Mester, Carly

AbstractINTRODUCTIONUnderstanding the relationship between amyloid beta (Aβ) positron emission tomography (PET) and Aβ cerebrospinal fluid (CSF) biomarkers will define their potential utility in Aβ treatment. Few population‐based or neuropathologic comparisons have been reported.METHODSParticipants 50+ years with Aβ PET and Aβ CSF biomarkers (phosphorylated tau [p‐tau]181/Aβ42, n = 505, and Aβ42/40, n = 54) were included from the Mayo Clinic Study on Aging. From these participants, an autopsy subgroup was identified (n = 47). The relationships of Aβ PET and Aβ CSF biomarkers were assessed cross‐sectionally in all participants and longitudinally in autopsy data.RESULTSCross‐sectionally, more participants were Aβ PET+ versus Aβ CSF− than Aβ PET− versus Aβ CSF+ with an incremental effect when using Aβ PET regions selected for early Aβ deposition. The sensitivity for the first detection of Thal phase ≥ 1 in longitudinal data was higher for Aβ PET (89%) than p‐tau181/Aβ42 (64%).DISCUSSIONAβ PET can detect earlier cortical Aβ deposition than Aβ CSF biomarkers. Aβ PET+ versus Aβ CSF− findings are several‐fold greater using regional Aβ PET analyses and in peri‐threshold‐standardized uptake value ratio participants.Highlights Amyloid beta (Aβ) positron emission tomography (PET) has greater sensitivity for Aβ deposition than Aβ cerebrospinal fluid (CSF) in early Aβ development. A population‐based sample of participants (n = 505) with PET and CSF tests was used. Cortical regions showing early Aβ on Aβ PET were also used in these analyses. Neuropathology was used to validate detection of Aβ by Aβ PET and Aβ CSF biomarkers.

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.

Abstract Background The ALLFTD (ARTFL‐LEFFTDS Longitudinal Frontemporal Lobar Degeneration) study is an NIH‐funded effort to prepare for clinical trials in sporadic (s‐FTLD) and familial (f‐FTLD) FTLD syndromes by characterizing cohorts, developing new clinical trial outcome measures, and evaluating disease progression. ALLFTD represents the merger and continuation of two prior studies: Advancing Research and Treatment of Frontotemporal Lobar Degeneration (ARTFL) and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS). Methods ALLFTD recruits participants with FTLD spectrum disorders (bvFTD, svPPA, nfvPPA, FTD‐ALS, CBS or PSP), with strong family histories of FTLD, or known FTLD‐associated genetic variants within the family. 26 sites in North America enroll participants for clinical and neuropsychological evaluations, MR Imaging, blood draws, and CSF collection in willing participants; participants are requested to return annually for follow‐up evaluation. All participants are genotyped for dementia‐associated mutations. Participants also enroll in the FTD Disorders Registry for follow‐up surveys. A subset of participants undergo remote assessment via the ALLFTD app. Visits conducted under ARTFL and LEFFTDS are included in the ALLFTD dataset. Results ALLFTD has been successful in developing methods to conduct remote or hybrid visits and in actively evaluating participants. Since ALLFTD began in January 2020, 1034 participants (736 new; 298 previously enrolled in ARTFL or LEFFTDS) have been evaluated in the longitudinal arm; an additional 119 participants have been participated in a shortened, biofluid‐focused visit. 349 have returned for follow‐up visits. Including prior participants, the ALLFTD dataset contains 3871 visits from 2343 individuals. 1277 people have sporadic syndromes; 1045 were enrolled as familial. Among s‐FTLD, bvFTD is the most common phenotype (32.8%) followed by PSP (15.9%). 61.4% of f‐FTLD participants were clinically normal at first visit; bvFTD is the most common syndrome in symptomatic f‐FTLD (40%). 477 are confirmed carriers of FTLD‐associated genetic mutations. MRIs were obtained for 2594 visits; 3492 visits have associated blood biospecimens banked; 978 visits have associated CSF samples. Conclusions The ALLFTD consortium is actively evaluating participants across North America to better characterize FTLD syndromes and support the planning and development of FTLD clinical trials. Longitudinal datasets including clinical, genetic, and imaging data are available by request.

Abstract Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer’s disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging’s use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

AbstractIntroductionRemote smartphone assessments of cognition, speech/language, and motor functioning in frontotemporal dementia (FTD) could enable decentralized clinical trials and improve access to research. We studied the feasibility and acceptability of remote smartphone data collection in FTD research using the ALLFTD Mobile App (ALLFTD‐mApp).MethodsA diagnostically mixed sample of 214 participants with FTD or from familial FTD kindreds (asymptomatic: CDR®+NACC‐FTLD = 0 [N = 101]; prodromal: 0.5 [N = 49]; symptomatic ≥1 [N = 51]; not measured [N = 13]) were asked to complete ALLFTD‐mApp tests on their smartphone three times within 12 days. They completed smartphone familiarity and participation experience surveys.ResultsIt was feasible for participants to complete the ALLFTD‐mApp on their own smartphones. Participants reported high smartphone familiarity, completed ∼ 70% of tasks, and considered the time commitment acceptable (98% of respondents). Greater disease severity was associated with poorer performance across several tests.DiscussionThese findings suggest that the ALLFTD‐mApp study protocol is feasible and acceptable for remote FTD research.HIGHLIGHTS The ALLFTD Mobile App is a smartphone‐based platform for remote, self‐administered data collection. The ALLFTD Mobile App consists of a comprehensive battery of surveys and tests of executive functioning, memory, speech and language, and motor abilities. Remote digital data collection using the ALLFTD Mobile App was feasible in a multicenter research consortium that studies FTD. Data was collected in healthy controls and participants with a range of diagnoses, particularly FTD spectrum disorders. Remote digital data collection was well accepted by participants with a variety of diagnoses.

AbstractBackgroundIn familial frontotemporal lobar degeneration (f‐FTLD), plasma levels of neurofilament light (NfL) are elevated at least two years prior to symptom onset and predict future clinical status. The Neurofilament Surveillance Project (NSP), an actively recruiting observational study, seeks to obtain higher resolution data of temporal changes in plasma NfL to better understand its clinical utility in f‐FTLD interventional trials. The NSP is a pre‐competitive collaboration sponsored by the Bluefield Project to Cure FTD and funded by biotechnology and pharmaceutical companies and nonprofit organizations.MethodParticipants must be members of families with disease‐causing mutations in C9orf72, MAPT or GRN and enrolled in the ALLFTD natural history study. Up to 335 participants, with roughly equal representation across mutation type, provide plasma quarterly for 3 years (4355 samples total) to enable the observation of peripheral NfL levels longitudinally during disease onset and progression. Plasma is collected by certified mobile research nurses during remote visits, and NfL is measured in batch at least every 6 months. NfL measurements from one plasma aliquot per draw will be correlated with annual assessments obtained in the ALLFTD study, including measures of disease severity (CDRÒ+NACC‐FTLD, CGI, etc.), cognitive function from the UDS Neuropsychology Battery, and volumetric MRI data. Additional plasma aliquots are reserved for future analyses of other biomarkers during the course of the study; another subset of aliquots is reserved to share with interested investigators after the study ends.ResultAs of 1/19/22, the NSP had enrolled 161 participants, including 62 from C9orf72 kindreds, 60 from MAPT kindreds, and 28 from GRN kindreds. One‐hundred forty‐five participants had completed their initial visit, and 109 had completed more than 3, and up to 5, quarterly blood draws. Enrollment and study visits are ongoing despite COVID‐related limitations.ConclusionThe quarterly blood sampling from this study, combined with annual comprehensive clinical assessments through the ALLFTD natural history study, will enable a more detailed understanding of when and how NfL levels change during phenoconversion and/or disease progression in f‐FTLD mutation carriers. These data will be especially informative in the context of the targeted investigational therapeutics for f‐FTLD now entering clinical trials.

AbstractBackgroundIn vivo tau‐positron emission tomography (PET) is an attractive biomarker for Alzheimer’s disease (AD) diagnosis and treatment. However, tau‐PET is less widely available than other modalities. In this study, we tested cross‐modality synthesis of tau‐PET brain images from fluorodeoxyglucose F‐18 (FDG)‐PET using a deep convolutional neural network (CNN).MethodParticipants (n=1,192) who had brain FDG‐PET with 18F‐FDG and tau‐PET with Flortaucipir (F‐18‐AV‐1451) were included for training and testing. This cohort spanned normal aging (ages 26‐98), pre‐clinical, and clinical AD and related disorders including the FTD and DLB spectrum. External validation was done using ADNI (n=288). The PET scans were co‐registered to the corresponding MRI and subsequently warped to Mayo Clinic Adult Lifespan Template (MCALT) space. Tau‐PET images were SUVR‐normalized to the cerebellar crus, and FDG to the pons. A 3D dense‐U‐net model was utilized as an architecture. Cross‐validation experiments were conducted using 5‐fold validations (60% training set, 20% validation set, and 20% test set) with mean squared error as the loss function.ResultOur dense‐U‐net model successfully synthesized tau‐PET from metabolic images with good correlation and low prediction error for regional SUVRs (Figure 1A‐C). The model showed a robust prediction ability, performing accurately in an independent, external ADNI cohort (Figure 1D‐F). The model‐imputed tau‐PET significantly improved performance in classifying tau positivity (mean AUROC(±SD)=0.78±0.04 and 0.85±0.03 for FDG‐PET and synthesized tau‐PET, respectively) and diagnostic groups (cognitively unimpaired with abnormal amyloid‐PET vs. cognitively impaired with abnormal amyloid‐PET) compared to the original input FDG data (mean AUROC(±SD)=0.89±0.04, 0.85±0.05 0.91±0.04 for actual tau‐PET, FDG‐PET and synthesized tau‐PET, respectively) (Figure 2), suggesting enhanced clinical utility for metabolic images. The ADNI cohort also showed similar results (for tau positivity: AUROC=0.66 and 0.78 for FDG‐PET and synthesized tau‐PET, respectively; for CU A+ vs. CI A+: AUROC=0.86, 0.62, and 0.73 for actual tau‐PET, FDG‐PET, and synthesized tau‐PET; Figure 3).ConclusionWe showed that using a CNN model to predict tau‐PET from FDG‐PET is feasible. The synthesized tau‐PET can augment the value of FDG‐PET, facilitating the multi‐modal diagnosis of AD.

AbstractBackgroundMotor disturbances are common in patients with a variety of neurodegenerative and neurological syndromes, such as Parkinson’s disease and Frontotemporal Dementia (FTD). Accurately quantifying motoric changes could enable earlier detection and monitoring of these syndromes, and remote quantification may allow for more frequent assessments and may improve access to research for underrepresented and geographically dispersed participants. With these benefits in mind, investigators from the ALLFTD consortium developed smartphone tests of balance and finger tapping speed. Here we present preliminary test‐retest reliability and associations with gold standard tests.MethodThe tasks were analyzed in two independent samples. The first was an in‐person cohort of healthy older adults (n = 7), mild cognitive impairment (MCI, n = 1) and FTD (n = 3) who completed the smartphone standing/balance and finger tapping tests (FTT) twice in person. The second sample included 31 ALLFTD participants with a range of clinical presentations (healthy older adults [n = 11], FTD [n = 5], MCI [n = 2], unknown [n = 13]) who were asked to complete the balance test three times over two weeks at home. The primary outcomes were total taps for the FTT and root mean squared acceleration for the balance test. Test‐retest reliability was calculated using an intraclass correlation coefficient (ICC); given the small sample size, outliers (n = 6) were removed from the balance test. We tested the association of smartphone FTT with the gold standard mechanical FTT and the balance task with the CDR®+NACC‐FTLD‐SB (disease severity) using Spearman’s correlations. A Wilcoxon‐sign rank test compared participants with motor features on the Progressive Supranuclear Palsy Rating Scale (PSPRS>0) to those without (PSPRS = 0).ResultThe smartphone FTT showed good test‐retest reliability (ICC = 0.78) and a strong correlation (r = 0.8,p<0.05) with mechanical FTT. The test‐retest reliability for the balance task was excellent for the remote sample (ICC = 0.97) and in‐person (ICC = 0.89) samples. In the remote sample, greater acceleration was correlated with higher CDR®+NACC‐FTLD‐SB (r = 0.7,p = 0.003), and participants with motor features on the PSPRS had higher acceleration than those without (W = 11,p = 0.04).ConclusionThese findings provide preliminary support for the reliability and validity of the ALLFTD Mobile App tests of motor function. Replication in a larger sample is required.