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ImportanceFrontotemporal lobar degeneration (FTLD) is relatively rare, behavioral and motor symptoms increase travel burden, and standard neuropsychological tests are not sensitive to early-stage disease. Remote smartphone-based cognitive assessments could mitigate these barriers to trial recruitment and success, but no such tools are validated for FTLD.ObjectiveTo evaluate the reliability and validity of smartphone-based cognitive measures for remote FTLD evaluations.Design, Setting, and ParticipantsIn this cohort study conducted from January 10, 2019, to July 31, 2023, controls and participants with FTLD performed smartphone application (app)–based executive functioning tasks and an associative memory task 3 times over 2 weeks. Observational research participants were enrolled through 18 centers of a North American FTLD research consortium (ALLFTD) and were asked to complete the tests remotely using their own smartphones. Of 1163 eligible individuals (enrolled in parent studies), 360 were enrolled in the present study; 364 refused and 439 were excluded. Participants were divided into discovery (n = 258) and validation (n = 102) cohorts. Among 329 participants with data available on disease stage, 195 were asymptomatic or had preclinical FTLD (59.3%), 66 had prodromal FTLD (20.1%), and 68 had symptomatic FTLD (20.7%) with a range of clinical syndromes.ExposureParticipants completed standard in-clinic measures and remotely administered ALLFTD mobile app (app) smartphone tests.Main Outcomes and MeasuresInternal consistency, test-retest reliability, association of smartphone tests with criterion standard clinical measures, and diagnostic accuracy.ResultsIn the 360 participants (mean [SD] age, 54.0 [15.4] years; 209 [58.1%] women), smartphone tests showed moderate-to-excellent reliability (intraclass correlation coefficients, 0.77-0.95). Validity was supported by association of smartphones tests with disease severity (r range, 0.38-0.59), criterion-standard neuropsychological tests (r range, 0.40-0.66), and brain volume (standardized β range, 0.34-0.50). Smartphone tests accurately differentiated individuals with dementia from controls (area under the curve [AUC], 0.93 [95% CI, 0.90-0.96]) and were more sensitive to early symptoms (AUC, 0.82 [95% CI, 0.76-0.88]) than the Montreal Cognitive Assessment (AUC, 0.68 [95% CI, 0.59-0.78]) (z of comparison, −2.49 [95% CI, −0.19 to −0.02]; P = .01). Reliability and validity findings were highly similar in the discovery and validation cohorts. Preclinical participants who carried pathogenic variants performed significantly worse than noncarrier family controls on 3 app tasks (eg, 2-back β = −0.49 [95% CI, −0.72 to −0.25]; P < .001) but not a composite of traditional neuropsychological measures (β = −0.14 [95% CI, −0.42 to 0.14]; P = .32).Conclusions and RelevanceThe findings of this cohort study suggest that smartphones could offer a feasible, reliable, valid, and scalable solution for remote evaluations of FTLD and may improve early detection. Smartphone assessments should be considered as a complementary approach to traditional in-person trial designs. Future research should validate these results in diverse populations and evaluate the utility of these tests for longitudinal monitoring.

AbstractINTRODUCTIONGlial fibrillary acidic protein (GFAP) in plasma is a proxy for astrocytic activity and is elevated in amyloid‐β (Aβ)‐positive individuals, making GFAP a potential blood‐based biomarker for Alzheimer's disease (AD).METHODSWe assessed plasma GFAP in 72 Aβ‐positive participants diagnosed with the visual or language variant of AD who underwent Aβ‐ and tau‐PET. Fifty‐nine participants had follow‐up imaging. Linear regression was applied on GFAP and imaging quantities.RESULTSGFAP did not correlate with Aβ‐ or tau‐PET cross‐sectionally. There was a limited positive correlation between GFAP and rates of tau accumulation, particularly in the language variant of AD, although associations were weaker after removing one outlier patient with the highest GFAP level.DISCUSSIONAmong Aβ‐positive AD participants with atypical presentations, plasma GFAP did not correlate with levels of AD pathology on PET, suggesting that the associations between GFAP and AD pathology might plateau during the advanced phase of the disease.

Abstract Background White matter hyperintensities (WMH) are considered hallmark features of cerebral small vessel disease and have recently been linked to Alzheimer’s disease pathology. Their distinct spatial distributions, namely periventricular versus deep WMH, may differ by underlying age-related and pathobiological processes contributing to cognitive decline. We aimed to identify the spatial patterns of WMH using the 4-scale Fazekas visual assessment and explore their differential association with age, vascular health, Alzheimer’s imaging markers, namely amyloid and tau burden, and cognition. Because our study consisted of scans from GE and Siemens scanners with different resolutions, we also investigated inter-scanner reproducibility and combinability of WMH measurements on imaging. Methods We identified 1144 participants from the Mayo Clinic Study of Aging consisting of older adults from Olmsted County, Minnesota with available structural magnetic resonance imaging (MRI), amyloid, and tau positron emission tomography (PET). WMH distribution patterns were assessed on FLAIR-MRI, both 2D axial and 3D, using Fazekas ratings of periventricular and deep WMH severity. We compared the association of periventricular and deep WMH scales with vascular risk factors, amyloid-PET and tau-PET standardized uptake value ratio, WMH volume, and cognition using Pearson partial correlation after adjusting for age. We also evaluated vendor compatibility and reproducibility of the Fazekas scales using intraclass correlations (ICC). Results Periventricular and deep WMH measurements showed similar correlations with age, cardiometabolic conditions score (vascular risk), and cognition, (p < 0.001). Both periventricular WMH and deep WMH showed weak associations with amyloidosis (R = 0.07, p = < 0.001), and none with tau burden. We found substantial agreement between data from the two scanners for Fazekas measurements (ICC = 0.78). The automated WMH volume had high discriminating power for identifying participants with Fazekas ≥ 2 (area under curve = 0.97). Conclusion Our study investigates risk factors underlying WMH spatial patterns and their impact on global cognition, with no discernible differences between periventricular and deep WMH. We observed minimal impact of amyloidosis on WMH severity. These findings, coupled with enhanced inter-scanner reproducibility of WMH data, suggest the combinability of inter-scanner data assessed by harmonized protocols in the context of vascular contributions to cognitive impairment and dementia biomarker research.

AbstractINTRODUCTIONPatients with dementia with Lewy bodies (DLB) may have Alzheimers disease (AD) pathology that can be detected by plasma biomarkers. Our objective was to evaluate plasma biomarkers of AD and their association with positron emission tomography (PET) biomarkers of amyloid and tau deposition in the continuum of DLB, starting from prodromal stages of the disease.METHODSThe cohort included patients with isolated rapid eye movement (REM) sleep behavior disorder (iRBD), mild cognitive impairment with Lewy bodies (MCI‐LB), or DLB, with a concurrent blood draw and PET scans.RESULTSAbnormal levels of plasma glial fibrillary acidic protein (GFAP) were found at the prodromal stage of MCI‐LB in association with increased amyloid PET. Abnormal levels of plasma phosphorylated tau (p‐tau)‐181 and neurofilament light (NfL) were found at the DLB stage. Plasma p‐tau‐181 showed the highest accuracy in detecting abnormal amyloid and tau PET in patients with DLB.DISCUSSIONThe range of AD co‐pathology can be detected with plasma biomarkers in the DLB continuum, particularly with plasma p‐tau‐181 and GFAP.

Introduction: Patients diagnosed with ischemic stroke routinely undergo prolonged cardiac telemetry (PCT) to evaluate for occult atrial fibrillation (AF), but this approach is limited by high cost and low yield. Clinical scores including CHA2DS2-VASc and CHARGE-AF can risk stratify patients but lack desired accuracy. Therefore, we aimed to evaluate an artificial intelligence (AI)-enabled ECG algorithm to predict AF detected by PCT after index stroke. Methods: This retrospective study included all adult patients with ischemic stroke evaluated at Mayo Clinic with baseline electrocardiogram (ECG) and PCT between 2018-2020. We recorded demographics, CHA2DS2-VASc score, CHARGE-AF score, stroke etiology, duration of PCT, and if PCT detected AF. ECGs were analyzed using the AI-ECG algorithm to determine AF likelihood. Stroke etiology was adjudicated using TOAST and embolic stroke of undetermined source (ESUS) definitions. The ability of the AI-ECG algorithm, CHA2DS2-VASc, and CHARGE-AF to predict AF detected by PCT was assessed via receiver operating characteristics analysis, calculating the area under curve (AUC) (c-statistic). Sensitivity and specificity analyses were performed for each tool using optimal cutoffs (using maximum Youden indices). Results: 863 patients were included; median age was 69 years, 496 (57.5%) were male, and 561 (65%) patients were categorized as ESUS. PCT detected AF in 85 (9.8%) patients. Median duration of cardiac telemetry was 30 (IQR 25-30) days. Sensitivity and specificity analyses are outlined in Table 1. The AI-ECG algorithm outperformed the CHA2DS2-VASc score (AUC 0.690 vs. 0.597, p=0.013), but was similar to the CHARGE-AF score (AUC 0.690 vs 0.651, p=0.233). Conclusions: Our AI-ECG algorithm demonstrated improved ability to predict AF detection on PCT after index stroke when compared to the CHA2DS2-VASc score, but was similar in performance to the CHARGE-AF score. All three methods had suboptimal accuracy.

Introduction: Incident intracerebral hemorrhage (ICH) is an uncommon medical event that can lead to devastating outcomes, including death. Small vessel disease (SVD), as measured by cerebral microbleeds (CMBs) and white matter hyperintensities (WMH), has been associated with risk factors and might be a predictor of incident ICH. CMBs result from irregularities in brain vessel structure due to chronic hypertension and cerebral amyloid angiopathy. These microhemorrhages are a possible risk factor for ICH. Methods: A cohort of ARIC (Atherosclerosis Risk in Communities) participants underwent 3T MRI at visit 5 (2011-2013). CMBs were assessed using a T2* gradient echo. CMBs are homogenous hypointense lesions ≤10 mm in diameter. WMH volumes were derived from FLAIR images using an automated program to measure the volumetric burden of leukoaraiosis. During 10 years of follow-up, incident ICH was noted in 7 (0.4%) out of 1,656 participants were dichotomized based on the presence of CMB and the median of WMH volume. Results: ARIC subjects (n=1656, mean age=76±5 years; 40% men; 26% African American race) underwent MRI. CMBs were present in 385 (23.25%) patients out of 1656. ICH occurred in 0.16% and 1.30% of patients without CMBs and with CMBs, respectively. Kaplan Meier (KM) survival analysis between CMBs and ICH was significant (logrank p=0.002). Cox Regression yielded a significant crude hazards ratio (HR 8.7, 95% CI 1.7-44.7, p=0.010). Adjustment for age, gender, race, and hypertension was also significant (HR 9.9, 95% CI 1.9-52.0, p=0.07). KM survival analysis between WMH median (11.68 cm 3 ) and ICH was significant (logrank p=0.006). Figure 1 displays the KM curves of those with and without CMBs and the incidence of ICH as well as those below and above WMH median and incidence of ICH. Conclusion: CMBs and WMH detected by MRI appear to be an important predictor for the occurrence of incident ICH. Further research is needed to observe if MRI could be used to screen subjects at high-risk for elevated risk of incident ICH.