Machulda, Mary M.

Abstract Background Brief self‐administered cognitive assessments deployed remotely and combined with plasma biomarkers of Alzheimer’s disease (AD) are well suited to assess clinical symptoms and underlying biology in large‐scale decentralized studies. We investigated whether associations of self‐administered cognitive measures and plasma biomarkers were comparable to associations of in‐person cognitive measures and plasma biomarkers. Method Participants included 327 adults from the Mayo Clinic Study of Aging (mean age = 70.8; 48% female; mean education = 16.0 years; 96.9% white). Most (94.5%) were cognitively unimpaired; 5.5% had consensus diagnosis of mild cognitive impairment. Participants completed Mayo Test Drive (MTD; Stricker et al., 2022), a remote, self‐administered, multi‐device compatible web‐based cognitive assessment comprised of the Stricker Learning Span (SLS) and Symbols test that are combined into a MTD composite. Participants also completed in‐person neuropsychological assessment (global‐z; Mayo Preclinical Alzheimer’s disease Cognitive Composite [Mayo‐PACC]) and fasting venipuncture. Plasma was assayed via HD‐X simoa platform for amyloid‐beta (Aß)‐42 and Aß‐40 to create an Aß42/40 ratio, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) using the N4PE Quanterix kit as well as phosphorylated‐tau protein 181 (p‐tau181). P‐tau181, GFAP, and NfL were log10‐transformed. Multiple regression analyses adjusting for age, sex, and education examined associations between plasma biomarkers and composite and subtest scores from remote and in‐person measures. Davidson‐Mackinnon J‐test compared R‐squared values of models with remote versus in‐person composites. Result Lower performances on MTD, global‐z, and Mayo‐PACC (Table 1) were associated with higher GFAP (p’s = .02), NfL (p’s = .003), and p‐tau181 (p’s = .04). For memory, worse SLS and AVLT sum of trials were associated with higher GFAP (p’s = .01) and NfL (p’s = .03). For processing speed/executive function, worse Symbols and Trails B were associated with higher p‐tau181 (p’s = .02). Worse Trails B and Digit Symbol Coding were associated with higher NfL (p’s = .02), and worse Digit Symbol Coding was associated with lower Aß42/40 (p = 0.03; Table 2). A multivariable model with MTD explained significantly more variance (p = .04) for NfL (adjusted R‐squared = 0.461) than a multivariable model with Mayo‐PACC (adjusted R‐squared = 0.447); remote and in‐person composite measures were comparable for other associations (p’s>.05). Conclusion Remote and in‐person composite measures showed comparable associations with AD‐related plasma biomarkers, supporting MTD’s criterion validity.

AbstractBackgroundPredictive models of tau‐PET accumulation have largely relied on the functional connectome to estimate its spatial patterns in Alzheimer’s disease (AD). Patterns of global functional organization represent a promising avenue as they encapsulate many biological properties relevant to neurodevelopment, cognition and degeneration. We compared approaches based on local connectivity (from task‐free fMRI) and global organization (from FDG‐PET) to predict regional tau‐PET across AD phenotypes.MethodWe included 430 cognitively unimpaired (CU) individuals (214 A+T‐, 216 A+T+) and 62 amnestic mild cognitive impairment (aMCI), 64 amnestic AD, 21 dysexecutive AD (dAD), 41 logopenic progressive aphasia (LPA) and 49 posterior cortical atrophy (PCA) patients (all A+T+) from the Alzheimer’s Disease Research Center, Mayo Clinic Study of Aging, and Neurodegenerative Research Groups (referred to as “Mayo cohort”). The connectivity approach consisted of assessing the relationship between a region’s tau level and its functional connectivity to the region of highest tau (epicenter). The global organization approach consisted of 1) performing a principal component analysis on 3000 FDG‐PET images that did not overlap with the Mayo cohort, 2) projecting FDG‐PET images of the Mayo cohort onto the low‐dimensional space generated in Step 1 and extracting corresponding weighted values across the first 100 components, 3) using these weighted values as predictors of regional tau‐PET in a linear model and determining optimal parameters based on predicted R2 values, and 4) using the parameters from the model built in Step 3 to predict tau‐PET in the ADNI cohort (n = 86) (Fig.2).ResultThe connectivity‐based approach yielded adjusted R2 values ranging from 0.19‐0.45 across diagnostic groups (Fig.1). The global organization approach yielded averaged adjusted R2 and predicted R2 values of 0.71 and 0.63, respectively, in the Mayo cohort. Out‐of‐sample median R2 in the ADNI cohort was 0.41 (range 0.10‐0.60) (Fig.3).ConclusionAn unbiased approach based on the global functional organization of the brain and using an imaging modality widely used in clinical settings outperformed a connectome‐based one to predict regional tau‐PET across the AD phenotypic spectrum. Patterns of tau accumulation may better relate to the large‐scale physiology of the brain underlying specific mental functions rather than local connectivity factors.

AbstractBackgroundNeurite orientation dispersion and density imaging (NODDI) measures from diffusion MRI provide information about tissue microstructure. Our goal was to compare the sensitivity of NODDI to structural MRI (sMRI) in capturing neurodegenerative changes in the gray matter due to aging and dementia.MethodWe included 815 participants [644 cognitively unimpaired (CU), 103 MCI, 68 AD] from the Mayo Clinic Study of Aging (MCSA) and Mayo ADRC with sMRI and multi‐shell diffusion MRI. We assessed gray matter volumes scaled by head size and cortical thicknesses from sMRI and neurite density index (NDI) and isotropic volume fraction (ISOVF) from NODDI. Pairwise Spearman correlations were calculated between imaging measures and Tau‐PET SUVRs in Braak I‐II, III‐IV, and V‐VI regions of interest (ROI) in participants = 60 years. Separate multivariable linear regression models were fit for the prediction of brain age using sMRI and NODDI in 515 CU participants. The predicted age gaps were calculated as chronological age minus predicted age in the remaining participants. We compared age gaps computed by sMRI and NODDI using disease surrogates (APOE4 genotype, AD pathologies via PET, WMH via FLAIR‐MRI, vascular risk, cognition via MMSE).ResultBraak ROIs, sMRI, and NODDI measures were correlated. The correlations between ISOVF and Tau‐PET SUVRs were stronger than correlations between NDI, volume, and thickness and Tau‐PET SUVR (Fig.1). MMSE was more associated with ISOVF than Tau‐PET and other imaging measures. The sMRI regression models had a higher mean absolute error (MAE) of 4.13 years than NODDI models with an MAE of 4.02 years. The NODDI model better predicted age gap separation between CU, MCI, and AD compared to the sMRI model (Fig.2). The NODDI predicted age gap showed stronger correlations with global amyloid‐PET SUVR, meta‐ROI Tau‐PET SUVR, CMC, APOE4, and MMSE (Fig. 3).ConclusionNODDI, especially ISOVF, had stronger correlations with MMSE and Tau‐PET SUVRs across all Braak ROIs. The brain age prediction model trained using NODDI was more sensitive in detecting neurodegeneration changes than sMRI based on associations with disease surrogates. Our work suggests that NODDI in gray matter may be a more sensitive surrogate of neurodegeneration compared to sMRI.

AbstractBackgroundDisruption of default mode network (DMN) connectivity is common in Alzheimer’s disease. We investigated cross‐sectional and one‐year longitudinal changes in DMN sub‐systems in atypical clinical presentations of AD, in relation to age and tau.MethodSixty‐four amyloid‐positive atypical AD (Aty‐AD) patients diagnosed with posterior cortical atrophy (n = 35) or logopenic progressive aphasia (n = 29) were recruited by the Neurodegenerative Research Group. Patients underwent structural and resting‐state functional MRI, and [18F]flortaucipir PET at baseline and one‐year follow‐up. 140 amyloid‐negative cognitively unimpaired (CU) individuals from the Mayo Clinic Study of Aging were included as controls. Global tau SUVR was calculated in a meta‐ROI including parietal, temporal, and occipital regions. DMN connectivity was calculated in native space for posterior (pDMN), ventral (vDMN), anterior ventral (avDMN) and anterior dorsal (adDMN) DMN sub‐systems using the dual regression method. A global measure of DMN connectivity, the Network Failure Quotient (NFQ), was calculated as (vDMN_to_pDMN+ adDMN_to_pDMN)/(pDMN+vDMN). Linear mixed‐effects models were computed for each DMN connectivity measure, including diagnosis (Aty‐AD, CU), age, time and their interaction, and a per‐person random effect. SPM paired t‐tests were run to compare baseline and follow‐up images in Aty‐AD and CU separately. Linear mixed‐effect models were run in the Aty‐AD cohort only to investigate the effect of global baseline tau SUVR on each DMN connectivity measure.ResultMixed‐effects models showed evidence of lower cross‐sectional pDMN and higher adDMN connectivity in Aty‐AD relative to CU, with no significant time effects (Fig. 1). While connectivity in all DMN sub‐systems declined with age in CU, connectivity was higher in older Aty‐AD patients relative to younger, particularly in vDMN and pDMN. A similar trend was observed for NFQ, which was significantly higher in Aty‐AD, with a negative age effect (Fig. 1). In Aty‐AD, voxel‐based analyses showed moderate evidence of decline in vDMN connectivity and increase in avDMN connectivity over time (Fig. 2). Greater baseline global tau SUVR was associated with lower vDMN connectivity, cross‐sectionally and longitudinally (Fig. 3).ConclusionDMN connectivity was altered in Aty‐AD, with strongest alterations in younger patients. The vDMN declined over time, suggesting a shift in DMN connectivity with disease progression that was likely related to tau pathology.

AbstractBackgroundPosterior cortical atrophy (PCA) and logopenic progressive aphasia (LPA) are atypical variants of Alzheimer’s disease which typically show different cross‐sectional patterns of atrophy and tau deposition. Patterns of atrophy remain distinct longitudinally. A direct comparison of rates of atrophy and tau accumulation between these syndromes has not yet been reported.MethodWe evaluated 20 PCA and 20 LPA patients; all were amyloid‐positive on [11C]PiB‐PET. Structural MRI and tau‐PET were performed at baseline and one‐year follow‐up. Annualized rates of change in grey matter volume (log Jacobians from tensor‐based morphometry) and flortaucipir‐PET SUVRs were calculated at the lobar level. A global tau SUVR was calculated using parietal, temporal, and occipital regions. Lobar rates were compared between PCA and LPA with linear models, accounting for education, sex, age, baseline global tau SUVR (for tau‐PET), with significance set at p = 0.05. SPM voxel‐level comparisons were performed, with the same covariates, with significance set at p = 0.001.ResultGroups did not differ in age, Apoe status, baseline global tau SUVR or whole brain volume, or their annualized rates of change. LPA had shorter disease duration. In the lobar models, PCA had faster rates of atrophy in the occipital lobe and there were no significant differences on rates of tau accumulation, but a trend for LPA having faster rates of tau accumulation in all lobes. Baseline global tau SUVR was negatively associated with rate of tau accumulation in parietal and temporal lobes. Age was negatively associated with rates of tau accumulation in frontal, temporal and occipital lobes and with rates of atrophy in frontal and parietal lobes. Voxel‐level analysis confirmed these results, with PCA showing faster rates of occipital atrophy, and LPA showing subtle evidence for diffuse faster rates of tau accumulation (Figure 1).ConclusionLongitudinal patterns of neuroimaging abnormalities differ between PCA and LPA, although with divergent results for tau accumulation and atrophy that may reflect differences in disease stage. Rates of change are strongly influenced by age and severity of baseline tau, with faster rates of tau accumulation in patients with younger age and lower baseline tau deposition.

Background: Posterior cortical atrophy (PCA) and logopenic progressive aphasia (LPA) are two common atypical Alzheimer’s disease (AD) variants. Little is known about behavioral and neuropsychiatric symptoms or activities of daily living (ADLs) in PCA and LPA, and whether they differ across syndromes. Objective: To characterize the behavioral and neuropsychiatric profiles and ADLs of PCA and LPA and compare presence/absence and severity of symptoms between syndromes. Methods: Seventy-eight atypical AD patients, 46 with PCA and 32 with LPA, completed the Neuropsychiatric Inventory Questionnaire (NPI-Q) and Cambridge Behavioral Inventory-Revised (CBI-R) at baseline and longitudinally over-time. Mann-Whitney U and Fisher’s Exact Tests assessed for differences in symptoms between the two syndromes with significance set at p ≤0.01. To eliminate demographic differences as confounders the groups were matched, and differences reanalyzed. Results: PCA were younger at onset ( p = 0.006), at time of baseline assessment ( p = 0.02) and had longer disease duration ( p = 0.01). Neuropsychiatric symptoms were common in PCA and LPA, although more common and severe in PCA. At baseline, PCA had a higher NPI-Q total score ( p = 0.01) and depression subscore ( p = 0.01) than LPA. Baseline total CBI-R scores were also higher in PCA than LPA ( p = 0.001) with PCA having worse scores in all 10 CBI-R categories. Longitudinally, there was no difference between groups on the NPI-Q. However, on the CBI-R, PCA had faster rates of worsening on self-grooming ( p = 0.01) and self-dressing ( p = 0.01) compared to LPA. Conclusions: Behavioral and neuropsychiatric symptoms are common in PCA and LPA although these symptoms are more common and severe in PCA.

Abstract Transactive response DNA-binding protein 43 (TDP-43) pathology is categorized as type A-E in frontotemporal lobar degeneration and as type α-β in Alzheimer disease (AD) based on inclusion type. We screened amygdala slides of 131 cases with varying ages at death, clinical/neuroimaging findings, and AD neuropathologic changes for TDP-43 pathology using anti-phospho-TDP-43 antibodies. Seven cases (5%) only showed atypical TDP-43 inclusions that could not be typed. Immunohistochemistry and immunofluorescence assessed the atypical star-shaped TDP-43 pathology including its distribution, species, cellular localization, and colocalization with tau. All 7 had died at an extremely old age (median: 100 years [IQR: 94–101]) from nonneurological causes and none had dementia (4 cognitively unimpaired, 3 with amnestic mild cognitive impairment). Neuroimaging showed mild medial temporal involvement. Pathologically, the star-shaped TDP-43-positive inclusions were found in medial (subpial) amygdala and, occasionally, in basolateral regions. Hippocampus only showed TDP-43-positive neurites in the fimbria and subiculum while the frontal lobe was free of TDP-43 inclusions. The star-shaped inclusions were better detected with antibodies against N-terminal than C-terminal TDP-43. Double-labeling studies confirmed deposition of TDP-43 within astrocytes and colocalization with tau. We have identified a novel TDP-43 pathology with star-shaped morphology associated with superaging, with a homogeneous clinicopathologic picture, possibly representing a novel, true aging-related TDP-43 pathology.