Chen Xu, Yu ji, Zhenyu Lv, Yang Yi, Yizhe Yang, Luyao Ji, Chaoyi Chen, Xianyang Wang, Tian Lan, Zhihua Wang, Juan Wang, Xunde Dong, Fuze Tian, Qunxi Dong, Bin Hu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Critique ability, defined as the capacity to identify and rectify flaws in text generation, is crucial for the applications of Large Language Models (LLMs). As a meta-cognitive capability, enhancing the critique ability of LLMs poses significant challenges. Recent studies have proposed improving this ability through fine-tuning on critique datasets. However, the static data distribution of existing datasets often leads to a mismatch between the training data and the diverse optimization needs of target models, thereby hindering their effectiveness. To address this issue, we introduce a novel Dynamic Iterative Data Distribution Optimization Method (DIDD) that dynamically adjusts training data distributions to align with the specific optimization requirements of target models. Specifically, DIDD detects the vulnerable data distribution of target optimization models by conducting the meta-critique on synthesized test set. The detected vulnerable data distribution are then leveraged to construct the training dataset that aligns with target model more closely, improving the effectiveness of the training dataset. Extensive experimental results across four benchmarks demonstrate that our proposed DIDD effectively alleviates the mismatch between the training dataset and target optimization models.

Multimodal fusion of Electroencephalography (EEG) and functional Near-Infrared Spectroscopy (fNIRS) has shown great promise in Brain-Computer Interface (BCI) tasks. However, due to differences in physical mechanisms, temporal dynamics, and semantic representations between the two modalities, the fusion process faces significant challenges such as heterogeneity and temporal misalignment. To address this, we propose a cross-modal decoupled deformable distillation (CMD$^{3}$) method, which aims to achieve flexible, efficient, and interpretable EEG-fNIRS fusion learning. CMD$^{3}$first decouples the feature representations of each modality into modality-independent and modality-specific spaces to separately model commonality and complementary information. A deformable feature extraction network is then designed to process shared and specific features individually, enabling cross-modal temporal alignment via predicted dynamic offsets, thereby mitigating response delays between modalities. Furthermore, to facilitate inter-modal knowledge transfer, we construct a dual-space graph distillation module to explicitly migrate semantic information across modalities, with learnable edge weights used to adaptively regulate the distillation strength. CMD$^{3}$is systematically evaluated on public datasets covering emotion recognition and motor imagery tasks. Experimental results demonstrate that CMD$^{3}$consistently outperforms existing fusion approaches in classification performance. Offset visualization further reveals physiologically meaningful temporal attention patterns learned by the model, validating the effectiveness and explainability of the proposed method.

Researchers have long sought objective and quantifiable methods for recognizing depression. Electroencephalography (EEG) signals, which reflect brain activities objectively, have emerged as a promising tool for this purpose. However, the practical application of EEG signals faces significant challenges arising from distribution variability across different datasets and subjects. In addition, conventional methods often struggle to effectively capture information related to dynamic transformations in distributions. To address these issues, we propose a Wasserstein distribution-inspired dynamic adversarial network (WDANet) for EEG-based depression recognition. Specifically, WDANet includes a global discriminator that focuses on the marginal distribution of EEG features, a local discriminator that concentrates on the conditional distribution of EEG features, and a Wasserstein distribution discriminator that utilizes Wasserstein distributions derived from various processed EEG features. The experimental results show that WDANet achieved classification accuracies of 83.33%, 75.52%, 73.93%, 76.04%, and 70.94% in cross-subject, cross-dataset experiments conducted on three datasets, demonstrating its effectiveness and superiority compared to state-of-the-art methods. These results support our claim that WDANet enhances the accuracy and interpretability of depression recognition, providing insights and new research directions for the integration of neuroscience and artificial intelligence technologies.

Cross-subject electroencephalogram (EEG) emotion recognition is an important task in affective computing, which aims to learn discriminative and generalizable EEG features to reveal emotions across individuals. To achieve robust generalization, existing methods primarily focus on developing domain alignment constraints to learn features that remain consistent across all source domains, while ignoring the potential benefits of domain-specific information in enhancing model discriminability. As a result, these approaches struggle to utilize relevant source domain information to improve predictions for unseen domains. To address this limitation, we propose a novel prompt-guided domain generalization (PGDG) framework that extends the invariance perspective by incorporating domain-specific information through prompt learning. Specifically, the variational autoencoder is first used to extract domain-invariant features, and then the prompt of each source domain is learned by guiding the classifier in the optimal direction. Finally, a multi-head cross-attention mechanism adaptively integrates these domain prompts from multiple source domains to improve the model's generalization ability in target domains. Experimental results on the SEED and SEED-IV datasets show that PGDG outperforms state-of-the-art methods, demonstrating the potential of prompt-guided generalization in improving cross-subject EEG emotion recognition performance.

To objectively evaluate the intervention effectiveness of exercise rehabilitation on drug addicts, this study proposes a neural-assessment method for measuring rehabilitation efficacy. Taking subjects from isolation rehabilitation centers as the research objects, they were divided into an experimental group (receiving exercise rehabilitation training), a control group (not receiving exercise rehabilitation training), and a newly admitted group (just admitted to the rehabilitation center). Brain activities of the three groups under resting state and audio-stimulated state were evaluated, and power features and nonpower features were extracted. A comparative study between groups was conducted by combining statistical analysis and machine learning models. The results show that the power index of the FPz channel is the most sensitive for distinguishing whether exercise intervention is received, and the nonpower features of the FP1 and FP2 channels are the core basis for identifying different withdrawal stages. Various machine learning models have achieved effective identification of subjects in different intervention states and withdrawal stages, confirming the reliability and potential of our method for evaluating exercise rehabilitation effectiveness. This study provides technical support and theoretical basis for optimizing exercise-assisted rehabilitation strategies and improving drug control governance effectiveness.

Event-driven neuromorphic computing involves sparse and asynchronous signal activity, which leads to irregular computation patterns and fine-grained concurrency. As a result, processing architectures need to support both high parallelism and energy efficiency. Among existing architectural solutions, superscalar designs exhibit significant potential for addressing high parallelism demands. However, conventional superscalar processors, which rely on synchronous circuits, maintain high-frequency clocking at all times, leading to substantial power inefficiency in sparse computation scenarios. To address this issue, we propose an asynchronous superscalar architecture that replaces global clocking with fully local handshake-based control, implemented using a bundled-data asynchronous protocol. The design supports decoding of up to 64 scalar instructions per cycle and implements the RISC-V RV32IMC instruction set. A prototype was fabricated using a 110 nm complementary metal oxide semiconductor (CMOS) process and was evaluated through post-layout simulation. Operating at 1.2 V, the processor delivers a peak INT8 throughput of 669.4 GOPS, with a static power consumption of 421 mW.

Depression has become one of the most prevalent mental health disorders worldwide, highlighting the urgent need for objective and reliable auxiliary diagnostic methods. Electroencephalography (EEG), as a non-invasive technique with high temporal resolution, shows great promise in depression recognition. However, the significant inter-subject variability inherent in EEG signals limits the generalization ability of traditional machine learning and deep learning models in cross-subject scenarios. Although incorporating multi-source data can enhance the representational capacity of transfer learning, it also introduces new challenges, such as distributional conflicts and adaptation strategy inconsistencies between sources, which can lead to negative transfer. To address these issues, we propose a Multi-Source Dynamic Adversarial Adaptation Network (MS-DAAN). This framework constructs independent feature extraction and adversarial adaptation branches for each source domain, incorporates an unsupervised EEG-based source clustering mechanism to form semantically coherent subdomains, and introduces a target-guided source attention module to dynamically weight each source according to its statistical similarity to the target domain. Experimental results demonstrate that MS-DAAN significantly outperforms existing methods across multiple evaluation metrics, validating its effectiveness and robustness in cross-subject EEGbased depression recognition.

Emotions regulation play an important role in human behavior, but exhibit considerable heterogeneity among individuals, which attenuates the generalization ability of emotion models. In this work, we aim to achieve robust emotion prediction through efficient disentanglement of affective semantic representations. In detail, the data generation mechanism behind observations from different perspectives is causally set, where latent variables that relate to emotion are explicitly separate into three parts: the intrinsic-related part, the extrinsic-related part, and the spurious-related part. Affective semantic features consist of the first two parts, with the understanding that spurious latent variables generate the inherent biases in the data. Furthermore, a variational autoencoder with a reformulated objective function is proposed to learn such disentangled latent variables, and only adopts semantic representations to perform the final classification task, avoiding the interference of spurious variables. In addition, for electroencephalography (EEG) data used in this article, a space-frequency mapping method is introduced to improve information utilization. Comprehensive experiments on popular emotion datasets show that the proposed method can achieve competitive intersubject generalization performance. Our results highlight the potential of efficient latent representation disentanglement in addressing the complexity challenges of emotion recognition.

Emotion inhibitory control is a key executive function of the human brain, which regulates behavior by suppressing inappropriate responses. It plays an integral part in alleviating negative emotions, improving mood, and preventing depression. Transcutaneous auricular vagus nerve stimulation (taVNS) has been proved to enhance behavioral control, potentially suppressing negative emotions or facilitating their reduction in healthy individuals. However, the neurocomputational mechanisms underlying taVNS-induced neuroenhancement remain unclear. In this work, a portable electroencephalography (EEG) acquisition and stimulation device is designed to collect eight-channel EEG signals and deliver taVNS to both sides of ears. Then, we design a protocol that successfully induced negative emotions in healthy subjects. Next, we conduct a sham-controlled experiment, involving 28 healthy subjects, to explore the changes in EEG of negative emotions under taVNS. Finally, we primarily analyze the power spectrum density (PSD) of EEG signals and the functional connectivity network of the brain, based on the phase locking value (PLV), to assess the effect of taVNS on neural activity induced by negative emotions. The results of the experiment reveal that taVNS is a promising method for enhancing emotional inhibitory control by reducing PSD in the alpha band and enhancing PLV within prefrontal inhibitory control networks. In addition, differences in graph theory parameters between the Sham and taVNS conditions indicate that taVNS helps regulate negative emotions. In conclusion, this study demonstrates that taVNS enhances inhibitory control and reveals its neurocomputational mechanisms of EEG in healthy individuals during the development of negative emotions. And results indicate that taVNS could serve as a promising neuromodulation therapy for psychiatric disorders and individuals with depression or emotional distress.

Functional brain network modeling plays a crucial role in uncovering cognitive mechanisms and identifying abnormalities associated with brain disorders. However, traditional approaches—such as Pearson correlation and mutual information—typically assume that interregional relationships are static and linear, limiting their ability to capture dynamic interactions and nonlinear features. To address this limitation, we propose a nonlinear dynamic low-rank representation method. This approach constructs interregional similarity weights using physiological state matrices and Frobenius distance, while incorporating neighborhood information to generate a row-stochastic transition matrix, thereby enhancing the robustness of local connections. Additionally, a dynamic interaction effect matrix is constructed based on the stationary distribution eigenvectors, enabling the identification of both direct and indirect information transmission processes between brain regions. The method preserves the brain’s modular structure through low-rank representation and manifold regularization. Experimental results demonstrate that the proposed method not only effectively reveals dynamic information transmission pathways, cross-modular cooperative effects, and task-dependent hub reorganization patterns, but also significantly outperforms traditional static connectivity approaches. This study offers a mathematically rigorous and physiologically interpretable framework for dynamic brain network modeling and lays a theoretical foundation for the detection of dynamic abnormalities in mental disorders.

Schizophrenia (SZ) is a severe mental disorder characterized by hallucinations, delusions, cognitive impairments, and social withdrawal. It leads to a series of brain abnormalities, particularly the deformation of the hippocampus and amygdala, which are highly associated with emotion, memory, and motivation. Most previous studies have used the hippocampal and amygdaloid volume, whereas surface-based morphometry reflects nuclear deformation more finely, but it is unclear the hippocampal and amygdaloid morphometry relates to schizophrenic pathology and its potential as a biomarker. In this study, we extracted individual multivariate morphometry statistics (MMS) of hippocampus and amygdala from MRI images and analyzed the morphometric differences between groups. After dictionary learning and max pooling, we obtain reduced dimensional features and use machine learning algorithms for individual diagnosis. The results showed that the hippocampus of the schizophrenia group was significantly atrophied bilaterally and the atrophied areas were symmetrical. Subregions of the amygdala are both atrophied and expanded, and in particular, the right amygdala shows a greater degree and extent of deformation. Using the random forest classifier, the accuracy of classification using hippocampal and amygdaloid morphometric features are 94.52% and 94.57%, respectively, and the accuracy of classification combining the two morphometric features reached 96.57%. Our study demonstrates the efficacy of MMS in identifying morphometric differences of the hippocampus and amygdala between healthy controls and schizophrenic, and these findings emphasize the potential of MMS as a reliable biomarker for the diagnosis of schizophrenia.

Constrained decoding approaches aim to control the meaning or style of text generated by a Pre-trained Language Model (PLM) for various task-specific objectives at inference time. However, these methods often guide plausible continuations by greedily and explicitly selecting targets, which, while fulfilling the task requirements, may overlook the natural patterns of human language generation. In this work, we propose a novel decoding framework,Decider, which enables us to program high-level rules on how we might effectively complete tasks to control a PLM. Differing from previous works, our framework transforms the encouragement of concrete target words into the encouragement of all words that satisfy the high-level rules. Specifically,Decideris a dual system in which a PLM is equipped and controlled by a First-Order Logic (FOL) reasoner to express and evaluate the rules, along with a decision function that merges the outputs from both systems to guide the generation. Experiments on CommonGen and PersonaChat demonstrate thatDecidercan effectively follow given rules to guide a PLM in achieving generation tasks in a more human-like manner.

Vigilance estimation is a critical task within the field of brain-computer interfaces, extensively applied in monitoring and optimizing user states during human-machine interaction using electroencephalography (EEG). However, most existing vigilance prediction frameworks are prone to spurious correlations stemming from inherent biases in collected data. These biases involve relevant but vigilance-independent information, which may lack robustness when applied to different data distributions, i.e., out-of-distribution (OOD) scenarios. The core idea of this study is to learn constraints that capture causal information from the input based on the assumed underlying data generating process. Leveraging the disentanglement and invariance principles behind the assumptions, we propose a constraint-driven causal representation learning (CCRL) to identify and separate spurious latent variables from biased training data for generalized vigilance estimation. The CCRL training process consists of two phases: self-supervised pretraining and constraint-driven causal information disentanglement. In the first phase, based on the masked autoencoder (MAE) architecture, unlabeled training data are used for reconstructing pretext tasks to capture the comprehensive and intrinsic contextual information from EEG data, which provides a powerful input for downstream disentanglement learning. In the second phase, we propose a novel disentanglement strategy to learn spurious-free latent representations causally related to the vigilance state driven by adversarial and invariance constraints. Comprehensive validation experiments conducted on two well-known public datasets demonstrate the effectiveness and superiority of the proposed framework. In general, this work has promising implications for addressing OOD challenges in vigilance estimation.

Detecting and diagnosing major depressive disorder (MDD) is greatly crucial for appropriate treatment and support. In recent years, there have been efforts to develop automated methods for depression detection using machine learning techniques, which mainly analyze various data sources such as text, speech, and social media posts. However, the effectiveness and reliability of these methods may vary and more importantly, they fail to provide timely intervention and treatment to MDD patients. To address these challenges, we propose a novel electroencephalogram (EEG)-based MDD detection framework, which is named as multiview sparse dynamic graph convolution-based region-attention feature fusion network (MV-SDGC-RAFFNet). Specifically, we first design a multiview (MV) feature extractor to concurrently characterize EEG signals from temporal, spectral, and time-frequency views, providing rich semantic information on the emotional status of patients. Secondly, we introduce a sparse dynamic graph convolution network (SDGCN) to map the multidomain features into high-level representations, which avoids the limitation of over-smoothing and redundant edges existing in the conventional graph neural networks (GNNs). Finally, to efficiently fuse multidomain features, we propose a region-attention feature fusion network (RAFFNet), which applies different attention weights for brain regions and is greatly beneficial to boost the accuracy (ACC) of MDD detection. We validate the efficacy of the proposed MV-SDGC-RAFFNet framework on two public MDD datasets, and it achieves more promising detection performance against the state-of-the-art methods, indicating that our method has a prospect on clinical MDD detection.

With great pride and enthusiasm, we present the inaugural edition of IEEE Transactions on Computational Social Systems (TCSS) for 2024. Reflecting on the year gone by, 2023 stands as a hallmark of academic excellence and prolific output, wherein our journal has successfully disseminated a substantial volume of scholarly work—301 articles encompassing approximately 3600 pages, distributed across six distinct issues.

Many headache patients often report cognitive disturbances, but tactile cognitive data are limited. Applying computing-aided strategies to reveal the association between migraine without aura (MOA) or tension-type headache (TTH) and tactile cognition is one of the research highlights. The aim of this study was to investigate whether MOA or TTH patients had a decline in tactile discrimination by utilizing a tactile angle discrimination tester. A cross-sectional study was performed between 1 January 2021, and 1 January 2022. A total of 301 participants were enrolled, with 107 in control, 90 in MOA, and 104 in TTH groups. A tactile cognition tester was used to objectively examine tactile discrimination in all participants. Tactile angle discrimination thresholds were measured to compare tactile cognitive functions among three groups. There were no statistically significant differences in their demographic characteristics. Compared to the normal control group, the MOA and TTH groups exhibited significantly higher tactile angle discrimination thresholds (showing decline in tactile discrimination), whereas no significant differences were found between the MOA and TTH groups. Differences in tactile angle discrimination thresholds were observed between young (≤ 44 years old) and middle-aged/elderly (≥ 45 years old) participants in the normal control group but not in the MOA and TTH groups. Moreover, the tactile deficits shown in the MOA or TTH groups were evident only in young participants. This study first demonstrated that patients with MOA or TTH, especially those patients younger than 44 years old, had decreased tactile angle discrimination ability, suggesting decline of tactile cognition.

Functional connectivity (FC) network derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been extensively employed in the automated identification of brain disorders. Conventional FC network modeling methods typically assign equal importance to different sampling points of rs-fMRI and brain regions of interest (ROIs). Nevertheless, considering temporal and regional heterogeneity, this assumption may not always be applicable. Moreover, sparse regression algorithms with regularization terms are commonly adopted to eliminate spurious FC. However, these conventional regularization terms impose a uniform sparse penalty on all FC without considering the prior knowledge that the brain tends to transmit information in an energetically efficient manner. To address the above problems, we propose an adaptive weight and Wasserstein distance constrained low-rank sparse representation (AW-WD-LSR) method to construct FC networks. Specifically, we employ adaptive weights to the reconstruction errors of rs-fMRI time series across various ROIs and sampling points, thereby amplifying the significance of crucial features in the joint representation, leading to a more robust FC network. Meanwhile, we incorporate a local constraint by introducing the optimal transport distance (i.e., Wasserstein distance) between ROIs to adjust the sparse penalty on the corresponding FC. The larger Wasserstein distance, the higher information transmission cost between two ROIs, resulting in a greater penalty imposed on the corresponding FC. The efficacy of the innovation modules is demonstrated in classification tasks involving major depressive disorder (MDD) with mixed features (MMF), MDD without mixed features (MDDnoMF), and healthy controls (HCs).

As an essential challenge within the realm of affective computing, emotion recognition assumes a vital role in bestowing computers with a higher level and comprehensive intelligence. Furthermore, it has emerged as a crucial research topic in both human–computer interaction (HCI) and medical rehabilitation related to mental illnesses. However, the related fusion studies for modeling physiological signals in emotion recognition are less based on multimodal coordinated representation and lack the exploration of multimodal physiological signal correlation. In this article, we propose a tensor correlation fusion framework for emotion recognition based on multimodal physiological signals. After extracting effective features from various physiological signals, the coordinated representation module of the framework first simultaneously learns the linear correlation of all input physiological signals based on the covariance tensor. An optimized solution strategy is constructed to obtain the coordinated representation corresponding to each physiological signal. Finally, an emotion recognition module fuses the correlation information of the coordinated representation of different physiological signals as input to the emotion recognition classifier. This framework constructs coordinated representation by introducing a strategy to simultaneously capture the correlation among multiple physiological signals, providing a fresh perspective with a well-defined mathematical foundation for the fusion of multimodal physiological signals in the realm of emotion recognition. The experiments conducted on the DEAP dataset demonstrate that compared with related methods, the framework achieves relatively higher emotion recognition performance while obtaining a coordinated representation of multimodal physiological signal correlations of emotions, all while achieving superior processing speed.

Intelligence evaluation is a desirable intelligent application for sensing and interaction in various scenarios, e.g., education, office, and the aviation industry. For example, identifying gifted students, who learn faster and more efficiently than general students due to their neurophysiological advantages, and teaching different students according to their intelligence are urgent requirements in school education. However, current intelligence evaluation mainly relies on intelligence quotient (IQ) tests, which have a problem of decreasing reliability in repeated tests. In addition, no objective assessment criteria are available in the present intelligence evaluation process. Electroencephalogram (EEG) signals, which reflect the neuroelectrical activities of the brain, can be utilized to develop an objective and promising tool for investigating the neurophysiological advantages of gifted groups and augmenting the effects of intelligence evaluation. Consequently, we proposed a novel real-time intelligence evaluation framework based on users’ psychophysiological data. Then, we leveraged the framework to investigate a case study to asses which EEG patterns could be used to effectively characterize gifted students and distinguish them from average students. Experimental results reveal the great differences in the chaos degree of the brain (CDB) between different groups of subjects and the effectiveness of the model in identifying gifted students, thus verifying the practicability and validity of the proposed framework.

Depression is a complex mental disease with its pathological mechanism unclear. To depict the complete picture of the abnormal information interaction in a depressed brain, this study is the first to apply fully connected multilayer brain functional (FCMBF) network framework and proposed composite FCMBF (CFCMBF) network framework, combined with graph theory to analyze the within-frequency coupling (WFC) and cross-frequency coupling (CFC) of sensor-layer and source-layer electroencephalography (EEG) signals in relevant subjects. Results showed that in the sensor-layer FCMBF network, depressive patients showed significantly reduced functional connectivity, as well as abnormal global and local information processing abilities of the network, and these network properties were significantly correlated with depressive symptoms. In addition, from the perspective of depression recognition, we found that the sensor-layer CFCMBF network could achieve better classification accuracy, especially when using the overlapping degree of node under the right center region, its accuracy could reach$86.88\% \pm 9.25 \%$. More importantly, the construction of the CFCMBF network has higher time efficiency and less information loss, since it not only measures the WFC and CFC between brain region representative signals (BRRSs) extracted from different brain regions, but also measures these two couplings between all nodes within each brain region. Although the FCMBF network contains more complete information by calculating WFC and CFC between all nodes distributed in each region, it will result in an enormous computational cost. In summary, this study proved the utility of multilayer brain network in revealing the abnormal brain interaction patterns of depression, and our proposed method might provide methodological support for efficient depression recognition research based on multilayer brain networks.

Currently, the integration of artificial intelligence (AI) techniques with multimodal physiological signals represents a pivotal approach to detect affective disorders (ADs). With the increasing complexity and diversity of physiological signal modalities, researchers have introduced various AI methods using multimodal physiological signals to improve model classification performance and explainability to increase trust and facilitate clinical adoption. Among these methods, spiking neural networks (SNNs) stand out as a promising avenue due to their alignment with the operating principles of the human brain, robust biological explainability, and adeptness in processing spatial–temporal information in an efficient event-driven manner with low power consumption. Furthermore, the emergence of neuromorphic computing (NC) chips based on SNNs has greatly bolstered the field of NC, enabling effective support for objective, pervasive, and wearable AI-assisted medical diagnostic devices for ADs and other diseases. This article presents a review of recent achievements in multimodal AD detection and points out the associated challenges in utilizing multimodal physiological signals and NC based on SNNs for AD detection. Building upon this foundation, we give perspectives on future work. The intended readership for this review consists of researchers in the fields of cognitive computing, computational psychophysiology, affective computing, NC, and brain-inspired computing. We hope that this survey not only garners increased attention from the scientific community but also serves as a valuable guide for future studies in this field.

Functional connectivity (FC) networks, built from analyses of resting-state magnetic resonance imaging (rs-fMRI), serve as efficacious biomarkers for identifying Autism Spectrum Disorders (ASD) patients. Given the neurobiological heterogeneity across individuals and the unique presentation of ASD symptoms, the fusion of individualized information into diagnosis becomes essential. However, this aspect is overlooked in most methods. Furthermore, the existing methods typically focus on studying direct pairwise connections between brain ROIs, while disregarding interactions between indirectly connected neighbors. To overcome above challenges, we build common FC and individualized FC by tangent pearson embedding (TP) and common orthogonal basis extraction (COBE) respectively, and present a novel multiview brain transformer (MBT) aimed at effectively fusing common and indivinformation of subjects. MBT is mainly constructed by transformer layers with diffusion kernel (DK), fusion quality-inspired weighting module (FQW), similarity loss and orthonormal clustering fusion readout module (OCFRead). DK transformer can incorporate higher-order random walk methods to capture wider interactions among indirectly connected brain regions. FQW promotes adaptive fusion of features between views, and similarity loss and OCFRead are placed on the last layer to accomplish the ultimate integration of information. In our method, TP, DK and FQW modules all help to model wider connectivity in the brain that make up for the shortcomings of traditional methods. We conducted experiments on the public ABIDE dataset based on AAL and CC200 respectively. Our framework has shown promising results, outperforming state-of-the-art methods on both templates. This suggests its potential as a valuable approach for clinical ASD diagnosis.

Conventional functional connectivity network (FCN) based on resting-state fMRI (rs-fMRI) can only reflect the relationship between pairwise brain regions. Thus, the hyper-connectivity network (HCN) has been widely used to reveal high-order interactions among multiple brain regions. However, existing HCN models are essentially spatial HCN, which reflect the spatial relevance of multiple brain regions, but ignore the temporal correlation among multiple time points. Furthermore, the majority of HCN construction and learning frameworks are limited to using a single template, while the multi-template carries richer information. To address these issues, we first employ multiple templates to parcellate the rs-fMRI into different brain regions. Then, based on the multi-template data, we propose a spatio-temporal weighted HCN (STW-HCN) to capture more comprehensive high-order temporal and spatial properties of brain activity. Next, a novel deep fusion model of multi-template called spatio-temporal weighted multi-hypergraph convolutional network (STW-MHGCN) is proposed to fuse the STW-HCN of multiple templates, which extracts the deep interrelation information between different templates. Finally, we evaluate our method on the ADNI-2 and ABIDE-I datasets for mild cognitive impairment (MCI) and autism spectrum disorder (ASD) analysis. Experimental results demonstrate that the proposed method is superior to the state-of-the-art approaches in MCI and ASD classification, and the abnormal spatio-temporal hyper-edges discovered by our method have significant significance for the brain abnormalities analysis of MCI and ASD.

Alzheimer's disease(AD) is being the burden of society and family. Applying computing-aided strategies to reveal its pathology is one of the research highlights. Plasma neurofilament light (NFL) is an emerging noninvasive and economic biomarker for AD molecular pathology. It is valuable to reveal the correlations between the plasma NFL levels and neurodegeneration, especially hippcampal deformations at the preclinical stage. The negative correlation between plasma NFL levels and hippocampal volumes has been documented. However, the relationship between the plasma NFL levels and the hippocampal morphometry details at the preclinical stage is still elusive. This study seeks to demonstrate the capacity of our proposed surface-based hippocampal morphometry system to discern the plasma NFL positive (NFL+>41.9 pg/L) level and plasma NFL negative (NFL-<41.9pg/L) level and illustrate its superiority to the hippocampal volume measurement by drawing the cohort of 154 CU middle aged and elderly adults. We also apply this morphometry measure and a proposed sparse coding based classification algorithm to classify CU individuals with NFL+ and NFL- levels. Experimental results show that the proposed hippocampal morphometry system offers stronger statistical power to discriminate CU subjects with NFL+ and NFL- levels, comparing with the hippocampal volume measure. Furthermore, this system can discriminate plasma NFL levels in CU individuals (Accuracy=0.86). Both the group level and individual level analysis results indicate that the association between plasma NFL levels and the hippocampal shapes can be mapped at the preclinical stage.

Depression is a serious and common psychiatric disease characterized by emotional and cognitive dysfunction. In addition, the rates of clinical diagnosis and treatment for depression are low. Therefore, the accurate recognition of depression is important for its effective treatment. Electroencephalogram (EEG) signals, which can objectively reflect the inner states of human brains, are regarded as promising physiological tools that can enable effective and efficient clinical depression diagnosis and recognition. However, one of the challenges regarding EEG-based depression recognition involves sufficiently optimizing the spatial information derived from the multichannel space of EEG signals. Consequently, we propose an adaptive channel fusion method via improved focal loss (FL) functions for depression recognition based on EEG signals to effectively address this challenge. In this method, we propose two improved FL functions that can enhance the separability of hard examples by upweighting their losses as optimization objectives and can optimize the channel weights by a proposed adaptive channel fusion framework. The experimental results obtained on two EEG datasets show that the developed channel fusion method can achieve improved classification performance. The learned channel weights include the individual characteristics of each EEG epoch, which can effectively optimize the spatial information of each EEG epoch via the channel fusion method. In addition, the proposed method performs better than the state-of-the-art channel fusion methods.

Depression is a heterogeneous syndrome with certain individual differences among subjects. Exploring a feature selection method that can effectively mine the commonness intra-groups and the differences inter-groups in depression recognition is therefore of great significance. This study proposed a new clustering-fusion feature selection method. Hierarchical clustering (HC) algorithm was used to capture the heterogeneity distribution of subjects. Average and similarity network fusion (SNF) algorithms were adopted to characterize the brain network atlas of different populations. Differences analysis was also utilized to obtain the features with discriminant performance. Experiments showed that compared with traditional feature selection methods, HCSNF method yielded the optimal classification results of depression recognition in both sensor and source layers of electroencephalography (EEG) data. Especially in the beta band of EEG data at sensor layer, the classification performance was improved by more than 6%. Moreover, the long-distance connections between parietal-occipital lobe and other brain regions not only have high discriminative power, but also significantly correlate with depressive symptoms, indicating the important role of these features in depression recognition. Therefore, this study may provide methodological guidance for the discovery of reproducible electrophysiological biomarkers and new insights into common neuropathological mechanisms of heterogeneous depression diseases.

Welcome to the fifth issue of IEEE Transactions on Computational Social Systems (TCSS) in 2022. After the usual introduction of our 24 regular articles, we would like to discuss the topic of “Psychological Field Versus Physiological Field: From Qualitative Analysis to Quantitative Modelling of the Mental Status.”

Welcome to the second issue of IEEE Transactions on Computational Social Systems (TCSS) in 2022. In this issue, we are going to present 25 regular articles. After the “scanning the issue,” I would like to share some of my opinions and perspectives on the fundamentals of computational psychophysiology: theory and methodology.

In an increasingly knowledge based world, people are confronted with an explosion of information from the environment which must be viewed in restricted attention spans. Hence there is a need to investigate how best to model our Visual Attention (VA) with a view to allocate our attention efficiently. We use the color-word Stroop task combined with electroencephalogram (EEG) to model VA: subjects undertake the Stroop task and their EEG is recorded. This is in contrast to other studies that use techniques such as Event Related Potentials (ERP), Contextual Modeling Frameworks, eye movements and facial recognition. The paper presents a simple and useful model to recognize VA dynamically. We use the linear EEG features of different cortical fields as the main inference factors, and take the response time (RT) of the Stroop task as a metric to quantify subject performance. First, we obtain the most relevant EEG feature vectors from the recording, using a correlation analysis. Second, we use experimental data for training the VA model, using a regression method. Last, we then apply further experimental data to test the proposed model. The results from the tests conducted demonstrate that our model maps visual attention very closely.