Botha, Hugo

AbstractBackgroundPatients with a yes‐no reversal verbally and non‐verbally respond “yes” when they mean “no” and vice versa. They are typically aware of the error and able to correct it given additional time. This symptom commonly, but not exclusively, occurs in patients with primary progressive apraxia of speech (PPAOS). PPAOS is a form of frontotemporal dementia associated with hypometabolism in the supplementary motor area (SMA; e.g., Utianski et al., 2018); as the disease progresses, a wider network of frontal and subcortical regions become implicated. Behaviorally, this symptom seems to reflect poor response inhibition, but the specific anatomical underpinning is not yet understood. Given the brain regions known to be involved in PPAOS, we hypothesized that yes‐no reversals emerge due to degeneration of regions near the SMA and/or frontostriatal regions.MethodWe conducted an exploratory retrospective study of 28 patients who presented with PPAOS and had an FDG‐PET scan; 14 (50%) endorsed yes‐no reversals (see Table 1 for group characteristics). Clinically, AOS severity was measured using clinician ratings (0 = absent, 4 = severe) and frontal dysfunction was captured by the Frontal Assessment Battery (FAB) and the Frontal Behavioral Inventory (FBI). We also measured median standardized uptake value ratios (SUVRs) in the SMA and pre‐SMA, as well as the head and body of the caudate with automated, in‐house software. The caudate ROI was selected based on visual comparison of the FDG‐PET scans between groups and the association between this region and inhibition during response interference (e.g., Schmidt et al., 2020).ResultNeither AOS severity nor hypometabolism in the pre‐SMA/SMA regions differed significantly between groups. In contrast, patients with a yes‐no reversal demonstrated significantly lower scores on the FAB and greater hypometabolism in the caudate body relative to patients without (see Figure 1).ConclusionResults suggest that yes‐no reversals relate to frontostriatal dysfunction rather than AOS severity or SMA activity in these patients. Further research is needed to address why a disproportionate number of patients with PPAOS develop this symptom.

AbstractBackgroundThe cardiac and respiratory cycles have been suggested as major drivers of cerebrospinal fluid(CSF) flow. The glymphatic system, one of the mechanisms for the clearance of amyloid‐beta and tau proteins, relies heavily on the movement(flow) of the CSF, and thus, investigating the forces that modulate the CSF flow is essential for understanding the glymphatic flux. Because breathing is trainable through exercise and self‐directed methods, various breathing methods may enhance the CSF flow.MethodReal‐time phase‐contrast MRI(pc‐MRI) was used to measure the CSF flow at the foramen magnum on a 3T. Fast resting‐state fMRI(rsfMRI;TR400ms) was conducted on a compact 3T. Under an IRB‐approved protocol, participants were instructed to breathe slowly and deeply, and subsequently switch to their spontaneous breathing. For rsfMRI data preprocessing, we performed motion correction, skull stripping, spatial smoothing(4mm), temporal filtering(0.01 to 0.1Hz), and linear and quadratic temporal trends removal. fMRI time courses were then normalized to Z‐score at each voxel before being averaged. On the pcMRI, the phase was summed in a CSF region‐of‐interest and plotted vs time. Additionally, we confirmed for possible confounding effects such as head motion or motion‐induced susceptibility changes.ResultrsfMRI findings showed that deep breathing promoted large oscillations in the CSF flow in the ventricles, while spontaneous breathing did not(Figure 1). Additionally, deep breathing resulted in large oscillations in lobar cortical regions. Similar results were observed in the pcMRI, with larger CSF velocity through the foramen magnum during the deep breathing compared to the spontaneous breathing(Figure 2A‐B). The cumulative summation result showed that the volume of CSF flow was increased during deep breathing(Figure 2C).ConclusionThis study provides new evidence that slow and deep breathing promotes CSF flow and global brain BOLD signal, which may be useful in promoting circulation in the brain. The study’s findings also open new avenues for research in the field of neurovascular coupling and mechanisms associated with glymphatic clearance. As hampered CSF flow and/or worsen glymphatic clearance have been shown to associate with pathological aging and dementia, including Alzheimer’s disease and Parkinson’s disease, future research is needed explore the potential of breathing training methods as a non‐pharmacological intervention to support brain health.

AbstractBackgroundPatients with dementia with Lewy bodies (DLB) may have Alzheimer´s disease (AD) pathology. Plasma biomarkers of beta‐amyloid (Aβ), phosphorylated tau (pTau), and neurodegeneration are sensitive to AD neuropathologic changes in AD dementia. While these plasma biomarkers are well tested in the AD continuum, their performance for concomitant AD pathology in the DLB continuum is still unclear.MethodWe included patients with isolated REM sleep behavior disorder (iRBD; n = 15), with mild cognitive impairment with Lewy bodies (MCI‐LB; n = 37), and with DLB (n = 70) from the Mayo Clinic Alzheimer’s Disease Research Center. Cognitively unimpaired individuals (CU; n = 100) included from the Mayo Clinic Study of Aging were balanced on age and sex. A panel of plasma biomarkers of Aβ40, Aβ42, p‐tau181, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) measures was obtained. We calculated the ratio of plasma Aβ40/42. Lower Aβ40/42 indicates increased Aβ pathology. Plasma Aβ40, Aβ42, GFAP, and NfL were measured using the Simoa® Neurology 4‐Plex E Advantage kit and pTau‐181 was measured with the Simoa® pTau‐181 Advantage V2 kit. Both were run on a Quanterix HD‐X analyzer. AD imaging biomarkers were assessed on PET using Pittsburgh compound B (PiB) for Aβ and 18F‐Flortaucipir for tau.ResultHigher levels of plasma GFAP were observed in the MCI‐LB (p = 0.014) and DLB (p<0.001) groups compared to CU (Figure). DLB patients also showed higher levels of plasma pTau181 (p<0.001) and NfL (p<0.001). Higher amyloid and tau PET standardized uptake value ratio (SUVr) in the DLB continuum were associated with higher levels of plasma p‐tau181, NfL, and GFAP (p<0.001). After accounting for age, plasma Aβ40/42 did not correlate with amyloid or tau PET SUVr.ConclusionIn the DLB continuum, abnormal levels of GFAP in plasma can be detected as early as the prodromal stages of the disease. Plasma pTau181 reached abnormal levels in the DLB stage. Higher plasma GFAP, NfL, and p‐tau181 are associated with higher amyloid and tau PET SUVr in the DLB continuum. Plasma biomarkers have the potential to contribute to screening and early diagnosis in the DLB continuum, which has implications in designing new treatments.

AbstractBackgroundDisruption of default mode network (DMN) connectivity is common in Alzheimer’s disease. We investigated cross‐sectional and one‐year longitudinal changes in DMN sub‐systems in atypical clinical presentations of AD, in relation to age and tau.MethodSixty‐four amyloid‐positive atypical AD (Aty‐AD) patients diagnosed with posterior cortical atrophy (n = 35) or logopenic progressive aphasia (n = 29) were recruited by the Neurodegenerative Research Group. Patients underwent structural and resting‐state functional MRI, and [18F]flortaucipir PET at baseline and one‐year follow‐up. 140 amyloid‐negative cognitively unimpaired (CU) individuals from the Mayo Clinic Study of Aging were included as controls. Global tau SUVR was calculated in a meta‐ROI including parietal, temporal, and occipital regions. DMN connectivity was calculated in native space for posterior (pDMN), ventral (vDMN), anterior ventral (avDMN) and anterior dorsal (adDMN) DMN sub‐systems using the dual regression method. A global measure of DMN connectivity, the Network Failure Quotient (NFQ), was calculated as (vDMN_to_pDMN+ adDMN_to_pDMN)/(pDMN+vDMN). Linear mixed‐effects models were computed for each DMN connectivity measure, including diagnosis (Aty‐AD, CU), age, time and their interaction, and a per‐person random effect. SPM paired t‐tests were run to compare baseline and follow‐up images in Aty‐AD and CU separately. Linear mixed‐effect models were run in the Aty‐AD cohort only to investigate the effect of global baseline tau SUVR on each DMN connectivity measure.ResultMixed‐effects models showed evidence of lower cross‐sectional pDMN and higher adDMN connectivity in Aty‐AD relative to CU, with no significant time effects (Fig. 1). While connectivity in all DMN sub‐systems declined with age in CU, connectivity was higher in older Aty‐AD patients relative to younger, particularly in vDMN and pDMN. A similar trend was observed for NFQ, which was significantly higher in Aty‐AD, with a negative age effect (Fig. 1). In Aty‐AD, voxel‐based analyses showed moderate evidence of decline in vDMN connectivity and increase in avDMN connectivity over time (Fig. 2). Greater baseline global tau SUVR was associated with lower vDMN connectivity, cross‐sectionally and longitudinally (Fig. 3).ConclusionDMN connectivity was altered in Aty‐AD, with strongest alterations in younger patients. The vDMN declined over time, suggesting a shift in DMN connectivity with disease progression that was likely related to tau pathology.

AbstractBackgroundThe spectrum of what is referred to as nonfluent/agrammatic primary progressive aphasia includes patients with isolated apraxia of speech (PPAOS), progressive agrammatic aphasia (PAA), or a combination thereof (AOS‐PAA). When AOS is present, there is typically an underlying 4R tauopathy (corticobasal degeneration; progressive supranuclear palsy); isolated PAA is more often associated with a 3R tauopathy (Pick’s disease). While pathological studies confirm the clinical syndrome is the sequela of these proteinopathies, beta‐amyloid (Aβ) deposition has also been observed. Given the growing number of treatment options, it is important to better understand the incidence of Alzheimer’s disease (AD) in PPAOS‐PAA spectrum disorders.Methods65 patients recruited by the Neurodegenerative Research Group at Mayo Clinic underwent Aβ‐PET imaging using Pittsburgh Compound B (PiB) and tau‐PET imaging with [18F]flortaucipir at the same visit (PPAOS = 23; PAA = 6; AOS‐PAA = 36). A global PiB SUVR (standardized uptake value ratio)≥1.48 and temporal lobe tau meta‐ROI SUVR≥1.25 were deemed Aβ and tau positive, respectively. The incidence of Aβ and tau positivity and clinical‐demographic profiles were compared among groups.ResultsThere was no difference in age, sex, APOE status, tau/Aβ positivity or PET SUVRs across clinical groups (Table 1). Patients differed on indices of aphasia and AOS severity, in concordance with group membership; there was no difference on a memory measure. Eight patients (12%) were both Aβ and tau positive (indicative of the presence of AD), with an additional 20 patients (31%) positive on only one biomarker (Table 1). Of the eight Aβ and tau positive patients, seven had AOS (six prosodic predominant AOS). Aβ more than tau SUVRs increased with age and were related to each other (Figure 1).ConclusionsAβ and tau positivity is not uncommon in AOS and/or PAA patients, particularly in older patients. Enrollment criteria for Aβ or tau targeted pharmacological trials should consider concordance of biological variables and clinical presentations, rather than simply biological biomarkers. Alternatively, if such patients are enrolled, the outcome measures should reflect the clinical impairments, as it is not yet known if or how concomitant AD influences the clinical presentation.

AbstractBackgroundCo‐morbid Alzheimer’s disease (AD) pathology in the form of amyloid‐β plaques and tau neurofibrillary tangles are frequently observed in people with probable dementia with Lewy bodies (DLB). Neurite Orientation Dispersion and Density Imaging (NODDI) is an emerging technique to characterize white matter (WM) tissue microstructure, although NODDI alterations and associations with AD biomarkers remain undefined in DLB. The objectives of this study are to compare NODDI and DTI parameters between DLB and controls and (ii) evaluate the pattern of DTI and NODDI changes associated with amyloid and tau deposition.MethodConsecutive patients within the DLB spectrum (n = 45), including probable DLB (n = 32) and probable Mild Cognitive Impairment with Lewy bodies (MCI‐LB) (n = 13) from the Mayo Clinic ADRC underwent multi‐shell diffusion MRI, [18F]‐Flortaucipir and [11C]‐PiB PET. A comparison group of 45 age‐ and gender‐matched clinically unimpaired controls (CU) was included. Voxelwise non‐parametric permutation models were used to determine pair‐wise group differences and assess the associations of AD biomarkers with fractional anisotropy (FA), mean diffusivity (MD), tissue‐weighted neurite density index (tNDI; NDI x (1 – Free water fraction)) and orientation dispersion index (ODI), controlling for age. Complementary analyses were performed using regions‐of‐interest (ROI) obtained from the JHU atlas.ResultCompared to CU, the DLB spectrum group exhibited widespread decreases in FA and tNDI and increases in MD and ODI, with the most pronounced effects in the cingulum bundles, uncinate, inferior and superior longitudinal fasciculi (Fig. 1). Greater [18F]‐Flortaucipir uptake was also associated with lower FA and tNDI, and higher MD and ODI in the DLB spectrum compared to CU, independently of age and [11C]‐PiB uptake (Fig. 2). In contrast, no associations were found between [11C]‐PiB uptake and any of the DTI or NODDI parameters. Complementary ROI analyses corroborated these findings.ConclusionWidespread loss of WM integrity was observed on DTI and NODDI in probable DLB and prodromal DLB. These findings were associated with greater cortical tau, but not with amyloid‐β deposition. Although alpha‐synuclein pathology underlies the loss of WM integrity in DLB, amyloid‐independent tau pathology may also be an important influence on white matter integrity within the DLB spectrum.

AbstractBackgroundWe aimed to assess axonal integrity in MAPT mutation carriers and early white matter (WM) neurodegeneration in asymptomatic carriers using traditional diffusion tensor imaging (DTI) measurements, i.e., fractional anisotropy (FA) and mean diffusivity (MD), and neurite orientation dispersion and density imaging (NODDI).MethodWe included available multi‐shell diffusion MRI data from 21 MAPT mutation carriers (16 asymptomatic; 5 symptomatic) and 31 non‐carrier family members as controls from the ALLFTD consortium. We used linear mixed‐effect models to assess WM abnormalities measured with FA and MD, neurite density index (NDI) and orientation dispersion index (ODI), combined across hemispheres. Separate models were conducted in all MAPT mutation carriers relative to controls while accounting for relatedness and age, then in symptomatic and asymptomatic carriers relative to controls. ComBat was used to harmonize data across sites.ResultIn models contrasting all MAPT carriers versus controls, WM alterations involved the cingulum bundle (FA, ODI), fronto‐temporal, entorhinal (FA, MD, NDI), and the inferior temporal WM (FA) after correcting for multiple comparisons (Fig. 1). In models contrasting symptomatic and asymptomatic MAPT carriers versus controls, symptomatic carriers had widespread involvement of the fronto‐temporal WM including the cingulum (FA, ODI), entorhinal (MD, FA, NDI), amygdala (MD, NDI) and inferior temporal (FA) areas after correcting for multiple comparisons. Asymptomatic carriers showed higher entorhinal MD and lower inferior temporal and cingulum FA relative to controls, and a more widespread pattern of higher ODI involved the medial temporal, superior temporal and occipital WM as well as long association and projection fibers including the internal capsule, cingulate bundle and fronto‐occipital fasciculi, although they were not statistically significant after correction for multiple comparisons.ConclusionTraditional DTI and novel NODDI measurements revealed some extent of overlap as well as divergences in patterns of WM abnormalities, indicating axonal loss is a feature of neurodegeneration in MAPT mutation carriers. Our results suggest that traditional DTI and novel NODDI measurements may have different sensitivities to WM alterations in MAPT mutation carriers, and that axon‐based metrics, particularly ODI measurements, may be more sensitive to the early WM involvement during the asymptomatic stage of the disease.

AbstractBackgroundGenetic mutations causative of familial fronto‐temporal lobar degeneration (f‐FTLD) are highly predictive of a specific proteinopathy, but there exists substantial inter‐individual variability in their system‐level pathophysiology, which can be assessed with 18Fluorodeoxyglucose‐positron emission tomography (FDG‐PET). Determining data‐driven patterns of network degeneration suggestive of underlying specific FTLD‐related genetic mutations is paramount to guide complex clinical decision making supported by the quantification of FDG‐PET.MethodWe collected clinical and FDG‐PET data from 39 patients with f‐FTLD, including 11 carrying the C9orf72 hexanucleotide expansion (1 asymptomatic, 9 with a predominant behavioral phenotype, 1 with a predominant language phenotype), 12 carrying a GRN mutation (1 asymptomatic, 7 behavioral, 4 language), and 16 carrying a MAPT mutation (6 asymptomatic, 10 behavioral). We performed a spectral covariance decomposition analysis between FDG‐PET images to yield latent patterns reflective of a distribution of metabolism opposing poles of relative hyper‐ and hypo‐metabolism across the entire brain (“eigenbrains” or EBs). We then conducted linear discriminant analyses (LDAs) to perform EB‐based multiclass predictions of genetic mutation and clinical phenotype.ResultThe spectral decomposition analysis yielded five eigenbrains, which explained 58.79% of the covariance between FDG‐PET images (Fig. 1). Gradients indicative of relative hypometabolism in left fronto‐temporo‐parietal areas distinguished GRN mutation carriers from other genetic mutations (EB1, EB2, EB4) and were associated with clinical phenotypes predominantly involving language (EB1, EB2, EB3). Conversely, those indicative of relative hypometabolism in prefrontal areas (EB3) and temporopolar areas (EB2) with a right hemispheric predominance were mostly associated with clinical phenotypes predominantly involving behavior/personality, and EB2 distinguished MAPT mutation carriers from other genetic mutations. The LDAs yielded accuracies of 80% and 75% in predicting genetic status and predominant clinical phenotype, respectively (Fig. 2).ConclusionFive EBs explained a high proportion of covariance in patterns of network degeneration across FTLD‐related genetic mutations. These EBs contained information relevant to the system‐level physiology associated with FTLD‐related pathogenic mechanisms and that allowed the prediction of clinical phenotype with relatively high accuracy. This supports the utility of FDG‐PET to guide the development of tools supporting clinical decision making and ongoing efforts to develop network‐based biomarkers to track disease progression and risk of phenoconversion in f‐FTLD.

AbstractBackgroundThere is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behavior/personality. This is due to a lack of knowledge about the macro‐scale anatomy underlying these symptomatologies, a partial overlap in clinical features, and the fact that a single underlying pathology can give rise to both phenotypes and vice‐versa.MethodWe included data from patients with genetic, biomarker, and/or autopsy evidence for fronto‐temporal lobar degeneration (FTLD) diagnosed with behavioral variant fronto‐temporal dementia (bvFTD, n = 30) and patients with biomarker and/or autopsy evidence for Alzheimer’s disease (AD) pathology diagnosed with either an initial and predominant dysexecutive (dAD, n = 52), behavioral (bvAD, n = 7), or amnestic (aAD, n = 28) syndrome. We assessed group‐wise differences in clinical/cognitive features and 18Fluorodeoxyglucose‐positron emission tomography (FDG‐PET) patterns. This was followed by a spectral covariance decomposition between FDG‐PET images to yield latent patterns of relative metabolism (“eigenbrains”). These eigenbrains were subsequently linked to clinical/cognitive data and meta‐analytic topics reflecting a wide range of mental abilities. We used a linear discriminant analysis (LDA) to perform eigenbrain‐based diagnostic predictions.ResultdAD and bvFTD patients were the youngest at symptom onset, followed by bvAD, then aAD. dAD patients had worse cognitive performance on nearly all cognitive domains compared to other groups. Hypometabolism was observed across associative cortices in dAD, temporo‐parietal areas in aAD, and fronto‐temporal areas in bvFTD and bvAD (Fig. 1). The spectral covariance decomposition yielded nine eigenbrains which explained 61% of the variance in patterns of FDG‐PET, and only the first three are presented (Fig. 2). These eigenbrains revealed that relative hypometabolism in association cortices, notably lateral parietal areas, associated with dysexecutive symptomatology and a lower likelihood of behavior/personality problems, whereas relative hypometabolism restricted to frontal and temporopolar areas showed opposite associations. The LDA yielded an accuracy of 82.1% in predicting diagnostic category (Fig. 3).ConclusionThis study suggests distinct macro‐scale underpinnings underlying predominant dysexecutive versus behavioral symptomatology in degenerative dementia phenotypes. This has implication for the implementation of clinical and research criteria for these dementia syndromes and highlights the importance of data‐driven techniques to inform the classification of degenerative diseases.

AbstractBackgroundQuantitative rapid eye movement (REM) sleep without atonia (RSWA) recorded during polysomnography (PSG) represents loss of normal REM sleep atonia. RSWA is the neurophysiologic signature of REM sleep behavior disorder (RBD). Visually scored RSWA is a candidate biomarker for synucleinopathies. However, visual RSWA scoring requires expert scoring, limiting widespread application. We comparatively analyzed RSWA in DLB and iRBD using the automated Ferri REM Atonia Index (RAI).MethodWe quantitatively analyzed the RAI in 12 DLB and 20 iRBD patients, compared to 20 controls using Hypnolab Software for Sleep Analysis (SWS Soft, Inc, Colognola ai Colli, Italy). PSG data were converted to European Data Format (.EDF), and the submentalis surface electromyogram (EMG) signal was notch‐filtered at 60 Hertz and rectified. Automatically calculated RAI scores were comparatively analyzed across groups (DLB, iRBD, controls) using Kruskal Wallis H tests for group comparisons, and regression analyses determined relationships between diagnosis, age, and sex. Logistic regression ROC determined optimal RAI for distinguishing iRBD from DLB. When interpreting RAI, lower values represent greater amounts of RSWA, and higher values represent more normal REM atonia.ResultMean age was older in DLB than in iRBD or controls (72.6 vs. 68.7 vs. 66.5 years). All 12 DLB participants were men, the iRBD group had 18 men and 2 women, and controls were comprised of 16 men and 4 women. Mean RAI was lowest in DLB (0.46), intermediate in iRBD (0.71) and highest in controls (0.96), indicating a gradient in RSWA amounts from DLB patients (highest) to controls (lowest) (Figure). Regression demonstrated diagnosis independently predicted RAI, controlling for age and sex (adjusted R2 = 0.49, p = 0.0001). The RAI that optimally distinguished iRBD from DLB was 0.46 (specificity 85%, sensitivity 58%, AUC = 0.75).ConclusionThe RAI demonstrated a clear gradient throughout the Lewy body spectrum; highest (indicating lowest RSWA amounts) in controls, intermediate in iRBD, and lowest (indicating greatest RSWA) in DLB. The RAI is a promising, time efficient and broadly applicable candidate biomarker for DLB and iRBD. Automated quantitative RSWA could be a valuable tool for deep phenotyping throughout the Lewy body disease spectrum.