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Abstract Background White matter hyperintensity topography, particularly posterior confluent and subcortical multispot (WMH‐MS), has been implicated as surrogate imaging biomarker of cerebral amyloid angiopathy (CAA). Usefulness of WMH‐MS for prediction of future cerebral microbleeds (CMB's) has significant clinical implications for anti‐amyloid therapy risk assessment. Our primary goal was to identify the prevalence of positive WMH‐MS status and their utility for predicting the incident CMBs in a population‐based sample. Our secondary goal was to evaluate its usefulness after accounting for amyloid (surrogate for CAA) and WMH burden (surrogate of small vessel disease) which are both risk factors for CMBs. Method We identified 651 participants (mean age (SD) 69.8 (10.0) years, 55% male) from the Mayo Clinic Study of Aging, who had 3T baseline whole brain fluid‐attenuated inversion recovery (FLAIR) MRI and at least two T2*‐weighted gradient recalled echo (GRE) sequences (concomitant with or after FLAIR) which were used to ascertain CMBs. Amyloid positron emission tomography (PiB‐PET) scans were available in 87% of the sample. Visual reads on FLAIR scans were performed using a 4‐point Fazekas rating scale of periventricular and deep WMH (positivity: Fazekas ≥ 2) and a 3‐point MS severity visual scale (positivity: nWMH‐MS > 10 circular or ovoid WMH spots, Figure 1) according to established criteria. Using univariate and multivariable logistic regression, we assessed the independent association of WMH‐MS > 10 and Fazekas ≥ 2 for prediction of incident CMB's. Result The frequency of WMH‐MS pattern was greater in those who developed an incident CMB compared to those who did not develop a CMB (20.8% vs. 2.8%, p < 0.001). Older age and male sex were predictive of incident CMBs (Figure 2A). In univariate models, presence of WMH‐MS pattern but not Fazekas was associated with incident CMBs after adjusting for age and sex. The associations remained significant after additional adjustment for APOE4 , hypertension, number of baseline CMBs (Prior CMB), and amyloid effects (Figure 2B). Conclusion Visually identified WMH‐MS patterns on FLAIR‐MRI were predictive of incident CMBs in this population and may be useful markers of CAA. Future studies evaluating its utility as an additional biomarker of amyloid related imaging abnormalities in clinical trials are necessary.

Abstract Background Cognitive resilience to Alzheimer's disease (AD) pathology has been widely studied, but distinguishing resilient individuals from those with typical tau pathologies remains challenging due to the overlap in imaging and plasma biomarker profiles. We hypothesize that our machine learning‐based (ML) tau heterogeneity score, THETA (JNUMED, July 2024), could serve as an effective marker of resilience and provide a better estimation of tau's temporal progression. Method Longitudinal imaging data from the Mayo Clinic Study of Aging (MCSA, n = 1834) and ADRC ( n = 246) were analyzed. Participants were categorized into typical AD and resilient groups using amyloid PiB‐PET (abnormal PiB‐PET≥25 Centiloid units), Flortaucipir (FTP‐PET) (meta‐ROI≥1.29 SUVr, ERC≥1.27 SUVr) and Mini‐Mental State Examination (MMSE). Typical AD was defined as abnormal PiB‐PET and abnormal FTP‐PET for both biomarkers (Figure 1). Resilience to AD pathology was determined by predicted values of MMSE >28 from a linear mixed effects model for cognitively unimpaired participants. In addition, a resistant group was broadly defined as having normal PiB‐PET. To capture the spatial heterogeneity of tau distribution in the brain, regional THETA scores were generated using ML model and visual ratings. Additionally, a progression model, Sampled Iterative Local Approximation (SILA), was fitted using AD and plasma biomarkers ( p ‐tau181 and GFAP) for validation. Result THETA identified regions of reduced tau uptake in the frontal, parietal, temporal, occipital, and limbic regions when compared to the typical AD group (Figure 2A). This was confirmed by the preserved cortical thickness in these same regions (Figure 2D). In addition, SILA showed THETA can distinguish global tau temporal progressions of typical AD, resilient, and resistant groups compared to the other biomarkers (Figure 3). Interestingly, the onset age for GFAP was younger for resilient and typical AD, indicating earlier onset of inflammation in the presence of pathology. Conclusion The significant regions identified by THETA, showing reduced tau and preserved cortical thickness, combined with the clear offset in progression models between resilient and typical AD, suggest that THETA may help uncover pathological tau mechanisms that differentiate these groups. These findings highlight the potential of THETA to provide valuable insights into resilience mechanisms and enhance our understanding of AD.

Abstract Background Use of software to “de‐face” research brain images has grown as advances in automated face recognition have made it increasingly possible to re‐identify participants. Imaging studies of Alzheimer's Disease and related diseases have included more participants from historically underrepresented groups, but de‐facing software have primarily been validated using images from mostly non‐Hispanic White participants, and its performance has not been directly compared across ethnoracial groups. Method From The Health and Aging Brain Study: Health Disparities (HABS‐HD) study, we constructed a sample of 305 T1‐weighted MRI from HABS‐HD baseline visits, matched across three ethnoracial groups (non‐Hispanic White (NHW, n = 104), non‐Hispanic African American (AA, n = 104), Mexican American (MA, n = 97)) according to age‐decade, sex, cognitive status (impaired/unimpaired), and MRI scanner model. We ran mri_reface 0.3.5 on each image, and we ran FreeSurfer 7.4.1 on each unmodified image and on each de‐faced output. We then compared regional gray matter (GM) volume and cortical thickness measurements by subtracting each de‐faced image measurement from the corresponding unmodified image measurement. We compared these differences between de‐faced and unmodified images across the three matched groups using GLM analyses adjusted for age, sex, and cognitive status, and Bonferroni corrected. Result When aggregating across all brain regions, differences in cortical thickness measures due to de‐facing were significantly different between the NHW and MA groups, and between AA and MA, but not between NHW and AA (Figure 1). Differences between these group means were <0.005mm. For GM volume, differences were significant between AA and MA (magnitude < 0.2%), but not between NHW and AA or NHW and MA (Figure 2). Examining each region individually (Figure 3), cortical thickness differences were significant for only 4 regions (all with magnitudes <0.03mm), and GM volume differences were significant for only 6 regions (all with magnitudes <2%). Conclusion Effects of mri_reface on FreeSurfer brain measurements were significantly different across ethnoracial groups. Our future work will optimize mri_reface to eliminate these differences. However, the magnitudes of these differences are sufficiently small that they are unlikely to have a practical effect on analyses of data using current versions.

Abstract Background Individuals with coexisting Alzheimer's disease (AD) and disorders of CSF dynamics such as normal‐pressure hydrocephalus (NPH) have lower CSF Aβ42 levels, which can confound interpretation of AD CSF‐biomarkers. However, little is known about how CSF dynamics influences plasma AD biomarkers. The objective of this study was to determine whether mass spectrometry‐based blood tests for AD are associated with disproportionately enlarged subarachnoid space hydrocephalus (DESH) on MRI. Method 509 Individuals from the Mayo Clinic Study of Aging, with MRI, 11C‐Pittsburgh compound B (Aβ) PET scans, and plasma phosphorylated‐tau protein 217, Aβ40 and Aβ42 were included. We performed an age‐adjusted logistic regression to determine whether abnormal values of p ‐Tau 217 (Abnormal if >1.55 pg/mL), %p‐tau217 defined as the ratio of phosphorylated Tau217 to non‐phosphorylated tau x 100% (Abnormal if >1.55%), Aβ42 (Abnormal if <41.5 pg/mL), and Aβ42/40 ratio (Abnormal if <0.094) were associated with the presence of DESH, as classified by a previously validated automated algorithm. Result Thirty‐two participants were classified as DESH‐positive. The median [IQR] age was 76.8 yrs [70.6, 82.9] for participants with DESH and 72.4 [59.3, 85.5] without DESH (Table 1). An abnormal plasma Aβ42 was associated with DESH (OR, 2.56 [1.19, 5.74], p = 0.018), but having an abnormal Aβ42/40 ratio (OR 2.01 [0.94, 4.56], p = 0.079) was not. There was no association between being DESH‐positive and an abnormal plasma p ‐tau217 (OR 1.30 [0.51, 3.09], p = 0.560) or with abnormal %plasma p ‐tau217 (OR 1.82 [0.74, 4.20], p = 0.17) (Figure 1). Subgroup analysis showed that only those participants who had an abnormal Aβ42 and positive amyloid PET (≥25 Centiloids) showed an association with DESH compared to biomarker‐ and PET‐negative participants (OR 3.10 [1.15, 8.43], p = 0.024 ) . Conclusion Understanding how CSF dynamics affect plasma biomarkers is critical for implementation. The relationship between lower Aβ42 and DESH is expected based on prior CSF studies, which can be addressed by using an amyloid ratio. p ‐tau217 shows robustness against DESH, making it a suitable biomarker in cases of abnormal cerebrospinal fluid dynamics although validation in a more severe CSF dynamics group is warranted.

Abstract Background Brain age models are increasingly being used as measures of brain health and neurodegeneration. These models can be deep learning (DL) based and/or classical methods and provide a brain age gap (BAG) which is the difference between chronological and predicted brain age. Our goal was to develop DL‐based scanner invariant brain age models that can be applied in aging and dementia studies even when there are scanner changes that may hinder longitudinal follow‐up. Method We identified 3374 cognitively unimpaired (CU) participants with T1 scans from the Mayo Clinic Study of Aging (MCSA). A subset of 863 CU amyloidosis negative (PiB‐PET cutoff < 25 Centiloid) and no history of diabetes and hypertension was selected (CU‐noVAβ). We built two models – Model‐A trained on CU‐noVAβ, and Model‐B trained on all CU. We included 453 MCSA MCI participants as test set for both models. We used 113 participants (74 CU, 28 MCI, 10 dementia) from MCSA and Mayo ADRC with scans from two different vendors (scanned within a week of each other) for testing cross‐vendor/scanner differences (cross‐vendor data). The two unique features of our model included histogram matching and scanner type as an input to the DenseNet model for training (Figure 1). Result In the data, the inclusion of scanner as an input decreased the prediction of mean age differences between the scanners (Model‐A=2.17 years; Model‐B=1.71 years) (Figure 2). The mean absolute error (MAE=absolute value of BAG) for Model‐A in MCI was 5.72 years and Model‐B in MCI was 4.06 years, consistent across scanners (Figure 3). Longitudinal prediction increased with age (Model‐A slope=0.53; Model‐B slope=0.63). Conclusion We found that training on all CU (Model B) rather than a subset CU‐noVAβ (Model A) allowed for the DenseNet model to backpropagate on more variable data which lowered MAE in prediction error between scanners and lowered MAE for MCIs. Histogram matching and scanner as an input to the brain age model accounted for scanner changes and resulted in scanner invariant longitudinal brain age estimation.

Abstract Background Chronic traumatic encephalopathy (CTE) is a neurodegenerative tauopathy associated with repetitive head impacts (RHI). Autopsy studies show that RHI influences risk for other later‐life neuropathologies, particularly white matter and vascular injury. We have observed a unique pattern of white matter hyperintensities (WMH) on FLAIR MRI in people exposed to RHI (RHI‐WMH) characterized by small, discrete lesions near the depths of sulci (Figure 1a). The objective of this study was to characterize this pattern empirically in former American football players and to investigate neuropathology co‐localizing with the WMH. Method The sample included 164 male former football players ( n = 111 professional, n = 53 college) and 55 asymptomatic males without RHI from the DIAGNOSE CTE Project (Table 1). Visual ratings of WMH blind to the participant's RHI and clinical status were conducted to count lesions that were a.) within 1.0cm of the gray/white matter boundary, b.) as bright as the cortex, c.) spherical and d.) <1.0cm in size. ANCOVA compared groups on total number of WMH adjusted for age and vascular risk factors. We selected one individual (former professional player) with the typical RHI‐WMH pattern and who donated their brain for neuropathological examination. Guided by in vivo MRI, ex vivo MRI was done to locate the WMH near the depths of the sulci in the tissue. Tissue sections were tested for phosphorylated tau immunoreactivity and reactive astrocytes and microglia in areas surrounding the lesion seen on MRI. Result Number of RHI‐WMH was greater in football players compared to controls ( p = 0.007) (Figure 1b). Number of RHI‐WMH was greater in former professional players compared to controls ( p = 0.001) but not compared to the college players ( p = 0.983). In the neuropathology case study, we located the punctate RHI‐WMH on ex vivo MRI. Immunostaining revealed that the RHI‐WMH was adjacent to a sulcus with the pathognomonic CTE p ‐tau lesion and was characterized by myelin pallor, dense GFAP‐positive glial scarring, and increased total and activated microglia (Figure 2). Conclusion We provide empirical support for exposure to RHI as potentially leading to a unique pattern of WMH. Importantly, histopathological evidence showed that the RHI‐WMH co‐localized with RHI and CTE‐related neuropathologies at the depths of the sulci.

Abstract Background Neuropathologic evaluation is essential for diagnosing and treating dementia, but definitive diagnoses is only available after post‐mortem brain autopsy. Positron emission tomography (PET) offers a potential in vivo proxy for predicting neuropathology. This study proposes a semi‐supervised learning (SSL) model using a single FDG‐PET image to predict underlying neuropathology. Method The SSL model leverages a self‐organized graph structure based on a patient similarity network (PSN) created from 1,495 FDG‐PET images across the aging and neurodegenerative spectrum. Each patient is represented by a node, with edges indicating image similarity derived from principal component analysis features and cosine similarity weights. The PSN structure connects 20 nearest neighbors. Neuropathologic labels were available for 204 autopsied patients. The Poisson Learning Method was employed to propagate labels from these annotated nodes to unlabeled nodes, guided by the PSN structure. FDG‐PET images were collected from clinical and research participants at the Mayo Clinic and processed using an MRI‐free pipeline. Neuropathologic labels were assigned based on widely accepted diagnostic criteria. Leave‐one‐out cross‐validation (LOOCV) was used to assess model performance. Results The confusion matrix demonstrated high accuracy in predicting Alzheimer's disease, frontotemporal lobar degeneration with tau inclusions, and Lewy body disease pathology. A Sankey diagram and label distribution analysis revealed strong correlations between clinical and predicted neuropathologic labels. Conclusion This study highlights the potential of a graph‐based SSL pipeline to predict neuropathological changes using FDG‐PET imaging data, despite limited pathology labels. The findings underscore the value of SSL in bridging the gap between abundant neuroimaging data and scarce neuropathologic labels, contributing to improved dementia diagnosis and understanding.

Abstract Background Multi‐site aging and dementia studies are challenged by data heterogeneity caused by scanner (manufacturer and protocol) related variability. Quantitative analysis of white matter hyperintensity (WMH) from pooled samples, a traditional marker of cerebrovascular disease, is sensitive to machine variability. Our goal was to compare Deming (symmetric) linear regression and ComBat, a common method used for harmonizing neuroimaging measurements, for harmonizing WMH measurements on participants scanned on two different scanners (GE and Siemens) and protocols (2D FLAIR on GE and 3D FLAIR on Siemens). Methods Cognitively unimpaired (CU) ( n =53, age (mean (SD))=67 (15) years) and cognitively impaired (CI; MCI/dementia) ( n =34, age (mean (SD))=82 (9) years) participants from the Mayo Clinic Study of Aging and Mayo ADRC were scanned on GE and Siemens scanners within a week of each other (typically done on the same day). WMH as a percentage of total intracranial volume was measured using multispectral segmentation of FLAIR and T1‐weighted images (Gunter et. al. AAIC 2023). We used Deming regression as it includes errors in both input dimensions. ComBat was applied using age, sex, and diagnosis as covariates, with scanner vendor and protocol as the batch variable. We assessed the inter‐scanner similarity using Maximum Mean Discrepancy (MMD) analysis, where a value of zero indicates identical distributions, p >0.05 indicates no significant difference under H 0 . (Gretton et al., JMLR 2012). Results Our results showed that the distribution of WMH measurements, which were different in the CU group, were more similar after Deming than ComBat harmonization (Figure 1). This was confirmed by the lower MMD in the CU group from Deming regression (‐0.01 ( p =0.76) from 0.03 ( p =0.05)) compared to ComBat (0.04 ( p =0.02))(Figure 1). However, ComBat and Deming were unable to create distributions as compatible as original measurements for the CI group. Conclusion Using a unique dataset of the same participants scanned close in time on different scanner and protocols, we found that (1) MMD measurements were useful to test the need/improvement due to harmonization; (2) ComBat was not substantially better at creating compatible distributions between scanner vendors than Deming regression, and both were sometimes inadequate. Future work will require alternative models to linear scale and shift transformations.

Abstract Background Use of software to “de‐face” research brain images has grown as advances in automated face recognition have made it increasingly possible to re‐identify participants. Imaging studies of Alzheimer’s Disease and related diseases have included more participants from historically underrepresented groups, but de‐facing software have primarily been validated using images from mostly non‐Hispanic White participants, and its performance has not been directly compared across ethnoracial groups. Method From The Health and Aging Brain Study: Health Disparities (HABS‐HD) study, we constructed a sample of 305 T1‐weighted MRI from HABS‐HD baseline visits, matched across three ethnoracial groups (non‐Hispanic White (NHW, n = 104), non‐Hispanic African American (AA, n = 104), Mexican American (MA, n = 97)) according to age‐decade, sex, cognitive status (impaired/unimpaired), and MRI scanner model. We ran mri_reface 0.3.5 on each image, and we ran FreeSurfer 7.4.1 on each unmodified image and on each de‐faced output. We then compared regional gray matter (GM) volume and cortical thickness measurements by subtracting each de‐faced image measurement from the corresponding unmodified image measurement. We compared these differences between de‐faced and unmodified images across the three matched groups using GLM analyses adjusted for age, sex, and cognitive status, and Bonferroni corrected. Result When aggregating across all brain regions, differences in cortical thickness measures due to de‐facing were significantly different between the NHW and MA groups, and between AA and MA, but not between NHW and AA (Figure 1). Differences between these group means were <0.005mm. For GM volume, differences were significant between AA and MA (magnitude < 0.2%), but not between NHW and AA or NHW and MA (Figure 2). Examining each region individually (Figure 3), cortical thickness differences were significant for only 4 regions (all with magnitudes <0.03mm), and GM volume differences were significant for only 6 regions (all with magnitudes <2%). Conclusion Effects of mri_reface on FreeSurfer brain measurements were significantly different across ethnoracial groups. Our future work will optimize mri_reface to eliminate these differences. However, the magnitudes of these differences are sufficiently small that they are unlikely to have a practical effect on analyses of data using current versions.

Abstract Background & Aims: Alcohol is a leading global risk factor, over 200 diseases and injuries, but policy adoption varies. We updated the Alcohol Preparedness Index (API) to track global and regional policy trends from 2010–2019. Methods: Using WHO GISAH data for 141 countries, we scored five domains (policy frameworks; production/pricing/taxation; marketing/availability; drink-driving; monitoring/surveillance). The primary endpoint was % change in API. Trends were summarized by WHO region and correlates tested in adjusted regression. Results: In 2019, <25% of countries had a strong national alcohol plan and 40% had none. Production/pricing/taxation increased most (27%→78%); monitoring/surveillance reached strong levels in 47.5%. Median API increased from 57.0 [39.0–82.0] (2010) to 74.8 [58.3–83.5] (2016) and stayed above baseline in 2019 (67.8 [51.2–83.8]). Larger population predicted less API gain (β=−19.1, p<0.05); HDI and per-capita consumption did not. Conclusions: Global alcohol policy preparedness increased from 2010–2019.