1, indoleacetic acid and triacylglyceride (17:1_34:3), also significant in multivariable linear regression. The kPCr metabolite score was associated with cognitive or motor function, with 2.5-minute usual gait speed showing the strongest association (r = 0.182). Five lipids (lysophosphatidylcholine a C18:1, phosphatidylcholine ae C42:3, cholesteryl ester 18:1, sphingomyelin C26:0, octadecenoic acid) and 2 amino acids (leucine, cystine) were associated with both cognitive and motor function measures. CONCLUSION Our findings add evidence to the hypothesis that mitochondrial function is implicated in the pathogenesis of cognitive and physical decline with aging and suggest that targeting specific metabolites may prevent cognitive and mobility decline through their effects on mitochondria. Future omics studies are warranted to confirm these findings and explore mechanisms underlying mitochondrial dysfunction in aging phenotypes.
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Affiliation(s)
- Qu Tian
- Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Erin E Greig
- Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Kenneth W Fishbein
- Laboratory of Clinical Investigation, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Richard G Spencer
- Laboratory of Clinical Investigation, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore, MD 21224, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD 21224, USA
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Kanakaraj P, Yao T, Cai LY, Lee HH, Newlin NR, Kim ME, Gao C, Pechman KR, Archer D, Hohman T, Jefferson A, Beason-Held LL, Resnick SM, Garyfallidis E, Anderson A, Schilling KG, Landman BA, Moyer D. DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images. Neuroinformatics 2024; 22:193-205. [PMID: 38526701 DOI: 10.1007/s12021-024-09655-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/19/2023] [Indexed: 03/27/2024]
Abstract
T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4 .
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Affiliation(s)
- Praitayini Kanakaraj
- Department of Computer Science, Vanderbilt University, 400 24th Ave S, Nashville, TN, 37240, USA.
| | - Tianyuan Yao
- Department of Computer Science, Vanderbilt University, 400 24th Ave S, Nashville, TN, 37240, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Ho Hin Lee
- Department of Computer Science, Vanderbilt University, 400 24th Ave S, Nashville, TN, 37240, USA
| | - Nancy R Newlin
- Department of Computer Science, Vanderbilt University, 400 24th Ave S, Nashville, TN, 37240, USA
| | - Michael E Kim
- Department of Computer Science, Vanderbilt University, 400 24th Ave S, Nashville, TN, 37240, USA
| | - Chenyu Gao
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kimberly R Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Derek Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Timothy Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Angela Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute On Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute On Aging, National Institutes of Health, Baltimore, MD, USA
| | | | - Adam Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, 400 24th Ave S, Nashville, TN, 37240, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science, Vanderbilt University, 400 24th Ave S, Nashville, TN, 37240, USA
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5
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Sayyid ZN, Wang H, Cai Y, Gross AL, Swenor BK, Deal JA, Lin FR, Wanigatunga AA, Dougherty RJ, Tian Q, Simonsick EM, Ferrucci L, Schrack JA, Resnick SM, Agrawal Y. Sensory and motor deficits as contributors to early cognitive impairment. Alzheimers Dement 2024; 20:2653-2661. [PMID: 38375574 PMCID: PMC11032563 DOI: 10.1002/alz.13715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 01/03/2024] [Accepted: 01/04/2024] [Indexed: 02/21/2024]
Abstract
INTRODUCTION Age-related sensory and motor impairment are associated with risk of dementia. No study has examined the joint associations of multiple sensory and motor measures on prevalence of early cognitive impairment (ECI). METHODS Six hundred fifty participants in the Baltimore Longitudinal Study of Aging completed sensory and motor function tests. The association between sensory and motor function and ECI was examined using structural equation modeling with three latent factors corresponding to multisensory, fine motor, and gross motor function. RESULTS The multisensory, fine, and gross motor factors were all correlated (r = 0.74 to 0.81). The odds of ECI were lower for each additional unit improvement in the multisensory (32%), fine motor (30%), and gross motor factors (12%). DISCUSSION The relationship between sensory and motor impairment and emerging cognitive impairment may guide future intervention studies aimed at preventing and/or treating ECI. HIGHLIGHTS Sensorimotor function and early cognitive impairment (ECI) prevalence were assessed via structural equation modeling. The degree of fine and gross motor function is associated with indicators of ECI. The degree of multisensory impairment is also associated with indicators of ECI.
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Affiliation(s)
- Zahra N. Sayyid
- Department of Otolaryngology‐Head and Neck SurgeryJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Hang Wang
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Center on Aging and HealthJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Yurun Cai
- Department of Health and Community SystemsUniversity of Pittsburgh School of NursingPittsburghPennsylvaniaUSA
| | - Alden L. Gross
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Center on Aging and HealthJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Bonnielin K. Swenor
- The Johns Hopkins School of NursingBaltimoreMarylandUSA
- The Johns Hopkins Disability Health Research Center, Johns Hopkins UniversityBaltimoreMarylandUSA
| | - Jennifer A. Deal
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Cochlear Center for Hearing and Public Health, Johns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Frank R. Lin
- Department of Otolaryngology‐Head and Neck SurgeryJohns Hopkins School of MedicineBaltimoreMarylandUSA
- Cochlear Center for Hearing and Public Health, Johns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Amal A. Wanigatunga
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Center on Aging and HealthJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Ryan J. Dougherty
- Department of NeurologyJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Qu Tian
- Intramural Research Program, National Institute on Aging, BaltimoreBaltimoreMarylandUSA
| | - Eleanor M. Simonsick
- Intramural Research Program, National Institute on Aging, BaltimoreBaltimoreMarylandUSA
| | - Luigi Ferrucci
- Intramural Research Program, National Institute on Aging, BaltimoreBaltimoreMarylandUSA
| | - Jennifer A. Schrack
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Center on Aging and HealthJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Susan M. Resnick
- Intramural Research Program, National Institute on Aging, BaltimoreBaltimoreMarylandUSA
| | - Yuri Agrawal
- Department of Otolaryngology‐Head and Neck SurgeryJohns Hopkins School of MedicineBaltimoreMarylandUSA
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6
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Newlin NR, Kim ME, Kanakaraj P, Yao T, Hohman T, Pechman KR, Beason-Held LL, Resnick SM, Archer D, Jefferson A, Landman BA, Moyer D. MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal. Magn Reson Imaging 2024:S0730-725X(24)00089-4. [PMID: 38537892 DOI: 10.1016/j.mri.2024.03.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 03/09/2024] [Accepted: 03/20/2024] [Indexed: 04/09/2024]
Abstract
Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. We find that MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.
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Affiliation(s)
- Nancy R Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
| | - Michael E Kim
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | | | - Tianyuan Yao
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Timothy Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Kimberly R Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Derek Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Angela Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
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7
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Kac PR, González-Ortiz F, Emeršič A, Dulewicz M, Koutarapu S, Turton M, An Y, Smirnov D, Kulczyńska-Przybik A, Varma VR, Ashton NJ, Montoliu-Gaya L, Camporesi E, Winkel I, Paradowski B, Moghekar A, Troncoso JC, Lashley T, Brinkmalm G, Resnick SM, Mroczko B, Kvartsberg H, Gregorič Kramberger M, Hanrieder J, Čučnik S, Harrison P, Zetterberg H, Lewczuk P, Thambisetty M, Rot U, Galasko D, Blennow K, Karikari TK. Plasma p-tau212 antemortem diagnostic performance and prediction of autopsy verification of Alzheimer's disease neuropathology. Nat Commun 2024; 15:2615. [PMID: 38521766 PMCID: PMC10960791 DOI: 10.1038/s41467-024-46876-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 03/04/2024] [Indexed: 03/25/2024] Open
Abstract
Blood phosphorylated tau (p-tau) biomarkers, including p-tau217, show high associations with Alzheimer's disease (AD) neuropathologic change and clinical stage. Certain plasma p-tau217 assays recognize tau forms phosphorylated additionally at threonine-212, but the contribution of p-tau212 alone to AD is unknown. We developed a blood-based immunoassay that is specific to p-tau212 without cross-reactivity to p-tau217. Here, we examined the diagnostic utility of plasma p-tau212. In five cohorts (n = 388 participants), plasma p-tau212 showed high performances for AD diagnosis and for the detection of both amyloid and tau pathology, including at autopsy as well as in memory clinic populations. The diagnostic accuracy and fold changes of plasma p-tau212 were similar to those for p-tau217 but higher than p-tau181 and p-tau231. Immunofluorescent staining of brain tissue slices showed prominent p-tau212 reactivity in neurofibrillary tangles that co-localized with p-tau217 and p-tau202/205. These findings support plasma p-tau212 as a peripherally accessible biomarker of AD pathophysiology.
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Grants
- R01 AG075336 NIA NIH HHS
- R01 AG078796 NIA NIH HHS
- R01 AG083874 NIA NIH HHS
- R01 AG072641 NIA NIH HHS
- R01 AG068398 NIA NIH HHS
- R21 AG078538 NIA NIH HHS
- R01 MH108509 NIMH NIH HHS
- RF1 AG025516 NIA NIH HHS
- P30 AG066468 NIA NIH HHS
- R01 AG073267 NIA NIH HHS
- P01 AG025204 NIA NIH HHS
- #AARF-21-850325 Alzheimer's Association
- R01 MH121619 NIMH NIH HHS
- R37 AG023651 NIA NIH HHS
- R21 AG080705 NIA NIH HHS
- U24 AG082930 NIA NIH HHS
- RF1 AG052525 NIA NIH HHS
- R01 AG053952 NIA NIH HHS
- Demensförbundet (Dementia Association)
- Anna Lisa and Brother Björnsson’s Foundation
- BrightFocus Foundation (BrightFocus)
- Alzheimerfonden
- the Swedish Dementia Foundation, Gun and Bertil Stohnes Foundation, Åhlén-stifelsen, and Gamla Tjänarinnor Foundation.
- Vetenskapsrådet (Swedish Research Council)
- Alzheimer’s Drug Discovery Foundation (ADDF)
- EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)
- EU Joint Programme – Neurodegenerative Disease Research (Programi i Përbashkët i BE-së për Kërkimet mbi Sëmundjet Neuro-degjeneruese)
- Swedish State Support for Clinical Research (#ALFGBG-71320), the AD Strategic Fund and the Alzheimer’s Association (#ADSF-21-831376-C, #ADSF-21-831381-C, and #ADSF-21-831377-C) the Bluefield Project, the Olav Thon Foundation, the Erling-Persson Family Foundation, Hjärnfonden, Sweden (#FO2022-0270), the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, and the UK Dementia Research Institute at UCL (UKDRI-1003)
- the Swedish Alzheimer Foundation (#AF-930351, #AF-939721 and #AF-968270), Hjärnfonden, Sweden (#FO2017-0243 and #ALZ2022-0006), the Swedish state under the agreement between the Swedish government and the County Councils, the ALF-agreement (#ALFGBG-715986 and #ALFGBG-965240), the National Institute of Health (NIH), USA, (grant #1R01AG068398-01) the Alzheimer’s Association 2021 Zenith Award (ZEN-21-848495).
- Alzheimer’s Association
- National Institute of Health (NIH) - (R01 AG083874-01, U24 AG082930-01 1 RF1 AG052525-01A1, 5 P30 AG066468-04, 5 R01 AG053952-05, 3 R01 MH121619-04S1, 5 R37 AG023651-18, 2 RF1 AG025516-12A1, 5 R01 AG073267-02, 2 R01 MH108509-06, 5 R01 AG075336-02, 5 R01 AG072641-02, 2 P01 AG025204-16) the Swedish Alzheimer Foundation (Alzheimerfonden), the Aina (Ann) Wallströms and Mary-Ann Sjöbloms stiftelsen, and the Emil och Wera Cornells stiftelsen.
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Affiliation(s)
- Przemysław R Kac
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden.
| | - Fernando González-Ortiz
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, 431 80, Sweden
| | - Andreja Emeršič
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, 1000, Slovenia
- Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia
| | - Maciej Dulewicz
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
| | - Srinivas Koutarapu
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
| | | | - Yang An
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA
| | - Denis Smirnov
- Department of Neurosciences, University of California, San Diego, CA, 92161, USA
| | | | - Vijay R Varma
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
- Department of Old Age Psychiatry, King's College London, London, SE5 8AF, UK
- Centre for Age-Related Medicine, Stavanger University Hospital, 4011, Stavanger, Norway
- South London & Maudsley NHS Foundation, NIHR Biomedical Research Centre for Mental Health & Biomedical Research Unit for Dementia, SE5 8AF, London, UK
| | - Laia Montoliu-Gaya
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
| | - Elena Camporesi
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
| | - Izabela Winkel
- Dementia Disorders Center, Medical University of Wrocław, 59-330, Ścinawa, Poland
| | - Bogusław Paradowski
- Department of Neurology, Medical University of Wrocław, 50-556, Wrocław, Poland
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Juan C Troncoso
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
- Department of Pathology, John Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Tammaryn Lashley
- Department of Neurodegenerative diseases, UCL Queen Square Institute of Neurology, WC1N 1PJ, London, UK
| | - Gunnar Brinkmalm
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA
| | - Barbara Mroczko
- Department of Neurodegeneration Diagnostics, Medical University of Białystok, Białystok, 15-269, Poland
| | - Hlin Kvartsberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, 431 80, Sweden
| | - Milica Gregorič Kramberger
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, 1000, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, 141 52, Huddinge, Sweden
| | - Jörg Hanrieder
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, WC1E 6BT, UK
| | - Saša Čučnik
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, 1000, Slovenia
- Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia
- Department of Rheumatology, University Medical Center Ljubljana, Ljubljana, Slovenia
| | | | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, 431 80, Sweden
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, WC1E 6BT, UK
- UK Dementia Research Institute, University College London, London, WC1E 6BT, UK
- Hong Kong Center for Neurodegenerative Diseases, HKCeND, Hong Kong, 1512-1518, China
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53726, USA
| | - Piotr Lewczuk
- Department of Neurodegeneration Diagnostics, Medical University of Białystok, Białystok, 15-269, Poland
- Department of Psychiatry and Psychotherapy, Universitätsklinikum Erlangen, and Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, 91054, Germany
| | - Madhav Thambisetty
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA
| | - Uroš Rot
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, 1000, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Douglas Galasko
- Department of Neurosciences, University of California, San Diego, CA, 92161, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, 431 80, Sweden
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
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Dougherty RJ, Wang H, Gross AL, Schrack JA, Agrawal Y, Davatzikos C, Cai Y, Simonsick EM, Ferrucci L, Resnick SM, Tian Q. Shared and Distinct Associations of Manual Dexterity and Gross Motor Function With Brain Atrophy. J Gerontol A Biol Sci Med Sci 2024; 79:glad245. [PMID: 37837441 PMCID: PMC10876075 DOI: 10.1093/gerona/glad245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Indexed: 10/16/2023] Open
Abstract
BACKGROUND Poor motor function is associated with brain atrophy and cognitive impairment. Less is known about the relationship between motor domains and brain atrophy and whether associations are affected by cerebrovascular burden and/or physical activity. METHODS We analyzed data from 726 Baltimore Longitudinal Study of Aging participants (mean age 70.6 ± 10.1 years, 56% women, 27% Black), 525 of whom had repeat MRI scans over an average of 5.0 ± 2.1 years. Two motor domains, manual dexterity and gross motor, were operationalized as latent variables. Associations between the latent variables and cortical and subcortical brain volumes of interest were examined using latent growth curve modeling, adjusted for demographics, white matter hyperintensities, and physical activity. RESULTS Both higher manual dexterity and gross motor function were cross-sectionally associated with smaller ventricular volume and greater white matter volumes in the frontal, parietal, and temporal lobes (all p < .05). Manual dexterity was also cross-sectionally associated with parietal gray matter (B = 0.14; 95% CI: 0.05, 0.23), hippocampus (B = 0.10; 95% CI: 0.01, 0.20), postcentral gyrus (B = 0.11; 95% CI: 0.01, 0.20), and occipital white matter (B = 0.10; 95% CI: 0.01, 0.21) volumes, and gross motor function with temporal gray matter volume (B = 0.16; 95% CI: 0.05, 0.26). Longitudinally, both higher manual dexterity and gross motor function were associated with less temporal white matter and occipital gray matter atrophy (all p < .05). Manual dexterity was also associated with a slower rate of ventricular enlargement (B = -0.17; 95% CI: -0.29, -0.05) and less atrophy of occipital white matter (B = 0.39; 95% CI: 0.04, 0.71). CONCLUSIONS Among cognitively normal middle- and older-aged adults, manual dexterity and gross motor function exhibited shared as well as distinct associations with brain atrophy over time.
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Affiliation(s)
- Ryan J Dougherty
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
- Center on Aging and Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Hang Wang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Alden L Gross
- Center on Aging and Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Jennifer A Schrack
- Center on Aging and Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Yuri Agrawal
- Department of Otolaryngology—Head and Neck Surgery, Johns Hopkins University, Baltimore, Maryland, USA
| | - Christos Davatzikos
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yurun Cai
- Department of Health and Community Systems, University of Pittsburgh School of Nursing, Pittsburgh, Pennsylvania, USA
| | - Eleanor M Simonsick
- Intramural Research Program, National Institute on Aging, Baltimore, Maryland, USA
| | - Luigi Ferrucci
- Intramural Research Program, National Institute on Aging, Baltimore, Maryland, USA
| | - Susan M Resnick
- Intramural Research Program, National Institute on Aging, Baltimore, Maryland, USA
| | - Qu Tian
- Intramural Research Program, National Institute on Aging, Baltimore, Maryland, USA
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9
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Tian Q, An Y, Resnick SM, Ferrucci L. Presymptomatic Profiles of Cognitive Impairment with Prior Mobility Impairment. J Am Med Dir Assoc 2024; 25:480-487.e2. [PMID: 38307123 PMCID: PMC10951864 DOI: 10.1016/j.jamda.2023.12.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 12/26/2023] [Accepted: 12/26/2023] [Indexed: 02/04/2024]
Abstract
OBJECTIVES To identify cognitive and health profiles of cognitively impaired older adults with the presence of prior mobility impairment, which may represent a specific pathway to the development of cognitive impairment or dementia. DESIGN Retrospective longitudinal study. SETTING AND PARTICIPANTS In adults aged ≥65 years who developed cognitive impairment or dementia, we compared cognitive and health profiles of those who did (n = 57) and did not (n = 86) experience slow gait up to 14 years before symptom onset. Measures of cognitive and biomarkers assessed longitudinally over an average of 7 years before symptom onset were compared between groups using linear mixed effects models, adjusted age, sex, race, and additionally adjusted for education for cognitive outcomes. RESULTS Compared to those without prior slow gait, those with slow gait had lower Digit Symbol Substitution Test and Pegboard dominant and nondominant hand performance. The slow gait group also had greater body mass index (BMI), waist, systolic blood pressure, lower high-density lipoprotein and low-density lipoprotein, and lower lysophosphatidylcholine 18:2, a lipid associated with mitochondrial function, and showed greater increases in 2-hour glucose levels of an oral glucose tolerance test. The slow gait group was more likely to take medication for hypertension and hypercholesterolemia. CONCLUSIONS AND IMPLICATIONS During the presymptomatic stage, cognitively impaired older persons who experience prior slow gait are more likely to have deficits in psychomotor speed and manual dexterity, an unfavorable metabolic and vascular profile, and lower lipid levels related to mitochondrial function. Older persons who exhibit mobility impairment should be evaluated for metabolic and vascular dysfunction at an early stage, and successful treatment of these conditions may slow down the progression of cognitive impairment or dementia.
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Affiliation(s)
- Qu Tian
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD, USA.
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD, USA
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10
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Kim ME, Gao C, Cai LY, Yang Q, Newlin NR, Ramadass K, Jefferson A, Archer D, Shashikumar N, Pechman KR, Gifford KA, Hohman TJ, Beason-Held LL, Resnick SM, Winzeck S, Schilling KG, Zhang P, Moyer D, Landman BA. Empirical assessment of the assumptions of ComBat with diffusion tensor imaging. J Med Imaging (Bellingham) 2024; 11:024011. [PMID: 38655188 PMCID: PMC11034156 DOI: 10.1117/1.jmi.11.2.024011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 02/28/2024] [Accepted: 03/25/2024] [Indexed: 04/26/2024] Open
Abstract
Purpose Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI. Approach As a baseline, we match N = 358 participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i) β AGE , the linear regression coefficient of the relationship between FA and age; (ii) γ ^ s f * , the ComBat-estimated site-shift; and (iii) δ ^ s f * , the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions. Results ComBat remains well behaved for β AGE when N > 162 and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable. Conclusion Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.
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Affiliation(s)
- Michael E. Kim
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Chenyu Gao
- Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee, United States
| | - Leon Y. Cai
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt University, Medical Scientist Training Program, Nashville, Tennessee, United States
| | - Qi Yang
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Nancy R. Newlin
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Karthik Ramadass
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
- Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee, United States
| | - Angela Jefferson
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Medicine, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Neurology, Nashville, Tennessee, United States
| | - Derek Archer
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, Tennessee, United States
| | - Niranjana Shashikumar
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
| | - Kimberly R. Pechman
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
| | - Katherine A. Gifford
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
| | - Timothy J. Hohman
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, Tennessee, United States
| | - Lori L. Beason-Held
- National Institutes of Health, National Institute on Aging, Laboratory of Behavioral Neuroscience, Baltimore, Maryland, United States
| | - Susan M. Resnick
- National Institutes of Health, National Institute on Aging, Laboratory of Behavioral Neuroscience, Baltimore, Maryland, United States
| | - Stefan Winzeck
- Imperial College London, Department of Computing, BioMedIA Group, London, United Kingdom
| | - Kurt G. Schilling
- Vanderbilt University Medical Center, Department of Radiology, Nashville, Tennessee, United States
| | - Panpan Zhang
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Biostatistics, Nashville, Tennessee, United States
| | - Daniel Moyer
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Bennett A. Landman
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
- Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee, United States
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Biostatistics, Nashville, Tennessee, United States
- Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, United States
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11
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Spira AP, Liu F, Zipunnikov V, Bilgel M, Rabinowitz JA, An Y, Di J, Bai J, Wanigatunga SK, Wu MN, Lucey BP, Schrack JA, Wanigatunga AA, Rosenberg PB, Simonsick EM, Walker KA, Ferrucci L, Resnick SM. Evaluating a Novel 24-Hour Rest/Activity Rhythm Marker of Preclinical β-Amyloid Deposition. Sleep 2024:zsae037. [PMID: 38381532 DOI: 10.1093/sleep/zsae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Indexed: 02/23/2024] Open
Abstract
STUDY OBJECTIVES To compare sleep and 24-hour rest/activity rhythms (RARs) between cognitively normal older adults who are β-amyloid-positive (Aβ or Aβ- and replicate a novel time-of-day-specific difference between these groups identified in a previous exploratory study. METHODS We studied 82 cognitively normal participants from the Baltimore Longitudinal Study of Aging (aged 75.7 ±8.5 years, 55% female, 76% White) with wrist actigraphy data and Aβ vs. Aβ- status measured by [11C] Pittsburgh compound B positron emission tomography. RARs were calculated using epoch-level activity count data from actigraphy. We used novel, data-driven function-on-scalar regression (FOSR) analyses and standard RAR metrics to cross sectionally compare RARs between 25 Aβ+ and 57 Aβ- participants. RESULTS Compared to Aβ- participants, Aβ+ participants had higher mean activity from 1:00 PM-3:30 PM when using less conservative pointwise confidence intervals (CIs) and from 1:30 PM-2:30 PM using more conservative, simultaneous CIs. Further, Aβ+ participants had higher day-to-day variability in activity from 9:00 AM-11:30 AM and lower variability from 1:30 PM-4:00 PM and 7:30 PM-10:30 PM according to pointwise CIs, and lower variability from 8:30 PM-10:00 PM using simultaneous CIs. There were no Aβ-related differences in standard sleep or RAR metrics. CONCLUSIONS Findings suggest Aβ older adults have higher, more stable day-to-day afternoon/evening activity than Aβ- older adults, potentially reflecting circadian dysfunction. Studies are needed to replicate our findings and determine whether these or other time-of-day-specific RAR features have utility as markers of preclinical Aβdeposition and if they predict clinical dementia and agitation in the afternoon/evening (i.e., "sundowning").
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Affiliation(s)
- Adam P Spira
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, 8th Floor, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 5300 Alpha Commons Drive, Baltimore, MD 21224, USA
- Johns Hopkins Center on Aging and Health, 2024 E Monument St, Suite 2-700, Baltimore, MD 21205, USA
| | - Fangyu Liu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E Monument St, Suite 2-700, Baltimore, MD 21205, USA
| | - Vadim Zipunnikov
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street Baltimore, MD 21205, USA
| | - Murat Bilgel
- National Institute on Aging Intramural Research Program, 251 Bayview Blvd, Ste 100, Baltimore MD 21224, USA
| | - Jill A Rabinowitz
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Yang An
- National Institute on Aging Intramural Research Program, 251 Bayview Blvd, Ste 100, Baltimore MD 21224, USA
| | - Junrui Di
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street Baltimore, MD 21205, USA
| | - Jiawei Bai
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street Baltimore, MD 21205, USA
| | - Sarah K Wanigatunga
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, 8th Floor, Baltimore, MD, 21205, USA
| | - Mark N Wu
- Department of Neurology, Johns Hopkins School of Medicine, 855 N. Wolfe Street, Rangos Bldg, Room 294, Baltimore, MD 21205, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, 855 N. Wolfe Street, Rangos Bldg, Room 294, Baltimore, MD 21205, USA
| | - Brendan P Lucey
- Department of Neurology, Washington University School of Medicine, 660 S. Euclid Ave, Campus Box 8111, St Louis, MO 63110, USA
| | - Jennifer A Schrack
- Johns Hopkins Center on Aging and Health, 2024 E Monument St, Suite 2-700, Baltimore, MD 21205, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E Monument St, Suite 2-700, Baltimore, MD 21205, USA
| | - Amal A Wanigatunga
- Johns Hopkins Center on Aging and Health, 2024 E Monument St, Suite 2-700, Baltimore, MD 21205, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E Monument St, Suite 2-700, Baltimore, MD 21205, USA
| | - Paul B Rosenberg
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, 5300 Alpha Commons Drive, Baltimore, MD 21224, USA
| | - Eleanor M Simonsick
- National Institute on Aging Intramural Research Program, 251 Bayview Blvd, Ste 100, Baltimore MD 21224, USA
| | - Keenan A Walker
- National Institute on Aging Intramural Research Program, 251 Bayview Blvd, Ste 100, Baltimore MD 21224, USA
| | - Luigi Ferrucci
- National Institute on Aging Intramural Research Program, 251 Bayview Blvd, Ste 100, Baltimore MD 21224, USA
| | - Susan M Resnick
- National Institute on Aging Intramural Research Program, 251 Bayview Blvd, Ste 100, Baltimore MD 21224, USA
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12
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Skampardoni I, Nasrallah IM, Abdulkadir A, Wen J, Melhem R, Mamourian E, Erus G, Doshi J, Singh A, Yang Z, Cui Y, Hwang G, Ren Z, Pomponio R, Srinivasan D, Govindarajan ST, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Yaffe K, Völzke H, Ferrucci L, Benzinger TL, Ezzati A, Shinohara RT, Fan Y, Resnick SM, Habes M, Wolk D, Shou H, Nikita K, Davatzikos C. Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals. JAMA Psychiatry 2024:2814597. [PMID: 38353984 PMCID: PMC10867779 DOI: 10.1001/jamapsychiatry.2023.5599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 11/29/2023] [Indexed: 02/17/2024]
Abstract
Importance Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures Individuals WODCI at baseline scan. Main Outcomes and Measures Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed. Results In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aβ positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.
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Affiliation(s)
- Ioanna Skampardoni
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Ilya M. Nasrallah
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Ahmed Abdulkadir
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Junhao Wen
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles
| | - Randa Melhem
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Elizabeth Mamourian
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Guray Erus
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Jimit Doshi
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Ashish Singh
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Zhijian Yang
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Yuhan Cui
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Gyujoon Hwang
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Zheng Ren
- Laboratory of AI and Biomedical Science, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles
| | - Raymond Pomponio
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Dhivya Srinivasan
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | | | - Paraskevi Parmpi
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Centre for Neurodegenerative Diseases, Site Greifswald, Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle
- Department of Epidemiology, University of Washington, Seattle
| | - Thomas R. Austin
- Cardiovascular Health Research Unit, University of Washington, Seattle
- Department of Epidemiology, University of Washington, Seattle
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland
| | - Aristeidis Sotiras
- Department of Radiology and Institute of Informatics, Washington University in St Louis, St Louis, Missouri
| | - Mark A. Espeland
- Sticht Centre for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison
| | - John C. Morris
- Knight Alzheimer Disease Research Centre, Washington University in St Louis, St Louis, Missouri
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - R. Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine in St Louis, St Louis, Missouri
| | - Ali Ezzati
- Department of Neurology, University of California, Irvine
| | - Russell T. Shinohara
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia
| | - Yong Fan
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, Maryland
| | - Mohamad Habes
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
| | - David Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia
| | - Haochang Shou
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia
| | - Konstantina Nikita
- School of Electrical and Computer Engineering, National Technical University of Athens, Greece
| | - Christos Davatzikos
- Centre for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
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13
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Tian Q, Lee PR, Yang Q, Moore AZ, Landman BA, Resnick SM, Ferrucci L. The mediation roles of intermuscular fat and inflammation in muscle mitochondrial associations with cognition and mobility. J Cachexia Sarcopenia Muscle 2024; 15:138-148. [PMID: 38116708 PMCID: PMC10834332 DOI: 10.1002/jcsm.13413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 10/24/2023] [Accepted: 11/20/2023] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Mitochondrial dysfunction may contribute to brain and muscle health through inflammation or fat infiltration in the muscle, both of which are associated with cognitive function and mobility. We aimed to examine the association between skeletal muscle mitochondrial function and cognitive and mobility outcomes and tested the mediation effect of inflammation or fat infiltration. METHODS We analysed data from 596 Baltimore Longitudinal Study of Aging participants who had concurrent data on skeletal muscle oxidative capacity and cognitive and mobility measures of interest (mean age: 66.1, 55% women, 24% Black). Skeletal muscle oxidative capacity was assessed as post-exercise recovery rate (kPCr) via P31 MR spectroscopy. Fat infiltration was measured as intermuscular fat (IMF) via CT scan and was available for 541 participants. Inflammation markers [IL-6, C-reactive protein (CRP), total white blood cell (WBC), neutrophil count, erythrocyte sedimentation rate (ESR), or albumin] were available in 594 participants. We examined the association of kPCr and cognitive and mobility measures using linear regression and tested the mediation effect of IMF or inflammation using the mediation package in R. Models were adjusted for demographics and PCr depletion. RESULTS kPCr and IMF were both significantly associated with specific cognitive domains (DSST, TMA-A, and pegboard dominant hand performance) and mobility (usual gait speed, HABCPPB, 400 m walk time) (all P < 0.05). IMF significantly mediated the relationship between kPCr and these cognitive and mobility measures (all P < 0.05, proportion mediated 13.1% to 27%). Total WBC, neutrophil count, and ESR, but not IL-6 or CRP, also mediated at least one of the cognitive and mobility outcomes (all P < 0.05, proportion mediated 9.4% to 15.3%). CONCLUSIONS Skeletal muscle mitochondrial function is associated with cognitive performance involving psychomotor speed. Muscle fat infiltration and specific inflammation markers mediate the relationship between muscle mitochondrial function and cognitive and mobility outcomes. Future studies are needed to confirm these associations longitudinally and to understand their mechanistic underpinnings.
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Affiliation(s)
- Qu Tian
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Philip R Lee
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Qi Yang
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Anne Z Moore
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
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14
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Dark HE, Paterson C, Daya GN, Peng Z, Duggan MR, Bilgel M, An Y, Moghekar A, Davatzikos C, Resnick SM, Loupy K, Simpson M, Candia J, Mosley T, Coresh J, Palta P, Ferrucci L, Shapiro A, Williams SA, Walker KA. Proteomic Indicators of Health Predict Alzheimer's Disease Biomarker Levels and Dementia Risk. Ann Neurol 2024; 95:260-273. [PMID: 37801487 PMCID: PMC10842994 DOI: 10.1002/ana.26817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 09/06/2023] [Accepted: 10/03/2023] [Indexed: 10/08/2023]
Abstract
OBJECTIVE Few studies have comprehensively examined how health and disease risk influence Alzheimer's disease (AD) biomarkers. The present study examined the association of 14 protein-based health indicators with plasma and neuroimaging biomarkers of AD and neurodegeneration. METHODS In 706 cognitively normal adults, we examined whether 14 protein-based health indices (ie, SomaSignal® tests) were associated with concurrently measured plasma-based biomarkers of AD pathology (amyloid-β [Aβ]42/40 , tau phosphorylated at threonine-181 [pTau-181]), neuronal injury (neurofilament light chain [NfL]), and reactive astrogliosis (glial fibrillary acidic protein [GFAP]), brain volume, and cortical Aβ and tau. In a separate cohort (n = 11,285), we examined whether protein-based health indicators associated with neurodegeneration also predict 25-year dementia risk. RESULTS Greater protein-based risk for cardiovascular disease, heart failure mortality, and kidney disease was associated with lower Aβ42/40 and higher pTau-181, NfL, and GFAP levels, even in individuals without cardiovascular or kidney disease. Proteomic indicators of body fat percentage, lean body mass, and visceral fat were associated with pTau-181, NfL, and GFAP, whereas resting energy rate was negatively associated with NfL and GFAP. Together, these health indicators predicted 12, 31, 50, and 33% of plasma Aβ42/40 , pTau-181, NfL, and GFAP levels, respectively. Only protein-based measures of cardiovascular risk were associated with reduced regional brain volumes; these measures predicted 25-year dementia risk, even among those without clinically defined cardiovascular disease. INTERPRETATION Subclinical peripheral health may influence AD and neurodegenerative disease processes and relevant biomarker levels, particularly NfL. Cardiovascular health, even in the absence of clinically defined disease, plays a central role in brain aging and dementia. ANN NEUROL 2024;95:260-273.
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Affiliation(s)
- Heather E. Dark
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | | | - Gulzar N. Daya
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Zhongsheng Peng
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Michael R. Duggan
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Christos Davatzikos
- Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
| | | | | | - Julián Candia
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD USA
| | - Thomas Mosley
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Priya Palta
- Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA
- Department of Epidemiology, Columbia Mailman School of Public Health, New York, New York, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD USA
| | - Allison Shapiro
- Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus
| | | | - Keenan A. Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Intramural Research Program, Baltimore, MD, USA
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15
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Varma VR, An Y, Kac PR, Bilgel M, Moghekar A, Loeffler T, Amschl D, Troncoso J, Blennow K, Zetterberg H, Ashton NJ, Resnick SM, Thambisetty M. Longitudinal progression of blood biomarkers reveals a key role of astrocyte reactivity in preclinical Alzheimer's disease. medRxiv 2024:2024.01.25.24301779. [PMID: 38343809 PMCID: PMC10854357 DOI: 10.1101/2024.01.25.24301779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/26/2024]
Abstract
Defining the progression of blood biomarkers of Alzheimer's disease (AD) is essential for targeting treatments in patients most likely to benefit from early intervention. We delineated the temporal ordering of blood biomarkers a decade prior to the onset of AD symptoms in participants in the Baltimore Longitudinal Study of Aging. We show that increased astrocyte reactivity, assessed by elevated glial fibrillary acidic protein (GFAP) levels is an early event in the progression of blood biomarker changes in preclinical AD. In AD-converters who are initially cognitively unimpaired (N=158, 377 serial plasma samples), higher plasma GFAP levels are observed as early as 10-years prior to the onset of cognitive impairment due to incident AD compared to individuals who remain cognitively unimpaired (CU, N=160, 379 serial plasma samples). Plasma GFAP levels in AD-converters remain elevated 5-years prior to and coincident with the onset of cognitive impairment due to AD. In participants with neuropathologically confirmed AD, plasma GFAP levels are elevated relative to cognitively normal individuals and intermediate in those who remain cognitively unimpaired despite significant AD pathology (asymptomatic AD). Higher plasma GFAP levels at death are associated with greater severity of both neuritic plaques and neurofibrillary tangles. In the 5XFAD transgenic model of AD, we observed greater GFAP levels in the cortex and hippocampus of transgenic mice relative to wild-type prior to the development of cognitive impairment. Reactive astrocytosis, an established biological response to neuronal injury, may be an early initiator of AD pathogenesis and a promising therapeutic target.
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Affiliation(s)
- V R Varma
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, United States of America
| | - Y An
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, MD, USA
| | - P R Kac
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - M Bilgel
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, MD, USA
| | - A Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA
| | - T Loeffler
- Scantox Neuro GmbH, Parkring 12, 8074, Grambach, Austria
| | - D Amschl
- Scantox Neuro GmbH, Parkring 12, 8074, Grambach, Austria
| | - J Troncoso
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - K Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - H Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - N J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience & Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- King's College London, Institute of Psychiatry, Psychology and Neuroscience Maurice Wohl Institute Clinical Neuroscience Institute London UK
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation London UK
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - S M Resnick
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, MD, USA
| | - M Thambisetty
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, United States of America
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16
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Gao C, Yang Q, Kim ME, Khairi NM, Cai LY, Newlin NR, Kanakaraj P, Remedios LW, Krishnan AR, Yu X, Yao T, Zhang P, Schilling KG, Moyer D, Archer DB, Resnick SM, Landman BA. Characterizing patterns of DTI variance in aging brains. medRxiv 2024:2023.08.22.23294381. [PMID: 37662348 PMCID: PMC10473788 DOI: 10.1101/2023.08.22.23294381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Purpose We characterize the role of physiology, subject compliance, and the interaction of subject with the scanner in the understanding of DTI variability, as modeled in spatial variance of derived metrics in homogeneous regions. Methods We analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging (BLSA), with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. We assess variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as "interval"), motion, sex, and whether it is the first scan or the second scan in the session. Results Covariate effects are heterogeneous and bilaterally symmetric across ROIs. Inter-session interval is positively related (p ≪ 0.001) to FA variance in the cuneus and occipital gyrus, but negatively (p ≪ 0.001) in the caudate nucleus. Males show significantly (p ≪ 0.001) higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In 62 out of 176 ROIs defined by the Eve type-1 atlas, an increase in motion is associated (p < 0.05) with a decrease in FA variance. Head motion increases during the rescan of DTI (Δμ = 0.045 millimeters per volume). Conclusions The effects of each covariate on DTI variance, and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis. This work provides a foundation for study planning to account for regional variations in metric variance.
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Affiliation(s)
- Chenyu Gao
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, United States
| | - Qi Yang
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Michael E Kim
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Nazirah Mohd Khairi
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, United States
| | - Leon Y Cai
- Vanderbilt University, Department of Biomedical Engineering, Nashville, United States
| | - Nancy R Newlin
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | | | - Lucas W Remedios
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Aravind R Krishnan
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, United States
| | - Xin Yu
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Tianyuan Yao
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Panpan Zhang
- Vanderbilt University Medical Center, Department of Biostatistics, Nashville, United States
| | - Kurt G Schilling
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, USA
- Vanderbilt University, Vanderbilt University Institute of Imaging Science, Nashville, USA
| | - Daniel Moyer
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Derek B Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University School of Medicine, Nashville, USA
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, USA
| | - Susan M Resnick
- National Institute on Aging, Laboratory of Behavioral Neuroscience, Baltimore, United States
| | - Bennett A Landman
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, United States
- Vanderbilt University, Department of Computer Science, Nashville, United States
- Vanderbilt University, Department of Biomedical Engineering, Nashville, United States
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, USA
- Vanderbilt University, Vanderbilt University Institute of Imaging Science, Nashville, USA
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17
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Gao C, Kim ME, Lee HH, Yang Q, Khairi NM, Kanakaraj P, Newlin NR, Archer DB, Jefferson AL, Taylor WD, Boyd BD, Beason-Held LL, Resnick SM, Huo Y, Van Schaik KD, Schilling KG, Moyer D, Išgum I, Landman BA. Predicting Age from White Matter Diffusivity with Residual Learning. ArXiv 2024:arXiv:2311.03500v2. [PMID: 37986731 PMCID: PMC10659451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI's advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are non-linearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.
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Affiliation(s)
- Chenyu Gao
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
| | - Michael E Kim
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Ho Hin Lee
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Qi Yang
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Nazirah Mohd Khairi
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
| | | | - Nancy R Newlin
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Derek B Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
| | - Angela L Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Medicine, Vanderbilt University Medical Center, Nashville, USA
| | - Warren D Taylor
- Dept. of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Brian D Boyd
- Vanderbilt Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, USA
| | - Yuankai Huo
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Katherine D Van Schaik
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Kurt G Schilling
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
| | - Daniel Moyer
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
| | - Ivana Išgum
- Dept. of Biomedical Engineering and Physics, Dept. of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Bennett A Landman
- Dept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
- Dept. of Computer Science, Vanderbilt University, Nashville, USA
- Dept. of Neurology, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, USA
- Dept. of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, USA
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18
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Yang Z, Wen J, Abdulkadir A, Cui Y, Erus G, Mamourian E, Melhem R, Srinivasan D, Govindarajan ST, Chen J, Habes M, Masters CL, Maruff P, Fripp J, Ferrucci L, Albert MS, Johnson SC, Morris JC, LaMontagne P, Marcus DS, Benzinger TLS, Wolk DA, Shen L, Bao J, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering. Nat Commun 2024; 15:354. [PMID: 38191573 PMCID: PMC10774282 DOI: 10.1038/s41467-023-44271-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 12/06/2023] [Indexed: 01/10/2024] Open
Abstract
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.
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Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhao Wen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiong Chen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Biggs Alzheimer's Institute, University of Texas San Antonio Health Science Center, San Antonio, TX, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Paul Maruff
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, QLD, Australia
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel S Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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19
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Bouhrara M, Walker KA, R Alisch JS, Gong Z, Mazucanti CH, Lewis A, Moghekar AR, Turek L, Collingham V, Shehadeh N, Fantoni G, Kaileh M, Bergeron CM, Bergeron J, Resnick SM, Egan JM. Association of Plasma Markers of Alzheimer's Disease, Neurodegeneration, and Neuroinflammation with the Choroid Plexus Integrity in Aging. Aging Dis 2024:AD.2023.1226. [PMID: 38300640 DOI: 10.14336/ad.2023.1226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 12/26/2023] [Indexed: 02/02/2024] Open
Abstract
The choroid plexus (CP) is a vital brain structure essential for cerebrospinal fluid (CSF) production. Moreover, alterations in the CP's structure and function are implicated in molecular conditions and neuropathologies including multiple sclerosis, Alzheimer's disease, and stroke. Our goal is to provide the first characterization of the association between variation in the CP microstructure and macrostructure/volume using advanced magnetic resonance imaging (MRI) methodology, and blood-based biomarkers of Alzheimer's disease (Aß42/40 ratio; pTau181), neuroinflammation and neuronal injury (GFAP; NfL). We hypothesized that plasma biomarkers of brain pathology are associated with disordered CP structure. Moreover, since cerebral microstructural changes can precede macrostructural changes, we also conjecture that these differences would be evident in the CP microstructural integrity. Our cross-sectional study was conducted on a cohort of 108 well-characterized individuals, spanning 22-94 years of age, after excluding participants with cognitive impairments and non-exploitable MR imaging data. Established automated segmentation methods were used to identify the CP volume/macrostructure using structural MR images, while the microstructural integrity of the CP was assessed using our advanced quantitative high-resolution MR imaging of longitudinal and transverse relaxation times (T1 and T2). After adjusting for relevant covariates, positive associations were observed between pTau181, NfL and GFAP and all MRI metrics. These associations reached significance (p<0.05) except for CP volume vs. pTau181 (p=0.14), CP volume vs. NfL (p=0.35), and T2 vs. NFL (p=0.07). Further, negative associations between Aß42/40 and all MRI metrics were observed but reached significance only for Aß42/40 vs. T2 (p=0.04). These novel findings demonstrate that reduced CP macrostructural and microstructural integrity is positively associated with blood-based biomarkers of AD pathology, neurodegeneration/neuroinflammation and neurodegeneration. Degradation of the CP structure may co-occur with AD pathology and neuroinflammation ahead of clinically detectable cognitive impairment, making the CP a potential structure of interest for early disease detection or treatment monitoring.
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Affiliation(s)
- Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Joseph S R Alisch
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Zhaoyuan Gong
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Caio H Mazucanti
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Alexandria Lewis
- Johns Hopkins University School of Medicine, Baltimore, 21224 MD, USA
| | - Abhay R Moghekar
- Johns Hopkins University School of Medicine, Baltimore, 21224 MD, USA
| | - Lisa Turek
- Clinical Research Core, Baltimore, MD 21224, USA
| | | | | | | | - Mary Kaileh
- Clinical Research Core, Baltimore, MD 21224, USA
| | - Christopher M Bergeron
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Jan Bergeron
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Josephine M Egan
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
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20
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Yang Z, Wen J, Erus G, Govindarajan ST, Melhem R, Mamourian E, Cui Y, Srinivasan D, Abdulkadir A, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Yi D, Marcus DS, LaMontagne P, Benzinger TL, Heckbert SR, Austin TR, Waldstein SR, Evans MK, Zonderman AB, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Toga A, O’Bryant S, Chakravarty MM, Villeneuve S, Johnson SC, Morris JC, Albert MS, Yaffe K, Völzke H, Ferrucci L, Bryan NR, Shinohara RT, Fan Y, Habes M, Lalousis PA, Koutsouleris N, Wolk DA, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures. medRxiv 2023:2023.12.29.23300642. [PMID: 38234857 PMCID: PMC10793523 DOI: 10.1101/2023.12.29.23300642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials.
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Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T. Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Paraskevi Parmpi
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University of Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea
| | - Daniel S. Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Thomas R. Austin
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Shari R. Waldstein
- Department of Psychology, University of Maryland, Baltimore County, Catonsville, MD, USA
| | - Michele K. Evans
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Alan B. Zonderman
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute of Informatics, Washington University in St. Luis, St. Luis, MO63110, USA
| | - Mark A. Espeland
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Colin L. Masters
- Florey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia
| | - Paul Maruff
- Florey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Sid O’Bryant
- Institute for Translational Research University of North Texas Health Science Center Fort Worth Texas USA
| | - Mallar M. Chakravarty
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
| | - Sylvia Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA
| | - Nick R. Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T. Shinohara
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Biggs Alzheimer’s Institute, University of Texas San Antonio Health Science Center, USA
| | - Paris Alexandros Lalousis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Nikolaos Koutsouleris
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
- Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M. Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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21
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Wen J, Nasrallah IM, Abdulkadir A, Satterthwaite TD, Yang Z, Erus G, Robert-Fitzgerald T, Singh A, Sotiras A, Boquet-Pujadas A, Mamourian E, Doshi J, Cui Y, Srinivasan D, Skampardoni I, Chen J, Hwang G, Bergman M, Bao J, Veturi Y, Zhou Z, Yang S, Dazzan P, Kahn RS, Schnack HG, Zanetti MV, Meisenzahl E, Busatto GF, Crespo-Facorro B, Pantelis C, Wood SJ, Zhuo C, Shinohara RT, Gur RC, Gur RE, Koutsouleris N, Wolf DH, Saykin AJ, Ritchie MD, Shen L, Thompson PM, Colliot O, Wittfeld K, Grabe HJ, Tosun D, Bilgel M, An Y, Marcus DS, LaMontagne P, Heckbert SR, Austin TR, Launer LJ, Espeland M, Masters CL, Maruff P, Fripp J, Johnson SC, Morris JC, Albert MS, Bryan RN, Resnick SM, Fan Y, Habes M, Wolk D, Shou H, Davatzikos C. Genomic loci influence patterns of structural covariance in the human brain. Proc Natl Acad Sci U S A 2023; 120:e2300842120. [PMID: 38127979 PMCID: PMC10756284 DOI: 10.1073/pnas.2300842120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 10/31/2023] [Indexed: 12/23/2023] Open
Abstract
Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to patterns of structural covariance (PSC) across brain regions and individuals during brain aging and diseases. The genetic underpinnings of these patterns remain largely unknown. We apply a stochastic multivariate factorization method to a diverse population of 50,699 individuals (12 studies and 130 sites) and derive data-driven, multi-scale PSCs of regional brain size. PSCs were significantly correlated with 915 genomic loci in the discovery set, 617 of which are newly identified, and 72% were independently replicated. Key pathways influencing PSCs involve reelin signaling, apoptosis, neurogenesis, and appendage development, while pathways of breast cancer indicate potential interplays between brain metastasis and PSCs associated with neurodegeneration and dementia. Using support vector machines, multi-scale PSCs effectively derive imaging signatures of several brain diseases. Our results elucidate genetic and biological underpinnings that influence structural covariance patterns in the human brain.
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Affiliation(s)
- Junhao Wen
- Laboratory of AI and Biomedical Science, Department of Neurology, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ilya M. Nasrallah
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Ahmed Abdulkadir
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Theodore D. Satterthwaite
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zhijian Yang
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Guray Erus
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Timothy Robert-Fitzgerald
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ashish Singh
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Aristeidis Sotiras
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Aleix Boquet-Pujadas
- Biomedical Imaging Group, Department of Biomedical Engineering, École Polytechnique Fédérale de Lausanne, Lausanne1015, Switzerland
| | - Elizabeth Mamourian
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jimit Doshi
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Yuhan Cui
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Dhivya Srinivasan
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ioanna Skampardoni
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jiong Chen
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Gyujoon Hwang
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Mark Bergman
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Yogasudha Veturi
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Zhen Zhou
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, LondonWC2R 2LS, United Kingdom
| | - Rene S. Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Hugo G. Schnack
- Department of Psychiatry, University Medical Center Utrecht, Utrecht 3584 CX Ut, Netherlands
| | - Marcus V. Zanetti
- Institute of Psychiatry, Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo05508-070, Brazil
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Heinrich Heine University, Düsseldorf40204, Germany
| | - Geraldo F. Busatto
- Institute of Psychiatry, Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo05508-070, Brazil
| | - Benedicto Crespo-Facorro
- Hospital Universitario Virgen del Rocio, School of Medicine, University of Sevilla,Sevilla41004, Spain
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Stephen J. Wood
- Orygen and the Centre for Youth Mental Health, Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC 3052, Australia
| | - Chuanjun Zhuo
- Key Laboratory of Real Tine Tracing of Brain Circuits in Psychiatry and Neurology, Department of Psychiatry, Tianjin Medical University, Tianjin300070, China
| | - Russell T. Shinohara
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Ruben C. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Raquel E. Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian University, Munich 80539, Germany
| | - Daniel H. Wolf
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Andrew J. Saykin
- Indiana Alzheimer’s Disease Research Center, Department of Radiology, Indiana University School of Medicine, Indianapolis, IN46202-3082
| | - Marylyn D. Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA19104
| | - Paul M. Thompson
- Imaging Genetics Center, Department of Neurology, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA90033
| | - Olivier Colliot
- Institut du Cerveau, Sorbonne Université, Paris75013, France
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, German Center for Neurodegenerative Diseases, University Medicine Greifswald, Greifswald17475, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, German Center for Neurodegenerative Diseases, University Medicine Greifswald, Greifswald17475, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Daniel S. Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Pamela LaMontagne
- Department of Radiology, Washington University School of Medicine, St. Louis, MO63110
| | - Susan R. Heckbert
- Department of Epidemiology, University of Washington, Seattle, WA98195
| | - Thomas R. Austin
- Department of Epidemiology, University of Washington, Seattle, WA98195
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Washington, MD20817
| | - Mark Espeland
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Divisions of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, NC27101
| | - Colin L. Masters
- Florey Institute of Neuroscience and Mental Health, Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC3010, Australia
| | - Paul Maruff
- Florey Institute of Neuroscience and Mental Health, Medicine, Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC3010, Australia
| | - Jurgen Fripp
- Health and Biosecurity, Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Brisbane, QLD4029, Australia
| | - Sterling C. Johnson
- Wisconsin Alzheimer's Institute, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI53792
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Department of Neurology, Washington University in St. Louis, St. Louis, MO63110
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD21205
| | - R. Nick Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, NIH, Baltimore21224, MD
| | - Yong Fan
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Mohamad Habes
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX78229
| | - David Wolk
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Department of Neurology, University of Pennsylvania, Philadelphia, PA19104
| | - Haochang Shou
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
| | - Christos Davatzikos
- AI in Biomedical Imaging Laboratory, Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104
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22
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Chang K, Ling JP, Redding-Ochoa J, An Y, Li L, Dean SA, Blanchard TG, Pylyukh T, Barrett A, Irwin KE, Moghekar A, Resnick SM, Wong PC, Troncoso JC. Loss of TDP-43 splicing repression occurs early in the aging population and is associated with Alzheimer's disease neuropathologic changes and cognitive decline. Acta Neuropathol 2023; 147:4. [PMID: 38133681 DOI: 10.1007/s00401-023-02653-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/23/2023] [Accepted: 10/25/2023] [Indexed: 12/23/2023]
Abstract
LATE-NC, the neuropathologic changes of limbic-predominant age-related TAR DNA-binding protein 43 kDa (TDP-43) encephalopathy are frequently associated with Alzheimer's disease (AD) and cognitive impairment in older adults. The association of TDP-43 proteinopathy with AD neuropathologic changes (ADNC) and its impact on specific cognitive domains are not fully understood and whether loss of TDP-43 function occurs early in the aging brain remains unknown. Here, using a large set of autopsies from the Baltimore Longitudinal Study of Aging (BLSA) and another younger cohort, we were able to study brains from subjects 21-109 years of age. Examination of these brains show that loss of TDP-43 splicing repression, as judged by TDP-43 nuclear clearance and expression of a cryptic exon in HDGFL2, first occurs during the 6th decade, preceding by a decade the appearance of TDP-43+ neuronal cytoplasmic inclusions (NCIs). We corroborated this observation using a monoclonal antibody to demonstrate a cryptic exon-encoded neoepitope within HDGFL2 in neurons exhibiting nuclear clearance of TDP-43. TDP-43 nuclear clearance is associated with increased burden of tau pathology. Age at death, female sex, high CERAD neuritic plaque score, and high Braak neurofibrillary stage significantly increase the odds of LATE-NC. Faster rates of cognitive decline on verbal memory (California Verbal Learning Test immediate recall), visuospatial ability (Card Rotations Test), mental status (MMSE) and semantic fluency (Category Fluency Test) were associated with LATE-NC. Notably, the effects of LATE-NC on verbal memory and visuospatial ability are independent of ADNC. However, the effects of TDP-43 nuclear clearance in absence of NCI on the longitudinal trajectories and levels of cognitive measures are not significant. These results establish that loss of TDP-43 splicing repression is an early event occurring in the aging population during the development of TDP-43 proteinopathy and is associated with increased tau pathology. Furthermore, LATE-NC correlates with high levels of ADNC but also has an impact on specific memory and visuospatial functions in aging that is independent of AD.
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Affiliation(s)
- Koping Chang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department and Graduate Institute of Pathology, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, 100225, Taiwan
| | - Jonathan P Ling
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Javier Redding-Ochoa
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA
| | - Ling Li
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Office of the Chief Medical Examiner, State of Maryland, Baltimore, MD, 21223, USA
- Department of Pediatrics, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Stephanie A Dean
- Office of the Chief Medical Examiner, State of Maryland, Baltimore, MD, 21223, USA
| | - Thomas G Blanchard
- Department of Pediatrics, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Tatiana Pylyukh
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Alexander Barrett
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Katherine E Irwin
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, 21224, USA
| | - Philip C Wong
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Juan C Troncoso
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
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23
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Kac PR, González-Ortiz F, Emeršič A, Dulewicz M, Koutarapu S, Turton M, An Y, Smirnov D, Kulczyńska-Przybik A, Varma V, Ashton NJ, Montoliu-Gaya L, Camporesi E, Winkel I, Paradowski B, Moghekar A, Troncoso JC, Brinkmalm G, Resnick SM, Mroczko B, Kvartsberg H, Kramberger MG, Hanrieder J, Čučnik S, Harrison P, Zetterberg H, Lewczuk P, Thambisetty M, Rot U, Galasko D, Blennow K, Karikari TK. Plasma p-tau212: antemortem diagnostic performance and prediction of autopsy verification of Alzheimer's disease neuropathology. medRxiv 2023:2023.12.11.23299806. [PMID: 38168323 PMCID: PMC10760276 DOI: 10.1101/2023.12.11.23299806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Blood phosphorylated tau (p-tau) biomarkers, including p-tau217, show high associations with Alzheimer's disease (AD) neuropathologic change and clinical stage. Certain plasma p-tau217 assays recognize tau forms phosphorylated additionally at threonine-212, but the contribution of p-tau212 alone to AD is unknown. We developed a blood-based immunoassay that is specific to p-tau212 without cross-reactivity to p-tau217. Thereafter, we examined the diagnostic utility of plasma p-tau212. In five cohorts (n=388 participants), plasma p-tau212 showed high performances for AD diagnosis and for the detection of both amyloid and tau pathology, including at autopsy as well as in memory clinic populations. The diagnostic accuracy and fold changes of plasma p-tau212 were similar to those for p-tau217 but higher than p-tau181 and p-tau231. Immunofluorescent staining of brain tissue slices showed prominent p-tau212 reactivity in neurofibrillary tangles that co-localized with p-tau217 and p-tau202/205. These findings support plasma p-tau212 as a novel peripherally accessible biomarker of AD pathophysiology.
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Affiliation(s)
- Przemysław R Kac
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
| | - Fernando González-Ortiz
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, 431 80, Sweden
| | - Andreja Emeršič
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, 1000, Slovenia
- Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia
| | - Maciej Dulewicz
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
| | - Srinivas Koutarapu
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
| | | | - Yang An
- Brain Aging and Behavior Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, United States of America
| | - Denis Smirnov
- Department of Neurosciences, University of California, San Diego, CA 92161 United States of America
| | | | - Vijay Varma
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, United States of America
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
- Department of Old Age Psychiatry, King's College London, London SE5 8AF, United Kingdom
- Centre for Age-Related Medicine, Stavanger University Hospital, 4011 Stavanger, Norway
- South London & Maudsley NHS Foundation, NIHR Biomedical Research Centre for Mental Health & Biomedical Research Unit for Dementia, SE5 8AF London, United Kingdom
| | - Laia Montoliu-Gaya
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
| | - Elena Camporesi
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
| | - Izabela Winkel
- Dementia Disorders Center, Medical University of Wrocław, 59-330 Scinawa, Poland
| | - Bogusław Paradowski
- Department of Neurology, Medical University of Wrocław, 50-556 Wroclaw, Poland
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States of America
| | - Juan C Troncoso
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States of America
- Department of Pathology, John Hopkins University School of Medicine, Baltimore, MD 21287, United States of America
| | - Gunnar Brinkmalm
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, United States of America
| | - Barbara Mroczko
- Department of Neurodegeneration Diagnostics, Medical University of Białystok, Białystok 15-269, Poland
| | - Hlin Kvartsberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, 431 80, Sweden
| | - Milica Gregorič Kramberger
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, 1000, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Karolinska Institutet, Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, 141 52 Huddinge, Sweden
| | - Jörg Hanrieder
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, WC1E 6BT, United Kingdom
| | - Saša Čučnik
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, 1000, Slovenia
- Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia
- Department of Rheumatology, University Medical Center Ljubljana, Ljubljana, Slovenia
| | | | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, 431 80, Sweden
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, WC1E 6BT, United Kingdom
- UK Dementia Research Institute, University College London, London, WC1E 6BT, United Kingdom
- Hong Kong Center for Neurodegenerative Diseases, HKCeND, Hong Kong, 1512-1518, China
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI 53726, USA
| | - Piotr Lewczuk
- Department of Psychiatry and Psychotherapy, Universitätsklinikum Erlangen, and Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, 91054, Germany
- Department of Biochemical Diagnostics, University Hospital of Białystok, Białystok, 15-269, Poland
| | - Madhav Thambisetty
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, United States of America
| | - Uroš Rot
- Department of Neurology, University Medical Centre Ljubljana, Ljubljana, 1000, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Douglas Galasko
- Department of Neurosciences, University of California, San Diego, CA 92161 United States of America
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, 431 80, Sweden
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, 431 80, Sweden
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, United States of America
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24
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Bothwell AR, Resnick SM, Ferrucci L, Tian Q. Associations of olfactory function with brain structural and functional outcomes. A systematic review. Ageing Res Rev 2023; 92:102095. [PMID: 37913831 PMCID: PMC10872938 DOI: 10.1016/j.arr.2023.102095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/20/2023] [Accepted: 10/21/2023] [Indexed: 11/03/2023]
Abstract
In aging, olfactory deficits have been associated with lower cognition and motor function. Olfactory dysfunction is also one of the earliest features of neurodegenerative disease. A comprehensive review of the neural correlates of olfactive function may reveal mechanisms underlying the associations among olfaction, cognition, motor function, and neurodegenerative diseases. Here, we summarize existing knowledge on the relationship between brain structural and functional measures and olfaction in older adults without and with cognitive impairment, including Alzheimer's disease. We identified 33 eligible studies (30 MRI/DTI,3 fMRI); 31 were cross-sectional, most assessed odor identification, and few examined multiple brain areas. Lower olfactory function was associated with smaller volumes in the temporal lobe (hippocampus,parahippocampal gyrus,fusiform gyrus), olfactory-related regions (piriform cortex,amygdala,entorhinal cortex), pre- and postcentral gyri, and globus pallidus. During aging, olfactory impairment may be associated with pathology in brain areas important for motor function and cognition, especially memory. Future longitudinal studies that include neuroimaging across different brain areas are warranted to determine the neurobiological changes underlying olfactory changes in the aging brain and the progression of neurodegeneration.
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Affiliation(s)
- Adam R Bothwell
- Longitudinal Studies Section, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224, USA
| | - Qu Tian
- Longitudinal Studies Section, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224, USA.
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25
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Wang X, Salminen LE, Petkus AJ, Driscoll I, Millstein J, Beavers DP, Espeland MA, Erus G, Braskie MN, Thompson PM, Gatz M, Chui HC, Resnick SM, Kaufman JD, Rapp SR, Shumaker S, Brown M, Younan D, Chen JC. Association between late-life air pollution exposure and medial temporal lobe atrophy in older women. medRxiv 2023:2023.11.28.23298708. [PMID: 38077091 PMCID: PMC10705610 DOI: 10.1101/2023.11.28.23298708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Background Ambient air pollution exposures increase risk for Alzheimer's disease (AD) and related dementias, possibly due to structural changes in the medial temporal lobe (MTL). However, existing MRI studies examining exposure effects on the MTL were cross-sectional and focused on the hippocampus, yielding mixed results. Method To determine whether air pollution exposures were associated with MTL atrophy over time, we conducted a longitudinal study including 653 cognitively unimpaired community-dwelling older women from the Women's Health Initiative Memory Study with two MRI brain scans (MRI-1: 2005-6; MRI-2: 2009-10; Mage at MRI-1=77.3±3.5years). Using regionalized universal kriging models, exposures at residential locations were estimated as 3-year annual averages of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) prior to MRI-1. Bilateral gray matter volumes of the hippocampus, amygdala, parahippocampal gyrus (PHG), and entorhinal cortex (ERC) were summed to operationalize the MTL. We used linear regressions to estimate exposure effects on 5-year volume changes in the MTL and its subregions, adjusting for intracranial volume, sociodemographic, lifestyle, and clinical characteristics. Results On average, MTL volume decreased by 0.53±1.00cm3 over 5 years. For each interquartile increase of PM2.5 (3.26μg/m3) and NO2 (6.77ppb), adjusted MTL volume had greater shrinkage by 0.32cm3 (95%CI=[-0.43, -0.21]) and 0.12cm3 (95%CI=[-0.22, -0.01]), respectively. The exposure effects did not differ by APOE ε4 genotype, sociodemographic, and cardiovascular risk factors, and remained among women with low-level PM2.5 exposure. Greater PHG atrophy was associated with higher PM2.5 (b=-0.24, 95%CI=[-0.29, -0.19]) and NO2 exposures (b=-0.09, 95%CI=[-0.14, -0.04]). Higher exposure to PM2.5 but not NO2 was also associated with greater ERC atrophy. Exposures were not associated with amygdala or hippocampal atrophy. Conclusion In summary, higher late-life PM2.5 and NO2 exposures were associated with greater MTL atrophy over time in cognitively unimpaired older women. The PHG and ERC - the MTL cortical subregions where AD neuropathologies likely begin, may be preferentially vulnerable to air pollution neurotoxicity.
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Affiliation(s)
- Xinhui Wang
- Department of Neurology, University of Southern California, Los Angeles, California
| | - Lauren E Salminen
- Department of Neurology, University of Southern California, Los Angeles, California
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Andrew J Petkus
- Department of Neurology, University of Southern California, Los Angeles, California
| | - Ira Driscoll
- Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin
| | - Joshua Millstein
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California
| | - Daniel P Beavers
- Departments of Statistical Sciences, Wake Forest University, Winston-Salem, North Carolina
| | - Mark A Espeland
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
- Department of Biostatistics and Data Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, Pennsylvania
| | - Meredith N Braskie
- Department of Neurology, University of Southern California, Los Angeles, California
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Paul M Thompson
- Department of Neurology, University of Southern California, Los Angeles, California
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Margaret Gatz
- Center for Economic and Social Research, University of Southern California, Los Angeles, California
| | - Helena C Chui
- Department of Neurology, University of Southern California, Los Angeles, California
| | - Susan M Resnick
- The Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland
| | - Joel D Kaufman
- Departments of Environmental & Occupational Health Sciences, Medicine (General Internal Medicine), and Epidemiology, University of Washington, Seattle, Washington
| | - Stephen R Rapp
- Departments of Psychiatry and Behavioral Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Sally Shumaker
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Mark Brown
- Department of Biostatistics and Data Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Diana Younan
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California
| | - Jiu-Chiuan Chen
- Department of Neurology, University of Southern California, Los Angeles, California
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California
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26
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Petkus AJ, Salminen LE, Wang X, Driscoll I, Millstein J, Beavers DP, Espeland MA, Braskie MN, Thompson PM, Casanova R, Gatz M, Chui HC, Resnick SM, Kaufman JD, Rapp SR, Shumaker S, Younan D, Chen JC. Alzheimer's Related Neurodegeneration Mediates Air Pollution Effects on Medial Temporal Lobe Atrophy. medRxiv 2023:2023.11.29.23299144. [PMID: 38076972 PMCID: PMC10705654 DOI: 10.1101/2023.11.29.23299144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Exposure to ambient air pollution, especially particulate matter with aerodynamic diameter <2.5 μm (PM2.5) and nitrogen dioxide (NO2), are environmental risk factors for Alzheimer's disease and related dementia. The medial temporal lobe (MTL) is an important brain region subserving episodic memory that atrophies with age, during the Alzheimer's disease continuum, and is vulnerable to the effects of cerebrovascular disease. Despite the importance of air pollution it is unclear whether exposure leads to atrophy of the MTL and by what pathways. Here we conducted a longitudinal study examining associations between ambient air pollution exposure and MTL atrophy and whether putative air pollution exposure effects resembled Alzheimer's disease-related neurodegeneration or cerebrovascular disease-related neurodegeneration. Participants included older women (n = 627; aged 71-87) who underwent two structural brain MRI scans (MRI-1: 2005-6; MRI-2: 2009-10) as part of the Women's Health Initiative Memory Study of Magnetic Resonance Imaging. Regionalized universal kriging was used to estimate annual concentrations of PM2.5 and NO2 at residential locations aggregated to 3-year averages prior to MRI-1. The outcome was 5-year standardized change in MTL volumes. Mediators included voxel-based MRI measures of the spatial pattern of neurodegeneration of Alzheimer's disease (Alzheimer's disease pattern similarity scores [AD-PS]) and whole-brain white matter small-vessel ischemic disease (WM-SVID) volume as a proxy of global cerebrovascular damage. Structural equation models were constructed to examine whether the associations between exposures with MTL atrophy were mediated by the initial level or concurrent change in AD-PS score or WM-SVID while adjusting for sociodemographic, lifestyle, clinical characteristics, and intracranial volume. Living in locations with higher PM2.5 (per interquartile range [IQR]=3.17μg/m3) or NO2 (per IQR=6.63ppb) was associated with greater MTL atrophy (βPM2.5 = -0.29, 95% confidence interval [CI]=[-0.41,-0.18]; βNO2 =-0.12, 95%CI=[-0.23,-0.02]). Greater PM2.5 was associated with larger increases in AD-PS (βPM2.5 = 0.23, 95%CI=[0.12,0.33]) over time, which partially mediated associations with MTL atrophy (indirect effect= -0.10; 95%CI=[-0.15, -0.05]), explaining approximately 32% of the total effect. NO2 was positively associated with AD-PS at MRI-1 (βNO2=0.13, 95%CI=[0.03,0.24]), which partially mediated the association with MTL atrophy (indirect effect= -0.01, 95% CI=[-0.03,-0.001]). Global WM-SVID at MRI-1 or concurrent change were not significant mediators between exposures and MTL atrophy. Findings support the mediating role of Alzheimer's disease-related neurodegeneration contributing to MTL atrophy associated with late-life exposures to air pollutants. Alzheimer's disease-related neurodegeneration only partially explained associations between exposure and MTL atrophy suggesting the role of multiple neuropathological processes underlying air pollution neurotoxicity on brain aging.
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Affiliation(s)
- Andrew J. Petkus
- Department of Neurology, University of Southern California, Los Angeles, California, 90033, United States
| | - Lauren E. Salminen
- Department of Neurology, University of Southern California, Los Angeles, California, 90033, United States
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, 90033, United States
| | - Xinhui Wang
- Department of Neurology, University of Southern California, Los Angeles, California, 90033, United States
| | - Ira Driscoll
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, 53792, United States
| | - Joshua Millstein
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, 90033, United States
| | - Daniel P. Beavers
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, 27101, United States
| | - Mark A. Espeland
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, 27101, United States
| | - Meredith N. Braskie
- Department of Neurology, University of Southern California, Los Angeles, California, 90033, United States
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, 90033, United States
| | - Paul M. Thompson
- Department of Neurology, University of Southern California, Los Angeles, California, 90033, United States
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, 90033, United States
| | - Ramon Casanova
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, 27101, United States
| | - Margaret Gatz
- Center for Economic and Social Research, University of Southern California, Los Angeles, California, 90089, United States
| | - Helena C. Chui
- Department of Neurology, University of Southern California, Los Angeles, California, 90033, United States
| | - Susan M Resnick
- The Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, 20898, United States
| | - Joel D. Kaufman
- Departments of Environmental & Occupational Health Sciences, Medicine (General Internal Medicine), and Epidemiology, University of Washington, Seattle, Washington, 98195, United States
| | - Stephen R. Rapp
- Departments of Psychiatry and Behavioral Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina , 27101, United States
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina, 27101, United States
| | - Sally Shumaker
- Department of Social Sciences and Health Policy, Wake Forest School of Medicine, Winston-Salem, North Carolina, 27101, United States
| | - Diana Younan
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, 90033, United States
| | - Jiu-Chiuan Chen
- Department of Neurology, University of Southern California, Los Angeles, California, 90033, United States
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, 90033, United States
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Kanakaraj P, Yao T, Cai LY, Lee HH, Newlin NR, Kim ME, Gao C, Pechman KR, Archer D, Hohman T, Jefferson A, Beason-Held LL, Resnick SM, Garyfallidis E, Anderson A, Schilling KG, Landman BA, Moyer D. DeepN4: Learning N4ITK Bias Field Correction for T1-weighted Images. Res Sq 2023:rs.3.rs-3585882. [PMID: 38014176 PMCID: PMC10680935 DOI: 10.21203/rs.3.rs-3585882/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
T1-weighted (T1w) MRI has low frequency intensity artifacts due to magnetic field inhomogeneities. Removal of these biases in T1w MRI images is a critical preprocessing step to ensure spatially consistent image interpretation. N4ITK bias field correction, the current state-of-the-art, is implemented in such a way that makes it difficult to port between different pipelines and workflows, thus making it hard to reimplement and reproduce results across local, cloud, and edge platforms. Moreover, N4ITK is opaque to optimization before and after its application, meaning that methodological development must work around the inhomogeneity correction step. Given the importance of bias fields correction in structural preprocessing and flexible implementation, we pursue a deep learning approximation / reinterpretation of the N4ITK bias fields correction to create a method which is portable, flexible, and fully differentiable. In this paper, we trained a deep learning network "DeepN4" on eight independent cohorts from 72 different scanners and age ranges with N4ITK-corrected T1w MRI and bias field for supervision in log space. We found that we can closely approximate N4ITK bias fields correction with naïve networks. We evaluate the peak signal to noise ratio (PSNR) in test dataset against the N4ITK corrected images. The median PSNR of corrected images between N4ITK and DeepN4 was 47.96 dB. In addition, we assess the DeepN4 model on eight additional external datasets and show the generalizability of the approach. This study establishes that incompatible N4ITK preprocessing steps can be closely approximated by naïve deep neural networks, facilitating more flexibility. All code and models are released at https://github.com/MASILab/DeepN4.
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Affiliation(s)
| | - Tianyuan Yao
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Leon Y. Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Ho Hin Lee
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Nancy R. Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Michael E. Kim
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | | | - Kimberly R. Pechman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Derek Archer
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Timothy Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Angela Jefferson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lori L. Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | | | | | | | - Adam Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA
| | - Kurt G. Schilling
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A. Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology and Radiological Services, Vanderbilt University Medical Center, Vanderbilt University Medical, Nashville, TN, USA
- Vanderbilt University Institute for Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
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28
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Cai Y, Schrack JA, Agrawal Y, Armstrong NM, Wanigatunga AA, Kitner-Triolo M, Moghekar A, Ferrucci L, Simonsick EM, Resnick SM, Gross AL. Application and validation of an algorithmic classification of early impairment in cognitive performance. Aging Ment Health 2023; 27:2187-2192. [PMID: 37354067 PMCID: PMC10592406 DOI: 10.1080/13607863.2023.2227118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 06/09/2023] [Indexed: 06/26/2023]
Abstract
OBJECTIVE Due to the long prodromal period for dementia pathology, approaches are needed to detect cases before clinically recognizable symptoms are apparent, by which time it is likely too late to intervene. This study contrasted two theoretically-based algorithms for classifying early cognitive impairment (ECI) in adults aged ≥50 enrolled in the Baltimore Longitudinal Study of Aging. METHOD Two ECI algorithms were defined as poor performance (1 standard deviation [SD] below age-, sex-, race-, and education-specific means) in: (1) Card Rotations or California Verbal Learning Test (CVLT) immediate recall and (2) ≥1 (out of 2) memory or ≥3 (out of 6) non-memory tests. We evaluated concurrent criterion validity against consensus diagnoses of mild cognitive impairment (MCI) or dementia and global cognitive scores using receiver operating characteristic (ROC) curve analysis. Predictive criterion validity was evaluated using Cox proportional hazards models to examine the associations between algorithmic status and future adjudicated MCI/dementia. RESULTS Among 1,851 participants (mean age = 65.2 ± 11.8 years, 50% women, 74% white), the two ECI algorithms yielded comparably moderate concurrent criterion validity with adjudicated MCI/dementia. For predictive criterion validity, the algorithm based on impairment in Card Rotations or CVLT immediate recall was the better predictor of MCI/dementia (HR = 3.53, 95%CI: 1.59-7.84) over 12.3 follow-up years. CONCLUSIONS Impairment in visuospatial ability or memory may be capable of detecting early cognitive changes in the preclinical phase among cognitively normal individuals.
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Affiliation(s)
- Yurun Cai
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Health and Community Systems, University of Pittsburgh School of Nursing, Pittsburgh, PA, USA
| | - Jennifer A. Schrack
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center on Aging and Health, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Yuri Agrawal
- Department of Otolaryngology - Head and Neck Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Nicole M. Armstrong
- Department of Psychiatry and Human Behavior, Warren Alpert Medical School, Brown University, Providence, RI, USA
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - Amal A. Wanigatunga
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center on Aging and Health, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Abhay Moghekar
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Luigi Ferrucci
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | | | - Susan M. Resnick
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - Alden L. Gross
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Center on Aging and Health, Johns Hopkins School of Medicine, Baltimore, MD, USA
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29
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Zuo L, Liu Y, Xue Y, Dewey BE, Remedios SW, Hays SP, Bilgel M, Mowry EM, Newsome SD, Calabresi PA, Resnick SM, Prince JL, Carass A. HACA3: A unified approach for multi-site MR image harmonization. Comput Med Imaging Graph 2023; 109:102285. [PMID: 37657151 PMCID: PMC10592042 DOI: 10.1016/j.compmedimag.2023.102285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/11/2023] [Accepted: 08/08/2023] [Indexed: 09/03/2023]
Abstract
The lack of standardization and consistency of acquisition is a prominent issue in magnetic resonance (MR) imaging. This often causes undesired contrast variations in the acquired images due to differences in hardware and acquisition parameters. In recent years, image synthesis-based MR harmonization with disentanglement has been proposed to compensate for the undesired contrast variations. The general idea is to disentangle anatomy and contrast information from MR images to achieve cross-site harmonization. Despite the success of existing methods, we argue that major improvements can be made from three aspects. First, most existing methods are built upon the assumption that multi-contrast MR images of the same subject share the same anatomy. This assumption is questionable, since different MR contrasts are specialized to highlight different anatomical features. Second, these methods often require a fixed set of MR contrasts for training (e.g., both T1-weighted and T2-weighted images), limiting their applicability. Lastly, existing methods are generally sensitive to imaging artifacts. In this paper, we present Harmonization with Attention-based Contrast, Anatomy, and Artifact Awareness (HACA3), a novel approach to address these three issues. HACA3 incorporates an anatomy fusion module that accounts for the inherent anatomical differences between MR contrasts. Furthermore, HACA3 can be trained and applied to any combination of MR contrasts and is robust to imaging artifacts. HACA3 is developed and evaluated on diverse MR datasets acquired from 21 sites with varying field strengths, scanner platforms, and acquisition protocols. Experiments show that HACA3 achieves state-of-the-art harmonization performance under multiple image quality metrics. We also demonstrate the versatility and potential clinical impact of HACA3 on downstream tasks including white matter lesion segmentation for people with multiple sclerosis and longitudinal volumetric analyses for normal aging subjects. Code is available at https://github.com/lianruizuo/haca3.
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Affiliation(s)
- Lianrui Zuo
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA.
| | - Yihao Liu
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Yuan Xue
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Blake E Dewey
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Samuel W Remedios
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA; Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA
| | - Savannah P Hays
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Ellen M Mowry
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Scott D Newsome
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Peter A Calabresi
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD 21224, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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30
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Tian Q, Bilgel M, Walker KA, Moghekar AR, Fishbein KW, Spencer RG, Resnick SM, Ferrucci L. Skeletal muscle mitochondrial function predicts cognitive impairment and is associated with biomarkers of Alzheimer's disease and neurodegeneration. Alzheimers Dement 2023; 19:4436-4445. [PMID: 37530130 PMCID: PMC10592411 DOI: 10.1002/alz.13388] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 06/08/2023] [Accepted: 06/12/2023] [Indexed: 08/03/2023]
Abstract
INTRODUCTION Mitochondrial dysfunction is implicated in the pathophysiology of many chronic diseases. Whether it is related to cognitive impairment and pathological markers is unknown. METHODS We examined the associations of in vivo skeletal muscle mitochondrial function (post-exercise recovery rate of phosphocreatine [kPCr] via magnetic resonance [MR] spectroscopy with future mild cognitive impairment (MCI) or dementia, and with positron emission tomography (PET) and blood biomarkers of Alzheimer's disease [AD] and neurodegeneration (i.e., Pittsburgh Compound-B [PiB] distribution volume ratio [DVR] for amyloid beta [Aβ], flortaucipir (FTP) standardized uptake value ratio [SUVR] for tau, Aβ42 /40 ratio, phosphorylated tau 181 [p-tau181], neurofilament light chain [NfL], and glial fibrillary acidic protein [GFAP]). RESULTS After covariate adjustment, each standard deviation (SD) higher kPCr level was associated with 52% lower hazards of developing MCI/dementia, and with 59% lower odds of being PiB positive with specific associations in DVR of frontal, parietal, and temporal regions, and cingulate cortex and pallidum. Higher kPCr level was also associated with lower plasma GFAP. DISCUSSION In aging, mitochondrial dysfunction may play a vital role in AD pathological changes and neuroinflammation. Highlights Higher in vivo mitochondrial function is related to lower risk of mild cognitive impairment (MCI)/dementia. Higher in vivo mitochondrial function is related to lower amyloid tracer uptake. Higher in vivo mitochondrial function is related to lower plasma neuroinflammation. Mitochondrial dysfunction may play a key role in Alzheimer's disease (AD) and neurodegeneration.
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Affiliation(s)
- Qu Tian
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224
| | - Keenan A. Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224
| | - Abhay R. Moghekar
- Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287
| | - Kenneth W. Fishbein
- Laboratory of Clinical Investigation, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224
| | - Richard G. Spencer
- Laboratory of Clinical Investigation, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, 251 Bayview Blvd., Suite 100, Baltimore, MD 21224
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31
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Chuang YF, An Y, Bilgel M, Wong DF, Troncoso JC, O'Brien RJ, Breitner JC, Ferrucci L, Resnick SM, Thambisetty M. Correction: Midlife adiposity predicts earlier onset of Alzheimer's dementia, neuropathology and presymptomatic cerebral amyloid accumulation. Mol Psychiatry 2023; 28:4486. [PMID: 37563279 DOI: 10.1038/s41380-023-02210-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Affiliation(s)
- Y-F Chuang
- Clinical and Translational Neuroscience Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, MD, USA
- Institute of Public Health, National Yang-Ming University, Taipei, Taiwan
- Department of Psychiatry, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Y An
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - M Bilgel
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
- Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - D F Wong
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Science and Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - J C Troncoso
- Department of Pathology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - R J O'Brien
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - J C Breitner
- Centre for Studies on Prevention of Alzheimer's Disease, Douglas Mental Health University Institute Research Centre, Montreal, QC, Canada
| | - L Ferrucci
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - S M Resnick
- Intramural Research Program, National Institute on Aging, Baltimore, MD, USA
| | - M Thambisetty
- Clinical and Translational Neuroscience Unit, Laboratory of Behavioral Neuroscience, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, MD, USA.
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32
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Bilgel M, An Y, Walker KA, Moghekar AR, Ashton NJ, Kac PR, Karikari TK, Blennow K, Zetterberg H, Jedynak BM, Thambisetty M, Ferrucci L, Resnick SM. Longitudinal changes in Alzheimer's-related plasma biomarkers and brain amyloid. Alzheimers Dement 2023; 19:4335-4345. [PMID: 37216632 PMCID: PMC10592628 DOI: 10.1002/alz.13157] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/25/2023] [Accepted: 04/25/2023] [Indexed: 05/24/2023]
Abstract
INTRODUCTION Understanding longitudinal plasma biomarker trajectories relative to brain amyloid changes can help devise Alzheimer's progression assessment strategies. METHODS We examined the temporal order of changes in plasma amyloid-β ratio (A β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ ), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and phosphorylated tau ratios (p-tau181 / A β 42 $\text{p-tau181}/\mathrm{A}{\beta}_{42}$ ,p-tau231 / A β 42 $\text{p-tau231}/\mathrm{A}{\beta}_{42}$ ) relative to 11 C-Pittsburgh compound B (PiB) positron emission tomography (PET) cortical amyloid burden (PiB-/+). Participants (n = 199) were cognitively normal at index visit with a median 6.1-year follow-up. RESULTS PiB groups exhibited different rates of longitudinal change inA β 42 / A β 40 ( β = 5.41 × 10 - 4 , SE = 1.95 × 10 - 4 , p = 0.0073 ) ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}\ ( {\beta \ = \ 5.41 \times {{10}}^{ - 4},{\rm{\ SE\ }} = \ 1.95 \times {{10}}^{ - 4},\ p\ = \ 0.0073} )$ . Change in brain amyloid correlated with change in GFAP (r = 0.5, 95% CI = [0.26, 0.68]). The greatest relative decline inA β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ (-1%/year) preceded brain amyloid positivity by 41 years (95% CI = [32, 53]). DISCUSSION PlasmaA β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ may begin declining decades prior to brain amyloid accumulation, whereas p-tau ratios, GFAP, and NfL increase closer in time. HIGHLIGHTS PlasmaA β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ declines over time among PiB- but does not change among PiB+. Phosphorylated-tau to Aβ42 ratios increase over time among PiB+ but do not change among PiB-. Rate of change in brain amyloid is correlated with change in GFAP and neurofilament light chain. The greatest decline inA β 42 / A β 40 ${{\rm A}\beta }_{42}/{{\rm A}\beta }_{40}$ may precede brain amyloid positivity by decades.
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Affiliation(s)
- Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, 21224, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, 21224, USA
| | - Keenan A. Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, 21224, USA
| | - Abhay R. Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21287, USA
| | - Nicholas J. Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 80 Mölndal, Sweden
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, SE5 9RX, UK
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research, Unit for Dementia at South London and Maudsley, NHS Foundation, London, SE5 8AF, UK
- Centre for Age-Related Medicine, Stavanger University Hospital, 4019 Stavanger, Norway
| | - Przemysław R. Kac
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 80 Mölndal, Sweden
| | - Thomas K. Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 80 Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 80 Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 431 80 Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 80 Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 431 80 Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
- UK Dementia Research Institute at UCL, London, WC1E 6BT, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53792, USA
| | - Bruno M. Jedynak
- Department of Mathematics and Statistics, Portland State University, Portland, Oregon, 97201, USA
| | - Madhav Thambisetty
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, 21224, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, Baltimore, Maryland, 21224, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, 21224, USA
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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. Alzheimers Dement (Amst) 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Dougherty RJ, Wanigatunga AA, An Y, Tian Q, Simonsick EM, Albert MS, Resnick SM, Schrack JA. Walking energetics and white matter hyperintensities in mid-to-late adulthood. Alzheimers Dement (Amst) 2023; 15:e12501. [PMID: 38026756 PMCID: PMC10646278 DOI: 10.1002/dad2.12501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/11/2023] [Accepted: 10/22/2023] [Indexed: 12/01/2023]
Abstract
INTRODUCTION White matter hyperintensities (WMHs) increase with age and contribute to cognitive and motor function decline. Energy costs for mobility worsen with age, as the energetic cost of walking increases and energetic capacity declines. We examined the cross-sectional associations of multiple measures of walking energetics with WMHs in mid- to late-aged adults. METHODS A total of 601 cognitively unimpaired adults (mean age 66.9 ± 15.3 years, 54% women) underwent brain magnetic resonance imaging scans and completed standardized slow- and peak-paced walking assessments with metabolic measurement (V̇O2). T1-weighted scans and fluid-attenuated inversion recovery images were used to quantify WMHs. Separate multivariable linear regression models examined associations adjusted for covariates. RESULTS Lower slow-paced V̇O2 (B = 0.07; P = 0.030), higher peak-paced V̇O2 (B = -0.10; P = 0.007), and lower cost-to-capacity ratio (B = .12; P < 0.0001) were all associated with lower WMH volumes. DISCUSSION The cost-to-capacity ratio, which describes the percentage of capacity required for ambulation, was the walking energetic measure most strongly associated with WMHs.
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Affiliation(s)
- Ryan J. Dougherty
- Department of NeurologyJohns Hopkins School of MedicineBaltimoreMarylandUSA
- Center on Aging and HealthJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Amal A. Wanigatunga
- Center on Aging and HealthJohns Hopkins UniversityBaltimoreMarylandUSA
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Yang An
- Intramural Research ProgramNational Institute on AgingBaltimoreMarylandUSA
| | - Qu Tian
- Intramural Research ProgramNational Institute on AgingBaltimoreMarylandUSA
| | | | - Marilyn S. Albert
- Department of NeurologyJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Susan M. Resnick
- Intramural Research ProgramNational Institute on AgingBaltimoreMarylandUSA
| | - Jennifer A. Schrack
- Center on Aging and HealthJohns Hopkins UniversityBaltimoreMarylandUSA
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
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Gao Y, Zhao Y, Li M, Lawless RD, Schilling KG, Xu L, Shafer AT, Beason-Held LL, Resnick SM, Rogers BP, Ding Z, Anderson AW, Landman BA, Gore JC. Functional alterations in bipartite network of white and grey matters during aging. Neuroimage 2023; 278:120277. [PMID: 37473978 PMCID: PMC10529380 DOI: 10.1016/j.neuroimage.2023.120277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/23/2023] [Accepted: 07/11/2023] [Indexed: 07/22/2023] Open
Abstract
The effects of normal aging on functional connectivity (FC) within various brain networks of gray matter (GM) have been well-documented. However, the age effects on the networks of FC between white matter (WM) and GM, namely WM-GM FC, remains unclear. Evaluating crucial properties, such as global efficiency (GE), for a WM-GM FC network poses a challenge due to the absence of closed triangle paths which are essential for assessing network properties in traditional graph models. In this study, we propose a bipartite graph model to characterize the WM-GM FC network and quantify these challenging network properties. Leveraging this model, we assessed the WM-GM FC network properties at multiple scales across 1,462 cognitively normal subjects aged 22-96 years from three repositories (ADNI, BLSA and OASIS-3) and investigated the age effects on these properties throughout adulthood and during late adulthood (age ≥70 years). Our findings reveal that (1) heterogeneous alterations occurred in region-specific WM-GM FC over the adulthood and decline predominated during late adulthood; (2) the FC density of WM bundles engaged in memory, executive function and processing speed declined with age over adulthood, particularly in later years; and (3) the GE of attention, default, somatomotor, frontoparietal and limbic networks reduced with age over adulthood, and GE of visual network declined during late adulthood. These findings provide unpresented insights into multi-scale alterations in networks of WM-GM functional synchronizations during normal aging. Furthermore, our bipartite graph model offers an extendable framework for quantifying WM-engaged networks, which may contribute to a wide range of neuroscience research.
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Affiliation(s)
- Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Richard D Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Baxter P Rogers
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
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Tian Q, Mitchell BA, Erus G, Davatzikos C, Moaddel R, Resnick SM, Ferrucci L. Sex differences in plasma lipid profiles of accelerated brain aging. Neurobiol Aging 2023; 129:178-184. [PMID: 37336172 PMCID: PMC10527719 DOI: 10.1016/j.neurobiolaging.2023.05.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 05/18/2023] [Accepted: 05/23/2023] [Indexed: 06/21/2023]
Abstract
Lipids are essential components of brain structure and shown to affect brain function. Previous studies have shown that aging men undergo greater brain atrophy than women, but whether the associations between lipids and brain atrophy differ by sex is unclear. We examined sex differences in the associations between circulating lipids by liquid chromatography-tandem mass spectrometry and the progression of MRI-derived brain atrophy index Spatial Patterns of Atrophy for Recognition of Brain Aging (SPARE-BA) over an average of 4.7 (SD = 2.3) years in 214 men and 261 women aged 60 or older who were initially cognitively normal using multivariable linear regression, adjusted for age, race, education, and baseline SPARE-BA. We found significant sex interactions for beta-oxidation rate, short-chain acylcarnitines, long-chain ceramides, and very long-chain triglycerides. Lower beta-oxidation rate and short-chain acylcarnitines in women and higher long-chain ceramides and very long-chain triglycerides in men were associated with faster increases in SPARE-BA (accelerated brain aging). Circulating lipid profiles of accelerated brain aging are sex-specific and vary by lipid classes and structure. Mechanisms underlying these sex-specific lipid profiles of brain aging warrant further investigation.
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Affiliation(s)
- Qu Tian
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA.
| | - Brendan A Mitchell
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Guray Erus
- Radiology Department, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Radiology Department, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruin Moaddel
- Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
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Newlin NR, Kim ME, Kanakaraj P, Yao T, Hohman T, Pechman KR, Beason-Held LL, Resnick SM, Archer D, Jefferson A, Landman BA, Moyer D. MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal. bioRxiv 2023:2023.08.12.553099. [PMID: 37645973 PMCID: PMC10462069 DOI: 10.1101/2023.08.12.553099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Objective Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. Methods We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. Conclusion MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. Significance Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.
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Affiliation(s)
- Nancy R Newlin
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | - Michael E Kim
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | | | - Tianyuan Yao
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | - Timothy Hohman
- VMAC, VUMC, Nashville, TN, USA and Vanderbilt University, Nashville, TN, USA
| | | | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Derek Archer
- VMAC, VUMC, Nashville, TN, USA and Vanderbilt University, Nashville, TN, USA
| | - Angela Jefferson
- VMAC, VUMC, Nashville, TN, USA and Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
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Terracciano A, Walker K, An Y, Luchetti M, Stephan Y, Moghekar AR, Sutin AR, Ferrucci L, Resnick SM. The association between personality and plasma biomarkers of astrogliosis and neuronal injury. Neurobiol Aging 2023; 128:65-73. [PMID: 37210782 PMCID: PMC10247521 DOI: 10.1016/j.neurobiolaging.2023.04.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/31/2023] [Accepted: 04/22/2023] [Indexed: 05/23/2023]
Abstract
Personality traits have been associated with the risk of dementia and Alzheimer's disease neuropathology, including amyloid and tau. This study examines whether personality traits are concurrently related to plasma glial fibrillary acidic protein (GFAP), a marker of astrogliosis, and neurofilament light (NfL), a marker of neuronal injury. Cognitively unimpaired participants from the Baltimore Longitudinal Study on Aging (N = 786; age: 22-95) were assayed for plasma GFAP and NfL and completed the Revised NEO Personality Inventory, which measures 5 domains and 30 facets of personality. Neuroticism (particularly vulnerability to stress, anxiety, and depression) was associated with higher GFAP and NfL. Conscientiousness was associated with lower GFAP. Extraversion (particularly positive emotions, assertiveness, and activity) was related to lower GFAP and NfL. These associations were independent of demographic, behavioral, and health covariates and not moderated by age, sex, or apolipoprotein E genotype. The personality correlates of astrogliosis and neuronal injury tend to be similar, are found in individuals without cognitive impairment, and point to potential neurobiological underpinnings of the association between personality traits and neurodegenerative diseases.
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Affiliation(s)
- Antonio Terracciano
- Department of Geriatrics, Florida State University College of Medicine, Tallahassee, FL, USA; Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
| | - Keenan Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Martina Luchetti
- Department of Behavioral Sciences and Social Medicine, Florida State University College of Medicine, Tallahassee, FL, USA
| | | | - Abhay R Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Angelina R Sutin
- Department of Behavioral Sciences and Social Medicine, Florida State University College of Medicine, Tallahassee, FL, USA
| | - Luigi Ferrucci
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
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Tian Q, Shardell MD, Kuo PL, Tanaka T, Simonsick EM, Moaddel R, Resnick SM, Ferrucci L. Plasma metabolomic signatures of dual decline in memory and gait in older adults. GeroScience 2023; 45:2659-2667. [PMID: 37052768 PMCID: PMC10651620 DOI: 10.1007/s11357-023-00792-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
Abstract
Older adults experiencing dual decline in memory and gait have greater dementia risk than those with memory or gait decline only, but mechanisms are unknown. Dual decline may indicate specific pathophysiological pathways to dementia which can be reflected by circulating metabolites. We compared longitudinal changes in plasma metabolite biomarkers of older adults with and without dual decline in the Baltimore Longitudinal Study of Aging (BLSA). Participants were grouped into 4 phenotypes based on annual rates of decline in verbal memory and gait speed: no decline in memory or gait, memory decline only, gait decline only, and dual decline. Repeated measures of plasma metabolomics were measured by biocrates p500 kit during the same time of memory and gait assessments. In BLSA, 18 metabolites differed across groups (q-value < 0.05). Metabolites differentially abundant were enriched for lysophosphatidylcholines (lysoPC C18:0,C16:0,C17:0,C18:1,C18:2), ceramides (d18:2/24:0,d16:1/24:0,d16:1/23:0), and amino acids (glycine) classes. Compared to no decline, the dual decline group showed greater declines in lysoPC C18:0, homoarginine synthesis, and the metabolite module containing mostly triglycerides, and showed a greater increase in indoleamine 2,3-dioxygenase (IDO) activity. Metabolites distinguishing dual decline and no decline groups were implicated in metabolic pathways of the aminoacyl-tRNA biosynthesis, valine, leucine and isoleucine biosynthesis, histidine metabolism, and sphingolipid metabolism. Older adults with dual decline exhibit the most extensive alterations in metabolic profiling of lysoPCs, ceramides, IDO activity, and homoarginine synthesis. Alterations in these metabolites may indicate mitochondrial dysfunction, compromised immunity, and elevated burden of cardiovascular and kidney pathology.
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Affiliation(s)
- Qu Tian
- Translational Gerontology Branch, National Institute On Aging, 251 Bayview Blvd., Suite 100, Room 04B316, Baltimore, MD, 21224, USA.
| | | | - Pei-Lun Kuo
- Translational Gerontology Branch, National Institute On Aging, 251 Bayview Blvd., Suite 100, Room 04B316, Baltimore, MD, 21224, USA
| | - Toshiko Tanaka
- Translational Gerontology Branch, National Institute On Aging, 251 Bayview Blvd., Suite 100, Room 04B316, Baltimore, MD, 21224, USA
| | - Eleanor M Simonsick
- Translational Gerontology Branch, National Institute On Aging, 251 Bayview Blvd., Suite 100, Room 04B316, Baltimore, MD, 21224, USA
| | - Ruin Moaddel
- Laboratory of Clinical Investigation, National Institute on Aging, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute On Aging, 251 Bayview Blvd., Suite 100, Room 04B316, Baltimore, MD, 21224, USA
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Gong Z, Bilgel M, Kiely M, Triebswetter C, Ferrucci L, Resnick SM, Spencer RG, Bouhrara M. Lower myelin content is associated with more rapid cognitive decline among cognitively unimpaired individuals. Alzheimers Dement 2023; 19:3098-3107. [PMID: 36720000 PMCID: PMC10387505 DOI: 10.1002/alz.12968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 02/01/2023]
Abstract
INTRODUCTION The influence of myelination on longitudinal changes in cognitive performance remains unclear. METHODS For each participant (N = 123), longitudinal cognitive scores were calculated. Myelin content was probed using myelin water fraction (MWF) or longitudinal relaxation rate (R1 ); both are MRI measures sensitive to myelin, with MWF being specific. RESULTS Lower MWF was associated with steeper declines in executive function (p < .02 in all regions) and lower R1 was associated with steeper declines in verbal fluency (p < .03 in all regions). Additionally, lower R1 was associated with steeper declines in executive function (p < .02 in all regions) and memory (p < .04 in occipital and cerebral white matter) but did not survive Bonferroni correction. DISCUSSION We demonstrate significant relationships between myelin content and the rates of change in cognitive performance among cognitively normal individuals. These findings highlight the importance of myelin in cognitive functioning and suggest MWF and R1 as imaging biomarkers to predict cognitive changes.
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Affiliation(s)
- Zhaoyuan Gong
- Magnetic Resonance Physics of Aging and Dementia (MRPAD) Unit, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA
| | - Murat Bilgel
- Brain Aging and Behavior Section, NIA, NIH, Baltimore, Maryland, USA
| | - Matthew Kiely
- Magnetic Resonance Physics of Aging and Dementia (MRPAD) Unit, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA
| | - Curtis Triebswetter
- Magnetic Resonance Physics of Aging and Dementia (MRPAD) Unit, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA
| | - Luigi Ferrucci
- Longitudinal Studies Section, NIA, NIH, Baltimore, Maryland, USA
| | - Susan M Resnick
- Brain Aging and Behavior Section, NIA, NIH, Baltimore, Maryland, USA
| | - Richard G Spencer
- Magnetic Resonance Imaging and Spectroscopy Section, NIA, NIH, Baltimore, Maryland, USA
| | - Mustapha Bouhrara
- Magnetic Resonance Physics of Aging and Dementia (MRPAD) Unit, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, Maryland, USA
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Kaivola K, Chia R, Ding J, Rasheed M, Fujita M, Menon V, Walton RL, Collins RL, Billingsley K, Brand H, Talkowski M, Zhao X, Dewan R, Stark A, Ray A, Solaiman S, Alvarez Jerez P, Malik L, Dawson TM, Rosenthal LS, Albert MS, Pletnikova O, Troncoso JC, Masellis M, Keith J, Black SE, Ferrucci L, Resnick SM, Tanaka T, Topol E, Torkamani A, Tienari P, Foroud TM, Ghetti B, Landers JE, Ryten M, Morris HR, Hardy JA, Mazzini L, D'Alfonso S, Moglia C, Calvo A, Serrano GE, Beach TG, Ferman T, Graff-Radford NR, Boeve BF, Wszolek ZK, Dickson DW, Chiò A, Bennett DA, De Jager PL, Ross OA, Dalgard CL, Gibbs JR, Traynor BJ, Scholz SW. Genome-wide structural variant analysis identifies risk loci for non-Alzheimer's dementias. Cell Genom 2023; 3:100316. [PMID: 37388914 PMCID: PMC10300553 DOI: 10.1016/j.xgen.2023.100316] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 03/21/2023] [Accepted: 04/06/2023] [Indexed: 07/01/2023]
Abstract
We characterized the role of structural variants, a largely unexplored type of genetic variation, in two non-Alzheimer's dementias, namely Lewy body dementia (LBD) and frontotemporal dementia (FTD)/amyotrophic lateral sclerosis (ALS). To do this, we applied an advanced structural variant calling pipeline (GATK-SV) to short-read whole-genome sequence data from 5,213 European-ancestry cases and 4,132 controls. We discovered, replicated, and validated a deletion in TPCN1 as a novel risk locus for LBD and detected the known structural variants at the C9orf72 and MAPT loci as associated with FTD/ALS. We also identified rare pathogenic structural variants in both LBD and FTD/ALS. Finally, we assembled a catalog of structural variants that can be mined for new insights into the pathogenesis of these understudied forms of dementia.
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Affiliation(s)
- Karri Kaivola
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Ruth Chia
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Jinhui Ding
- Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Memoona Rasheed
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Masashi Fujita
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, New York, NY, USA
| | - Vilas Menon
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, New York, NY, USA
| | - Ronald L. Walton
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL, USA
| | - Ryan L. Collins
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (M.I.T.), Cambridge, MA, USA
- Division of Medical Sciences and Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kimberley Billingsley
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
- Centre for Alzheimer’s and Related Dementias, National Institute on Aging, Bethesda, MD, USA
| | - Harrison Brand
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (M.I.T.), Cambridge, MA, USA
- Division of Medical Sciences and Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Michael Talkowski
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (M.I.T.), Cambridge, MA, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Xuefang Zhao
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (M.I.T.), Cambridge, MA, USA
| | - Ramita Dewan
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Ali Stark
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Anindita Ray
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Sultana Solaiman
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Pilar Alvarez Jerez
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
- Centre for Alzheimer’s and Related Dementias, National Institute on Aging, Bethesda, MD, USA
| | - Laksh Malik
- Centre for Alzheimer’s and Related Dementias, National Institute on Aging, Bethesda, MD, USA
| | - Ted M. Dawson
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
- Neuroregeneration and Stem Cell Programs, Institute of Cell Engineering, Johns Hopkins University Medical Center, Baltimore, MD, USA
- Department of Pharmacology and Molecular Science, Johns Hopkins University Medical Center, Baltimore, MD, USA
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Liana S. Rosenthal
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Olga Pletnikova
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University of Buffalo, Buffalo, NY, USA
- Department of Pathology (Neuropathology), Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Juan C. Troncoso
- Department of Pathology (Neuropathology), Johns Hopkins University Medical Center, Baltimore, MD, USA
| | - Mario Masellis
- Cognitive & Movement Disorders Clinic, Sunnybrook Health Sciences Centre, University of Toronto, 1 King’s College Circle, Room 2374, Toronto, ON, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, 2075 Bayview Avenue, Toronto, ON, Canada
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, University of Toronto, 2075 Bayview Avenue, Toronto, ON, Canada
| | - Julia Keith
- Department of Anatomical Pathology, Sunnybrook Health Sciences Centre, University of Toronto, 1 King’s College Circle, Room 2374, Toronto, ON, Canada
| | - Sandra E. Black
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, 2075 Bayview Avenue, Toronto, ON, Canada
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, University of Toronto, 2075 Bayview Avenue, Toronto, ON, Canada
- Institute of Medical Science, Faculty of Medicine, University of Toronto, 1 King’s College Circle, Room 2374, Toronto, ON, Canada
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Health Sciences Centre, University of Toronto, 1 King’s College Circle, Room 2374, Toronto, ON, Canada
| | - Luigi Ferrucci
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Toshiko Tanaka
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD, USA
| | - PROSPECT Consortium
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
- Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, New York, NY, USA
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (M.I.T.), Cambridge, MA, USA
- Division of Medical Sciences and Department of Medicine, Harvard Medical School, Boston, MA, USA
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
- Centre for Alzheimer’s and Related Dementias, National Institute on Aging, Bethesda, MD, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
- Neuroregeneration and Stem Cell Programs, Institute of Cell Engineering, Johns Hopkins University Medical Center, Baltimore, MD, USA
- Department of Pharmacology and Molecular Science, Johns Hopkins University Medical Center, Baltimore, MD, USA
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University Medical Center, Baltimore, MD, USA
- Department of Pathology and Anatomical Sciences, Jacobs School of Medicine and Biomedical Sciences, University of Buffalo, Buffalo, NY, USA
- Department of Pathology (Neuropathology), Johns Hopkins University Medical Center, Baltimore, MD, USA
- Cognitive & Movement Disorders Clinic, Sunnybrook Health Sciences Centre, University of Toronto, 1 King’s College Circle, Room 2374, Toronto, ON, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada
- Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, University of Toronto, 2075 Bayview Avenue, Toronto, ON, Canada
- LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, University of Toronto, 2075 Bayview Avenue, Toronto, ON, Canada
- Department of Anatomical Pathology, Sunnybrook Health Sciences Centre, University of Toronto, 1 King’s College Circle, Room 2374, Toronto, ON, Canada
- Institute of Medical Science, Faculty of Medicine, University of Toronto, 1 King’s College Circle, Room 2374, Toronto, ON, Canada
- Heart and Stroke Foundation Canadian Partnership for Stroke Recovery, Sunnybrook Health Sciences Centre, University of Toronto, 1 King’s College Circle, Room 2374, Toronto, ON, Canada
- Longitudinal Studies Section, National Institute on Aging, Baltimore, MD, USA
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, USA
- Translational Immunology, Research Programs Unit, University of Helsinki, Helsinki, Finland
- Department of Neurology, Helsinki University Hospital, Helsinki, Finland
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA
- Department of Genetics and Genomic Medicine Research & Teaching, UCL GOS Institute of Child Health, University College London, London, UK
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
- UK Dementia Research Institute, Department of Neurogenerative Disease and Reta Lila Weston Institute, London, UK
- Institute of Advanced Study, The Hong Kong University of Science and Technology, Hong Kong SAR, China
- Maggiore della Carita University Hospital, Novara, Italy
- Department of Health Sciences, University of Eastern Piedmont, Novara, Italy
- Rita Levi Montalcini Department of Neuroscience, University of Turin, Turin, Italy
- Azienda Ospedaliero Universitaria Città, della Salute e della Scienza, Corso Bramante, 88, Turin, Italy
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, USA
- Department of Psychiatry and Psychology, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL, USA
- Department of Neurology, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL, USA
- Center for Sleep Medicine, Mayo Clinic, Rochester, MN, USA
- Institute of Cognitive Sciences and Technologies, C.N.R., Via S. Martino della Battaglia, 44, Rome, Italy
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Anatomy, Physiology and Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The American Genome Center, Collaborative Health Initiative Research Program, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- RNA Therapeutics Laboratory, Therapeutics Development Branch, National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Eric Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, USA
| | - Ali Torkamani
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA, USA
| | - Pentti Tienari
- Translational Immunology, Research Programs Unit, University of Helsinki, Helsinki, Finland
- Department of Neurology, Helsinki University Hospital, Helsinki, Finland
| | - Tatiana M. Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - John E. Landers
- Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Mina Ryten
- Department of Genetics and Genomic Medicine Research & Teaching, UCL GOS Institute of Child Health, University College London, London, UK
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, University College London, London, UK
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London, UK
| | - Huw R. Morris
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
| | - John A. Hardy
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, UK
- UCL Movement Disorders Centre, University College London, London, UK
- UK Dementia Research Institute, Department of Neurogenerative Disease and Reta Lila Weston Institute, London, UK
- Institute of Advanced Study, The Hong Kong University of Science and Technology, Hong Kong SAR, China
| | | | - Sandra D'Alfonso
- Department of Health Sciences, University of Eastern Piedmont, Novara, Italy
| | - Cristina Moglia
- Rita Levi Montalcini Department of Neuroscience, University of Turin, Turin, Italy
- Azienda Ospedaliero Universitaria Città, della Salute e della Scienza, Corso Bramante, 88, Turin, Italy
| | - Andrea Calvo
- Rita Levi Montalcini Department of Neuroscience, University of Turin, Turin, Italy
- Azienda Ospedaliero Universitaria Città, della Salute e della Scienza, Corso Bramante, 88, Turin, Italy
| | - Geidy E. Serrano
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, USA
| | - Thomas G. Beach
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, USA
| | - Tanis Ferman
- Department of Psychiatry and Psychology, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL, USA
| | | | | | - Zbigniew K. Wszolek
- Institute of Cognitive Sciences and Technologies, C.N.R., Via S. Martino della Battaglia, 44, Rome, Italy
| | - Dennis W. Dickson
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL, USA
| | - Adriano Chiò
- Rita Levi Montalcini Department of Neuroscience, University of Turin, Turin, Italy
- Azienda Ospedaliero Universitaria Città, della Salute e della Scienza, Corso Bramante, 88, Turin, Italy
- Institute of Cognitive Sciences and Technologies, C.N.R., Via S. Martino della Battaglia, 44, Rome, Italy
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Philip L. De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center and the Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, New York, NY, USA
| | - Owen A. Ross
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road South, Jacksonville, FL, USA
| | - Clifton L. Dalgard
- Department of Anatomy, Physiology and Genetics, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- The American Genome Center, Collaborative Health Initiative Research Program, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - J. Raphael Gibbs
- Computational Biology Group, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
| | - Bryan J. Traynor
- Neuromuscular Diseases Research Section, Laboratory of Neurogenetics, National Institute on Aging, Bethesda, MD, USA
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
- RNA Therapeutics Laboratory, Therapeutics Development Branch, National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Sonja W. Scholz
- Neurodegenerative Diseases Research Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, USA
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Archer DB, Schilling K, Shashikumar N, Jasodanand V, Moore EE, Pechman KR, Bilgel M, Beason-Held LL, An Y, Shafer A, Ferrucci L, Risacher SL, Gifford KA, Landman BA, Jefferson AL, Saykin AJ, Resnick SM, Hohman TJ. Leveraging longitudinal diffusion MRI data to quantify differences in white matter microstructural decline in normal and abnormal aging. bioRxiv 2023:2023.05.17.541182. [PMID: 37292885 PMCID: PMC10245725 DOI: 10.1101/2023.05.17.541182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
INTRODUCTION It is unclear how rates of white matter microstructural decline differ between normal aging and abnormal aging. METHODS Diffusion MRI data from several well-established longitudinal cohorts of aging [Alzheimer's Neuroimaging Initiative (ADNI), Baltimore Longitudinal Study of Aging (BLSA), Vanderbilt Memory & Aging Project (VMAP)] was free-water corrected and harmonized. This dataset included 1,723 participants (age at baseline: 72.8±8.87 years, 49.5% male) and 4,605 imaging sessions (follow-up time: 2.97±2.09 years, follow-up range: 1-13 years, mean number of visits: 4.42±1.98). Differences in white matter microstructural decline in normal and abnormal agers was assessed. RESULTS While we found global decline in white matter in normal/abnormal aging, we found that several white matter tracts (e.g., cingulum bundle) were vulnerable to abnormal aging. CONCLUSIONS There is a prevalent role of white matter microstructural decline in aging, and future large-scale studies in this area may further refine our understanding of the underlying neurodegenerative processes. HIGHLIGHTS Longitudinal data was free-water corrected and harmonizedGlobal effects of white matter decline were seen in normal and abnormal agingThe free-water metric was most vulnerable to abnormal agingCingulum free-water was the most vulnerable to abnormal aging.
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Affiliation(s)
- Derek B. Archer
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kurt Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Niranjana Shashikumar
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Varuna Jasodanand
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Elizabeth E. Moore
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Kimberly R. Pechman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Lori L. Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Andrea Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | | | - Shannon L. Risacher
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA
| | - Katherine A. Gifford
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Bennett A. Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Angela L. Jefferson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Andrew J. Saykin
- Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Timothy J. Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
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Bilgel M, An Y, Walker KA, Moghekar AR, Ashton NJ, Kac PR, Karikari TK, Blennow K, Zetterberg H, Jedynak BM, Thambisetty M, Ferrucci L, Resnick SM. Longitudinal changes in Alzheimer's-related plasma biomarkers and brain amyloid. medRxiv 2023:2023.01.12.23284439. [PMID: 36711545 PMCID: PMC9882432 DOI: 10.1101/2023.01.12.23284439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
INTRODUCTION Understanding longitudinal plasma biomarker trajectories relative to brain amyloid changes can help devise Alzheimer's progression assessment strategies. METHODS We examined the temporal order of changes in plasma amyloid-β ratio (Aβ 42 /Aβ 40 ), glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), and phosphorylated tau ratios (p-tau181/Aβ 42 , p-tau231/Aβ 42 ) relative to 11 C-Pittsburgh compound B (PiB) positron emission tomography (PET) cortical amyloid burden (PiB-/+). Participants (n = 199) were cognitively normal at index visit with a median 6.1-year follow-up. RESULTS PiB groups exhibited different rates of longitudinal change in Aβ 42 /Aβ 40 (β = 5.41 × 10^ -4 , SE = 1.95 × 10 -4 , p = 0.0073). Change in brain amyloid was correlated with change in GFAP (r = 0.5, 95% CI = [0.26, 0.68]). Greatest relative decline in Aβ 42 /Aβ 40 (-1%/year) preceded brain amyloid positivity onset by 41 years (95% CI = [32, 53]). DISCUSSION Plasma Aβ 42 /Aβ 40 may begin declining decades prior to brain amyloid accumulation, whereas p-tau ratios, GFAP, and NfL increase closer in time.
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Affiliation(s)
- Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, 21224, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, 21224, USA
| | - Keenan A. Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, 21224, USA
| | - Abhay R. Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21287, USA
| | - Nicholas J. Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 80 Mölndal, Sweden
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, SE5 9RX, UK
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research, Unit for Dementia at South London and Maudsley, NHS Foundation, London, SE5 8AF, UK
- Centre for Age-Related Medicine, Stavanger University Hospital, 4019 Stavanger, Norway
| | - Przemyslaw R. Kac
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 80 Mölndal, Sweden
| | - Thomas K. Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 80 Mölndal, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 80 Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 431 80 Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, 431 80 Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 431 80 Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
- UK Dementia Research Institute at UCL, London, WC1E 6BT, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53792, USA
| | - Bruno M. Jedynak
- Department of Mathematics and Statistics, Portland State University, Portland, Oregon, 97201, USA
| | - Madhav Thambisetty
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, 21224, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, Baltimore, Maryland, 21224, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, Maryland, 21224, USA
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Yesantharao L, Cai Y, Schrack JA, Gross AL, Wang H, Bilgel M, Dougherty R, Simonsick EM, Ferrucci L, Resnick SM, Agrawal Y. Sensory impairment and beta-amyloid deposition in the Baltimore longitudinal study of aging. Alzheimers Dement (Amst) 2023; 15:e12407. [PMID: 37139098 PMCID: PMC10150164 DOI: 10.1002/dad2.12407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 12/19/2022] [Accepted: 01/19/2023] [Indexed: 05/05/2023]
Abstract
Introduction Beta-amyloid (Aβ) plaque deposition is a biomarker of preclinical Alzheimer's disease (AD). Impairments in sensory function are associated with cognitive decline. We sought to investigate the relationship between PET-indicated Aβ deposition and sensory impairment. Methods Using data from 174 participants ≥55 years in the Baltimore Longitudinal Study of Aging, we analyzed associations between sensory impairments and Aβ deposition measured by PET and Pittsburgh Compound B (PiB) mean cortical distribution volume ratio (cDVR). Results The combinations of hearing and proprioceptive impairment and hearing, vision, and proprioceptive impairment, were positively correlated with cDVR (β = 0.087 and p = 0.036, β = 0.110 and p = 0.018, respectively). In stratified analyses of PiB+ participants, combinations of two, three, and four sensory impairments (all involving proprioception) were associated with higher cDVR. Discussion Our findings suggest a relationship between multi-sensory impairment (notably proprioceptive impairment) and Aβ deposition, which could reflect sensory impairment as an indicator or potentially a risk factor for Aβ deposition.
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Affiliation(s)
- Lekha Yesantharao
- Department of Otolaryngology ‐ Head and Neck SurgeryJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Yurun Cai
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Jennifer A. Schrack
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Center on Aging and HealthJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Alden L. Gross
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Center on Aging and HealthJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Hang Wang
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Murat Bilgel
- Intramural Research ProgramNational Institute on AgingBaltimoreMarylandUSA
| | - Ryan Dougherty
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | | | - Luigi Ferrucci
- Intramural Research ProgramNational Institute on AgingBaltimoreMarylandUSA
| | - Susan M. Resnick
- Intramural Research ProgramNational Institute on AgingBaltimoreMarylandUSA
| | - Yuri Agrawal
- Department of Otolaryngology ‐ Head and Neck SurgeryJohns Hopkins School of MedicineBaltimoreMarylandUSA
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45
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Beason-Held LL, Kerley CI, Chaganti S, Moghekar A, Thambisetty M, Ferrucci L, Resnick SM, Landman BA. Health Conditions Associated with Alzheimer's Disease and Vascular Dementia. Ann Neurol 2023; 93:805-818. [PMID: 36571386 DOI: 10.1002/ana.26584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 12/15/2022] [Accepted: 12/17/2022] [Indexed: 12/27/2022]
Abstract
OBJECTIVE We examined medical records to determine health conditions associated with dementia at varied intervals prior to dementia diagnosis in participants from the Baltimore Longitudinal Study of Aging (BLSA). METHODS Data were available for 347 Alzheimer's disease (AD), 76 vascular dementia (VaD), and 811 control participants without dementia. Logistic regressions were performed associating International Classification of Diseases, 9th Revision (ICD-9) health codes with dementia status across all time points, at 5 and 1 year(s) prior to dementia diagnosis, and at the year of diagnosis, controlling for age, sex, and follow-up length of the medical record. RESULTS In AD, the earliest and most consistent associations across all time points included depression, erectile dysfunction, gait abnormalities, hearing loss, and nervous and musculoskeletal symptoms. Cardiomegaly, urinary incontinence, non-epithelial skin cancer, and pneumonia were not significant until 1 year before dementia diagnosis. In VaD, the earliest and most consistent associations across all time points included abnormal electrocardiogram (EKG), cardiac dysrhythmias, cerebrovascular disease, non-epithelial skin cancer, depression, and hearing loss. Atrial fibrillation, occlusion of cerebral arteries, essential tremor, and abnormal reflexes were not significant until 1 year before dementia diagnosis. INTERPRETATION These findings suggest that some health conditions are associated with future dementia beginning at least 5 years before dementia diagnosis and are consistently seen over time, while others only reach significance closer to the date of diagnosis. These results also show that there are both shared and distinctive health conditions associated with AD and VaD. These results reinforce the need for medical intervention and treatment to lessen the impact of health comorbidities in the aging population. ANN NEUROL 2023;93:805-818.
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Affiliation(s)
- Lori L Beason-Held
- National Institute on Aging Intramural Research Program, Baltimore, Maryland, USA
| | - Cailey I Kerley
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Shikha Chaganti
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Madhav Thambisetty
- National Institute on Aging Intramural Research Program, Baltimore, Maryland, USA
| | - Luigi Ferrucci
- National Institute on Aging Intramural Research Program, Baltimore, Maryland, USA
| | - Susan M Resnick
- National Institute on Aging Intramural Research Program, Baltimore, Maryland, USA
| | - Bennett A Landman
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA
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46
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Tian Q, Montero-Odasso M, Buchman AS, Mielke MM, Espinoza S, DeCarli CS, Newman AB, Kritchevsky SB, Rebok GW, Resnick SM, Thambisetty M, Verghese J, Ferrucci L. Dual cognitive and mobility impairments and future dementia - Setting a research agenda. Alzheimers Dement 2023; 19:1579-1586. [PMID: 36637077 PMCID: PMC10101877 DOI: 10.1002/alz.12905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 10/28/2022] [Accepted: 11/15/2022] [Indexed: 01/14/2023]
Abstract
Dual cognitive and mobility impairments are associated with an increased risk of dementia. Recent studies examining temporal trajectories of mobility and cognitive function in aging found that dual decline is associated with higher dementia risk than memory decline or gait decline only. Although initial data show that individuals with dual decline or impairment have excessive cardiovascular and metabolic risk factors, the causes of dual decline or what underlies dual decline with a high risk of dementia remain largely unknown. In December 2021, the National Institute on Aging Intramural and Extramural Programs jointly organized a workshop on Biology Underlying Moving and Thinking to explore the hypothesis that older persons with dual decline may develop dementia through a specific pathophysiological pathway. The working group discussed assessment methods for dual decline and possible mechanisms connecting dual decline with dementia risk and pinpointed the most critical questions to be addressed from a translational perspective.
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Affiliation(s)
- Qu Tian
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
| | - Manuel Montero-Odasso
- Schulich School of Medicine and Dentistry, Department of Medicine and Division of Geriatric Medicine, The University of Western Ontario, London, ON, Canada
- Gait and Brain Lab, Parkwood Institute, Lawson Health Research Institute, London, ON, Canada
- Department of Epidemiology and Biostatistics, The University of Western Ontario, London, ON, Canada
| | - Aron S. Buchman
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Michelle M. Mielke
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Sara Espinoza
- Division of Geriatrics, Gerontology & Palliative Medicine, Sam and Ann Barshop Institute for Longevity and Aging Studies, UT Health San Antonio, San Antonio, TX, USA
- Geriatrics Research, Education and Clinical Center, South Texas Veterans Health Care System, Audie Murphy Veterans Hospital, San Antonio, TX, USA
| | | | - Anne B. Newman
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Stephen B. Kritchevsky
- Department of Internal Medicine: Gerontology & Geriatric Medicine, The Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - George W. Rebok
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Johns Hopkins Center on Aging and Health, Baltimore, MD, USA
- Johns Hopkins Alzheimer’s Disease Resource Center for Minority Aging Research, Baltimore, MD, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Madhav Thambisetty
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Joe Verghese
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, Baltimore, MD, USA
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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. Alzheimers Dement (Amst) 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] [What about the content of this article? (0)] [Affiliation(s)] [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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48
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Duan P, Xue Y, Han S, Zuo L, Carass A, Bernhard C, Hays S, Calabresi PA, Resnick SM, Duncan JS, Prince JL. RAPID BRAIN MENINGES SURFACE RECONSTRUCTION WITH LAYER TOPOLOGY GUARANTEE. Proc IEEE Int Symp Biomed Imaging 2023; 2023:10.1109/isbi53787.2023.10230668. [PMID: 37990735 PMCID: PMC10660710 DOI: 10.1109/isbi53787.2023.10230668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Abstract
The meninges, located between the skull and brain, are composed of three membrane layers: the pia, the arachnoid, and the dura. Reconstruction of these layers can aid in studying volume differences between patients with neurodegenerative diseases and normal aging subjects. In this work, we use convolutional neural networks (CNNs) to reconstruct surfaces representing meningeal layer boundaries from magnetic resonance (MR) images. We first use the CNNs to predict the signed distance functions (SDFs) representing these surfaces while preserving their anatomical ordering. The marching cubes algorithm is then used to generate continuous surface representations; both the subarachnoid space (SAS) and the intracranial volume (ICV) are computed from these surfaces. The proposed method is compared to a state-of-the-art deformable model-based reconstruction method, and we show that our method can reconstruct smoother and more accurate surfaces using less computation time. Finally, we conduct experiments with volumetric analysis on both subjects with multiple sclerosis and healthy controls. For healthy and MS subjects, ICVs and SAS volumes are found to be significantly correlated to sex (p<0.01) and age (p ≤ 0.03) changes, respectively.
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Affiliation(s)
- Peiyu Duan
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, USA
- Department of Biomedical Engineering, Yale University, USA
| | - Yuan Xue
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - Shuo Han
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, USA
| | - Lianrui Zuo
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - Caitlyn Bernhard
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | - Savannah Hays
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
| | | | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, USA
| | - James S Duncan
- Department of Biomedical Engineering, Yale University, USA
| | - Jerry L Prince
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, USA
- Department of Electrical and Computer Engineering, Johns Hopkins University, USA
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49
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Cai Y, Schrack JA, Gross AL, Armstrong NM, Swenor BK, Deal JA, Lin FR, Wang H, Tian Q, An Y, Simonsick EM, Ferrucci L, Resnick SM, Agrawal Y. Sensory impairment and algorithmic classification of early cognitive impairment. Alzheimers Dement (Amst) 2023; 15:e12400. [PMID: 37063388 PMCID: PMC10103182 DOI: 10.1002/dad2.12400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 11/10/2022] [Accepted: 01/12/2023] [Indexed: 04/18/2023]
Abstract
INTRODUCTION Sensory impairment (SI) is linked to cognitive decline, but its association with early cognitive impairment (ECI) is unclear. METHODS Sensory functions (vision, hearing, vestibular function, proprioception, and olfaction) were measured between 2012 and 2018 in 414 Baltimore Longitudinal Study of Aging (BLSA) participants (age 74 ± 9 years; 55% women). ECI was defined as 1 standard deviation below age-, sex-, race-, and education-specific mean performance in Card Rotations or California Verbal Learning Test immediate recall. Log binomial models (cross-sectional analysis) and Cox regression models (time-to-event analysis) were used to examine the association between SI and ECI. RESULTS Cross-sectionally, participants with ≥3 SI had twice the prevalence of ECI (prevalence ratio = 2.10, p = 0.02). Longitudinally, there was no significant association between SI and incident ECI over up to 6 years of follow-up. DISCUSSION SI is associated with higher prevalence, but not incident ECI. Future studies with large sample sizes need to further elucidate the relationship between SI and ECI. Highlights Sensory impairment is associated with high prevalence of early cognitive impairmentMultisensory impairment may pose a strong risk of early changes in cognitive functionIdentifying multisensory impairment may help early detection of dementia.
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Affiliation(s)
- Yurun Cai
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Department of Health and Community SystemsUniversity of Pittsburgh School of NursingPittsburghPennsylvaniaUSA
| | - Jennifer A. Schrack
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Center on Aging and HealthJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Alden L. Gross
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Center on Aging and HealthJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Nicole M. Armstrong
- Department of Psychiatry and Human BehaviorWarren Alpert Medical School of Brown UniversityProvidenceRhode IslandUSA
- Intramural Research ProgramNational Institute on AgingBaltimoreMarylandUSA
| | - Bonnielin K. Swenor
- Wilmer Eye InstituteJohns Hopkins School of MedicineBaltimoreMarylandUSA
- The Johns Hopkins Disability Health Research CenterJohns Hopkins UniversityBaltimoreMarylandUSA
| | - Jennifer A. Deal
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- The Johns Hopkins Disability Health Research CenterJohns Hopkins UniversityBaltimoreMarylandUSA
- Cochlear Center for Hearing and Public HealthJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Frank R. Lin
- Cochlear Center for Hearing and Public HealthJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
- Department of Otolaryngology ‐ Head and Neck SurgeryJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Hang Wang
- Center on Aging and HealthJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - Qu Tian
- Intramural Research ProgramNational Institute on AgingBaltimoreMarylandUSA
| | - Yang An
- Intramural Research ProgramNational Institute on AgingBaltimoreMarylandUSA
| | | | - Luigi Ferrucci
- Intramural Research ProgramNational Institute on AgingBaltimoreMarylandUSA
| | - Susan M. Resnick
- Intramural Research ProgramNational Institute on AgingBaltimoreMarylandUSA
| | - Yuri Agrawal
- Department of Otolaryngology ‐ Head and Neck SurgeryJohns Hopkins School of MedicineBaltimoreMarylandUSA
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50
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Zhou Z, Li H, Srinivasan D, Abdulkadir A, Nasrallah IM, Wen J, Doshi J, Erus G, Mamourian E, Bryan NR, Wolk DA, Beason-Held L, Resnick SM, Satterthwaite TD, Davatzikos C, Shou H, Fan Y. Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study. Neuroimage 2023; 269:119911. [PMID: 36731813 PMCID: PMC9992322 DOI: 10.1016/j.neuroimage.2023.119911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/06/2023] [Accepted: 01/28/2023] [Indexed: 02/03/2023] Open
Abstract
To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.
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Affiliation(s)
- Zhen Zhou
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Hongming Li
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ilya M Nasrallah
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Nick R Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX, 78705, USA
| | - David A Wolk
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lori Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, 20892, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, 20892, USA
| | - Theodore D Satterthwaite
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn Statistic in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychiatry, Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychiatry, Brain Behavior Laboratory and Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn Statistic in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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