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.

Ultrafast Decay Dynamics of the 21ππ* Electronic State of N-Methyl-2-pyridone.

The ultrafast decay dynamics of N-methyl-2-pyridone upon excitation in the near-ultraviolet range of 261.5-227.9 nm is investigated using the femtosecond time-resolved photoelectron spectroscopy method. Irradiation at 261.5 nm prepares N-methyl-2-pyridone molecules with high vibrational levels in the 11ππ* state. The radiation-less decay to the ground state via internal conversion is suggested to be the dominant channel for the 11ππ* state with large vibrational excess energy, which is revealed by a lifetime of 1.6 ± 0.2 ps. As the pump wavelength decreases, we found that irradiation at 238.5 and 227.9 nm results in the population of the 21ππ* state. This is in agreement with the assignment of the vapor-phase UV absorption bands of N-methyl-2-pyridone. On the basis of the detailed analysis of our measured time-resolved photoelectron spectra at all pump wavelengths, we conclude that the 21ππ* state has an ultrashort lifetime of 50 ± 10 fs. In addition, the S1(11ππ*) state is subsequently populated via internal conversion and decays over a lifetime of 680-620 fs. The most probable whole deactivation pathway of the 21ππ* state is discussed. This experimental study provides new insights into the excitation energy-dependent decay dynamics of electronically excited N-methyl-2-pyridone.

Real-time observation of two distinctive non-thermalized hot electron dynamics at MXene/molecule interfaces.

The photoinduced non-thermalized hot electrons at an interface play a pivotal role in determining plasmonic driven chemical events. However, understanding non-thermalized electron dynamics, which precedes electron thermalization (~125 fs), remains a grand challenge. Herein, we simultaneously captured the dynamics of both molecules and non-thermalized electrons in the MXene/molecule complexes by femtosecond time-resolved spectroscopy. The real-time observation allows for distinguishing non-thermalized and thermalized electron responses. Differing from the thermalized electron/heat transfer, our results reveal two non-thermalized electron dynamical pathways: (i) the non-thermalized electrons directly transfer to attached molecules at an interface within 50 fs; (ii) the non-thermalized electrons scatter at the interface within 125 fs, inducing adsorbed molecules heating. These two distinctive pathways are dependent on the irradiating wavelength and the energy difference between MXene and adsorbed molecules. This research sheds light on the fundamental mechanism and opens opportunities in photocatalysis and interfacial heat transfer theory.

Disentangling sex-dependent effects of APOE on diverse trajectories of cognitive decline in Alzheimer's disease.

Current diagnostic systems for Alzheimer's disease (AD) rely upon clinical signs and symptoms, despite the fact that the multiplicity of clinical symptoms renders various neuropsychological assessments inadequate to reflect the underlying pathophysiological mechanisms. Since putative neuroimaging biomarkers play a crucial role in understanding the etiology of AD, we sought to stratify the diverse relationships between AD biomarkers and cognitive decline in the aging population and uncover risk factors contributing to the diversities in AD. To do so, we capitalized on a large amount of neuroimaging data from the ADNI study to examine the inflection points along the dynamic relationship between cognitive decline trajectories and whole-brain neuroimaging biomarkers, using a state-of-the-art statistical model of change point detection. Our findings indicated that the temporal relationship between AD biomarkers and cognitive decline may differ depending on the synergistic effect of genetic risk and biological sex. Specifically, tauopathy-PET biomarkers exhibit a more dynamic and age-dependent association with Mini-Mental State Examination scores (p<0.05), with inflection points at 72, 78, and 83 years old, compared with amyloid-PET and neurodegeneration (cortical thickness from MRI) biomarkers. In the landscape of health disparities in AD, our analysis indicated that biological sex moderates the rate of cognitive decline associated with APOE4 genotype. Meanwhile, we found that higher education levels may moderate the effect of APOE4, acting as a marker of cognitive reserve.

Photodissociation dynamics of SO2 via the G̃1B1 state: The O(1D2) and O(1S0) product channels.

Produced by both nature and human activities, sulfur dioxide (SO2) is an important species in the earth's atmosphere. SO2 has also been found in the atmospheres of other planets and satellites in the solar system. The photoabsorption cross sections and photodissociation of SO2 have been studied for several decades. In this paper, we reported the experimental results for photodissociation dynamics of SO2 via the G̃1B1 state. By analyzing the images from the time-sliced velocity map ion imaging method, the vibrational state population distributions and anisotropy parameters were obtained for the O(1D2) + SO(X3Σ-, a1Δ, b1Σ+) and O(1S0) + SO(X3Σ-) channels, and the branching ratios for the channels O(1D2) + SO(X3Σ-), O(1D2) + SO(a1Δ), and O(1D2) + SO(b1Σ+) were determined to be ∼0.3, ∼0.6, and ∼0.1, respectively. The SO products were dominant in electronically and rovibrationally excited states, which may have yet unrecognized roles in the upper planetary atmosphere.

Spectroscopic and Theoretical Identifications of Two Structural Motifs of (H2O)10 Cluster.

Precise characterization of archetypal systems of aqueous hydrogen-bonding networks is essential for developing accurate potential functions and universal models of water. The structures of water clusters (H2O)n (n = 2-9) have been verified recently through size-specific infrared spectroscopy with a vacuum ultraviolet free electron laser (VUV-FEL) and quantum chemical studies. For (H2O)10, the pentagonal prism and butterfly motifs were proposed to be important building blocks and were observed in previous experiments. Here we report the size-specific infrared spectra of (H2O)10 via a joint experimental and theoretical study. Well-resolved spectra provide a unique signature for the coexistence of pentagonal prism and butterfly motifs. These (H2O)10 motifs develop from the dominant structures of (H2O)n (n = 8, 9) clusters. This work provides an intriguing prelude to the diverse structure of liquid water and opens avenues for size-dependent measurement of larger systems to understand the stepwise formation mechanism of hydrogen-bonding networks.

Roaming in highly excited states: The central atom elimination of triatomic molecule decomposition.

Chemical reactions are generally assumed to proceed from reactants to products along the minimum energy path (MEP). However, straying from the MEP-roaming-has been recognized as an unconventional reaction mechanism and found to occur in both the ground and first excited states. Its existence in highly excited states is however not yet established. We report a dissociation channel to produce electronically excited fragments, S(1D)+O2(a1Δg), from SO2 photodissociation in highly excited states. The results revealed two dissociation pathways: One proceeds through the MEP to produce vibrationally colder O2(a1Δg) and the other yields vibrationally hotter O2(a1Δg) by means of a roaming pathway involving an intramolecular O abstraction during reorientation motion. Such roaming dynamics may well be the rule rather than the exception for molecular photodissociation through highly excited states.

Development of PainFace software to simplify, standardize, and scale up mouse grimace analyses.

ABSTRACT: Facial grimacing is used to quantify spontaneous pain in mice and other mammals, but scoring relies on humans with different levels of proficiency. Here, we developed a cloud-based software platform called PainFace (http://painface.net) that uses machine learning to detect 4 facial action units of the mouse grimace scale (orbitals, nose, ears, whiskers) and score facial grimaces of black-coated C57BL/6 male and female mice on a 0 to 8 scale. Platform accuracy was validated in 2 different laboratories, with 3 conditions that evoke grimacing-laparotomy surgery, bilateral hindpaw injection of carrageenan, and intraplantar injection of formalin. PainFace can generate up to 1 grimace score per second from a standard 30 frames/s video, making it possible to quantify facial grimacing over time, and operates at a speed that scales with computing power. By analyzing the frequency distribution of grimace scores, we found that mice spent 7x more time in a "high grimace" state following laparotomy surgery relative to sham surgery controls. Our study shows that PainFace reproducibly quantifies facial grimaces indicative of nonevoked spontaneous pain and enables laboratories to standardize and scale-up facial grimace analyses.

Time-Resolved Dynamics of Metal Halide Perovskite under High Pressure: Recent Progress and Challenges.

Metal halide perovskites have garnered significant attention in the scientific community for their promising applications in optoelectronic devices. The application of pressure engineering, a viable technique, has played a crucial role in substantially improving the optoelectronic characteristics of perovskites. Despite notable progress in understanding ground-state structural changes under high pressure, a comprehensive exploration of excited-state dynamics influencing luminescence remains incomplete. This Perspective delves into recent advances in time-resolved dynamics studies of photoexcited metal halide perovskites under high pressure. With a focus on the intricate interplay between structural alterations and electronic properties, we investigate electron-phonon interactions, carrier transport mechanisms, and the influential roles of self-trapped excitons (STEs) and coherent phonons in luminescence. However, significant challenges persist, notably the need for more advanced measurement techniques and a deeper understanding of the phenomena induced by high pressure in perovskites.

Insights into ultrafast decay dynamics of electronically excited pyridine-N-oxide.

The ultrafast decay dynamics of pyridine-N-oxide upon excitation in the near-ultraviolet range of 340.2-217.6 nm is investigated using the femtosecond time-resolved photoelectron imaging technique. The time-resolved photoelectron spectra and photoelectron angular distributions at all pump wavelengths are carefully analyzed and the following view is derived: at the longest pump wavelengths (340.2 and 325.6 nm), pyridine-N-oxide is excited to the S1(1ππ*) state with different vibrational levels. The depopulation rate of the S1 state shows a marked dependence on vibrational energy and mode, and the lifetime is in the range of 1.4-160 ps. At 289.8 and 280.5 nm, both the second 1ππ* state and the S1 state are initially prepared. The former has an extremely short lifetime of ∼60 fs, which indicates that the ultrafast deactivation pathway such as a rapid internal conversion to one close-lying state is its dominant decay channel, while the latter is at high levels of vibrational excitation and decays within the range of 380-520 fs. At the shortest pump wavelengths (227.3 and 217.6 nm), another excited state of Rydberg character is mostly excited. We assign this state to the 3s Rydberg state which has a lifetime of 0.55-2.2 ps. This study provides a comprehensive picture of the ultrafast excited-state decay dynamics of the photoexcited pyridine-N-oxide molecule.

Unraveling how the adolescent brain deals with criticism using dynamic causal modeling.

Sensitivity to criticism, which can be defined as a negative evaluation that a person receives from someone else, is considered a risk factor for the development of psychiatric disorders in adolescents. They may be more vulnerable to social evaluation than adults and exhibit more inadequate emotion regulation strategies such as rumination. The neural network involved in dealing with criticism in adolescents may serve as a biomarker for vulnerability to depression. However, the directions of the functional interactions between the brain regions within this neural network in adolescents are still unclear. In this study, 64 healthy adolescents (aged 14 to 17 years) were asked to listen to a series of self-referential auditory segments, which included negative (critical), positive (praising), and neutral conditions, during fMRI scanning. Dynamic Causal Modeling (DCM) with Parametric Empirical Bayesian (PEB) analysis was performed to map the interactions within the neural network that was engaged during the processing of these segments. Three regions were identified to form the interaction network: the left pregenual anterior cingulate cortex (pgACC), the left dorsolateral prefrontal cortex (DLPFC), and the right precuneus (preCUN). We quantified the modulatory effects of exposure to criticism and praise on the effective connectivity between these brain regions. Being criticized was found to significantly inhibit the effective connectivity from the preCUN to the DLPFC. Adolescents who scored high on the Perceived Criticism Measure (PCM) showed less inhibition of the preCUN-to-DLPFC connectivity when being criticized, which may indicate that they required more engagement of the Central Executive Network (which includes the DLPFC) to sufficiently disengage from negative self-referential processing. Furthermore, the inhibitory connectivity from the DLPFC to the pgACC was strengthened by exposure to praise as well as criticism, suggesting a recruitment of cognitive control over emotional responses when dealing with positive and negative evaluative feedback. Our novel findings contribute to a more profound understanding of how criticism affects the adolescent brain and can help to identify potential biomarkers for vulnerability to develop mood disorders before or during adulthood.

Developing Explainable Deep Model for Discovering Novel Control Mechanism of Neuro-Dynamics.

Human brain is a complex system composed of many components that interact with each other. A well-designed computational model, usually in the format of partial differential equations (PDEs), is vital to understand the working mechanisms that can explain dynamic and self-organized behaviors. However, the model formulation and parameters are often tuned empirically based on the predefined domain-specific knowledge, which lags behind the emerging paradigm of discovering novel mechanisms from the unprecedented amount of spatiotemporal data. To address this limitation, we sought to link the power of deep neural networks and physics principles of complex systems, which allows us to design explainable deep models for uncovering the mechanistic role of how human brain (the most sophisticated complex system) maintains controllable functions while interacting with external stimulations. In the spirit of optimal control, we present a unified framework to design an explainable deep model that describes the dynamic behaviors of underlying neurobiological processes, allowing us to understand the latent control mechanism at a system level. We have uncovered the pathophysiological mechanism of Alzheimer's disease to the extent of controllability of disease progression, where the dissected system-level understanding enables higher prediction accuracy for disease progression and better explainability for disease etiology than conventional (black box) deep models.

Pathology steered stratification network for subtype identification in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by three neurobiological factors beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for AD at a late stage, urging for early detection and prevention. However, existing statistical inference approaches in neuroimaging studies of AD subtype identification do not take into account the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles. PURPOSE: Integrating systems biology modeling with machine learning, the study aims to assist clinical AD prognosis by providing a subpopulation classification in accordance with essential biological principles, neurological patterns, and cognitive symptoms. METHODS: We propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along the brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term evolution trajectories that capture individual characteristic progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. RESULTS: Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. CONCLUSIONS: The proposed PSSN (i) reduces neuroimage data to low-dimensional feature vectors, (ii) combines AT[N]-Net based on real pathological pathways, (iii) predicts long-term biomarker trajectories, and (iv) stratifies subjects into fine-grained subtypes with distinct neurological underpinnings. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases.

The interaction effects of age, APOE and common environmental risk factors on human brain structure.

Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.

Excitation Energy-Dependent Decay Dynamics of the S1 State of N-Methyl-2-pyridone.

The UV-induced decay dynamics of N-methyl-2-pyridone is investigated using a femtosecond time-resolved photoelectron spectroscopy method. Irradiation in the wavelength range of 339.3-258.9 nm prepares N-methyl-2-pyridone molecules with very different vibrational levels of the S1(11ππ*) state. For v' = 0 (origin) and a few low-energy vibrational levels slightly above the S1 state origin, the radiative decay channel is in operation for some specific vibrations. This is revealed by the excited-state lifetime of ≫1 ns. In addition, some other nearby S1 vibronic states have a much shorter lifetime in the range of several picoseconds to a few tens of picoseconds, indicating that the radiation-less decay to the ground state (S0) via internal conversion is the dominant channel for them. As the pump wavelength slightly decreases, the radiative decay is suddenly not important at all, and the deactivation rate of the S1 state becomes faster. At shorter pump wavelengths, the lifetime of highly excited vibrational states of the S1 state further decreases with the increase in the vibrational excess energy. This study provides quantitative information about the excitation energy-dependent decay dynamics of the S1 state of N-methyl-2-pyridone. Methyl substitution effects on the excited-state dynamics of 2-pyridone are also discussed.

Solid-State Nanopore Distinguishes Ferritin and Apo-Ferritin with Identical Exteriors through Amplified Flexibility at Single-Molecule Level.

Protein identification and discrimination at the single-molecule level are big challenges. Solid-state nanopores as a sensitive biosensor have been used for protein analysis, although it is difficult to discriminate proteins with similar structures in the traditional discrimination method based on the current blockage fraction. Here, we select ferritin and apo-ferritin as the model proteins that exhibit identical exterior and different interior structures and verify the practicability of their discrimination with flexibility features by the strategy of gradually decreasing the nanopore size. We show that the larger nanopore (relative to the protein size) has no obvious effect on discriminating two proteins. Then, the comparable-sized nanopore plays a key role in discriminating two proteins based on the dwell time and fraction distribution, and the conformational changes of both proteins are also studied with this nanopore. Finally, in the smaller nanopore, the protein molecules are trapped rather than translocated, where two proteins are obviously discriminated through the current fluctuation caused by the vibration of proteins. This strategy has potential in the discrimination of other important similar proteins.

Evidence of Surface-Mediated Carrier-Phonon Scattering in MXene.

In a two-dimensional (2D) metallic nanostructure, when a sample's thickness is shorter than a carrier mean free path, the ultrathin thickness may influence carrier and energy transport, owing to surface scattering. However, to date, for metallic 2D transition-metal carbides (MXenes), experiments and calculations related to surface scattering have not been performed. The contribution of ultrathin structures to carrier surface scattering in MXene is yet to be explored. Herein, to reveal this effect, we design various models, including metal/MXene, dielectric/MXene, and bulk structure, and analyze their carrier dynamics via ultrafast spectroscopy. The results related to carrier dynamics indicate that the influence of the dielectric/MXene interface and the temperature is negligible. In contrast, the carrier dynamic lifetimes are prolonged owing to weakened surface scattering in metal/MXene, which is supported by ab initio calculations. These results suggest that the carrier-phonon scattering is dominated by surface scattering. These findings can help guide effective energy transport and enhance energy conversion and catalysis.

Manipulating Lone-Pair-Driven Luminescence in 0D Tin Halides by Pressure-Tuned Stereochemical Activity from Static to Dynamic.

The excellent luminescence properties and structural dynamics driven by the stereoactivity of the lone pair in a variety of low-dimensional ns2 metal halides have attracted growing investigations for optoelectronic applications. However, the structural and photophysical aspects of the excited state associated with the lone pair expression are currently open questions. Herein, zero-dimensional Sn-based halides with static stereoactive 5 s2 lone pairs are selected as a model system to understand the correlations between the distinctive lone pair expression and the excited-state structural relaxation and charge carrier dynamics by continuous lattice manipulation. Lattice compression drives 5 s2 lone pair active switching and self-trapped exciton (STE) redistribution by suppressing excited-state structural deformation of the isolated SnBr4 2- units. Our results demonstrate that the static expression of the 5 s2 lone pair results in a red broadband triplet STE emission with a large Stokes shift, while its dynamic expression creates a sky-blue narrowband emission dominated by the radiative recombination of singlet STEs. Our findings and the photophysical mechanism proposed highlight the stereochemical effects of lone pair expression in controlling light emission properties and offer constructive guidelines for tuning the optoelectronic properties in diverse ns2 metal halides.

The moderating effect of resting heart rate variability on the relationship between internet addiction tendency and brain morphology.

Previous neuroimaging studies have investigated brain morphology associated with internet addiction tendency (IAT) in healthy subjects. However, whether resting vagally-mediated heart rate variability (HRV) exerting influences on the association of IAT and brain morphology remains unclear. This study used voxel-based morphometry (VBM) and multiple regression analyses to assess the interaction effect of IAT and resting vagally-mediated HRV on regional grey matter volumes in 82 healthy subjects. To further illustrate the observed interaction effect, the moderated hierarchical regression analysis was performed. The results showed that resting vagally-mediated HRV moderated the relationship between IAT scores and grey matter volume (GMV) in the precuneus and cerebellum. Specifically, individuals with higher resting vagally-mediated HRV showed a significant positive relationship between IAT scores and GMV in the precuneus, whereas individuals with lower resting vagally-mediated HRV showed a significant negative relationship between IAT scores and GMV in the precuneus. In addition, IAT scores were negatively correlated with GMV in the cerebellum among individuals with lower resting vagally-mediated HRV, but not among individuals with higher resting vagally-mediated HRV. These findings have demonstrated a moderating role of resting vagally-mediated HRV on the association of IAT and brain morphology.

Uncovering the System Vulnerability and Criticality of Human Brain Under Dynamical Neuropathological Events in Alzheimer's Disease.

BACKGROUND: Despite the striking efforts in investigating neurobiological factors behind the acquisition of amyloid-ß (A), protein tau (T), and neurodegeneration ([N]) biomarkers, the mechanistic pathways of how AT[N] biomarkers spreading throughout the brain remain elusive. OBJECTIVE: To disentangle the massive heterogeneities in Alzheimer's disease (AD) progressions and identify vulnerable/critical brain regions to AD pathology. METHODS: In this work, we characterized the interaction of AT[N] biomarkers and their propagation across brain networks using a novel bistable reaction-diffusion model, which allows us to establish a new systems biology underpinning of AD progression. We applied our model to large-scale longitudinal neuroimages from the ADNI database and studied the systematic vulnerability and criticality of brains. RESULTS: Our model yields long term prediction that is statistically significant linear correlated with temporal imaging data, produces clinically consistent risk prediction, and captures the Braak-like spreading pattern of AT[N] biomarkers in AD development. CONCLUSIONS: Our major findings include (i) tau is a stronger indicator of regional risk compared to amyloid, (ii) temporal lobe exhibits higher vulnerability to AD-related pathologies, (iii) proposed critical brain regions outperform hub nodes in transmitting disease factors across the brain, and (iv) comparing the spread of neuropathological burdens caused by amyloid-ß and tau diffusions, disruption of metabolic balance is the most determinant factor contributing to the initiation and progression of AD.