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Abstract INTRODUCTION Limited data exist on the utility of plasma biomarkers to predict incident abnormal amyloid positron emission tomography (PET). In this study we evaluate the association of plasma Alzheimer's disease (AD) biomarkers with amyloid PET progression among initially amyloid PET negative (A−) individuals. METHODS We included 290 A−, cognitively unimpaired Mayo Clinic Study of Aging participants. We estimated the association of each baseline plasma biomarker with progression from A− to A+ and with rate of amyloid PET change. RESULTS Interquartile range differences in amyloid beta 42/40, percent phosphorylated tau 217 (%p‐tau217), and Amyloid Probability Score 2 were associated with 1.29 ( P = 0.09), 1.38 ( P < 0.001), and 1.20 ( P = 0.05) increases, respectively, in the hazard of progression from A− to A+ and 0.27 ( P = 0.16), 0.50 ( P = 0.007), and 0.28 ( P = 0.15) Centiloid/year increases, respectively, in annual rate of amyloid PET change. DISCUSSION Plasma %p‐tau217 may be a useful screening tool to enrich for participants with increased likelihood of progressing from normal to abnormal amyloid PET in a primary prevention trial. Highlights Plasma phosphorylated tau 217 was associated with amyloid positron emission tomography progression, negative to positive. The associations were weaker for amyloid beta 42/40 and Amyloid Probability Score 2. Age and apolipoprotein E ε4 carriership were also important predictors. These markers may be useful for enrichment of a primary prevention trial.

Abstract INTRODUCTION We investigated the association between alpha‐synuclein (α‐syn) pathology and brain glucose metabolism across the cognitive spectrum of Alzheimer's disease (AD) co‐pathologies. METHODS Fluorodeoxyglucose positron emission tomography (FDG‐PET) data from 829 Alzheimer's Disease Neuroimaging Initiative participants (648 cognitively impaired [CI], 181 unimpaired [CU]) were compared between α‐syn seed amplification assay (SAA) positive and negative groups. Interactions with cerebrospinal fluid (CSF) AD biomarkers were examined. RESULTS SAA+ was associated with widespread hypometabolism among CI individuals, particularly in posterior cortical regions, independent of CSF amyloid and tau levels in the occipital lobes. Regional hypometabolism mediated the effect of α‐syn SAA on disease severity in CI individuals, independent of CSF amyloid and tau levels. There were no influences of SAA on FDG‐PET in CU individuals. DISCUSSION This study supports a model in which α‐syn aggregation influences metabolic dysfunction, which then influences clinical disease severity, independent of AD. SAA+ could help optimize participant selection and outcome measures for clinical trials in AD. Highlights α‐synuclein seed amplification positivity (SAA+) was associated with hypometabolism in cognitively impaired individuals. Hypometabolism mediated the influence of α‐synuclein on disease severity. Occipital hypometabolism in SAA+ was independent of cerebrospinal fluid levels of Alzheimer's disease pathology. These findings can optimize future clinical trials targeting α‐synuclein pathology.

Background: Spinal cord (SC) atrophy is a key imaging biomarker of progressive multiple sclerosis (MS). Progressive MS is more common in men and postmenopausal women. Objective: Investigate the impact of sex and menopause on SC measurements in persons with MS (pwMS). Methods: In pwMS and age- and sex-matched controls, upper cervical SC area from brain MRI (UCC brain ) was obtained. Impact of sex and menopause on UCC brain (adjusted for total intracranial volume) and its association with progression and disability, including MS functional composite (MSFC), were investigated. Results: UCC brain was smaller in pwMS ( n = 118, 51.4 ± 5.3 mm 2 ) than controls ( n = 118, 54.2 ± 4.4 mm 2 , p < 0.001) and inversely correlated with older age in pwMS ( r = −0.24, p = 0.010) but not in controls ( r = −0.025, p = 0.786). In 173 pwMS (413 brain MRIs), UCC brain was smaller in men (49.5 ± 5.9 mm 2 ) than women (51.6 ± 5.5 mm 2 , p = 0.001), postmenopausal women (49.4 ± 5.6 mm 2 ) than premenopausal women (52.9 ± 4.1 mm 2 , p < 0.001), and progressive (47.5 ± 5.6 mm 2 ) than relapsing MS (52.1 ± 5.2 mm 2 , p < 0.001). UCC brain also correlated with disease duration ( r = −0.39, p < 0.001), 9-hole peg test ( r = −0.26, p = 0.005), and severe ambulatory disability (Expanded Disability Status Scale ⩾6) ( r = −0.27, p < 0.001). Conclusion: UCC brain , a biomarker of progressive MS, is inversely associated with age, disease duration, male sex, and menopause, highlighting the potential impact of sex and hormones on neurodegeneration in MS.

Importance Although 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established cross-sectional biomarker of brain metabolism in dementia with Lewy bodies (DLB), the longitudinal change in FDG-PET has not been characterized. Objective To investigate longitudinal FDG-PET in prodromal DLB and DLB, including a subsample with autopsy data, and report estimated sample sizes for a hypothetical clinical trial in DLB. Design, Setting, and Participants Longitudinal case-control study with mean (SD) follow-up of 3.8 (2.3) years. Cases were recruited consecutively between 2007 and 2022 at a referral center and among the population. Patients with probable DLB or mild cognitive impairment with Lewy bodies (MCI-LB) were included. Individuals without cognitive impairment were included from a population-based cohort balanced on age and sex for comparison. All participants completed at least 1 follow-up assessment by design. Exposure Patients with MCI-LB and DLB. Main Outcomes and Measures Rate of change in FDG-PET was assessed as standardized uptake value ratios (SUVr). Clinical progression was assessed with the Clinical Dementia Rating Sum of Boxes (CDR-SB) score. Results Thirty-five patients with probable DLB, 37 patients with MCI-LB, and 100 individuals without cognitive impairment were included. The mean (SD) age of the DLB and MCI-LB groups combined (n = 72) was 69.6 (8.2) years; 66 patients (92%) were men and 6 (8%) were women. At follow-up, 18 participants (49%) with MCI-LB had progressed to probable DLB. Patients with MCI-LB had a faster decline in FDG-SUVr, compared with that of participants without cognitive impairment, in the posterior cingulate, occipital, parietal, temporal, and lateral frontal cortices. The same regions showed greater metabolic decline in patients with DLB than in participants without cognitive impairment, with the addition of anterior-middle cingulate, insula, and medial frontal orbital cortices. Rates of change in FDG-PET in these brain regions were combined into a region of interest (ROI) labeled longitudinal FDG-PET LB meta-ROI. The rate of change in FDG-SUVr in the meta-ROI correlated with the rate of change in CDR-SB, and sample size estimates were reported for potential clinical trials in DLB. Findings were confirmed in the subsample with neuropathologic confirmation (n = 20). Conclusions and Relevance This study found that brain hypometabolism begins to evolve during the prodromal stages of DLB with changes paralleling symptomatic progression. These data may inform clinical practice and trials planning to use FDG-PET for biologic staging, monitoring disease progression, and potentially assessing treatment response.

AbstractBackgroundRecent advances in automatic face recognition have increased the risk that de‐identified research imaging data could be re‐identified from face imagery in brain scans.MethodAn ADNI committee of independent imaging experts evaluated 11 published techniques for face‐deidentification (“de‐facing”) and selected four algorithms (FSL‐UK Biobank, HCP/XNAT, mri_reface, and BIC) for formal testing using 183 longitudinal scans of 61 racially and ethnically diverse ADNI participants, evaluated by their facial feature removal on 3D rendered surfaces (confirming sufficient privacy protection) and by comparing measurements from ADNI routine image analyses on unmodified vs. de‐faced images (confirming negligible side effects on analyses).ResultEffects of the de‐facing methods on FreeSurfer at UCSF (Figure 1‐top), ASHS at University of Pennsylvania (Figure 1 bottom‐left), and longitudinal atrophy at Mayo Clinic (Figure 1 bottom‐right) were comparable with each other and sufficiently small vs. unmodified scans to recommend de‐facing in ADNI‐4. The FSL and mri_reface methods were more thorough in removing facial features, partly because these also removed ears (Figure 2‐top). mri_reface was recommended for ADNI‐4 primarily because fewer (zero) images had modified brain (Figure 2‐bottom), and ADNI leadership approved de‐facing with mri_reface. ADNI performed a second validation of its latest version on another diverse sample of 100 ADNI‐3 participants. These analyses included: boundary shift interval at University College London, where average differences in measurements were all <0.05%; cortical white matter hyperintensities at UC Davis, where they found “minimum impact” (R>0.97); and others.ConclusionADNI‐4 de‐faces all indicated image types (Table 1) before subsequent pre‐processing, analyses, and public release. Sites upload images to the central repository (LONI), where they are queued to the MRI Core at Mayo Clinic for de‐facing. Image types requiring de‐facing are de‐faced using mri_reface. Trained analysts inspect de‐faced images to confirm complete removal of the face and complete non‐modification of the brain, and any flagged images are re‐processed manually until issues are resolved. De‐faced images are converted back to DICOM and re‐uploaded for further distribution. Image types not requiring de‐facing are indicated to proceed directly to subsequent steps. In summary, ADNI‐4 has adopted mri_reface because it successfully de‐faces without substantially affecting brain measurements.

AbstractBackgroundParkinson’s disease and dementia with Lewy bodies are characterized by abnormal iron deposition in the substantia nigra, which can be measured with quantitative susceptibility mapping (QSM) on MRI. However, neuropathologic validation of the increased QSM in the substantia nigra associated with Lewy body disease (LBD) is lacking. We compared substantia nigra QSM on antemortem MRI between patients with and without Lewy‐related pathology which was confirmed by autopsy in all cases.MethodWe studied cases with antemortem QSM who underwent autopsy from the Mayo Clinic Alzheimer’s Disease Research Center and Mayo Clinic Study of Aging (n=54). Immunohistochemistry was performed with rabbit NACP antibody against the amino acids 98‐115 of the α‐synuclein protein to detect Lewy‐related pathology. After neuropathologic evaluation, cases were classified as LBD‐present (n=13) if they had Lewy‐related pathology and LBD‐absent (n=42) if they did not have Lewy‐related pathology. Alzheimer’s disease co‐pathology was present in 9 of the LBD‐present cohort (70%). QSM was calculated for the whole substantia nigra and the two sub‐regions: pars compacta and pars reticulata. Non‐parametric Wilcoxon rank‐sum test was used to compare substantia nigra QSM values between LBD‐present and LBD‐absent cases. Area under the curve (AUC) analyses tested the accuracy of QSM to distinguish the two groups.ResultsLBD‐present group had higher QSM values in the substantia nigra than LBD‐absent group. Furthermore, sub‐regional analyses showed that the LBD‐present group had higher QSM in the substantia nigra pars compacta (p=0.008) but not in the pars reticulata than the LBD‐absent group (Figure 1). QSM of the substantia nigra pars compacta distinguished LBD‐present and LBD‐absent cases with good accuracy (AUC=0.74; p=0.006; Figure 2).ConclusionThis study provides neuropathologic insight into the relevance of QSM of substantia nigra pars compacta as an in‐vivo biomarker of autopsy‐confirmed LBD. These findings indicate that imaging evidence of abnormal iron deposition in the substantia nigra may serve as a biomarker of Lewy‐related pathology in patients with primary LBD (i.e., Parkinson disease, dementia with Lewy bodies) and patients with potential LBD co‐pathology (e.g., patients with Alzheimer disease).

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.

AbstractBackgroundApproximately 10% of patients with acute SARS‐CoV‐2 infection present with persistent symptoms recognized as the long‐COVID. Neurological and cognitive symptoms are prevalent in long‐COVID, requiring a deeper understanding of the biological basis of this condition for potential therapeutic interventions. Cerebrovascular complications are observed during acute infection, underscoring the importance of understanding cerebrovascular outcomes. Our objective was to investigate white matter hyperintensities (WMH) as a potential indicator of microvascular disease associated with long‐COVID in the brainMethodParticipants with long‐COVID (n=71; hospitalized, n=9, non‐hospitalized, n=62 during the acute infection) and controls (n=50) who did not have a reported infection were recruited from the community at five US sites that participated in the COVID‐Brain‐Advanced‐Imaging‐Network (COVID‐BRAIN) project. To be included, participants with long‐COVID had to experience neurological or cognitive sequelae within 6 months after a confirmed infection and continued to show at least one neurological symptom. Clinical evaluation and MRI were performed an average of 20±10 months after the acute infection. WMH volumes were measured from the 3D T2‐weighted FLAIR MRIs using a semi‐automated approach for the WMH segmentation. A linear regression model predicting log transformed WMH volume was used to investigate the association of age, groups (hospitalized long‐COVID, non‐hospitalized long‐COVID, controls), and the interaction of these two predictors along with an adjustment for total intracranial volume. Additionally, predicted regression lines have been plotted on the untransformed WMH scale.ResultWe observed a greater age‐dependent increase in WMH volumes in the hospitalized patients with long‐COVID compared to the control group (group*age interaction, p=0.005). We did not observe this difference in slopes in the non‐hospitalized patients with long‐COVID compared to the controls. Figure shows the slopes of WMH volume as a percentage of the total intracranial volume by age in the three groups.ConclusionOur preliminary observation in this ongoing long‐Covid study suggests that hospitalization during acute COVID is associated with the age‐dependent increase in microvascular injury in the white matter. Older adults with acute infection that require hospitalization are more susceptible to cerebrovascular adverse outcomes than controls or those who do not require hospitalization. Impact of such microvascular changes on cognitive outcomes and persistence of post‐COVID symptoms is yet to be studied.

AbstractBackgroundPlasma Aβ42/40 and p‐tau217 can predict amyloid positivity in cross‐sectional studies. However, it is unclear how plasma biomarkers perform longitudinally, which is important to inform their utility in tracking disease progression. The goal of this study is to describe temporal evolutions of plasma Aβ42/40 and ptau217 ratio (p‐tau217r) measured via mass spectrometry, p‐tau217 measured via an immunoassay, and amyloid PET.MethodsWe included participants in the Mayo Clinic Study of Aging with amyloid PET (PiB) and plasma Aβ42/40 + ptau217r (mass spectrometry, C2N) and/or p‐tau217 (Lilly, MSD). We fit a joint non‐linear mixed effects model with Aβ42/40, p‐tau217r, p‐tau217 (Lilly), and amyloid PET as outcomes and age, sex, APOE ε4 carriership and education as covariates. The model included a per‐participant adjustment (time‐shift) for each biomarker indicating how much earlier or later in time the biomarker was estimated to progress relative to the average participant, a correlation (R) between adjustments for each pair of biomarkers, and the impact of covariates on each biomarker’s timing.ResultsThe study included 1459 participants, age 77 (10) years, 44% female, 82% cognitively unimpaired. Mean (SD) years of followup was 5.6(3.0) years for mass spectrometry plasma measures, 2.5(0.7) years for p‐tau217 (Lilly), and 4.6(3.3) for amyloid PET. Model fits are shown for each biomarker, with greater variability in serial measurements for plasma than PET (Figure 1). Individual adjustments between each of the plasma biomarkers and amyloid PET were highly correlated: p‐tau217r, R=0.73 (95%CI: 0.67, 0.78); p‐tau217 (Lilly), R=0.66 (0.62, 0.7); Aβ42/40, R=0.6 (0.52, 0.68) (Figure 2). The p‐tau217r and Aβ42/40 individual adjustments were modestly correlated, R=0.39 (0.28, 0.49). APOE ε4 carriership had the strongest covariate effect on all biomarkers, e.g. p‐tau217r progression 9.4 (6.7, 12) years earlier (Figure 3).ConclusionsPlasma Aβ42/40 and p‐tau217 measured with currently available assays may both be amenable to longitudinal modeling, although the degree of variability in Aβ42/40 over time may pose challenges. Findings suggest that both plasma Aβ42/40 and p‐tau217r may provide information regarding an individual’s timing of progression on amyloid PET; lower correlations with each other than amyloid PET suggests that each analyte may provide complimentary information.

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