Development of segregation and integration of functional connectomes during the first 1,000 days

The first 1,000 days of human life lay the foundation for brain development and later cognitive growth. However, the developmental rules of the functional connectome during this critical period remain unclear. Using high-resolution, longitudinal, task-free functional magnetic resonance imaging data from 930 scans of 665 infants aged 28 postmenstrual weeks to 3 years, we report the early maturational process of connectome segregation and integration. We show the dominant development of local connections alongside a few global connections, the shift of brain hubs from primary regions to high-order association cortices, the developmental divergence of network segregation and integration along the anterior-posterior axis, the prediction of neurocognitive outcomes, and their associations with gene expression signatures of microstructural development and neuronal metabolic pathways. These findings advance our understanding of the principles of connectome remodeling during early life and its neurobiological underpinnings and have implications for studying typical and atypical development.

Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study

While videolaryngoscopy has resulted in better overall success rates of tracheal intubation, airway assessment is still an important prerequisite for safe airway management. This study aimed to create an artificial intelligence model to identify difficult videolaryngoscopy using a neural network. Baseline characteristics, medical history, bedside examination and seven facial images were included as predictor variables. ResNet-18 was introduced to recognise images and extract features. Different machine learning algorithms were utilised to develop predictive models. A videolaryngoscopy view of Cormack-Lehane grade of 1 or 2 was classified as 'non-difficult', while grade 3 or 4 was classified as 'difficult'. A total of 5849 patients were included, of whom 5335 had non-difficult and 514 had difficult videolaryngoscopy. The facial model (only including facial images) using the Light Gradient Boosting Machine algorithm showed the highest area under the curve (95%CI) of 0.779 (0.733-0.825) with a sensitivity (95%CI) of 0.757 (0.650-0.845) and specificity (95%CI) of 0.721 (0.626-0.794) in the test set. Compared with bedside examination and multivariate scores (El-Ganzouri and Wilson), the facial model had significantly higher predictive performance (p < 0.001). Artificial intelligence-based facial analysis is a feasible technique for predicting difficulty during videolaryngoscopy, and the model developed using neural networks has higher predictive performance than traditional methods.

Study of High-Transverse-Momentum Higgs Boson Production in Association with a Vector Boson in the qqbb Final State with the ATLAS Detector

This Letter presents the first study of Higgs boson production in association with a vector boson (V=W or Z) in the fully hadronic qqbb final state using data recorded by the ATLAS detector at the LHC in proton-proton collisions at sqrt[s]=13  TeV and corresponding to an integrated luminosity of 137  fb^{-1}. The vector bosons and Higgs bosons are each reconstructed as large-radius jets and tagged using jet substructure techniques. Dedicated tagging algorithms exploiting b-tagging properties are used to identify jets consistent with Higgs bosons decaying into bb[over ¯]. Dominant backgrounds from multijet production are determined directly from the data, and a likelihood fit to the jet mass distribution of Higgs boson candidates is used to extract the number of signal events. The VH production cross section is measured inclusively and differentially in several ranges of Higgs boson transverse momentum: 250-450, 450-650, and greater than 650 GeV. The inclusive signal yield relative to the standard model expectation is observed to be μ=1.4_{-0.9}^{+1.0} and the corresponding cross section is 3.1±1.3(stat)_{-1.4}^{+1.8}(syst)  pb.

Electroconvulsive Therapy Regulates Brain Connectome Dynamics in Patients With Major Depressive Disorder

BACKGROUND

Electroconvulsive therapy (ECT) is an effective treatment for patients with major depressive disorder (MDD), but its underlying neural mechanisms remain largely unknown. The aim of this study was to identify changes in brain connectome dynamics after ECT in MDD and to explore their associations with treatment outcome.

METHODS

We collected longitudinal resting-state functional magnetic resonance imaging data from 80 patients with MDD (50 with suicidal ideation [MDD-SI] and 30 without [MDD-NSI]) before and after ECT and 37 age- and sex-matched healthy control participants. A multilayer network model was used to assess modular switching over time in functional connectomes. Support vector regression was used to assess whether pre-ECT network dynamics could predict treatment response in terms of symptom severity.

RESULTS

At baseline, patients with MDD had lower global modularity and higher modular variability in functional connectomes than control participants. Network modularity increased and network variability decreased after ECT in patients with MDD, predominantly in the default mode and somatomotor networks. Moreover, ECT was associated with decreased modular variability in the left dorsal anterior cingulate cortex of MDD-SI but not MDD-NSI patients, and pre-ECT modular variability significantly predicted symptom improvement in the MDD-SI group but not in the MDD-NSI group.

CONCLUSIONS

We highlight ECT-induced changes in MDD brain network dynamics and their predictive value for treatment outcome, particularly in patients with SI. This study advances our understanding of the neural mechanisms of ECT from a dynamic brain network perspective and suggests potential prognostic biomarkers for predicting ECT efficacy in patients with MDD.

Measurement of the Centrality Dependence of the Dijet Yield in p+Pb Collisions at sqrt[s_{NN}]=8.16 TeV with the ATLAS Detector

ATLAS measured the centrality dependence of the dijet yield using 165  nb^{-1} of p+Pb data collected at sqrt[s_{NN}]=8.16  TeV in 2016. The event centrality, which reflects the p+Pb impact parameter, is characterized by the total transverse energy registered in the Pb-going side of the forward calorimeter. The central-to-peripheral ratio of the scaled dijet yields, R_{CP}, is evaluated, and the results are presented as a function of variables that reflect the kinematics of the initial hard parton scattering process. The R_{CP} shows a scaling with the Bjorken x of the parton originating from the proton, x_{p}, while no such trend is observed as a function of x_{Pb}. This analysis provides unique input to understanding the role of small proton spatial configurations in p+Pb collisions by covering parton momentum fractions from the valence region down to x_{p}∼10^{-3} and x_{Pb}∼4×10^{-4}.

Search for New Phenomena in Two-Body Invariant Mass Distributions Using Unsupervised Machine Learning for Anomaly Detection at sqrt[s]=13 TeV with the ATLAS Detector

Searches for new resonances are performed using an unsupervised anomaly-detection technique. Events with at least one electron or muon are selected from 140  fb^{-1} of pp collisions at sqrt[s]=13  TeV recorded by ATLAS at the Large Hadron Collider. The approach involves training an autoencoder on data, and subsequently defining anomalous regions based on the reconstruction loss of the decoder. Studies focus on nine invariant mass spectra that contain pairs of objects consisting of one light jet or b jet and either one lepton (e,μ), photon, or second light jet or b jet in the anomalous regions. No significant deviations from the background hypotheses are observed. Limits on contributions from generic Gaussian signals with various widths of the resonance mass are obtained for nine invariant masses in the anomalous regions.

Development of sensorimotor-visual connectome gradient at birth predicts neurocognitive outcomes at 2 years of age

Functional connectome gradients represent fundamental organizing principles of the brain. Here, we report the development of the connectome gradients in preterm and term babies aged 31-42 postmenstrual weeks using task-free functional MRI and its association with postnatal cognitive growth. We show that the principal sensorimotor-to-visual gradient is present during the late preterm period and continuously evolves toward a term-like pattern. The global measurements of this gradient, characterized by explanation ratio, gradient range, and gradient variation, increased with age (p < 0.05, corrected). Focal gradient development mainly occurs in the sensorimotor, lateral, and medial parietal regions, and visual regions (p < 0.05, corrected). The connectome gradient at birth predicts cognitive and language outcomes at 2-year follow-up (p < 0.005). These results are replicated using an independent dataset from the Developing Human Connectome Project. Our findings highlight early emergent rules of the brain connectome gradient and their implications for later cognitive growth.

Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence

Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Here, we show cortical thinning patterns primarily located in the lateral frontal and parietal heteromodal nodes during childhood and adolescence, which are structurally constrained by white matter network architecture and are particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible in another independent dataset. These findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.

Observation of WZγ Production in pp Collisions at sqrt[s]=13 TeV with the ATLAS Detector

This Letter reports the observation of WZγ production and a measurement of its cross section using 140.1±1.2  fb^{-1} of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. The WZγ production cross section, with both the W and Z bosons decaying leptonically, pp→WZγ→ℓ^{'}^{±}νℓ^{+}ℓ^{-}γ (ℓ^{(^{'})}=e, μ), is measured in a fiducial phase-space region defined such that the leptons and the photon have high transverse momentum and the photon is isolated. The cross section is found to be 2.01±0.30(stat)±0.16(syst)  fb. The corresponding standard model predicted cross section calculated at next-to-leading order in perturbative quantum chromodynamics and at leading order in the electroweak coupling constant is 1.50±0.06  fb. The observed significance of the WZγ signal is 6.3σ, compared with an expected significance of 5.0σ.

Combined Measurement of the Higgs Boson Mass from the H→γγ and H→ZZ^{*}→4ℓ Decay Channels with the ATLAS Detector Using sqrt[s]=7, 8, and 13 TeV pp Collision Data

A measurement of the mass of the Higgs boson combining the H→ZZ^{*}→4ℓ and H→γγ decay channels is presented. The result is based on 140  fb^{-1} of proton-proton collision data collected by the ATLAS detector during LHC run 2 at a center-of-mass energy of 13 TeV combined with the run 1 ATLAS mass measurement, performed at center-of-mass energies of 7 and 8 TeV, yielding a Higgs boson mass of 125.11±0.09(stat)±0.06(syst)=125.11±0.11  GeV. This corresponds to a 0.09% precision achieved on this fundamental parameter of the Standard Model of particle physics.

Search for Dark Photons in Rare Z Boson Decays with the ATLAS Detector

A search for events with a dark photon produced in association with a dark Higgs boson via rare decays of the standard model Z boson is presented, using 139  fb^{-1} of sqrt[s]=13  TeV proton-proton collision data recorded by the ATLAS detector at the Large Hadron Collider. The dark boson decays into a pair of dark photons, and at least two of the three dark photons must each decay into a pair of electrons or muons, resulting in at least two same-flavor opposite-charge lepton pairs in the final state. The data are found to be consistent with the background prediction, and upper limits are set on the dark photon's coupling to the dark Higgs boson times the kinetic mixing between the standard model photon and the dark photon, α_{D}ϵ^{2}, in the dark photon mass range of [5, 40] GeV except for the ϒ mass window [8.8, 11.1] GeV. This search explores new parameter space not previously excluded by other experiments.

Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome

BACKGROUND

Major depressive disorder (MDD) is a highly heterogeneous disorder that typically emerges in adolescence and can occur throughout adulthood. Studies aimed at quantitatively uncovering the heterogeneity of individual functional connectome abnormalities in MDD and identifying reproducibly distinct neurophysiological MDD subtypes across the lifespan, which could provide promising insights for precise diagnosis and treatment prediction, are still lacking.

METHODS

Leveraging resting-state functional magnetic resonance imaging data from 1148 patients with MDD and 1079 healthy control participants (ages 11-93), we conducted the largest multisite analysis to date for neurophysiological MDD subtyping. First, we characterized typical lifespan trajectories of functional connectivity strength based on the normative model and quantitatively mapped the heterogeneous individual deviations among patients with MDD. Then, we identified neurobiological MDD subtypes using an unsupervised clustering algorithm and evaluated intersite reproducibility. Finally, we validated the subtype differences in baseline clinical variables and longitudinal treatment predictive capacity.

RESULTS

Our findings indicated great intersubject heterogeneity in the spatial distribution and severity of functional connectome deviations among patients with MDD, which inspired the identification of 2 reproducible neurophysiological subtypes. Subtype 1 showed severe deviations, with positive deviations in the default mode, limbic, and subcortical areas and negative deviations in the sensorimotor and attention areas. Subtype 2 showed a moderate but converse deviation pattern. More importantly, subtype differences were observed in depressive item scores and the predictive ability of baseline deviations for antidepressant treatment outcomes.

CONCLUSIONS

These findings shed light on our understanding of different neurobiological mechanisms underlying the clinical heterogeneity of MDD and are essential for developing personalized treatments for this disorder.

Observation of Single-Top-Quark Production in Association with a Photon Using the ATLAS Detector

This Letter reports the observation of single top quarks produced together with a photon, which directly probes the electroweak coupling of the top quark. The analysis uses 139  fb^{-1} of 13 TeV proton-proton collision data collected with the ATLAS detector at the Large Hadron Collider. Requiring a photon with transverse momentum larger than 20 GeV and within the detector acceptance, the fiducial cross section is measured to be 688±23(stat) _{-71}^{+75}(syst)  fb, to be compared with the standard model prediction of 515_{-42}^{+36}  fb at next-to-leading order in QCD.

Measurement of Suppression of Large-Radius Jets and Its Dependence on Substructure in Pb+Pb Collisions at sqrt[s_{NN}]=5.02 TeV with the ATLAS Detector

This letter presents a measurement of the nuclear modification factor of large-radius jets in sqrt[s_{NN}]=5.02  TeV Pb+Pb collisions by the ATLAS experiment. The measurement is performed using 1.72  nb^{-1} and 257  pb^{-1} of Pb+Pb and pp data, respectively. The large-radius jets are reconstructed with the anti-k_{t} algorithm using a radius parameter of R=1.0, by reclustering anti-k_{t} R=0.2 jets, and are measured over the transverse momentum (p_{T}) kinematic range of 158 Collapse

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Measurement of the Sensitivity of Two-Particle Correlations in pp Collisions to the Presence of Hard Scatterings

A key open question in the study of multiparticle production in high-energy pp collisions is the relationship between the "ridge"-i.e., the observed azimuthal correlations between particles in the underlying event that extend over all rapidities-and hard or semihard scattering processes. In particular, it is not known whether jets or their soft fragments are correlated with particles in the underlying event. To address this question, two-particle correlations are measured in pp collisions at sqrt[s]=13  TeV using data collected by the ATLAS experiment at the LHC, with an integrated luminosity of 15.8  pb^{-1}, in two different configurations. In the first case, charged particles associated with jets are excluded from the correlation analysis, while in the second case, correlations are measured between particles within jets and charged particles from the underlying event. Second-order flow coefficients, v_{2}, are presented as a function of event multiplicity and transverse momentum. These measurements show that excluding particles associated with jets does not affect the measured correlations. Moreover, particles associated with jets do not exhibit any significant azimuthal correlations with the underlying event, ruling out hard processes contributing to the ridge.

Observation of an Excess of Dicharmonium Events in the Four-Muon Final State with the ATLAS Detector

A search is made for potential ccc[over ¯]c[over ¯] tetraquarks decaying into a pair of charmonium states in the four muon final state using proton-proton collision data at sqrt[s]=13  TeV, corresponding to an integrated luminosity of 140  fb^{-1} recorded by the ATLAS experiment at LHC. Two decay channels, J/ψ+J/ψ→4μ and J/ψ+ψ(2S)→4μ, are studied. Backgrounds are estimated based on a hybrid approach involving Monte Carlo simulations and data-driven methods. Statistically significant excesses with respect to backgrounds dominated by the single parton scattering are seen in the di-J/ψ channel consistent with a narrow resonance at 6.9 GeV and a broader structure at lower mass. A statistically significant excess is also seen in the J/ψ+ψ(2S) channel. The fitted masses and decay widths of the structures are reported.

Observation of the γγ→ττ Process in Pb+Pb Collisions and Constraints on the τ-Lepton Anomalous Magnetic Moment with the ATLAS Detector

This Letter reports the observation of τ-lepton-pair production in ultraperipheral lead-lead collisions Pb+Pb→Pb(γγ→ττ)Pb and constraints on the τ-lepton anomalous magnetic moment a_{τ}. The dataset corresponds to an integrated luminosity of 1.44  nb^{-1} of LHC Pb+Pb collisions at sqrt[s_{NN}]=5.02  TeV recorded by the ATLAS experiment in 2018. Selected events contain one muon from a τ-lepton decay, an electron or charged-particle track(s) from the other τ-lepton decay, little additional central-detector activity, and no forward neutrons. The γγ→ττ process is observed in Pb+Pb collisions with a significance exceeding 5 standard deviations and a signal strength of μ_{ττ}=1.03_{-0.05}^{+0.06} assuming the standard model value for a_{τ}. To measure a_{τ}, a template fit to the muon transverse-momentum distribution from τ-lepton candidates is performed, using a dimuon (γγ→μμ) control sample to constrain systematic uncertainties. The observed 95% confidence-level interval for a_{τ} is -0.057 Collapse

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Functional connectome through the human life span

The functional connectome of the human brain represents the fundamental network architecture of functional interdependence in brain activity, but its normative growth trajectory across the life course remains unknown. Here, we aggregate the largest, quality-controlled multimodal neuroimaging dataset from 119 global sites, including 33,809 task-free fMRI and structural MRI scans from 32,328 individuals ranging in age from 32 postmenstrual weeks to 80 years. Lifespan growth charts of the connectome are quantified at the whole cortex, system, and regional levels using generalized additive models for location, scale, and shape. We report critical inflection points in the non-linear growth trajectories of the whole-brain functional connectome, particularly peaking in the fourth decade of life. Having established the first fine-grained, lifespan-spanning suite of system-level brain atlases, we generate person-specific parcellation maps and further show distinct maturation timelines for functional segregation within different subsystems. We identify a spatiotemporal gradient axis that governs the life-course growth of regional connectivity, transitioning from primary sensory cortices to higher-order association regions. Using the connectome-based normative model, we demonstrate substantial individual heterogeneities at the network level in patients with autism spectrum disorder and patients with major depressive disorder. Our findings shed light on the life-course evolution of the functional connectome and serve as a normative reference for quantifying individual variation in patients with neurological and psychiatric disorders.

Removal of site effects and enhancement of signal using dual projection independent component analysis for pooling multi-site MRI data

Combining magnetic resonance imaging (MRI) data from multi-site studies is a popular approach for constructing larger datasets to greatly enhance the reliability and reproducibility of neuroscience research. However, the scanner/site variability is a significant confound that complicates the interpretation of the results, so effective and complete removal of the scanner/site variability is necessary to realise the full advantages of pooling multi-site datasets. Independent component analysis (ICA) and general linear model (GLM) based harmonisation methods are the two primary methods used to eliminate scanner/site effects. Unfortunately, there are challenges with both ICA-based and GLM-based harmonisation methods to remove site effects completely when the signals of interest and scanner/site effects-related variables are correlated, which may occur in neuroscience studies. In this study, we propose an effective and powerful harmonisation strategy that implements dual projection (DP) theory based on ICA to remove the scanner/site effects more completely. This method can separate the signal effects correlated with site variables from the identified site effects for removal without losing signals of interest. Both simulations and vivo structural MRI datasets, including a dataset from Autism Brain Imaging Data Exchange II and a travelling subject dataset from the Strategic Research Program for Brain Sciences, were used to test the performance of a DP-based ICA harmonisation method. Results show that DP-based ICA harmonisation has superior performance for removing site effects and enhancing the sensitivity to detect signals of interest as compared with GLM-based and conventional ICA harmonisation methods.

Strong Constraints on Jet Quenching in Centrality-Dependent p+Pb Collisions at 5.02 TeV from ATLAS

Jet quenching is the process of color-charged partons losing energy via interactions with quark-gluon plasma droplets created in heavy-ion collisions. The collective expansion of such droplets is well described by viscous hydrodynamics. Similar evidence of collectivity is consistently observed in smaller collision systems, including pp and p+Pb collisions. In contrast, while jet quenching is observed in Pb+Pb collisions, no evidence has been found in these small systems to date, raising fundamental questions about the nature of the system created in these collisions. The ATLAS experiment at the Large Hadron Collider has measured the yield of charged hadrons correlated with reconstructed jets in 0.36  nb^{-1} of p+Pb and 3.6  pb^{-1} of pp collisions at 5.02 TeV. The yields of charged hadrons with p_{T}^{ch}>0.5  GeV near and opposite in azimuth to jets with p_{T}^{jet}>30 or 60 GeV, and the ratios of these yields between p+Pb and pp collisions, I_{pPb}, are reported. The collision centrality of p+Pb events is categorized by the energy deposited by forward neutrons from the struck nucleus. The I_{pPb} values are consistent with unity within a few percent for hadrons with p_{T}^{ch}>4  GeV at all centralities. These data provide new, strong constraints that preclude almost any parton energy loss in central p+Pb collisions.

Search for Heavy Neutral Leptons in Decays of W Bosons Using a Dilepton Displaced Vertex in sqrt[s]=13 TeV pp Collisions with the ATLAS Detector

A search for a long-lived, heavy neutral lepton (N) in 139  fb^{-1} of sqrt[s]=13  TeV pp collision data collected by the ATLAS detector at the Large Hadron Collider is reported. The N is produced via W→Nμ or W→Ne and decays into two charged leptons and a neutrino, forming a displaced vertex. The N mass is used to discriminate between signal and background. No signal is observed, and limits are set on the squared mixing parameters of the N with the left-handed neutrino states for the N mass range 3  GeV Collapse

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Test of CP Invariance in Higgs Boson Vector-Boson-Fusion Production Using the H→γγ Channel with the ATLAS Detector

A test of CP invariance in Higgs boson production via vector-boson fusion has been performed in the H→γγ channel using 139  fb^{-1} of proton-proton collision data at sqrt[s]=13  TeV collected by the ATLAS detector at the LHC. The optimal observable method is used to probe the CP structure of interactions between the Higgs boson and electroweak gauge bosons, as described by an effective field theory. No sign of CP violation is observed in the data. Constraints are set on the parameters describing the strength of the CP-odd component in the coupling between the Higgs boson and the electroweak gauge bosons in two effective field theory bases: d[over ˜] in the HISZ basis and c_{HW[over ˜]} in the Warsaw basis. The results presented are the most stringent constraints on CP violation in the coupling between Higgs and weak bosons. The 95% C.L. constraint on d[over ˜] is derived for the first time and the 95% C.L. constraint on c_{HW[over ˜]} has been improved by a factor of 5 compared to the previous measurement.

Frequency-resolved connectome alterations in major depressive disorder: A multisite resting fMRI study

BACKGROUND

Functional connectome studies have revealed widespread connectivity alterations in major depressive disorder (MDD). However, the low frequency bandpass filtering (0.01-0.08 Hz or 0.01-0.1 Hz) in most studies have impeded our understanding on whether and how these alterations are affected by frequency of interest.

METHODS

Here, we performed frequency-resolved (0.01-0.06 Hz, 0.06-0.16 Hz and 0.16-0.24 Hz) connectome analyses using a large-sample resting-state functional MRI dataset of 1002 MDD patients and 924 healthy controls from seven independent centers.

RESULTS

We reported significant frequency-dependent connectome alterations in MDD in left inferior parietal, inferior temporal, precentral, and fusiform cortices and bilateral precuneus. These frequency-dependent connectome alterations are mainly derived by abnormalities of medium- and long-distance connections and are brain network-dependent. Moreover, the connectome alteration of left precuneus in high frequency band (0.16-0.24 Hz) is significantly associated with illness duration.

LIMITATIONS

Multisite harmonization model only removed linear site effects. Neurobiological underpinning of alterations in higher frequency (0.16-0.24 Hz) should be further examined by combining fMRI data with respiration, heartbeat and blood flow recordings in future studies.

CONCLUSIONS

These results highlight the frequency-dependency of connectome alterations in MDD and the benefit of examining connectome alteration in MDD under a wider frequency band.

Connectomics reconciles seemingly irreconcilable neuroimaging findings

Neuroimaging studies have reported heterogeneity of regional anatomical localization for the same disease, impeding reproducible conclusions regarding brain alterations. In recent work, Cash and colleagues help to reconcile inconsistent findings in functional neuroimaging studies in depression by identifying reliable and clinically valuable distributed brain networks from a connectomic perspective.

First-in-human, phase I study of AK109, an anti-VEGFR2 antibody in patients with advanced or metastatic solid tumors

BACKGROUND

Vascular endothelial growth factor receptor 2 (VEGFR2) plays a key role in antiangiogenesis which has been an essential strategy for cancer treatment. We report the first-in-human study of AK109, a novel anti-VEGFR2 monoclonal antibody, to characterize the safety profile and pharmacokinetics/pharmacodynamics (PK/PD) properties, and explore the preliminary antitumor efficacy in patients with solid tumors.

PATIENTS AND METHODS

This was a multicenter, open-label, phase I study, including dose escalation and dose expansion (NCT04547205). Patients with advanced cancers were treated 2 and 3 weekly with escalating doses of AK109. A 3 + 3 design was used to determine the maximum tolerated dose. Blood was sampled for PK/PD analysis. The primary endpoint was safety and recommended phase II dose (RP2D).

RESULTS

A total of 40 patients were enrolled. No dose-limiting toxicity was observed. However, 38 patients reported treatment-related adverse events (TRAEs); grade ≥3 TRAEs occurred in 10 patients. The most common TRAEs were proteinuria (n = 24, 60%), hypertension (n = 13, 32.5%), increased aspartate transaminase (n = 11, 27.5%), thrombopenia (n = 10, 25%), and anemia (n = 10, 25%). A total of 28 patients (70%) reported adverse events of special interest (AESIs). The most common AESIs were proteinuria (60%), hypertension (32.5%), and hemorrhage (32.5%), mainly including gum bleeding and urethrorrhagia. AK109 exhibited an approximately linear PK exposure with dose escalation at 2-12 mg/kg. PD analyses showed rapid target engagement. Among the 40 patients, 4 achieved partial response and 21 achieved stable disease with an objective response rate of 10% and a disease control rate of 62.5%. Based on the safety profile, the PK/PD profile, and preliminary antitumor activities, 12 mg/kg Q2W and 15 mg/kg Q3W were selected as RP2D.

CONCLUSIONS

AK109 showed manageable safety profile and promising antitumor activity, supporting further clinical development in a large population.

First Report of Colletotrichum godetiae Causing Anthracnose on Walnut (Juglans regia and Juglans sigillata) in China

In August 2020, anthracnose lesions were observed on fruits of Juglans regia and J. sigillata in walnut orchards, in Yijun (Shaanxi Province) and Nanhua (Yunnan Province) counties, China. Symptoms on walnut fruits first appeared as small necrotic spots that rapidly enlarged into subcircular or irregular sunken black lesions (Fig. 1a, b). Sixty diseased walnut fruits (30 fruits of J. regia and J. sigillata, respectively) were randomly sampled from six orchards (10-15 ha each orchard, three orchards were selected in each county) with severe anthracnose (incidence rate of fruit anthracnose is over 60% in the orchard.) in two counties. Twenty-six single spore isolates were obtained from diseased fruits as described by Cai et al. (2009). After seven days, isolates formed grey to milky white colony with abundant aerial hyphae on the upper surface of colony, and milky white to light olive on the back of PDA (Fig. 1c). Conidiogenous cells were hyaline, smooth-walled, and cylindrical to clavate (Fig. 1d). Conidia were smooth-walled, aseptate, cylindrical to fusiform, with both ends acute or one end round and one end slightly acute (Fig. 1e), and ranged in size from 15.5-24.3×4.9-8.1 µm (n=30). Appressoria were brown to medium brown, clavate to elliptical, with the edge entire or undulate (Fig. 1f), and ranged in size from 8.0-27.6×4.7-13.7μm (n=30). The morphological characteristics of 26 isolates were similar to those of the species complex Colletotrichum acutatum (Damm et al. 2012). Six representative isolates were randomly selected (three isolates for each province) for molecular analysis. The ribosomal internal transcribed spacers (ITS) (White et al. 1990), beta-tubulin (TUB2) (Glass and Donaldson 1995), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Templeton et al.1992) and chitin synthase 1 (CHS-1) (Carbone and Kohn 1999) genes were amplified and sequenced. Sequences of 6 of 26 isolates were submitted to GenBank (Accession Nos: ITS: MT799938-MT799943, TUB: MT816321-MT816326, GAPDH:MT816327-MT816332, CHS-1: MT816333-816338). Multi-locus phylogenetic analyses revealed that six isolates clustered together with Colletotrichum godetiae ex-type culture isolates CBS133.44 and CBS130251, and the bootstrap support value was 100% (Fig.2). The pathogenicity of two representative isolates (CFCC54247 and CFCC54244) was tested using healthy fruits of the " J. regia cv. Xiangling" and " J. sigillata cv. Yangbi" varieties. Forty sterilized fruits (20 fruits were inoculated with CFCC54247, and 20 fruits with CFCC54244) were wounded by puncturing with a sterile needle through walnut pericarp and inoculated in the wound site with 10 μl of conidial suspension (106 conidia/ml) from seven day old colonies grown on PDA at 25℃. Twenty wounded fruits were inoculated with sterile water as control. Inoculated and control fruits were incubated in containers at 25℃ in a 12/12h light/dark cycle. The experiment was repeated three times. Anthracnose symptoms (Fig. 1g-h) were observed in all inoculated fruits after 12 days, whereas controls showed no symptoms. Fungal isolates from inoculated diseased fruits showed the same morphological and molecular characteristics as the isolates obtained in this study, confirming Koch's postulates. To our knowledge, this is the first report of C. godetiae causing anthracnose on the two walnut species in China. The result will be helpful for providing a basis for further research on the control of the disease.

3D-MASNet: 3D mixed-scale asymmetric convolutional segmentation network for 6-month-old infant brain MR images

Precise segmentation of infant brain magnetic resonance (MR) images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are essential for studying neuroanatomical hallmarks of early brain development. However, for 6-month-old infants, the extremely low-intensity contrast caused by inherent myelination hinders accurate tissue segmentation. Existing convolutional neural networks (CNNs) based segmentation models for this task generally employ single-scale symmetric convolutions, which are inefficient for encoding the isointense tissue boundaries in baby brain images. Here, we propose a 3D mixed-scale asymmetric convolutional segmentation network (3D-MASNet) framework for brain MR images of 6-month-old infants. We replaced the traditional convolutional layer of an existing to-be-trained network with a 3D mixed-scale convolution block consisting of asymmetric kernels (MixACB) during the training phase and then equivalently converted it into the original network. Five canonical CNN segmentation models were evaluated using both T1- and T2-weighted images of 23 6-month-old infants from iSeg-2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the most considerable improvement in the fully convolutional network model (CC-3D-FCN) and the highest performance in the Dense U-Net model. This approach further obtained Dice coefficient accuracies of 0.931, 0.912, and 0.961 in GM, WM, and CSF, respectively, ranking first among 30 teams on the validation dataset of the iSeg-2019 Grand Challenge. Thus, the proposed 3D-MASNet can improve the accuracy of existing CNNs-based segmentation models as a plug-and-play solution that offers a promising technique for future infant brain MRI studies.

Common and distinct roles of amygdala subregional functional connectivity in non-motor symptoms of Parkinson's disease

Neuroimaging studies suggest a pivotal role of amygdala dysfunction in non-motor symptoms (NMS) of Parkinson's disease (PD). However, the relationship between amygdala subregions (the centromedial (CMA), basolateral (BLA) and superficial amygdala (SFA)) and NMS has not been delineated. We used resting-state functional MRI to examine the PD-related alterations in functional connectivity for amygdala subregions. The left three subregions and right BLA exhibited between-group differences, and were commonly hypo-connected with the frontal, temporal, insular cortex, and putamen in PD. Each subregion displayed distinct hypoconnectivity with the limbic systems. Partial least-squares analysis revealed distinct amygdala subregional involvement in diverse NMS. Hypo-connectivity of all four subregions was associated with emotion, pain, olfaction, and cognition. Hypo-connectivity of the left SFA was associated with sleepiness. Our findings highlight the hypofunction of the amygdala subregions in PD and their preliminary associations with NMS, providing new insights into the pathogenesis of NMS.

Resting-state functional connectivity of the raphe nuclei in major depressive Disorder: A Multi-site study

Accumulating evidence showed that major depressive disorder (MDD) is characterized by a dysfunction of serotonin neurotransmission. Raphe nuclei are the sources of most serotonergic neurons that project throughout the brain. Incorporating measurements of activity within the raphe nuclei into the analysis of connectivity characteristics may contribute to understanding how neurotransmitter synthesized centers are involved in thepathogenesisof MDD. Here, we analyzed the resting-state functional magnetic resonance imaging (RS-fMRI) dataset from 1,148 MDD patients and 1,079 healthy individuals recruited across nine centers. A seed-based analysis with the dorsal raphe and median raphe nuclei was performed to explore the functional connectivity (FC) alterations. Compared to controls, for dorsal raphe, the significantly decreased FC linking with the right precuneus and median cingulate cortex were found; for median raphe, the increased FC linking with right superior cerebellum (lobules V/VI) was found in MDD patients. In further exploratory analyzes, MDD-related connectivity alterations in dorsal and median raphe nuclei in different clinical factors remained highly similar to the main findings, indicating these abnormal connectivities are a disease-related alteration. Our study highlights a functional dysconnection pattern of raphe nuclei in MDD with multi-site big data. These findings help improve our understanding of the pathophysiology of depression and provide evidence of the theoretical foundation for the development of novel pharmacotherapies.

Programmed Death of Microglia in Alzheimer's Disease: Autophagy, Ferroptosis, and Pyroptosis

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline, amyloid-β (Aβ) plaques and the formation of neurofibrillary tangles (NFTs) composed of hyperphosphorylated tau. Increasing evidence has demonstrated that the damage of cell plays an important role in AD. Cell death is a critical phenomenon for physiological functions, which promotes AD pathogenesis. Programmed cell death, including necroptosis, pyroptosis, autophagy, and ferroptosis, have been discovered that have unique biological functions and pathophysiological characteristics. Here, we review the available evidence detailing the mechanisms of programmed microglial death, including pyroptosis, autophagy, and ferroptosis. We also highlight the role of programmed death of microglia during the process of AD and focus on the connection between the disease and cell death.

Baseline cerebral structural morphology predict freezing of gait in early drug-naïve Parkinson's disease

Freezing of gait (FOG) greatly impacts the daily life of patients with Parkinson's disease (PD). However, predictors of FOG in early PD are limited. Moreover, recent neuroimaging evidence of cerebral morphological alterations in PD is heterogeneous. We aimed to develop a model that could predict the occurrence of FOG using machine learning, collaborating with clinical, laboratory, and cerebral structural imaging information of early drug-naïve PD and investigate alterations in cerebral morphology in early PD. Data from 73 healthy controls (HCs) and 158 early drug-naïve PD patients at baseline were obtained from the Parkinson's Progression Markers Initiative cohort. The CIVET pipeline was used to generate structural morphological features with T1-weighted imaging (T1WI). Five machine learning algorithms were calculated to assess the predictive performance of future FOG in early PD during a 5-year follow-up period. We found that models trained with structural morphological features showed fair to good performance (accuracy range, 0.67-0.73). Performance improved when clinical and laboratory data was added (accuracy range, 0.71-0.78). For machine learning algorithms, elastic net-support vector machine models (accuracy range, 0.69-0.78) performed the best. The main features used to predict FOG based on elastic net-support vector machine models were the structural morphological features that were mainly distributed in the left cerebrum. Moreover, the bilateral olfactory cortex (OLF) showed a significantly higher surface area in PD patients than in HCs. Overall, we found that T1WI morphometric markers helped predict future FOG occurrence in patients with early drug-naïve PD at the individual level. The OLF exhibits predominantly cortical expansion in early PD.

Altered functional connectivity of nucleus accumbens subregions associates with non-motor symptoms in Parkinson's disease

AIMS

This study aimed to identify the functional connectivity (FC) changes of nucleus accumbens (NAc) subregions and characterize the association of network changes and non-motor symptoms (NMS) in Parkinson's disease (PD).

METHODS

We enrolled 129 PD patients and 106 healthy controls from our center and the PPMI (Parkinson's Progression Markers Initiative) database. The FC of the bilateral core and shell of the NAc were measured and compared between the two groups. We further used partial least squares correlation to reveal the relationships between altered FC of NAc subregions and manifestations of NMS of PD.

RESULTS

The subregions of left core, left shell, and right core had reduced FC with extensive brain regions in PD patients compared with healthy controls. These three subregions were commonly associated with depression, anxiety, apathy, and cognitive impairment. Moreover, the left core and left shell were associated with excessive daytime sleepiness, whereas the right core was associated with olfactory impairment and rapid eye movement sleep behavior disorder.

CONCLUSION

This study for the first time identified the neural network changes of NAc subregions in PD and the associations between network changes and phenotypes of NMS. Our findings provide new insights into the pathogenesis of NMS in PD.

[Effect of dyslipidemia on clinical outcome of infertility patients receiving donor eggs]

Objective: To explore the effect of dyslipidemia on the clinical outcome of intracytoplasmic sperm injection-embryo transfer (ICSI-ET) in infertility patients receiving donor eggs. Methods: A total of 118 patients were selected to receive egg donors and ICSI-ET at the First Affiliated Hospital of Nanjing Medical University between April 2007 and December 2020. According to the levels of triacylglycerol, serum cholesterol, high density lipoprotein (HDL), and low density lipoprotein, they were divided into dyslipidemia group (35 cases) and normal blood lipids group (83 cases). The influence of body mass index (BMI) and age was adjusted by 1∶1 propensity score matching, and the general condition and clinical outcome of the two groups were analyzed retrospectively. Finally, the relationship between lipid composition and clinical outcome was analyzed according to patients' age and BMI. Results: (1) Comparing the pre-matching dyslipidemia group with the normal blood lipids group, the BMI of the dyslipidemia group was significantly higher than that of the normal blood lipids group [(23.5±2.4) vs (22.4±2.7) kg/m²], and the embryo implantation rate was significantly lower than that of the normal blood lipids group [13.6% (8/59) vs 27.3% (36/132)], the differences were statistically significant (both P<0.05). (2) There were no significant differences in years of infertility, number of pregnancies, number of abortions, number of transplanted embryos, protocol of endometrial preparation, endometrial thickness on transplantation day and high quality embryo rate between the two groups, through propensity score matching (all P>0.05). The biochemical pregnancy rate [28.6% (10/35)], embryo implantation rate [13.6% (8/59)] and live birth rate [20.0% (7/35)] in dyslipidemia group were significantly lower than those in the normal blood lipids group (P<0.05). The clinical pregnancy rate was lower than that of the normal blood lipids group (P>0.05). (3) The results of stratified analysis showed that the level of HDL in the clinically non-pregnant group was significantly lower than that in the pregnant group in patients ≤ 35 years old [(1.5±0.3) vs (1.8±0.5) mmol/L; P<0.05]. In the overweight recipient patients, the level of HDL of the clinically non-pregnant group was lower than that of the pregnant group (P>0.05). Conclusions: Dyslipidemia significantly reduces the biochemical pregnancy rate, embryo implantation rate and live birth rate in patients with receiving donor eggs. Especially in patients aged ≤35 years old, the reduction of HDL is closely related to adverse pregnancy outcomes.

Observation of WWW Production in pp Collisions at sqrt[s]=13 TeV with the ATLAS Detector

This Letter reports the observation of WWW production and a measurement of its cross section using 139  fb^{-1} of proton-proton collision data recorded at a center-of-mass energy of 13 TeV by the ATLAS detector at the Large Hadron Collider. Events with two same-sign leptons (electrons or muons) and at least two jets, as well as events with three charged leptons, are selected. A multivariate technique is then used to discriminate between signal and background events. Events from WWW production are observed with a significance of 8.0 standard deviations, where the expectation is 5.4 standard deviations. The inclusive WWW production cross section is measured to be 820±100 (stat)±80 (syst)  fb, approximately 2.6 standard deviations from the predicted cross section of 511±18  fb calculated at next-to-leading-order QCD and leading-order electroweak accuracy.

A detailed map of Higgs boson interactions by the ATLAS experiment ten years after the discovery

The standard model of particle physics1-4 describes the known fundamental particles and forces that make up our Universe, with the exception of gravity. One of the central features of the standard model is a field that permeates all of space and interacts with fundamental particles5-9. The quantum excitation of this field, known as the Higgs field, manifests itself as the Higgs boson, the only fundamental particle with no spin. In 2012, a particle with properties consistent with the Higgs boson of the standard model was observed by the ATLAS and CMS experiments at the Large Hadron Collider at CERN10,11. Since then, more than 30 times as many Higgs bosons have been recorded by the ATLAS experiment, enabling much more precise measurements and new tests of the theory. Here, on the basis of this larger dataset, we combine an unprecedented number of production and decay processes of the Higgs boson to scrutinize its interactions with elementary particles. Interactions with gluons, photons, and W and Z bosons-the carriers of the strong, electromagnetic and weak forces-are studied in detail. Interactions with three third-generation matter particles (bottom (b) and top (t) quarks, and tau leptons (τ)) are well measured and indications of interactions with a second-generation particle (muons, μ) are emerging. These tests reveal that the Higgs boson discovered ten years ago is remarkably consistent with the predictions of the theory and provide stringent constraints on many models of new phenomena beyond the standard model.

Structural insight into the individual variability architecture of the functional brain connectome

Human cognition and behaviors depend upon the brain's functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher accuracy in primary regions and lower accuracy in heteromodal regions. We further decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.

Alterations in Connectome Dynamics in Autism Spectrum Disorder: A Harmonized Mega- and Meta-analysis Study Using the Autism Brain Imaging Data Exchange Dataset

BACKGROUND

Neuroimaging studies have reported functional connectome aberrancies in autism spectrum disorder (ASD). However, the time-varying patterns of connectome topology in individuals with ASD and the connection between these patterns and gene expression profiles remain unknown.

METHODS

To investigate case-control differences in dynamic connectome topology, we conducted mega- and meta-analyses of resting-state functional magnetic resonance imaging data of 939 participants (440 patients with ASD and 499 healthy control subjects, all males) from 18 independent sites, selected from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Functional data were preprocessed and analyzed using harmonized protocols, and brain module dynamics was assessed using a multilayer network model. We further leveraged postmortem brain-wide gene expression data to identify transcriptomic signatures associated with ASD-related alterations in brain dynamics.

RESULTS

Compared with healthy control participants, individuals with ASD exhibited a higher global mean and lower standard deviation of whole-brain module dynamics, indicating an unstable and less regionally differentiated pattern. More specifically, individuals with ASD showed higher module switching, primarily in the medial prefrontal cortex, posterior cingulate gyrus, and angular gyrus, and lower switching in the visual regions. These alterations in brain dynamics were predictive of social impairments in individuals with ASD and were linked with expression profiles of genes primarily involved in the regulation of neurotransmitter transport and secretion as well as with previously identified autism-related genes.

CONCLUSIONS

This study is the first to identify consistent alterations in brain network dynamics in ASD and the transcriptomic signatures related to those alterations, furthering insights into the biological basis behind this disorder.

Development of functional connectome gradients during childhood and adolescence

Connectome mapping studies have documented a principal primary-to-transmodal gradient in the adult brain network, capturing a functional spectrum that ranges from perception and action to abstract cognition. However, how this gradient pattern develops and whether its development is linked to cognitive growth, topological reorganization, and gene expression profiles remain largely unknown. Using longitudinal resting-state functional magnetic resonance imaging data from 305 children (aged 6-14 years), we describe substantial changes in the primary-to-transmodal gradient between childhood and adolescence, including emergence as the principal gradient, expansion of global topography, and focal tuning in primary and default-mode regions. These gradient changes are mediated by developmental changes in network integration and segregation, and are associated with abstract processing functions such as working memory and expression levels of calcium ion regulated exocytosis and synaptic transmission-related genes. Our findings have implications for understanding connectome maturation principles in normal development and developmental disorders.

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset

The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies. Here, we propose a deep learning-based framework to harmonize imaging data obtained from pairs of sites, in which site factors and brain features can be disentangled and encoded. We trained the proposed framework with a publicly available traveling subject dataset from the Strategic Research Program for Brain Sciences (SRPBS) and harmonized the gray matter volume maps derived from eight source sites to a target site. The proposed framework significantly eliminated intersite differences in gray matter volumes. The embedded encoders successfully captured both the abstract textures of site factors and the concrete brain features. Moreover, the proposed framework exhibited outstanding performance relative to conventional statistical harmonization methods in terms of site effect removal, data distribution homogenization, and intrasubject similarity improvement. Finally, the proposed harmonization network provided fixable expandability, through which new sites could be linked to the target site via indirect schema without retraining the whole model. Together, the proposed method offers a powerful and interpretable deep learning-based harmonization framework for multisite neuroimaging data that can enhance reliability and reproducibility in multisite studies regarding brain development and brain disorders.

Altered topological properties of the intrinsic functional brain network in patients with right-sided unilateral hearing loss caused by acoustic neuroma

Neuroimaging studies have identified alterations in functional connectivity between specific brain regions in patients with unilateral hearing loss (UHL) and different influence of the side of UHL on neural plasticity. However, little is known about changes of whole-brain functional networks in patients with UHL and whether differences exist in topological organization between right-sided UHL (RUHL) and left-sided UHL (LUHL). To address this issue, we employed resting-state fMRI (rs-fMRI) and graph-theoretical approaches to investigate the topological alterations of brain functional connectomes in patients with RUHL and LUHL. Data from 44 patients with UHL (including 22 RUHL patients and 22 LUHL patients) and 37 healthy control subjects (HCs) were collected. Functional brain networks were constructed for each participant, following by graph-theoretical network analyses at connectional and global (e.g., small-worldness) levels. The correlations between brain network topologies and clinical variables were further studied. Using network-based analysis, we found a subnetwork in the visual cortex which had significantly lower connectivity strength in patients with RUHL as compared to HCs. At global level, all participants showed small-world architecture in functional brain networks, however, significantly lower normalized clustering coefficient and small-worldness were observed in patients with RUHL than in HCs. Moreover, these abnormal network metrics were demonstrated to be correlated with the clinical variables and cognitive performance of patients with RUHL. Notably, no significant alterations in the functional brain networks were found in patients with LUHL. Our findings demonstrate that RUHL (rather than LUHL) is accompanied with aberrant topological organization of the functional brain connectome, indicating different pathophysiological mechanisms between RUHL and LUHL from a viewpoint of network topology.

Connectome gradient dysfunction in major depression and its association with gene expression profiles and treatment outcomes

Patients with major depressive disorder (MDD) exhibit concurrent deficits in both sensory and higher-order cognitive processing. Connectome studies have suggested a principal primary-to-transmodal gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether this gradient structure is disrupted in patients with MDD and how this disruption associates with gene expression profiles and treatment outcome remain unknown. Using a large cohort of resting-state fMRI data from 2227 participants (1148 MDD patients and 1079 healthy controls) recruited at nine sites, we investigated MDD-related alterations in the principal connectome gradient. We further used Neurosynth, postmortem gene expression, and an 8-week antidepressant treatment (20 MDD patients) data to assess the meta-analytic cognitive functions, transcriptional profiles, and treatment outcomes related to MDD gradient alterations, respectively. Relative to the controls, MDD patients exhibited global topographic alterations in the principal primary-to-transmodal gradient, including reduced explanation ratio, gradient range, and gradient variation (Cohen's d = 0.16-0.21), and focal alterations mainly in the primary and transmodal systems (d = 0.18-0.25). These gradient alterations were significantly correlated with meta-analytic terms involving sensory processing and higher-order cognition. The transcriptional profiles explained 53.9% variance of the altered gradient pattern, with the most correlated genes enriched in transsynaptic signaling and calcium ion binding. The baseline gradient maps of patients significantly predicted symptomatic improvement after treatment. These results highlight the connectome gradient dysfunction in MDD and its linkage with gene expression profiles and clinical management, providing insight into the neurobiological underpinnings and potential biomarkers for treatment evaluation in this disorder.

[Association between serum uric acid to creatinine ratio and metabolic syndrome based on community residents in Chashan town, Dongguan city]

Objective: To analyze the association between serum uric acid to creatinine ratio (SUA/Cr) and metabolic syndrome among community residents in Chashan town, Dongguan city. Methods: Participants were from the prospective cohort study of chronic diseases in natural populations in South China conducted in Chashan town, Dongguan city from 2018 to 2019. A total of 11 334 participants with complete data were included by using convenient sampling method. Demographic characteristics, lifestyle and health status were collected through questionnaire and physical examination. The venous blood of the subjects was collected to detect the levels of serum uric acid, creatinine and blood lipid. All participants were divided into four groups (Q1-Q4) according to the quartile of SUA/Cr level. The relationship between SUA/Cr and metabolic syndrome and its components (abdominal obesity, high triglyceride, low level of high density lipoprotein cholesterol, hypertension and abnormal glucose metabolism) were analyzed by using logistic regression model. Results: The mean age of 11 334 participants was (49.52±10.02) years. Male participants accounted for 44.2% (5 015/11 334). The prevalence of metabolic syndrome was 31.2% (3 532/11 334), and the level of SUA/Cr was 5.17±1.53. The prevalence of metabolic syndrome in group Q1-Q4 was 22.3% (631/2 834), 26.5% (752/2 833), 34.9% (988/2 833) and 41.0% (1 161/2 834), respectively. After adjusting for relevant confounding factors, the result of logistic regression model showed that compared with group Q1, the risk of metabolic syndrome in group Q2-Q4 was significantly higher, with OR (95%CI) values about 1.41 (1.23-1.60), 2.19 (1.93-2.49) and 3.01 (2.65-3.42) respectively. The risk of each component of metabolic syndrome in group Q2-Q4 was higher (P_trend<0.001). The SUA/Cr level of participants with normal uric acid level was significantly positively correlated with metabolic syndrome. The risk of metabolic syndrome increased with the increase of SUA/Cr level, but there was the same trend without significant differences in patients with hyperuricemia (P_interaction=0.008). Conclusion: There is a positive correlation between SUA/Cr level and the risk of metabolic syndrome among community residents in Chashan town, Dongguan city.

Progressive Stabilization of Brain Network Dynamics during Childhood and Adolescence

Functional brain networks require dynamic reconfiguration to support flexible cognitive function. However, the developmental principles shaping brain network dynamics remain poorly understood. Here, we report the longitudinal development of large-scale brain network dynamics during childhood and adolescence, and its connection with gene expression profiles. Using a multilayer network model, we show the temporally varying modular architecture of child brain networks, with higher network switching primarily in the association cortex and lower switching in the primary regions. This topographical profile exhibits progressive maturation, which manifests as reduced modular dynamics, particularly in the transmodal (e.g., default-mode and frontoparietal) and sensorimotor regions. These developmental refinements mediate age-related enhancements of global network segregation and are linked with the expression profiles of genes associated with the enrichment of ion transport and nucleobase-containing compound transport. These results highlight a progressive stabilization of brain dynamics, which expand our understanding of the neural mechanisms that underlie cognitive development.

[Relationship of quality and duration of sleep with hypertension among adults in Guangzhou]

Objective: To explore the relationship of sleep quality and sleep duration with hypertension among adults aged 30-79 years old in Guangzhou. Methods: According to multi-stage stratified cluster sampling, 12 747 residents aged 30-79 years old were sampled and surveyed in Guangzhou from January 2018 to March 2019. Data on general demographic characteristics, sleep quality, sleep duration and hypertension were collected through questionnaire survey, the Pittsburgh sleep quality index (PSQI) and physical examination. Multivariate logistic regression models were used to analyze the putative association between sleep quality, sleep duration and hypertension. Restrictive cubic spline curve was used to draw the dose-response relationship curve between sleep quality, sleep time and hypertension. Results: The mean age of the subjects was (52.68±12.17) years, the prevalence of hypertension was 36.6% (4 664/12 747), the average score of PSQI was (4.70±2.88), and the average sleep time was (7.00±1.32) hours. The prevalence of hypertension was positively associated with the PSQI score. Compared to the subjects with a score less than 3, OR (95%CI) of hypertension with a PSQI score of 3-5, 5-8, ≥9 were 1.14 (1.02-1.27), 1.17 (1.03-1.34), 1.41 (1.21-1.64), respectively. The relationship between sleep duration and hypertension appeared U-shaped. Compared with 6 to 8 hours sleep duration, both sleep duration<6 hours with OR(95%CI) of 1.27(1.12-1.43) or >8 hours with OR(95%CI) of 1.20(1.05-1.38) was associated with hypertension. Conclusion: Both poor sleep quality, longer or shorter sleep duration were responsible for increased risk of cognitive impairment in older Chinese.