Citations

AbstractBackgroundThere has been a recent proliferation of various quantities based on single shell Diffusion Tensor Imaging (DTI) for capturing overall cerebrovascular health, especially Small Vessel Disease (SVD) as a single number. The existing literature has gaps in the comparison of these measures, so we evaluated them as predictors of both current and future White Matter Hyperintensity (WMH) as a proxy of SVD.MethodUsing baseline 3T MRI examinations (T1, DTI, FLAIR scans) of 598 participants (>60 years, amyloid negative, to limit to those on the SVD pathway) from the Mayo Clinic Study of Aging, we calculated five summary DTI quantities with varying degree of complexity. We estimated their partial R2 from cross‐sectional linear regression models and computed semi‐partial R2 from mixed models predicting WMH volume as a percentage of Total Intracranial Volume (TIV). All models were adjusted for age, sex, and scanner model, and the WMH and based were log‐transformed before fitting. We also conducted subset analyses with high (WMH+) vs. low (WMH‐) baseline WMH load (cut at 1.62%) to compare the performance of the measures in early vs. widespread WMH.ResultAll MD‐based quantities computed on the whole brain, including FWF, performed similarly to each other, but Genu FA underperformed at explaining WMH variance (Table 1). PSMD (skeletonized or atlas based) was more sensitive in WMH+ but dramatically less so in WMH‐. In WMH‐ and overall cohort, FWF and JHU MD (which is a simple averaging of MD in the JHU atlas) performed well.ConclusionAll MD‐based measures performed similarly to each other in the whole dataset but dichotomizing by WMH load showed interesting differences. PSMD measurements are driven by the extreme end (95th percentile) of the MD distribution and therefore more sensitive in WMH+. Genu FA, a regional measure, was not useful for predicting whole brain WMH change. We noticed some variation in how the quantities are affected by scanner model (GE Signa HDxt and Discovery 750) which needs broader investigation. Simply averaging MD over WM avoids additional processing and is more sensitive to WM degeneration that is not (yet) severe.

AbstractBackgroundDementia with Lewy bodies (DLB) frequently co‐occurs with Alzheimer’s disease (AD) pathologies, exacerbating disease progression. Biophysical models of diffusion imaging data, such as Neurite Orientation Dispersion and Density Imaging (NODDI), may reveal novel insights into the neurobiological substrates of AD on cortical and white matter microstructural injury.MethodA cohort of 57 DLB patients on the DLB spectrum (33 clinically probable DLB and 14 prodromal DLB) and 57 cognitively unimpaired (CU) controls underwent NODDI and PET imaging with [18F]‐Flortaucipir and [11C]‐PiB (Table 1). NODDI parameters were derived from white matter tracts and grey matter regions using the JHU “Eve” and Mayo Clinic Adult Lifespan Template atlases, respectively. Conditional logistic models were used to compare these parameters between the two groups, accounting for the 1:1 matching on age and sex. The associations of tau and amyloid‐β with NODDI parameters across the white matter tracts were examined with linear regressions. A region‐by‐region approach was adopted to evaluate the relationships of tau and amyloid‐β with grey matter NODDI, adjusting for age, APOE, and local grey matter volumes.ResultDLB spectrum patients exhibited extensive white matter abnormalities relative to CU, including lower fractional anisotropy, higher mean diffusivity (MD), and lower tissue‐weighted neurite density (tNDI). In grey matter, these deficits were paralleled with higher MD and lower tNDI and ODI, primarily in the temporoparietal and posterior cortices. Regression models revealed significant associations of elevated [18F]‐Flortaucipir uptake with deficits in temporal and limbic white matter tracts and a temporal‐lobe pattern of higher MD and lower tNDI in the cortex (Figure 1). No associations were observed with [11C]‐PiB in the DLB spectrum.ConclusionThese findings demonstrated an in vivo relationship between tau deposition and neurite deficits that spatially aligned with the canonical AD “cortical signature” of neurodegeneration in both white matter tracts and grey matter regions in DLB. Our study underscores the detrimental impact of AD pathology in DLB and provides a rationale for further investigations into the underlying mechanisms of AD in DLB. Future longitudinal studies tracking the progression of these changes could inform therapeutic strategies for DLB in the presence of AD pathologies

AbstractBackgroundLate‐Onset Alzheimer’s Disease (LOAD) is characterized by genetic heterogeneity and there is no single model explaining the genetic mode of inheritance. To date, more than 70 genetic loci associated with AD have been identified but they explain only a small proportion of AD heritability. Structural variants (SVs) may explain some of the missing AD heritability, and specifically, their segregation in AD families has yet to be investigated.MethodWe analyzed WGS data from 197 NHW families (926 subjects, 58.5% affected) and 214 CH families (1,340 subjects, 59.17% affected). Manta, Absinthe, and MELT were used for large insertions/deletions calling from short‐read WGS, combined with Sniffles2 calls from 4 ONT‐sequenced genomes and an external SV call set from HGSVC on 32 PacBio‐sequenced genomes from the 1000 Genomes Project. Genotyping produced a unified project‐level VCF. We identified 45,251 insertions and 76,566 deletions genome‐wide. Variants were tested for segregation and pathogenicity using Annot‐SV, cadd‐SV, and Variant Effect Predictor. Segregation required SV presence in all affected family members and only in unaffected members five years younger than average disease onset.ResultWe identified 453 insertions and 598 deletions segregating in 78.68% and 87.31% of NHW families, respectively. In CH families, 432 insertions and 460 deletions were segregating in 75.23% and 72.90% of the families, respectively. Genes overlapping with the SVs exhibited high expression levels in brain tissues. Notably, around 93% of insertions and 76% of deletions segregating in NHW and CH families were less than 1 kilobase pair (1kbp) in length. A total of 79 insertions and 96 deletions were found to be segregating in both NHW and CH families. Interestingly, a segregating insertion was observed in CH families overlapping within the CACNA2D3 gene, which was previously reported in a CH GWAS for clinical AD. A deletion segregating in NHW overlapped with the PSEN1, and another in a CH family overlapped with the PTK2B gene.ConclusionOur findings suggested that there are several SVs associated with familial AD across CH and NHW families. Prioritizing the SVs based on their effects on gene function and expression will be helpful in understanding their contributions in AD.

AbstractBackgroundUnderstanding the relationship between AβPET and AβCSF biomarkers will define their potential utility in Aβ treatment. Few longitudinal or neuropathological comparisons have been reported. We assessed the relationship of AβPET and AβCSF biomarkers in a large community cohort.MethodParticipants 50+ years with coincident AβPET and AβCSF biomarkers (pTau181/Aβ42, n=505, and Aβ42/40, n=54) were included. Of these participants, a subgroup was identified that had neuropathology data (n=47) of which many had longitudinal AβPET and CSF testing. The relationships of AβPET and CSF biomarkers were assessed cross‐sectionally in all participants and longitudinally in those with autopsy data. Analyses were performed using AβPET Meta‐ROI and AβPET early‐Aβ‐deposition ROIs (ROIs with early Aβ deposition: fusiform, angular, inferior temporal, middle temporal, middle occipital, and calcarine).ResultIn biomarker tests performed at the same time, more participants were AβPET+ and CSF‐ than AβPET‐ and CSF+ using Meta‐ROI AβPET. This discordance occurred more frequently when using early‐Aβ‐deposition regions (Figure 1). Of 47 participants with autopsy data, 35 had Thal stage ≥1. The sensitivity for earliest detection of cortical Aβ neuropathology by AβPET was 89% (31/35) and by CSF was 66%, (23/35). Eight of these 35 had positive AβPET findings and negative coincident CSF tests at the first time tested. Of these, 5/8 were Meta‐ROI and early AβPET ROI positive. The other 3/8 were only positive on early‐Aβ AβPET ROIs. Four of the 8 with negative CSF and positive AβPET had subsequent testing and 2/4 became positive on CSF tests after 1.5 years while 2/4 continued to be negative on subsequent testing.ConclusionAβPET is more sensitive for detection of early cortical Aβ deposition than CSF biomarkers in cross‐sectional or autopsy data. While the assessment of AβPET by standard Meta‐ROI AβPET methods showed cortical Aβ deposition occurring when not indicated by CSF biomarkers, AβPET ROIs for early Aβ deposition detected additional participants that were AβPET positive and AβCSF negative. These findings have implications for detecting early Aβ neuropathology among CU participants in Aβ prevention trials when robust detection is needed. Further work is needed to compare Aβ plasma biomarkers and AβPET using these methods.

AbstractBackgroundCorticobasal syndrome (CBS) is a progressive neurodegenerative syndrome that can result from several underlying neuropathologies, including Alzheimer’s disease (AD). Little is known about the utility of blood plasma measures to predict PET biomarker confirmed AD in CBS.MethodEighteen CBS patients (8 PET Aβ+; 10 PET Aβ‐) and 8 cognitively unimpaired Aβ‐ individuals (CU) were recruited by the Neurodegenerative Research group from the Department of Neurology, Mayo Clinic, Rochester, MN, underwent Aβ (Pittsburgh Compound‐B) and tau (18F‐flortaucipir) PET and provided a blood sample. Blood plasma analysis was performed to assess p‐tau 181, total tau, Aβ 42/40, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) using the Quanterix Simoa platform. Plasma concentrations were compared between Aβ‐, and Aβ+ CBS and controls using Kruskal‐Wallis corrected for multiple comparisons using Dunn’s test, with effect size measured with area under the receiver operating characteristic curve (AUROC) analysis. Spearman correlations were calculated to assess relationships between plasma concentrations and Aβ and tau PET uptake.ResultThe CBS Aβ+ group showed reduced Aβ42/40 ratio, with elevated p‐tau 181, GFAP and NfL plasma concentrations compared to CU, while the CBS Aβ‐ group only showed elevated NfL concentration compared to CU (Figure 1). Both p‐tau 181 (AUROC=0.95) and GFAP (AUROC=0.91) plasma concentrations were able to differentiate CBS Aβ‐ from CBS Aβ+. Plasma p‐tau 181 and GFAP concentrations showed a positive correlation to both Aβ and tau PET uptake, and the Aβ42/40 ratio showed a negative correlation to Aβ PET uptake (Figure 2). No correlations were observed between NfL and Aβ and tau PET.ConclusionThis study supports the use of plasma p‐tau 181 and GFAP to detect AD in CBS, while NfL shows potential as a nonspecific disease biomarker of CBS regardless of underlying pathology. Plasma concentrations of these biomarkers may potentially be incorporated into clinical trials targeting CBS patients with underlying AD pathology.

AbstractBackgroundBlood plasma metrics provide excellent biomarkers of the presence of Alzheimer’s disease (AD) pathology in population cohorts. However, it is less clear whether plasma metrics correlate with PET measures of AD pathology in cohorts of patients with advanced AD disease, particularly in those with atypical clinical presentations of AD.MethodSeventy‐seven patients (visual variant=43, language variant=32, motor variant=1 and dysexecutive variant=1) with PET biomarker‐confirmed atypical AD were recruited by the Neurodegenerative Research group at Mayo Clinic, Rochester, MN, underwent Aβ (Pittsburgh Compound‐B) and tau (18F‐flortaucipir) PET and provided a blood sample. Blood plasma analysis was performed to assess p‐tau 181 and Aβ42/40 ratio concentrations using the Quanterix Simoa platform. Multivariate linear regression models assessed the relationship between plasma concentrations and both global Aβ, atypical AD summary and regional tau PET measures. For each PET measure, two separate models were fit to assess the influence of each plasma biomarker separately, while the third model included all plasma measures to assess the combined influence of all biomarkers on PET uptake. To investigate differences in plasma concentrations between atypical AD phenotypes, we performed Mann‐Whitney’s test across the visual and language variants of AD.ResultPlasma Aβ42/40 ratio showed positive associations with tau PET uptake, specifically within precuneus, parietal and temporal regions and was not associated with Aβ PET. There was no relationship between plasma p‐tau 181 and Aβ or tau PET uptake. However, the model that included both plasma biomarkers showed that with the addition of p‐tau 181, all significant associations between Aβ42/40 ratio and regional tau uptake were weakened to trends. The Aβ42/40 ratio continued to show no relationship with global Aβ‐PET uptake and plasma p‐tau 181 showed no relationship to PET measures (Figure 1). Lastly, no significant difference in plasma Aβ42/40 and p‐tau 181 levels was noted between the visual and language variants of AD (Figure 2).ConclusionThe lack of associations between plasma concentrations and PET measures of Aβ and tau in this relatively advanced AD cohort may suggest that plasma concentrations have greater utility as diagnostic biomarkers of early AD as compared to advanced AD.

AbstractBackgroundSeveral programs are available for automatically “de‐facing” brain images. These are designed to prevent the potential use of face imagery to re‐identify research participants. Previous works have compared available software and mri_reface is frequently among the top methods. The “Minimally Invasive DeFacing of MRI images” (MiDeFace) program was more recently added to FreeSurfer, but minimal validation of MiDeFace has been published.MethodWe ran mri_reface 0.3.3 and MiDeFace 7.3.2 on two datasets: A) 207 participants with face photographs and 3D T1‐weighted and 3D T2‐weighted‐FLAIR MRI from the Mayo Clinic Study of Aging (all unimpaired, Siemens); and B) 300 participants from ADNI3 with 3D T1‐weighted MRI (50 unimpaired + 50 with clinical AD, from each of 3 vendors). For dataset A, we tested the rate at which Microsoft Azure face recognition matched each participant’s photographs with the correct MRI, comparing unmodified images, those from mri_reface, and those from MiDeFace. For dataset B, we compared measured regional brain volume and cortical thickness from original vs. de‐faced images with SPM12, FreeSurfer 7.4.1, and the FSL‐UK Biobank pipeline. For both datasets, images were visually rated to assess removal of the face and retention of brain tissue.ResultAutomated face recognition match rates were lower with mri_reface (17/207 8.2%) than with MiDeFace (24/207 11.6%), indicating stronger identity protection with mri_reface, but both were greatly reduced vs. unmodified images (203/207 98.1%). Example images with both programs are shown in Figure 1. From visual ratings (Table 1), rates of complete success (modification of the entire face while retaining the entire brain) were much higher with mri_reface, but the great majority of failures from MiDeFace were subtle and occurred in relatively less recognizable face regions. The effects of both programs on downstream imaging measurements (Figure 2) were small and very comparable, but MiDeFace was favored mildly for SPM12 and for FSL, while mri_reface was more favored for FreeSurfer.ConclusionAltogether, mri_reface more reliably removed the complete face, leading to smaller rates of potential re‐identification (8.2% vs 11.6%). Effects on downstream image measurements were small and comparable with both methods.

Abstract Background The Mayo Clinic Study of Aging (MCSA) is a longitudinal, population‐based study of residents of Olmsted County, Minnesota, USA. MCSA is releasing de‐identified clinical and imaging data on GAAIN.org to benefit the research community. Method We included longitudinal clinical data from all participants of the Mayo Clinic Study of Aging 30‐90 years of age (average 70.0y) through the first 15 years of the study. Clinical data includes age, sex, self‐reported race/ethnicity, APoE4 allele status, cognitive impairment status (normal, MCI, dementia, or other), height, weight, blood pressure, multi‐domain neuropsychological scores, neuroimaging summary measurements, medications, cardiovascular risk factors, social activity, sleep, mood/anxiety, functional measures, and more. For our initial release of imaging data, we selected the first imaging timepoint acquired on 3T GE MRI scanners and amyloid (PiB) PET during the same visit, from all available imaging participants 50‐90 years of age (average 70.7y). Currently, we included 3D T1‐weighted (MPRAGE) MRI (1x1x1.2mm 3 ), T2‐weighted FLAIR MRI (0.85x0.85x3.6mm 3 ), diffusion MRI (2.7mm resolution with 41 directions at b=1000 and 5 b=0), and numeric cortical global SUVR and Centiloid values from PiB PET scans at 40‐60 minutes post‐injection. All images are released in DICOM format and thoroughly de‐identified using CTP while ensuring preservation of necessary fields for analyses. T1‐weighted and T2‐FLAIR MRI were also de‐faced (identifiable face imagery was removed and replaced with an average) using mri_reface version 0.3.3 to further protect participants’ privacy, and all de‐faced images were visually inspected by a trained data scientist for quality assurance. Result The first phase of our public data release includes clinical data from 5925 unique participants from the Mayo Clinic Study of Aging (MCSA), each ranging from 1‐12 visits at roughly 15‐month intervals, and three MRI sequences (T1‐w, T2‐w‐FLAIR, and diffusion) from 1802 unique participants. Demographic summaries are given in Table 1. Conclusion This large, richly characterized, de‐identified clinical and imaging dataset is available to the research community at https://www.gaaindata.org/partner/MCSA , and it is suitable for a wide range of research uses. Additional data will be released over time, including de‐faced amyloid PET images.

AbstractBackgroundWhite matter (WM) damage is seen with neurodegenerative and cerebrovascular pathologies and contributes to cognitive dysfunction. We hypothesized that there are multiple disease progression patterns in WM microstructural changes related to aging and dementia, and these can be identified using SuStaIn (data‐driven clustering algorithm for disease subtype and stage discovery) on multishell diffusion MRI (NODDI) data. NODDI provides information on cellular tissue architecture – neurite density index (NDI) measures packing density of axons and dendrites, and free water fraction (FWF) measures unrestricted water, e.g., interstitial and cerebrospinal fluid.MethodWe identified 1136 participants (mean age=70 years, 51% male, 40% Aβ‐positive, 19% MCI/AD dementia) from the Mayo ADRC and Mayo Clinic Study of Aging. We analyzed NDI and FWF in 55 WM regions. We selected a subset of WM features that monotonically changed with clinical disease progression and then applied the Z‐score SuStaIn algorithm.ResultNDI in seven regions and FWF in six regions survived selection criteria (Figure 1). Noteworthy, the NDI regions were predominantly located in the temporal lobe, while FWF regions were primarily distributed in the frontal and subcortical regions. The SuStaIn algorithm discovered two stable clusters: the NDI‐First (Temporal) and FWF‐First (Frontal) groups (Figure 2). For the NDI‐First group, temporal lobe NDI changed early followed by frontal FWF. The FWF‐First group had the reverse order. There was clear phase separation based on which measure changed first. Stages of the NDI‐First group correlated better with meta‐ROI Tau‐PET SUVR and with MMSE than the FWF‐First group (p<0.01), suggesting that NDI‐First may be indicative of a primarily AD pathway.ConclusionGiven NDI changes are likely degenerative and FWF are suggested to be other processes such as neurodegeneration or inflammatory, the observed changes in the temporal and frontal regions may be due to distinct etiologies, i.e., neurodegenerative in the temporal lobes vs. vascular dysfunction often observed in the frontal lobes. The SuStaIn algorithm showed promise in discovering disease subtypes of WM microstructural changes. Further work is needed to understand the mechanistic basis of these observed changes and integrate WM changes for differential etiology identification and disease staging.

AbstractBackgroundTau phosphorylated at threonine 217 (pTau217) is one of the most promising blood‐based biomarkers of Alzheimer’s disease (AD). This study compares the performance of two plasma pTau217 immunoassays on the Simoa platform for detection of abnormal amyloid‐PET.MethodPlasma samples from 112 individuals without cognitive impairment and 114 with mild cognitive impairment (MCI) or AD related dementia were evaluated. Testing was performed on the Quanterix Simoa® HD‐X Analyzer using the ALZpath Simoa® pTau217 and the Simoa® ACC pTau217 (Janssen antibodies) commercial assays. Amyloid‐PET imaging was performed using 11C‐Pittsburgh Compound B and a standard uptake value ratio (SUVR) cutoff of ≥1.52 (≥25 Centiloid) was used for abnormal (n= 147) vs. normal (n= 79) classification. Passing‐Bablok regression analysis and Spearman’s correlation coefficient (rho) were used to estimate bias and correlation between the assays, respectively. Correlation of each assay with Amyloid‐PET SUVR was evaluated. Amyloid‐PET status as the outcome and plasma pTau217 as predictor was used to estimate the area under the receiver operating characteristic (ROC) curve (AUC).ResultThe pTau217 concentrations between the two assays showed an excellent correlation (Spearman’s rho= 0.94 [95% CI= 0.93 to 0.96, p<0.0001]). However, the Passing‐Bablok regression analysis equation for ALZpath (x‐axis) and ACC (y‐axis) was y= 0.1324x ‐ 0.0019, showing that measured pTau217 concentrations with the ALZpath assay were higher (∼87%) than with the ACC assay (concentrations ranged 0.057 to 3.5 pg/mL and 0.003 to 0.5 pg/mL, respectively). Correlations between amyloid‐PET SUVR and the two pTau217 immunoassays were almost identical with Spearman’s rho= 0.70 (95% CI= 0.62 to 0.76, p<0.0001) for ALZpath and 0.69 (95% CI= 0.61 to 0.75, p<0.0001) for ACC. The AUC for detection of an abnormal amyloid‐PET among the MCI/AD group was 0.88 (95% CI= 0.81 to 0.95) for ALZpath, and 0.89 (95% CI= 0.83 to 0.96) for ACC.ConclusionAnalytically, the two Simoa® pTau217 assays correlate well with each other, but results are not interchangeable due to a significant proportional bias. Clinically, the performance of the assays was largely equivalent for the detection of abnormal amyloid‐PET by AUC analysis based on their almost identical correlations with amyloid‐PET SUVR.