Drug-resistant epilepsy (DRE) patients typically require surgical intervention or neurostimulation. Therefore, accurate localization of the seizure onset zone (SOZ) is essential for effective clinical intervention. Although some physiologically meaningful parameters of neural computational models show substantial differences across brain regions during seizures, few studies pay attention to applying these model parameters to SOZ localization. To investigate whether the parameter can be used for accurate SOZ localization, the unscented kalman filter (UKF) is employed to estimate the excitatory-inhibitory balance parameter c from the Z6 neural computational model using DRE patients' electrocorticography (ECoG). The results indicate that this parameter follows a unimodal distribution during the pre-ictal period and the post-ictal period, while exhibiting a bimodal distribution during the ictal period. Then, the distribution of this parameter is combined with machine learning methods, and a bagged tree classifier is constructed to localize the SOZ. The classification results demonstrate that the classifier based on parameter distributions exhibits excellent performance, particularly during the post-ictal period, with an average accuracy of 91.60% . Interestingly, SOZ localization is more accurate when no lesions are detected on magnetic resonance imaging (MRI) compared to when lesions are present. Finally, the model parameter distributions of the SOZs are utilized to predict the outcome of epilepsy surgery. Of note, the results demonstrate that the parameter distribution accurately predicts surgical outcomes with an average accuracy of 92.56% . These findings suggest that the distribution of neural computational model parameters may serve as biomarkers for SOZ localization and epilepsy surgery outcome prediction, providing valuable support and assistance for clinical decision-making.

Despite numerous studies aimed at improving accuracy, the accurate prediction of epileptic seizures remains a challenge in clinical practice due to the high computational cost, poor real-time performance, and over-reliance on labelled data. To address these issues, a real-time seizure prediction method with spatio-temporal information transfer learning (RTSPM-STITL) has been proposed in this study. In the RTSPM-STITL method, the human brain is regarded as a time-varying high-dimensional neurodynamic system, in which epileptic seizures are viewed as state transitions caused by time-varying system parameters. Specifically, the spatio-temporal information transfer (STIT) model is firstly constructed by the recurrent neural network (RNN) and trained by the Force Learning (a real-time learning mechanism). Then the STIT model is utilized to transform the high-dimensional neurodynamic data into low-dimensional time series to capture the dynamic features of epileptic seizures. Also, the critical slowing down effect (CSD) of seizure dynamics is used to detect warning signals. The experimental results demonstrate that the proposed method can achieve higher accuracy and sensitivity without labeled data on both the CHB-MIT and Siena scalp EEG databases. Especially, the parameters of the STIT model can be updated in real-time based on patient data, without iterative training. More importantly, the STIT model can maintain high sensitivity and accuracy with only 48400 parameters, which is reduced by more than 91% compared with contrast models in this experiment. Therefore, the proposed method can significantly reduce the computational cost and accurately predict epileptic seizures, as well as with high real-time, practicality, applicability, and interpretability.

Epilepsy, a chronic neuropsychiatric brain disorder characterized with recurrent seizures, is closely associated with abnormal neural communications within the brain. Despite that the phase-amplitude coupling (PAC) has been suggested to offer a new way to observe neural interactions during epilepsy, however, few studies pay attention to alterations of the epileptic functional brain network based on PAC, especially on the [Formula: see text] PAC. Therefore, we use scalp electroencephalography (EEG) data of epileptic patients and the [Formula: see text] PAC modulation index (MI) to construct functional brain networks to examine variations of neural interactions during different epileptic phases. Statistically, the findings show that between-channel MI values in the post-ictal period significantly increase compared to that in the pre-ictal period, and the between-channel MI value has a close association with the information of phase and amplitude provided by the channels. Importantly, in both the phase-amplitude and amplitude-phase functional brain networks, the average node degree is remarkably higher in the post-ictal period than that in the pre-ictal period, whereas the characteristic path length in the ictal and post-ictal periods is significantly lower than that in the pre-ictal period. Besides, the average betweenness centrality in the post-ictal period is remarkably higher than that in the ictal period. Interestingly, the positive correlations between within-channel MI values and between-channel MI values can be observed during the pre-ictal, ictal and post-ictal periods. These findings suggest that the [Formula: see text] PAC-based functional brain network may provide a novel perspective to understanding alterations of neural interactions during the epileptic evolution, and may contribute to effectively controlling the spread of epileptic seizures.

The explorations of brain functional connectivity (FC) network using resting-state functional magnetic resonance imaging (rs-fMRI) can provide crucial insights into discriminative analysis of neuropsychiatric disorders such as schizophrenia (SZ). Graph attention network (GAT), which could capture the local stationary on the network topology and aggregate the features of neighboring nodes, has advantages in learning the feature representation of brain regions. However, GAT only can obtain the node-level features that reflect local information, ignoring the spatial information within the connectivity-based features that proved to be important for SZ diagnosis. In addition, existing graph learning techniques usually rely on a single graph topology to represent neighborhood information, and only consider a single correlation measure for connectivity features. Comprehensive analysis of multiple graph topologies and multiple measures of FC can leverage their complementary information that may contribute to identifying patients. In this paper, we propose a multi-graph attention network (MGAT) with bilinear convolution (BC) neural network framework for SZ diagnosis and functional connectivity analysis. Besides multiple correlation measures to construct connectivity networks from different perspectives, we further propose two different graph construction methods to capture both the low- and high-level graph topologies, respectively. Especially, the MGAT module is developed to learn multiple node interaction features on each graph topology, and the BC module is utilized to learn the spatial connectivity features of the brain network for disease prediction. Importantly, the rationality and advantages of our proposed method can be validated by the experiments on SZ identification. Therefore, we speculate that this framework may also be potentially used as a diagnostic tool for other neuropsychiatric disorders.

It is quite challenging to establish a prompt and reliable prognosis assessment for acquired brain injury (ABI) patients with persistent severe disorders of consciousness (DOC) like unconscious comatose and unresponsive wakefulness syndrome (a.k.a., vegetative state). Recent advances in brain functional imaging and functional net-work analysis have demonstrated its potential in determining the consciousness level and prognostic outcome for ABI patients with DOC. However, the diagnostic and prognostic usefulness of the whole-brain functional connectome based on advanced machine learning techniques has not been fully evaluated. The first aim of this study is to predict the outcome of individual unconscious ABI patients during a three-month follow-up. The second aim is to conduct precise individualized differentiation among different consciousness levels for exploring the neurobiological mechanisms underlying DOC. Based on resting-state fMRI, we construct large-scale functional networks by using a weighted sparse model, which ensures sparsity and interpretability by preserving strong functional connections. The functional connection strengths are exploited as features for outcome prediction and consciousness level differentiation. We achieve significantly improved consciousness level classification (accuracy: 84.78%) and recovery outcome prediction (accuracy: 89.74%) compared to other network construction methods. More importantly, we reveal the contributive connections across the entire brain in both tasks. These connections could serve as the potential biomarkers for better understanding of consciousness and further provide new insight into the development of diagnostic, prognostic, and effective therapeutic guidelines for ABI patients with DOC.

Compressed sensing (CS) multi‐camera network reconstruction has attracted much attention in the field of distributed CS networks. However, many multi‐camera network reconstructions based on CS usually recover every image separately; the view's dependency and geometrical structure among these multi‐view images could be rarely considered in this way, which will result in some unsatisfied joint reconstruction results. Here, the authors introduce to extract the multiple view geometry from multi‐view images to construct the view's dependency observation model. Based on the proposed parametric transformation observation model, they propose a novel CS joint reconstruction method of multi‐view image that guided by the spatial correlation and low‐rank background constraints. The eventual optimisation model could be relaxed to a series of convex optimisation problems, which could be efficiently solved by combining the variable splitting and alternate iteration technique. The extended experimental results indicate that they proposed method has achieved a remarkable improvement in both objective criterion and visual fidelity compared with other competitive reconstruction methods.

Generally, the improvement in resolution will lead to larger data volume and higher data dimension for Hyperspectral image, which raise a higher requirement for previous image processing algorithms. In this paper, a novel coupled spectral-spatial tensor representation framework (CSSTR) is proposed for denoising of hyperspectral images. Specifically, the proposed method is applied to describe the spectral-spatial features which decomposes a third-order tensor into the sum of several component tensors, with each component tensor being the outer product of a matrix and a vector. Owing to the spatial-spectral constraint fed back from the tensor representation method, CSSTR can capture the structural correlations and inherent feature information of data. Finally, several experiments were conducted to illustrate the advantage of the proposed algorithm.