Boeve, Bradley F.

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.

AbstractBackgroundEvent‐related‐potentials (ERPs) during cognitive tasks are sensitive measures of cognitive decline [1]‐[2] and could quantitatively measure the trajectory of disease progression or response to clinical interventions. In this longitudinal study, we aimed to compare ERPs between individuals with mild cognitive impairment (MCI) and controls, and to demonstrate the test‐retest reliability of ERP measures to assess their potential utility for future observational studies and treatment trials.MethodsParticipants included individuals with mild cognitive impairment (MCI, n=31) and healthy controls stratified by age: HC2 (n=46, 40‐60 yrs.), HC3 (n=56, 60‐90 yrs.). ERPs during attention, image recognition, and verbal memory tasks were recorded using a wireless EEG to determine amplitudes and latencies of the P200 and late positive potential (LPP) ERP components in response to target (memorized) and non‐target (novel) stimuli. Differences between MCI and HC3 groups were characterized at the initial visit, and longitudinal changes in ERP components were determined at 1‐year follow‐up for both groups. Reliability of ERP measures was demonstrated by computing intraclass correlation coefficient (ICC) measures of test‐retest variability in all healthy controls (HC2 and HC3) from initial visit to 1‐year follow‐up.ResultsAt the initial visit, the MCI group demonstrated reduced LPP amplitude (Hedges‐g=0.8, p=10‐3) and delayed P200‐latency (Hedges‐g=0.89, p=10‐4) when compared to HC3. Reliability in healthy controls was high for LPP (0.85 and 0.75 for target and non‐target stimuli, respectively) and moderate for P200‐latency (0.67 and 0.53 for target and non‐target stimuli, respectively), without longitudinal changes after 1‐year follow‐up. Individuals with MCI demonstrated increased P200‐latency (Hedges‐g= 1.05, p=0.02, df=8).ConclusionsIndividuals with MCI demonstrated quantifiable memory ERP deficits at baseline with detectable change in the P200 component at 1‐year follow‐up, demonstrating that ERPs are sensitive measures for quantifying mild cognitive impairment. Test‐retest reliability of these measures was good or moderate after 1‐year, supporting their feasibility for longitudinal monitoring of patients during disease progression or quantifying the cognitive impact of potential interventions.

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.

AbstractBackgroundDisruption of the default mode network (DMN) is thought to be a common feature of aging and across Alzheimer’s disease (AD) clinical phenotypes, while syndromic variability could be reflected in selective degeneration of macro‐scale networks optimally supporting specific mental abilities (Fig 1). As part of a collaborative effort between Mayo Clinic and the Human Connectome Project‐Aging (HCP‐A), we aimed to delineate a biomarker of global network failure across aging and the dysexecutive (dAD) and amnestic phenotypes of AD, and biomarkers of focal degeneration specific to each AD phenotype.MethodWe used a regression framework to assess the relationships between a marker of DMN dysfunction, the network failure quotient (NFQ), and factors relevant to AD such as age, cortical thickness (global cortical thickness, inferior parietal lobe), hippocampal volume, and cognition (episodic memory, cognitive flexibility) in the HCP‐A cohort, which consists of 725 non‐demented older adults. We then fit centile curves to the HCP‐A cohort to assess whether the NFQ and focal degeneration markers could distinguish dAD (n=10) and amnestic AD (n=8) patients from the HCP‐A cohort at the group‐ and individual‐level. Participants were scanned using the HCP‐A protocol, allowing for the acquisition of high‐resolution multi‐echo structural images and longer resting‐state connectivity acquisition time compared to traditional protocols.ResultThe NFQ related to age, cortical thickness, hippocampal volume, and cognition in the HCP‐A cohort. It also strongly discriminated dAD and amnestic AD from the HCP‐A cohort but did not discriminate between AD phenotypes. In contrast, markers of focal degeneration were phenotype‐specific; parieto‐frontal areas yielded largest effect sizes for dAD patients, and hippocampal and temporal areas best discriminated patients with amnestic AD (Fig 2).ConclusionThe NFQ may reflect shared system‐level pathophysiological mechanisms across aging and the dysexecutive and amnestic phenotypes of AD whereas focal degeneration markers highlight distinct pathognomonic processes across AD phenotypes. These findings suggest that inter‐individual variability in cognitive impairment in AD would be closely tied to modular, phenotype‐specific network degeneration combined with a common global functional disruption of the DMN. Such information has crucial implications for clinical biomarkers and to provide a deeper understanding of heterogeneity in AD.

AbstractBackgroundDysexecutive Alzheimer's disease (dAD) is a relatively early‐onset variant of AD primarily degrading core executive functions in the absence of predominant behavioral symptoms. Clinical observations suggest substantial variability in clinical, cognitive and neuroimaging profiles within this syndrome. In this presentation, we will discuss our ongoing work disentangling this heterogeneity using unsupervised machine learning techniques.MethodWe collected clinical, neuropsychological and multimodal imaging (MRI, FDG‐PET, amyloid‐PET, tau‐PET) data on 52 dAD patients assessed in our behavioral neurology clinic at Mayo Clinic Rochester. We first performed a spectral clustering analysis based on FDG‐PET to delineate latent patterns of network degeneration (referred as “eigenbrains”) and assessed the relationships between these eigenbrains and clinical and cognitive symptomatology. We then performed a hierarchical clustering on these eigenbrains to derive data‐driven subtypes of dAD. We compared clinical and cognitive data between subtypes, and then compared the imaging profiles to 52 amyloid‐negative age‐ and sex‐matched controls.ResultSix eigenbrains explained approximatively 48% of the variance in FDG‐PET patterns and primarily reflected heteromodal to primary motor/sensory, left‐right hemispheric asymmetry, and anterior‐to‐posterior gradients of macro‐scale cortical organization (Fig 1). These eigenbrains differentially related to reported age at symptom onset, degree of clinical impairment, and performance on a wide range of cognitive domains (executive functions, episodic memory, and visuospatial). Hierarchical clustering revealed four dAD subtypes (diffuse, biparietal, left‐hemisphere, right‐hemisphere). These subtypes differed in reported age at symptom onset and cognitive profile, where the heteromodal‐diffuse subtype exhibited an overall worse clinical picture and the biparietal had a milder profile compared to other subtypes, which was not explained by disease duration. Additionally, spatial patterns of tau distribution and neurodegeneration overlapped with patterns of FDG‐PET hypometabolism in each subtype, whereas patterns of amyloid deposition were similar across subtypes (Figs 2 & 3).ConclusionAlmost half of the variance in macro‐scale patterns of hypometabolism in this dAD cohort was accounted for by six eigenbrains. These eigenbrains captured the inter‐individual variability in age at symptom onset and cognitive impairment. Four dAD subtypes derived from these eigenbrains revealed meaningful differences in clinical, cognitive, and imaging profiles. Recognizing this heterogeneity has significant clinical implications for diagnosis, prognosis, and symptom monitoring.

AbstractBackgroundTAR DNA‐binding protein 43 (TDP‐43) mislocalization is present in over 50% of patients with frontotemporal dementia and about 20% of patients with Alzheimer’s disease, but there is still an unmet need for clinical trials targeting sporadic TDP‐43 pathology in patients with dementia. The semantic variant of primary progressive aphasia (svPPA) is highly predictive of frontotemporal lobar degeneration with TDP‐43 mislocalization (FTLD‐TDP) and presents an ideal cohort in which to explore therapeutics with potential benefit across the spectrum of TDP‐43 pathology. Oxidative stress promotes mislocalization of TDP‐43 in neurons and may be a viable therapeutic target in svPPA and other FTLD‐TDP disorders. Verdiperstat is a potent, oral, CNS penetrant, myeloperoxidase inhibitor that reduces production of oxidative species from microglia. The Veri‐T trial (NCT05184569) explores the therapeutic potential of verdiperstat and is the first clinical trial to focus on patients suffering from svPPA.MethodsVeri‐T is a multisite, phase 1, randomized, double‐blind, placebo‐controlled trial. N = 64 participants with svPPA will be randomized 3:1 to oral verdiperstat (titrated to a dose of 600mg BID) or placebo for 6 months of double‐blind therapy. Neuropsychological assessments, plasma, CSF, and volumetric brain imaging are collected prior to and upon conclusion of treatment. Recruitment is ongoing at 5 ARTFL LEFFTDS Longitudinal FTLD (ALLFTD) research network clinical centers.ResultsThe primary objective is to determine the safety and tolerability of verdiperstat in svPPA. The secondary objective is to determine the pharmacokinetic profile of verdiperstat in this cohort. Exploratory endpoints are focused on identifying potential pharmacodynamic effects and identifying endpoints for future efficacy trials including plasma myeloperoxidase activity, CSF biomarkers of glial activity (chitinase‐family proteins), neurodegeneration (neurofilament light chain), and unbiased CSF proteomics (Somalogics), as well volumetric MRI changes unique to svPPA, and cognitive and language impairments measured using the ALLFTD Mobile App.ConclusionThe Veri‐T trial examines the safety, tolerability and pharmacokinetic properties of verdiperstat in svPPA, and explores novel pharmacodynamic biomarkers and outcome measures that could be employed in future efficacy studies. Veri‐T is the first multicenter clinical trial of a potentially disease modifying agent for svPPA, and the first to leverage resources of the ALLFTD network.

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.

AbstractBackgroundFrontotemporal dementia (FTD) is a collection of neurodegenerative diseases affecting behavior, cognition, and motor functions. FTD treatment trials will benefit from sensitive assessments capable of detecting and longitudinally tracking cognitive changes. Remote smartphone cognitive tests may improve the sensitivity to detect and monitor cognitive decline via novel methods of data capture and more frequent assessments within ecologically valid environments, while simultaneously reducing patient burden. The first step in determining the utility of these measures is establishing their reliability. We previously reported baseline reliability estimates for the ALLFTD Mobile App cognitive tests in a small pilot sample. Here we extend these results using a larger sample and evaluate six‐month stability.MethodWe enrolled a diagnostically mixed sample of 128 participants (CDR®+NACC‐FTLD = 0 [n = 84]; CDR®+NACC‐FTLD = 0.5 [n = 21]; CDR®+NACC‐FTLD≥1 [n = 23]) from ongoing studies of healthy aging (Hillblom Network) and frontotemporal dementia (ALLFTD). Participants self‐administered five cognitive tests from the ALLFTD Mobile App on their own smartphone; they were asked to complete each test three times within two weeks. Tests included gamified versions of N‐back, Stroop, Flanker, and Go/No‐Go paradigms, and an adaptive short‐term memory test. We evaluated the internal consistency of each test’s first occurrence using Spearman‐Brown split‐half reliability (100 simulations). Test‐retest reliability over the first three baseline replicates (as available) was evaluated by calculating intraclass correlations (ICC). In a preliminary analysis, we tested the six‐month stability of test performance in nine functionally intact participants who completed baseline and follow‐up Flanker, N‐Back, and adaptive memory assessments by calculating Spearman’s rho.ResultSplit‐half reliability estimates were in the good to excellent range (r’s: 0.81–0.97). Test‐retest reliability estimates were in the adequate to excellent range (ICCs: 0.72–0.96). ICC estimates were comparable for participants with and without clinical impairment (CDR®+NACC‐FTLD = 0 (0.74–0.95) and CDR®+NACC‐FTLD >0 (0.67–0.95). Among functionally intact individuals, baseline and 6‐months scores were highly correlated (r’s: 0.76–0.93, p’s<.05).ConclusionOur findings of adequate to excellent internal consistency, test‐retest reliability, and 6‐month stability support the feasibility of remote, smartphone‐based cognitive testing among a diagnostically mixed sample typical of healthy aging and frontotemporal dementia research studies.

AbstractBackgroundRemote digital cognitive assessments validated for use in frontotemporal dementia (FTD) and other neurodegenerative diseases would benefit clinical trials and clinical care. The ALLFTD Mobile App includes several smartphone‐based measures of cognition, and prior studies have provided preliminary support for their reliability. Here we evaluate their validity by testing associations with gold standard clinical outcomes and expected neural correlates.MethodA diagnostically mixed sample of 92 participants (CDR®+NACC‐FTLD = 0 [n = 33]; CDR®+NACC‐FTLD = 0.5 [n = 15]; CDR®+NACC‐FTLD≥1 [n = 16]; unknown [n = 28]) were recruited from a UCSF healthy aging study (Hillblom Network) and a North American FTD study (ALLFTD). Participants were asked to remotely complete four ALLFTD Mobile App tests of executive functioning (EF) and a memory test on their smartphones. Gold standard clinical measures were also collected; outcomes were the UDSv3 EF composite, CVLT‐SF Total Immediate Recall (memory), and CDR®+NACC‐FTLD Box Score (disease severity). Volumetric brain MRI measurements were available for a subset (n = 37). Expected neural correlates were frontoparietal gray matter volume for EF tests and hippocampal volume for the memory test. Linear models were fitted to evaluate the association of app tests with the gold standard outcomes, controlling for education, sex, and total intracranial volume (imaging only).ResultSmartphone EF measures were strongly and positively associated with the UDS3‐EF composite (ß’s = 0.6–0.8, all p<.001). The memory test was strongly correlated with the CVLT‐SF (ß = 0.7, p<.001). Tests were less strongly associated with divergent cognitive domains (e.g., associations with a visuospatial test were ß’s = 0.1–0.3). Worse performance on all tests was significantly associated with greater disease severity (ß’s = 0.5–0.7, all p<.001). Worse app‐based EF scores were associated with lower frontoparietal volume (ß’s = 0.4–0.6, all p<.015) and worse memory scores with lower hippocampal volume (ß = 0.5, p<.001).ConclusionALLFTD Mobile App scores were strongly associated with gold standard neuropsychological tests of the same cognitive domain (i.e., convergent validity) and less associated with measures of a different domain (i.e., divergent validity). Worse performance on the smartphone tests was associated with increasing disease severity and volume loss in expected brain regions. These results provide preliminary support for the construct validity of the ALLFTD Mobile App cognitive tests.

AbstractBackgroundSeveral hypotheses exist regarding the neurologic substrate underlying visual hallucinations (VH) in Dementia with Lewy Bodies (DLB). Studies have suggested an association with regional cerebral hypometabolism in the primary visual or visual association cortices on FDG‐PET. We compared regional cerebral glucose metabolism and posterior cingulate island sign (CIS) ratios in DLB and in Mild Cognitive Impairment with Lewy Bodies (MCI‐LB) participants with (VH+) and without (VH‐) VH.MethodParticipants with clinically probable DLB or MCI‐LB and an FDG‐PET scan were identified from the Mayo ADRC database. Responses on the Visual Hallucinations and Delusions Questionnaire and Neuropsychiatric Inventory were used to assess presence or absence of VH. Standardized uptake value ratios (SUVr) were used to calculate regional cerebral glucose hypometabolism in the primary visual cortex (PVC), lateral occipital cortex (LOC), and whole occipital cortex (WOC). The CIS ratio was defined as posterior cingulate divided by the sum of the precuneus plus cuneus FDG‐PET uptake ratio.ResultTwo cohorts were identified (27 DLB and 18 MCI‐LB, 87% male). Eighteen DLB participants were VH+ and 9 were VH‐, whereas 5 MCI‐LB were VH+ and 13 were VH‐. There were no statistically significant differences in demographic (age, sex, education) and clinical features (MoCA, CDR, presence of other core DLB features, and UPDRS) between VH+ vs VH‐ in those with DLB, and between VH+ vs VH‐ in those with MCI‐LB. Similarly, there were no statistically significant differences in DLB between VH+ vs VH‐ in PVC (1.55±0.14 vs 1.58±0.15), LOC (1.22±0.16 vs 1.28±0.12), WOC (1.29±0.15 vs 1.34±0.11), or CIS ratio (1.16±0.08 vs 1.14±0.10), nor in MCI‐LB between VH+ vs VH‐ in PVC (1.68±0.19 vs 1.58±0.14), LOC (1.40±0.21 vs 1.32±0.14), WOC (1.45±0.20 vs 1.37±0.14), or CIS ratio (1.17±0.08 vs 1.19±0.10) (P>0.05 for all comparisons).ConclusionThese findings do not support a direct correlation between regional cerebral glucose metabolism and VH in DLB or MCI‐LB. Further work in DLB and MCI‐LB with larger numbers is warranted. Other neurologic substrates and mechanisms, such as REM sleep intrusions during wakefulness, neurotransmitter dysfunction, and disruption of functional connectivity across brain regions should be explored as contributors to VH in DLB.