Botha, Hugo

Purpose This study describes motor speech disorders and associated communication limitations in six variants of progressive supranuclear palsy (PSP). Method The presence, nature, and severity of dysarthria and apraxia of speech (AOS) were documented, along with scores on the Apraxia of Speech Rating Scale–Version 3 (ASRS-3) for 77 (40 male and 37 female) patients with PSP. Clinician-estimated and patient-estimated communication limitations were rated using the Motor Speech Disorders Severity Rating (MSDSR) Scale and the Communicative Effectiveness Survey (CES), respectively. Descriptive statistics were calculated for each of these dependent variables. One-tailed t tests were conducted to test mean differences in ASRS-3 and CES between participants with and without AOS and between participants with and without dysarthria. Spearman rank correlations were calculated between ASRS-3 scores and clinical judgments of AOS and dysarthria severity and between MSDSR and CES ratings. Results Nine participants (12%) had normal speech. Eighty-seven percent exhibited dysarthria; hypokinetic and mixed hypokinetic–spastic dysarthria were observed most frequently. AOS was observed in 19.5% of participants across all variants, but in only 10% exclusive of the PSP speech and language variant. Nearly half presented with AOS in which neither phonetic nor prosodic features clearly predominated. The mean ASRS-3 score for participants with AOS was significantly higher than for those without and correlated strongly with clinician judgment of AOS severity. Mean ASRS-3 was higher for participants with dysarthria than for those without but correlated weakly with dysarthria severity. Mean MSDSR and CES ratings were lower in participants with AOS compared to those without and moderately correlated with each other. Conclusions Motor speech disorders that negatively impact communicative effectiveness are common in PSP and occur in many variants. This is the first description of motor speech disorders across PSP variants, setting the stage for future research characterizing neuroanatomical correlates, progression of motor speech disorders, and benefits of targeted interventions. Supplemental Material https://doi.org/10.23641/asha.14111837

Purpose Individuals with primary progressive apraxia of speech (PPAOS) have apraxia of speech (AOS) in which disruptions in articulation or prosody predominate the speech pattern, referred to, respectively, as phonetic or prosodic subtypes. Many develop aphasia and/or dysarthria. Past research has demonstrated that simple temporal acoustic measures are sensitive to the presence of AOS. The aim of this study was to describe the change in temporal acoustic measures over time and assess if specific patterns of AOS or co-occurring aphasia or dysarthria impact the rate of change over time. Method Durations for multiple productions of the words cat , catnip , catapult , and catastrophe , in an imitative speech task, were recorded for 73 patients, with two to six visits each. A linear mixed-effects model was used to assess the cross-sectional differences and longitudinal influence of AOS subtype and presence of aphasia/dysarthria on speech rate. Pearson correlations were calculated between rate measures and performance on other clinical measures. Results Cross-sectionally, patients with prosodic-predominant PPAOS produced words more slowly than those with phonetic-predominant PPAOS. Patients with either aphasia or dysarthria produced words more slowly than those without. Longitudinally, the speech rate of patients with phonetic-predominant PPAOS had a reduction of 0.5 syllables per second per year. Patients with prosodic-predominant AOS changed less quickly, as did those who developed aphasia. Dysarthria did not impact rate of change. There were strong associations between speech rate measures and other clinical indices of speech and language functioning. Conclusion Simple temporal acoustic measures may reflect the subtype of AOS (phonetic or prosodic predominant), serve as an index of progression of AOS, and inform prognostication relative to the presenting combination of speech and language features. Supplemental Material https://doi.org/10.23641/asha.13564724

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.

AbstractBackgroundPast research has demonstrated that clinical EEG is sensitive to dementia associated with Alzheimer's (AD) and Parkinson’s diseases (PD), and nonfluent/agrammatic Primary Progressive Aphasia (agPPA), but not Primary Progressive Apraxia of Speech (PPAOS). However, clinical EEG studies do not quantify interactions of multiple brain areas, or network activity. Graph theory (GT) simplifies analyzing and comparing complex brain networks. Studies have shown changes in EEG GT measures in AD and PD, but have not been studied in agPPA or PPAOS. The goal of this study was to use GT to characterize network‐level changes and associations with speech and language functioning in patients with agPPA and PPAOS.MethodsScalp EEGs of 14 agPPA and 7 PPAOS patients were collected during relaxed wakefulness (10‐20 positions, 256Hz sampling, 1–200Hz bandpass filter). Eight artifact‐free epochs were selected from non‐overlapping 1024‐point epochs. Via Brainwave software, GT weighted connectivity and minimum spanning tree (MST) measures were calculated for theta and alpha frequency bands. Differences in theta MST measures between agPPA and PPAOS were assessed with Wilcoxon rank‐sum tests. Spearman correlations were computed between behavioral and all network measures across all patients.ResultsTheta frequency MST diameter and eccentricity were significantly lower and leaf was significantly higher in agPPA compared to PPAOS patients; betweenness centrality did not differ. This indicates agPPA networks are more integrated (star‐like) than PPAOS. There were significant correlations between several network measures (e.g. kappa, lambda, PLI) and indices of speech‐language functioning.ConclusionsThe results provide EEG evidence of network alteration in agPPA and PPAOS patients. The GT measures and associations with behavioral measures provide support for reduced global efficiency, or network specificity, in these patients. The correlation of language measures with lambda (path length) in the theta frequency band suggests agPPA is associated with an inefficient path length and/or increased integration. The correlation between apraxia of speech (AOS) measures and MST eccentricity in the alpha frequency band suggests that more severe AOS is associated with reduced distance between two nodes. This study demonstrates potential for EEG GT measures to quantify network changes and distinguish network changes between related but different clinical syndromes.

AbstractBackgroundAlzheimer’s disease (AD) is marked by selective disruptions to anatomically‐distinct brain regions and their connectivity which collectively support cognition. Based on a variation of pattern‐recognition analysis of FDG‐PET images, we previously developed a framework, the global functional state space (GFSS), which captures this macroscale architecture by assigning each individual a series of weightings on 10 independent patterns (eigenbrains=EBs). In this study, we applied the GFSS as a quantitative endophenotype to test whether known AD genetic risk factors were associated with variability in large‐scale neurodynamics represented by the GFSS.MethodThe study sample included 410 participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) with evidence of cognitive impairment (CDR‐global>0) and amyloidosis (positive amyloid‐PET). The GFSS was estimated for each individual using the set of 10 EBs to describe global patterns of variation in glucose metabolism. AD genetic risk variants identified in prior large consortium case‐control studies were included for analysis if the variant (or a proxy by linkage disequilibrium r 2>0.7) was assayed on both genotyping platforms used in the sample and if the minor allele frequency exceeded 1%. Genetic associations with the collective set of EBs were tested using multivariate linear regression under a dominant genetic model.ResultSignificant multivariate associations were identified for CD33 (p=7.04x10‐4), MEF2C (p=0.001), and SORL1 (p=0.02). These findings remained significant after covarying for age and sex. The strongest component loadings for CD33 (p=9.09x10‐4; std. β=‐0.16) and MEF2C (p=1.59x10‐4; std. β=‐0.18) were for EB1, with the minor allele in each case associated with a negative axis deviation in the direction of the GFSS area representing episodic memory dysfunction. SORL1 displayed its strongest loading onto EB4 (p=0.03, std. β=‐0.11). The association of APOE ɛ4 with the GFSS was marginally nonsignificant (p=0.09).ConclusionWe identified associations of risk variants in CD33, MEF2C, and SORL1 with differences in patterns of FDG hypometabolism in persons with symptomatic AD. These findings establish the GFSS as a useful endophenotype for genetic analyses, particularly as an avenue toward a mechanistic understanding of how heritable factors may influence inter‐individual variability and syndromic diversity in AD. Further investigation in larger and independent cohorts is warranted.

AbstractBackgroundThe Network Failure Quotient (NFQ) metric summarizes functional MRI network integrity. We wanted to investigate the stability of the NFQ results across time, hardware, software and protocol changes during the last decade in ADNI Resting State (RS‐) fMRI.MethodIn ADNI‐2 (2009‐2016) RS‐fMRI was acquired on Philips scanners. In ADNI‐3 (2017‐present) RS‐fMRI is acquired on GE, Philips, and Siemens systems with “Basic” and “Advanced” variants based on the availability of simultaneous multi‐slice (SMS, multi‐band) imaging. (Table 1.) Resting state images were preprocessed by performing slice timing correction, trimming volumes from the first 10 seconds of acquired data, despiking and within‐series alignment. Simultaneous bandpass and regression was performed using spatial registration parameters and their first temporal derivatives, mean WM signal, mean CSF signal and global signal. Dual regression via GIFT was applied creating regional time courses. The NFQ is calculated as NFQ=(pDMN_to_vDMN + pDMN_to_adDMN) / (pDMN +vDMN) where pDMN_to_vDMN and pDMN_to_adDMN are correlations between time courses in the posterior DMN, ventral DMN and anterior‐dorsal DMN. pDMN and vDMN are the within‐region correlations. NFQ values from 1447 scans of 739 individual study participants (Table 1) were analyzed using linear mixed effects models to account for age, clinical diagnosis, vendor, and time trends.ResultBox plots of NFQ values by diagnosis within ADNI‐phase and protocol are shown in Figure 2 and suggest important differences among clinical diagnoses and between ADNI‐2 and ADNI‐3. The mixed model analysis indicates that a 10‐year increase in age is associated with a 0.05±0.01 (mean±SD) increase in NFQ; individuals with MCI average roughly 0.05±0.01 higher than unimpaired and individuals with dementia average 0.14±0.02 higher. Differences by vendor for ADNI‐2 and ADNI‐3 are summarized in Figures 2 and 3 with effect sizes on par with these clinical factors. Mean NFQ for Phillips ADNI‐2 were highest (0.54±0.02); somewhat lower were Philips ADNI‐3 (0.51±0.02) and Siemens ADNI‐3 advanced (0.49±0.03); Siemens ADNI‐3 basic (0.40±0.02) and GE ADNI‐3 were the lowest (0.38±0.02).ConclusionNFQ values consistently increase with impairment. Vendor‐specific differences are observed and vendor changes over the life of a study introduce variability on the scale of disease‐related changes in NFQ.

AbstractBackgroundIt is important to determine the natural history of sporadic and familial frontotemporal lobar degeneration (FTLD) and generate clinical, neuropsychological, neuroimaging and biofluid data for planning disease‐modifying trials.MethodAs part of the ARTFL LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD; U19 AG063911) protocol, investigators at 19 centers in North America will enroll 2100 participants with FTLD over the next 5 years beginning in early 2020.ResultAs of 1/20/20, the ARTFL/LEFFTDS (A/L) Consortium had enrolled 1832 participants, including 850 in kindreds with familial FTLD (239 associated with mutations in MAPT, 192 in GRN, 370 in C9orf72, 4 with mutations in both GRN and C9orf72, and 45 with a mutation in a different gene or no mutation in any known FTLD‐associated gene). Over 500 participants have undergone 2 or more annual visits to date. MRI has been performed in 1105. Biofluid samples have also been collected, with blood (DNA, plasma, serum, mRNA, PBMC) in 1413 and CSF in 303. Over 60 manuscripts using A/L data or samples have been published to date. Five clinical trials involving A/L and ALLFTD participants are in progress or planned. The longitudinal arm in ALLFTD will enroll 500 of existing A/L and 600 future participants for annual assessments with similar methodology to A/L. An additional 1000 FTLD patients will undergo focused one‐time clinical evaluations and biofluid collection.ConclusionThe data/samples from already‐enrolled and planned participants in ALLFTD and findings published to date underscore the utility of evaluating FTLD subjects. The absence of identifiable mutations in some with familial FTLD suggests that other genes are yet to be discovered. ALLFTD data will inform clinical trial design, and many participants will be eligible for future trials. The key data and samples in ALLFTD are available to interested investigators worldwide. Supported by: AG063911, AG045390, NS092089, AG016976, AG21886.

AbstractBackgroundWe report a group of individuals presenting with a progressive dementia syndrome characterized by predominant dysfunction in core executive functions and positive biomarkers for Alzheimer’s pathophysiology.MethodIn this retrospective review, we report on 55 participants with a clinically defined progressive dysexecutive syndrome with 18F‐fluorodeoxyglucose‐positron emission tomography (FDG) and AD biomarker positivity. A subset of these individuals enrolled in our ADRC were compared to other phenotypic variants of AD.ResultSixty‐two percent were female with a mean of 15.2 years of education. Mean age of symptom onset was 53.8 while mean age at diagnosis was 57.2 years. Multi‐domain cognitive impairment was evident in neuropsychological testing with executive dysfunction most consistently and severely affected. The frontal and/or parietal regions demonstrate clear hypometabolism on positron emission tomography in all cases. Genetic testing for autosomal dominant genes was negative in all 8 participants tested and at least one APOE ε4 allele was present in 14/26. EEG was abnormal in 14/17 cases. CSF and neuroimaging biomarkers (PIB‐PET and Tau‐PET) were consistent with Alzheimer’s disease pathophysiology, although CSF p‐tau was normal in 24% of cases. In two participants that came to autopsy, the hippocampus was relatively spared compared to frontal and parietal cortex. Participants presenting with a progressive dysexecutive syndrome and evidence of AD pathology are labeled dAD. Comparing FDG in dAD relative to other AD phenotypes revealed unique areas of relatively greater hypometabolism in parieto‐frontal cortex and relative sparing of medial temporal (vs typical AD), visual (vs PCA), and left temporal (vs lvPPA) (Figure 1). Controlling for age did not alter patterns of regional differences. This demonstrates regional variability related to clinical phenotype not accountable by variation in age, duration, stage, or severity (Figure 2).ConclusionA progressive dysexecutive syndrome should be recognized as a distinct clinical phenotype. This clinical presentation can be due to Alzheimer’s disease but is not specific for any single etiology (non‐AD cases of progressive dysexecutive syndrome will be discussed). Prominent impairment of executive task performance, young‐age of onset, normal CSF p‐tau, and relative preservation of the hippocampus were commonly encountered in this case series of dAD.

AbstractThe mechanisms through which tau and amyloid-beta (Aβ) accumulate in the brain of Alzheimer’s disease patients may differ but both are related to neuronal networks. We examined such mechanisms on neuroimaging in 58 participants with atypical Alzheimer’s disease (posterior cortical atrophy or logopenic progressive aphasia). Participants underwent Aβ-PET, longitudinal tau-PET, structural MRI and resting-state functional MRI, which was analyzed with graph theory. Regions with high levels of Aβ were more likely to be functional hubs, with a high number of functional connections important for resilience to cascading network failures. Regions with high levels of tau were more likely to have low clustering coefficients and degrees, suggesting a lack of trophic support or vulnerability to local network failures. Regions strongly functionally connected to the disease epicenters were more likely to have higher levels of tau and, less strongly, of Aβ. The regional rate of tau accumulation was associated with tau levels in functionally connected regions, in support of tau accumulation in a functional network. This study elucidates the relations of tau and Aβ to functional connectivity metrics in atypical Alzheimer’s disease, strengthening the hypothesis that the spread of the 2 proteins is driven by different biological mechanisms related to functional networks.

ObjectiveTo examine associations between tau and amyloid β (Aβ) molecular positron emission tomography (PET) and both Alzheimer‐related pathology and 4‐repeat tau pathology in autopsy‐confirmed frontotemporal lobar degeneration (FTLD).MethodsTwenty‐four patients had [18F]‐flortaucipir–PET and died with FTLD (progressive supranuclear palsy [PSP], n = 10; corticobasal degeneration [CBD], n = 10; FTLD‐TDP, n = 3; and Pick disease, n = 1). All but 1 had Pittsburgh compound B (PiB)‐PET. Braak staging, Aβ plaque and neurofibrillary tangle counts, and semiquantitative tau lesion scores were performed. Flortaucipir standard uptake value ratios (SUVRs) were calculated in a temporal meta region of interest (meta‐ROI), entorhinal cortex and cortical/subcortical regions selected to match the tau lesion analysis. Global PiB SUVR was calculated. Autoradiography was performed in 1 PSP patient, with digital pathology used to quantify tau burden.ResultsNine cases (37.5%) had Aβ plaques. Global PiB SUVR correlated with Aβ plaque count, with 100% specificity and 50% sensitivity for diffuse plaques. Twenty‐one (87.5%) had Braak stages I to IV. Flortaucipir correlated with neurofibrillary tangle counts in entorhinal cortex, but entorhinal and meta‐ROI SUVRs were not elevated in Braak IV or primary age‐related tauopathy. Flortaucipir uptake patterns differed across FTLD pathologies and could separate PSP and CBD. Flortaucipir correlated with tau lesion score in red nucleus and midbrain tegmentum across patients, but not in cortical or basal ganglia regions. Autoradiography demonstrated minimal uptake of flortaucipir, although flortaucipir correlated with quantitative tau burden across regions.InterpretationMolecular PET shows expected correlations with Alzheimer‐related pathology but lacks sensitivity to detect mild Alzheimer pathology in FTLD. Regional flortaucipir uptake was able to separate CBD and PSP. ANN NEUROL 2020;88:1009–1022