Citations

Abstract Associations of Alzheimer’s disease biomarker progression with cognitive decline are important to inform patient prognosis. Of particular interest is how newly available plasma biomarkers evolve relative to cognitive decline. The goals of this work are to measure how much earlier versus later an individual’s progression on plasma and PET Alzheimer’s disease biomarkers is associated with earlier versus later cognitive progression and to estimate the average timeline of progression of these processes in the population. In this cohort study of 2369 Mayo Clinic Study of Aging (MCSA) and 1591 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants, we fit non-linear mixed-effects models to estimate how much earlier versus later each individual progresses on plasma phosphorylated tau (p-tau)217, amyloid PET, tau PET and auditory verbal learning test (AVLT) sum of trials relative to the population mean (individual adjustment), the associations of these individual adjustments among biomarker pairs and how covariates affect the timing of biomarker progression. The association of individual adjustments implies mechanistic associations and the amount of variability in cognitive decline accounted for by each biomarker. By applying cut-off points, we also estimated the relative timing that these biomarkers become abnormal in the population. Associations of individual adjustments were moderate between all biomarkers and AVLT (R = 0.38–0.47) in the MCSA and stronger (R = 0.74–0.81) in ADNI; plasma p-tau217 accounted for 16% of the variability in timing of AVLT decline in the MCSA and 64% in ADNI. APOE ɛ4 carriership was associated with earlier biomarker progression. AVLT became abnormal after the biomarkers up to age 90, after which AVLT was estimated to become abnormal prior to tau biomarkers. The association of the timing of plasma and PET Alzheimer’s disease biomarker progression with cognitive decline was modest in the MCSA population-based sample and stronger in the Alzheimer’s disease-enriched ADNI cohort. The timing of plasma p-tau217 progression explained a similar degree of variability in AVLT progression as amyloid PET, supporting its utility as a marker of disease progression. The estimated temporal ordering of biomarkers and cognitive abnormality was as anticipated (amyloid, tau, cognition) up to the age of 90, beyond which AVLT was estimated to become abnormal prior to tau biomarkers, likely related to the effects of non-Alzheimer’s disease co-pathologies.

Abstract Distinguishing atypical parkinsonian disorders (APS) from Parkinson’s disease (PD) remains challenging due to overlapping clinical features, yet accurate differentiation is critical for prognosis and treatment. Here, we employed multi-model diffusion MRI (dMRI) analysis to characterize microstructural alterations across corticobasal syndrome (CBS), progressive supranuclear palsy-Richardson syndrome (PSP-RS) and PD, with the aim of identifying which dMRI model provides optimum differentiation. We analyzed 25 CBS, 42 PSP-RS, and 21 PD participants compared to 35 age and sex-matched controls. Using a clinically feasible 3-shell high angular resolution diffusion imaging (HARDI) protocol, we applied 11 metrics from five complementary dMRI models—diffusion tensor imaging (DTI), free-water-eliminated model of DTI (FWE), neurite orientation dispersion and density imaging (NODDI), tissue-weighted NODDI, and Fixel Density (FD) in fixel-based analysis (FBA) —to comprehensively assess regional white and gray matter integrity. Group differentiation was assessed using Cohen’s d effect sizes and spearman correlations were assessed between dMRI metrics and clinical scales. Distinct microstructural signatures were observed across disorders and the sensitivity of the dMRI models differed. In group contrasts, DTI and NODDI-derived metrics consistently captured the strongest effects in midbrain and peduncular pathways for PSP-RS, whereas precentral and corticospinal alterations in CBS were most prominent using NODDI and FBA measures. Free-water-corrected metrics showed attenuated group differences. Across clinical-diffusion analyses, NODDI metrics exhibited the most robust associations with disease severity, while DTI and FWE measures detected more limited, regionally constrained effects. Together, these findings highlight complementary yet distinct sensitivities of tensor, free-water, multi-compartment, and fixel-based models to APS-related neurodegeneration.

Abstract Background Atypical clinical presentations of Alzheimer's disease (AD) are associated with heterogeneous patterns of cortical tau‐PET uptake. The medial‐temporal (MTL) meta‐ROI is typically used to determine tau‐PET abnormality in AD, but this approach may not detect focal uptake in atypical patients. Our goal was to develop regional tau‐PET cut‐points and determine whether the number of positive regions better captures positive tau‐PET signal in atypical AD. Method 127 amyloid‐positive atypical AD (visual=68, language=39, motor=15, others=5) and 62 amyloid‐negative cognitively unimpaired (CU) individuals were recruited by the Neurodegenerative Research Group, Mayo Clinic, and underwent, amyloid‐PET and 18 F‐AV‐1451(tau)‐PET. Images were parcellated using the modified Harvard‐Oxford atlas and we focused on 67 AD‐specific regions. Rather than using the average SUVR across voxels within a region to quantify uptake, we used the 95 th percentile of SUVR values across voxels within a region to capture focal cases. We then used receiver operating characteristic (ROC) methods and a Youden threshold to define regional abnormality. The number of abnormal regions was calculated for each participant and used to discriminate between CU and atypical AD at the individual level. Result Across 64 regions, our approach showed excellent (AUROC>0.90) differentiation between atypical AD and CU. The temporal‐fusiform, parahippocampal gyrus and inferior temporal gyrus showed the best differentiation (Figure 1). All 67 regions showed excellent specificity (>85%), with 63 regions showing excellent sensitivity (≥80%). The lateral occipital cortex, temporal‐fusiform and parahippocampal gyrus showed the best specificity and sensitivity (Figure 2). The distribution of the number of tau‐elevated regions is shown in Figure 3. 74% of CU had no positive regions, while 43% of the atypical AD patients had all 67 regions affected. An optimal threshold of 7 tau‐elevated regions classified all CU below, and 123 (97%) atypical AD above threshold. Using a standard approach based on the average SUVR in the MTL meta‐ROI, only 116 (91%) atypical AD patients were tau‐PET positive. Conclusion Tau uptake in temporal regions showed the greatest group‐level differentiation. However, examining regional tau‐positivity may offer better sensitivity in atypical AD patients compared to the temporal meta‐ROI, likely due to the presence of focal non‐temporal patterns of uptake.

Abstract Background Quantitative susceptibility mapping (QSM) can detect iron deposition or myelin loss in the brain by estimating magnetic susceptibility properties. Iron is an important component in neurofibrillary tangles and is known to co‐localize in regions with tangle burden in Alzheimer's disease (AD). Abnormal magnetic susceptibility measured on QSM has been observed in atypical AD, although it is unclear if susceptibility is regionally related to AV‐1451‐PET uptake in atypical AD. Method Forty atypical AD patients (visual variant=24, language variant=7, others=9) were recruited by the Neurodegenerative Research group from the Department of Neurology, Mayo Clinic, Rochester, MN. They underwent a 3T MRI scan with a five‐echo gradient echo sequence for calculation of QSM ( n = 80), Aβ (Pittsburgh Compound‐B) and a 18 F‐ AV‐1451 PET scan ( n = 73). All patients showed evidence of Aβ positivity on PET. To assess the relationship between QSM and AV‐1451‐PET, two approaches were used i) Voxel‐based regression analysis using VoxelStats for the whole brain, and ii) region‐of‐interest (ROI)‐based Spearman's correlation analyses using cortical and subcortical ROIs. All models were adjusted for age and sex as covariates and were corrected for multiple comparisons, specifically the VoxelStats regression maps were corrected using random field theory and the ROI‐based correlation plots were corrected using false discovery rate. Result At the voxel‐level, positive correlations between AV‐1451‐PET and QSM were only observed in the left caudate (Figure 1). At the ROI‐level, no significant associations were found, although uncorrected data showed a positive association in the occipital lobe (red circle) and a negative association between substantia nigra susceptibility and occipital AV‐1451 uptake (blue circle) (Figure 2). Since these findings did not survive corrections, they should be interpreted cautiously. Conclusion Our data provides little evidence that regional AV‐1451 PET uptake is related to susceptibility changes in patients with atypical AD, suggesting that iron deposition may not be associated with regional tau accumulation in AD.

Abstract Background Self‐administered and remote digital cognitive assessments provide a scalable method for screening and longitudinal cognitive monitoring. Mayo Test Drive (MTD) is one such assessment with previously demonstrated usability, reliability, validity and multi‐device compatibility. MTD includes a computer‐adaptive word list memory test (Stricker Learning Span) and a measure of processing speed/executive functioning (Symbols Test) that combine to provide an MTD Composite. This study aimed to develop a conditional normative model predicting longitudinal cognitive change for MTD, and to validate the model in two independent samples of cognitively unimpaired (CU) and mild cognitive impairment (MCI)/dementia (DEM) participants. Method 1355 CU participants at initial MTD assessment with 2‐4 consecutive MTD assessments every 7.5 months comprised the development dataset. Linear mixed effects models were used to construct a conditional normative model for MTD assessments 2‐4. The baseline model was chosen a priori , consisting of age, sex, education, baseline score, number of assessments, and time. Device type, race, and baseline score by time interactions were also considered. Variables were only added if a >1% increment in r‐squared was observed. The models were externally validated in 113 CU and 51 MCI/DEM patients. Following model development, the data were updated and additional follow‐up MTD assessments were added for many participants, including the addition of additional CU validation data at test 3 ( N = 126) and 4 ( N = 279). T‐tests compared observed and predicted differences and residual z‐scores between development and validation at each follow‐up assessment. Result Table 1 presents demographic variables. No additional variables increased the r‐squared by >1%. The final models are presented in Table 2. Age, baseline score, sex, and education were significant. Residual z‐scores were not significantly different between CU development and CU validation cohorts, indicating observed scores were in line with model predicted scores. Residual z‐scores were significantly worse among MCI/DEM compared to CU development in 11/12 comparisons (Figure 1), indicating observed scores were below model predicted expectations and implying sensitivity to longitudinal cognitive decline. Conclusion These longitudinal normative models can be used to identify individuals with follow‐up scores outside of expected predicted ranges, signifying abnormal longitudinal cognitive decline.

Abstract Background In addition to visuospatial and visuoperceptual deficits, most PCA patients also report deficits in visual attention, episodic memory, and working memory. The dorsal attention network is known to play a critical role in modulating these clinical functions. However, little is known about the relationship of the dorsal attention network with other core networks and relationships to volume loss and memory functions in PCA. Method Fifty‐seven PCA patients were recruited by the Neurodegenerative Research group, Mayo Clinic, and compared to 60 cognitively unimpaired (CU) individuals from the Mayo Clinic Study of Aging. The CONN functional connectivity toolbox was used to calculate within‐ and between‐network connectivity. Functional connectivity within the key regions (i.e., inter‐hemisphere connectivity) of the dorsal attention network, namely frontal eye field (FEF) and intraparietal sulcus (IPS), and the entire network was generated. Functional connectivity between the dorsal attention network and the frontoparietal, visual, and default mode networks (DMN) were also generated using CONN's network parcellation atlas. Multivariate linear regression models were fit i) to compare within‐ and between‐network connectivity between both groups and ii) to assess relationships between within‐network connectivity and both gray matter volumes and clinical test scores. Result PCA showed reduced within‐network connectivity in the dorsal attention network, specifically within the IPS and the entire network compared to CU. An increase in between‐network connectivity between the frontoparietal network and both FEF and the entire dorsal attention network was noted in PCA compared to CU (Figure 1). Lower dorsal attention within‐network connectivity was associated with a trend for lower volumes in the entire network (Figure 2) and significantly lower scores on the auditory verbal learning test‐recognition percent correct (AVLT‐RCP) and Wechsler Memory Scale (WMS) III‐digit span backward in PCA (Figure 3). Conclusion These findings suggest that within‐ and between‐network connectivity of the dorsal attention network, both within key regions and the entire network, was disrupted in PCA patients. However, this network did not show disrupted connectivity with the core PCA networks. Within‐network connectivity disruptions in the dorsal attention network are associated with aspects of memory function, and to some extent, with volume loss.

Abstract Background Aging and dementia are generally associated with lower metabolism in gray matter on FDG‐PET and have been widely studied. However, white matter (WM) metabolic changes remain understudied. We investigated the metabolic changes in WM associated with aging and dementia and their relationships with established Alzheimer's Disease (AD) biomarkers and cognitive outcomes. Method We analyzed data from 3,169 participants (72 ± 9 years; 53% male; 65% cognitively unimpaired) enrolled in the Mayo Clinic Study of Aging (MCSA) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). All participants underwent baseline MRI, amyloid‐PET, and FDG‐PET scans, as well as neuropsychological evaluations, with at least one clinical follow‐up. Voxel‐wise multiple regression analysis identified WM metabolic changes associated with global cognition in cognitively unimpaired MCSA participants ( n = 1,754). Univariate analyses and structural equation models were applied to elucidate the driving factors underlying the WM metabolic changes in MCSA data. Multiple regression and linear mixed‐effects models evaluated the explanatory utility of WM metabolism for baseline global cognition and cognitive decline in both cohorts. In a subset of MCSA participants, we evaluated neurite density index from diffusion MRI as a surrogate of WM health and glial fibrillary acidic protein (GFAP) as a surrogate of neuroinflammation to understand underlying mechanisms. Result We identified an atypical WM signature (AWM‐Signature) in the anterior and superior corona radiata, where glucose metabolism increased as cognitive performance worsened (Figure 1). Hypermetabolism in AWM‐Signature was related to increased age, amyloidosis, vascular disease, and neuroinflammation, as well as decreasing WM neurite density in the same regions (Figure 2). Hypermetabolism in AWM‐Signature regions predicted faster future cognitive decline, even after accounting for demographics, amyloid, and vascular disease metrics. The results observed in the AWM‐Signature regions were consistently in the opposite direction of the associations seen with gray matter and typical WM regions. Conclusion We found unexpected metabolism increases in corona radiata, a hub that carries significant portion of the brain's neural traffic, with aging and disease which likely indicates a compensatory response. These changes may be useful cognitive resilience measures and require further investigation to understand their role in maintaining cognitive function.

Abstract Background Neuropathologic evaluation is essential for diagnosing and treating dementia, but definitive diagnoses is only available after post‐mortem brain autopsy. Positron emission tomography (PET) offers a potential in vivo proxy for predicting neuropathology. This study proposes a semi‐supervised learning (SSL) model using a single FDG‐PET image to predict underlying neuropathology. Method The SSL model leverages a self‐organized graph structure based on a patient similarity network (PSN) created from 1,495 FDG‐PET images across the aging and neurodegenerative spectrum. Each patient is represented by a node, with edges indicating image similarity derived from principal component analysis features and cosine similarity weights. The PSN structure connects 20 nearest neighbors. Neuropathologic labels were available for 204 autopsied patients. The Poisson Learning Method was employed to propagate labels from these annotated nodes to unlabeled nodes, guided by the PSN structure. FDG‐PET images were collected from clinical and research participants at the Mayo Clinic and processed using an MRI‐free pipeline. Neuropathologic labels were assigned based on widely accepted diagnostic criteria. Leave‐one‐out cross‐validation (LOOCV) was used to assess model performance. Results The confusion matrix demonstrated high accuracy in predicting Alzheimer's disease, frontotemporal lobar degeneration with tau inclusions, and Lewy body disease pathology. A Sankey diagram and label distribution analysis revealed strong correlations between clinical and predicted neuropathologic labels. Conclusion This study highlights the potential of a graph‐based SSL pipeline to predict neuropathological changes using FDG‐PET imaging data, despite limited pathology labels. The findings underscore the value of SSL in bridging the gap between abundant neuroimaging data and scarce neuropathologic labels, contributing to improved dementia diagnosis and understanding.

Background: Posterior cortical atrophy (PCA) patients typically show progressive visual decline, although language impairment can be a prominent feature. Previous studies have explored language connectivity impairment in PCA; not much is known about the exact nature of language processing disruptions in PCA. Methods: Thirty-seven PCA patients were compared with 39 cognitively unimpaired (CU) individuals. Functional connectivity was evaluated for the language processing system, which consisted of language, perceptual, motor, and cognitive networks. Linear regression was performed to compare within- and between-network connectivity across networks and within the language network in PCA compared to CU individuals. Relationships between language network connectivity, clinical performance, and gray matter volumes were also evaluated. Results: PCA showed reduced connectivity within the language and perceptual networks, along with reduced between-network connectivity compared to CU individuals. Regional connectivity breakdown within the language network was also noted. Language processing system connectivity was not associated with clinical performance on language processing tests. However, a breakdown in Broca-Wernicke, Wernicke-middle frontal gyrus, and Wernicke-frontal orbital gyrus connectivity was related to worse performance on sentence repetition. Lastly, gray matter volume was not associated with language network connectivity. Discussion: Together, these results reiterate the breakdown of language network connectivity in PCA and highlight the disruption in connectivity at the regional level within the language network. Reduced connectivity within the perceptual network and associations between reduced language network connectivity and worse performance on sentence repetition imply a breakdown within the language processing systems in PCA.