Jones, David T.

Abstract Background Neuroinflammation plays a crucial role in neuronal damage, synaptic dysfunction, and impaired neurogenesis, significantly contributing to Alzheimer's Disease (AD) progression. To address this, we assessed ER176's effectiveness in detecting neuroinflammation in cognitively unimpaired (CU) A–, CU A+, mild cognitive impairment (MCI), and AD participants, with a particular focus on their amyloid and tau status. This research aims to help identify suitable candidates for specialized treatment when adversely impacted by neuroinflammation. Method Participants from the Mayo Clinic Study of Aging were recruited for this study. The study included four participant groups: CU A– ( n = 25), CU A+ ( n = 25), MCI A+ ( n = 25), and AD ( n = 15). We explored the variations in neuroinflammation PET results among different groups. Result When compared to CU A–, both CU A+ and MCI A+ groups (Figure 1) showed greater regional neuroinflammation. AD exhibited more widely affected regions with neuroinflammation (temporal, occipital, and parietal lobes) than CU A‐ over the PVC spectrum (Figure 1). Minimally more neuroinflammation was seen (temporal lobes) in MCI A+ than CU A+ (Figure 1). AD had more widespread regional neuroinflammation vs CU A+ (Figure 1) than in MCI A+ vs CU+. The least difference in neuroinflammation is seen in MCI A+ vs AD (Figure 1). For A+ vs A‐, A+ showed more widespread neuroinflammation (Figure 2). T+ (tau>=1.29) vs T‐ (tau<1.29) showed more neuro inflammation with T+ (Figure 2). Comparisons varied modestly in significance level with PVC method (Figures 1 and 2). Conclusion The findings demonstrate that neuroinflammatory changes are associated with AD pathology. Even at early stages, neuroinflammation is associated with CU+. As the condition progresses, neuroinflammation extends to more brain regions and is associated with increased A+ and T+ pathology in MCI A+ and AD. These results suggest an early and persistent role of neuroinflammation in the progression AD pathology, indicating that therapy options could include early anti‐inflammatory intervention, even prior to clinical symptoms, along with treatment during clinical manifestations. PVC methods used may provide somewhat variable results.

Abstract Background The Overlap Index (OI) as previously published (Lee et al., 2022) is a reliable method for detecting tau accumulation using tau‐PET imaging (flortaucipir; AV1451). It identifies voxel‐wise standardized uptake value ratio (SUVr) elevations across serial scans. Although OI showed more sensitivity to tau signal elevation and longitudinal change than standard ROI measures, to our knowledge, the relationship of the tau PET overlap index to neuropathologic findings is still unclear. This study aims to investigate the potential of OI to detect NFTs of the neuropathologic Braak tangle stage, especially the early Braak tangle stage (I–IV). Method This study included 57 participants from the Mayo Clinic Study of Aging or Mayo Alzheimer's Disease Research Center, all of whom underwent autopsy and at least two serial AV1451 tau‐PET scans and 3T T1‐weighted MRI within an average of 16 months. Tau‐PET SUVr images were normalized to cerebellar crus grey matter, and MR images were co‐registered to a single‐subject MRI mean. Tau‐PET scans were resampled into MRI space, and OI was calculated by applying intensity thresholds 1.3. Clusters with fewer than 20 contiguous voxels were excluded. An OI cutoff of 0.5 was used and an SUVr cutoff of 1.29. Postmortem immunohistochemistry (IHC) using phospho‐tau antibodies (AT8, PHF‐1) was performed on the amygdala, temporal cortex (middle and superior), and anterior/posterior hippocampus. Neuropathological Braak staging was determined based on AT8‐stained sections. We conducted visual comparison of tau‐PET overlap signal in these regions with IHC‐confirmed tau pathology across Braak tangle stages. Result OI‐detected voxels corresponded to NFT distribution across Braak stages. In early Braak stages, standard SUVr‐based META‐ROI values remained negative, whereas OI showed positive signals, indicating greater sensitivity to early tau pathology (Figure 1). The spatial distribution of OI‐derived overlap regions aligned with high NFT burden, suggesting that OI captures early‐stage tau pathology more effectively than conventional SUVr methods. Conclusion OI in serial tau‐PET imaging demonstrated better detection of early‐stage tau pathology than standard ROI‐based SUVr measures. The alignment between OI‐detected voxels and neuropathological tau burden supports its potential as an in vivo biomarker for detecting early neurofibrillary tangles.

Abstract Background To promote equitable access to neurologic diagnostics, our team developed MASTERMIND—the Mobile Assessment of Speech To Evaluate, Recognize, and Monitor Indicators of Neurologic health and Disease. One subtest, the MASTERMIND Assessment of Naming, includes ten pictures randomly selected from a list of 45 words varying in frequency. This study aimed to 1) evaluate the feasibility of a remotely self‐administered naming test for patients with cognitive, linguistic, and/or communication difficulties and 2) assess the sensitivity and specificity of performance measures (accuracy, error type, and response time) for detecting aphasia and cognitive‐communication difficulties. Method The task was remotely administered to 138 controls, 26 cognitively impaired individuals, 15 with motor speech disorders, 8 with cognitive concerns, and 6 with aphasia. Participants named pictures displayed on‐screen, advancing through the task by clicking “Next.” Responses were processed using natural language processing (NLP). Audio was transcribed with Whisper Large‐v3, and responses were matched to stimuli within a 0.4‐second threshold. Errors were categorized by type (semantic, phonemic, or unclear source) through consensus between two raters. Performance metrics (accuracy, error type, response time) were analyzed using Kruskal‐Wallis and Pearson's Chi‐Square tests, with AUROC analysis evaluating diagnostic performance. Results Differences in accuracy, response time, and error type were observed across groups (Table 1). Controls and cognitively impaired participants performed similarly, with longer response times observed in patients with aphasia and motor speech disorders. Accuracy alone was a poor predictor of all patients versus controls (AUROC = 0.468) but prediction was better in distinguishing aphasia patients from 1) controls (AUROC = 0.698), 2) cognitively impaired participants (AUROC = 0.679), and 3) those with MSDs (AUROC = 0.670). Conclusions The MASTERMIND Assessment of Naming is a feasible, scalable, self‐administered diagnostic tool enhanced by automated NLP. It provides critical insights through response time, accuracy, and error analysis. Limitations include potential bias in error classification and fixed processing thresholds. The study highlights MASTERMIND's potential for patient triaging and screening. Future research will evaluate the sensitivity of combined performance metrics to disease progression, expand sample sizes and diversity, and refine usability based on participant feedback.