Citations

AbstractBackgroundDementia with Lewy bodies (DLB), the second most prevalent form of degenerative dementia, is characterized by pathological α‐synuclein (αSyn) protein aggregation in neurons. Pathological αSyn shows a protein‐seeding phenomenon that may result in aggregation years before clinical symptoms. Studies using Real‐time quaking‐induced conversion (RT‐QuIC) assay show αSyn seeding from peripheral tissue including skin and cerebrospinal Fluid (CSF). Identifying early biomarkers for DLB is essential for accurate diagnosis, and developing effective treatments to slow progression. Herein, we investigate αSyn seeding activities in CSF from patients with mild cognitive impairment with Lewy bodies (MCI‐LB) phenoconverting to DLB.MethodCSF samples were obtained from 3 MCI‐LB and 2 DLB patients at baseline and two‐year follow‐up appointments. CSF samples were tested for their potential to seed α‐synuclein aggregation using an RT‐QuIC assay. CSF samples were tested by seeding 2 μl of CSF in 98 ul of RT‐QuIC reaction mixture with human recombinant αSyn protein, analyzed on a FLUOstar Omega plate reader. Results were recorded every 30 minutes over a period of 45 hours analyzed based on the reaction kinetics to determine the seeding ability of the α‐synuclein in the CSF samples.ResultWe observed seed amplification at first visit for both DLB patients and one MCI‐LB patient. The absolute quantification of aggregation increased at second visit (2 years apart) for one DLB patient and one MCI‐LB. The other two MCI‐LB patients showed no amplification at baseline visit but showed signal at two‐year visit, by which time all three had phenoconverted to DLB. Interestingly the second DLB patient showed a decline at two‐year follow‐up. The medical and neuroimaging records of that patient indicate that the patient’s cognitive decline began as early‐onset disease (first symptoms at age 50 years).ConclusionWe demonstrate that measures of seed aggregation in CSF with RT‐QuIC are informative in MCI‐LB and DLB, and may also reflect changes in disease progression and dynamics.

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 (WM) plays an important role in cognitive function and undergoes substantial changes due to aging and increase in AD and AD related dementia (AD/ADRD) pathologies. While recent work proposed measurement of global WM changes from diffusion MRI as biomarkers for Cerebrovascular disease (CVD) such as PSMD and free water, there needs to be a better understanding of WM changes due to AD/ADRD pathologies. The goal of our recent work was to understand white matter changes with CVD and AD – common pathologies in AD/ADRD ‐ utilizing data from Mayo Clinic Study of Aging and Mayo ADRC.MethodUsing diffusion MRI (dMRI) in 718 participants from Mayo Clinic Study of Aging (mean age = 71.1 years, 56% male), we investigated relationships between commonly used dMRI measures and vascular risk as well as global amyloid PET. In a subset of 233 participants who were amyloid‐PET positive and also competed Tau‐PET (flortaucipir) from Mayo Clinic Study of Aging and Mayo ADRC, we investigated associations between regional flortaucipir SUVRs in Braak stages and regional WM changes.ResultsWe found greater frontal lobe WM damage with increasing vascular risk across all dMRI measures. We previously validated the strong relationship between the genu of the corpus callosum (anterior interhemispheric connections) dMRI measurements made on antemortem scans and cerebrovascular pathology observed on postmortem tissue (Nguyen et. al. Acta Neuropathologica 2022) supporting the utility of genu of the corpus callosum dMRI measures as a potential CVD biomarker. Also, we found greater posterior WM damage with increasing amyloid burden. Among the 233 amyloid positive participants, we found spatially dependent WM degeneration associated with regional flortaucipir SUVRs in Braak stages with greater extent of posterior WM damage. Further, dMRI measurements of the WM damage provided complementary information about disease staging and progression of AD in addition to flortaucipir SUVR measurements.ConclusionsAnterior WM measurements may be more specific to CVD and posterior WM changes are seen with increasing AD pathological burden. Our results suggest that WM changes observed with CVD and typical AD may be different and have clinical utility for differential diagnosis and disease staging.

AbstractBackgroundInter‐cellular communication within the gliovascular unit (GVU) is critical for cerebral blood flow regulation, and maintenance of the blood‐brain‐barrier (BBB) properties. The breakdown of BBB in Alzheimer’s disease (AD) is well‐established, but precise underlying molecular changes remain unclear. Additionally, whether GVU molecular alterations observed in AD brains are also detected in blood from living patients is unknown. Further, these GVU molecular perturbations require further investigation in different model systems to identify both human brain‐specific and cross‐species conserved alterations. In this study, we investigated prioritized GVU molecules altered in AD brains for their conservation in blood and cross‐species model systems.MethodsWe performed single nucleus RNA sequencing (snRNAseq) of temporal cortex tissue in AD and control brains. We analyzed this data to detect cell‐specific GVU molecular perturbations and their interactions. We investigated molecular interactions between vascular and astrocyte clusters, the major cell types of the GVU of the BBB. To determine whether GVU transcriptional alterations detected in the brain are preserved in the blood, existing blood expression, genetic, and neuroimaging data from two longitudinal antemortem cohorts were analyzed. Using model systems, including mouse, drosophila, and zebrafish, we evaluated the cross‐species conservation of the top GVU alterations detected in AD brains.ResultsBrain snRNAseq revealed transcriptional profiles of 6,541 astrocytes and 2,210 vascular cells. The latter formed three distinct vascular clusters characterized as pericytes, endothelia and perivascular fibroblasts. We identified differentially expressed genes and their enriched pathways within these clusters and observed the highest levels of transcriptional changes within pericytes. Vascular targets that interact with astrocytic ligands have biological functions in cell signaling, angiogenesis, amyloid ß metabolism, and cytoskeletal architecture. We discovered that genetic variants influencing blood expression levels of some of the prioritized GVU genes were associated with neuroimaging burden of cerebrovascular disease in living human cohorts. Our ongoing studies in model systems revealed conservation of some of the top prioritized molecular perturbations across species.ConclusionOur findings prioritized by multiscale, cross‐tissue human data revealed GVU perturbations within interacting pericyte and astrocyte molecules, which are conserved across multiple cross‐species models. These results nominate new molecular targets and mechanistic insights for BBB disruptions in AD.

AbstractBackgroundTwo major risk factors of Alzheimer’s disease (AD) are advanced age and the APOE‐ε4 risk allele. However, despite having these risk factors, some individuals escape from developing AD or have slower cognitive decline. In this study we hypothesized that “resilient” individuals, defined as those who remain cognitively normal despite being >80 years‐old and carrying at least one copy of the APOE‐ε4 allele, have concerted changes in their blood transcriptomes that are associated with resilience to AD.MethodWe generated blood RNA‐sequence data from 105 APOE‐ε4 carriers from the Mayo Clinic Study of Aging (MCSA) who remained cognitively normal after age 80 (resilient), or who developed mild cognitive impairment (MCI) or AD (non‐resilient). Weighted gene co‐expression network analysis (WGCNA) was performed to evaluate the association of the blood co‐expression network with primary phenotypes of resilience and memory scores. To validate our findings, we constructed a consensus co‐expression network using both MCSA and a replication cohort of blood microarray data from 91 Alzheimer’s Disease Neuroimaging Initiative (ADNI) study participants who had a similar demographic profile. We annotated the consensus modules for their enrichment of cell types, biological processes, genes within previously published genome‐wide significant (GWS) AD‐associated loci, and genes that reside in consensus modules built from seven brain regions across three different autopsy cohorts. To identify genetic variants regulating blood consensus module co‐expression, we performed module quantitative trait locus (modQTL) analysis.ResultWe found co‐expression modules significantly (q<0.05) correlated with the memory phenotype, logical memory delayed recall, across both MCSA and ADNI cohorts. Some of these modules were enriched with blood cell types and biological processes, including telomere maintenance, mitochondrial functions, and immune response. We found 1 module significantly (q<0.05) enriched with genes near GWS AD‐associated loci and 4 modules with genes in brain consensus modules. Lastly, we found 8 modQTLs significantly (Bonferroni‐p<0.05) associated with the co‐expression of 5 different modules.ConclusionWe identified blood co‐expression modules, biological pathways and specific genetic variants associated with cognitive resilience in the presence of AD risk factors. This study provides novel insights into the potential role of peripheral blood in the development of and resilience to AD.

AbstractBackgroundPatients with dementia with Lewy bodies (DLB) may have Alzheimer´s disease (AD) pathology. Plasma biomarkers of beta‐amyloid (Aß), phosphorylated tau (pTau), and neurodegeneration are sensitive to AD neuropathologic changes in AD dementia. While these plasma biomarkers are well tested in the AD continuum, their performance for concomitant AD pathology in the DLB continuum is still unclear.MethodWe included patients with isolated REM sleep behavior disorder (iRBD; n = 15), with mild cognitive impairment with Lewy bodies (MCI‐LB; n = 37), and with DLB (n = 70) from the Mayo Clinic Alzheimer’s Disease Research Center. Cognitively unimpaired individuals (CU; n = 100) included from the Mayo Clinic Study of Aging were balanced on age and sex. A panel of plasma biomarkers of Aß40, Aß42, p‐tau181, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) measures was obtained. We calculated the ratio of plasma Aß40/42. Lower Aß40/42 indicates increased Aß pathology. Plasma Aß40, Aß42, GFAP, and NfL were measured using the Simoa® Neurology 4‐Plex E Advantage kit and pTau‐181 was measured with the Simoa® pTau‐181 Advantage V2 kit. Both were run on a Quanterix HD‐X analyzer. AD imaging biomarkers were assessed on PET using Pittsburgh compound B (PiB) for Aß and 18F‐Flortaucipir for tau.ResultHigher levels of plasma GFAP were observed in the MCI‐LB (p = 0.014) and DLB (p<0.001) groups compared to CU (Figure). DLB patients also showed higher levels of plasma pTau181 (p<0.001) and NfL (p<0.001). Higher amyloid and tau PET standardized uptake value ratio (SUVr) in the DLB continuum were associated with higher levels of plasma p‐tau181, NfL, and GFAP (p<0.001). After accounting for age, plasma Aß40/42 did not correlate with amyloid or tau PET SUVr.ConclusionIn the DLB continuum, abnormal levels of GFAP in plasma can be detected as early as the prodromal stages of the disease. Plasma pTau181 reached abnormal levels in the DLB stage. Higher plasma GFAP, NfL, and p‐tau181 are associated with higher amyloid and tau PET SUVr in the DLB continuum. Plasma biomarkers have the potential to contribute to screening and early diagnosis in the DLB continuum, which has implications in designing new treatments.

AbstractBackgroundCharacterizing disease trajectories in frontotemporal lobar degeneration (FTLD) is essential to determine the optimal timing for initiating therapeutic interventions, and there is an urgent need for novel and sensitive disease biomarkers. Intrinsic connectivity network (ICN) mapping using task‐free fMRI is a promising biomarker which reveals early abnormalities even before symptomatic onset (Dopper et al. 2014; Lee et al. 2016). Yet, ICN trajectories from the presymptomatic to symptomatic stages in FTLD mutation carriers remain unclear.MethodIn this cross‐sectional study, we used UCSF and ALLFTD Consortia data to study 129 carriers with variants in MAPT (presymptomatic/prodromal/symptomatic: 34/4/7), C9orf72 (30/9/12) and GRN (22/4/7). Using cross‐sectional data to construct ICN trajectories by genotype, we built separate generalized additive models for each genetic group to estimate non‐linear relationships between standardized connectivity changes and disease age across clinical stages. ICN connectivity was based on seed‐based analyses for networks associated with common clinical syndromes for each genetic group (i.e., salience and default mode [MAPT/C9orf72/GRN], corticobasal syndrome [MAPT/GRN], progressive supranuclear palsy syndrome [MAPT], sensorimotor and medial pulvinar‐related [C9orf72], and non‐fluent variant primary progressive aphasia (nfvPPA) syndrome networks [GRN]). We included 127 healthy controls as a reference to calculate ICN z‐scores for each carrier. Each carrier’s disease age was defined as the difference between the carrier’s chronological age and estimated age of onset based on multimodal measures of structural imaging, fluid biomarker and clinical assessments (Staffaroni et al. 2022).ResultIn MAPT+, presymptomatic carriers showed declining salience network connectivity starting approximately 30 years before estimated onset, which plateaued about 20 years before onset. In C9orf72+, salience network hyperconnectivity emerged about 30 years before onset and declined throughout the presymptomatic and symptomatic stages. For GRN+, connectivity changes as a function of disease age were found within the salience and nfvPPA networks, with connectivity stable until around 10 years before estimated onset, followed by decline starting in the late presymptomatic phase and continuing through the prodromal and symptomatic stages.ConclusionThese findings demonstrate that FTLD genes show distinct ICN trajectories throughout the clinical spectrum. These results help pave the way for determining the optimal timing to initiate therapeutic interventions.

AbstractBackgroundWe have previously shown that 3D T1‐weighted and 3D T2‐FLAIR brain MRI can potentially be re‐identified using face recognition, but this has not been tested for other MRI sequences commonly used in imaging studies of aging and dementia. Here we compare match rates of automated face recognition to re‐identify 3D T1‐weighted, 3D T2‐FLAIR, 3D T2‐weighted, 2D Axial T2‐FLAIR, multi‐echo GRE (T2*), diffusion MRI, functional MRI, ASL, and T2* GRE images.MethodWe used a dataset of 182 Mayo Clinic participants each with MRI and face photographs. MRIs used Siemens and GE vendor‐product sequences similar to ADNI3 and ADNI4 protocols. We created face reconstructions (“basic”, with minimal preprocessing, and “advanced” that automatically filled missing face regions using a template) from each MRI sequence, and we trained a Microsoft Azure PersonGroup face recognition classifier to recognize these MRI‐based faces. We then queried the classifier with five face photographs for each participant, and we recorded how often the classifier chose the correct MRI as the top‐ranked match. For image types with match rates > = 10%, we then repeated the experiment after de‐facing each MRI with mri_reface, our leading software that replaces faces in each image (“re‐faces”) with an average face.ResultResults are presented in Table 1. 3D T2‐FLAIR, 3D T1‐weighted, and 3D T2‐weighted images were matched at 96‐98%, and mri_reface reduced all to 8%. Axial T2‐FLAIR and ME‐GRE were matched at 32‐45%, and mri_reface reduced both to 1%. T2*‐GRE matched at 10%, and mri_reface reduced this to 1%. ASL matched at 8%, while dMRI and fMRI matched at only 0‐2%.ConclusionAmong current‐generation vendor‐product sequences, we recommend that de‐facing be applied before research data sharing of T1‐weighted, T2‐weighted, T2‐FLAIR, T2*, and ME‐GRE images. Minimal match rates for current‐generation product fMRI, ASL, and fMRI images suggest that they present low risk of re‐identification and can be shared without de‐facing, but this conclusion should be re‐evaluated if they are acquired without fat‐suppression, with a larger field of view, or if newer developments reduce the current levels of artifacts and distortion around the face.

AbstractBackgroundCo‐morbid Alzheimer’s disease (AD) pathology in the form of amyloid‐ß 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‐ß 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.

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.