工程技术最新文献

Although electroencephalography (EEG) offers significant advantages in terms of high temporal resolution and cost-effectiveness, its application is often constrained by limited spatial resolution. This limitation makes it challenging to accurately localize and characterize activity within specific target regions of the brain. To address this, we propose a computational model for brain-network analysis based on independent component analysis (ICA) and source-space clustering. First, repetitive ICA decomposition is performed on a trial-by-trial basis, followed by clustering to extract stable independent components and their corresponding spatial mapping vectors. Subsequently, standardized low-resolution brain electromagnetic tomography (sLORETA) is employed for source localization. The resulting source locations are then clustered across trials to define network nodes, which are utilized to construct a source-level brain network for the investigation of neural mechanisms. The efficacy of this algorithm was validated using two datasets: the international Brain-Computer Interface (BCI) competition dataset involving motor imagery, and a self-collected dataset recorded during the preparatory phase of pistol shooting. Analysis of the motor-imagery dataset demonstrated that the proposed method identified active brain regions consistent with those observed in previous functional magnetic resonance imaging (fMRI) studies. Regarding the pistol-shooting preparation dataset, the method revealed heightened activity in the frontal, occipital, and bilateral temporal lobes. Furthermore, the intensity of information interaction among multiple brain regions exhibited a significant correlation with shooting performance. These findings not only corroborate prior research but also uncover novel features regarding source-level functional connectivity. Consequently, this novel framework achieves precise source localization and network analysis using EEG, significantly enhancing spatial resolution and providing a more accurate elucidation of target brain activities and information-interaction mechanisms during motor tasks.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10405-z.

This study investigated the electrophysiological correlates of anticipatory and reactive processing and behavior associated with a visuomotor discrimination response task of professional dancers to test the effect of dance practice on their cognitive functions. To control the physical activity practice effects, dancers were compared with non-dancers matched for physical activity level. Considering the intrinsic features of the training routine to which professional dancers are constantly exposed - characterized by high temporal anticipation, continuous spatial monitoring and complex sensorimotor integration - we hypothesized differences in attentional control mechanisms and anticipatory processes compared to physically active controls in a discrimination response task. Behavioral data showed that dancers were more accurate than controls, and they had comparable response times. This effect was paralleled by the analysis of event-related potential (ERP), showing dancers compared to controls larger cognitive preparation in the prefrontal cortex (PFC), indexed by the prefrontal negativity (pN) ERP component. This may indicate a more intense top-down attentional control of the upcoming task. Dancers also showed reduced early sensory processing (P1 component) and less intense stimulus-response mapping (pP2 component), suggesting more efficient reactive processing in early sensory processing and associative brain areas. In contrast, the pP1 component was enhanced in dancers, likely reflecting superior sensory-motor integration, a pivotal function in choreographic demands. No difference emerged in the P3, signaling a similar workload load for the two groups. The results outline a peculiar neurofunctional profile of professional dancers, relying on intense cognitive anticipatory control and optimized proactive processing, allowing them superior response precision in sensory-motor performance. Further studies are needed to fully understand the specific trajectories of brain plasticity found here associated with dance practice.

We introduce a brain-constrained neurocomputational model designed to simulate higher cognitive functions of the human brain, implemented using NEST, a widely used open-source simulator optimised for high-performance spiking neural network simulations. Previously implemented in the custom-built C-based Felix simulation library, transitioning the model to NEST enhances accessibility, reproducibility, and computational efficiency. At the cellular level, the model comprises spiking excitatory neurons and local inhibitory neurons, whereas at the network level, it replicates the structural and functional organisation of 12 cortical regions spanning frontal, temporal, and occipital cortices, along with their associated inter-area connectivity. Additionally, global inhibition mechanisms and neuronal noise are integrated. Learning in the model follows biologically plausible Hebbian plasticity principles, incorporating both long-term potentiation and long-term depression. To validate the NEST implementation, we replicated previous simulation findings obtained with the Felix-based model. The new implementation successfully reproduced the same topographical distribution of cell assemblies following associative learning of object and action words within action and perception systems, replicating a range of previous neuroimaging results. Although the NEST model produced larger cell assemblies than Felix, the overall topographical patterns remained similar, indicating preservation of fundamental network characteristics. Moreover, the transition to NEST significantly enhanced computational efficiency, reducing simulation runtime nearly sixfold compared to Felix. This improvement in computational speed is crucial for expanding the model to include additional cortical regions, such as extending to the right hemisphere, which necessitates increased computational resources.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-026-10415-5.

Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures. Altered connectivity within the default mode network (DMN) has been associated with epilepsy, highlighting its role in seizure propagation. In this study, we investigate the temporal patterns of DMN connectivity in epilepsy patients compared to healthy controls using data-driven models of dynamic functional connectivity (dFC). Specifically, we employ one Hidden Markov Model (HMM) and two Hidden Semi-Markov Models (HSMMs) with Gamma and Poisson sojourn distributions to capture latent brain state transitions, as well as hidden connectivity states and their temporal properties. Dynamic metrics (i.e., fractional occupancy, switching rate, and mean lifetime) were derived for each subject, revealing prolonged dwell times in low-connectivity states and reduced flexibility in state transitions, particularly in low-connectivity DMN states. HSMMs, especially the Gamma variant, demonstrated superior sensitivity in capturing these alterations compared to the standard HMM, highlighting the importance of flexible sojourn modeling in dynamic functional connectivity analysis. Additionally, group-specific transition patterns suggested disrupted temporal progression of DMN state transitions. Our findings highlight the potential of HSMMs in capturing alterations in functional brain states and provide new insights into the dynamic reorganization of the DMN in epilepsy.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10382-3.

Detecting Psychogenic Nonepileptic Seizures (PNES) is vital because PNES mimics epileptic seizures but has psychological-not electrical-origins, leading to frequent misdiagnosis and ineffective treatment. Electroencephalography (EEG) provides a non-invasive view of brain activity for distinguishing PNES from true epilepsy. Current PNES detection methods remain limited. This study introduces a curated PNES EEG dataset and a novel explainable feature-engineering (XFE) model. Expert neurologists annotated three classes: Normal, PNES with Verbal Suggestion Provocation (VSP+), and PNES without VSP (VSP -). The introduced explainable feature engineering (XFE) framework includes four components: (i) Distance Counter Pattern (DCPat) for channel-pair feature extraction (190 features for 20 channels), (ii) Cumulative Weight-based Neighborhood Component Analysis (CWNCA) for feature selection (threshold = 0.99), (iii) t-algorithm k-Nearest Neighbors (tkNN) ensemble classifier with Iterative Majority Voting (IMV) and greedy optimization, and (iv) Directed Lobish (DLob) for symbolic interpretation and cortical connectome mapping. For this research, we curated an EEG dataset and four cases are created using the curated dataset. These four cases are: Case 1 (Normal vs. PNES VSP+), Case 2 (Normal vs. PNES VSP-), Case 3 (PNES VSP + vs. PNES VSP-), and Case 4 (all three classes).). The introduced DCPat XFE framework reached accuracy above 96.5% in all four cases; Case 2 attained the best overall value (99.11%). DLob strings and connectome diagrams provided clear symbolic explanations of PNES-related patterns. The DCPat-based XFE framework yields high accuracy and interpretable outputs for PNES detection on EEG. These results support its use as a reliable, explainable tool for clinical decision support.

EEG microstates sequence analysis gained a lot of attention in recent years and different sequence analysis methods have been applied to study microstates sequence randomness, complexity, speed, periodicity, and long-range memory. Although several studies have reported on the reliability of temporal parameters, the stability of sequence-based metrics within subjects has not yet been systematically examined. In this study, we analysed EEG recordings from 60 healthy young adults and assessed short-term (90 min) and long-term (30 days) test-retest reliability and agreement of sequence measures: long-range memory (Hurst exponent), complexity (two Lempel-Ziv algorithms), and randomness (entropy and entropy rate). Across metrics, short-term reliability was consistently good to excellent (ICC = 0.831-0.902), and long-term reliability was moderate to good (ICC = 0.651-0.793). Entropy and entropy rate emerged as the most stable measures across both intervals, confirmed by minimal bias and strong agreement. These findings demonstrate that EEG microstate sequence dynamics represent a stable trait of neural activity, providing a solid methodological foundation for future studies that aim to embed these metrics into computational models and explore their translational value as neurophysiological biomarkers.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10391-2.

Alzheimer's disease (AD) and frontotemporal dementia (FTD) have insidious, similar and ambiguous clinical symptoms, which make their diagnosis difficult. Currently, in the field of EEG signal analysis, there are relatively few studies on the interpretability analysis of feature selection using intelligent optimization algorithms. To analyze the EEG signals of AD and FTD patients more comprehensively, first, 16 features in three dimensions of entropy, time-frequency domain and SODP were extracted in this paper. Secondly, Pearson correlation analysis, importance ranking and SHAP interpretability analysis methods were adopted to select SE, SW, ZCR, STA, CTM2 and CTM5 as the best discriminative features, and the Relief algorithm was used for fusion and dimension reduction based on weights. Thirdly, GWOCS was used for channel screening to determine the optimal channel combination of Fz, F7, Fp1, Fp2, F3, T3, P4 and C3, achieving the three-classification identification of the two patient groups and the normal control group, with the classification accuracy reaching 89.35[Formula: see text] and 81.12[Formula: see text] in cross-validation and LOSO validation, respectively. Finally, the SHAP method was used to prove that for the diagnosis of dementia, the prefrontal and temporal lobe brain regions play a decisive role, verifying the effectiveness of this framework in rapid channel selection and improving the efficiency of disease detection.

Alzheimer's disease (AD) remains a major global health challenge, with current therapies offering only symptomatic relief. A significant constraint in the development of effective treatments is the blood-brain barrier (BBB), as it greatly limits the access of therapeutic drugs targeting amyloid-β (Aβ) aggregation, tau hyperphosphorylation and neuroinflammation. Nanobodies, single-domain antibody fragments derived from camelids, have emerged as versatile tools with unique properties such as small size, high stability and the ability to penetrate the BBB. Engineered formats allow for specific targeting of Aβ and tau, receptor-mediated transcytosis, and conjugation with therapeutic or diagnostic substances. Preclinical studies show that nanobody-based strategies can reduce pathological burden, attenuate neuroinflammation and improve cognitive outcomes in AD models. Manufacturing scale-up, long-term safety and regulatory validation are among the remaining challenges, yet nanobody engineering represents a viable path to disease-modifying medicines. Innovative approaches, including artificial intelligence-driven design, i.e. 4-1BB agonist nanobodies, and clustered regularly interspaced short palindromic repeat-facilitated diversification of nanobody libraries - such as targeted complementarity-determining region 3 mutagenesis followed by functional screening against disease-relevant tau or Aβ conformers - alongside half-life extension strategies, are commencing to surmount these obstacles and enhance the potential of nanobody platforms to develop into clinically viable disease-modifying therapies.

Early detection of depressive symptoms is crucial for reducing their impact on social and cognitive functioning and can be effectively supported by non-invasive, cost-effective biomarkers derived from brain electrical activity. Previous research has identified altered temporal and transition patterns of EEG microstates in clinical populations diagnosed with major depressive disorder (MDD) as well as in healthy individuals exhibiting elevated depressive symptoms. In this study, we aimed to replicate recent EEG microstate findings in young, generally healthy adults who reported high (N = 38) versus low (N = 38) levels of depressive symptoms, while also examining the long-range dependencies of microstate sequences. Microstate analysis was performed on 5-minute resting-state EEG recordings obtained with eyes closed. EEG data were categorized into five microstate classes, revealing significant differences in parameters between groups. Participants with high depressive symptoms exhibited decreased occurrence of microstate A, reduced coverage of microstates A and D, and diminished bidirectional transition probabilities between microstates A and D. Conversely, increased values were found for the Hurst exponent and bidirectional transition probabilities between microstates B and C, between microstates C and E, and from microstate B to E. Linear regression analysis demonstrated that these microstate parameters can predicted depressive symptom scores (R² = 0.145). Our results underscore the potential of resting-state EEG microstate temporal and sequence parameters as biomarkers for the early identification of depressive symptoms in generally healthy young adults.

Hopfield networks are widely used models of associative memory. When the number of stored patterns exceeds the network's storage capacity, theoretical predictions show that the overlap between final states and memorized patterns should vanish. However, numerical simulations show that a small, non-zero overlap persists, indicating that the network retains residual memory. To investigate the origin of this phenomenon, we analyze the network's dynamics during the initial update steps. Using a signal-to-noise-ratio analysis, we demonstrate that when a node undergoes a state flip, the signal term of its neighbors is enhanced by the connecting link. This effect improves the stability of these neighboring neurons, facilitating a fraction of the network to remain aligned with the memory pattern and preventing a total loss of memory. Our findings elucidate the mechanism by which residual memory traces emerge in Hopfield networks beyond the storage limit.