Citations

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.

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 R 2 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 R 2 values ranging from 0.19‐0.45 across diagnostic groups (Fig.1). The global organization approach yielded averaged adjusted R 2 and predicted R 2 values of 0.71 and 0.63, respectively, in the Mayo cohort. Out‐of‐sample median R 2 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.

AbstractBackgroundWhite matter hyper‐intensities (WMH) are observed on FLAIR MRI, increase with age and are more prominent in neurodegenerative diseases such as in Alzheimer’s disease (AD) and Parkinson’s disease (PD). The aim of this study was to assess the association between WMH and neuropathological staging of amyloid‐beta, neurofibrillary tangles (NFT) and α‐synuclein (α‐syn).MethodIn‐situ 3T‐MRI 3DT1‐weighted and 3D‐FLAIR data were collected for 77 clinically defined and pathologically‐confirmed brain donors; 31 AD, 21 PD and PD dementia (PDD), and 25 controls. WMH were segmented on FLAIR using an in‐house developed multiview convolutional neural network, followed by manual checking, and volume was obtained with FSL. Fifteen brain regions were dissected at autopsy, immunostained and evaluated for neuropathological diagnosis, including Thal amyloid phase, CAA type, and Braak stages for NFT and α‐syn. Group differences were assessed with linear models (corrected for age, gender, post‐mortem delay), and associations with Spearman and Pearson partial correlations.ResultCompared to controls, WMH load was higher in AD (p<0.001), specifically amnestic AD (p = 0.003) rather than non‐amnestic AD (p = 0.053), and higher in PD (p = 0.016), specifically non‐demented PD (p = 0.015) rather than PDD (p = 1.0). No difference in gender was observed (p = 0.38). Without the influence of pathology (in controls), WMH was associated with age (r = 0.49,p = 0.016).In the whole cohort, WMH was associated with Fazekas score (r = 0.67; p<0.001), normalized brain volume (NBV; r = ‐0.31,p = 0.007), gray matter volume (NGMV; r = ‐0.36,p = 0.002), Thal phase (r = 0.24,p0.034), Braak NFT stage (r = 0.33,p = 0.004), and Braak α‐syn stage (r = 0.27, p0.018).Across controls and AD, WMH associated with NBV (r = ‐0.39,p = 0.004), NGMV (r = ‐0.42,p = 0.002), left and right hippocampal volume (r = ‐0.36,p = 0.008 and r = ‐0.32,p = 0.022), Braak NFT (r = 0.41,p<0.001), Thal phase (r = 0.41,p = 0.002), and CAA (r = 0.34,p = 0.01). Amnestic and non‐amnestic AD showed similar results, except for CAA, which was associated with WMH in non‐amnestic AD only (r = 0.33,p = 0.04).Across controls and PD(D), WMH associated with Braak NFT stage (r = 0.39,p = 0.008) and Braak α‐syn stage (r = 0.60,p<0.001), but not with MRI outcome measures. In this respect PD and PDD showed different results; in PD WMH associated with NGMV (r = ‐0.37,p = 0.031), in PDD with left and right hippocampal volume (r = ‐0.44,p = 0.014 and r = ‐0.44,p = 0.015).ConclusionWMH associate with pathological stages of AD and PD(D), and contributes to MRI atrophy.

AbstractBackgroundWhite matter hyper‐intensities (WMH) are observed on FLAIR MRI, increase with age and are more prominent in neurodegenerative diseases such as in Alzheimer’s disease (AD) and Parkinson’s disease (PD). The aim of this study was to assess the association between WMH and neuropathological staging of amyloid‐beta, neurofibrillary tangles (NFT) and a‐synuclein (a‐syn).MethodIn‐situ 3T‐MRI 3DT1‐weighted and 3D‐FLAIR data were collected for 77 clinically defined and pathologically‐confirmed brain donors; 31 AD, 21 PD and PD dementia (PDD), and 25 controls. WMH were segmented on FLAIR using an in‐house developed multiview convolutional neural network, followed by manual checking, and volume was obtained with FSL. Fifteen brain regions were dissected at autopsy, immunostained and evaluated for neuropathological diagnosis, including Thal amyloid phase, CAA type, and Braak stages for NFT and a‐syn. Group differences were assessed with linear models (corrected for age, gender, post‐mortem delay), and associations with Spearman and Pearson partial correlations.ResultCompared to controls, WMH load was higher in AD (p<0.001), specifically amnestic AD (p = 0.003) rather than non‐amnestic AD (p = 0.053), and higher in PD (p = 0.016), specifically non‐demented PD (p = 0.015) rather than PDD (p = 1.0). No difference in gender was observed (p = 0.38). Without the influence of pathology (in controls), WMH was associated with age (r = 0.49,p = 0.016). In the whole cohort, WMH was associated with Fazekas score (r = 0.67; p<0.001), normalized brain volume (NBV; r = ‐0.31,p = 0.007), gray matter volume (NGMV; r = ‐0.36,p = 0.002), Thal phase (r = 0.24,p0.034), Braak NFT stage (r = 0.33,p = 0.004), and Braak a‐syn stage (r = 0.27, p0.018). Across controls and AD, WMH associated with NBV (r = ‐0.39,p = 0.004), NGMV (r = ‐0.42,p = 0.002), left and right hippocampal volume (r = ‐0.36,p = 0.008 and r = ‐0.32,p = 0.022), Braak NFT (r = 0.41,p<0.001), Thal phase (r = 0.41,p = 0.002), and CAA (r = 0.34,p = 0.01). Amnestic and non‐amnestic AD showed similar results, except for CAA, which was associated with WMH in non‐amnestic AD only (r = 0.33,p = 0.04). Across controls and PD(D), WMH associated with Braak NFT stage (r = 0.39,p = 0.008) and Braak a‐syn stage (r = 0.60,p<0.001), but not with MRI outcome measures. In this respect PD and PDD showed different results; in PD WMH associated with NGMV (r = ‐0.37,p = 0.031), in PDD with left and right hippocampal volume (r = ‐0.44,p = 0.014 and r = ‐0.44,p = 0.015).ConclusionWMH associate with pathological stages of AD and PD(D), and contributes to MRI atrophy.

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.

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.

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.