Machulda, Mary M.

AbstractIntroductionPresence of apolipoprotein E (APOE) ε4 has shown greater predisposition to medial temporal involvement in posterior cortical atrophy (PCA) and logopenic progressive aphasia (LPA). Little is known about its influence on memory network connectivity, a network comprised of medial temporal structures.MethodsFifty‐eight PCA and 82 LPA patients underwent structural and resting state functional magnetic resonance imaging (MRI). Bayesian hierarchical linear models assessed the influence of APOE ε4 on within and between‐network connectivity for five networks.ResultsAPOE ε4 carriers showed reduced memory and language within‐network connectivity in LPA and increased salience within‐network connectivity in PCA compared to non‐carriers. Between‐network analysis showed evidence of reduced DMN connectivity in APOE ε4 carriers, with reduced DMN‐to‐salience and DMN‐to‐language network connectivity in PCA, and reduced DMN‐to‐visual network connectivity in LPA.DiscussionThe APOE genotype influences brain connectivity, both within and between‐networks, in atypical Alzheimer's disease. However, there was evidence that the modulatory effects of APOE differ across phenotype.HIGHLIGHTS APOE genotype is associated with reductions in within‐network connectivity for the memory and language networks in LPA APOE genotype is associated with reductions in language‐to‐visual connectivity in LPA and PCA APOE genotype has no effect on the memory network in PCA

AbstractObjectivePrimary progressive apraxia of speech (PPAOS) is associated with imaging abnormalities in the lateral premotor cortex (LPC) and supplementary motor area (SMA). It is not known whether greater involvement of these regions in either hemisphere is associated with demographics, presenting, and/or longitudinal features.MethodsIn 51 prospectively recruited PPAOS patients who completed [18F]‐fluorodeoxyglucose (FDG) positron emission tomography (PET), we classified patients as left‐dominant, right‐dominant, or symmetric, based on visual assessment of the LPC and SMA on FDG‐PET. SPM and statistical analyses of regional metabolic values were performed. Diagnosis of PPAOS was made if apraxia of speech was present and aphasia absent. Thirteen patients completed ioflupane‐123I (dopamine transporter [DAT]) scans. We compared cross‐sectional and longitudinal clinicopathological, genetic, and neuroimaging characteristics across the three groups, with area under the receiver‐operating curve (AUROC) determined as a measure of effect size.ResultsIn all, 49% of the PPAOS patients were classified as left‐dominant, 31% as right‐dominant, and 20% as symmetric, which was supported by results from the SPM and regional analyses. There were no differences in baseline characteristics. Longitudinally, right‐dominant PPAOS showed faster rates of progression of ideomotor apraxia (AUROC 0.79), behavioral disturbances (AUROC 0.84), including disinhibition symptoms (AUROC 0.82) and negative behaviors (AUROC 0.82), and parkinsonism (AUROC 0.75) compared to left‐dominant PPAOS. Symmetric PPAOS showed faster rates of dysarthria progression compared to left‐dominant (AUROC 0.89) and right‐dominant PPAOS (AUROC 0.79). Five patients showed abnormal DAT uptake. Braak neurofibrillary tangle stage differed across groups (p = 0.01).ConclusionsPatients with PPAOS and a right‐dominant pattern of hypometabolism on FDG‐PET have the fastest rates of decline of behavioral and motor features.

Abstract Dysexecutive Alzheimer’s disease (dAD) manifests as a progressive dysexecutive syndrome without prominent behavioral features, and previous studies suggest clinico-radiological heterogeneity within this syndrome. We uncovered this heterogeneity using unsupervised machine learning in 52 dAD patients with multimodal imaging and cognitive data. A spectral decomposition of covariance between FDG-PET images yielded six latent factors (“eigenbrains”) accounting for 48% of variance in patterns of hypometabolism. These eigenbrains differentially related to age at onset, clinical severity, and cognitive performance. A hierarchical clustering on the eigenvalues of these eigenbrains yielded four dAD subtypes, i.e. “left-dominant,” “right-dominant,” “bi-parietal-dominant,” and “heteromodal-diffuse.” Patterns of FDG-PET hypometabolism overlapped with those of tau-PET distribution and MRI neurodegeneration for each subtype, whereas patterns of amyloid deposition were similar across subtypes. Subtypes differed in age at onset and clinical severity where the heteromodal-diffuse exhibited a worse clinical picture, and the bi-parietal had a milder clinical presentation. We propose a conceptual framework of executive components based on the clinico-radiological associations observed in dAD. We demonstrate that patients with dAD, despite sharing core clinical features, are diagnosed with variability in their clinical and neuroimaging profiles. Our findings support the use of data-driven approaches to delineate brain–behavior relationships relevant to clinical practice and disease physiology.

Abstract Introduction We aimed to define a Mayo Preclinical Alzheimer's disease Cognitive Composite (Mayo‐PACC) that prioritizes parsimony and use of public domain measures to facilitate clinical translation. Methods Cognitively unimpaired participants aged 65 to 85 at baseline with amyloid PET imaging were included, yielding 428 amyloid negative (A‐) and 186 amyloid positive (A+) individuals with 7 years mean follow‐up. Sensitivity to amyloid‐related cognitive decline was examined using slope estimates derived from linear mixed models (difference in annualized change across A+ and A‐ groups). We compared differences in rates of change between Mayo‐PACC and other composites (A+ > A‐ indicating more significant decline in A+). Results All composites showed sensitivity to amyloid‐related longitudinal cognitive decline (A+ > A‐ annualized change p < 0.05). Comparisons revealed that Mayo‐PACC (AVLT sum of trials 1‐5+6+delay, Trails B, animal fluency) showed comparable longitudinal sensitivity to other composites. Discussion Mayo‐PACC performs similarly to other composites and can be directly translated to the clinic.

AbstractBackgroundAmyloidosis‐driven tau deposition has been a significant focus of imaging research in Alzheimer’s disease (AD). However, the 3R/4R tau seen in AD can be observed in the transentorhinal/entorhinal regions (Braak I/II) in the absence of amyloidosis. The underlying drivers of this phenomenon are not well understood. Our goal was to examine amyloid‐independent pathways of longitudinal Tau‐PET SUVR progression in the entorhinal cortex (ERC) using a broad range of risk and protective factors.MethodWe identified 449 participants from the population‐based sample of Mayo Clinic Study of Aging >50 years old with baseline amyloid PET and at least two serial tau PET scans (mean number of scans=2.3; mean follow‐up time=3.2 years). Using an amyloid SUVR cut‐point of 1.48, we categorized participants into A+ (n=167; mean age=77.5 years; 56% male; baseline ERC tau=1.20; 83% cognitively unimpaired) and A‐ (n=282; mean age=67.5; 56% male; baseline ERC tau=1.07; 92% cognitively unimpaired). We used an ERC tau SUVR cut‐point of 1.21 to determine T+. We fit mixed effects and growth curve models with age, sex, baseline amyloid, risk factors (midlife activities, sleep, stress, education/occupation, cardiovascular metabolic conditions) as predictors and longitudinal ERC tau as the outcome. We fit separate models in A‐, A+, and all participants.ResultsFor models of all participants and A+ participants, the only predictors of higher rates of ERC tau deposition were increasing age and amyloid SUVR (p<0.05). In the A‐ models (figure below), amyloid SUVR and age were predictors of baseline, but not longitudinal ERC tau. However, a greater number of cardiovascular metabolic conditions predicted a higher rate of ERC tau accumulation (0.007 (0.002), p<0.05). Only 13% of the 23 A‐T+ participants were APOE4 carriers (compared to 31% in the overall sample), suggesting the role of non‐APOE4 pathways to tau deposition.ConclusionOur results support previous evidence that significant amyloidosis is causal to higher rates of tau deposition. However, in those without significant amyloidosis, cardiovascular metabolic conditions predicted higher rates of ERC tau accumulation. Further work is warranted to investigate the mechanisms through which genetics, comorbidities, and lifestyle may play a role in early tau deposition.

AbstractBackgroundPosterior Cortical Atrophy (PCA) is one of the atypical Alzheimer’s Disease (AD) variants, characterized by predominant visuospatial and visuoperceptual deficits, with dorsal and ventral subtypes recognized clinically and radiographically. A third primary occipital (caudal) variant has been suggested. The objective of this study was to determine its demographics, clinical manifestations, and biomarker findings.MethodFifty‐two PCA patients with positive amyloid PET scans were investigated. Patients underwent neuropsychological assessment, 3T MRI imaging and FDG‐, amyloid‐ and tau‐PET scans. Regional FDG‐PET values were normalized to the pons and represented as z‐scores relative to a control population. Patients were divided into “primary occipital” and “other PCA” subgroups according to FDG‐PET defined criteria, with primary occipital defined as patients in which the z‐scores for occipital subregions were at least one standard deviation lower (i.e., more abnormal) than the z scores in all other brain regions. Global amyloid‐PET, temporoparietal FDG‐PET and temporal tau‐PET regions‐of‐interest (ROI) were calculated. Student’s T test and Fisher’s exact analyses were used to investigate differences in demographics, clinical findings, and imaging biomarkers across the two groups.ResultNine patients were classified as primary occipital; primary occipital patients were older (64.8±6.9 versus 59.3±6.8, p=0.034) and had more years of education (17.7±1.7 versus 15.1±2.6, p=0.007) compared to the other PCA patients. Clinical evaluation revealed worse performance for the primary occipital group on the Ishihara test for color perception (p<0.001), while the other PCA patients performed worse on the Western Aphasia Battery (WAB) praxis scale (p=0.005). Overall neuropsychiatric symptom burden as defined by the NPI‐Q was lower in the primary occipital group (p<0.001). The FDG‐PET meta‐ROI (weighted median uptake in angular gyrus, posterior cingulate, and inferior temporal ROIs, normalized to the pons and vermis) was higher in the primary occipital subtype (p=0.006), but no differences were observed in amyloid and tau‐PET.ConclusionClinical and biomarker findings suggest primary occipital PCA is characterized by an older age at onset, more color perception dysfunction, less severe ideomotor apraxia and less hypometabolism in temporal‐parietal meta‐ROI compared to more established phenotypes.

AbstractBackgroundResting state functional connectivity (rsfMRI) studies have shown functional network disruptions in logopenic progressive aphasia (LPA) (reduced connectivity within the left temporal language network) and posterior cortical atrophy (PCA) (reduced connectivity within ventral visual network). However, little is known about how these networks compare and how connectivity differs within and between networks in these atypical Alzheimer’s Disease (AD) phenotypes.MethodFifty‐nine amyloid‐positive PCA patients and eighty‐five amyloid‐positive LPA patients were recruited by the Neurodegenerative Research Group 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 the default mode network (DMN), salience, sensorimotor, left language, visual and memory networks, which were generated by averaging multiple regions‐of‐interest (ROI) within each network. The data was analyzed at both the voxel‐level and network‐level (ROI‐ROI).ResultVoxel‐level differences in functional connectivity between PCA and LPA were observed in all networks (p<0.05; cluster size, FWE=0.05). PCA showed reduced connectivity in the occipital lobe in visual and memory networks, parietal lobe in language and DMN, and sensorimotor strip in sensorimotor network, particularly on the right, compared to LPA. Conversely, LPA showed reduced connectivity in the frontal cortex in salience, memory, visual and sensorimotor networks, inferior parietal lobe in visual network, and anterior temporal pole and sensorimotor cortex in the language network, compared to PCA. Between network differences (p<0.05; cluster size, FDR=0.05) were observed between groups, with PCA showing reduced connectivity between the visual and sensorimotor networks, while LPA showed reduced connectivity between the language and sensorimotor networks. The DMN was anti‐correlated to the language network in PCA, while it was positively correlated to the language network in LPA. In both phenotypes, the DMN was anticorrelated with the salience network. The visual network in PCA showed connections to all networks, while in LPA, it was only connected to the salience, language and DMN.ConclusionThe atypical AD phenotypes were associated with multiple disruptions in connectivity, both within and between brain networks. Phenotype specific differences in connectivity patterns were noted in visual network for PCA and language network for LPA.