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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.

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.

Clinical trials frequently include multiple end points that mature at different times. The initial report, typically based on the primary end point, may be published when key planned co-primary or secondary analyses are not yet available. Clinical trial updates provide an opportunity to disseminate additional results from studies, published in JCO or elsewhere, for which the primary end point has already been reported. Patients with early-stage non–small-cell lung cancer (NSCLC) who undergo curative surgical resection are at risk for developing second primary lung cancer (SPLC). Cancer and Leukemia Group B 140503 (Alliance) was a multicenter, international, randomized, phase III trial in patients with stage T1aN0 NSCLC (using the TNM staging system seventh edition) and demonstrated the noninferiority for disease-free survival between sublobar resection (SLR) and lobar resection (LR). After surgery, patients underwent computed tomography surveillance as defined by the protocol. The determination of a SPLC was done by the treating physician and recorded in the study database. We performed an analysis of the rate of SPLC (per patient per year) and the 5-year cumulative incidence in the study population and within the SLR and LR arms. Median follow-up was 7 years. The rate per patient per year in the study population, in the SLR arm, and in the LR arm was 3.4% (95% CI, 2.9 to 4.1), 3.8% (95% CI, 2.9 to 4.9), and 3.1% (95% CI, 2.4 to 4.1), respectively. The estimated 5-year cumulative incidence of SPLC in the study population, SLR arm, and LR arm was 15.9% (95% CI, 12.9 to 18.9), 17.2% (95% CI, 12.7 to 21.5), and 14.7% (95% CI, 10.6 to 18.7), respectively.

Abstract Disruption of the default mode network is a hallmark of Alzheimer’s disease, which has not been extensively examined in atypical phenotypes. We investigated cross-sectional and 1-year longitudinal changes in default mode network sub-systems in the visual and language variants of Alzheimer’s disease, in relation to age and tau. Sixty-one amyloid-positive Alzheimer’s disease participants diagnosed with posterior cortical atrophy (n = 33) or logopenic progressive aphasia (n = 28) underwent structural MRI, resting-state functional MRI and [18F]flortaucipir PET. One-hundred and twenty-two amyloid-negative cognitively unimpaired individuals and 60 amyloid-positive individuals diagnosed with amnestic Alzheimer’s disease were included as controls and as a comparison group, respectively, and had structural and resting-state functional MRI. Forty-one atypical Alzheimer’s disease participants, 26 amnestic Alzheimer’s disease participants and 40 cognitively unimpaired individuals had one follow-up functional MRI ∼1–2 years after the baseline scan. Default mode network connectivity was calculated using the dual regression method for posterior, ventral, anterior ventral and anterior dorsal sub-systems derived from independent component analysis. A global measure of default mode network connectivity, the network failure quotient, was also calculated. Linear mixed-effects models and voxel-based analyses were computed for each connectivity measure. Both atypical and amnestic Alzheimer’s disease participants had lower cross-sectional posterior and ventral and higher anterior dorsal connectivity and network failure quotient relative to cognitively unimpaired individuals. Age had opposite effects on connectivity in Alzheimer’s disease participants and cognitively unimpaired individuals. While connectivity declined with age in cognitively unimpaired individuals, younger Alzheimer’s disease participants had lower connectivity than the older ones, particularly in the ventral default mode network. Greater baseline tau-PET uptake was associated with lower ventral and anterior ventral default mode network connectivity in atypical Alzheimer’s disease. Connectivity in the ventral default mode network declined over time in atypical Alzheimer’s disease, particularly in older participants, with lower tau burden. Voxel-based analyses validated the findings of higher anterior dorsal default mode network connectivity, lower posterior and ventral default mode network connectivity and decline in ventral default mode network connectivity over time in atypical Alzheimer’s disease. Visuospatial symptoms were associated with default mode network connectivity disruption. In summary, default mode connectivity disruption was similar between atypical and amnestic Alzheimer’s disease variants, and discriminated Alzheimer’s disease from cognitively unimpaired individuals, with decreased posterior and increased anterior connectivity and with disruption more pronounced in younger participants. The ventral default mode network declined over time in atypical Alzheimer’s disease, suggesting a shift in default mode network connectivity likely related to tau pathology.

AbstractINTRODUCTIONThe 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‐.METHODSWe 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.RESULTSThe 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+.DISCUSSIONA‐T+ participants show an association with lower cognitive performance.

AbstractBackgroundWe examined neuropsychiatric symptoms (NPS) and psychotropic medication use at the midway point of data collection of the Longitudinal Early‐onset Alzheimer’s Disease Study (LEADS). We compared amyloid‐positive early‐onset Alzheimer’s disease (EOAD) participants and amyloid‐negative early‐onset non‐Alzheimer’s disease [EOnonAD]) participants across NPS domains and psychotropic medication categories.Method282 participants enrolled in LEADS were compared across diagnostic groups – amyloid‐positive EOAD (n = 212) and amyloid‐negative EOnonAD (n = 70). All participants were between ages 40 and 64 at baseline. NPS were measured with the Neuropsychiatric Inventory – Questionnaire (NPI‐Q) and Geriatric Depression Scale – Short Form (GDS‐SF). Medications were categorized into psychotropic category (e.g., antidepressants, antipsychotics, benzodiazepines). Groups were compared on NPI‐Q using binary logistic regression adjusting for sex and disease severity, on GDS‐SF using linear regression adjusting for sex and disease severity, and on psychotropic medication use using Chi square or Fisher’s exact test.ResultDemographic data are presented in Table 1. Average total NPI‐Q score was higher in EOnonAD than EOAD (p = .002, ŋp 2 = .03). Regardless of diagnostic group, informants endorsed Affective behaviors most frequently (76.2%). EOAD informants endorsed fewer Distress‐Tension behaviors (p = .02, OR = .498) and Impulse Control behaviors (p = .01, OR = .438). EOAD informants also endorsed apathy (p < .001, OR = .316), irritability (p = .02, OR = .498), and disinhibition (p = .04, OR = .476) at lower frequency than EOnonAD. GDS‐SF showed that the EOnonAD participants self‐reported more symptoms of depression than the EOAD group (p = .002, ŋp 2 = .03). (Table 2A). A minority of participants, regardless of diagnostic group, reported psychotropic medication use (35%). There was higher use in EOnonAD (38%) than EOAD (24%) (p = .03, Cohen W = .14) (Table 2B).ConclusionNPS burden and psychotropic medication use were higher in EOnonAD than EOAD participants, despite EOnonAD being less severely impaired. LEADS continues to collect data. Future studies will examine etiologies that may be larger drivers of NPS than AD neuropathology, mediators (e.g., race, ethnicity) in the association between NPS and diagnostic group classication, compare NPS in EOAD vs. LOAD, and examine longitudinal changes in neuropsychiatric profiles.

AbstractBackgroundDecreased image self‐similarity of repeated FDG‐PET scans has been suggested to indicate increased risk for disease progression. Here, we compared several self‐similarity measures based on conventionally used voxel‐ and ROI‐based approaches and those based on patterns of cortical metabolism.MethodFrom the MCSA/ADRC, we included 376 cognitively unimpaired (CU) individuals and those with a clinical diagnosis of MCI (n = 55) or AD (n = 20) who had two FDG‐PET images within 2‐3 years and clinical follow‐up available. We compared image self‐similarity for three approaches: voxel, ROI and “eigenbrain” (Fig.1). For the voxel approach, FDG‐PET images were normalized to template space, grey matter masked, and intensity normalized to the pons to create standard uptake value ratios (SUVR) for each voxel. From these images, mean SUVR were extracted for 122 ROIs (MCALT‐ADIR122) for the ROI approach. For the “eigenbrain” approach, the SUVR images were transformed to a latent space embedding composed of 253 components (“eigenbrains”) and reflecting metabolism patterns, that was generated in an independent sample of 7008 FDG‐PET images. For each approach, image self‐similarity between baseline and follow‐up values (voxel, ROI or eigenbrain) was determined using three metrics (Pearson correlation, cosine, Euclidean distance). We computed AUC to determine which approach and metric would best discriminate between groups. We used Cox regression analyses to assess whether the metrics could predict disease progression.ResultComparing each distance metric across approaches, AUC was highest in differentiating clinical diagnoses using self‐similarity measures based on eigenbrains, then voxels, then ROIs (e.g., Euclidean distance CU vs AD AUC eigenbrain: 0.86, voxel: 0.75, ROI: 0.71; Fig.1). Across all approaches, Euclidean distance always discriminated best between classes. Eigenbrain‐based Euclidean distance was associated with risk for progression to MCI in CU (HR [95%CI]: 1.25 [1.05, 1.49]), and eigenbrain‐based cosine and Euclidean distances were associated with risk for progression to AD in MCI (1.61 [1.1, 2.34] and 1.53 [1.01, 2.31], respectively), while we found no associations for voxel‐ or ROI‐based distance measures.ConclusionEigenbrain‐based measures outperformed other, more conventional approaches in discriminating clinical diagnoses, and were associated with risk for disease progression. Our results suggest that decreased self‐similarity in metabolism patterns informs on disease stage and prognosis.