Citations

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 Monoclonal antibodies targeting amyloid‐β (Aβ), including aducanumab, have been tested to treat Alzheimer's disease (AD). However, limited data exist on neuropathology following treatment and the effects of amyloid‐related imaging abnormalities (ARIA). We report on such data from five aducanumab‐treated study participants with AD of whom two experienced ARIA. Methods Aducanumab‐treated study participants who came to autopsy ( n = 5) were matched to AD controls ( n = 12) based on the presence/type of autosomal dominant AD mutation, APOE genotype, age at cognitive symptom onset, and sex. We assessed cognitive measures, 18 F‐florbetapir PET centiloid, ARIA risk factors, and AD neuropathologic change. Using multiplex immunofluorescence, brain regions affected along Thal Aβ phases were stained for Aβ isoforms and phosphorylated tau, as well as for ARIA‐associated markers, including Perls’ Prussian blue to detect ferric iron, fibrinogen‐α for blood brain barrier integrity, membrane attack complex (C5b‐C9) for complement activation, and activated microglia (IBA1, CD68). Results Study participants included four males and one female, all carrying at least one APOE ε4 allele. Two participants carried a PSEN1 NM_000021.4:c.817G>A , p.Glu273Lys mutation. Cumulative aducanumab dosage ranged between 5–241 mg/kg, with death occurring 5–41 months after the last infusion. Amyloid burden on PET declined in all participants (range 15‐100 centiloids). Two participants experienced ARIA. Compared to AD controls, Aβ aa1‐8 and Aβ 42 were selectively reduced in cortical layer I of aducanumab‐treated study participants ( p <0.05) but not in the total cortex, as shown for the middle frontal cortex in Figure 1. No differences were observed in phosphorylated tau burden. Prussian blue‐stained hemosiderin was present in both treated and untreated AD cases. However, in participants with ARIA, hemosiderin accumulated in superficial cortical layers near CAA‐laden meningeal and penetrating vessels, which also exhibited extensive complement and microglial activation (Figure 2). Conclusions Aβ clearance and ARIA‐related findings were localized predominantly in superficial cortical layers, suggesting that aducanumab biodistribution is more pronounced in these regions rather than deeper cortical layers.

Abstract Background Aging and dementia are generally associated with lower metabolism in gray matter on FDG‐PET and have been widely studied. However, white matter (WM) metabolic changes remain understudied. We investigated the metabolic changes in WM associated with aging and dementia and their relationships with established Alzheimer's Disease (AD) biomarkers and cognitive outcomes. Method We analyzed data from 3,169 participants (72 ± 9 years; 53% male; 65% cognitively unimpaired) enrolled in the Mayo Clinic Study of Aging (MCSA) and the Alzheimer's Disease Neuroimaging Initiative (ADNI). All participants underwent baseline MRI, amyloid‐PET, and FDG‐PET scans, as well as neuropsychological evaluations, with at least one clinical follow‐up. Voxel‐wise multiple regression analysis identified WM metabolic changes associated with global cognition in cognitively unimpaired MCSA participants ( n = 1,754). Univariate analyses and structural equation models were applied to elucidate the driving factors underlying the WM metabolic changes in MCSA data. Multiple regression and linear mixed‐effects models evaluated the explanatory utility of WM metabolism for baseline global cognition and cognitive decline in both cohorts. In a subset of MCSA participants, we evaluated neurite density index from diffusion MRI as a surrogate of WM health and glial fibrillary acidic protein (GFAP) as a surrogate of neuroinflammation to understand underlying mechanisms. Result We identified an atypical WM signature (AWM‐Signature) in the anterior and superior corona radiata, where glucose metabolism increased as cognitive performance worsened (Figure 1). Hypermetabolism in AWM‐Signature was related to increased age, amyloidosis, vascular disease, and neuroinflammation, as well as decreasing WM neurite density in the same regions (Figure 2). Hypermetabolism in AWM‐Signature regions predicted faster future cognitive decline, even after accounting for demographics, amyloid, and vascular disease metrics. The results observed in the AWM‐Signature regions were consistently in the opposite direction of the associations seen with gray matter and typical WM regions. Conclusion We found unexpected metabolism increases in corona radiata, a hub that carries significant portion of the brain's neural traffic, with aging and disease which likely indicates a compensatory response. These changes may be useful cognitive resilience measures and require further investigation to understand their role in maintaining cognitive function.

Abstract Background Apolipoprotein E (APOE), the major genetic risk factor for Alzheimer's disease (AD), has three common alleles (2, 3 and 4), giving rise to genotypes that are associated with six levels of AD risk: (2/2<2/3<3/3<2/4<3/4<4/4). In our new Arizona APOE Cohort Study, we have begun to establish a longitudinal cohort of more than 300 cognitively unimpaired persons (50‐90 year‐old) stratified by APOE genotype and age decile, matched by age and sex, and have an extensive battery of cognitive, clinical, amyloid (NAV4694) and tau (MK6240) PET, MRI, cerebrospinal fluid (CSF), plasma, and DNA. The study is intended to clarify the impact of APOE4 and APOE2 allelic dose on the predisposition to and protection from AD and inform the design of secondary and primary prevention therapies. Here, we describe the study and the resource of data and samples that are now available to researchers. Methods Participants were recruited from across the United States using GeneMatch and other sources. Comprehensive clinical assessments, brain images, and lumbar punctures are performed every 2 years, 50mL blood samples are acquired annually, and whole genome sequencing is in progress. Results As of January 2025, baseline assessments were completed in 303 participants, 68±9 years of age, including 205 females and 98 males. Sixteen percent are from underrepresented groups. Baseline CSF samples were obtained in 89% of the participants. The complete cohort consists of 17, 63, 41, 65, 66, and 51 persons with the APOE 2/2, 2/3, 3/3, 2/4, 3/4, and 4/4 genotype, respectively. In the first 287 participants with available amyloid PET scans 6% of 2/2, 5% of 2/3, 14% of 3/3, 26% of 2/4, 29% of 3/4, and 52% of 4/4 participants had a positive amyloid PET (≥20 centiloids). Conclusions Building up on our decades of experience in APOE research, we launched the new Arizona APOE Cohort which promises to provide a valuable resource of data and samples to advance the study of APOE, its variants and interactions with other risk and protective factors, and the development, treatment, and prevention of AD. Baseline data are now available to interested and eligible research collaborators.

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 To date, no scoring system has adequately defined clinical efficacy of erectile dysfunction (ED) therapies when evaluating differences between cohorts or in meta-analytic studies. Aim To outline the minimal criteria required for any therapy to be considered clinically effective in the treatment of ED. Methods An analytic review was performed of standardized ED questionnaires, with a particular emphasis placed on the International Index of Erectile Function, Erectile Function Domain (IIEF-EFD), and the minimally clinically important difference (MCID) concept. The data were used to create a societal position statement and recommendations on best practices within clinical trials to determine treatment efficacy. Outcomes The SMSNA provided 6 key statements and recommendations to suggest best practices and define minimal thresholds for improvements required for an ED therapy to be considered clinically effective. Results The IIEF-EFD currently represents the preferred standardized questionnaire to evaluate for changes in erectile function post-treatment and is the only questionnaire adequate to assess primary outcomes. To claim efficacy, investigational treatments must demonstrate MCID between cohorts (eg, 2-, 5-, or 7-point median improvements between groups among mild, moderate, or severe ED cohorts) in prospective, randomized, placebo-controlled, and adequately powered studies. In studies employing mixed-severity ED populations, a method of calculating an adjusted MCID was proposed based on statistical weighting principles. Similarly, a novel method for analyzing differences between cohorts in meta-analytic studies was presented to incorporate the concept of MCID. Based on these principles, the SMSNA would not support efficacy of a therapy based on statistical significance in a meta-analytic study alone. Similarly, studies claiming efficacy utilizing responder approaches (ie, statistically higher percentage of men achieving MCID between cohorts) would not be acceptable unless the median difference between groups on the IIEF-EFD achieved MCID. Clinical Implications The current position statement provides important, evidence-based data and scientific principles to help define what may be considered as an effective therapy in the treatment of ED. This framework provides a basis that decision-makers, clinicians, and patients may use to help differentiate clinically effective therapies. Strengths & Limitations The published literature is limited by a lack of validated instruments and data defining MCID for ED therapies between cohorts and with meta-analytic studies. The current position statement provides important guidance on these areas of ambiguity pending further, definitive data. Conclusion The current position statement provides detailed guidance on key criteria required to conclude efficacy of therapies used to treat ED.

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 Our group recently performed the largest‐to‐date X‐chromosome wide association study (XWAS) of Alzheimer's disease (AD). Here, we expand on this with additional samples, novel sex‐ and APOE *4‐stratified analyses, and evaluate escape from random X chromosome inactivation (eXCI) to better capture female‐biased risk genes. We then integrate these results with XWASs of various AD‐relevant traits to elucidate shared genetic signals and ultimately uncover novel AD risk genes and mechanisms. Method The AD XWAS design is shown in Figure 1A and includes random X chromosome inactivation (rXCI) and eXCI models. We further assessed suggestive AD signals ( p <1x10 ‐3 ) for pleiotropy using genetic colocalization with 2852 AD‐relevant traits (Figure 2A). Loci exhibiting X chromosome‐wide significance ( p <1x10 ‐5 ) or apparent trait colocalization underwent quantitative trait locus (QTL) colocalization analyses across 62 tissues and cell types to functionally prioritize AD risk genes. Result Full sample analyses reproduced Belloy et al. 2024 findings and identified 3 additional loci, including a male‐specific signal near GLUD2 that impacts glutamate/glutamine metabolism (Figure 1B). APOE *4‐stratified analyses revealed 9 additional loci, including 3 eXCI hits in APOE *4 carriers (Figure 1C‐D). Pleiotropy colocalization identified 38 loci, most being novel and many driven by eXCI and/or female‐specific AD XWAS, including MOSPD2, PNMA3 and GABRE loci that showed extensive colocalization with brain imaging traits (Figure 2B). The most significant XWAS locus, SLC9A7 , colocalized with total testosterone, HDL‐C, and various brain imaging traits. Only the NLGN4X locus colocalized with amyloid positivity, linking its proposed role in synaptic dysfunction to amyloid pathology. The locus at KDM6A , previously implicated as an AD resilience gene, colocalized with a functional MRI (fMRI) phenotype. QTL colocalization analyses confirmed gene prioritizations in Belloy et al. 2024, and additionally provided support for PNMA3 , an antigen highly expressed in the brain, and 3 APOE ‐stratified loci, including ACLS4 , which impacts lipid metabolism. Conclusion This study provides novel insights into biological mechanisms that contribute to X‐linked genetic risk for AD in an APOE *4 and sex‐specific manner, identifying various novel risk loci. Notably, 29 female‐biased risk loci were uncovered, which may shed light on sex disparities in AD. Overall, our findings will aid in elucidating AD pathways and drug targets relevant to personalized medicine.