Pressure-Induced Changes in the Phase Distribution and Carrier Dynamics of Quasi-Two-Dimensional Ruddlesden-Popper Perovskites.

Quasi-two-dimensional (quasi-2D) perovskites hold significant potential for diverse design strategies due to their tunable structures, exceptional optical properties, and environmental stability. Due to the complexity of the structure and carrier dynamics, characterization methods such as photoluminescence and absorption spectroscopy can observe but cannot precisely distinguish or identify the phase distribution within quasi-2D perovskite films or correlate phases with carrier dynamics. In this study, we used pressure to modulate the intralayer and interlayer structures of (PEA)2Csn-1PbnBr3n+1 quasi-2D perovskite films, investigating charge carrier dynamics. Steady-state spectroscopy revealed phase transitions at 1.62, 3, and 8 GPa, with free excitons transforming into self-trapped excitons after 8 GPa. Transient absorption spectroscopy elucidated the structural evolution, energy transfer, and pressure-induced transition mechanisms. The results demonstrate that combining pressure and spectroscopy enables the precise identification of phase distribution and pressure response ranges and reveals photophysical mechanisms, providing new insights for optimizing optoelectronic materials.

Process Optimization and Characterization of Polysaccharides with Potential Antioxidant and Hypoglycemic Activity from Cissus repens.

Ultrasound-assisted extraction of Cissus repens polysaccharides (CRPs) was optimized through response surface methodology (RSM) based on Box-Behnken design (BBD). The maximum CRPs yield (16.18%) was achieved under the optimum extraction conditions: extraction time 72 min, extraction temperature 74 ℃, extraction power 240 W. Then three-phase partitioning (TPP) method combined with gradient alcohol precipitation was used to obtained CRP20, CRP40, CRP60 and CRP80 from CRPs, and CRP80 has a higher purity than others. The primary chemical and structural characteristics of CRP80 were investigated by UV, FT-IR, high-performance liquid chromatography (HPLC) and high-performance gel-permeation chromatography (HPGPC). CRP80 is mainly composed of glucose, galactose, arabinose and mannose, with a molecular weights of approximately 2.95 kDa. Furthermore, the antioxidant activity and hypoglyceamic activity of CRP80 in vitro were evaluated. The results showed that CRP80 had strong scavenging activities on ABTS, hydroxyl and DPPH radicals, as well as high scavenging activities on α-glucosidase and α-amylase. Our research provided an efficient method for the extraction of polysaccharides from C. repens and CRP80 has potential as a promising source of natural antioxidants and hypoglycemic agent for the functional food and medicinal industries.

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.

Modulating Carrier Dynamics in PdSe2: The Role of Pressure in Electronic and Phononic Interactions.

PdSe2 is a puckered transition metal dichalcogenide that has been reported to undergo a two-dimensional to three-dimensional structural transition under pressure. Here, we investigated the electronic and phononic evolution of PdSe2 under high pressure using pump-probe spectroscopy. We observed the electronic intraband and interband transitions occurring in the d orbitals of Pd, revealing the disappearance of the Jahn-Teller effect under high pressure. Furthermore, we found that the decay rates of interband recombination and intraband relaxation lifetimes change at 3 and 7 GPa, respectively. First-principles calculations suggest that the bandgap closure slows the decay rate of interband recombination after 3 GPa, while the saturation of phonon-phonon scattering is the main reason for the relatively constant intraband relaxation lifetime. Our work provides a novel perspective for understanding the evolution of the electron and modulation of the carrier dynamics by phonons under pressure.

Cardiac vagal activity changes moderated the association of cognitive and cerebral hemodynamic variations in the prefrontal cortex.

Phasic cardiac vagal activity (CVA), reflecting ongoing, moment-to-moment psychophysiological adaptations to environmental changes, can serve as a predictor of individual difference in executive function, particularly executive performance. However, the relationship between phasic CVA and executive function demands requires further validation because of previous inconsistent findings. Moreover, it remains unclear what types of phasic changes of CVA may be adaptive in response to heightened executive demands. This study used the standard N-back task to induce different levels of working memory (WM) load and combined functional Near-Infrared Spectroscopy (fNIRS) with a multipurpose polygraph to investigate the variations of CVA and its interactions with cognitive and prefrontal responses as executive demands increased in fifty-two healthy young subjects. Our results showed phasic decreases in CVA as WM load increased (t (51) = -3.758, p < 0.001, Cohen's d = 0.526). Furthermore, phasic changes of CVA elicited by increased executive demands moderated the association of cognitive and cerebral hemodynamic variations in the prefrontal cortex (B = 0.038, SE = 0.014, p < 0.05). Specifically, as executive demands increased, individuals with larger phasic CVA withdrawal showed a positive relationship between cognitive and hemodynamic variations in the prefrontal cortex (ß = 0.281, p = 0.031). No such significant relationship was observed in individuals with smaller phasic CVA withdrawal. The current findings demonstrate a decrease in CVA with increasing executive demands and provide empirical support for the notion that a larger phasic CVA withdrawal can be considered adaptive in situations requiring high executive function demands.

Shared and distinctive brain networks underlying trait and state rumination.

Although trait and state rumination play a central role in the exacerbation of negative affect, evidence suggests that they are weakly correlated and exert distinct influences on emotional reactivity to stressors. Whether trait and state rumination share a common or exhibit distinct neural substrate remains unclear. In this study, we utilized functional near-infrared spectroscopy (fNIRS) combined with connectome-based predictive modeling (CPM) to identify neural fingerprints associated with trait and state rumination. CPM identified distinctive functional connectivity (FC) profiles that contribute to the prediction of trait rumination, primarily involving FC within the default mode network (DMN) and the dorsal attention network (DAN) as well as FC between the DMN, control network (CN), DAN, and salience network (SN). Conversely, state rumination was predominantly associated with FC between the DMN and CN. Furthermore, the predictive features of trait rumination can be robustly generalized to predict state rumination, and vice versa. In conclusion, this study illuminates the importance of both DMN and non-DMN systems in the emergence and persistence of rumination. While trait rumination was associated with stronger and broader FC than state rumination, the generalizability of the predictive features underscores the presence of shared neural mechanisms between the two forms of rumination. These identified connectivity fingerprints may hold promise as targets for innovative therapeutic interventions aimed at mitigating rumination-related negative affect.

Harmonic Wavelet Neural Network for Discovering Neuropathological Propagation Patterns in Alzheimer's Disease.

Emerging research indicates that the degenerative biomarkers associated with Alzheimer's disease (AD) exhibit a non-random distribution within the cerebral cortex, instead following the structural brain network. The alterations in brain networks occur much earlier than the onset of clinical symptoms, thereby affecting the progression of brain disease. In this context, the utilization of computational methods to ascertain the propagation patterns of neuropathological events would contribute to the comprehension of the pathophysiological mechanism involved in the evolution of AD. Despite the encouraging findings achieved by existing graph-based deep learning approaches in analyzing irregular graph data, their applications in identifying the spreading pathway of neuropathology are limited due to two disadvantages. They include (1) lack of a common brain network as an unbiased reference basis for group comparison, and (2) lack of an appropriate mechanism for the identification of propagation patterns. To this end, we propose a proof-of-concept harmonic wavelet neural network (HWNN) to predict the early stage of AD and localize disease-related significant wavelets, which can be used to characterize the spreading pathways of neuropathological events across the brain network. The extensive experiments constructed on both synthetic and real datasets demonstrate that our proposed method achieves superior performance in classification accuracy and statistical power of identifying propagation patterns, compared with other representative approaches.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.