Citations

Abstract Two variants of semantic dementia are recognized based on the laterality of temporal lobe involvement: a left-predominant variant associated with verbal knowledge impairment and a right-predominant variant associated with behavioural changes and non-verbal knowledge loss. This cross-sectional clinicoradiologic study aimed to assess whole hippocampal, subregion, and/or subfield volume loss in semantic dementia versus controls and across its variants. Thirty-five semantic dementia participants and 15 controls from the Neurodegenerative Research Group at Mayo Clinic who had completed 3.0-T volumetric magnetic resonance imaging and 18F-fluorodeoxyglucose-positron emission tomography were included. Classification as left-predominant (n = 25) or right-predominant (n = 10) variant was based on temporal lobe hypometabolism. Volumes of hippocampal subregions (head, body, and tail) and subfields (parasubiculum, presubiculum, subiculum, cornu ammonis 1, cornu ammonis 3, cornu ammonis 4, dentate gyrus, molecular layer, hippocampal–amygdaloid transition area, and fimbria) were obtained using FreeSurfer 7. Subfield volumes were measured separately from head and body subregions. We fit linear mixed-effects models using log-transformed whole hippocampal/subregion/subfield volumes as dependent variables; age, sex, total intracranial volume, hemisphere and a group-by-hemisphere interaction as fixed effects; and subregion/subfield nested within hemisphere as a random effect. Significant results (P < 0.05) are hereby reported. At the whole hippocampal level, the dominant (predominantly involved) hemisphere of both variants showed 23–27% smaller volumes than controls. The non-dominant (less involved) hemisphere of the right-predominant variant also showed volume loss versus controls and the left-predominant variant. At the subregional level, both variants showed 17–28% smaller dominant hemisphere head, body, and tail than controls, with the right-predominant variant also showing 8–12% smaller non-dominant hemisphere head than controls and left-predominant variant. At the subfield level, the left-predominant variant showed 12–36% smaller volumes across all dominant hemisphere subfields and 14–15% smaller non-dominant hemisphere parasubiculum, presubiculum (head and body), subiculum (head) and hippocampal–amygdaloid transition area than controls. The right-predominant variant showed 16–49% smaller volumes across all dominant hemisphere subfields and 14–22% smaller parasubiculum, presubiculum, subiculum, cornu ammonis 3, hippocampal–amygdaloid transition area (all from the head) and fimbria of non-dominant hemisphere versus controls. Comparison of dominant hemispheres showed 16–29% smaller volumes of the parasubiculum, presubiculum (head) and fimbria in the right-predominant than left-predominant variant; comparison of non-dominant hemispheres showed 12–15% smaller cornu ammonis 3, cornu ammonis 4, dentate gyrus, hippocampal–amygdaloid transition area (all from the head) and cornu ammonis 1, cornu ammonis 3 and cornu ammonis 4 (all from the body) in the right-predominant variant. All hippocampal subregion/subfield volumes are affected in semantic dementia, although some are more affected in both dominant and non-dominant hemispheres of the right-predominant than the left-predominant variant by the time of presentation. Involvement of hippocampal structures is apparently more subregion dependent than subfield dependent, indicating possible superiority of subregion volumes as disease biomarkers.

Purpose: Apraxia of speech (AOS) is a motor speech disorder affecting articulatory planning and speech programming. When AOS is the sole manifestation of neurodegeneration, it is termed primary progressive apraxia of speech (PPAOS). Recent work has shown that there are distinct PPAOS subtypes: phonetic, prosodic, and those that do not clearly align with either (mixed). PPAOS subtypes differ with respect to the predominating motor speech difficulties, as well as disease progression and underlying pathology. Because past studies have determined PPAOS subtype based on clinical impression, the goal of the present study was to quantitatively determine the distribution of speech error types across PPAOS subtypes in a word repetition task and to investigate how word complexity affects the type and number of speech errors across PPAOS subtypes. Method: Forty-five patients with PPAOS (13 phonetic, 23 prosodic, and nine mixed) and 45 healthy controls produced multiple repetitions of words that varied in phonetic complexity. Sound additions, deletions, and substitutions/distortions (phonetic errors) and within-word segmentations (prosodic errors) were calculated. Results: All three PPAOS groups produced significantly more errors than controls, but the total number of errors was comparable among subtypes. The phonetic group produced more phonetic-type errors compared to the prosodic group but comparable to the mixed group. The prosodic group produced more segmentations compared to the phonetic and mixed PPAOS groups. As word complexity increased, the total number of errors increased for PPAOS patients. The phonetic and prosodic groups were more likely to produce phonetic- and prosodic-type errors, respectively, as word complexity increased. Conclusions: This study provides novel quantitative data showing that PPAOS subtype can be supported by the type and distribution of speech errors in a word repetition task. This may facilitate earlier, more reliable differential diagnosis and aid in disease prognosis, as PPAOS subtypes have distinct disease trajectories.

Abstract Progressive apraxia of speech (PAOS) is a neurodegenerative motor-speech disorder that most commonly arises from a four-repeat tauopathy. Recent studies have established that progressive apraxia of speech is not a homogenous disease but rather there are distinct subtypes: the phonetic subtype is characterized by distorted sound substitutions, the prosodic subtype by slow and segmented speech and the mixed subtype by a combination of both but lack of predominance of either. There is some evidence that cross-sectional patterns of neurodegeneration differ across subtypes, although it is unknown whether longitudinal patterns of neurodegeneration differ. We examined longitudinal patterns of atrophy on MRI, hypometabolism on 18F-fluorodeoxyglucose-PET and tau uptake on flortaucipir-PET in a large cohort of subjects with PAOS that had been followed for many years. Ninety-one subjects with PAOS (51 phonetic, 40 prosodic) were recruited by the Neurodegenerative Research Group. Of these, 54 (27 phonetic, 27 prosodic) returned for annual follow-up, with up to seven longitudinal visits (total visits analysed = 217). Volumes, metabolism and flortaucipir uptake were measured for subcortical and cortical regions, for all scans. Bayesian hierarchical models were used to model longitudinal change across imaging modalities with PAOS subtypes being compared at baseline, 4 years from baseline, and in terms of rates of change. The phonetic group showed smaller volumes and worse metabolism in Broca’s area and the striatum at baseline and after 4 years, and faster rates of change in these regions, compared with the prosodic group. There was also evidence of faster spread of hypometabolism and flortaucipir uptake into the temporal and parietal lobes in the phonetic group. In contrast, the prosodic group showed smaller cerebellar dentate, midbrain, substantia nigra and thalamus volumes at baseline and after 4 years, as well as faster rates of atrophy, than the phonetic group. Greater hypometabolism and flortaucipir uptake were also observed in the cerebellar dentate and substantia nigra in the prosodic group. Mixed findings were observed in the supplementary motor area and precentral cortex, with no clear differences observed across phonetic and prosodic groups. These findings support different patterns of disease spread in PAOS subtypes, with corticostriatal patterns in the phonetic subtype and brainstem and thalamic patterns in the prosodic subtype, providing insight into the pathophysiology and heterogeneity of PAOS.

Abstract Disruption of the default mode network is a hallmark of Alzheimer’s disease, which has not been extensively examined in atypical phenotypes. We investigated cross-sectional and 1-year longitudinal changes in default mode network sub-systems in the visual and language variants of Alzheimer’s disease, in relation to age and tau. Sixty-one amyloid-positive Alzheimer’s disease participants diagnosed with posterior cortical atrophy (n = 33) or logopenic progressive aphasia (n = 28) underwent structural MRI, resting-state functional MRI and [18F]flortaucipir PET. One-hundred and twenty-two amyloid-negative cognitively unimpaired individuals and 60 amyloid-positive individuals diagnosed with amnestic Alzheimer’s disease were included as controls and as a comparison group, respectively, and had structural and resting-state functional MRI. Forty-one atypical Alzheimer’s disease participants, 26 amnestic Alzheimer’s disease participants and 40 cognitively unimpaired individuals had one follow-up functional MRI ∼1–2 years after the baseline scan. Default mode network connectivity was calculated using the dual regression method for posterior, ventral, anterior ventral and anterior dorsal sub-systems derived from independent component analysis. A global measure of default mode network connectivity, the network failure quotient, was also calculated. Linear mixed-effects models and voxel-based analyses were computed for each connectivity measure. Both atypical and amnestic Alzheimer’s disease participants had lower cross-sectional posterior and ventral and higher anterior dorsal connectivity and network failure quotient relative to cognitively unimpaired individuals. Age had opposite effects on connectivity in Alzheimer’s disease participants and cognitively unimpaired individuals. While connectivity declined with age in cognitively unimpaired individuals, younger Alzheimer’s disease participants had lower connectivity than the older ones, particularly in the ventral default mode network. Greater baseline tau-PET uptake was associated with lower ventral and anterior ventral default mode network connectivity in atypical Alzheimer’s disease. Connectivity in the ventral default mode network declined over time in atypical Alzheimer’s disease, particularly in older participants, with lower tau burden. Voxel-based analyses validated the findings of higher anterior dorsal default mode network connectivity, lower posterior and ventral default mode network connectivity and decline in ventral default mode network connectivity over time in atypical Alzheimer’s disease. Visuospatial symptoms were associated with default mode network connectivity disruption. In summary, default mode connectivity disruption was similar between atypical and amnestic Alzheimer’s disease variants, and discriminated Alzheimer’s disease from cognitively unimpaired individuals, with decreased posterior and increased anterior connectivity and with disruption more pronounced in younger participants. The ventral default mode network declined over time in atypical Alzheimer’s disease, suggesting a shift in default mode network connectivity likely related to tau pathology.

Abstract Background Progressive apraxia of speech (PAOS) is characterized by difficulties with motor speech programming and planning. PAOS targets gray matter (GM) and white matter (WM) microstructure that can be assessed using diffusion tensor imaging (DTI) and multishell applications, such as neurite orientation dispersion and density imaging (NODDI). In this study, we aimed to apply DTI and NODDI to add further insight into PAOS tissue microstructure. Methods Twenty‐two PAOS patients and 26 age‐ and sex‐matched controls, recruited by the Neurodegenerative Research Group (NRG) at Mayo Clinic, underwent diffusion MRI on 3T MRI. Brain maps of fractional anisotropy (FA) and mean diffusivity (MD) from DTI and intracellular volume fraction (ICVF) and isotropic volume fraction (IsoVF) from NODDI were generated. Global WM and GM, and specific WM tracts were identified using tractography and lobar GM regions. Results Global WM differences between PAOS and controls were greatest for ICVF, and global GM differences were greatest for MD and IsoVF. Abnormalities in key WM tracts involved in PAOS, including the body of the corpus callosum and frontal aslant tract, were identified with FA, MD, and ICVF, with excellent differentiation of PAOS from controls (area under the receiver operating characteristic curves >.90). MD and ICVF identified abnormalities in arcuate fasciculus, thalamic radiations, and corticostriatal tracts. Significant correlations were identified between an index of articulatory errors and DTI and NODDI metrics from the arcuate fasciculus, frontal aslant tract, and inferior longitudinal fasciculus. Conclusions DTI and NODDI represent different aspects of brain tissue microstructure, increasing the number of potential biomarkers for PAOS.

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 language and visual variants are two common clinical phenotypes of atypical Alzheimer’s disease (AD). There are reports of overlap between these clinical phenotypes, however, the extent and frequency of overlap, and its neuroanatomical underpinnings remain unclear.MethodEighty‐two patients with biomarker‐confirmed AD who presented with either predominant language (n = 34) or visuospatial/perceptual (n = 48) deficits were recruited by the Neurodegenerative Research Group and underwent neurological and neuropsychological testing, MRI and [18F]flortaucipir‐PET. The clinical examination included testing for simultanagnosia, Gerstmann’s syndrome, ideomotor apraxia, visuospatial‐visuoperceptual function, confrontational naming, sentence repetition and letter and animal fluency. Cut‐points for abnormality were defined for each test and used to classify the following groups: language‐pure (no visual‐function deficit), language‐plus (impaired on one visual‐function), language‐mixed (impaired on two visual‐functions), visual‐pure (no language‐function deficit), visual‐plus (impaired on one language‐function) and visual‐mixed (impaired on two language‐functions). Patterns of relative volume loss and flortaucipir uptake were assessed using SPM12.ResultOf the 82 patients, 11 (13%) were language‐pure, 16 (20%) language‐plus, 7 (8%) language‐mixed, 10 (12%) visual‐pure, 17 (21%) visual‐plus and 21 (26%) visual‐mixed, with no difference in disease duration across groups. Visual deficits in the language‐plus group consisted of mild simultanagnosia in 29%, Gerstmann’s syndrome in 24%, and mild visuospatial deficits. In the language‐mixed group 86% had simultanagnosia and 43% had Gerstmann’s syndrome, with visuospatial‐visuoperceptual deficits comparable to the visual‐pure group. Language deficits in the visual‐plus group consisted of mild anomia and reduced letter and animal fluency. The visual‐mixed group reported naming and repetition deficits, and reduced fluency, comparable to the language‐predominant groups. All three language groups showed left temporal volume loss and flortaucipir uptake, with greater involvement of the parieto‐occipital and frontal lobes in language‐plus and language‐mixed groups. All three visual groups showed occipital volume loss and flortaucipir uptake, with greater involvement of the parieto‐temporal and frontal lobes in visual‐plus and visual‐mixed groups.ConclusionOnly 25% of our cohort showed a pure language or visual presentation, highlighting the high frequency of syndromic overlap in atypical AD and the diagnostic challenge of categorical phenotyping. Syndromic overlap was associated with neuroanatomical overlap and more widespread patterns of atrophy and tau deposition.

AbstractBackgroundPosterior cortical atrophy (PCA) and dementia with Lewy bodies (DLB) show overlapping patterns of hypometabolism affecting posterior regions of the brain. Both syndromes show disruptions in functional connectivity, but it is unknown how patterns of disruption within and between networks compare between PCA and DLB.MethodThirty amyloid‐positive PCA, 27 amyloid‐positive DLB (DLB+), 24 amyloid‐negative DLB (DLB‐) patients and 30 cognitively normal amyloid‐negative (CN) individuals were recruited at Mayo Clinic, Rochester, MN, and underwent structural and resting state functional MRI. Spatially preprocessed data was analyzed using the CONN functional connectivity toolbox to explore nine networks: visual, sensorimotor, dorsal‐attention, cerebellar, frontoparietal, memory, language, salience, and default mode networks (DMN). Multivariate linear regression models corrected for multiple comparisons and adjusted for age and sex were fit to analyze within‐network connectivity (Fisher’s R‐to‐Z transformations). Between‐network connectivity was assessed with group level difference maps (two‐sample t‐test) corrected for false discovery rate at p<0.05.ResultMultivariate linear regression models showed reduced within‐network connectivity across all DMN components, sensorimotor, and dorsal‐attention networks, with trends for the visual network in PCA compared to CN. Both DLB‐ and DLB+ showed reduced within‐network connectivity for the cerebellar network, with DLB+ showing reduced connectivity in the posterior‐DMN, memory, and salience networks compared to CN. DLB‐ showed increased connectivity in dorsal‐attention and DMN, while DLB+ showing reduced connectivity in the memory network compared to PCA. Between‐network analysis showed an increase in cerebellar‐to‐sensorimotor connectivity in all three groups, with increase in cerebellar‐to‐dorsal‐attention connectivity in DLB‐ and PCA compared to CN. DLB‐ showed a decrease in posterior‐DMN‐to‐sensorimotor connectivity, while PCA showed reduced connectivity between all DMN components and dorsal‐attention‐to‐sensorimotor connectivity compared to CN. There was an increase in connectivity between all DMN components and a reduction in dorsal‐DMN‐to‐sensorimotor connectivity in both DLB‐ and DLB+ compared to PCA.ConclusionPCA was associated with connectivity disruptions across all DMN components, sensorimotor, dorsal‐attention and visual networks, while both DLB groups showed disruptions in cerebellar networks, with DLB+ showing further disruptions in posterior‐DMN, memory and salience networks. These findings suggest that both PCA and DLB have unique brain network disruptions, with amyloid status influencing connectivity in DLB.