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INTRODUCTION: The contribution of neuropsychological assessments to risk assessment for incident dementia is underappreciated. METHODS: We analyzed neuropsychological testing results in dementia‐free participants in the Atherosclerosis Risk in Communities (ARIC) study. We examined associations of index domain–specific neuropsychological test performance with incident dementia using cumulative incidence curves and Cox proportional hazards models. RESULTS: Among 5296 initially dementia‐free participants (mean [standard deviation] age of 75.8 [5.1] years; 60.1% women, 22.2% Black) over a median follow‐up of 7.9 years, the covariate‐adjusted hazard ratio varied substantially depending on the pattern of domain‐specific performance and age, in an orderly manner from single domain language abnormalities (lowest risk) to single domain executive or memory abnormalities, to multidomain abnormalities including memory (highest risk). DISCUSSION: By identifying normatively defined cognitive abnormalities by domains based on neuropsychological test performance, there is a conceptually orderly and age‐sensitive spectrum of risk for incident dementia that provides valuable information about the likelihood of progression. Highlights: Domain‐specific cognitive profiles carry enhanced prognostic value compared to mild cognitive impairment.Single‐domain non‐amnestic cognitive abnormalities have the most favorable prognosis.Multidomain amnestic abnormalities have the greatest risk for incident dementia.Patterns of domain‐specific risks are similar by sex and race. [ABSTRACT FROM AUTHOR]

BACKGROUND: We compared the ability of several plasma biomarkers versus amyloid positron emission tomography (PET) to predict rates of memory decline among cognitively unimpaired individuals. METHODS: We studied 645 Mayo Clinic Study of Aging participants. Predictor variables were age, sex, education, apolipoprotein E (APOE) ε4 genotype, amyloid PET, and plasma amyloid beta (Aβ)42/40, phosphorylated tau (p‐tau)181, neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and p‐tau217. The outcome was a change in a memory composite measure. RESULTS: All plasma biomarkers, except NfL, were associated with mean memory decline in models with individual biomarkers. However, amyloid PET and plasma p‐tau217, along with age, were key variables independently associated with mean memory decline in models combining all predictors. Confidence intervals were narrow for estimates of population mean prediction, but person‐level prediction intervals were wide. DISCUSSION: Plasma p‐tau217 and amyloid PET provide useful information about predicting rates of future cognitive decline in cognitively unimpaired individuals at the population mean level, but not at the individual person level. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The implications of positive tau positron emission tomography (T) with negative beta amyloid positron emission tomography (A) are not well understood. We investigated cognitive performance in participants who were T+ but A‐. METHODS: We evaluated 98 participants from the Mayo Clinic who were T+ and A‐. Participants were matched 2:1 to A‐ and T‐ cognitively unimpaired (CU) controls. Cognitive test scores were compared between different groups. RESULTS: The A‐T+ group demonstrated lower performance than the A‐T‐ group on the Mini‐Mental Status Exam (MMSE) (p < 0.001), Wechsler Memory Scale‐Revised Logical Memory I (p < 0.001) and Logical Memory II (p < 0.001), Auditory Verbal Learning Test (AVLT) delayed recall (p = 0.004), category fluency (animals p = 0.005; vegetables p = 0.021), Trail Making Test A and B (p < 0.001), and others. There were no significant differences in demographic features or apolipoprotein E (APOE) e4 genotype between CU A‐T+ and CI A‐T+. DISCUSSION: A‐T+ participants show an association with lower cognitive performance. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Cost‐effective screening tools for vascular contributions to cognitive impairment and dementia (VCID) has significant implications. We evaluated non‐imaging indicators of VCID using magnetic resonance imaging (MRI)‐measured white matter (WM) damage and hypothesized that these indicators differ based on age. METHODS: In 745 participants from the Mayo Clinic Study of Aging (≥50 years of age) with serial WM assessments from diffusion MRI and fluid‐attenuated inversion recovery (FLAIR)‐MRI, we examined associations between baseline non‐imaging indicators (demographics, vascular risk factors [VRFs], gait, behavioral, plasma glial fibrillary acidic protein [GFAP], and plasma neurofilament light chain [NfL]) and WM damage across three age tertiles. RESULTS: VRFs and gait were associated with diffusion changes even in low age strata. All measures (VRFs, gait, behavioral, plasma GFAP, plasma NfL) were associated with white matter hyperintensities (WMHs) but mainly in intermediate and high age strata. DISCUSSION: Non‐imaging indicators of VCID were related to WM damage and may aid in screening participants and assessing outcomes for VCID. Highlights: Non‐imaging indicators of VCID can aid in prediction of MRI‐measured WM damage but their importance differed by age.Vascular risk and gait measures were associated with early VCID changes measured using diffusion MRI.Plasma markers explained variability in WMH across age strata.Most non‐imaging measures explained variability in WMH and vascular WM scores in intermediate and older age groups.The framework developed here can be used to evaluate new non‐imaging VCID indicators proposed in the future. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The timing of plasma biomarker changes is not well understood. The goal of this study was to evaluate the temporal co‐evolution of plasma and positron emission tomography (PET) Alzheimer's disease (AD) biomarkers. METHODS: We included 1408 Mayo Clinic Study of Aging and Alzheimer's Disease Research Center participants. An accelerated failure time (AFT) model was fit with amyloid beta (Aβ) PET, tau PET, plasma p‐tau217, p‐tau181, and glial fibrillary acidic protein (GFAP) as endpoints. RESULTS: Individual timing of plasma p‐tau progression was strongly associated with Aβ PET and GFAP progression. In the population, GFAP became abnormal first, then Aβ PET, plasma p‐tau, and tau PET temporal meta‐regions of interest when applying cut points based on young, cognitively unimpaired participants. DISCUSSION: Plasma p‐tau is a stronger indicator of a temporally linked response to elevated brain Aβ than of tau pathology. While Aβ deposition and a rise in GFAP are upstream events associated with tau phosphorylation, the temporal link between p‐tau and Aβ PET was the strongest. Highlights: Plasma p‐tau progression was more strongly associated with Aβ than tau PET.Progression on plasma p‐tau was associated with Aβ PET and GFAP progression.P‐tau181 and p‐tau217 become abnormal after Aβ PET and before tau PET.GFAP became abnormal first, before plasma p‐tau and Aβ PET. [ABSTRACT FROM AUTHOR]

Background: Automated face recognition can potentially re‐identify de‐identified research brain MRI, PET, and CT, and direct differences of brain measurements from original and de‐faced images are statistically significant but very minor, i.e. below scan‐rescan differences. Their effects on analyses correlating brain imaging biomarkers with clinical variables remain unknown. Method: We sampled 3 age‐and‐sex‐matched groups (CU, MCI, and clinical AD) from Mayo Clinic aging studies, each with 61 participants with same‐visit 3D T1‐weighted and 3D T2‐FLAIR MRI, PiB amyloid PET, and Flortaucipir tau PET (total n = 183). We automatically measured hippocampal volume, PIB global SUVR, and tau temporal meta‐ROI SUVR using a) our in‐house pipeline with SPM12, MCALT, and ANTs, and b) FreeSurfer 7.3.2 (PETSurfer), and we measured voxels associated with clinical group separation using SPM12. We also measured WMH from FLAIR using a) our in‐house pipeline, b) Lesion Segmentation Tool (LST), and c) FreeSurfer's Samseg. For each measurement, we calculated pair‐wise group separation AUC values and Spearman's rho with age and CDR‐SOB. We then de‐faced all PET and MRI using mri_reface, our top‐performing automated de‐facing software that replaces imaged faces with an average face ("re‐facing"), and we repeated all measurements and analyses with de‐faced images. We then compared the magnitudes of clinical correlations with original images and those of de‐faced images. Result: Analyses of original and de‐faced images had very high agreement. Only 2/55 paired comparisons were significant (Tables 1‐2): FreeSurfer‐measured global PIB SUVR from de‐faced images was more correlated with age (rho 0.003 vs ‐0.227, p = 0.046), and Mayo‐measured WMH volume from original images was more correlated with CDR‐SOB (rho 0.321 vs 0.308, p = 0.020). Another comparison‐pair tested p = 0.041 for an AUC change +0.008, which we consider not practically meaningful. All others had p>0.05. In voxel‐based analyses of CU vs AD participants, all comparisons were qualitatively similar between original and de‐faced variants (Figure 1). Conclusion: De‐facing PET and MRI using mri_reface did not significantly affect 53/55 correlations between imaging and clinical variables; of the two affected, one became stronger with re‐faced images. Stronger correlations with clinical variables may be explained by de‐facing's standardizing face voxels and artifacts that irrelevantly affect brain segmentations. [ABSTRACT FROM AUTHOR]

Background: Genetic mutations causative of familial fronto‐temporal lobar degeneration (f‐FTLD) are highly predictive of a specific proteinopathy, but there exists substantial inter‐individual variability in their system‐level pathophysiology, which can be assessed with 18Fluorodeoxyglucose‐positron emission tomography (FDG‐PET). Determining data‐driven patterns of network degeneration suggestive of underlying specific FTLD‐related genetic mutations is paramount to guide complex clinical decision making supported by the quantification of FDG‐PET. Method: We collected clinical and FDG‐PET data from 39 patients with f‐FTLD, including 11 carrying the C9orf72 hexanucleotide expansion (1 asymptomatic, 9 with a predominant behavioral phenotype, 1 with a predominant language phenotype), 12 carrying a GRN mutation (1 asymptomatic, 7 behavioral, 4 language), and 16 carrying a MAPT mutation (6 asymptomatic, 10 behavioral). We performed a spectral covariance decomposition analysis between FDG‐PET images to yield latent patterns reflective of a distribution of metabolism opposing poles of relative hyper‐ and hypo‐metabolism across the entire brain ("eigenbrains" or EBs). We then conducted linear discriminant analyses (LDAs) to perform EB‐based multiclass predictions of genetic mutation and clinical phenotype. Result: The spectral decomposition analysis yielded five eigenbrains, which explained 58.79% of the covariance between FDG‐PET images (Fig. 1). Gradients indicative of relative hypometabolism in left fronto‐temporo‐parietal areas distinguished GRN mutation carriers from other genetic mutations (EB1, EB2, EB4) and were associated with clinical phenotypes predominantly involving language (EB1, EB2, EB3). Conversely, those indicative of relative hypometabolism in prefrontal areas (EB3) and temporopolar areas (EB2) with a right hemispheric predominance were mostly associated with clinical phenotypes predominantly involving behavior/personality, and EB2 distinguished MAPT mutation carriers from other genetic mutations. The LDAs yielded accuracies of 80% and 75% in predicting genetic status and predominant clinical phenotype, respectively (Fig. 2). Conclusion: Five EBs explained a high proportion of covariance in patterns of network degeneration across FTLD‐related genetic mutations. These EBs contained information relevant to the system‐level physiology associated with FTLD‐related pathogenic mechanisms and that allowed the prediction of clinical phenotype with relatively high accuracy. This supports the utility of FDG‐PET to guide the development of tools supporting clinical decision making and ongoing efforts to develop network‐based biomarkers to track disease progression and risk of phenoconversion in f‐FTLD. [ABSTRACT FROM AUTHOR]

Background: Knowledge of the temporal evolution among plasma Alzheimer's biomarkers and PET will inform disease pathophysiology and appropriate implementation of plasma biomarkers (Therneau, NeuroImage 2021; Betthauser, Brain 2022). This study investigates the relative timing of progression of plasma p‐tau181, p‐tau217, GFAP, and amyloid PET on individual and population levels. Method: We included 957 participants from the Mayo Clinic Study of Aging with clinical diagnoses of cognitively unimpaired (n = 732), MCI (n = 205), and Alzheimer's clinical syndrome dementia (n = 20). An accelerated failure time (AFT) model was fit with amyloid (PiB) PET, plasma p‐tau217 (Meso Scale Discovery, Lilly), plasma p‐tau181, and GFAP (Simoa, Quanterix HD‐X) as quad‐variate endpoints, and sex, APOE genotype status, and education as covariates. The model output included a per subject adjustment or time‐shift for each biomarker indicating how much earlier or later it was estimated to progress relative to the average participant, the correlation (R) between adjustments for each pair of biomarkers, and the impact of covariates on each biomarker's timing. Secondly, we estimated progression at a given age for selected cutpoints. Result: Temporal trajectories for each biomarker are shown in Figure 1. Individual‐level adjustments between plasma p‐tau217 and amyloid PET were highly correlated, R = 0.69(0.64‐0.74); how early or late an individual progressed on plasma p‐tau217 explained 48% of the variation in relative timing of amyloid PET progression. Associations of plasma p‐tau181 and GFAP with amyloid PET individual adjustments were moderate and low, respectively (Figure 2). Using the chosen cutpoints, the estimated order of biomarker progression was GFAP, amyloid PET, plasma p‐tau181, and plasma p‐tau217 (Figure 3). Conclusion: On the individual level, the strong association of the relative timing of progression of plasma p‐tau217 and amyloid PET supports that these processes are mechanistically related. The relatively lower associations of the timing of p‐tau181 and GFAP with amyloid PET likely reflect a lower biological association and greater measurement noise for p‐tau181. The individual adjustments, the primary output of this model, are independent of cutpoints. On the population level, relative timing of biomarker change is highly dependent on the cutpoints applied. This true for any analysis that employs cutpoints, regardless of the approach. [ABSTRACT FROM AUTHOR]

Background: Automated face recognition can potentially re‐identify de‐identified research brain MRI, PET, and CT, and direct differences of brain measurements from original and de‐faced images are statistically significant but very minor, i.e. below scan‐rescan differences. Their effects on analyses correlating brain imaging biomarkers with clinical variables remain unknown. Method: We sampled 3 age‐and‐sex‐matched groups (CU, MCI, and clinical AD) from Mayo Clinic aging studies, each with 61 participants with same‐visit 3D T1‐weighted and 3D T2‐FLAIR MRI, PiB amyloid PET, and Flortaucipir tau PET (total n = 183). We automatically measured hippocampal volume, PIB global SUVR, and tau temporal meta‐ROI SUVR using a) our in‐house pipeline with SPM12, MCALT, and ANTs, and b) FreeSurfer 7.3.2 (PETSurfer), and we measured voxels associated with clinical group separation using SPM12. We also measured WMH from FLAIR using a) our in‐house pipeline, b) Lesion Segmentation Tool (LST), and c) FreeSurfer's Samseg. For each measurement, we calculated pair‐wise group separation AUC values and Spearman's rho with age and CDR‐SOB. We then de‐faced all PET and MRI using mri_reface, our top‐performing automated de‐facing software that replaces imaged faces with an average face ("re‐facing"), and we repeated all measurements and analyses with de‐faced images. We then compared the magnitudes of clinical correlations with original images and those of de‐faced images. Result: Analyses of original and de‐faced images had very high agreement. Only 2/55 paired comparisons were significant (Tables 1‐2): FreeSurfer‐measured global PIB SUVR from de‐faced images was more correlated with age (rho 0.003 vs ‐0.227, p = 0.046), and Mayo‐measured WMH volume from original images was more correlated with CDR‐SOB (rho 0.321 vs 0.308, p = 0.020). Another comparison‐pair tested p = 0.041 for an AUC change +0.008, which we consider not practically meaningful. All others had p>0.05. In voxel‐based analyses of CU vs AD participants, all comparisons were qualitatively similar between original and de‐faced variants (Figure 1). Conclusion: De‐facing PET and MRI using mri_reface did not significantly affect 53/55 correlations between imaging and clinical variables; of the two affected, one became stronger with re‐faced images. Stronger correlations with clinical variables may be explained by de‐facing's standardizing face voxels and artifacts that irrelevantly affect brain segmentations. [ABSTRACT FROM AUTHOR]

Introduction: We examined the association between cerebrospinal fluid (CSF)‐derived biomarkers of Alzheimer's disease and neuropsychiatric symptoms (NPS) in older non‐demented adults. Methods: We included 784 persons (699 cognitively unimpaired, 85 with mild cognitive impairment) aged ≥ 50 years who underwent CSF amyloid beta (Aβ42), hyperphosphorylated tau 181 (p‐tau), and total tau (t‐tau) as well as NPS assessment using Beck Depression and Anxiety Inventories (BDI‐II, BAI), and Neuropsychiatric Inventory Questionnaire (NPI‐Q). Results: Lower CSF Aβ42, and higher t‐tau/Aβ42 and p‐tau/Aβ42 ratios were associated with BDI‐II and BAI total scores, clinical depression (BDI‐II ≥ 13), and clinical anxiety (BAI ≥ 10), as well as NPI‐Q–assessed anxiety, apathy, and nighttime behavior. Discussion: CSF Aβ42, t‐tau/Aβ42, and p‐tau/Aβ42 ratios were associated with NPS in community‐dwelling individuals free of dementia. If confirmed by a longitudinal cohort study, the findings have clinical relevance of taking into account the NPS status of individuals with abnormal CSF biomarkers. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The fraction of dementia attributable to hypertension might vary depending on the age of the population considered and the age through which dementia occurs. METHODS: In the Atherosclerosis Risk in Communities study, we quantified population attributable fractions (PAF) of dementia by age 80 and 90 from hypertension assessed at ages of 45–54 (n = 7572), 55–64 (n = 12,033), 65–74 (n = 6561), and 75–84 (n = 2086). RESULTS: The PAF for dementia by age 80 from all non‐normal blood pressure at ages 45–54 was 15.3% (95% confidence interval [CI] = 6.9%–22.3%), 19.1% (95% CI = 9.9%–26.9%) at ages 55–64, and 19.9% (95% CI = −4.4%–38.5%) at ages 65–74. The strongest PAFs were from stage 2 hypertension (11.9%–21.3%). For dementia by age 90, PAFs from non‐normal blood pressure up through age 75 were smaller (10.9%–13.8%), and non‐significant by age 75–84. DISCUSSION: Interventions targeting hypertension even in early late life might reduce a sizeable proportion of dementia. Highlights: We estimated prospective population attributable risks of dementia for hypertension.15%–20% of dementia cases by age 80 are from non‐normal blood pressure (BP).Associations between hypertension and dementia persisted through age 75.Midlife to early late‐life BP control might reduce a large proportion of dementia. [ABSTRACT FROM AUTHOR]

Background: Hearing loss is associated with greater cognitive decline and incident dementia. Whether hearing intervention could reduce cognitive decline in older adults with hearing loss is unknown. Method: The ACHIEVE study is a randomized trial (NCT03243422) of 70‐84 year‐old adults with untreated hearing loss and free from substantial cognitive impairment that took place at four U.S. sites. Participants were recruited from two study populations: 1) a group of adults participating in a longstanding observational study of cardiovascular health (Atherosclerosis Risk in Communities [ARIC] study), and 2) a group of healthy de novo community volunteers. Participants were randomised (1:1) to hearing intervention (HI; audiological counseling and provision of hearing aids) or a successful aging health education control intervention (SA; sessions with a health educator on chronic disease prevention). The primary intention‐to‐treat endpoint was 3‐year change in a global cognition standardized factor score from a comprehensive neurocognitive battery. Result: 977 participants (238 ARIC, 739 de novo) underwent randomisation; 490 were assigned to HI and 487 to SA control. Participants from ARIC were older, had more risk factors for cognitive decline, and had lower baseline cognitive scores than the de novo group. In the primary analysis combining the ARIC and de novo groups, 3‐year cognitive change (in S.D. units) was not significantly different between HI and SA control (HI: ‐0·200 [95% CI: ‐0·256,‐0·144]; SA: ‐0·202 [‐0·258,‐0·145]; Difference 0·002 [‐0·077,0·081],p = 0·96). However, prespecified analyses demonstrated significant differences in the effect of HI on cognitive change between the ARIC and de novo group (p interaction = 0·010). In the ARIC group, HI was associated with a 48% reduction in 3‐year cognitive change compared to SA control (HI: ‐0·211 [‐0·349,‐0·073]; SA: ‐0·402 [‐0·536, ‐0·267]; Difference 0·191 [0·022,0·360,p = 0·027]). In the de novo group, cognitive change was not significantly different between HI and SA control (HI: ‐0·213 [‐0·277,‐0·148]; SA: ‐0·151 [‐0·215,‐0·087]; Difference ‐0·061 [‐0·151,0·028,p = 0·18]). The rate of cognitive change among control participants was observed to be 2.7‐fold faster in the ARIC versus de novo group. Conclusion: Hearing intervention may have a significant effect on reducing cognitive change over 3 years in populations of older adults at increased risk for cognitive decline. [ABSTRACT FROM AUTHOR]

Background: The mapping between risk factors and biomarkers with cognitive performance and decline in the older adults is complex and varies significantly across different age and sex groups. Our goals were to (i) develop a machine learning (ML) model to predict baseline cognition and cognitive decline over a 5‐year follow‐up using only baseline assessments, and (ii) identify important modifiable and non‐modifiable predictors of cognitive decline across different age strata. Method: We utilized longitudinal data from 2219 participants (mean age (SD): 70 (12) years, 49% male) from Mayo Clinic Study of Aging who had 43 features belonging to cardiovascular health, plasma, MRI (diffusion and structural), and lifestyle measures. Cognition was measured using a global cognition z‐score. We built ML models to predict baseline cognition and rate of cognitive decline over 5‐years (linear regression slopes over all individual followup measurements for each participant) using baseline assessments as inputs. We then built a model for each category separately and as a pooled model with all measures included, incorporating education and sex as demographic features in all models (Fig. 1). We applied a multi‐layer stack ensemble ML technique that utilized repeated k‐fold bagging to maximize accuracy. Importance of the top predictors were assessed in three age strata (55‐65 years (31%), 65‐80 years (46%), 80‐95 years (23%)). Result: The pooled model explained the largest variance for prediction of baseline cognition and cognitive decline (Fig. 2). In individuals ≤80 years, lower NfL was most predictive of higher baseline cognition. A combination of imaging and plasma features predicted the 5‐year cognitive decline. Additionally, in individuals ≤65 years, diabetes and hypertension were important predictors of faster cognitive decline and in those >80, lower BMI and midlife physical/cognitive activity was an important predictor of faster cognitive decline (Fig. 3). Conclusion: The plasma model was best for baseline cognition prediction and imaging model was best for cognitive decline prediction. There was significant heterogeneity in important features across the age strata. In individuals >65 years, plasma GFAP was the most important predictor of cognitive decline. Our model allowed identification of key risk factors that predicted cognitive decline. [ABSTRACT FROM AUTHOR]

Background: The implications of a positive tau (T) Positron Emission Tomography (PET) with a negative beta amyloid (A) PET are not well understood. We investigated cognitive performance in participants who were positive on tau PET but negative on beta amyloid PET. Method: We evaluated 98 participants from the Mayo Clinic Study of Aging (MCSA) and the Mayo Clinic Alzheimer's Disease Research Center (ADRC) who were positive for Flortaucipir (FTP) and negative for PiB PET. Participants were classified as A‐ and T+ based on Standardized Uptake Value Ratios (SUVr) of FTP and PiB PET scans, and were matched 2:1 to amyloid negative and tau negative cognitively unimpaired controls from the MCSA. Cognitive test scores were compared between the combined ADRC/MCSA group and MCSA only group. We also examined clinical diagnoses in our cohort. The most common were Alzheimer's Disease, mild cognitive impairment (MCI), Dementia with Lewy Bodies (DLB) and Frontotemporal dementia (FTD) amongst others. Result: There were no significant differences in demographic features or PiB SUVr between groups. There were significant differences in APOE genotype between groups. In the combined ADRC/MCSA group, 25% of A‐T‐ and 13% of A‐T+ were APOE e4 carriers (p = 0.016), and in the MCSA only group, 27% of A‐T‐and 10% of A‐T+ were APOE e4 carriers (p = 0.010). In the combined ADRC/MCSA group, the A‐T+ group demonstrated lower performance than the A‐T‐ group on the Mini Mental Status Exam (MMSE) (p<0.001), Wechsler Memory Scale‐Revised Logical Memory I (p<0.001) and Logical Memory II (p<0.001), Auditory verbal Learning Test Delayed Recall (AVLT) (p = 0.004), Category fluency animals (p = 0.005), Category fluency vegetables (p = 0.021), Trailmaking test A (p<0.001), Trail making test B (p<0.001), Wechsler Adult Intelligence Scale‐IV (WAIS‐IV) (p = 0.002), and Unified Parkinson's Disease Rating Scale part III (UPDRS III) (p = 0.003). In the MCSA only group, the A‐T+ group had lower scores on Logical Memory II (p = 0.022), AVLT delated recall (p = 0.04), and WAIS‐R block design (p = 0.035) compared to the A‐T‐group. Conclusion: Positive tau PET in the absence of beta‐amyloid positivity is associated with lower cognitive performance. [ABSTRACT FROM AUTHOR]

Background: Although amyloid deposition in the brain is often associated with subsequent dementia risk, not everyone with brain amyloid will develop dementia. This discrepancy illustrates the potential importance that risk factors and lived experiences may have in modifying this association. Compared to participants with low social engagement (SE) in mid‐life, we hypothesized that participants with high mid‐life SE will show a weaker association between amyloid burden and incident dementia. Method: We included data from 310 non‐demented participants of the Atherosclerosis Risk in Communities (ARIC)‐PET study. Social support and isolation were assessed via interviewer‐administered questionnaires (Visit 2; 1990 – 1992). Based upon categorization of both factors, participants were classified as having high, intermediate, or low mid‐life SE (Table 1). Brain amyloid was evaluated with florbetapir PET (Visit 5; 2011‐2014). Elevated amyloid burden was defined as standardized uptake value ratio (SUVR) >1.2 in the global cortex. Incident dementia cases were identified from visit 5 through 2019 through ongoing surveillance utilizing in‐person neurocognitive testing, informant interviews, and hospitalization codes. Relative contributions of mid‐life SE and elevated florbetapir SUVR to incident dementia, independently and with multiplicative interaction terms, were evaluated with Cox regression models, adjusted for demographics, APOEε4 and vascular risk factors Result: Among 310 participants, 48 developed dementia (median follow‐up: 4.7 years). Mid‐life SE and elevated SUVR each independently predicted dementia risk but did not interact on a multiplicative scale. Participants with high or intermediate mid‐life SE, relative to low mid‐life SE, were less likely to develop dementia (Table 2). Although the interaction between mid‐life SE and elevated SUVR was not significant, stratified models suggested a stronger association between amyloid burden and dementia in participants with high mid‐life SE (Table 3). Conclusion: Greater mid‐life SE was associated with lower odds of developing dementia, independent of elevated SUVR. Although protective, there was no strong evidence for effect modification of mid‐life SE on the association between amyloid and dementia risk. Future longitudinal studies evaluating the potential influence of social factors measured throughout the life course are needed to inform our understanding as to what factors may preserve cognition in the presence of elevated brain pathology. [ABSTRACT FROM AUTHOR]

Background: Easily accessible and self‐administered cognitive assessments that can aide early identification of individuals at risk for Alzheimer's disease (AD) dementia are critical for timely intervention. Mayo Test Drive (MTD) is a remote, self‐administered, multi‐device compatible, web‐based cognitive assessment (Stricker et al., 2022). We investigated continuous associations between MTD and amyloid and tau PET biomarkers and compared the strength of association to PET biomarkers and in‐person cognitive measures. Method: 556 adults from the Mayo Clinic Study of Aging participated (mean age = 70.6; mean education = 16.0 years; 50.7% female; 97.5% white). Participants were predominantly cognitively unimpaired (95.3%; 4.7% had a consensus diagnosis of mild cognitive impairment). Participants completed (1) brain amyloid and tau PET scans, (2) in‐person neuropsychological assessment including Mayo Preclinical Alzheimer's disease Cognitive Composite (Mayo‐PACC) and Global‐z, and (3) remote MTD comprised of the Stricker Learning Span (SLS) and the Symbols test, which are combined into an MTD composite. Multiple regressions adjusted for age, sex, and education queried associations between imaging biomarkers [amyloid meta‐region of interest (ROI), tau meta‐ROI, and tau entorhinal cortex (EC)‐ROI)] and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites. Result: Lower performances on MTD composite and Mayo‐PACC were associated with higher amyloid meta‐ROI (p's<.05; Table 1). MTD composite was also negatively associated with tau meta‐ROI and EC‐ROI (p's<.05). For memory measures, SLS sum of trials was negatively associated with tau meta‐ROI (p =.01) and EC‐ROI (p<.001) with no associations between AVLT sum of trials and PET (p's≥.06; Table 2). Among processing speed/executive function measures, worse Trails B performance was associated with higher amyloid meta‐ROI (p<.001). Symbols and Digit Symbol Coding were not associated with PET (p's≥.29). A multivariable model with MTD explained significantly less variance (p =.04) for amyloid meta‐ROI (adjusted R‐squared = 0.165) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.176); remote and in‐person composite measures were comparable for other associations (p's>.05). Conclusion: MTD is associated with continuous measures of amyloid and tau PET biomarkers of AD, demonstrating criterion validity for MTD. Several associations were comparable to relationships between gold‐standard in‐person neuropsychological tests and imaging biomarkers. [ABSTRACT FROM AUTHOR]

Background: Brief self‐administered cognitive assessments deployed remotely and combined with plasma biomarkers of Alzheimer's disease (AD) are well suited to assess clinical symptoms and underlying biology in large‐scale decentralized studies. We investigated whether associations of self‐administered cognitive measures and plasma biomarkers were comparable to associations of in‐person cognitive measures and plasma biomarkers. Method: Participants included 327 adults from the Mayo Clinic Study of Aging (mean age = 70.8; 48% female; mean education = 16.0 years; 96.9% white). Most (94.5%) were cognitively unimpaired; 5.5% had consensus diagnosis of mild cognitive impairment. Participants completed Mayo Test Drive (MTD; Stricker et al., 2022), a remote, self‐administered, multi‐device compatible web‐based cognitive assessment comprised of the Stricker Learning Span (SLS) and Symbols test that are combined into a MTD composite. Participants also completed in‐person neuropsychological assessment (global‐z; Mayo Preclinical Alzheimer's disease Cognitive Composite [Mayo‐PACC]) and fasting venipuncture. Plasma was assayed via HD‐X simoa platform for amyloid‐beta (Aβ)‐42 and Aβ‐40 to create an Aβ42/40 ratio, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) using the N4PE Quanterix kit as well as phosphorylated‐tau protein 181 (p‐tau181). P‐tau181, GFAP, and NfL were log10‐transformed. Multiple regression analyses adjusting for age, sex, and education examined associations between plasma biomarkers and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites. Result: Lower performances on MTD, global‐z, and Mayo‐PACC (Table 1) were associated with higher GFAP (p's≤.02), NfL (p's≤.003), and p‐tau181 (p's =.04). For memory, worse SLS and AVLT sum of trials were associated with higher GFAP (p's≤.01) and NfL (p's≤.03). For processing speed/executive function, worse Symbols and Trails B were associated with higher p‐tau181 (p's≤.02). Worse Trails B and Digit Symbol Coding were associated with higher NfL (p's≤.02), and worse Digit Symbol Coding was associated with lower Aβ42/40 (p = 0.03; Table 2). A multivariable model with MTD explained significantly more variance (p =.04) for NfL (adjusted R‐squared = 0.461) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.447); remote and in‐person composite measures were comparable for other associations (p's>.05). Conclusion: Remote and in‐person composite measures showed comparable associations with AD‐related plasma biomarkers, supporting MTD's criterion validity. [ABSTRACT FROM AUTHOR]

Background: Small vessel disease (SVD) is a common vascular contributor to cognitive impairment and dementia. In this work, we evaluated and compared non‐imaging measures that can predict variability in SVD related white matter (WM) damage in a population‐based sample. Method: We identified 752 participants in the population‐based sample of Mayo Clinic Study of Aging (≥50 years of age), who had at least 2 diffusion tensor imaging (DTI) scans and white matter hyperintensity (WMH) assessments along with baseline demographics, systemic vascular health, lifestyle, motor impairment, behavioral symptoms, and plasma markers (Summarized in Figure 1). We computed the WM markers of SVD: fractional anisotropy of genu of the corpus callosum (Genu‐FA) representative of early WM changes, WMH representative of late WM changes, and Vascular WM score (combination of Genu‐FA and WMH). Using all available data, we fit linear mixed effect models and evaluated predictors of WM markers by considering each category of features independently (with demographics) as well as by combining all categories of features Result: Plasma, gait, systemic vascular, and life‐style measures were significant predictors of all three WM markers of SVD (Figure 2). The predictors of early and late WM slightly differed such that there were no additional significant contributions from behavioral and plasma measures to Genu‐FA. In the final composite models shown in Table 1, higher gait speed, lower number of cardiovascular and metabolic conditions (CMC) and lower Unified Parkinson's disease rating scale scores were associated with better Genu‐FA whereas higher CMC, Beck Anxiety Inventory scores (BAI), and glial acidic protein (GFAP) levels were associated with higher burden of WMH. Conclusion: Our observations evaluated the non‐imaging markers of greater WM damage that are relevant to vascular contributions to cognitive impairment to dementia (VCID). We found that in addition to higher vascular risk, worse plasma GFAP, gait measures, and BAI scores were predictors of greater WM damage. Some non‐imaging markers were more associated with early WM changes and others with late WM changes. [ABSTRACT FROM AUTHOR]

Background: Neurite orientation dispersion and density imaging (NODDI) measures from diffusion MRI provide information about tissue microstructure. Our goal was to compare the sensitivity of NODDI to structural MRI (sMRI) in capturing neurodegenerative changes in the gray matter due to aging and dementia. Method: We included 815 participants [644 cognitively unimpaired (CU), 103 MCI, 68 AD] from the Mayo Clinic Study of Aging (MCSA) and Mayo ADRC with sMRI and multi‐shell diffusion MRI. We assessed gray matter volumes scaled by head size and cortical thicknesses from sMRI and neurite density index (NDI) and isotropic volume fraction (ISOVF) from NODDI. Pairwise Spearman correlations were calculated between imaging measures and Tau‐PET SUVRs in Braak I‐II, III‐IV, and V‐VI regions of interest (ROI) in participants ≥ 60 years. Separate multivariable linear regression models were fit for the prediction of brain age using sMRI and NODDI in 515 CU participants. The predicted age gaps were calculated as chronological age minus predicted age in the remaining participants. We compared age gaps computed by sMRI and NODDI using disease surrogates (APOE4 genotype, AD pathologies via PET, WMH via FLAIR‐MRI, vascular risk, cognition via MMSE). Result: Braak ROIs, sMRI, and NODDI measures were correlated. The correlations between ISOVF and Tau‐PET SUVRs were stronger than correlations between NDI, volume, and thickness and Tau‐PET SUVR (Fig.1). MMSE was more associated with ISOVF than Tau‐PET and other imaging measures. The sMRI regression models had a higher mean absolute error (MAE) of 4.13 years than NODDI models with an MAE of 4.02 years. The NODDI model better predicted age gap separation between CU, MCI, and AD compared to the sMRI model (Fig.2). The NODDI predicted age gap showed stronger correlations with global amyloid‐PET SUVR, meta‐ROI Tau‐PET SUVR, CMC, APOE4, and MMSE (Fig. 3). Conclusion: NODDI, especially ISOVF, had stronger correlations with MMSE and Tau‐PET SUVRs across all Braak ROIs. The brain age prediction model trained using NODDI was more sensitive in detecting neurodegeneration changes than sMRI based on associations with disease surrogates. Our work suggests that NODDI in gray matter may be a more sensitive surrogate of neurodegeneration compared to sMRI. [ABSTRACT FROM AUTHOR]

Background: Large neuroimaging studies like ADNI‐4 now include T1‐weighted, T2‐weighted and FLAIR images at isotropic1mm resolution for investigation of both neurodegenerative and cerebrovascular disease. These three input types allow segmentation of GM, WM, DeepGM, WMH, and CSF compartments. Additionally, this supports automated assessment of enlarged perivascular spaces (PVS). Our objective was to propose a unified framework for segmentation of intracranial classes and PVS while reducing human effort. Methods: FLAIR and T2‐weighted images are aligned to the T1‐weighted image.. Standard SPM12 unified segmentation using all three input images establishes deformations between subject and template space and bias field corrections. Extended tissue priors including deep GM are deformed into subject space. Adding mean intensities from high probability deep GM voxels as seeds and using the extended prior set, segmentation is re‐started, refining model parameters, deformations and bias‐field estimates. The procedure repeats, adding a WMH channel, resulting in a segmentation including GM, WM, CSF, deep GM, WMH and nuisance classes. Frangi filters are applied to the T1‐ and T2‐ weighted images. Filter response images are smoothed, averaged, masked and inserted in place of the CSF prior. Probability maps of vessel‐like shapes with CSF‐like contrast are estimated to identify PVS. Visual grading was also performed for comparison. Results: The method was applied to 300 participants. Visual quality control found two instances of poor WMH over‐segmentation. Perivascular space maps were all visually reasonable. Standard segmentation classes were visibly superior to single T1‐based segmentation. Figures 1,2 present examples. Pearson correlations of PVS fractional volume with multiple human raters' PVS counts in CSO/CR was over 0.7 in a subset of 125 participants (Figure 3). Conclusion: Modern dementia protocols include high‐resolution T1‐weighted, T2‐weighted, and FLAIR images that can be used together as inputs to extend probabilistic segmentation adding deep grey and WMH classes with automated PVS assessment. [ABSTRACT FROM AUTHOR]

Background: Co‐morbid Alzheimer's disease (AD) pathology in the form of amyloid‐β plaques and tau neurofibrillary tangles are frequently observed in people with probable dementia with Lewy bodies (DLB). Neurite Orientation Dispersion and Density Imaging (NODDI) is an emerging technique to characterize white matter (WM) tissue microstructure, although NODDI alterations and associations with AD biomarkers remain undefined in DLB. The objectives of this study are to compare NODDI and DTI parameters between DLB and controls and (ii) evaluate the pattern of DTI and NODDI changes associated with amyloid and tau deposition. Method: Consecutive patients within the DLB spectrum (n = 45), including probable DLB (n = 32) and probable Mild Cognitive Impairment with Lewy bodies (MCI‐LB) (n = 13) from the Mayo Clinic ADRC underwent multi‐shell diffusion MRI, [18F]‐Flortaucipir and [11C]‐PiB PET. A comparison group of 45 age‐ and gender‐matched clinically unimpaired controls (CU) was included. Voxelwise non‐parametric permutation models were used to determine pair‐wise group differences and assess the associations of AD biomarkers with fractional anisotropy (FA), mean diffusivity (MD), tissue‐weighted neurite density index (tNDI; NDI x (1 – Free water fraction)) and orientation dispersion index (ODI), controlling for age. Complementary analyses were performed using regions‐of‐interest (ROI) obtained from the JHU atlas. Result: Compared to CU, the DLB spectrum group exhibited widespread decreases in FA and tNDI and increases in MD and ODI, with the most pronounced effects in the cingulum bundles, uncinate, inferior and superior longitudinal fasciculi (Fig. 1). Greater [18F]‐Flortaucipir uptake was also associated with lower FA and tNDI, and higher MD and ODI in the DLB spectrum compared to CU, independently of age and [11C]‐PiB uptake (Fig. 2). In contrast, no associations were found between [11C]‐PiB uptake and any of the DTI or NODDI parameters. Complementary ROI analyses corroborated these findings. Conclusion: Widespread loss of WM integrity was observed on DTI and NODDI in probable DLB and prodromal DLB. These findings were associated with greater cortical tau, but not with amyloid‐β deposition. Although alpha‐synuclein pathology underlies the loss of WM integrity in DLB, amyloid‐independent tau pathology may also be an important influence on white matter integrity within the DLB spectrum. [ABSTRACT FROM AUTHOR]

Background: There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behavior/personality. This is due to a lack of knowledge about the macro‐scale anatomy underlying these symptomatologies, a partial overlap in clinical features, and the fact that a single underlying pathology can give rise to both phenotypes and vice‐versa. Method: We included data from patients with genetic, biomarker, and/or autopsy evidence for fronto‐temporal lobar degeneration (FTLD) diagnosed with behavioral variant fronto‐temporal dementia (bvFTD, n = 30) and patients with biomarker and/or autopsy evidence for Alzheimer's disease (AD) pathology diagnosed with either an initial and predominant dysexecutive (dAD, n = 52), behavioral (bvAD, n = 7), or amnestic (aAD, n = 28) syndrome. We assessed group‐wise differences in clinical/cognitive features and 18Fluorodeoxyglucose‐positron emission tomography (FDG‐PET) patterns. This was followed by a spectral covariance decomposition between FDG‐PET images to yield latent patterns of relative metabolism ("eigenbrains"). These eigenbrains were subsequently linked to clinical/cognitive data and meta‐analytic topics reflecting a wide range of mental abilities. We used a linear discriminant analysis (LDA) to perform eigenbrain‐based diagnostic predictions. Result: dAD and bvFTD patients were the youngest at symptom onset, followed by bvAD, then aAD. dAD patients had worse cognitive performance on nearly all cognitive domains compared to other groups. Hypometabolism was observed across associative cortices in dAD, temporo‐parietal areas in aAD, and fronto‐temporal areas in bvFTD and bvAD (Fig. 1). The spectral covariance decomposition yielded nine eigenbrains which explained 61% of the variance in patterns of FDG‐PET, and only the first three are presented (Fig. 2). These eigenbrains revealed that relative hypometabolism in association cortices, notably lateral parietal areas, associated with dysexecutive symptomatology and a lower likelihood of behavior/personality problems, whereas relative hypometabolism restricted to frontal and temporopolar areas showed opposite associations. The LDA yielded an accuracy of 82.1% in predicting diagnostic category (Fig. 3). Conclusion: This study suggests distinct macro‐scale underpinnings underlying predominant dysexecutive versus behavioral symptomatology in degenerative dementia phenotypes. This has implication for the implementation of clinical and research criteria for these dementia syndromes and highlights the importance of data‐driven techniques to inform the classification of degenerative diseases. [ABSTRACT FROM AUTHOR]

Background: The Alzheimer's Association and Society for Nuclear Medicine and Molecular Imaging convened a Workgroup to update previous Appropriate Use Criteria (AUC) for amyloid PET (Johnson 2013) and develop the first AUC for tau PET. The AUC represent general guidelines and should not be considered a substitute for clinical judgment exercised in the care of individual patients. Method: The Workgroup included 14 researchers with multidisciplinary expertise. Meetings were held between June 2020 and August 2021. Key research questions were identified using the PICOTS framework, triggering an independent systematic literature review. Using established methods (Fitch 2001), the Workgroup identified 17 clinical scenarios in which amyloid/tau PET may be considered. Appropriateness of amyloid and tau PET as independent, stand‐alone modalities were ranked for each scenario using a 9‐point scale (Table 1). Consensus was achieved using a modified Delphi process. Online surveys were completed iteratively by Workgroup members, followed by discussion, until all votes for each scenario fell within one of the following categories: Appropriate/Uncertain/Rarely Appropriate. Result: As an over‐arching principle, amyloid/tau PET should be considered in patients who: (1) have undergone a comprehensive assessment by a dementia expert; (2) Alzheimer's disease (AD) is a diagnostic possibility but uncertainty remains; and (3) knowledge of amyloid/tau PET results is expected to help establish diagnosis and guide patient management. Consensus ratings for clinical scenarios are shown in Table 2. More evidence was available to inform recommendations for amyloid than tau PET. Amyloid PET was considered "Appropriate" to inform diagnosis of MCI or dementia due to suspected AD; clarify diagnosis when CSF biomarkers are equivocal; to inform prognosis in MCI; and to determine eligibility for amyloid‐targeting therapies. Tau PET was considered "Appropriate" to clarify diagnosis in patients with MCI/dementia under age 65 or those with atypical presentations; and to inform prognosis in MCI or dementia due to suspected AD. Conclusion: The updated AUC highlight a growing role for amyloid PET and an emerging role for tau PET in the clinical evaluation of cognitively impaired patients. AUC are expected to further evolve based on data from ongoing studies of clinical utility and a rapidly developing therapeutic landscape. [ABSTRACT FROM AUTHOR]

Background: Predictive models of tau‐PET accumulation have largely relied on the functional connectome to estimate its spatial patterns in Alzheimer's disease (AD). Patterns of global functional organization represent a promising avenue as they encapsulate many biological properties relevant to neurodevelopment, cognition and degeneration. We compared approaches based on local connectivity (from task‐free fMRI) and global organization (from FDG‐PET) to predict regional tau‐PET across AD phenotypes. Method: We included 430 cognitively unimpaired (CU) individuals (214 A+T‐, 216 A+T+) and 62 amnestic mild cognitive impairment (aMCI), 64 amnestic AD, 21 dysexecutive AD (dAD), 41 logopenic progressive aphasia (LPA) and 49 posterior cortical atrophy (PCA) patients (all A+T+) from the Alzheimer's Disease Research Center, Mayo Clinic Study of Aging, and Neurodegenerative Research Groups (referred to as "Mayo cohort"). The connectivity approach consisted of assessing the relationship between a region's tau level and its functional connectivity to the region of highest tau (epicenter). The global organization approach consisted of 1) performing a principal component analysis on 3000 FDG‐PET images that did not overlap with the Mayo cohort, 2) projecting FDG‐PET images of the Mayo cohort onto the low‐dimensional space generated in Step 1 and extracting corresponding weighted values across the first 100 components, 3) using these weighted values as predictors of regional tau‐PET in a linear model and determining optimal parameters based on predicted R2 values, and 4) using the parameters from the model built in Step 3 to predict tau‐PET in the ADNI cohort (n = 86) (Fig.2). Result: The connectivity‐based approach yielded adjusted R2 values ranging from 0.19‐0.45 across diagnostic groups (Fig.1). The global organization approach yielded averaged adjusted R2 and predicted R2 values of 0.71 and 0.63, respectively, in the Mayo cohort. Out‐of‐sample median R2 in the ADNI cohort was 0.41 (range 0.10‐0.60) (Fig.3). Conclusion: An unbiased approach based on the global functional organization of the brain and using an imaging modality widely used in clinical settings outperformed a connectome‐based one to predict regional tau‐PET across the AD phenotypic spectrum. Patterns of tau accumulation may better relate to the large‐scale physiology of the brain underlying specific mental functions rather than local connectivity factors. [ABSTRACT FROM AUTHOR]

Background: Easily accessible and self‐administered cognitive assessments that can aide early identification of individuals at risk for Alzheimer's disease (AD) dementia are critical for timely intervention. Mayo Test Drive (MTD) is a remote, self‐administered, multi‐device compatible, web‐based cognitive assessment (Stricker et al., 2022). We investigated continuous associations between MTD and amyloid and tau PET biomarkers and compared the strength of association to PET biomarkers and in‐person cognitive measures. Method: 556 adults from the Mayo Clinic Study of Aging participated (mean age = 70.6; mean education = 16.0 years; 50.7% female; 97.5% white). Participants were predominantly cognitively unimpaired (95.3%; 4.7% had a consensus diagnosis of mild cognitive impairment). Participants completed (1) brain amyloid and tau PET scans, (2) in‐person neuropsychological assessment including Mayo Preclinical Alzheimer's disease Cognitive Composite (Mayo‐PACC) and Global‐z, and (3) remote MTD comprised of the Stricker Learning Span (SLS) and the Symbols test, which are combined into an MTD composite. Multiple regressions adjusted for age, sex, and education queried associations between imaging biomarkers [amyloid meta‐region of interest (ROI), tau meta‐ROI, and tau entorhinal cortex (EC)‐ROI)] and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites. Result: Lower performances on MTD composite and Mayo‐PACC were associated with higher amyloid meta‐ROI (p's<.05; Table 1). MTD composite was also negatively associated with tau meta‐ROI and EC‐ROI (p's<.05). For memory measures, SLS sum of trials was negatively associated with tau meta‐ROI (p =.01) and EC‐ROI (p<.001) with no associations between AVLT sum of trials and PET (p's =.06; Table 2). Among processing speed/executive function measures, worse Trails B performance was associated with higher amyloid meta‐ROI (p<.001). Symbols and Digit Symbol Coding were not associated with PET (p's =.29). A multivariable model with MTD explained significantly less variance (p =.04) for amyloid meta‐ROI (adjusted R‐squared = 0.165) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.176); remote and in‐person composite measures were comparable for other associations (p's>.05). Conclusion: MTD is associated with continuous measures of amyloid and tau PET biomarkers of AD, demonstrating criterion validity for MTD. Several associations were comparable to relationships between gold‐standard in‐person neuropsychological tests and imaging biomarkers. [ABSTRACT FROM AUTHOR]

Background: Brief self‐administered cognitive assessments deployed remotely and combined with plasma biomarkers of Alzheimer's disease (AD) are well suited to assess clinical symptoms and underlying biology in large‐scale decentralized studies. We investigated whether associations of self‐administered cognitive measures and plasma biomarkers were comparable to associations of in‐person cognitive measures and plasma biomarkers. Method: Participants included 327 adults from the Mayo Clinic Study of Aging (mean age = 70.8; 48% female; mean education = 16.0 years; 96.9% white). Most (94.5%) were cognitively unimpaired; 5.5% had consensus diagnosis of mild cognitive impairment. Participants completed Mayo Test Drive (MTD; Stricker et al., 2022), a remote, self‐administered, multi‐device compatible web‐based cognitive assessment comprised of the Stricker Learning Span (SLS) and Symbols test that are combined into a MTD composite. Participants also completed in‐person neuropsychological assessment (global‐z; Mayo Preclinical Alzheimer's disease Cognitive Composite [Mayo‐PACC]) and fasting venipuncture. Plasma was assayed via HD‐X simoa platform for amyloid‐beta (Aß)‐42 and Aß‐40 to create an Aß42/40 ratio, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) using the N4PE Quanterix kit as well as phosphorylated‐tau protein 181 (p‐tau181). P‐tau181, GFAP, and NfL were log10‐transformed. Multiple regression analyses adjusting for age, sex, and education examined associations between plasma biomarkers and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites. Result: Lower performances on MTD, global‐z, and Mayo‐PACC (Table 1) were associated with higher GFAP (p's =.02), NfL (p's =.003), and p‐tau181 (p's =.04). For memory, worse SLS and AVLT sum of trials were associated with higher GFAP (p's =.01) and NfL (p's =.03). For processing speed/executive function, worse Symbols and Trails B were associated with higher p‐tau181 (p's =.02). Worse Trails B and Digit Symbol Coding were associated with higher NfL (p's =.02), and worse Digit Symbol Coding was associated with lower Aß42/40 (p = 0.03; Table 2). A multivariable model with MTD explained significantly more variance (p =.04) for NfL (adjusted R‐squared = 0.461) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.447); remote and in‐person composite measures were comparable for other associations (p's>.05). Conclusion: Remote and in‐person composite measures showed comparable associations with AD‐related plasma biomarkers, supporting MTD's criterion validity. [ABSTRACT FROM AUTHOR]

Background: Large neuroimaging studies like ADNI‐4 now include T1‐weighted, T2‐weighted and FLAIR images at isotropic1mm resolution for investigation of both neurodegenerative and cerebrovascular disease. These three input types allow segmentation of GM, WM, DeepGM, WMH, and CSF compartments. Additionally, this supports automated assessment of enlarged perivascular spaces (PVS). Our objective was to propose a unified framework for segmentation of intracranial classes and PVS while reducing human effort. Method: FLAIR and T2‐weighted images are aligned to the T1‐weighted image.. Standard SPM12 unified segmentation using all three input images establishes deformations between subject and template space and bias field corrections. Extended tissue priors including deep GM are deformed into subject space. Adding mean intensities from high probability deep GM voxels as seeds and using the extended prior set, segmentation is re‐started, refining model parameters, deformations and bias‐field estimates. The procedure repeats, adding a WMH channel, resulting in a segmentation including GM, WM, CSF, deep GM, WMH and nuisance classes. Frangi filters are applied to the T1‐ and T2‐ weighted images. Filter response images are smoothed, averaged, masked and inserted in place of the CSF prior. Probability maps of vessel‐like shapes with CSF‐like contrast are estimated to identify PVS. Visual grading was also performed for comparison. Result: The method was applied to 300 participants. Visual quality control found two instances of poor WMH over‐segmentation. Perivascular space maps were all visually reasonable. Standard segmentation classes were visibly superior to single T1‐based segmentation. Figures 1,2 present examples. Pearson correlations of PVS fractional volume with multiple human raters' PVS counts in CSO/CR was over 0.7 in a subset of 125 participants (Figure 3). Conclusion: Modern dementia protocols include high‐resolution T1‐weighted, T2‐weighted, and FLAIR images that can be used together as inputs to extend probabilistic segmentation adding deep grey and WMH classes with automated PVS assessment. [ABSTRACT FROM AUTHOR]

Background: Co‐morbid Alzheimer's disease (AD) pathology in the form of amyloid‐ß plaques and tau neurofibrillary tangles are frequently observed in people with probable dementia with Lewy bodies (DLB). Neurite Orientation Dispersion and Density Imaging (NODDI) is an emerging technique to characterize white matter (WM) tissue microstructure, although NODDI alterations and associations with AD biomarkers remain undefined in DLB. The objectives of this study are to compare NODDI and DTI parameters between DLB and controls and (ii) evaluate the pattern of DTI and NODDI changes associated with amyloid and tau deposition. Method: Consecutive patients within the DLB spectrum (n = 45), including probable DLB (n = 32) and probable Mild Cognitive Impairment with Lewy bodies (MCI‐LB) (n = 13) from the Mayo Clinic ADRC underwent multi‐shell diffusion MRI, [18F]‐Flortaucipir and [11C]‐PiB PET. A comparison group of 45 age‐ and gender‐matched clinically unimpaired controls (CU) was included. Voxelwise non‐parametric permutation models were used to determine pair‐wise group differences and assess the associations of AD biomarkers with fractional anisotropy (FA), mean diffusivity (MD), tissue‐weighted neurite density index (tNDI; NDI x (1 – Free water fraction)) and orientation dispersion index (ODI), controlling for age. Complementary analyses were performed using regions‐of‐interest (ROI) obtained from the JHU atlas. Result: Compared to CU, the DLB spectrum group exhibited widespread decreases in FA and tNDI and increases in MD and ODI, with the most pronounced effects in the cingulum bundles, uncinate, inferior and superior longitudinal fasciculi (Fig. 1). Greater [18F]‐Flortaucipir uptake was also associated with lower FA and tNDI, and higher MD and ODI in the DLB spectrum compared to CU, independently of age and [11C]‐PiB uptake (Fig. 2). In contrast, no associations were found between [11C]‐PiB uptake and any of the DTI or NODDI parameters. Complementary ROI analyses corroborated these findings. Conclusion: Widespread loss of WM integrity was observed on DTI and NODDI in probable DLB and prodromal DLB. These findings were associated with greater cortical tau, but not with amyloid‐ß deposition. Although alpha‐synuclein pathology underlies the loss of WM integrity in DLB, amyloid‐independent tau pathology may also be an important influence on white matter integrity within the DLB spectrum. [ABSTRACT FROM AUTHOR]

Background: Predictive models of tau‐PET accumulation have largely relied on the functional connectome to estimate its spatial patterns in Alzheimer's disease (AD). Patterns of global functional organization represent a promising avenue as they encapsulate many biological properties relevant to neurodevelopment, cognition and degeneration. We compared approaches based on local connectivity (from task‐free fMRI) and global organization (from FDG‐PET) to predict regional tau‐PET across AD phenotypes. Method: We included 430 cognitively unimpaired (CU) individuals (214 A+T‐, 216 A+T+) and 62 amnestic mild cognitive impairment (aMCI), 64 amnestic AD, 21 dysexecutive AD (dAD), 41 logopenic progressive aphasia (LPA) and 49 posterior cortical atrophy (PCA) patients (all A+T+) from the Alzheimer's Disease Research Center, Mayo Clinic Study of Aging, and Neurodegenerative Research Groups (referred to as "Mayo cohort"). The connectivity approach consisted of assessing the relationship between a region's tau level and its functional connectivity to the region of highest tau (epicenter). The global organization approach consisted of 1) performing a principal component analysis on 3000 FDG‐PET images that did not overlap with the Mayo cohort, 2) projecting FDG‐PET images of the Mayo cohort onto the low‐dimensional space generated in Step 1 and extracting corresponding weighted values across the first 100 components, 3) using these weighted values as predictors of regional tau‐PET in a linear model and determining optimal parameters based on predicted R2 values, and 4) using the parameters from the model built in Step 3 to predict tau‐PET in the ADNI cohort (n = 86) (Fig.2). Result: The connectivity‐based approach yielded adjusted R2 values ranging from 0.19‐0.45 across diagnostic groups (Fig.1). The global organization approach yielded averaged adjusted R2 and predicted R2 values of 0.71 and 0.63, respectively, in the Mayo cohort. Out‐of‐sample median R2 in the ADNI cohort was 0.41 (range 0.10‐0.60) (Fig.3). Conclusion: An unbiased approach based on the global functional organization of the brain and using an imaging modality widely used in clinical settings outperformed a connectome‐based one to predict regional tau‐PET across the AD phenotypic spectrum. Patterns of tau accumulation may better relate to the large‐scale physiology of the brain underlying specific mental functions rather than local connectivity factors. [ABSTRACT FROM AUTHOR]

Background: Neurite orientation dispersion and density imaging (NODDI) measures from diffusion MRI provide information about tissue microstructure. Our goal was to compare the sensitivity of NODDI to structural MRI (sMRI) in capturing neurodegenerative changes in the gray matter due to aging and dementia. Method: We included 815 participants [644 cognitively unimpaired (CU), 103 MCI, 68 AD] from the Mayo Clinic Study of Aging (MCSA) and Mayo ADRC with sMRI and multi‐shell diffusion MRI. We assessed gray matter volumes scaled by head size and cortical thicknesses from sMRI and neurite density index (NDI) and isotropic volume fraction (ISOVF) from NODDI. Pairwise Spearman correlations were calculated between imaging measures and Tau‐PET SUVRs in Braak I‐II, III‐IV, and V‐VI regions of interest (ROI) in participants = 60 years. Separate multivariable linear regression models were fit for the prediction of brain age using sMRI and NODDI in 515 CU participants. The predicted age gaps were calculated as chronological age minus predicted age in the remaining participants. We compared age gaps computed by sMRI and NODDI using disease surrogates (APOE4 genotype, AD pathologies via PET, WMH via FLAIR‐MRI, vascular risk, cognition via MMSE). Result: Braak ROIs, sMRI, and NODDI measures were correlated. The correlations between ISOVF and Tau‐PET SUVRs were stronger than correlations between NDI, volume, and thickness and Tau‐PET SUVR (Fig.1). MMSE was more associated with ISOVF than Tau‐PET and other imaging measures. The sMRI regression models had a higher mean absolute error (MAE) of 4.13 years than NODDI models with an MAE of 4.02 years. The NODDI model better predicted age gap separation between CU, MCI, and AD compared to the sMRI model (Fig.2). The NODDI predicted age gap showed stronger correlations with global amyloid‐PET SUVR, meta‐ROI Tau‐PET SUVR, CMC, APOE4, and MMSE (Fig. 3). Conclusion: NODDI, especially ISOVF, had stronger correlations with MMSE and Tau‐PET SUVRs across all Braak ROIs. The brain age prediction model trained using NODDI was more sensitive in detecting neurodegeneration changes than sMRI based on associations with disease surrogates. Our work suggests that NODDI in gray matter may be a more sensitive surrogate of neurodegeneration compared to sMRI. [ABSTRACT FROM AUTHOR]

Background: Although amyloid deposition in the brain is often associated with subsequent dementia risk, not everyone with brain amyloid will develop dementia. This discrepancy illustrates the potential importance that risk factors and lived experiences may have in modifying this association. Compared to participants with low social engagement (SE) in mid‐life, we hypothesized that participants with high mid‐life SE will show a weaker association between amyloid burden and incident dementia. Method: We included data from 310 non‐demented participants of the Atherosclerosis Risk in Communities (ARIC)‐PET study. Social support and isolation were assessed via interviewer‐administered questionnaires (Visit 2; 1990 – 1992). Based upon categorization of both factors, participants were classified as having high, intermediate, or low mid‐life SE (Table 1). Brain amyloid was evaluated with florbetapir PET (Visit 5; 2011‐2014). Elevated amyloid burden was defined as standardized uptake value ratio (SUVR) >1.2 in the global cortex. Incident dementia cases were identified from visit 5 through 2019 through ongoing surveillance utilizing in‐person neurocognitive testing, informant interviews, and hospitalization codes. Relative contributions of mid‐life SE and elevated florbetapir SUVR to incident dementia, independently and with multiplicative interaction terms, were evaluated with Cox regression models, adjusted for demographics, APOEe4 and vascular risk factors Result: Among 310 participants, 48 developed dementia (median follow‐up: 4.7 years). Mid‐life SE and elevated SUVR each independently predicted dementia risk but did not interact on a multiplicative scale. Participants with high or intermediate mid‐life SE, relative to low mid‐life SE, were less likely to develop dementia (Table 2). Although the interaction between mid‐life SE and elevated SUVR was not significant, stratified models suggested a stronger association between amyloid burden and dementia in participants with high mid‐life SE (Table 3). Conclusion: Greater mid‐life SE was associated with lower odds of developing dementia, independent of elevated SUVR. Although protective, there was no strong evidence for effect modification of mid‐life SE on the association between amyloid and dementia risk. Future longitudinal studies evaluating the potential influence of social factors measured throughout the life course are needed to inform our understanding as to what factors may preserve cognition in the presence of elevated brain pathology. [ABSTRACT FROM AUTHOR]

Introduction: Alzheimer's disease (AD) patients ≥70 years show smaller medial temporal volumes despite less 18F‐flortaucipir‐positron emission tomography (PET) uptake than younger counterparts. We investigated whether TAR DNA‐binding protein 43 (TDP‐43) was contributing to this volume‐uptake mismatch. Methods: Seventy‐seven participants with flortaucipir‐PET and volumetric magnetic resonance imaging underwent postmortem AD and TDP‐43 pathology assessments. Bivariate‐response linear regression estimated the effect of age and TDP‐43 pathology on volume and/or flortaucipir standardized uptake volume ratios of the hippocampus, amygdala, entorhinal, inferior temporal, and midfrontal cortices. Results: Older participants had lower hippocampal volumes and overall flortaucipir uptake. TDP‐43‐immunoreactivity correlated with reduced medial temporal volumes but was unrelated to flortaucipir uptake. TDP‐43 effect size was consistent across the age spectrum. However, at older ages, the cohort mean volumes moved toward those of TDP‐43‐positives, reflecting the increasing TDP‐43 pathology frequency with age. Discussion: TDP‐43 pathology is a relevant contributor driving the volume‐uptake mismatch in older AD participants. Highlights: TDP‐43 pathology affects medial temporal volume loss but not tau radiotracer uptake.Greater TDP‐43 pathology effect is seen in old age due to its increasing frequency.TDP‐43 pathology is a relevant driver of the volume‐uptake mismatch in old AD patients. [ABSTRACT FROM AUTHOR]

Introduction: We aimed to define a Mayo Preclinical Alzheimer's disease Cognitive Composite (Mayo‐PACC) that prioritizes parsimony and use of public domain measures to facilitate clinical translation. Methods: Cognitively unimpaired participants aged 65 to 85 at baseline with amyloid PET imaging were included, yielding 428 amyloid negative (A‐) and 186 amyloid positive (A+) individuals with 7 years mean follow‐up. Sensitivity to amyloid‐related cognitive decline was examined using slope estimates derived from linear mixed models (difference in annualized change across A+ and A‐ groups). We compared differences in rates of change between Mayo‐PACC and other composites (A+ > A‐ indicating more significant decline in A+). Results: All composites showed sensitivity to amyloid‐related longitudinal cognitive decline (A+ > A‐ annualized change p < 0.05). Comparisons revealed that Mayo‐PACC (AVLT sum of trials 1‐5+6+delay, Trails B, animal fluency) showed comparable longitudinal sensitivity to other composites. Discussion: Mayo‐PACC performs similarly to other composites and can be directly translated to the clinic. [ABSTRACT FROM AUTHOR]

Introduction: We evaluated whether better cardiovascular health at midlife and improvement of cardiovascular health within midlife were associated with dementia risk. Methods: Two longitudinal population‐based studies were used: Atherosclerosis Risk in Communities (ARIC) (n = 11,460/visits at ages 54 and 60), and Age, Gene/Environment Susceptibility (AGES)‐Reykjavik (n = 3907/visit at age 51). A cardiovascular health score (range 0–12/0–14, depending on diet availability) including six/seven items was calculated at each visit, with weight assigned to each item as poor (0), intermediate (1), or ideal (2). Cardiovascular health was defined as low (score 0–4/0–5), intermediate (5–7/6–9), or high (8–12/10–14). Incident dementia was ascertained through linkage to health records and with neuropsychological examinations. Results: Midlife high compared to low cardiovascular health (hazard ratios [HRs]: for ARIC: 0.60 [95% confidence interval: 0.52, 0.69]); for AGES‐Reykjavik: 0.83 [0.66, 0.99] and improvement of cardiovascular health score within midlife (HR per one‐point increase: ARIC: 0.94 [0.92, 0.96]) were associated with lower dementia risk. Discussion: Better cardiovascular health at midlife and improvement of cardiovascular health within midlife are associated with lower dementia risk. Highlights: Cardiovascular health and dementia were studied in two large cohort studies.Better cardiovascular health at midlife relates to lower dementia risk.Improvement of cardiovascular health within midlife relates to lower dementia risk.Promotion of cardiovascular health at midlife can help to reduce dementia risk. [ABSTRACT FROM AUTHOR]

Introduction: We investigated the association of the area deprivation index (ADI) with cognitive decline, mild cognitive impairment (MCI), and dementia in older adults (≥50 years old). ADI is a neighborhood socioeconomic disadvantage measure assessed at the census block group level. Methods: The study included 4699 participants, initially without dementia, with available ADI values for 2015 and at least one study visit in 2008 through 2018. Using logistic regression and Cox proportional hazards models with age as the time scale, we assessed the odds for MCI and the risk for dementia, respectively. Results: In cognitively unimpaired (CU) adults at baseline, the risk for progression to dementia increased for every decile increase in the ADI state ranking (hazard ratio = 1.06, 95% confidence interval (1.01–1.11), P =.01). Higher ADI values were associated with subtly faster cognitive decline. Discussion: In older CU adults, higher baseline neighborhood socioeconomic deprivation levels were associated with progression to dementia and slightly faster cognitive decline. Highlights: The study used area deprivation index, a composite freely available neighborhood deprivation measure.Higher levels of neighborhood deprivation were associated with greater mild cognitive impairment odds.Higher neighborhood deprivation levels were associated with higher dementia risk. [ABSTRACT FROM AUTHOR]