Neural synchronization predicts marital satisfaction.

Marital attachment plays an important role in maintaining intimate personal relationships and sustaining psychological well-being. Mate-selection theories suggest that people are more likely to marry someone with a similar personality and social status, yet evidence for the association between personality-based couple similarity measures and marital satisfaction has been inconsistent. A more direct and useful approach for understanding fundamental processes underlying marital satisfaction is to probe similarity of dynamic brain responses to maritally and socially relevant communicative cues, which may better reflect how married couples process information in real time and make sense of their mates and themselves. Here, we investigate shared neural representations based on intersubject synchronization (ISS) of brain responses during free viewing of marital life-related, and nonmarital, object-related movies. Compared to randomly selected pairs of couples, married couples showed significantly higher levels of ISS during viewing of marital movies and ISS between married couples predicted higher levels of marital satisfaction. ISS in the default mode network emerged as a strong predictor of marital satisfaction and canonical correlation analysis revealed a specific relation between ISS in this network and shared communication and egalitarian components of martial satisfaction. Our findings demonstrate that brain similarities that reflect real-time mental responses to subjective perceptions, thoughts, and feelings about interpersonal and social interactions are strong predictors of marital satisfaction, reflecting shared values and beliefs. Our study advances foundational knowledge of the neurobiological basis of human pair bonding.

High-throughput computational materials screening of transition metal peroxides.

Semiconductor materials of abnormal stoichiometric ratio often exhibit unique properties, yet it is still a challenge to determine the structures of such materials in an efficient way. Herein, we propose a method for structurally biased screening according to the coordination numbers and the numbers of Wyckoff positions, balancing the atom local environment and the global symmetry of structures. Based on first-principles calculations, we have predicted two metastable peroxides P21/c-ScO2 and Pmmn-TiO3 with more than six coordination points. For these two structures, the most stable intrinsic defect is the oxygen vacancy (VO) at the peroxide anion (O2-2), which induces the absence of antibonding orbital formed by O2-2 near the valence band maximum. With the introduction of VO, the decrease of coordination numbers leads to charge recombination, and results in the appearance of an ordered phase TiO2.5 with stronger Ti-O orbital hybridization. The proposed method presents a promising and feasible approach for the screening of novel compounds.

Generalized Octet Rule with Fractional Occupancies for Boron.

The octet rule is a fundamental theory in the chemical bonding of main-group elements, which achieve stable configurations by gaining, losing, or sharing electrons. However, the conventional octet rule, as depicted through Lewis structures, is inadequate for describing the electron delocalization in boron allotropes and boron-rich compounds due to the electron deficiency of boron. To address this, we introduce the concept of fractional electron occupancies, which more accurately reflect the electron delocalization in boron systems. Based on this, we propose a generalized octet rule that provides a more comprehensive understanding of the complex bonding configurations in boron allotropes and boron-rich compounds. Importantly, our predictions for α-B12 are validated by both first-principles calculations and existing experimental data. Beyond boron, this generalized octet rule is also applicable to systems with multiple resonance structures.

Hollow Heterostructured Nanocatalysts for Boosting Electrocatalytic Water Splitting.

The implementation of electrochemical water splitting demands the development and application of electrocatalysts to overcome sluggish reaction kinetics of hydrogen/oxygen evolution reaction (HER/OER). Hollow nanostructures, particularly for hollow heterostructured nanomaterials can provide multiple solutions to accelerate the HER/OER kinetics owing to their advantageous merit. Herein, the recent advances of hollow heterostructured nanocatalysts and their excellent performance for water splitting are systematically summarized. Starting by illustrating the intrinsically advantageous features of hollow heterostructures, achievements in engineering hollow heterostructured electrocatalysts are also highlighted with the focus on structural design, interfacial engineering, composition regulation, and catalytic evaluation. Finally, some perspective insights and future challenges of hollow heterostructured nanocatalysts for electrocatalytic water splitting are also discussed.

Dimensional Analysis of Atypical Functional Connectivity of Major Depression Disorder and Bipolar Disorder.

Literatures have reported considerable heterogeneity with atypical functional connectivity (FC) pattern of psychiatric disorders. However, traditional statistical methods are hard to explore this heterogeneity pattern. We proposed a "brain dimension" method to describe the atypical FC patterns of major depressive disorder and bipolar disorder (BD). The approach was firstly applied to a simulation dataset. It was then utilized to a real resting-state functional magnetic resonance imaging dataset of 47 individuals with major depressive disorder, 32 individuals with BD, and 52 well matched health controls. Our method showed a better ability to extract the FC dimensions than traditional methods. The results of the real dataset revealed atypical FC dimensions for major depressive disorder and BD. Especially, an atypical FC dimension which exhibited decreased FC strength of thalamus and basal ganglia was found with higher severity level of individuals with BD than the ones with major depressive disorder. This study provided a novel "brain dimension" method to view the atypical FC patterns of major depressive disorder and BD and revealed shared and specific atypical FC patterns between major depressive disorder and BD.

Linked Social-Communication Dimensions and Connectivity in Functional Brain Networks in Autism Spectrum Disorder.

Much recent attention has been directed toward elucidating the structure of social interaction-communication dimensions and whether and how these symptom dimensions coalesce with each other in individuals with autism spectrum disorder (ASD). However, the underlying neurobiological basis of these symptom dimensions is unknown, especially the association of social interaction and communication dimensions with brain networks. Here, we proposed a method of whole-brain network-based regression to identify the functional networks linked to these symptom dimensions in a large sample of children with ASD. Connectome-based predictive modeling (CPM) was established to explore neurobiological evidence that supports the merging of communication and social interaction deficits into one symptom dimension (social/communication deficits). Results showed that the default mode network plays a core role in communication and social interaction dimensions. A primary sensory perceptual network mainly contributed to communication deficits, and high-level cognitive networks mainly contributed to social interaction deficits. CPM revealed that the functional networks associated with these symptom dimensions can predict the merged dimension of social/communication deficits. These findings delineate a link between brain functional networks and symptom dimensions for social interaction and communication and further provide neurobiological evidence supporting the merging of communication and social interaction deficits into one symptom dimension.

Mapping Progressive Gray Matter Alterations in Early Childhood Autistic Brain.

Autism spectrum disorder is an early-onset neurodevelopmental condition. This study aimed to investigate the progressive structural alterations in the autistic brain during early childhood. Structural magnetic resonance imaging scans were examined in a cross-sectional sample of 67 autistic children and 63 demographically matched typically developing (TD) children, aged 2-7 years. Voxel-based morphometry and a general linear model were used to ascertain the effects of diagnosis, age, and a diagnosis-by-age interaction on the gray matter volume. Causal structural covariance network analysis was performed to map the interregional influences of brain structural alterations with increasing age. The autism group showed spatially distributed increases in gray matter volume when controlling for age-related effects, compared with TD children. A significant diagnosis-by-age interaction effect was observed in the fusiform face area (FFA, Fpeak = 13.57) and cerebellum/vermis (Fpeak = 12.73). Compared with TD children, the gray matter development of the FFA in autism displayed altered influences on that of the social brain network regions (false discovery rate corrected, P < 0.05). Our findings indicate the atypical neurodevelopment of the FFA in the autistic brain during early childhood and highlight altered developmental effects of this region on the social brain network.

Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder.

Individual-based morphological brain networks built from T1-weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual-based morphological networks were constructed by using high-resolution structural MRI data from 40 young children with ASD (age range: 2-8 years) and 38 age-, gender-, and handedness-matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small-worldness (i.e., σ) of individual-level morphological brain networks, increased morphological connectivity in cortico-striatum-thalamic-cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico-cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio-cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically-relevant biomarkers for ASD.

Structure-Function Connectomics Reveals Aberrant Developmental Trajectory Occurring at Preadolescence in the Autistic Brain.

Accumulating neuroimaging evidence shows that age estimation obtained from brain connectomics reflects the level of brain maturation along with neural development. It is well known that autism spectrum disorder (ASD) alters neurodevelopmental trajectories of brain connectomics, but the precise relationship between chronological age (ChA) and brain connectome age (BCA) during development in ASD has not been addressed. This study uses neuroimaging data collected from 50 individuals with ASD and 47 age- and gender-matched typically developing controls (TDCs; age range: 5-18 years). Both functional and structural connectomics were assessed using resting-state functional magnetic resonance imaging and diffusion tensor imaging data from the Autism Brain Imaging Data Exchange repository. For each participant, BCA was estimated from structure-function connectomics through linear support vector regression. We found that BCA matched well with ChA in TDC children and adolescents, but not in ASD. In particular, our findings revealed that individuals with ASD exhibited accelerated brain maturation in youth, followed by a delay of brain development starting at preadolescence. Our results highlight the critical role of BCA in understanding aberrant developmental trajectories in ASD and provide the new insights into the pathophysiological mechanisms of this disorder.

Altered inter- and intrahemispheric functional connectivity dynamics in autistic children.

Emerging evidence has associated autism spectrum disorder (ASD) with static functional connectivity abnormalities between multiple brain regions. However, the temporal dynamics of intra- and interhemispheric functional connectivity patterns remain unknown in ASD. Resting-state functional magnetic resonance imaging data were analyzed for 105 ASD and 102 demographically matched typically developing control (TC) children (age range: 7-12 years) available from the Autism Brain Imaging Data Exchange database. Whole-brain functional connectivity was decomposed into ipsilateral and contralateral functional connectivity, and sliding-window analysis was utilized to capture the intra- and interhemispheric dynamic functional connectivity density (dFCD) patterns. The temporal variability of the functional connectivity dynamics was further quantified using the standard deviation (SD) of intra- and interhemispheric dFCD across time. Finally, a support vector regression model was constructed to assess the relationship between abnormal dFCD variance and autism symptom severity. Both intra- and interhemispheric comparisons showed increased dFCD variability in the anterior cingulate cortex/medial prefrontal cortex and decreased variability in the fusiform gyrus/inferior temporal gyrus in autistic children compared with TC children. Autistic children additionally showed lower intrahemispheric dFCD variability in sensorimotor regions including the precentral/postcentral gyrus. Moreover, aberrant temporal variability of the contralateral dFCD predicted the severity of social communication impairments in autistic children. These findings demonstrate altered temporal dynamics of the intra- and interhemispheric functional connectivity in brain regions incorporating social brain network of ASD, and highlight the potential role of abnormal interhemispheric communication dynamics in neural substrates underlying impaired social processing in ASD.

Atom Classification Model for Total Energy Evaluation of Two-Dimensional Multicomponent Materials.

The tunable properties of materials originate from variety of structures; however, it is still a challenge to give an accurate and fast evaluation of stabilities for screening numerous candidates. Herein, we propose an atom classification model to describe the multicomponent materials based on the structural recognition, in which the atoms are classified to estimate the total energies. Taking two-dimensional planar C1-xBx and C1-2x(BN)x as examples, we have found that the test error of total energies is about 3 meV per atom. Notably, the distributions of classified atoms demonstrate the evolution of configurations as a function of temperature, providing a clearer picture of phase transition. In addition, our method is universal, which can be flexibly extended to the bulk structures with more components.

Plasmon-Stimulated Colorimetry Biosensor Array for the Identification of Multiple Metabolites.

Rationally designing highly catalytic and stable nanozymes for metabolite monitoring is of great importance because of their huge potential in early disease diagnosis. Herein, a novel nanozyme based on hierarchically structured CuS/ZnS with a highly efficient peroxidase (POD)-mimic capability was developed and synthesized for multiple metabolite determination and recognition via the plasmon-stimulated biosensor array strategy. The designed nanozyme can simultaneously harvest plasmon triggered hot electron-hole pairs and generate photothermal properties, leading to a sharply boosted POD-mimic capability under 808 nm laser irradiation. Interestingly, because of the interaction diversity of the metabolite with POD-like nanomaterials, the unique inhibitory effect of metabolites on the POD-mimic activity could be the signal response as the differentiation. Thus, utilizing TMB as a typical chromogenic substrate in the addition of H2O2, the designed colorimetric biosensor array can produce diverse fingerprints for the three vital metabolisms (cysteine (Cys), ascorbic acid (AA), and glutathione (GSH)), which can be precisely identified by principal component analysis (PCA). Notably, a distinct fingerprint of a single metabolite with different levels and metabolite mixtures is also achieved with a detection limit of 1 µM. Most importantly, cell lysis could be effectively discriminated by the biosensor assay, implying its great potential in clinical diagnosis.

Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder.

BACKGROUND: Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder. METHODS: We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns. RESULTS: Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network-related and cingulo-opercular network-related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD. CONCLUSIONS: Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.

Exploring functional and structural connectivity disruptions in spinocerebellar ataxia type 3: Insights from gradient analysis.

AIMS: Spinocerebellar Ataxia Type 3 (SCA3) is a rare genetic ataxia that impacts the entire brain and is characterized as a neurodegenerative disorder affecting the neural network. This study explores how alterations in the functional hierarchy, connectivity, and structural changes within specific brain regions significantly contribute to the heterogeneity of symptom manifestations in patients with SCA3. METHODS: We prospectively recruited 51 patients with SCA3 and 59 age-and sex-matched healthy controls. All participants underwent comprehensive multimodal neuroimaging and clinical assessments. In SCA3 patients, an innovative approach utilizing gradients in resting-state functional connectivity (FC) was employed to examine atypical patterns of hierarchical processing topology from sensorimotor to supramodal regions in the cerebellum and cerebrum. Coupling analyses of abnormal FC and structural connectivity among regions of interest (ROIs) in the brain were also performed to characterize connectivity alterations. Additionally, relationships between quantitative ROI values and clinical variables were explored. RESULTS: Patients with SCA3 exhibited either compression or expansion within the primary sensorimotor-to-supramodal gradient through four distinct calculation methods, along with disruptions in FC and structural connectivity coupling. A comprehensive correlation was identified between the altered gradients and the clinical manifestations observed in patients. Notably, altered fractional anisotropy values were not significantly correlated with clinical variables. CONCLUSION: Abnormal gradients and connectivity in the cerebellar and cerebral cortices in SCA3 patients may contribute to disrupted motor-to-supramodal functions. Moreover, these findings support the potential utility of FCG analysis as a biomarker for diagnosing SCA3 and assessing treatment efficacy.

Synergistic coupling of FeNi3 alloy with graphene carbon dots for advanced oxygen evolution reaction electrocatalysis.

Realizing the simultaneous morphology and electrical conductivity tuning of non-noble metal nanocatalysts is urgently desired for promoting their intrinsic activity toward oxygen evolution reaction (OER), while it is still challenging. Herein, we have demonstrated that the morphology and conductivity of FeNi3 alloy can be finely tailored via introducing the graphene carbon dots (GCDs). Benefitting from the enlarged active areas, significantly improved electrical conductivity, and strong synergistic coupling effect, the optimized FeNi3@GCDs-10 shows extraordinary electrocatalytic performance towards OER by delivering a current density of 10 mA cm-2 with the overpotential of 238 mV, as well as small Tafel slope of 48.7 mV dec-1. Density functional theory (DFT) calculations reveal that Fe is the dominated active sites for boosting water dissociation. In addition, the incorporation of GCDs can also strength the adsorption of *O and lower the energy barrier for the transformation from *OH to *O, thereby optimizing the free energy of *O and greatly promoting the OER activity.

Predictive Biomarkers for Antipsychotic Treatment Response in Early Phase of Schizophrenia: Multi-Omic Measures Linking Subcortical Covariant Network, Transcriptomic Signatures, and Peripheral Epigenetics.

Background: Volumetric alterations of subcortical structures as predictors of antipsychotic treatment response have been previously corroborated, but less is known about whether their morphological covariance relates to treatment outcome and is driven by gene expression and epigenetic modifications. Methods: Subcortical volumetric covariance was analyzed by using baseline T1-weighted magnetic resonance imaging (MRI) in 38 healthy controls and 38 drug-naïve first-episode schizophrenia patients. Patients were treated with 8-week risperidone monotherapy and divided into responder and non-responder groups according to the Remission in Schizophrenia Working Group (RSWG). We utilized partial least squares (PLS) regression to examine the spatial associations between gene expression of subcortical structures from a publicly available transcriptomic dataset and between-group variances of structural covariance. The peripheral DNA methylation (DNAm) status of a gene of interest (GOI), overlapping between genes detected in the PLS and 108 schizophrenia candidate gene loci previously reported, was examined in parallel with MRI scanning. Results: In the psychotic symptom dimension, non-responders had a higher baseline structural covariance in the putamen-hippocampus-pallidum-accumbens pathway compared with responders. For disorganized symptoms, significant differences in baseline structural covariant connections were found in the putamen-hippocampus-pallidum-thalamus circuit between the two subgroups. The imaging variances related to psychotic symptom response were spatially related to the expression of genes enriched in neurobiological processes and dopaminergic pathways. The DNAm of GOI demonstrated significant associations with patients' improvement of psychotic symptoms. Conclusion: Baseline subcortical structural covariance and peripheral DNAm may relate to antipsychotic treatment response. Phenotypic variations in subcortical connectome related to psychotic symptom response may be transcriptomically and epigenetically underlaid. This study defines a roadmap for future studies investigating multimodal imaging epigenetic biomarkers for treatment response in schizophrenia.

Configuration Sensitivity of Electrocatalytic Oxygen Reduction Reaction on Nitrogen-Doped Graphene.

As one of the most promising nonprecious metal catalysts for the oxygen reduction reaction (ORR), the structure of the active site on nitrogen-doped carbon materials is still under debate. Here, we report that the sensitivity of the ORR on the local configuration of multiple nitrogen dopants may be overlooked. Combining global structure searching with density functional theory calculations, we established the structure-activity relationship for 19 and 298 possible configurations of graphitic nitrogen-doped graphene with N content of 2 and 3%, respectively. It was revealed that the stability cannot be a screener to determine the major contributor to the activity. 77.5% of current density is contributed by the active configuration with 4.59% population on the graphene containing 3% nitrogen. It unambiguously demonstrates the configuration sensitivity of N-doped graphene for ORR and opens a new window to identifying the optimal structure of N-doped carbons for various applications.

Mapping the Heterogeneous Brain Structural Phenotype of Autism Spectrum Disorder Using the Normative Model.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by substantial clinical and biological heterogeneity. Quantitative and individualized metrics for delineating the heterogeneity of brain structure in ASD are still lacking. Likewise, the extent to which brain structural metrics of ASD deviate from typical development (TD) and whether deviations can be used for parsing brain structural phenotypes of ASD is unclear. METHODS: T1-weighted magnetic resonance imaging data from the Autism Brain Imaging Data Exchange (ABIDE) II (nTD = 564) were used to generate a normative model to map brain structure deviations of ABIDE I subjects (nTD = 560, nASD = 496). Voxel-based morphometry was used to compute gray matter volume. Non-negative matrix factorization was employed to decompose the gray matter matrix into 6 factors and weights. These weights were used for normative modeling to estimate the factor deviations. Then, clustering analysis was used to identify ASD subtypes. RESULTS: Compared with TD, ASD showed increased weights and deviations in 5 factors. Three subtypes with distinct neuroanatomical deviation patterns were identified. ASD subtype 1 and subtype 3 showed positive deviations, whereas ASD subtype 2 showed negative deviations. Distinct clinical manifestations in social communication deficits were identified among the three subtypes. CONCLUSIONS: Our findings suggest that individuals with ASD have heterogeneous deviation patterns in brain structure. The results highlight the need to test for subtypes in neuroimaging studies of ASD. This study also presents a framework for understanding neuroanatomical heterogeneity in this increasingly prevalent neurodevelopmental disorder.

Mo doping and Se vacancy engineering for boosting electrocatalytic water oxidation by regulating the electronic structure of self-supported Co9Se8@NiSe.

Oxygen evolution reactions (OERs) are regarded as the rate-determining step of electrocatalytic overall water splitting, which endow OER electrocatalysts with the advantages of high activity, low cost, good conductivity, and excellent stability. Herein, a facile H2O2-assisted etching method is proposed for the fabrication of Mo-doped ultrathin Co9Se8@NiSe/NF-X heterojunctions with rich Se vacancies to boost electrocatalytic water oxidation. After step-by-step electronic structure modulation by Mo doping and Se vacancy engineering, the self-standing Mo-Co9Se8@NiSe/NF-60 heterojunctions deliver a current density of 50 mA cm-2 with an overpotential of 343 mV and a cell voltage of only 1.87 V at 50 mA cm-2 for overall water splitting in 1.0 M KOH. Our study opens up the possibility of realizing step-by-step electronic structure modulation of nonprecious OER electrocatalysts via heteroatom doping and vacancy engineering.

Five-fold symmetry in Au-Si metallic glass.

The first metallic glass of Au-Si alloy for over half a century has been discovered, but its atomic structure is still puzzling. Herein, Au8Si dodecahedrons with local five-fold symmetry are revealed as building blocks in Au-Si metallic glass, and the interconnection modes of Au8Si dodecahedrons determine the medium-range order. With dimensionality reduction, the surface ordering is attributed to the motif transformation of Au8Si dodecahedrons into planar Au5Si pyramids with five-fold symmetry, and thus the self-assembly of Au5Si pyramids leads to the formation of the ordered Au2Si monolayer with the lowest energy. Furthermore, structural similarity analysis is performed to unveil the physical origin of the structural characteristics in different dimensions. The amorphism of Au-Si is due to the smooth energy landscape around the global minimum, while the ordered surface structure occurs due to the steep energy landscape.