Uncovering distinct progression patterns of tau deposition in progressive supranuclear palsy using [18F]Florzolotau PET imaging and subtype/stage inference algorithm.

BACKGROUND: Progressive supranuclear palsy (PSP) is a primary 4-repeat tauopathy with diverse clinical phenotypes. Previous post-mortem studies examined tau deposition sequences in PSP, but in vivo scrutiny is lacking. METHODS: We conducted [18F]Florzolotau tau positron emission tomography (PET) scans on 148 patients who were clinically diagnosed with PSP and 20 healthy controls. We employed the Subtype and Stage Inference (SuStaIn) algorithm to identify PSP subtype/stage and related tau patterns, comparing clinical features across subtypes and assessing PSP stage-clinical severity association. We also evaluated functional connectivity differences among subtypes through resting-state functional magnetic resonance imaging. FINDINGS: We identified two distinct subtypes of PSP: Subtype1 and Subtype2. Subtype1 typically exhibits a sequential progression of the disease, starting from subcortical and gradually moving to cortical regions. Conversely, Subtype2 is characterized by an early, simultaneous onset in both regions. Interestingly, once the disease is initiated, Subtype1 tends to spread more rapidly within each region compared to Subtype2. Individuals categorized as Subtype2 are generally older and exhibit less severe dysfunctions in areas such as cognition, bulbar, limb motor, and general motor functions compared to those with Subtype1. Moreover, they have a more favorable prognosis in terms of limb motor function. We found significant correlations between several clinical variables and the identified PSP SuStaIn stages. Furthermore, Subtype2 displayed a remarkable reduction in functional connectivity compared to Subtype1. INTERPRETATION: We present the evidence of distinct in vivo spatiotemporal tau trajectories in PSP. Our findings can contribute to precision medicine advancements for PSP. FUNDING: This work was supported by grants from the National Natural Science Foundation of China (number 82272039, 81971641, 82021002, and 92249302); Swiss National Science Foundation (number 188350); the STI2030-Major Project of China (number 2022ZD0211600); the Clinical Research Plan of Shanghai Hospital Development Center of China (number SHDC2020CR1038B); and the National Key R&D Program of China (number 2022YFC2009902, 2022YFC2009900), the China Scholarship Council (number 202006100181); the Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy, ID 390857198).

Improved interpretation of 18F-florzolotau PET in progressive supranuclear palsy using a normalization-free deep-learning classifier.

While 18F-florzolotau tau PET is an emerging biomarker for progressive supranuclear palsy (PSP), its interpretation has been hindered by a lack of consensus on visual reading and potential biases in conventional semi-quantitative analysis. As clinical manifestations and regions of elevated 18F-florzolotau binding are highly overlapping in PSP and the Parkinsonian type of multiple system atrophy (MSA-P), developing a reliable discriminative classifier for 18F-florzolotau PET is urgently needed. Herein, we developed a normalization-free deep-learning (NFDL) model for 18F-florzolotau PET, which achieved significantly higher accuracy for both PSP and MSA-P compared to semi-quantitative classifiers. Regions driving the NFDL classifier's decision were consistent with disease-specific topographies. NFDL-guided radiomic features correlated with clinical severity of PSP. This suggests that the NFDL model has the potential for early and accurate differentiation of atypical parkinsonism and that it can be applied in various scenarios due to not requiring subjective interpretation, MR-dependent, and reference-based preprocessing.

The heterogeneity of asymmetric tau distribution is associated with an early age at onset and poor prognosis in Alzheimer's disease.

PURPOSE: Left-right asymmetry, an important feature of brain development, has been implicated in neurodegenerative diseases, although it's less discussed in typical Alzheimer's disease (AD). We sought to investigate whether asymmetric tau deposition plays a potential role in AD heterogeneity. METHODS: Two independent cohorts consisting of patients with mild cognitive impairment due to AD and AD dementia with tau PET imaging were enrolled [the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort with 18F-Flortaucipir, the Shanghai Memory Study (SMS) cohort with 18F-Florzolotau]. Based on the absolute global tau interhemispheric differences, each cohort was divided into two groups (asymmetric versus symmetric tau distribution). The two groups were cross-sectionally compared in terms of demographic, cognitive characteristics, and pathological burden. The cognitive decline trajectories were analyzed longitudinally. RESULTS: Fourteen (23.3%) and 42 (48.3%) patients in the ADNI and SMS cohorts showed an asymmetric tau distribution, respectively. An asymmetric tau distribution was associated with an earlier age at disease onset (proportion of early-onset AD: ADNI/SMS/combined cohorts, p = 0.093/0.026/0.001) and more severe pathological burden (i.e., global tau burden: ADNI/SMS cohorts, p < 0.001/= 0.007). And patients with an asymmetric tau distribution were characterized by a steeper cognitive decline longitudinally (i.e., the annual decline of Mini-Mental Status Examination score: ADNI/SMS/combined cohorts, p = 0.053 / 0.035 / < 0.001). CONCLUSIONS: Asymmetry in tau deposition, which may be associated with an earlier age at onset, more severe pathological burden, and a steeper cognitive decline, is potentially an important characteristic of AD heterogeneity.

Concept development of an on-chip PET system.

BACKGROUND: Organs-on-Chips (OOCs), microdevices mimicking in vivo organs, find growing applications in disease modeling and drug discovery. With the increasing number of uses comes a strong demand for imaging capabilities of OOCs as monitoring physiologic processes within OOCs is vital for the continuous improvement of this technology. Positron Emission Tomography (PET) would be ideal for OOC imaging, however, current PET systems are insufficient for this task due to their inadequate spatial resolution. In this work, we propose the concept of an On-Chip PET system capable of imaging OOCs and optimize its design using a Monte Carlo Simulation (MCS). MATERIAL AND METHODS: The proposed system consists of four detectors arranged around the OOC device. Each detector is made of two monolithic LYSO crystals and covered with Silicon photomultipliers (SiPMs) on multiple surfaces. We use a Convolutional Neural Network (CNN) trained with data from a MCS to predict the first gamma-ray interaction position inside the detector from the light patterns that are recorded by the SiPMs on the detector's surfaces. RESULTS: The CNN achieves a mean average prediction error of 0.80 mm in the best configuration. The proposed system achieves a sensitivity of 34.81% for 13 mm thick crystals and does not show a prediction degradation near the boundaries of the detector. We use the trained network to reconstruct an image of a grid of 21 point sources spread across the field-of-view and obtain a mean spatial resolution of 0.55 mm. We show that 25,000 Line of Responses (LORs) are needed to reconstruct a realistic OOC phantom with adequate image quality. CONCLUSIONS: We demonstrate that it is possible to achieve a spatial resolution of almost 0.5 mm in a PET system made of multiple monolithic LYSO crystals by directly predicting the scintillation position from light patterns created with SiPMs. We observe that a thinner crystal performs better than a thicker one, that increasing the SiPM size from 3 mm to 6 mm only slightly decreases the prediction performance, and that certain surfaces encode significantly more information for the scintillation-point prediction than others.