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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.

AbstractBackgroundTransactive response DNA‐binding protein of 43 kDa (TDP‐43) is implicated in numerous neurodegenerative diseases, including Alzheimer’s disease (AD). Immunohistochemically stained TDP‐43 is linked to faster rates of hippocampal atrophy (HA) in AD. However, it is unclear whether phosphorylated TDP‐43 (pTDP‐43) measured using an immunoassay is also associated with HA. We explored associations between pTDP‐43 levels, TDP‐43 immunohistochemistry, and annual rate of HA in AD. We hypothesized that higher pTDP‐43 levels would be associated with positive TDP‐43 immunohistochemistry, and faster rates of HA.MethodThirty‐seven cognitively impaired participants from the Mayo Clinic Alzheimer’s Disease Research Center underwent serial brain MRIs and brain autopsy. Hippocampal TDP‐43 was detected histochemically, and pTDP‐43 was quantified using biochemical assay. Hippocampal volumes were measured using the longitudinal pipeline in FreeSurfer‐7 and annualized HA rates were calculated. Linear regression analyzed the relationship between HA and pTDP‐43 biochemical assay levels. pTDP‐43 was categorized as positive (≥400) or negative (<400), and logistic regression tested the association of these levels with HA rates. The correlation between TDP‐43 immunohistochemistry status and biochemical assay levels was evaluated using descriptive statistics. All analyses were adjusted for potential confounders.ResultsThe mean age at death was 83.5 (±9.9) years with 49% females. Median scan interval was 3‐years. A negative relationship was observed between age at death and annual HA rate (β=‐3.1; 95% CI [‐5.3,‐0.9]; p=0.007), and a positive relationship was observed between pTDP‐43 level and annual HA rate (β=0.02; 95% CI [0.003,0.03]; p=0.015). A one‐unit increase in HA rate was associated with a 3% increase in the odds of being pTDP‐43 positive (≥400) (odds ratio=1.03; CI [1.007,1.05]; p=0.014). Cases that tested positive for Immunohistochemistry TDP‐43 had significantly higher mean pTDP‐43 immunoassay levels (1508 ± 2072 vs. 202 ± 131; p=0.008). More cases with positive IHC TDP‐43 surpassed the pTDP‐43 immunoassay cut‐off level of 400 vs the negative participants (p=0.003).ConclusionHigher pTDP‐43 levels in the hippocampus are linked to faster HA in AD and correlated with immunohistochemistry TDP‐43 positivity. These findings underscore the role of TDP‐43 in AD neurodegeneration. The biological and clinical implications of these observations necessitate further exploration in subsequent studies.

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.

AbstractBackgroundAlzheimer’s disease (AD) patients have decline in cognitive domains including memory, language, visuospatial, and/or executive function and brain pathology including amyloid‐β and tau deposition, neurodegeneration, and frequent vascular co‐pathologies detectable by neuroimaging and/or cerebrospinal fluid biomarkers. However, molecular disease mechanisms are complex and heterogeneous. It is necessary to develop cost‐effective blood‐based biomarkers reflecting brain molecular perturbations in AD. We identified blood‐based gene and co‐expression network level changes associated with AD/mild cognitive impairment (MCI) diagnosis and AD‐related phenotypes.MethodWe performed differential gene expression and weighted gene co‐expression network analysis, followed by meta‐analysis, using blood transcriptome data of 391 participants from the Mayo Clinic Study of Aging and 654 participants from the Alzheimer's Disease Neuroimaging Initiative. The neuroimaging phenotypes include microhemorrhages, infarcts, amyloid burden, hippocampal volume, and white matter hyperintensities. The cognitive phenotypes include standardized cognitive subtest scores and composite scores for memory, language, visuospatial, and executive function.ResultFive out of 18 modules(M) are significantly associated with diagnosis or cognition (FDR‐adjusted p<0.05). M1 and M15 both positively associates with memory, M1 positively associated with language and M15 with visuospatial function. M1 and M15 are enriched in differentially expressed genes (DEGs) associated with language and executive function, respectively. M2 negatively associates with logical memory delayed recall scores(LMDR), memory, executive, and language functions and is enriched in DEGs for these phenotypes. M8 negatively associates with memory, language and executive functions and is enriched in DEGs for memory and language. M12 positively associates with LMDR. M1 and M15 are down‐regulated while M2 and M8 are up‐regulated in AD/MCI patients. Cell‐type enrichment analysis showed M2 is enriched in monocytes and neutrophils; M8 in monocytes; M15 in B cells (FDR <0.05). Gene ontology terms enriched in these modules indicated broad consistency with their cell types.ConclusionWe identified five modules significantly associated with AD/MCI or cognitive phenotypes using blood transcriptome data. These findings nominate blood transcriptome changes and their enriched biological processes as potential pathomechanisms in cognitive decline and AD/MCI development. We aim to investigate these blood transcripts as potential biomarkers for AD or AD‐related phenotypes and therapeutic targets through additional replication and experimental validation studies.

AbstractBackgroundScalable, efficient methods are needed to enroll diverse populations of older adults into AD observational studies and clinical trials. We evaluated preliminary feasibility of a novel, digital, culturally informed approach to recruit and screen participants for the Alzheimer’s Disease Neuroimaging Initiative (ADNI4).MethodsDigital advertising tailored towards Black/African American and Latinx older adults residing near six clinical ADNI sites directed potential participants to a recruitment website (Figure 1). They completed digital surveys of demographics, ADNI exclusion criteria, memory concerns and changes, self‐report cognitive impairment, and the Everyday Cognition Scale (ECog)‐12 item. Participants also completed Novoic Storyteller, a self‐administered speech‐based cognitive test. Digital assessment results were used to prioritize those from minoritized ethnocultural groups, with low educational attainment, and possible Mild Cognitive Impairment (MCI) for remote blood draw at local Quest phlebotomy centers to obtain AD plasma biomarkers. Completion rates and digital assessment performance were compared between minoritized and non‐minoritized groups.ResultsOf 989 participants recruited from digital advertising efforts who provided contact information, 230 (23%) signed consent, and 183 (19%) completed at least one remote assessment (Table 1). 45% were from minoritized ethnocultural groups, 4% had ≤12 years of education, and 53% had evidence of possible MCI from digital assessments. 34 (19%) were referred to the plasma biomarker study, of which 21% completed blood draw. Compared to non‐minoritized participants, minoritized participants were less likely to report a diagnosed cognitive impairment or a family history of AD, less likely to have ECog scores indicative of MCI, and less likely to have a study partner. Minoritized participants had lower ECog and Novoic Storyteller completion rates.ConclusionRecruitment and assessment of a diverse cohort of older adults, including those with possible cognitive impairment, is feasible using culturally informed digital advertising. Improving study engagement and achieving educational diversity are key challenges. This approach is now being scaled up to facilitate recruitment into in‐clinic ADNI4, with the goal of enrolling 500 new participants: >50% from minoritized groups, 40% with MCI, and 80% amyloid positive across diagnostic groups. This approach can be adapted to facilitate recruitment and longitudinal assessment in other AD studies and trials.

AbstractBackgroundChronic medical conditions increase dementia risk, and the mechanism may be captured computationally by measures of accelerated brain aging. We hypothesized that quantifying the impact of chronic conditions on GM and WM aging will aid in measuring the differential impact of each condition on brain aging and shed light on the mechanisms through which they increase dementia risk. We specifically focused on microstructural brain changes with Neurite Orientation Dispersion and Density Imaging (NODDI) based on advanced diffusion MRI ‐ neurite density index (NDI), free water fraction (FWF), and orientation dispersion index (ODI).MethodWe identified 1123 participants [913 cognitively unimpaired (CU), 114 with MCI, 96 with AD] from the Mayo Clinic Study of Aging (MCSA) and Mayo ADRC with multi‐shell diffusion MRI. MCSA participants had chronic medical conditions’ diagnosis (based on US Dept of HHS) extracted from the electronic health care records. Using the BorutaSHAP algorithm on NDI, ODI, and ISOVF features from GM and WM separately, we constructed two distinct brain age models for GM and WM. First, we evaluated the usefulness of the predicted age gap (PAG), which is the difference between predicted age by model and chronological age, for both WM and GM NODDI models in CU/MCI/AD. We then developed separate GM and WM models for each condition using samples from condition‐negative participants to compare PAG of GM and WM for that condition.ResultThere was greater PAG in GM (avg = ) than WM (avg = 9.15 yrs) in MCI/AD participants (Fig. 1A). Each condition shifted the GM PAG by 0.10 to 3.56 years and WM PAG by 0.38 to 3.72 years with stroke and congestive heart failure showing the greater PAG overall for GM and WM PAD respectively (Fig. 2). COPD, stroke, chronic kidney disease, hypertension had comparatively greater GM PAG than WM PAG; while hyperlipidemia, congestive heart failure and coronary artery disease had greater WM PAG than GM PAG (Fig. 1B).ConclusionChronic conditions are associated with increased GM and WM Brain aging models can aid in quantifying the impact of chronic conditions on dementia risk and understanding mechanisms.

AbstractBackgroundThe higher prevalence and incidence of later life dementia among older Black Americans compared to older White Americans is incompletely understood and understudied. HATS is designed as a companion to the Mayo Clinic Study of Aging (MCSA) with a focus on identification of modifiable cardiovascular contributions to brain health among Black older adults living in urban areas in the upper Midwest.MethodHATS is enrolling 300 U.S. Black Minnesotans from the Twin Cities area aged 55 to 90 years without advanced dementia. Enrollment started 4/2023 and is expected to be complete by the end of 2025. Recruitment is primarily community‐based, led by two community engagement partners, the Linc and the Hue‐MAN Partnership. Electronic health record‐based recruitment will begin in 2024. Data collection includes medical comorbidities, structural and social determinants of health, cardiovascular health, functional status, physical performance, specific and non‐specific blood‐based biomarkers of Alzheimer’s disease, and comprehensive assessment of cognitive and neuropsychiatric status. Brain MRIs will be obtained on a subset of participants. A core component of HATS is distribution of meaningful health information back to participants and their community, guided by input from the HATS community engagement partners. The HATS cohort will serve as an initial effort to develop age and education adjusted normative cognitive test data for urban Black older adults in the upper Midwest.ResultAs of January 22 2024, HATS has enrolled 40 participants with a mean age of 70 years (range 51‐90) and mean education of 15 years. Mean informant‐reported Everyday Cognition scale‐12 score is 1.1 (range 1‐1.7), mean Short Test of Mental Status (STMS) score is 31 (range 24‐37), and CDR sum of boxes scores range from 0‐4 consistent with enrollment of participants with normal and mildly impaired cognition.ConclusionHATS is a novel longitudinal cohort study that is well‐positioned to advance understanding of risk factors for dementia among Black Americans living in urban areas in the upper Midwest. Recruitment is ongoing with a focus on enrolling older adults with a wide range of cognitive function. Ongoing community engagement is a central feature of HATS.

AbstractBackgroundCortical structural changes in the temporal lobes including global and hippocampal volume loss and decrease in cortical thickness are well‐associated findings linked to cognitive decline in MCI and dementia patients. Neurite orientation dispersion and density imaging (NODDI) is a multishell diffusion MRI method enabling assessment of tissue microstructural integrity at the axonal and dendritic level. We wished to determine whether changes in temporal cortex orientation dispersion index (ODI) & neurite density index (NDI) correlate with decreases in memory function.MethodThe MCALT_ADIR122 cortical atlas was used for ROI designation. NODDI imaging was obtained on a 3T Siemens Prisma scanner with 2 mm isotropic resolution. Participants (ages 60‐95 years) were cognitively unimpaired (CU) or had mild cognitive impairment (MCI). CU & MCI subjects were enrolled in the Mayo Clinic Study of Aging (MCSA) and underwent neuropsychological testing including 3 memory tests from which a composite memory z score was calculated. A generalized additive regression model was used to estimate z scores as a smooth function of ODI, NDI and cortical thickness or volume with adjustment for age, gender, education, MCSA cycle number, WMH and intracranial volume.ResultThe Table displays demographic data. Figures 1 and 2 show the relationship of NDI and ODI measures to memory z scores across participants in individual ROIs both before and after adjustment for cortical thickness (“structural MRI”) as well as for confounds listed above. Both higher cortical thickness and higher NDI values in the hippocampus and entorhinal cortex were associated with significantly higher memory z‐scores (p<0.001) (Figure 1). Higher ODI values in addition to greater cortical thickness in temporal lobe regions, including within the hippocampus, parahippocampal gyrus, entorhinal cortex and inferior temporal gyrus, were associated with higher memory z‐scores (Figure 2). These significant associations (p <=0.01) were maintained after adjusting for cortical thickness in all these regions except entorhinal cortex for ODI.ConclusionNODDI‐derived measures of microstructural integrity (NDI & ODI) within the temporal cortex may provide information about memory decline, beyond what cortical thickness analysis provides, and may be potentially useful for monitoring therapeutic effects of AD immunotherapy in the future.

AbstractBackgroundResearch subjects can potentially be re‐identified from de‐identified MRI, CT, and PET brain scans with up to 98% accuracy using Microsoft Azure’s cloud‐based commercial facial recognition software. This showed the need to “de‐face” publicly shared research brain scans. Subsequently, Microsoft has begun restricting its face recognition services, intending to prevent misuse. This study tests a variety of popular open‐source computer vision and facial recognition software packages to measure the re‐identification risk with only unrestricted free software.MethodThis study used brain MRI from Siemens 3T scanners and face photographs from 182 participants (ages 30‐90+) in the Mayo Clinic Study of Aging. Each participant had photos taken with an iPad, which were cropped loosely around the head and converted to black and white. A total of 81 renders (2D reconstruction images; Figure 1) were made from each 3D T2‐FLAIR MRI, simulating various camera and lighting positions. These face renders approximate black and white photos, allowing use with software packages developed for matching faces/features between photographs. Open‐source Python packages (OpenCV, DeepFace, face_recognition, scikit‐learn) and their included pre‐trained models (designed for photographs) were used to match participant photographs to the labelled library of MRI‐based face renders. A correct match was recorded when the top‐scoring render or set of renders automatically chosen by the algorithm was from the MRI of the correct participant.ResultFeature matching with ORB and SWIFT (both from OpenCV) produced no correct matches, despite a prior publication reporting moderate success. The best match rates (Figure 2) were 58‐59% accuracy using face_recognition or DeepFace packages with support vector machines (SVMs). Without SVMs, these packages performed modestly worse (51%‐55%).ConclusionWhile the highest match rates obtained by widely available open‐source packages (roughly 60%) are much lower than those of a limited‐access commercial product (Microsoft Azure, with 98%), they show that actors with only access to no‐cost readily available software could still re‐identify a research subject from a brain scan most of the time. This further highlights the need for image refacing software, such as mri_reface, to protect the privacy and identifies of research volunteers.

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.