Przybelski, Scott A.

Abstract Background Tau‐PET neuroimaging is increasingly applied to diagnose and stage, track progression, and assess response to putative disease‐modifying treatments in individuals with Alzheimer disease. However, it remains to be seen whether findings established in predominantly non‐Hispanic White (nHW) cohorts will generalize to Black and Latino participants who are underrepresented in dementia research. Method Community‐dwelling Black ( n = 83) and Latino participants ( n = 27) completed MRI (with quantification of white matter hyperintensity volumes), and amyloid‐ (PiB or florbetapir) and tau‐ (flortaucipir) PET brain imaging as part of a longitudinal study of memory and aging at Mayo Clinic in Florida. Global and regional tau‐PET standardized uptake value ratios (normalized to the cerebellar crus) were determined in Black and Latino participants and compared with findings from an independent cohort of nHW participants ( n = 110), 1:1‐matched for age, sex, education, and global Clinical Dementia Rating®. Conditional logistic modeling compared 47 regions of interest, adjusted for false discovery rate. Relevant associations between flortaucipir retention and clinical outcomes were assessed using Spearman rank correlations. Result Eighty‐three Black (mean 72±10.8‐years‐old, 63% female, 33% cognitively impaired) and 27 Latino participants (mean 65.8‐years‐old, 56% female, 11% cognitively impaired) completed neuroimaging. Data were compared with findings in 110 matched nHW individuals. Global tau‐PET burden was similar between matched cohorts (Black and nHW participants, mean SUVR: 1.34±0.39 vs 1.36±0.37, p = 0.71; Latino and nHW participants, mean SUVR: 1.17±0.09 vs 1.20±0.11, p = 0.66). Lower flortaucipir uptake was noted in the caudate ( p = 0.005) and putamen ( p = 0.022) of Black/Latino participants (vs nHW), although differences attenuated following adjustment for white matter hyperintensity volume. Global flortaucipir retention increased with age (Black: Rho=0.31; Latino: Rho=0.46; nHW: Rho=0.27; p <0.05), amyloid Centiloids (Black: Rho=0.65; Latino: Rho=0.41; nHW: Rho=0.58; p <0.05), and cognitive impairment (i.e., Clinical Dementia Rating® Sum‐of‐Box scores, Black/Latino: Rho=0.31, p = 0.07; nHW: 0.76, p <0.001). Conclusion Tau‐PET patterns were similar across ethnoracial groups after adjusting for white matter hyperintensity volumes—a biomarker of small vessel disease. Flortaucipir retention increased with age, amyloid accumulation, and cognitive impairment across all cohorts. These findings affirm the clinical and research applications of tau PET in diverse cohorts. Acknowledgements : Flortaucipir precursor and technology was supported by Avid Radiopharmaceuticals.

Abstract Background Dementia with Lewy Bodies (DLB) has been overlooked or misdiagnosed as Alzheimer's disease (AD) dementia due to its frequent concomitant AD pathology with Lewy Body Disease (LBD) pathology. Although autopsy confirmation has been the gold standard for the precise diagnosis of DLB, time‐course 123 I‐FP‐CIT SPECT (DaT‐SPECT) findings in autopsy‐confirmed DLB are not well characterized. We previously reported that the DaTQUANT z‐score of the more affected side of the putamen has the best discriminatory power in detecting LBD pathology and nigrostriatal degeneration in dementia patients and demonstrated z‐score of −1 as the cutoff value. We sought to examine how DaT‐SPECT findings change over time in autopsy confirmed LBD patients in preparation for its use in LBD targeted clinical trials. Method Eight autopsy confirmed neocortical or limbic LBD patients who underwent two or more DaT‐SPECT were included. Nigrostriatal dopamine transporter (DaT) binding and age‐adjusted z‐scores were analyzed using the DaTQUANT 2.0 software (GE Healthcare). Result Six patients (75%) were male. The patients had 2.5±0.7 longitudinal DaT‐SPECT scans. Clinical diagnosis at initial scan was: DLB (75%), mild cognitive impairment (MCI) with REM sleep behavior disorder (RBD) (13%), and isolated RBD (iRBD) (13%). Clinical diagnosis at last scan was: DLB (88%) and iRBD (12%). All patients developed dementia during life. Mean age at initial scan/last scan/autopsy were 71.1±7.0/73.4±6.9/75.1±7.3 years old. Time from initial and last scan to autopsy were 4.0±1.9 and 1.8±1.9 years respectively. Seven patients (88%) had neocortical LBD and one (12%) had limbic LBD. Concomitant AD pathology was observed in four patients (two males (33%), two females (100%)). All patients had DaTQUANT putamen z‐score <‐1.0 at their initial scan including in MCI+RBD (‐1.76) and iRBD (‐1.53) patients. In the DLB patients at their initial scans, five (83%) had DaTQUANT putamen z‐score <‐2.0 and one (17%) had z‐score of ‐1.55. All patients (100%) showed DaTQUANT putamen z‐score <‐2.0 at their last scans. Conclusion DaTQUANT using cut‐off putamen z‐score of ‐1 was associated with underlying LBD pathology including at their iRBD and MCI stages. Nigrostriatal DaT binding tends to decline over time with DaTQUANT z‐score <‐2 at their last scans.

Abstract Background T2*‐weighted imaging is of paramount importance for patient treatment eligibility and safety monitoring in anti‐amyloid pharmaceutical clinical trials and clinical care. Recently with the adoption of multi‐echo GRE (GRE) sequences, susceptibility weighted imaging (SWI), and quantitative susceptibility mapping (QSM) can be obtained in the same acquisition. In 2024, the QSM Consensus Organization Committee released a white paper recommending at least 1 mm isotropic voxel size for QSM. In clinical workflows microbleed detection is performed with higher in‐plane resolution (0.5x0.5mm 2 ), and large out‐of‐plane resolution (∼1.8mm), which was used in the ADNI4 protocol. In the CLARiTI protocol, a 1mm isotropic resolution was adopted. The objective of this work was to compare susceptibility estimates between the ADNI4 (0.5x0.5x1.8mm 3 ) and CLARiTI (1mm 3 ) protocols. Method The ADNI4 and CLARiTI QSM protocols were acquired on forty‐seven subjects (33 cognitively unimpaired (CU), 14 cognitively impaired (CI), in the same scan session. The groups were defined by their Clinical Demential Rating (CDR) global score (CDR global = 0 for CU, and CDR global >= 0.5 for CI subjects). QSM images were generated using publically available software (STI Suite, UC Berkeley). Mean susceptibility values in atlas regions were compared. Result Significant group differences (CU vs. CI) were observed in the fusiform and middle occipital gyrus with the ADNI4 protocol, only in the rectus with the CLARiTI protocol (Figure 1). These three regions are small in size and closer to surface‐air interfaces, which may have impacted estimation accuracy. However, in the substantia nigra and its sub‐regions (pars compacta and pars reticulata), which shows increased susceptibility in Lewy body disease, the ADNI4 and CLARiTI protocols were in good agreement (r 2 > 0.9) (Figure 2). Conclusion Group differences were observed between the ADNI4 and CLARiTI QSM only in 3 small regions close to surface‐air interfaces in this small dataset. The two techniques showed highly correlated (r 2 = 0.93) estimates in the substantia nigra, a region with high susceptibility due to iron deposition. Strong correlations between the two protocols are encouraging. Radiologists have historically preferred submillimeter in‐plane resolution for microbleed detection, which is more time manageable with anisotropic imaging.

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 White matter (WM) plays a dual role in interacting with tau pathology. There is an intricate dynamic relationship with WM acting as highways for tau spreading, while the increasing tau burden ultimately compromises WM health. We aimed at studying the associations between tau accumulation and WM integrity using longitudinal data with focus on sex differences. Method We included 374 participants who had longitudinal multishell diffusion MRI and flortaucipir PET (baseline mean age = 70.9 years, 52% male, 17% MCI/AD dementia) scans from the Mayo ADRC and Mayo Clinic Study of Aging. Rate of tau accumulation was calculated from the entorhinal cortex (ERC) and inferior temporal cortex (IT) SUVRs, with IT‐WM serving as the WM highway between the two regions. IT‐WM was defined by JHU “Eve” WM atlas, and its integrity was assessed using neurite density index (NDI), which reflects the packing density of neurites. Structural equation modeling (SEM) evaluated relationships between tau and WM metrics. Result At baseline, males and females showed no significant differences in tau SUVR or IT‐WM NDI ( p >0.05), though males had higher vascular risk and averaged two years older than females (Table 1). Figure 1a showed that baseline ERC‐tau influenced baseline IT‐tau, as well as both rate of ERC‐tau and baseline IT‐tau influenced rate of IT‐tau. After incorporating IT‐WM NDI as a mediator (Figure 1b), we discovered 1) baseline ERC‐tau influencing rate of IT‐tau accumulation through baseline IT‐WM NDI, suggesting the role of WM health in the rate of tau accumulation; 2) Rate of IT‐tau accumulation negatively affected follow‐up IT‐WM NDI suggesting the deterioration of WM health as tau accumulation progresses. These relationships were stronger in males compared to females. We also observed greater impact of vascular risk on WM health in males and greater rates of IT‐tau accumulation in females. Conclusion Our work sheds light on the mechanistic role of intact WM in tau accumulation (through dense microtubules and synaptic connections) and the deterioration of WM that is associated with higher rates of tau accumulation. We found interesting sex differences that warrant further investigation.

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 Chronic disease conditions lead to brain changes elevating dementia risk. We hypothesized that tissue microstructural changes caused by type 2 diabetes mellitus (T2DM) and hypertension could be effectively captured using an advanced diffusion MRI technique, Neurite Orientation Dispersion and Density Imaging (NODDI). Our goal was to evaluate the usefulness of composite NODDI scores, customized for increased hemoglobin (Hb) A1c and systolic blood pressure as predictors of cognition and compare their performance to other measures typically used to assess dementia risk. Method We collected multi‐shell diffusion MR on 1024 Mayo Clinic Study of Aging participants. NODDI measures (neurite density index (NDI), orientation dispersion index (ODI), and free water fraction (FWF)) in gray matter (GM) and white matter (WM) regions were estimated. To identify brain regions susceptible to both conditions, a multiple linear regression model was fitted for each NODDI metric, using hemoglobin (Hb) A1c (in N = 305 participants) or systolic blood pressure ( N = 906) as predictor, with age, sex, and body mass index (BMI) as covariates. Event‐based modeling was performed with the identified NODDI metrics to investigate the chronological pattern along the course of chronic condition development from this cross‐sectional dataset. Composite metrics were created as a weighted sum of susceptible NODDI metrics. We evaluated the impact of both conditions on cognition using gradient boosting models incorporating composite metrics, additional brain measurements, and demographics. Result Increased HbA1c impacts both GM and WM NODDI relatively evenly, whereas increased systolic blood pressure related NODDI changes were mainly seen in WM (data not shown). With increased HbA1c, the earliest changes were observed in GM ODI, followed by changes in the pons and cortical FWF (Figure 1A). With increased systolic blood pressure, the earliest changes occurred in association fibers and middle cerebellar peduncle, followed by FWF change in lobar and projection WM fibers (Figure 1B). Average feature importance from 100 gradient boosting models for cognition prediction suggests that diabetes‐induced GM changes play a moderately important role in cognitive decline (Figure 2). Conclusion Chronic medical conditions, especially T2DM, result in brain changes that are captured by NODDI and contribute moderately to cognitive performance.

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 Cognitive resilience is the discrepancy in individuals with intermediate to high Alzheimer's disease neuropathologic change (ADNC) and normal cognition. Currently, the role of microglial activation in cognitive resilience is understudied. Method In the MCSA brain bank, high‐cognitively unimpaired (high‐CU) participants were defined as those performing in the upper quartile of their respective age groups based upon antemortem global cognition z‐scores, computed from memory, language, executive function, and visuospatial domain z‐scores. From the high‐CU group ( N = 118, global z‐scores from 1.55 to 1.78), cognitively resilient (CR) individuals were identified as those with intermediate to high ADNC upon neuropathologic evaluation. First, regression models were fit to evaluate global cognition as a function of age, sex, amyloid PET, and microglial‐specific genes of interest, including MS4A gene cluster SNPs. Second, a subset of CR participants was analyzed for postmortem ARM expression in the dorsolateral prefrontal cortex. Single immunohistochemistry was performed on formalin‐fixed, paraffin‐embedded sections using antibodies against beta‐amyloid (6F3D) and CD163 (10D6). Digital image analysis was performed on scanned sections (40x, Leica Aperio GT 450), and ARM (CD163%area) and beta‐amyloid (%area) measurements were obtained using QuPath. Cognitively impaired (CI) control group was matched on age, sex, ADNC level, and APOE4 status. Spearman correlations were performed between postmortem ARM and antemortem MRI, PET, plasma, and global cognition metrics. Result A third (39/118) of high‐CU cases showed intermediate/high ADNC (CR group). Furthermore, 28% of CR individuals harbored an MS4A6A SNP, which showed a protective effect on cognition via amyloid‐PET ( p = 0.043, Figure 1). Moreover, CR individuals harbored significantly higher ARM:amyloid than the CI group ( p <0.001, Figure 2) and fewer neuritic plaques ( p = 0.03, Figure 3). Antemortem white matter hyperintensities and global cognition were significantly correlated to ARM expression (r=0.556 [p=0.042] and r=0.648 [p=0.02], respectively). Conclusion Microglial specific MS4A6A SNP, which modulates microglial activation, was seen in a third of CR individuals. Postmortem analyses in CR individuals revealed increased ARM:amyloid, which was also associated with higher antemortem cognitive performance. These findings underscore the role of microglial activation in cognitive resilience. Future work is needed to investigate microglial protective mechanisms and links between vascular disease and microglial activation.