Citations

Background: The agrammatic variant of primary progressive aphasia (PAA), primary progressive apraxia of speech (PPAOS), or a combination of both (AOS-PAA) are neurodegenerative disorders characterized by speech-language impairments and together compose the AOS-PAA spectrum disorders. These patients typically have an underlying 4-repeat tauopathy, although they sometimes show evidence of amyloid-β and tau deposition on PET, suggesting Alzheimer’s disease (AD). Given the growing number of pharmacologic treatment options for AD, it is important to better understand the incidence of AD pathology in these patients. Objective: This study aimed to evaluate the frequency of amyloid-β and tau positivity in AOS-PAA spectrum disorders. Sixty-five patients with AOS-PAA underwent a clinical speech-language battery and PiB PET and flortaucipir PET imaging. Methods: Global PiB PET standardized uptake value ratios (SUVRs) and flortaucipir PET SUVRs from the temporal meta region of interest were compared between patient groups. For 19 patients who had died and undergone autopsy, their PET and pathology findings were also compared. Results: The results showed that although roughly half of the patients are positive for at least one biomarker, their clinical symptoms and biomarker status were not related, suggesting that AD is not the primary cause of their neurodegeneration. All but one patient in the autopsy subset had a Braak stage of IV or less, despite four being positive on tau PET imaging. Conclusions: Inclusion criteria for clinical trials should specify clinical presentation or adjust the evaluation of such treatments to be specific to disease diagnosis beyond the presence of certain imaging biomarkers.

Abstract There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer’s disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer’s disease pathology (behavioural Alzheimer’s disease) and 28 had amnestic Alzheimer’s disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as ‘eigenbrains’. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer’s disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer’s disease, then amnestic Alzheimer’s disease. Dysexecutive Alzheimer’s disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer’s disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer’s disease, temporo-parietal areas in amnestic Alzheimer’s disease and frontotemporal areas in bvFTD and behavioural Alzheimer’s disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer’s disease patient for behavioural Alzheimer’s disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.

Abstract Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer’s disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging’s use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

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.

e24089 Background: Little is known about risk and protective factors for dementia in cancer survivors. With increasing longevity due to improved cancer therapy, it’s prudent to study factors contributing to cognitive impairment in this patient population. Methods: We performed multivariable cox regression analyses to assess established risk factors for all-cause dementia (age, education, APOE4-status, social and physical activity engagement) in breast and prostate cancer survivors. Patients 50+ years old with pathology-confirmed cancer diagnosis at Mayo Clinic were identified from a longitudinal, population-based cohort – Mayo Clinic Study of Aging. Analyses were controlled for age, APOE4-status, education, and cancer treatment. Results: We included a total of 209 patients with breast cancer (mean age 75.4 years, 24% APOE4-positive, median follow-up 6.8 years) and 352 with prostate cancer (mean age 77 years, 26% APOE4-positive, median follow-up 6.4 years). 31 patients with breast cancer (15%) and 66 patients with prostate cancer (20%) were diagnosed with dementia at last follow-up. Older age was associated with increased risk for dementia (5-year increase – breast: hazard ratio (HR) 1.9 [95%CI 1.3-2.7, p = 0.001]; prostate: HR 2.4 [95%CI 1.8-3.2, p < 0.0001]). APOE4-positivity was associated with increased dementia risk in breast (HR 3.6 [95%CI 1.6-7.8, p < 0.002]), but not prostate cancer (HR 1.2 [95%CI 0.7-2.0, p = 0.5]). More education was associated with lower dementia risk in prostate cancer (education year: HR 0.91 [95%CI 0.84-0.98, p < 0.02]; education years > 12: HR 0.26 [95%CI 0.1-0.6, p < 0.001]). Physical activity engagement in mid-life was associated with lower dementia risk (prostate: HR 0.56 [95%CI 0.4-0.8, p < 0.005]; breast: HR 0.48 [95%CI 0.3-0.9, p < 0.02]). Conclusions: In this longitudinal, population-based study we found that some risk and protective factors for dementia in cancer survivors vary by cancer type. While advanced age is an expected dementia risk factor, there were mixed results for APOE4-positivity (increased risk only in breast cancer) and education (only protective in prostate cancer). Our results support further study into factors influencing dementia risk by cancer type in hopes of identifying preventative strategies.