Citations

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.

AbstractBackgroundPositron emission tomography (PET) imaging with [18F]flortaucipir allows for in‐vivo visualization of aggregated tau in Alzheimer’s disease (AD). The FDA‐approved label for [18F]flortaucipir PET provides a standardized, clinically applicable definition of tau‐PET positivity by visual interpretation. Here, we studied the concordance between this visual interpretation and quantitative approaches employed in research.MethodWe included 2692 participants (cognitively unimpaired [CU] and impaired [CI, MCI or AD dementia]) from four cohorts (Mayo Clinic Study of Aging and ADRC, ADNI, A4, and A05) with available [18F]flortaucipir PET (mean age: 70.2 y, 50.0% females). Three trained readers assessed each [18F]flortaucipir PET scan according to the FDA‐approved method. Visual reads were compared to an established approach for defining tau‐PET positivity based on SUVR values in a temporal meta‐ROI (Jack, et al. Alzheimers Dement. 2017). Previously defined “lenient” (SUVR = 1.22) and “conservative” (SUVR = 1.30) cut‐points were explored. In addition, visual reads were compared to tau‐PET positivity as defined by a scheme based on the expected spatial progression of tau pathology from the medial temporal lobe (MTL) to the temporal neocortex (NEO) (Ossenkoppele, et al. Nat Med. 2022; cut‐points: SUVRMTL = 1.30, SUVRNEO = 1.37).ResultConcordance between visual and quantitative approaches was limited (Figure 1): the “lenient” and MTL‐NEO methods yielded high rates of quantitative‐positive, visual‐negative scans (73‐52% and 26‐18% for CU and CI, respectively), while the “conservative” cut‐point failed to detect 45% and 13% of the CU and CI visual‐positive scans, respectively. The prevalence of tau‐PET positivity in Aβ‐negative individuals was highest with the “lenient” cut‐point (∼24%) and lowest with visual interpretation (∼3%) (Figure 2). Visual‐positive, quantitative‐negative individuals were more frequently Aβ‐positive than visual‐negative, quantitative‐positive individuals (“lenient”: 86% vs 50%, p<0.001; “conservative”: 87% vs 58%, p<0.001; MTL‐NEO: 85% vs 70%, p = 0.005). Among visually‐positive participants, 164 (28%) showed a tau deposition pattern that deviated from the expected MTL to NEO progression (Figure 3).ConclusionVisual and quantitative methods for [18F]flortaucipir PET are non‐exchangeable. Tau‐PET positivity based on visual interpretation aligns better with Aβ‐pathology. A significant number of visually‐positive participants showed a pattern of tau accumulation that deviated from the expected stereotypical spatial progression and was not detected by standard quantitative methods.

AbstractBackgroundLarge neuroimaging studies like ADNI‐4 now include T1‐weighted, T2‐weighted and FLAIR images at isotropic1mm resolution for investigation of both neurodegenerative and cerebrovascular disease. These three input types allow segmentation of GM, WM, DeepGM, WMH, and CSF compartments. Additionally, this supports automated assessment of enlarged perivascular spaces (PVS). Our objective was to propose a unified framework for segmentation of intracranial classes and PVS while reducing human effort.MethodsFLAIR and T2‐weighted images are aligned to the T1‐weighted image.. Standard SPM12 unified segmentation using all three input images establishes deformations between subject and template space and bias field corrections. Extended tissue priors including deep GM are deformed into subject space. Adding mean intensities from high probability deep GM voxels as seeds and using the extended prior set, segmentation is re‐started, refining model parameters, deformations and bias‐field estimates. The procedure repeats, adding a WMH channel, resulting in a segmentation including GM, WM, CSF, deep GM, WMH and nuisance classes. Frangi filters are applied to the T1‐ and T2‐ weighted images. Filter response images are smoothed, averaged, masked and inserted in place of the CSF prior. Probability maps of vessel‐like shapes with CSF‐like contrast are estimated to identify PVS. Visual grading was also performed for comparison.ResultsThe method was applied to 300 participants. Visual quality control found two instances of poor WMH over‐segmentation. Perivascular space maps were all visually reasonable. Standard segmentation classes were visibly superior to single T1‐based segmentation. Figures 1,2 present examples. Pearson correlations of PVS fractional volume with multiple human raters’ PVS counts in CSO/CR was over 0.7 in a subset of 125 participants (Figure 3).ConclusionModern dementia protocols include high‐resolution T1‐weighted, T2‐weighted, and FLAIR images that can be used together as inputs to extend probabilistic segmentation adding deep grey and WMH classes with automated PVS assessment.

AbstractBackgroundThe Standardized Centralized Alzheimer’s & related dementias Neuroimaging (SCAN) project harmonizes processing of prospectively acquired PET and MRI scans from Alzheimer’s disease research centers (ADRCs) across the USA. SCAN data will be integrated with other ADRC data streams within the National Alzheimer’s Coordinating Center (NACC), including the Uniform Data Set which includes rich longitudinal demographic, cognitive, and neuropathological data, resulting in a large, potentially representative cohort. The project has sought to accommodate site‐to‐site variation in image acquisition while permitting harmonization of imaging data, necessitating new analytic approaches for different PET tracers and PET scans without MRIs.MethodPET scans underwent preprocessing for quality control and smoothing to a uniform resolution. MRI‐free processing pipelines and positivity thresholds were developed for each beta‐amyloid (Aβ)‐PET tracer: [11C]PiB, [18F]florbetaben (FBB), [18F]florbetapir (FBP) and [18F]flutafuranol (NAV). A generic Aβ‐PET template was used for spatial normalization and cortical summary SUVRs were calculated in template space. Aβ‐positivity thresholds were determined by examining both MRI‐dependent thresholds transformed into MRI‐free units and a young control mean+2SD. Centiloid (CL) conversions were completed for each Aβ‐PET tracer. Tau‐PET tracers ([18F]flortaucipir (FTP) and [18F]MK‐6240) required tracer‐specific warping templates for best performance relative to gold‐standard MRI‐dependent processing. Demographic and clinical data were obtained from NACC.Result15 ADRCs have contributed data to SCAN as of January 2023. 477 participants had preprocessed, SCAN‐compliant (https://scan.naccdata.org/) Aβ‐PET scans and NACC data; 219 of these participants (46%) also had tau‐PET scans (Table 1). This cohort included 18% of individuals from underrepresented groups. Clinical diagnostic groups ranged from cognitively unimpaired to MCI and dementia (74% of whom had AD dementia). Using pooled Aβ‐PET data across 4 tracers in CLs, there were expected differences in Aβ burden by diagnosis group (Fig. 1) and positive associations with tau (Fig. 2).ConclusionSCAN has successfully harmonized PET data from more than a dozen US ADRCs yielding a large, clinically heterogeneous cohort of deeply phenotyped research participants. SCAN data will be compatible with ADNI data allowing for further pooling of data and greater statistical power to drive critical questions in AD and related dementias research.

AbstractBackgroundA hallmark of Alzheimer’s disease is amyloid‐β (Aβ) deposition in the brain parenchyma as senile plaques which Aß frequently accompanies in the cerebral vasculature as cerebral amyloid angiopathy (CAA). Previous studies demonstrated that the cerebrovascular system is essential for Aβ clearance and interactions exist between cerebrovascular disease and AD. Neuroimaging data, including measures on small vessel disease, amyloid deposits, and microhemorrhages (MCH), can reflect both cerebrovascular disease and AD pathology, thereby serving as intermediate phenotypes of both conditions. We aim to identify genes and co‐expression networks in blood associated with these neuroimaging endophenotypes and the biological pathways enriched in these perturbed transcriptional networks.MethodWe conducted differential gene expression and Weighted Gene Co‐Expression Network analyses (WGCNA) on blood RNA sequencing data of 407 participants from the longitudinally followed Mayo Clinic Study of Aging cohort. The neuroimaging phenotypes include MCH, infarction, and amyloid deposits measured by Pittsburgh compound B positron emission tomography (PiB PET).ResultWGCNA analysis showed a nominally significant association (p<0.05) between several module eigengenes (MEs) and MCH and PiB PET. One module, M42, remained significantly associated with PiB PET after Bonferroni correction for all 43 modules tested. Pathway enrichment analysis of M42 identified two significant pathways (FDR‐adjusted p<0.05) including phosphodiesterase I activity and IgE binding. These pathways contain genes such as MS4A2 encoding the high‐affinity IgE receptor involved in the immune response. Similarly, ENPP2 in the phosphodiesterase I activity pathway represents an additional candidate gene previously implicated in Alzheimer’s disease.ConclusionWe generated co‐expression networks and identified one network module with a statistically significant association between brain amyloid deposits and blood transcriptome data. Further investigations of this network and its enriched pathways are necessary to understand their roles in amyloidosis, CAA, and neurodegeneration. Future studies will include seeking replication of these results in other datasets. Such studies can help elucidate the biological mechanisms of pathogenesis of cerebrovascular damage and amyloid deposits. In addition, in a clinical setting, blood levels of transcripts discovered in this study may serve as predictive or diagnostic biomarkers with mechanistic implications.

AbstractBackgroundCo‐morbid Alzheimer’s disease (AD) pathology in the form of amyloid‐β plaques and tau neurofibrillary tangles are frequently observed in people with probable dementia with Lewy bodies (DLB). Neurite Orientation Dispersion and Density Imaging (NODDI) is an emerging technique to characterize white matter (WM) tissue microstructure, although NODDI alterations and associations with AD biomarkers remain undefined in DLB. The objectives of this study are to compare NODDI and DTI parameters between DLB and controls and (ii) evaluate the pattern of DTI and NODDI changes associated with amyloid and tau deposition.MethodConsecutive patients within the DLB spectrum (n = 45), including probable DLB (n = 32) and probable Mild Cognitive Impairment with Lewy bodies (MCI‐LB) (n = 13) from the Mayo Clinic ADRC underwent multi‐shell diffusion MRI, [18F]‐Flortaucipir and [11C]‐PiB PET. A comparison group of 45 age‐ and gender‐matched clinically unimpaired controls (CU) was included. Voxelwise non‐parametric permutation models were used to determine pair‐wise group differences and assess the associations of AD biomarkers with fractional anisotropy (FA), mean diffusivity (MD), tissue‐weighted neurite density index (tNDI; NDI x (1 – Free water fraction)) and orientation dispersion index (ODI), controlling for age. Complementary analyses were performed using regions‐of‐interest (ROI) obtained from the JHU atlas.ResultCompared to CU, the DLB spectrum group exhibited widespread decreases in FA and tNDI and increases in MD and ODI, with the most pronounced effects in the cingulum bundles, uncinate, inferior and superior longitudinal fasciculi (Fig. 1). Greater [18F]‐Flortaucipir uptake was also associated with lower FA and tNDI, and higher MD and ODI in the DLB spectrum compared to CU, independently of age and [11C]‐PiB uptake (Fig. 2). In contrast, no associations were found between [11C]‐PiB uptake and any of the DTI or NODDI parameters. Complementary ROI analyses corroborated these findings.ConclusionWidespread loss of WM integrity was observed on DTI and NODDI in probable DLB and prodromal DLB. These findings were associated with greater cortical tau, but not with amyloid‐β deposition. Although alpha‐synuclein pathology underlies the loss of WM integrity in DLB, amyloid‐independent tau pathology may also be an important influence on white matter integrity within the DLB spectrum.

AbstractBackgroundWe aimed to assess axonal integrity in MAPT mutation carriers and early white matter (WM) neurodegeneration in asymptomatic carriers using traditional diffusion tensor imaging (DTI) measurements, i.e., fractional anisotropy (FA) and mean diffusivity (MD), and neurite orientation dispersion and density imaging (NODDI).MethodWe included available multi‐shell diffusion MRI data from 21 MAPT mutation carriers (16 asymptomatic; 5 symptomatic) and 31 non‐carrier family members as controls from the ALLFTD consortium. We used linear mixed‐effect models to assess WM abnormalities measured with FA and MD, neurite density index (NDI) and orientation dispersion index (ODI), combined across hemispheres. Separate models were conducted in all MAPT mutation carriers relative to controls while accounting for relatedness and age, then in symptomatic and asymptomatic carriers relative to controls. ComBat was used to harmonize data across sites.ResultIn models contrasting all MAPT carriers versus controls, WM alterations involved the cingulum bundle (FA, ODI), fronto‐temporal, entorhinal (FA, MD, NDI), and the inferior temporal WM (FA) after correcting for multiple comparisons (Fig. 1). In models contrasting symptomatic and asymptomatic MAPT carriers versus controls, symptomatic carriers had widespread involvement of the fronto‐temporal WM including the cingulum (FA, ODI), entorhinal (MD, FA, NDI), amygdala (MD, NDI) and inferior temporal (FA) areas after correcting for multiple comparisons. Asymptomatic carriers showed higher entorhinal MD and lower inferior temporal and cingulum FA relative to controls, and a more widespread pattern of higher ODI involved the medial temporal, superior temporal and occipital WM as well as long association and projection fibers including the internal capsule, cingulate bundle and fronto‐occipital fasciculi, although they were not statistically significant after correction for multiple comparisons.ConclusionTraditional DTI and novel NODDI measurements revealed some extent of overlap as well as divergences in patterns of WM abnormalities, indicating axonal loss is a feature of neurodegeneration in MAPT mutation carriers. Our results suggest that traditional DTI and novel NODDI measurements may have different sensitivities to WM alterations in MAPT mutation carriers, and that axon‐based metrics, particularly ODI measurements, may be more sensitive to the early WM involvement during the asymptomatic stage of the disease.

AbstractBackgroundGenetic mutations causative of familial fronto‐temporal lobar degeneration (f‐FTLD) are highly predictive of a specific proteinopathy, but there exists substantial inter‐individual variability in their system‐level pathophysiology, which can be assessed with 18Fluorodeoxyglucose‐positron emission tomography (FDG‐PET). Determining data‐driven patterns of network degeneration suggestive of underlying specific FTLD‐related genetic mutations is paramount to guide complex clinical decision making supported by the quantification of FDG‐PET.MethodWe collected clinical and FDG‐PET data from 39 patients with f‐FTLD, including 11 carrying the C9orf72 hexanucleotide expansion (1 asymptomatic, 9 with a predominant behavioral phenotype, 1 with a predominant language phenotype), 12 carrying a GRN mutation (1 asymptomatic, 7 behavioral, 4 language), and 16 carrying a MAPT mutation (6 asymptomatic, 10 behavioral). We performed a spectral covariance decomposition analysis between FDG‐PET images to yield latent patterns reflective of a distribution of metabolism opposing poles of relative hyper‐ and hypo‐metabolism across the entire brain (“eigenbrains” or EBs). We then conducted linear discriminant analyses (LDAs) to perform EB‐based multiclass predictions of genetic mutation and clinical phenotype.ResultThe spectral decomposition analysis yielded five eigenbrains, which explained 58.79% of the covariance between FDG‐PET images (Fig. 1). Gradients indicative of relative hypometabolism in left fronto‐temporo‐parietal areas distinguished GRN mutation carriers from other genetic mutations (EB1, EB2, EB4) and were associated with clinical phenotypes predominantly involving language (EB1, EB2, EB3). Conversely, those indicative of relative hypometabolism in prefrontal areas (EB3) and temporopolar areas (EB2) with a right hemispheric predominance were mostly associated with clinical phenotypes predominantly involving behavior/personality, and EB2 distinguished MAPT mutation carriers from other genetic mutations. The LDAs yielded accuracies of 80% and 75% in predicting genetic status and predominant clinical phenotype, respectively (Fig. 2).ConclusionFive EBs explained a high proportion of covariance in patterns of network degeneration across FTLD‐related genetic mutations. These EBs contained information relevant to the system‐level physiology associated with FTLD‐related pathogenic mechanisms and that allowed the prediction of clinical phenotype with relatively high accuracy. This supports the utility of FDG‐PET to guide the development of tools supporting clinical decision making and ongoing efforts to develop network‐based biomarkers to track disease progression and risk of phenoconversion in f‐FTLD.

AbstractBackgroundThere is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behavior/personality. This is due to a lack of knowledge about the macro‐scale anatomy underlying these symptomatologies, a partial overlap in clinical features, and the fact that a single underlying pathology can give rise to both phenotypes and vice‐versa.MethodWe included data from patients with genetic, biomarker, and/or autopsy evidence for fronto‐temporal lobar degeneration (FTLD) diagnosed with behavioral variant fronto‐temporal dementia (bvFTD, n = 30) and patients with biomarker and/or autopsy evidence for Alzheimer’s disease (AD) pathology diagnosed with either an initial and predominant dysexecutive (dAD, n = 52), behavioral (bvAD, n = 7), or amnestic (aAD, n = 28) syndrome. We assessed group‐wise differences in clinical/cognitive features and 18Fluorodeoxyglucose‐positron emission tomography (FDG‐PET) patterns. This was followed by a spectral covariance decomposition between FDG‐PET images to yield latent patterns of relative metabolism (“eigenbrains”). These eigenbrains were subsequently linked to clinical/cognitive data and meta‐analytic topics reflecting a wide range of mental abilities. We used a linear discriminant analysis (LDA) to perform eigenbrain‐based diagnostic predictions.ResultdAD and bvFTD patients were the youngest at symptom onset, followed by bvAD, then aAD. dAD patients had worse cognitive performance on nearly all cognitive domains compared to other groups. Hypometabolism was observed across associative cortices in dAD, temporo‐parietal areas in aAD, and fronto‐temporal areas in bvFTD and bvAD (Fig. 1). The spectral covariance decomposition yielded nine eigenbrains which explained 61% of the variance in patterns of FDG‐PET, and only the first three are presented (Fig. 2). These eigenbrains revealed that relative hypometabolism in association cortices, notably lateral parietal areas, associated with dysexecutive symptomatology and a lower likelihood of behavior/personality problems, whereas relative hypometabolism restricted to frontal and temporopolar areas showed opposite associations. The LDA yielded an accuracy of 82.1% in predicting diagnostic category (Fig. 3).ConclusionThis study suggests distinct macro‐scale underpinnings underlying predominant dysexecutive versus behavioral symptomatology in degenerative dementia phenotypes. This has implication for the implementation of clinical and research criteria for these dementia syndromes and highlights the importance of data‐driven techniques to inform the classification of degenerative diseases.