Search Results (259)

Background/Objectives: Epilepsy is a chronic brain disease characterized by recurrent epileptic seizures. It affects roughly 50 million people worldwide and around one third of the patients have drug-resistant epilepsy (DRE). The current study aimed to find differences in the whole-brain functional connectivity (FC) in patients with DRE compared to patients with well-controlled epilepsy (WCE) and healthy controls (HCs). Methods: This explorative, cross-sectional study included 92 participants (n_DRE = 30; n_WCE = 30; n_HC = 32) who underwent resting-state functional magnetic resonance imaging (fMRI). The CONN Toolbox was used to process and analyze the FC changes among the three groups. Results: There was a statistically significant increase of the FC between the left lateral prefrontal cortex, left inferior temporal gyrus (temporo-occipital), left lobules IV and V of the cerebellum and multiple cortical and subcortical structures in patients with DRE as opposed to WCE and HC. On the other hand, decreased FC was observed between three seeds (the posterior cingulate cortex, precuneus cortex, the right planum polare) and different frontal, temporal and occipital regions. Interestingly, the right nucleus accumbens (r_NAc) showed increased FC with the inferior frontal gyrus in DRE compared to WCE, whereas the r_NAc-left precentral gyrus FC was reduced in DRE as opposed to HC. Conclusions: The acquired information offers valuable insights into the neuronal networks associated with DRE. These data could be used for advancing diagnostic accuracy and future therapeutic strategies. Full article

Viral vaccine development requires both mechanistic understanding of protective immunity and translational study designs that connect preclinical data with human outcomes. Animal models remain important for early assessment of safety, immunogenicity and protective efficacy, but their predictive value depends on the question being asked, the pathophysiology of infection, the immune mechanisms expected to mediate protection, and the biomarkers chosen to bridge animal and human data. This review focuses on viral vaccines and examines innate and adaptive mechanisms of vaccine-induced protection, including B cell and antibody responses, Fc-mediated functions, Fc glycosylation, T cell memory and CD8+ cytotoxic responses. We discuss common reasons for clinical failure and show how preclinical endpoints can be classified as human-counterpart, surrogate or comparative/mechanistic readouts. Influenza and COVID-19 examples illustrate how different models can be combined across discovery, challenge, transmission and late-stage bridging studies. Emerging tools such as systems serology, omics, AI/ML and new approach methods can improve candidate prioritization, but their value depends on assay standardization, biological validation and cautious interpretation. A mechanism-driven model cascade, paired with human-relevant immunological readouts, can improve preclinical interpretation and reduce the risk of advancing candidates that are unlikely to succeed in clinical trials. Full article

The pathological mechanism of autism spectrum disorder (ASD) exhibits dual heterogeneity: abnormal local energy metabolism and brain-wide high-order topological failure. To synergistically characterize these complex signals captured by advanced neuroimaging sensors, we propose the Quantum-Enhanced Graph Convolutional Wide Residual Network (QG-WRN), a modality-specific, decoupled parallel dual-stream architecture. In the classical branch, to accurately capture the spatial distribution of local metabolic abnormalities, we employ a wide residual network (WRN) to extract amplitude of low-frequency fluctuation (ALFF) features, leveraging its expanded feature channels to effectively mine regional neurodynamic properties. Furthermore, to overcome the representational bottlenecks of classical linear operators in parsing hidden, long-range network connections, we introduce quantum computing, exploiting its exponentially expansive state space and intrinsic low-parameter regularization mechanism. Guided by these properties, the quantum branch utilizes a variational quantum graph convolutional (QGCN) module—featuring a trainable circular encoding strategy and a hardware-efficient 4-qubit configuration—with a 2-layer nested message passing structure to process the functional connectivity (FC) matrix, harnessing quantum interference in Hilbert space to parse complex topology while effectively mitigating overfitting on small-sample medical data. A unified training scheme achieves full-dimensional fusion of node activity and topology. Achieving 68.49% accuracy, our method outperforms 10 classic and recent new baselines, providing a powerful computational intelligence tool for sensor-based ASD clinical diagnosis. Furthermore, interpretability analysis successfully maps core disease hubs to standard AAL116 atlas coordinates, providing a powerful tool for computationally aided ASD diagnosis. Full article

Background/Objectives: Brain dysfunction and symptoms can be improved with a sensory trick (ST) in more than 80% of patients with cervical dystonia (CD). This study aimed to investigate the functional connectivity (FC) of CD patients with different types of STs using functional near-infrared spectroscopy (fNIRS) and to explore the underlying neural mechanisms of STs. Methods: In this study, 35 CD patients (including 15 with classic STs, 15 with forcible STs, 5 with non-STs) and 29 healthy controls (HCs) underwent resting-state fNIRS. We subsequently analyzed FC differences between the groups and their correlations with clinical characteristics. Results: The grand-average FC was significantly higher in the non-ST group than in the forcible ST group. Furthermore, compared to the ST group, the non-ST group exhibited significantly increased FC, primarily involving the prefrontal and sensorimotor networks. In the forcible ST group, this hypoconnectivity was negatively correlated with disease severity scores. Conclusions: This study supports the concept of CD as a networkopathy, suggesting that both the severity and topology of cortical coherence impairment are modulated by the ST phenotype. Full article

Background/Objectives: Intentional inhibition reflects voluntary control abilities and is assumed to be an indicator of overweight. The medial frontal cortex is an important brain region associated with intentional inhibition. Nevertheless, it is uncertain whether being overweight is connected to impaired food-related intentional inhibition (FII), and if so, what its underlying neural correlates are. The present study therefore aims to provide increased support for overweight due to impairment of FII. Methods: Firstly, 55 overweight and 45 normal-weight college students (Sample 1) were instructed to perform a go/no-go/choose task, which included a resting-state fMRI. Neural correlates of FII were examined using regional homogeneity (ReHo) analyses. Subsequently, an additional 180 undergraduates (87 overweight and 93 normal-weight; Sample 2) were examined to ascertain the differences in resting-state functional connectivity (rsFC) between overweight and normal-weight participants. The study also investigated whether restrained eating mediated the effect of rsFCs on one-year body index changes. Results: FII demonstrated a positive correlation with the cerebellum, inferior temporal gyrus, orbitofrontal cortex, inferior frontal gyrus, and cingulate gyrus. Additionally, in comparison with participants with normal weight, overweight participants demonstrated diminished rsFC between the FII-related areas and the postcentral gyrus, while heightened rsFC strengths were found between these areas and the middle temporal gyrus and precuneus. Furthermore, mediation analyses demonstrated that cingulate–precuneus connectivity is linked to fat mass index change a year later through restrained eating. Conclusions: FII was associated with connectivity between brain regions involved in inhibitory control and maladaptive eating. Furthermore, we investigated how these connectivity patterns could potentially affect future body fat loss through restrained eating. Full article

The explosive growth of the Internet of Things (IoT) has brought an array of resource-constrained devices to domains such as smart homes, industrial automation, and healthcare, raising substantial cybersecurity challenges. Lightweight wireless protocols, such as Thread, Zigbee, and Z-Wave, are integral to IoT connectivity, but the degree to which their embedded cryptographic mechanisms satisfy formal cybersecurity certification schemes remains underexplored. This work draws primarily on recent peer-reviewed publications and major conference proceedings to rigorously evaluate Thread, Zigbee, and Z-Wave against the Common Criteria (CC) Functional Requirements for Cryptography (FCS) as specified in CC:2022 and the EU cybersecurity certification scheme on Common Criteria (EUCC). The assessment focuses on essential CC cryptographic components, including key generation (FCS_CKM.1), secure key distribution (FCS_CKM.2), agreement protocols (FCS_CKM_EXT.7), cryptographic operations (FCS_COP.1), and random bit generators (FCS_RBG.1). The analysis reveals that Thread demonstrates the strongest alignment with CC requirements by leveraging Advanced Encryption Standard—Counter with CBC-MAC mode (AES-CCM) authenticated encryption and Elliptic Curve Diffie-Hellman (ECDH)-based key exchange within a decentralized trust framework. Zigbee matches this cryptographic strength at the primitive level, but its dependency on a centralized Trust Center for key management complicates full compliance with key lifecycle and distribution controls. Z-Wave, especially through its S2 Security framework, improves by incorporating authenticated ECDH exchanges, though proprietary constraints and limited protocol transparency remain obstacles to independent assurance. This comparative study concludes that while all three protocols provide a baseline of robust cryptographic security, only Thread currently aligns with CC and EUCC certification schemes. Zigbee and Z-Wave will require additional protocol hardening and enhancement of cryptographic key lifecycle management to achieve comparable assurance levels. Ensuring conformance with formal cybersecurity standards is imperative for building trust and resilience across critical IoT infrastructures. Full article

Background/Objectives: Chronic pain in osteoarthritis alters large-scale brain networks, including the insular cortex and default mode network. While total hip arthroplasty (THA) provides substantial relief, the early postoperative reorganization of functional connectivity (FC) remains unclear. This longitudinal fMRI study exploratively investigated how early improvements in pain intensity and catastrophizing are associated with insular FC alterations following THA. Methods: In this exploratory, longitudinal observational study, 10 female patients with hip osteoarthritis underwent resting-state fMRI and clinical assessments—Pain Visual Analogue Scale (VAS), Pain Catastrophizing Scale (PCS), and Japanese Orthopaedic Association (JOA) hip score—preoperatively and two weeks post-THA Whole-brain seed-to-voxel FC analyses were conducted using the bilateral anterior insular cortex as the seed. Changes in FC (ΔFC) were correlated with preoperative scores and postoperative clinical changes (ΔVAS, ΔPCS). Results: Following THA, VAS and PCS scores decreased significantly, while JOA scores improved. rs-fMRI analysis revealed that FC between the left anterior insula and major DMN regions as well as the right anterior cingulate cortex (ACC) increased significantly overall. Correlation analysis showed that greater reductions in pain intensity (ΔVAS) were significantly associated with increased ΔFC across these regions. Conversely, greater reductions in pain catastrophizing (ΔPCS) were associated with a suppression of these FC increases. Conclusions: Given the preliminary nature of this study, these findings suggest that the alleviation of pain catastrophizing following THA may be associated with the initial reorganization of the aIC network, rather than establishing a definitive causal relationship. Further large-scale longitudinal studies are required to confirm these potential neural signatures. Full article

Understanding the pore structure of high-rank coals is essential in evaluating gas storage and transport. Here, twelve semianthracite samples from the early Permian Shanxi Formation were investigated by proximate analysis, optical microscopy, low-temperature N₂ adsorption, and fractal analysis, coupled with diffusion coefficient modeling. The coals exhibit diverse pore types (plant-cellular, interparticle, and dissolution pores) shaped by coalification and minerals and show Type IV (a) isotherms with H4 hysteresis loops, indicating complex pore networks. Pore-size partitioning reveals that mesopores and macropores dominate total pore volume, whereas mesopores contribute most of the specific surface area. The pore structure exhibits strong fractal characteristics with an average comprehensive fractal dimension (F_c) of 2.628. The calculated gas diffusion coefficient decreases monotonically with increasing pressure from 1 MPa to 5.8 MPa, with a more pronounced decline at low pressure, indicating a clear pressure-dependent attenuation effect. Diffusion capacity is weakly related to average pore diameter but shows positive correlations with total pore volume and, particularly, macropore volume. Multiple linear regression further demonstrates that pore volume structure is the dominant control on diffusion under both low- and high-pressure conditions, with the relative importance ranked as macropores > mesopores > micropores. Macropores provide the main low-resistance transport framework, mesopores serve as transitional pathways linking storage and transport domains, whereas micropores mainly contribute to gas storage and may even suppress apparent diffusion when overly developed. These results reveal a clear functional differentiation of multiscale pore systems and highlight that gas migration in semianthracite is jointly governed by pore size distribution, connectivity, tortuosity, and fractal network topology. Full article

Background: Lumbar disc herniation (LDH) is the most common etiological cause of low back pain (LBP). Objective and precise pain evaluation is of significant clinical value. Functional near-infrared spectroscopy (fNIRS) as a noninvasive neuroimaging modality, has been increasingly validated to reflect subjective pain perception through hemodynamic correlates. This study aimed to analyze the fNIRS changes in patients with LDH about to receive Unilateral Biportal Endoscopy and to further explore the feasibility of fNIRS as an objective biomarkers for clinical assessment of LDH. Methods: Resting-state fNIRS data were acquired from 67 preoperative LDH patients and 20 healthy controls (HC). Brain functional maps—including z-standardized fractional amplitude of low-frequency fluctuations (zfALFF) and seed-based functional connectivity (FC)—were extracted and quantified. Group-level comparisons were performed between LDH and HC groups across four predefined regions of interest; additionally, correlation analyses were conducted between fNIRS metrics and clinical assessment scores within the LDH cohort. Results: Compared with HC, LDH patients exhibited significantly altered zfALFF in the medial prefrontal cortex (mPFC): decreased amplitude at channel CH12 (t = −2.031, p = 0.045) and increased amplitude at CH21 (t = 2.462, p = 0.016). Whole-brain FC analysis further revealed widespread changes—particularly between the parietal somatosensory cortex and prefrontal regions. Among all tested FC–clinical indicator associations, 56 reached statistical significance after FDR correction (q < 0.05). VAS_ lumbar and SF-36_SF exhibited the highest number of significant connections. Conclusions: LDH patients with LBP exhibit notable alterations in prefrontal resting-state ALFF and FC between the parietal somatosensory cortex and prefrontal cortex relative to HC. Importantly, these neural alterations exhibit significant associations with both pain severity (VAS) and long-term health-related quality of life (SF-36), thereby strengthening their candidacy as neural correlates meriting prospective validation as objective, mechanism-informed biomarkers for clinical evaluation of lumbar disc herniation (LDH). Moreover, these findings highlight candidate neural targets for future longitudinal studies investigating early prognostic prediction and treatment response monitoring in LDH. Full article

Parkinson’s disease (PD) may benefit from non-pharmacological motor–cognitive rehabilitation, but sensitive neuroimaging markers of training-related brain changes remain limited. This study investigated whether 4 weeks of daily Quadrato Motor Training (QMT) modulate resting-state functional connectivity (FC) in PD and secondarily explored whether whole-brain radiomic features derived from T1-weighted and fractional anisotropy (FA) images could detect pre–post differences over this short intervention interval. Fifty patients with idiopathic PD were randomized to QMT or a SHAM repetitive stepping condition, and 48 completed the protocol (25 SHAM, 23 QMT). MRI was acquired at baseline and after 4 weeks and included resting-state fMRI, 3D T1-weighted imaging, and diffusion-derived FA maps. Resting-state fMRI was analyzed using independent component analysis and dual regression, whereas an IBSI-compliant radiomics workflow and machine-learning models were used for exploratory scan-level classification. Compared with baseline, the SHAM group showed reduced synchronization across several resting-state networks, whereas the QMT group showed increased synchronization in the right sensorimotor and frontoparietal networks and no significant reductions. Between-group analyses showed lower delta-FC in SHAM than QMT in the cerebellar and sensorimotor networks. In contrast, radiomics showed limited discrimination between pre- and post-QMT scans; the best model achieved a ROC-AUC of 0.65 with near-chance accuracy, and no selected predictor remained significant after multiple-comparison correction. These findings suggest that QMT may support short-term functional network stability or task-relevant reorganization in PD relative to the SHAM condition, whereas whole-brain structural radiomics appears less sensitive for detecting early training-related effects in this setting. Full article

Background/Objectives: Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder that imposes a profound burden on global public health. While resting-state functional magnetic resonance imaging (rs-fMRI)-based dynamic functional connectivity (dFC) analysis has demonstrated promise in capturing time-varying brain network abnormalities, existing deep learning methods suffer from three fundamental limitations: (1) an inability to model temporal dependencies across dynamic connectivity windows, (2) reliance on post hoc black-box explainability tools, and (3) misalignment between feature learning and classification objectives. Methods: To address these challenges, we propose MambaKAN, an end-to-end interpretable framework integrating a Variational Autoencoder (VAE), a Selective State Space Model (Mamba), and a Kolmogorov–Arnold Network (KAN). The VAE encodes each dFC snapshot into a compact latent representation, preserving nonlinear connectivity patterns. The Mamba encoder captures long-range temporal dynamics across the sequence of latent representations via input-selective state transitions. The KAN classifier provides intrinsic interpretability through learnable B-spline activation functions, enabling direct visualization of how latent features influence diagnostic decisions without post-hoc approximation. The entire pipeline is trained end-to-end with a joint loss function that aligns feature learning with classification. Results: Evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset across five classification tasks (CN vs. AD, CN vs. EMCI, EMCI vs. LMCI, LMCI vs. AD, and four-class), MambaKAN achieves accuracies of 95.1%, 89.8%, 84.0%, 86.7%, and 70.5%, respectively, outperforming strong baselines including LSTM, Transformer, and MLP-based variants. Conclusions: Comprehensive ablation studies confirm the indispensable contribution of each module, and the three-layer interpretability analysis reveals key temporal patterns and brain regions associated with AD progression. Full article

Individual differences in second-language (L2) proficiency are expected to influence how listeners parse and represent continuous speech, yet their neural signatures under naturalistic conditions remain unclear. We investigated this question using task-based fMRI during continuous speech listening. A total of 43 healthy participants completed four listening runs synchronized with MRI acquisition via PsychoPy(Peirce 2007), with eyes open throughout scanning. To promote sustained attention and comprehension, participants provided a native-language oral recall after each run. Based on behavioral proficiency scores, participants were grouped into low- (LP, n = 14), moderate- (MP, n = 14), and high-proficiency (HP, n = 15) groups. We evaluated three temporal information-encoding frameworks derived from BOLD dynamics: direct temporal series, functional connectivity (FC), and self-information weighted inter-subject correlation (ISC-W). Using a 10 × 5-fold nested cross-validation scheme, we tested both categorical classification (Support Vector Machines) for discrete proficiency groups (LP, MP, HP) and continuous multivariate regression (Ridge/Lasso) for continuous proficiency scores. Furthermore, we applied ROI-based ANOVA and univariate Neural Correlation Analysis (NCA) to identify key brain regions, evaluating significance via nonparametric permutation testing (1000 permutations) and False Discovery Rate (FDR) correction. Results indicated that while categorical classification yielded numerical trends—with ISC-W performing best—it did not reach statistical significance under stringent permutation testing. However, multivariate continuous regression using ISC-W features successfully predicted continuous proficiency scores with statistical significance (p < 0.05). Exploratory ROI analysis highlighted the bilateral orbital inferior frontal gyrus (IFG_orb_bilat) as a highly sensitive region. These findings suggest that L2 proficiency is best represented as a distributed, continuous neural variable, and that self-information weighting effectively filters background noise to capture cognitive variance. Methodologically, this study provides a reproducible pipeline integrating information-theoretic feature construction with rigorous whole-brain nonparametric inference. Full article

Background/Objectives: Migraine with aura (MwA) is a heterogeneous disorder comprising pure visual aura (MwAv) and more complex phenotypes with additional somatosensory and/or dysphasic symptoms (MwAvsd). Previous structural magnetic resonance imaging (MRI) studies have demonstrated hippocampal subfield volume reductions associated with aura complexity, suggesting a role for the hippocampus in MwA pathophysiology. However, functional network mechanisms underlying these structural differences remain unclear. This study aimed to investigate hippocampal resting-state functional connectivity (FC) in MwA subtypes and healthy controls (HCs), and to determine whether hippocampal connectivity patterns differ according to aura complexity. Methods: In this comparative cross-sectional study, 27 patients with MwAvsd, 18 with MwAv, and 29 age- and sex-matched HCs underwent resting-state functional MRI on a 3T scanner. Seed-based FC analyses were performed using both hippocampi as regions of interest. Results: MwAvsd patients demonstrated significantly increased FC between the right hippocampus and the left dorsal parietal cortex and right sensory association cortex compared with MwAv patients. In contrast, MwAv patients showed increased FC between the left hippocampus and the right dorsolateral prefrontal cortex compared with MwAvsd patients. Additionally, MwAv patients exhibited stronger FC between the left hippocampus and bilateral anterior prefrontal cortices and the left angular cortex compared with HCs. No other significant hippocampal FC differences were observed. Conclusions: Hippocampal FC is altered in MwA and varies according to aura phenotype. Complex aura is characterized by enhanced hippocampal coupling with multisensory integration regions and reduced connectivity with executive control areas, whereas pure visual aura demonstrates increased hippocampal–prefrontal and hippocampal–parietal associative connectivity compared with HCs. These findings suggest that the hippocampus might serve as a target for future neuromodulatory and therapeutic investigations in MwA patients. Full article

Background: The recognition of autism spectrum disorder (ASD) has been a challenge due to the heterogeneity in symptoms and complex variations in brain function. Resting-state functional magnetic resonance imaging (rs-fMRI) has become instrumental in studying these disorders by accessing underlying abnormal neural activity and connectivity. Recently, deep learning approaches have shifted the analysis of brain networks by capturing spatiotemporal information from fMRI sequences. Nonetheless, most existing studies are limited by relying on a single representational scale, typically restricting analysis to either voxel-level spatiotemporal patterns or static connectivity matrices. Additionally, the dynamic reconfiguration of functional coupling and its variations across different anatomical parcellations are often ignored, which obscures neurobiologically meaningful dynamics. Methods: In this regard, we propose a multi-atlas dynamic connectivity transformer fused with 4D spatiotemporal modeling for ASD recognition (MADCT-4D). Specifically, the framework comprises two complementary branches. The 4D spatiotemporal branch encodes raw rs-fMRI volumes to learn hierarchical representations of evolving neural activity, while the dynamic-connectivity branch models time-resolved functional connectivity sequences constructed from multiple atlases, enabling the network to capture dynamic reconfiguration at the connectome level under different parcellation granularities. Moreover, we perform late fusion by combining the branch-specific decision scores with a learnable gate, allowing the model to adaptively weight voxel-level dynamics and multi-atlas connectivity evidence for each subject. Results: Extensive experiments on the publicly available ABIDE dataset demonstrate that the proposed method achieves 90.2% accuracy for ASD recognition, outperforming multiple competitive baselines. Conclusions: The proposed framework yields interpretable biomarkers based on learned dynamic connectivity patterns that are consistent with altered functional coupling in ASD. Full article

The diagnosis of attention-deficit/hyperactivity disorder (ADHD) has long relied on subjective scales, lacking objective neuroimaging biomarkers. Static functional connectivity (sFC) and dynamic functional connectivity (dFC), as commonly used metrics in resting-state functional magnetic resonance imaging (rs-fMRI) analysis, provide important perspectives for related research. However, existing unimodal approaches struggle to effectively integrate the spatiotemporal characteristics of functional connectivity. To address this, this paper proposes the multimodal fusion framework D-SFANet, which synergistically models the static and dynamic features of brain functional connectivity through an attention mechanism: in the static path, it integrates a multi-scale convolutional network with phenotypic information extraction to extract hierarchical topological features; in the dynamic path, it combines graph theory with a bidirectional long short-term memory network (BiLSTM) to capture key state transition patterns in brain networks. Experimental validation demonstrates that D-SFANet achieves significantly higher classification accuracy than existing mainstream methods, robustly validating the effectiveness of its spatiotemporal fusion strategy. Full article