Citations

AbstractBackgroundPosterior cortical atrophy (PCA) and dementia with Lewy bodies (DLB) show overlapping patterns of hypometabolism affecting posterior regions of the brain. Both syndromes show disruptions in functional connectivity, but it is unknown how patterns of disruption within and between networks compare between PCA and DLB.MethodThirty amyloid‐positive PCA, 27 amyloid‐positive DLB (DLB+), 24 amyloid‐negative DLB (DLB‐) patients and 30 cognitively normal amyloid‐negative (CN) individuals were recruited at Mayo Clinic, Rochester, MN, and underwent structural and resting state functional MRI. Spatially preprocessed data was analyzed using the CONN functional connectivity toolbox to explore nine networks: visual, sensorimotor, dorsal‐attention, cerebellar, frontoparietal, memory, language, salience, and default mode networks (DMN). Multivariate linear regression models corrected for multiple comparisons and adjusted for age and sex were fit to analyze within‐network connectivity (Fisher’s R‐to‐Z transformations). Between‐network connectivity was assessed with group level difference maps (two‐sample t‐test) corrected for false discovery rate at p<0.05.ResultMultivariate linear regression models showed reduced within‐network connectivity across all DMN components, sensorimotor, and dorsal‐attention networks, with trends for the visual network in PCA compared to CN. Both DLB‐ and DLB+ showed reduced within‐network connectivity for the cerebellar network, with DLB+ showing reduced connectivity in the posterior‐DMN, memory, and salience networks compared to CN. DLB‐ showed increased connectivity in dorsal‐attention and DMN, while DLB+ showing reduced connectivity in the memory network compared to PCA. Between‐network analysis showed an increase in cerebellar‐to‐sensorimotor connectivity in all three groups, with increase in cerebellar‐to‐dorsal‐attention connectivity in DLB‐ and PCA compared to CN. DLB‐ showed a decrease in posterior‐DMN‐to‐sensorimotor connectivity, while PCA showed reduced connectivity between all DMN components and dorsal‐attention‐to‐sensorimotor connectivity compared to CN. There was an increase in connectivity between all DMN components and a reduction in dorsal‐DMN‐to‐sensorimotor connectivity in both DLB‐ and DLB+ compared to PCA.ConclusionPCA was associated with connectivity disruptions across all DMN components, sensorimotor, dorsal‐attention and visual networks, while both DLB groups showed disruptions in cerebellar networks, with DLB+ showing further disruptions in posterior‐DMN, memory and salience networks. These findings suggest that both PCA and DLB have unique brain network disruptions, with amyloid status influencing connectivity in DLB.

AbstractBackgroundPatients with dementia with Lewy bodies (DLB) may have Alzheimer´s disease (AD) pathology. Plasma biomarkers of beta‐amyloid (Aβ), 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 Aβ40, Aβ42, p‐tau181, neurofilament light chain (NfL), and glial fibrillary acidic protein (GFAP) measures was obtained. We calculated the ratio of plasma Aβ40/42. Lower Aβ40/42 indicates increased Aβ pathology. Plasma Aβ40, Aβ42, 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 Aβ 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 Aβ40/42 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.

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.

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.

AbstractBackgroundAutomated face recognition can potentially re‐identify de‐identified research brain MRI, PET, and CT, and direct differences of brain measurements from original and de‐faced images are statistically significant but very minor, i.e. below scan‐rescan differences. Their effects on analyses correlating brain imaging biomarkers with clinical variables remain unknown.MethodWe sampled 3 age‐and‐sex‐matched groups (CU, MCI, and clinical AD) from Mayo Clinic aging studies, each with 61 participants with same‐visit 3D T1‐weighted and 3D T2‐FLAIR MRI, PiB amyloid PET, and Flortaucipir tau PET (total n = 183). We automatically measured hippocampal volume, PIB global SUVR, and tau temporal meta‐ROI SUVR using a) our in‐house pipeline with SPM12, MCALT, and ANTs, and b) FreeSurfer 7.3.2 (PETSurfer), and we measured voxels associated with clinical group separation using SPM12. We also measured WMH from FLAIR using a) our in‐house pipeline, b) Lesion Segmentation Tool (LST), and c) FreeSurfer’s Samseg. For each measurement, we calculated pair‐wise group separation AUC values and Spearman’s rho with age and CDR‐SOB. We then de‐faced all PET and MRI using mri_reface, our top‐performing automated de‐facing software that replaces imaged faces with an average face (“re‐facing”), and we repeated all measurements and analyses with de‐faced images. We then compared the magnitudes of clinical correlations with original images and those of de‐faced images.ResultAnalyses of original and de‐faced images had very high agreement. Only 2/55 paired comparisons were significant (Tables 1‐2): FreeSurfer‐measured global PIB SUVR from de‐faced images was more correlated with age (rho 0.003 vs ‐0.227, p = 0.046), and Mayo‐measured WMH volume from original images was more correlated with CDR‐SOB (rho 0.321 vs 0.308, p = 0.020). Another comparison‐pair tested p = 0.041 for an AUC change +0.008, which we consider not practically meaningful. All others had p>0.05. In voxel‐based analyses of CU vs AD participants, all comparisons were qualitatively similar between original and de‐faced variants (Figure 1).ConclusionDe‐facing PET and MRI using mri_reface did not significantly affect 53/55 correlations between imaging and clinical variables; of the two affected, one became stronger with re‐faced images. Stronger correlations with clinical variables may be explained by de‐facing’s standardizing face voxels and artifacts that irrelevantly affect brain segmentations.

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.

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.

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.