When more control means better choices: Cognitive control networks drive expected-value maximization under uncertainty.

Multi-path neural mechanisms of self-other reward integration impairment in schizophrenia.

Wired for conflict? Neurocognitive mechanisms linking threat perception and support for war.

A multi-scale dual-stream time-frequency fusion network for clutch actuator delay compensation control

Cholesterol-driven mitochondrial rejuvenation by quercetin nanotherapeutics restores implant osseointegration in diabetes.

The rhythms of trance: Cultural phenomenology and neural mechanisms of music-induced non-ordinary states of consciousness.

Distinct frontal lobe subregions mediate the emergence and reporting of visual consciousness.

Alpha connectivity in the frontoparietal network is increased in individuals with familial risk for depression: A resting-state EEG study towards a potential endophenotype.

Dissociable Neurocognitive Mechanisms of State and Trait Anxiety in Working Memory: Threat-Induced Alterations in Decision Dynamics and Attenuation of Large-Scale Network Reconfiguration.

This work reports a dual-modulus microcone array for graded tactile sensing and intelligent slip detection. The asymmetric microstructure─comprising hollow and solid polydimethylsiloxane/carbon nanotube (PDMS/CNT) microneedle arrays with distinct Young's moduli (460.8 kPa vs 581.2 kPa)─produces a hierarchical mechanical response fundamentally different from conventional single-modulus designs. This structural design yields high sensitivity (9.55 kPa-1) over a broad pressure range (0.1-450 kPa), fast response/recovery (68/51 ms), and durability exceeding 10000 cycles. The superhydrophobic surface (contact angle 156.5 ± 1.0°, sliding angle <2°) ensures stable operation in wet and variable-temperature environments (10-70 °C). Integrated with a one-dimensional convolutional neural network for slip detection and adaptive feedback control, the sensor enables real-time grip force regulation during delicate object manipulation, minimizing mechanical damage and contamination. This work establishes a materials platform that couples interfacial engineering with machine learning-enhanced perception, with implications for soft robotics, wearable electronics, and intelligent human-machine interfaces.

Cocaine Use Disorder (CUD) poses a major public health challenge, with no Food and Drug Administration-approved pharmacotherapies currently available. A deeper understanding of Morphometric Similarity Network (MSN) alterations and their associations with neurotransmitter systems in CUD may facilitate the development of targeted therapeutic strategies. We aimed to investigate the aberrant MSN patterns and their relationships with neurotransmitter distributions in CUD. This case-control study enrolled 70 patients with CUD and 57 age- and sex-matched healthy controls (HCs). Individual-level MSNs were constructed for each participant, and regional morphometric similarity (MS) strength was computed. Group differences in MSN connectivity and regional MS strength were compared between the CUD and HC groups. The JuSpace toolbox was employed to assess spatial correlations between regional MS alterations and specific neurotransmitter maps. Network-based statistic analysis revealed disrupted MSN connectivity in patients with CUD, primarily involving the prefrontal cortex, striatum, thalamus, frontoparietal control network, and insula. Regional MS strength abnormalities demonstrated significant spatial correlations with the neurotransmitter density distributions of the serotonergic, dopaminergic, glutamatergic, GA-BAergic, and cholinergic systems. These spatial correlations were further associated with CUD severity and weekly cocaine dose. These findings provide novel insights into the neuropathological mechanisms of CUD from the perspectives of gray matter morphometric covariance patterns and neurotransmitter systems. The consistency with existing findings further supports the clinical translational value of the neurotransmitter targets we have identified. Our findings revealed co-altered patterns of gray matter morphometric covariance and neurotransmitter interactions in CUD, providing novel insights into the neuropathological mechanisms of CUD and identifying potential therapeutic targets.

This work proposes a novel control technique to ensure stable operation of direct current (DC) microgrids powered by renewable energy in various operating modes. The proposed control framework combines Frilled Lizard Optimization (FLO) with Mix Style Neural Network (MSNN) for adaptive control of charge/discharge cycle and energy availability prediction. FLO algorithm ensures fast and adaptive response by optimizing power flow under nonlinear and unstable operating conditions. Simultaneously, MSNN improves robustness in predicting learning relationship between voltage, current and irradiance patterns. The aim of the proposed framework is to minimize the operational cost and improve system efficiency. The proposed method is simulated utilizing MATLAB and its performance is benchmarked against hybrid models such as cuckoo search–recurrent neural network (CS-RNN) and Ant Colony Optimization-Feed forward Neural Network (ACO-FNN) with cost reduction upto 28.57% and efficiency improvement upto 8.89% in comparison to these hybrid methods. The proposed framework also had the lowest voltage deviation of 0.64%. These findings highlight the reliability, adaptiveness and cost-effectiveness of the proposed energy management system in renewable-based DC microgrids.

Growing evidence indicates that bulimia nervosa (BN) is more likely driven by network-level dysfunctions rather than abnormalities in isolated brain regions. However, previous studies focusing on specific regions or connectivity features have led to a limited understanding of the complexity and integrality of network in BN. This study aimed to investigate large- and mesoscale network alterations in BN from the perspective of network segregation and integration. Using resting-state functional magnetic resonance imaging data from 85 BN patients and 71 healthy controls (HCs) matched for age, sex, and education, we applied a graph-theoretic framework to analyze functional network architecture in BN. Specifically, we examined group differences in within- and between-system functional connectivity (FC) as well as system segregation at both the whole-brain and system levels. Associations between network measures and clinical features were further explored. Compared with HCs, BN patients exhibited whole-brain reduced within- and between-system FC accompanied by increased system segregation. At the system level, patients showed increased between-system FC of the default mode network (DMN), control network and ventral attention network, along with decreased DMN system segregation. The increased integration of DMN with other systems was associated with higher Beck Depression Inventory (BDI-21) scores in BN patients. The neural mechanisms of BN involve an imbalance between network segregation and integration at both global and system levels, most pronounced within the DMN, whose alterations are associated with depression and disordered eating behavior. These findings may provide potential neural substrates for some core behavioral deficits in BN.

This guideline aims to provide systematic guidance for establishing "Metabolic-Associated Fatty Liver Disease (MAFLD) Tiered Diagnosis, Treatment, and Standardized Management Centers" at various levels of medical institutions. Faced with the severe challenges of high prevalence and low diagnosis and treatment rates for this disease, the guidelines clarify the background, necessity, tiered setting standards, multidisciplinary collaboration models, and clinical diagnosis and treatment pathways for center construction. The core is to establish a standardized management framework and integrate multidisciplinary resources to achieve standardized management of the entire process, from screening, diagnosis, and treatment to long-term follow-up, thereby improving the overall prevention and control level in China and building a chronic disease prevention and control network and data platform for MAFLD.

Multi-level pathways linking personality and mood to problematic smartphone use: A behavioral and neuroimaging study.

Targeting ncRNA control networks with engineered exosomes to overcome therapy resistance in thyroid cancer.

Brain tumors impair brain function both locally and across distant regions by disrupting network connectivity, contributing to cognitive deficits and triggering compensatory plasticity. Traditional resting-state fMRI methods average brain activity over time, missing transient, dynamic events critical to cognition. Co-Activation Pattern (CAP) analysis captures these brief brain states, enabling quantification of state engagement and duration. To investigate alterations in transient brain states in patients with brain tumors using CAP analysis of resting-state fMRI, and to assess whether these changes reflect modified engagement of cognitive states compared to healthy controls. This retrospective cross-sectional study included 106 patients with left-hemispheric brain tumors (72 high-grade gliomas, 19 low-grade gliomas, 15 metastases; mean age 61.15 ± 8.95 years; 43 women) and 100 age-matched healthy controls. Resting-state fMRI data were analyzed using a seed-free clustering method (TbCAPs toolbox) to extract CAPs. CAPs were first identified in controls and then matched to patients via spatial similarity. Each CAP was assigned to a canonical brain network using the GIFT toolbox. Dynamic metrics computed included: persistence (duration of a CAP), transitions (switching frequency), in-degree, and out-degree. Group comparisons used two-tailed t-tests with Benjamini-Hochberg correction (p < 0.05). Six CAPs were identified. Compared to controls, patients showed significantly increased transitions, in-degree, and out-degree, and decreased persistence in CAPs linked to the default mode and executive control networks (all p < 0.01), suggesting more frequent but less stable engagement. Visuospatial network CAPs demonstrated lower transition, in/out-degree and persistence in patients (p < 0.05). No significant differences were observed among tumor types. Patients with brain tumors display altered CAP dynamics involving higher-order cognitive networks and perceptual networks. These alterations may reflect the combined effects of tumor-related network damage and potential adaptive reorganization, with potential implications for functional preoperative planning and prognosis. However, in the absence of direct clinical or neuropsychological correlations, these interpretations remain hypothesis-generating. The findings are also limited by the retrospective cross-sectional design preventing causal or longitudinal interpretation, and the restriction to left-hemispheric tumors. Future studies integrating CAP dynamics with cognitive and clinical outcomes will be necessary to determine whether these changes reflect compensatory functional reorganization in brain tumor patients.

Schizophrenia (SCZ) and bipolar disorder (BD) are severe psychiatric conditions with overlapping clinical presentations, genetic risk factors, and brain network dysfunction. Whether alterations in large-scale intrinsic brain networks reflect shared or disorder-specific genetic influences remains poorly understood. Clarifying this distinction is essential for refining etiological models and improving diagnostic precision. Genome-wide inferred statistics (GWIS) were applied to decompose the genetic architecture of SCZ and BD into shared and unique components. Using resting-state network (RSN) data from the UK Biobank, functional connectivity (FC) and structural connectivity (SC) were extracted as neuroimaging phenotypes. Causal inference approaches were subsequently employed to infer potential directional relationships between brain network connectivity and each disorder. Analyses revealed both common and distinct patterns of brain network connectivity associated with SCZ and BD. Notably, SC within the default mode network (DMN) exhibited opposing effects across the two disorders, suggesting divergent structural underpinnings despite clinical overlap. Additionally, SC within the limbic network (LN) and frontotemporal control network demonstrated potential causal relationships with both conditions, implicating these circuits astransdiagnostic neural substrates. These findings illuminate the shared and disorder-specific genetic and neural architecture underlying SCZ and BD. Integrating genome-wide genetic methods with large-scale neuroimaging data offers a powerful framework for disentangling psychiatric comorbidity and may inform more targeted diagnostic criteria and individualized treatment strategies.

Post-traumatic stress disorder (PTSD) is characterized by exaggerated fear response, anxiety, hyperarousal and sleep disturbances. One of the major pathophysiological mechanisms underlying the development of PTSD is the dysregulation of the hypothalamic-pituitary-adrenal axis. However, the molecular and cellular mechanisms underlying the development of the disease remain poorly understood due to the heterogeneity of the clinical symptoms and disease course. There is emerging evidence that peripheral immune signaling, endocrine regulation, and glial-vascular interactions play a role in modulating neural circuit functions, which in turn can impact synaptic plasticity and network dynamics. In this review, we integrate evidence from cytokine and immune cell profiling, epigenetic and transcriptomic analyses, experimental immune activation studies. We elaborated neuroimaging, including the translocator protein-positron emission tomography (TSPO-PET) as a marker of glial activation state, to summarize the potential role of immune-brain interactions in modulating circuit and network phenotypes in PTSD. Recently, studies emphasized the role of inflammatory biomarkers, such as C-reactive protein (CRP), interleukin-6 (IL-6), Tumor necrosis factor-alpha (TNF-α), as well as changes in monocyte and T-cell phenotypes, in PTSD. In this review, we focus on the neuroimmune interface in PTSD, including immune-activated and immune-altered phenotypes that may relate to different underlying dysfunctions in neural circuits. We also elaborated the interactions between immune signaling, endocrine control, and neural networks that may trigger clinical heterogeneity in PTSD. Notably, future studies that incorporate peripheral markers with multimodal neuroimaging will be critical for validating these underlying mechanisms.

Adult language learning varies widely among individuals, with some learners quickly acquiring knowledge and skills while others struggle with specific components or overall proficiency despite similar exposure. This variability, once linked to frontotemporal language regions, is increasingly seen as originating from distributed networks involved in attention, control, and memory. The role and organization of these networks in explaining these differences remain unclear. We hypothesized that intrinsic multi-network connectivity underpins these variations, revealing potential neuromarkers of interactions among systems beyond language regions. We tested this in 101 healthy adults (72 females and 29 males) using multimodal neuroimaging before seven days of artificial language training across six tasks targeting auditory and speech categories, words, morphosyntax, and sentence structures. We identified one general component shared across tasks and five task-specific ones. Using cross-validated predictive modeling and graph-theoretic metrics, we found that the general component's learning outcome (LO) and rate (LR) were primarily driven by the dorsal attention and frontoparietal networks. Their local efficiency was a strong predictor, highlighting local resilience and mesoscale segregation. Local connectivity dominated in association cortical networks, while global integration occurred in subcortical regions, reflecting a balance between segregation and integration influences learning. Only task-specific word learning was predictable, relying on default-mode and frontoparietal hubs. Single-modality predictions were weaker, emphasizing the value of multimodal approaches. These findings suggest that the intrinsic network topology underlies individual success in language learning, supporting a multiple-system model in which attention, default, and subcortical networks work together to shape learning trajectories and advance mechanistic understanding.Significance Statement Adult learners vary greatly in how effectively and successfully they learn a new language, but the neural basis for this variability remains unclear. Using multimodal MRI data collected before training and connectome-based graph-theoretic measures, we demonstrate that intrinsic brain network topology predicts both a general language-learning factor and learning speed across six artificial-language-learning tasks in 101 adults. The strongest predictive features were found in the dorsal attention and frontoparietal control networks, with nodal local efficiency emerging as the most consistent marker. A cortical-subcortical dissociation in local-efficiency and global-integration properties may underlie these individual differences. These findings highlight potential network-level neuromarkers for predicting adult language learning success beyond language areas.