TauFlowNet: Revealing latent propagation mechanism of tau aggregates using deep neural transport equations.

Mounting evidence shows that Alzheimer's disease (AD) is characterized by the propagation of tau aggregates throughout the brain in a prion-like manner. Since current pathology imaging technologies only provide a spatial mapping of tau accumulation, computational modeling becomes indispensable in analyzing the spatiotemporal propagation patterns of widespread tau aggregates from the longitudinal data. However, current state-of-the-art works focus on the longitudinal change of focal patterns, lacking a system-level understanding of the tau propagation mechanism that can explain and forecast the cascade of tau accumulation. To address this limitation, we conceptualize that the intercellular spreading of tau pathology forms a dynamic system where each node (brain region) is ubiquitously wired with other nodes while interacting with the build-up of pathological burdens. In this context, we formulate the biological process of tau spreading in a principled potential energy transport model (constrained by brain network topology), which allows us to develop an explainable neural network for uncovering the spatiotemporal dynamics of tau propagation from the longitudinal tau-PET scans. Specifically, we first translate the transport equation into a GNN (graph neural network) backbone, where the spreading flows are essentially driven by the potential energy of tau accumulation at each node. Conventional GNNs employ a l2-norm graph smoothness prior, resulting in nearly equal potential energies across nodes, leading to vanishing flows. Following this clue, we introduce the total variation (TV) into the graph transport model, where the nature of system's Euler-Lagrange equations is to maximize the spreading flow while minimizing the overall potential energy. On top of this min-max optimization scenario, we design a generative adversarial network (GAN-like) to characterize the TV-based spreading flow of tau aggregates, coined TauFlowNet. We evaluate our TauFlowNet on ADNI and OASIS datasets in terms of the prediction accuracy of future tau accumulation and explore the propagation mechanism of tau aggregates as the disease progresses. Compared to the current counterpart methods, our physics-informed deep model yields more accurate and interpretable results, demonstrating great potential in discovering novel neurobiological mechanisms through the lens of machine learning.

Uncovering Diverse Mechanistic Spreading Pathways in Disease Progression of Alzheimer's Disease.

Background: The AT[N] research framework focuses on three major biomarkers in Alzheimer's disease (AD): amyloid-ß deposition (A), pathologic tau (T), and neurodegeneration [N]. Objective: We hypothesize that the diverse mechanisms such as A⟶T and A⟶[N] pathways from one brain region to others, may underlie the wide variation in clinical symptoms. We aim to uncover the causal-like effect of regional AT[N] biomarkers on cognitive decline as well as the interaction with non-modifiable risk factors such as age and APOE4. Methods: We apply multi-variate statistical inference to uncover all possible mechanistic spreading pathways through which the aggregation of an upstream biomarker (e.g., increased amyloid level) in a particular brain region indirectly impacts cognitive decline, via the cascade build-up of a downstream biomarker (e.g., reduced metabolism level) in another brain region. Furthermore, we investigate the survival time for each identified region-to-region pathological pathway toward the AD onset. Results: We have identified a collection of critical brain regions on which the amyloid burdens exert an indirect effect on the decline in memory and executive function (EF) domain, being mediated by the reduction of metabolism level at other brain regions. APOE4 status has been found not only involved in many A⟶N mechanistic pathways but also significantly contributes to the risk of developing AD. Conclusion: Our major findings include 1) the region-to-region A⟶N⟶MEM and A⟶N⟶MEM pathways exhibit distinct spatial patterns; 2) APOE4 is significantly associated with both direct and indirect effects on the cognitive decline while sex difference has not been identified in the mediation analysis.

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy.

Tissue clearing followed by light-sheet microscopy (LSFM) enables cellular-resolution imaging of intact brain structure, allowing quantitative analysis of structural changes caused by genetic or environmental perturbations. Whole-brain imaging results in more accurate quantification of cells and the study of region-specific differences that may be missed with commonly used microscopy of physically sectioned tissue. Using light-sheet microscopy to image cleared brains greatly increases acquisition speed as compared to confocal microscopy. Although these images produce very large amounts of brain structural data, most computational tools that perform feature quantification in images of cleared tissue are limited to counting sparse cell populations, rather than all nuclei. Here, we demonstrate NuMorph (Nuclear-Based Morphometry), a group of analysis tools, to quantify all nuclei and nuclear markers within annotated regions of a postnatal day 4 (P4) mouse brain after clearing and imaging on a light-sheet microscope. We describe magnetic resonance imaging (MRI) to measure brain volume prior to shrinkage caused by tissue clearing dehydration steps, tissue clearing using the iDISCO+ method, including immunolabeling, followed by light-sheet microscopy using a commercially available platform to image mouse brains at cellular resolution. We then demonstrate this image analysis pipeline using NuMorph, which is used to correct intensity differences, stitch image tiles, align multiple channels, count nuclei, and annotate brain regions through registration to publicly available atlases. We designed this approach using publicly available protocols and software, allowing any researcher with the necessary microscope and computational resources to perform these techniques. These tissue clearing, imaging, and computational tools allow measurement and quantification of the three-dimensional (3D) organization of cell-types in the cortex and should be widely applicable to any wild-type/knockout mouse study design.

Marginal quality and fracture strength of root-canal treated mandibular molars with overlay restorations after thermocycling and mechanical loading.

PURPOSE: To evaluate marginal quality, fracture modes, and loads-to-failure of different overlay restorations in rootcanal treated molars in a laboratory setup. MATERIALS AND METHODS: Thirty-two mandibular first molars were randomly assigned to four groups (n = 8): UTR= untreated (control), RCT-COM= root canal treated (RCT)+ lab-made composite overlay, RCT-FRC= RCT+composite resin overlay with two layers of multidirectional woven glass fibers; RCT-CER: RCT+ceramic overlay. The teeth in all groups were subjected to thermocycling and mechanical loading (TCML) in a computer-controlled masticator (1,200,000 loads, 49 N, 1.7 Hz, 3000 temperature cycles of 5°C to 50°C). Marginal adaptation was evaluated before and after TCML with scanning electron microscopy at 200X at the tooth-to-luting composite (IF1) and luting composite-to restoration (IF2) interfaces. After TCML, all specimens were loaded to failure in a universal testing machine at 0.5 mm/min. Data were analyzed with ANOVA and Bonferroni correction. RESULTS: Marginal adaptation decreased from 93 ± 3.4 to 82 ± 6.5 % at IF1 after TCML (p > 0.001) but the decrease was not significant between the groups (p = 0.8130). At IF2, ceramic overlays showed about 10% lower marginal adaptation than composite overlays (p < 0.0001). Loads-to-failure (in N) were as follows in descending order: RCT-FRC: 3619 ± 520; UTR: 3048 ± 905; RCT-COM: 2770 ± 457; RCT-CER 2036 ± 319. RCT-FRC showed significantly higher results than those of RCT-COM (p = 0.0077) and RCT-CER (p < 0.0001). Only RCT-CER showed significantly lower results than that of the control (p = 0.0019). While the fractures in the UTR occurred exclusively above the cementoenamel junction (Mode 1 and Mode 2) and were rated reparable, RCT-COM and RCT-CER showed exclusively catastrophic failures in varying modes (nodes 3 to 5). Only in group RCT-FRC, half of the specimens fractured in a reparable fracture mode (modes 1 and 2) with veneering composite delamination from the glass-fiber weaver layer. CONCLUSION: As cusp-covering overlay restorations in root canal treated molars, composite resin overlays with and without fiber reinforcement performed similar to intact teeth with varying failure types. While intact teeth failed exclusively in reparable modes, all other restorations failed in a catastrophic manner, except half of the fiber reinforced composite group.