Josephs, Keith A.

Transactive response DNA‐binding protein 43 (TDP‐43) pathological inclusions are found in frontotemporal lobar degeneration (FTLD‐TDP) and Alzheimer's disease (AD‐TDP). While clinically different, TDP‐43 inclusions in FTLD‐TDP and AD can have similar morphological characteristics. However, TDP‐43 colocalizing with tau and forming “apple‐bite” or “flame‐shaped” neuronal cytoplasmic inclusions (NCI) are only found in AD‐TDP. Here, we describe a case with AD and neuritic plaque‐associated TDP‐43. The patient was a 96‐year‐old right‐handed Caucasian woman who had developed a slowly progressive amnestic syndrome compatible with typical AD at age 80. Genetic testing revealed APOE ε3/ε4, GRN r5848 CT, and MAPT H1/H2 genotype. Consistent with the old age at onset and long disease duration, limbic‐predominant AD was found at autopsy, with high hippocampal yet low cortical neurofibrillary tangle (NFT) counts. Hippocampal and amygdala sclerosis were present. Immunohistochemistry for phospho‐TDP‐43 showed NCIs, dystrophic neurites, and rare neuronal intranuclear inclusions consistent with FTLD‐TDP type A, as well as tau NFT‐associated TDP‐43 inclusions. These were frequent in the amygdala, entorhinal cortex, hippocampus, occipitotemporal gyrus, and inferior temporal gyrus but sparse in the mid‐frontal cortex. Additionally, there were TDP‐43‐immunoreactive inclusions forming plaque‐like structures in the molecular layer of the dentate fascia of the hippocampus. The presence of neuritic plaques in the same region was confirmed using thioflavin‐S fluorescent microscopy and immunohistochemistry for phospho‐tau. Double labeling immunofluorescence showed colocalization of TDP‐43 and tau within neuritic plaques. Other pathologies included mild Lewy body pathology predominantly affecting the amygdala and olfactory bulb, aging‐related tau astrogliopathy, and mixed small vessel disease (arteriolosclerosis and amyloid angiopathy) with several cortical microinfarcts. In conclusion, we have identified TDP‐43 colocalizing with tau in neuritic plaques in AD, which expands the association of TDP‐43 and tau in AD beyond NFTs. The clinical correlate of this plaque‐associated TDP‐43 appears to be a slowly progressive amnestic syndrome.

AbstractINTRODUCTIONAtypical variants of Alzheimer's disease (AD) include the visual variant, known as posterior cortical atrophy (PCA), and the language variant, known as logopenic progressive aphasia (LPA). Clinically, rates of disease progression differ between them.METHODSWe evaluated 34 PCA and 29 LPA participants. Structural magnetic resonance imaging and 18F‐flortaucipir positron emission tomography were performed at baseline and at 1‐year follow‐up. Rates of change in tau uptake and grey matter volumes were compared between PCA and LPA with linear mixed‐effects models and voxel‐based analyses.RESULTSPCA had faster rates of occipital atrophy. LPA had faster rates of left temporal atrophy and faster rates of tau accumulation in the parietal, right temporal, and occipital lobes. Age was negatively associated with rates of atrophy and tau accumulation.DISCUSSIONLongitudinal patterns of neuroimaging abnormalities differed between PCA and LPA, although with divergent results for tau accumulation and atrophy.HIGHLIGHTS The language variant of Alzheimer's disease accumulates tau faster than the visual variant. Each variant shows faster rates of atrophy than the other in its signature regions. Age negatively influences rates of atrophy and tau accumulation in both variants.

AbstractBackgroundThe semantic fluency task involves naming as many items as possible in a given category (e.g., animals) in one minute. It is often interpreted as a measure of word retrieval, but also draws on executive function abilities to organize responses and develop task‐specific strategies. Indeed, both language skills and dementia severity ratings have recently been found to correlate with semantic fluency performance in patients with primary progressive aphasia (Riello et al., 2022; Scheffel et al., 2021). Here, we focused instead on how performance on language and executive function measures predicts longitudinal change in semantic fluency performance in patients with progressive agrammatic aphasia and/or apraxia of speech (AOS).MethodPatients with isolated agrammatic aphasia (n = 10), isolated AOS (n = 42), or a combination of the two (n = 59) with 1‐7 visits (mean = 2.19) were included. We used Bayesian hierarchical linear models to investigate whether baseline measures of executive function or language were associated with the rate at which semantic fluency performance changes over time while accounting for baseline diagnosis group, sex, education, age at baseline, and baseline disease duration.ResultSemantic fluency performance declined over time, at a similar rate irrespective of diagnostic group (isolated or combined aphasia or AOS; Figure 1). Across all patients, higher executive function abilities at baseline, as indicated by faster Trails Making Test B completion times, predicted higher baseline semantic fluency performance and also a steeper annual rate of decline (Figure 2). Similarly, higher fluency ratings during the Western Aphasia Battery‐Revised picture description at baseline were associated with higher baseline semantic fluency performance and faster decline. Baseline performance on the Boston Naming Test and Token Test were also associated with baseline semantic fluency, but not with longitudinal change.ConclusionThese results better delineate how language and executive function abilities support performance in the semantic fluency task. Higher abilities in those areas at baseline do not appear to be protective over time. Rather, the differences in semantic fluency that are present at baseline disappear as the disease progresses.

Abstract Posterior cortical atrophy and logopenic progressive aphasia are atypical clinical presentations of Alzheimer’s disease. Resting-state functional connectivity studies have shown functional network disruptions in both phenotypes, particularly involving the language network in logopenic progressive aphasia and the visual network in posterior cortical atrophy. However, little is known about how connectivity differs both within and between brain networks in these atypical Alzheimer’s disease phenotypes. A cohort of 144 patients was recruited by the Neurodegenerative Research Group at Mayo Clinic, Rochester, MN, USA, and underwent structural and resting-state functional MRI. Spatially preprocessed data were analysed to explore the default mode network and the salience, sensorimotor, language, visual and memory networks. The data were analysed at the voxel and network levels. Bayesian hierarchical linear models adjusted for age and sex were used to analyse within- and between-network connectivity. Reduced within-network connectivity was observed in the language network in both phenotypes, with stronger evidence of reductions in logopenic progressive aphasia compared to controls. Only posterior cortical atrophy showed reduced within-network connectivity in the visual network compared to controls. Both phenotypes showed reduced within-network connectivity in the default mode and sensorimotor networks. No significant change was noted in the memory network, but a slight increase in the salience within-network connectivity was seen in both phenotypes compared to controls. Between-network analysis in posterior cortical atrophy showed evidence of reduced visual-to-language network connectivity, with reduced visual-to-salience network connectivity, compared to controls. An increase in visual-to-default mode network connectivity was noted in posterior cortical atrophy compared to controls. Between-network analysis in logopenic progressive aphasia showed evidence of reduced language-to-visual network connectivity and an increase in language-to-salience network connectivity compared to controls. Findings from the voxel-level and network-level analysis were in line with the Bayesian hierarchical linear model analysis, showing reduced connectivity in the dominant network based on diagnosis and more crosstalk between networks in general compared to controls. The atypical Alzheimer’s disease phenotypes were associated with disruptions in connectivity, both within and between brain networks. Phenotype-specific differences in connectivity patterns were noted in the visual network for posterior cortical atrophy and the language network for logopenic progressive aphasia.

Background: Posterior cortical atrophy (PCA) and logopenic progressive aphasia (LPA) are associated with characteristic patterns of structural network degeneration. Little is known about longitudinal patterns of white matter tract degeneration in these phenotypes. Objective: To assess longitudinal patterns of white matter degeneration and identify phenotype specific cross-sectional and longitudinal diffusion tensor imaging (DTI) biomarkers in PCA and LPA. Methods: Twenty-five PCA, 22 LPA and 25 cognitively unimpaired (CU) individuals were recruited and underwent structural MRI that included a DTI sequence with a follow-up one year later. Cross-sectional and longitudinal mixed effects models were fit to assess the effects of diagnosis on baseline and annualized change in regional DTI metrics. Discriminatory power was investigated using the area under the receiver operating characteristic curves (AUROC). Results: PCA and LPA showed overlapping white matter degeneration profiles predominantly in the left occipital and temporal lobes, the posterior thalamic radiation and sagittal stratum at baseline, as well as the parietal lobe longitudinally. PCA showed degeneration in the occipital and parietal white matter, cross-sectionally and longitudinally, compared to CU, while LPA showed greater degeneration in the temporal and inferior parietal white matter and the inferior fronto-occipital fasciculus cross-sectionally, and in parietal white matter longitudinally compared to CU. Cross-sectionally, integrity of the inferior occipital white matter was best able to differentiate PCA from LPA, with an AUROC of 0.82. Conclusion: These findings contribute to our understanding of white matter degeneration and support usage of DTI as a useful additional diagnostic biomarker for PCA and LPA.