Citations

Abstract Normal brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging at respective age ranges. Here, we developed a deep learning-based brain age prediction model using fluorodeoxyglucose (FDG) PET and structural MRI and tested how the brain age gap relates to degenerative cognitive syndromes including mild cognitive impairment, AD, frontotemporal dementia, and Lewy body dementia. Occlusion analysis, performed to facilitate interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap in dementia cohorts was highly correlated with the cognitive impairment and AD biomarker. However, regions generating brain age gaps were different for each diagnosis group of which the AD continuum showed similar patterns to normal aging in the CU.

Background: Chronic kidney disease (CKD), a growing public health issue in the elderly, is associated with increased risk of cognitive impairment. Objective: To investigate the mechanisms through which CKD impacts brain health using longitudinal imaging. Methods: We identified 97 participants (74 CKD and 23 non-CKD) from the BRINK (BRain IN Kidney Disease), a longitudinal study of CKD with two MRI scans (baseline and 3-year follow-up). We measured the associations between baseline and change in kidney disease biomarkers of estimated glomerular filtration rate (eGFR) and urinary albumin to creatinine ratio (UACR), considered a measure of microvascular inflammation, and imaging outcomes of cortical thickness and ventricular volume from structural MRI, white matter hyperintensities (WMH) volume from FLAIR images, and fractional anisotropy of the corpus callosum (FACC). Results: There were white matter-specific changes as observed by increased WMH volume and decreased FACC in CKD participants, as well as ventricular volume increase in both CKD and non-CKD groups reflective of aging-related changes. Decline in eGFR was associated with decrease in the FACC, suggesting that subtle early white matter changes due to kidney disease can be captured using DTI. An increase in UACR was associated with increase in ventricular volume. Conclusion: Our results support the role of eGFR as a measure of kidney microvascular disease which is associated with concurrent white matter damage in CKD. Future work is needed to investigate the possible link between endothelial microvascular inflammation (as measured by an increased UACR) and ventricular volume increase.

AbstractBackgroundDysphagia is a common symptom of progressive supranuclear palsy often leading to aspiration pneumonia and death.ObjectiveThe aim of this study was to examine how impairments of the oral and pharyngeal phases of the swallow and airway incursion during liquid swallows relate to gray and white matter integrity.MethodsThirty‐eight participants with progressive supranuclear palsy underwent videofluorographic swallowing assessment and structural and diffusion tensor head magnetic resonance imaging. Penalized linear regression models assessed relationships between swallowing metrics and regional gray matter volumes and white matter fractional anisotropy and mean diffusivity.ResultsOral phase impairments were associated with reduced superior parietal volumes and abnormal diffusivity in parietal and sensorimotor white matter, posterior limb of the internal capsule, and superior longitudinal fasciculus. Pharyngeal phase impairments were associated with disruption to medial frontal lobe, corticospinal tract, and cerebral peduncle. No regions were predictive of airway incursion.ConclusionsDifferential patterns of neuroanatomical impairment corresponded to oral and pharyngeal phase swallowing impairments. © 2021 International Parkinson and Movement Disorder Society

Abstract Posterior cortical atrophy is a neurodegenerative syndrome with a heterogeneous clinical presentation due to variable involvement of the left, right, dorsal and ventral parts of the visual system, as well as inconsistent involvement of other cognitive domains and systems. 18F-fluorodeoxyglucose (FDG)-PET is a sensitive marker for regional brain damage or dysfunction, capable of capturing the pattern of neurodegeneration at the single-participant level. We aimed to leverage these inter-individual differences on FDG-PET imaging to better understand the associations of heterogeneity of posterior cortical atrophy. We identified 91 posterior cortical atrophy participants with FDG-PET data and abstracted demographic, neurologic, neuropsychological and Alzheimer’s disease biomarker data. The mean age at reported symptom onset was 59.3 (range: 45–72 years old), with an average disease duration of 4.2 years prior to FDG-PET scan, and a mean education of 15.0 years. Females were more common than males at 1.6:1. After standard preprocessing steps, the FDG-PET scans for the cohort were entered into an unsupervised machine learning algorithm which first creates a high-dimensional space of inter-individual covariance before performing an eigen-decomposition to arrive at a low-dimensional representation. Participant values (‘eigenbrains’ or latent vectors which represent principle axes of inter-individual variation) were then compared to the clinical and biomarker data. Eight eigenbrains explained over 50% of the inter-individual differences in FDG-PET uptake with left (eigenbrain 1) and right (eigenbrain 2) hemispheric lateralization representing 24% of the variance. Furthermore, eigenbrain-loads mapped onto clinical and neuropsychological data (i.e. aphasia, apraxia and global cognition were associated with the left hemispheric eigenbrain 1 and environmental agnosia and apperceptive prosopagnosia were associated with the right hemispheric eigenbrain 2), suggesting that they captured important axes of normal and abnormal brain function. We used NeuroSynth to characterize the eigenbrains through topic-based decoding, which supported the idea that the eigenbrains map onto a diverse set of cognitive functions. These eigenbrains captured important biological and pathophysiologic data (i.e. limbic predominant eigenbrain 4 patterns being associated with older age of onset compared to frontoparietal eigenbrain 7 patterns being associated with younger age of onset), suggesting that approaches that focus on inter-individual differences may be important to better understand the variability observed within a neurodegenerative syndrome like posterior cortical atrophy.

Background: The modifying effect of sex on the relation between marital status and dementia has yet to be determined. Objective: To examine if sex modifies the association between marital status and incident dementia. Methods: Population-based samples from the Mayo Clinic Study of Aging (MCSA, N = 3,471) and the Gothenburg H70 Birth Cohort Study (H70-study, N = 913) were used. A multiplicative interaction term was used to analyze the modifying effect of sex on the relation between marital status (married versus not married) and incident dementia using Cox regression models. Further, risk of dementia by marital status was also evaluated in models separated by sex. Results: In the MCSA, there was an interaction between marital status and sex in relation to dementia (p = 0.015). In contrast, in the H70-study, no significant interaction was observed (p = 0.28). Nevertheless, in both studies, not married men had increased risk of dementia compared to married men in models adjusted for age, education, and number of children (H70-study: 1.99; 1.06–3.76, MCSA: 1.43; 1.08–1.89). Associations remained similar after additional adjustment for depression, BMI, hypertension, dyslipidemia, and diabetes mellitus (H70-study: 2.00; 1.05–3.82, MCSA: 1.32; 0.99–1.76). Further, no significant association was observed between marital status and dementia in women (H70-study: 1.24; 0.82–1.89, MCSA: 0.82; 0.64–1.04). Conclusion: Sex had a modifying effect on the association between marital status and incident dementia. In analyses separated by sex, not married men had an increased risk of dementia compared to married men, while no significant association was observed between marital status and risk of dementia in women.

Background: Longitudinal, but not cross-sectional, cognitive testing is one option proposed to define transitional cognitive decline for individuals on the Alzheimer’s disease continuum. Objective: Compare diagnostic accuracy of cross-sectional subtle objective cognitive impairment (sOBJ) and longitudinal objective decline (ΔOBJ) over 30 months for identifying 1) cognitively unimpaired participants with preclinical Alzheimer’s disease defined by elevated brain amyloid and tau (A+T+) and 2) incident mild cognitive impairment (MCI) based on Cogstate One Card Learning (OCL) accuracy performance. Methods: Mayo Clinic Study of Aging cognitively unimpaired participants aged 50 + with amyloid and tau PET scans (n = 311) comprised the biomarker-defined sample. A case-control sample of participants aged 65 + remaining cognitively unimpaired for at least 30 months included 64 who subsequently developed MCI (incident MCI cases) and 184 controls, risk-set matched by age, sex, education, and visit number. sOBJ was assessed by OCL z-scores. ΔOBJ was assessed using within subjects’ standard deviation and annualized change from linear regression or linear mixed effects (LME) models. Concordance measures Area Under the ROC Curve (AUC) or C-statistic and odds ratios (OR) from conditional logistic regression models were derived. sOBJ and ΔOBJ were modeled jointly to compare methods. Results: sOBJ and ΔOBJ-LME methods differentiated A+T+ from A-T- (AUC = 0.64, 0.69) and controls from incident MCI (C-statistic = 0.59, 0.69) better than chance; other ΔOBJ methods did not. ΔOBJ-LME improved prediction of future MCI over baseline sOBJ (p = 0.003) but not over 30-month sOBJ (p = 0.09). Conclusion: Longitudinal decline did not offer substantial benefit over cross-sectional assessment in detecting preclinical Alzheimer’s disease or incident MCI.

AbstractIntroductionThe prognostic utility of cerebrospinal fluid (CSF) phosphorylated tau 217 (p‐tau217) and p‐tau181 is not understood.MethodsAnalyses included 753 Mayo Clinic Study on Aging participants (median age = 71.6; 57% male). CSF amyloid beta (Aβ)42 and p‐tau181 were measured with Elecsys immunoassays. CSF p‐tau181 and p‐tau217 were also measured with Meso Scale Discovery (MSD). We used Cox proportional hazards models for risk of mild cognitive impairment (MCI) and linear mixed models for risk of global and domain‐specific cognitive decline and cortical thickness. Analyses were stratified by elevated brain amyloid based on CSF Aβ42 or amyloid positron emission tomography for those with imaging.ResultsCSF p‐tau217 was superior to p‐tau181 for the diagnosis of Alzheimer's disease (AD) pathology. CSF MSD p‐tau181 and p‐tau217 were associated with risk of MCI among amyloid‐positive individuals. Differences between CSF p‐tau measures predicting cortical thickness were subtle.DiscussionThere are subtle differences for CSF p‐tau217 and p‐tau181 as prognostic AD markers.

Abstract Introduction We aimed to provide cut points for the automated Elecsys Alzheimer's disease (AD) cerebrospinal fluid (CSF) biomarkers. Methods Cut points for Elecsys amyloid beta 42 (Aβ42), total tau (t‐tau), hyperphosphorylated tau (p‐tau), and t‐tau/Aβ42 and p‐tau/Aβ42 ratios were evaluated in Mayo Clinic Study of Aging (n = 804) and Mayo Clinic Alzheimer's Disease Research Center (n = 70) participants. Results The t‐tau/Aβ42 and p‐tau/Aβ42 ratios had a higher percent agreement with normal/abnormal amyloid positron emission tomography (PET) than the individual CSF markers. Reciever Operating Characteristic (ROC)‐based cut points were 0.26 (0.24–0.27) for t‐tau/Aβ42 and 0.023 (0.020–0.025) for p‐tau/Aβ42. Ratio cut points derived from other cohorts performed as well in our cohort as our own did. Individual biomarkers had worse diagnostic properties and more variable results in terms of positive and negative percent agreement (PPA and NPA). Conclusion CSF t‐tau/Aβ42 and p‐tau/Aβ42 ratios are very robust indicators of AD. For individual biomarkers, the intended use should determine which cut point is chosen.

Abstract Introduction Asymptomatic and mildly symptomatic dominantly inherited Alzheimer's disease mutation carriers (DIAD‐MC) are ideal candidates for preventative treatment trials aimed at delaying or preventing dementia onset. Brain atrophy is an early feature of DIAD‐MC and could help predict risk for dementia during trial enrollment. Methods We created a dementia risk score by entering standardized gray‐matter volumes from 231 DIAD‐MC into a logistic regression to classify participants with and without dementia. The score's predictive utility was assessed using Cox models and receiver operating curves on a separate group of 65 DIAD‐MC followed longitudinally. Results Our risk score separated asymptomatic versus demented DIAD‐MC with 96.4% (standard error = 0.02) and predicted conversion to dementia at next visit (hazard ratio = 1.32, 95% confidence interval [CI: 1.15, 1.49]) and within 2 years (area under the curve = 90.3%, 95% CI [82.3%–98.2%]) and improved prediction beyond established methods based on familial age of onset. Discussion Individualized risk scores based on brain atrophy could be useful for establishing enrollment criteria and stratifying DIAD‐MC participants for prevention trials.