Boeve, Bradley F.

Background: The relationship between cerebral microbleeds (CMBs) on hemosiderin-sensitive MRI sequences and cerebral amyloid angiopathy (CAA) remains unclear in population-based participants or in individuals with dementia. Objective: To determine whether CMBs on antemortem MRI correlate with CAA. Methods: We reviewed 54 consecutive participants with antemortem T2*GRE-MRI sequences and subsequent autopsy. CMBs were quantified on MRIs closest to death. Autopsy CAA burden was quantified in each region including leptomeningeal/cortical and capillary CAA. By a clustering approach, we examined the relationship among CAA variables and performed principal component analysis (PCA) for dimension reduction to produce two scores from these 15 interrelated predictors. Hurdle models assessed relationships between principal components and lobar CMBs. Results: MRI-based CMBs appeared in 20/54 (37%). 10 participants had ≥2 lobar-only CMBs. The first two components of the PCA analysis of the CAA variables explained 74% variability. The first rotated component (RPC1) consisted of leptomeningeal and cortical CAA and the second rotated component of capillary CAA (RPC2). Both the leptomeningeal and cortical component and the capillary component correlated with lobar-only CMBs. The capillary CAA component outperformed the leptomeningeal and cortical CAA component in predicting lobar CMBs. Both capillary and the leptomeningeal/cortical components correlated with number of lobar CMBs. Conclusion: Capillary and leptomeningeal/cortical scores correlated with lobar CMBs on MRI but lobar CMBs were more closely associated with the capillary component. The capillary component correlated with APOE ɛ4, highlighting lobar CMBs as one aspect of CAA phenotypic diversity. More CMBs also increase the probability of underlying CAA.

Abstract Introduction The Frontotemporal Lobar Degeneration Module (FTLD‐MOD) includes a neuropsychological battery designed to assess the clinical features of FTLD, although much is unknown about its utility. We investigated FTLD‐MOD and Uniform Data Set 3.0 (UDS) language tests for differential diagnosis and disease monitoring. Methods Linear regressions compared baseline performances in 1655 National Alzheimer's Coordinating Center participants (behavioral variant frontotemporal dementia (bvFTD, n = 612), semantic variant primary progressive aphasia (svPPA, n = 185), non‐fluent/agrammatic variant PPA (nfvPPA, n = 168), logopenic variant PPA (lvPPA, n = 109), and controls (n = 581)). Sample sizes to detect treatment effects were estimated using longitudinal data. Results Among PPAs, the FTLD‐MOD language tasks and UDS Multilingual Naming Test accurately discriminated svPPA. Number Span Forward best discriminated lvPPA; Phonemic:Semantic Fluency ratio was excellent for nfvPPA classification. UDS fluency and naming measures required the smallest sample size to detect meaningful change. Discussion The FTLD‐MOD and UDS differentiated among PPA subtypes. UDS 3.0 measures performed best for longitudinal monitoring.

Background: Transactive response DNA-binding protein of 43 kDa (TDP-43) is associated with memory impairment and overall cognitive decline. It is unclear how TDP-43 contributes to the rate of clinical decline. Objective: To determine whether cross-sectional and longitudinal cognitive and functional decline are associated with anatomical distribution of TDP-43 in the brain. Methods: Longitudinal clinical-neuropathologic autopsy cohort study of 385 initially cognitively normal/mildly impaired older adults prospectively followed until death. We investigated how TDP-43, amyloid-β (Aβ), tau neurofibrillary tangles (NFT), Lewy body disease (LBD), age, sex, and genetics are associated with clinical scores and rates of their longitudinal decline. Results: Of 385 participants, 260 (68%) had no TDP-43, 32 (8%) had TDP-43 limited to amygdala, and 93 (24%) had TDP-43 in the hippocampus and beyond. Higher TDP-43 and Braak NFT stages independently were associated with faster decline in global cognition, functional performance measured by Clinical Dementia Rating scale, and naming and episodic memory, whereas older age was associated with slower rate of cognitive, psychiatric, and functional decline. Cross-sectionally the following associations were found: higher TDP-43 and Braak NFT - worse performance; higher Aβ burden - worse global cognition, more behavioral changes, the latter also with higher LBD; older age - worse naming, lower frequency of behavioral changes; female sex - more impaired naming and better preserved episodic memory. There were no genetic associations. Conclusion: The association of TDP-43 distribution with decline in cognitive and functional performance suggests that TDP-43 is playing a role in the clinical progression to dementia. Further characterization of clinical features associated with TDP-43 can facilitate establishment of antemortem diagnosis.

AbstractTo examine the length of a hexanucleotide expansion in C9orf72, which represents the most frequent genetic cause of frontotemporal lobar degeneration and motor neuron disease, we employed a targeted amplification-free long-read sequencing technology: No-Amp sequencing. In our cross-sectional study, we assessed cerebellar tissue from 28 well-characterized C9orf72 expansion carriers. We obtained 3507 on-target circular consensus sequencing reads, of which 814 bridged the C9orf72 repeat expansion (23%). Importantly, we observed a significant correlation between expansion sizes obtained using No-Amp sequencing and Southern blotting (P = 5.0 × 10−4). Interestingly, we also detected a significant survival advantage for individuals with smaller expansions (P = 0.004). Additionally, we uncovered that smaller expansions were significantly associated with higher levels of C9orf72 transcripts containing intron 1b (P = 0.003), poly(GP) proteins (P = 1.3 × 10− 5), and poly(GA) proteins (P = 0.005). Thorough examination of the composition of the expansion revealed that its GC content was extremely high (median: 100%) and that it was mainly composed of GGGGCC repeats (median: 96%), suggesting that expanded C9orf72 repeats are quite pure. Taken together, our findings demonstrate that No-Amp sequencing is a powerful tool that enables the discovery of relevant clinicopathological associations, highlighting the important role played by the cerebellar size of the expanded repeat in C9orf72-linked diseases.

AbstractBackgroundHeterozygous progranulin (GRN) mutations are an important cause of familial Frontotemporal Lobar Degeneration (f‐FTLD) associated with TDP‐43 pathology. The cellular role of progranulin remains unclear, but alterations in lipid metabolism and lysosomal function have been suggested. Better understanding the pathophysiology of progranulin deficiency is critical as several clinical trials are underway in this population, and fluid biomarkers are urgently needed to assess pharmacodynamic effects. Therefore, we conducted an unbiased metabolomic analysis in GRN mutation carriers and family member controls, and identified at least two classes of lipids altered in symptomatic GRN carriers.MethodsMatched plasma and CSF samples were obtained from GRN mutation carriers (46 plasma, 36 CSF) and age‐matched blood relatives (36 plasma, 18 CSF). Untargeted metabolite detection was conducted via LC‐MS/MS in both plasma (1389 compounds) and CSF (508 compounds) by Metabolon. To identify important compounds in differentiating these groups, we conducted an unbiased analysis between family controls, asymptomatic carriers, and symptomatic carriers using univariate one‐way ANOVA with post‐hoc Fisher’s LSD corrected for false discovery rate (FDR) and partial least squares ‐ discriminant analysis (PLS‐DA, Metaboanalyst). For compounds identified by this unbiased approach, correlation with measures of disease severity was analyzed via linear regression, controlling for age and gender.ResultsIn plasma, 22 compounds were identified as significant in discriminating between groups via ANOVA (FDR<0.05), and 17/22 were also identified by PLS‐DA (VIP>2.4). Of these, 21/22 compounds were lipids; 11 were sphingomyelins. Comparatively, in the relatively lipid‐poor CSF samples, 6/15 compounds identified by PLS‐DA (VIP>2.0) were phosphatidylcholines. Pathway impact analysis revealed a large impact from the GRN mutation on sphingolipids in plasma and glycerophospholipids in CSF. In secondary analyses, sphingomyelins were elevated in plasma from symptomatic carriers, while phosphatidylcholines were decreased in CSF. Secondary analyses showed these compounds were correlated with clinical measures of disease severity, including MoCA and CDR+NACC‐FTLD.ConclusionIncreased plasma concentration of sphingomyelin and decreased CSF phosphatidylcholines are both associated with worse clinical status in FTLD‐GRN. These findings suggest that impaired lipid metabolism could play a pathogenic role in FTLD‐GRN. Targeted lipidomic measures in plasma and CSF may potentially reveal useful clinical trial biomarkers.

AbstractBackgroundMeasuring changes in the earliest phases of familial frontotemporal lobar degeneration (f‐FTLD) and estimating symptom onset has important implications for clinical care. Early stage clinical trials have begun for f‐FTD, but the low prevalence of f‐FTLD mutation carriers and heterogeneous symptomology challenges trial design. Bayesian disease progression models (DPM) incorporate clinical data, neuroimaging metrics, and fluid biomarker concentrations to estimate latent disease age, providing estimates of symptom onset and progression status. Such models may facilitate enrollment in studies (i.e., inclusion of presymptomatic individuals) and provide more powerful analysis methods for trials.MethodA Bayesian latent variable repeated measures model was used to estimate disease progression, conditional on latent disease age (proximity to symptom onset), in 275 participants with mutations in GRN (n=68), MAPT (n=80) and C9orf72 (n=127). Median follow‐up was one year (range: 0‐4). Jointly modeled longitudinal variables included neuropsychological scores, CDR®+NACC‐FTLD box score, 3T MRI frontotemporal brain volumes, and plasma levels of neurofilament light chain (NfL). Sample size estimates for detecting treatment effects were generated by varying thresholds of estimated proximity to symptom onset.ResultThe Bayesian DPM provided a clear picture of clinical and neurological progression within each mutation group. Neuroimaging and NfL captured early progression rates (1 SD from controls) 5‐10 years before symptom onset. After symptom onset, CDR®+NACC‐FTLD box score was the most sensitive measure of disease progression. GRN mutation carriers had the largest annual progression rates (and lowest variability) compared to MAPT and C9orf72. Between 5 years pre‐ and 10 years post‐onset, CDR®+NACC‐FTLD box score progressed by 2 units/year (SD=2.7) in GRN mutation carriers ‐ almost twice the rate of progression in MAPT (M=1.2;SD=2.5) and C9orf72 (M=1.1;SD=2.2) mutation carriers. Compared to a trial enrolling only early symptomatic cases (CDR®+NACC‐FTLD=0.5 or 1), utilizing latent disease age would facilitate enrollment of asymptomatic cases with the same power.ConclusionBased on an estimation of symptom onset, Bayesian DPMs suggest clinical trials in GRN mutation carriers would require the smallest sample sizes. Furthermore, leveraging these DPMs would allow novel, powerful trial designs that may facilitate greater participant enrollment.

AbstractBackgroundImmune dysregulation may influence disease pathogenesis in familial frontotemporal dementia (fFTD), but the utility of peripheral biomarkers of inflammation remains unclear in this cohort. We sought to investigate candidate plasma biomarkers of immune function within a large cohort of fFTD kindreds from the ARTFL and LEFFTDS Consortia.MethodsWe examined plasma samples from 148 non‐carrier healthy controls and 256 asymptomatic and symptomatic individuals carrying autosomal dominant fFTD‐associated mutations (n=73 MAPT, n=120 C9ORF72, and n=63 GRN). ELISA was used to measure three plasma biomarkers of inflammation (YKL‐40, TNF‐α,IL‐6). We used Mann–Whitney U tests to compare concentrations amongst controls and subgroups of fFTD differing in disease severity, defined by the Clinical Dementia Rating Scale plus National Alzheimer’s Disease Coordinating Center Frontotemporal Lobar Degeneration Behavior and Language Domains (CDR® plus NACC FTLD) global score (asymptomatic=0, early symptomatic=0.5, late symptomatic ≥1). We also used linear mixed models to assess the utility of baseline inflammatory biomarkers in predicting the subsequent annual rate of decline in functional clinical severity (CDR® plus NACC FTLD sum of boxes) and psychomotor speed (Trails A).ResultsPlasma YKL‐40 concentrations were elevated in late symptomatic fFTD (59.3ng/ml [50.1]) relative to controls (32.9ng/ml [24.0]) (p=0.0003) and asymptomatic mutation carriers (30.9ng/ml [25.4], p=0.0001). Plasma TNF‐α concentrations were relatively low in asymptomatic mutation carriers (1.96pg/ml [0.56]) compared to early symptomatic fFTD (2.35pg/ml [0.83], p= 0.009). Amongst mutation carriers (including 140 with follow up clinical data), higher baseline plasma IL‐6 and YKL‐40 concentrations were associated with greater increase in functional clinical severity and declines in psychomotor speed, respectively.ConclusionPlasma biomarkers of inflammation increase with disease severity in fFTD and predict worse clinical trajectories. While the role of inflammation remains unclear in familial FTD, plasma markers of inflammation appear to reflect the processes influencing disease trajectory in patients carrying mutations associated with familial frontotemporal dementia.

AbstractBackgroundRecent evidence has implicated brain networks in the pathophysiology of Alzheimer’s disease (AD). However, the phenotypic variability in AD, inter‐individual differences in tau PET uptake patterns, and dynamic nature of functional connectivity have been relatively neglected. In this talk, I will discuss our recent work linking measures of dynamic brain organization to spatially independent tau PET patterns defined in a phenotypically diverse cohort.MethodsWe identified all Mayo Clinic participants with concurrent amyloid PET (PiB), tau PET (AV1451) and task‐free fMRI (impaired participants were all Aβ+). We used independent component analysis (ICA) to identify spatially independent patterns of tau PET uptake. For the fMRI data, we used a Hidden‐Markov Model (HMM) design to identify distinct patterns of activity/connectivity (‘states’). The ICA and HMM parameters were used to project/segment an independent cohort (ADNI) and subsequent longitudinal timepoints for the Mayo cohort. Multilevel Bayesian regression models were fit in Stan to explore the association between phenotype, component load and brain state.ResultsThe optimal HMM model identified 10 brain states (Figure 1). The optimal ICA decomposition identified 42 tau PET components, of which 13 were potentially related to AD pathobiology (Figure 1). Component loads and brain state dwell time differed between diagnostic groups in both Mayo and ADNI cohorts. One state was much more likely to occur in AD in both cohorts (State 8). Cross‐sectionally, State 8 dwell time was associated with higher tau loads, while higher baseline dwell‐time was predictive of ∆tau in participants with at least 2 timepoints (Figure 2).ConclusionWe found links between dynamics of functional connectivity, tau deposition, and cognitive impairment across the AD spectrum using two different cohorts and longitudinal data. Specifically: 1) our method for characterizing tau PET uptake was able to capture the magnitude and spatial pattern of binding across diverse phenotypes; 2) Our data supports a role for dynamic brain connectivity in cognitive impairment due to Alzheimer’s disease; 3) Dynamic connectivity appears to be directly related to AD pathobiology as measured by tau PET. Incorporating the time‐varying nature of connectivity will be important in future studies of connectome‐based disease progression in AD.