Lee, Jeyeon

Abstract INTRODUCTION The tau positron emission tomography (PET) overlap index (OI) has shown promise in maximizing signal‐to‐noise for longitudinal tau PET imaging, particularly for early tau pathology, but requires validation against neuropathology. METHODS Fifty‐seven participants who underwent serial tau PET imaging (flortaucipir) and subsequent autopsy were included. Tau PET OI and standardized uptake value ratios (SUVRs) were compared across neuropathological diagnoses. RESULTS Tau PET OI showed greater concordance with neurofibrillary tangle (NFT) severity in the entorhinal cortex (a key region for Alzheimer's disease [AD] tauopathy) than SUVR, particularly in early Braak tangle stages (positivity: 52.2% for OI vs. 13.0% for SUVR). OI detected overlapping tau voxels that exhibited spatial correspondence with immunohistochemical and autoradiography measures of tau deposition across both AD and non‐AD tauopathies. DISCUSSION These findings demonstrate the enhanced capacity of OI in serial tau PET to robustly detect early and spatially localized tau pathology, supporting its application as a sensitive imaging metric in AD and select non‐AD tauopathies.

Abstract INTRODUCTION Understanding neuroinflammation across the Alzheimer's disease (AD) spectrum is essential to elucidate disease mechanisms and individualize therapy. METHODS Human microglial translocator protein (TSPO) expression under inflammatory stimuli was assessed by immunoblot experiments. Ninety‐six participants were enrolled across four groups: cognitively unimpaired amyloid (CU A) + and −, mild cognitive impaired (MCI) A+, and Alzheimer's disease dementia (ADD) A+. Neuroinflammation using 11 C‐ER176 TSPO positron emission tomography (PET) was compared to amyloid and tau PET. Correlations between neuroinflammation, amyloid, and tau pathology were examined across disease stages. RESULTS TSPO was upregulated in human microglia under AD‐like inflammatory conditions. Neuroinflammation, defined by PET TSPO, increased in CU A+ participants and became more widespread with increasing disease severity, aligning with worsening amyloid and tau pathology. Associations with tau were particularly extensive in temporal and parietal regions. DISCUSSION These findings suggest probable amyloid association with early microglial activation, while tau pathology is closely tied to wider distribution of neuroinflammation. Highlights TSPO expression increases in human microglia under AD‐like inflammation. 11 C‐ER176 PET shows stage‐dependent neuroinflammation across the AD spectrum. Neuroinflammation overlaps with tau and is more widespread in amyloid‐positive individuals. Activity peaks in MCI, stabilizes later, and relates to vascular and neurodegenerative changes.

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 Respiration has traditionally been considered a source of artifact in functional MRI (fMRI) due to chest motion. However, the respiratory cycle also induces changes in local oxygen concentration, resulting in fluctuations in the fMRI signal. These breathing‐triggered signals (BTS) may reflect underlying oxygen metabolism. This preliminary study developed a method to extract BTS from resting‐state BOLD fMRI and examined differences between healthy and mild cognitive impairment (MCI) groups. Method MRI scans were conducted under IRB approval on a compact 3T MRI system. Participants included two groups of 18 healthy volunteers (42.6±16.0 years) and 12 MCI patients (70.9±7.3 years). Each MRI scan included T 1 ‐weighted anatomy imaging and multi‐echo resting‐state fMRI acquisitions. The preprocessing pipeline was implemented by AFNI and FreeSurfer. Figure 1 illustrates the workflow for extracting and averaging fMRI data based on respiratory or cardiac cycle windows. As depicted in Figure 2(c,d), the mean fMRI signals corresponding to the breathing or cardiac cycles were modeled using a low‐order Fourier series to assess their amplitudes. A linear mixed‐effects model was employed to examine the effects of group and breathing cycle period, using MATLAB's ‘fitlme’ function with the formula: BTS ∼ Period + Group. Result As shown in Figure 2(e,f), breathing‐triggered BOLD signals were prominent, while cardiac‐driven BOLD signals were rarely observed. Figure 2(g) shows that global BTSs in the healthy group exhibited a linear relationship with the breathing cycle period, persisting even after artifact correction (RETROICOR for respiration and cardiac cycles and RVT). Figure 3 highlights significant regional differences between groups, particularly in the hippocampus and thalamus ( p < 0.05, uncorrected), where BTS amplitudes were greater in the MCI group. Potentially, the detected BTS could provide some insights into the coupling between respiration and cerebral oxygen metabolism and potentially serve as an imaging biomarker to assess brain health. However, due to the limited number of samples, these findings should be interpreted with caution, and further analysis is needed. Conclusion This preliminary study suggests that breathing‐triggered signals in resting‐state BOLD fMRI have the potential to serve as a noninvasive biomarker for early detection and monitoring of neurodegenerative conditions such as MCI.