Diversity-enabled sweet spots in layered architectures and speed-accuracy trade-offs in sensorimotor control.

Nervous systems sense, communicate, compute, and actuate movement using distributed components with severe trade-offs in speed, accuracy, sparsity, noise, and saturation. Nevertheless, brains achieve remarkably fast, accurate, and robust control performance due to a highly effective layered control architecture. Here, we introduce a driving task to study how a mountain biker mitigates the immediate disturbance of trail bumps and responds to changes in trail direction. We manipulated the time delays and accuracy of the control input from the wheel as a surrogate for manipulating the characteristics of neurons in the control loop. The observed speed-accuracy trade-offs motivated a theoretical framework consisting of two layers of control loops-a fast, but inaccurate, reflexive layer that corrects for bumps and a slow, but accurate, planning layer that computes the trajectory to follow-each with components having diverse speeds and accuracies within each physical level, such as nerve bundles containing axons with a wide range of sizes. Our model explains why the errors from two control loops are additive and shows how the errors in each control loop can be decomposed into the errors caused by the limited speeds and accuracies of the components. These results demonstrate that an appropriate diversity in the properties of neurons across layers helps to create "diversity-enabled sweet spots," so that both fast and accurate control is achieved using slow or inaccurate components.

Remote Ischemic Conditioning and Outcomes in Acute Ischemic Stroke With Versus Without Large Artery Atherosclerosis.

BACKGROUND: RICAMIS trial (The Remote Ischemic Conditioning for Acute Moderate Ischemic Stroke) has demonstrated efficacy of remote ischemic conditioning (RIC) in acute ischemic stroke. We conducted a post hoc analysis of RICAMIS to investigate whether large artery atherosclerosis (LAA) subtype contributed to the outcomes. METHODS: This is a post hoc analysis of the RICAMIS trial. Patients randomized to RIC group and Control group in full analysis set of RICAMIS were classified into LAA and non-LAA subtypes. The primary outcome was excellent functional outcome at 90 days, defined as modified Rankin Scale score of 0 to 1. Compared with patients receiving usual care, we investigated the association of RIC effect with outcomes in stroke subtypes and the interaction between RIC effect and stroke subtypes. The primary analysis was adjusted analysis. RESULTS: Among 1773 patients, 516 were assigned to LAA subtype (229 in the RIC group and 287 in the control group) and 1257 to non-LAA subtype (633 in the RIC group and 624 in the control group). Median age was 65 years, and 34.2% were women. A higher proportion of primary outcome was found to be associated with RIC treatment in LAA subtype (adjusted risk difference, 11.4% [95% CI, 3.6%-19.2%]; P=0.004), but not in non-LAA subtype (adjusted risk difference, 4.1% [95% CI, -1.1% to 9.3%]; P=0.12). There was no significant interaction between RIC effect and stroke subtypes (P=0.12). CONCLUSIONS: Patients with LAA subtype may benefit from RIC after stroke with respect to excellent functional outcome at 90 days. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03740971.

Multi-objective optimization via evolutionary algorithm (MOVEA) for high-definition transcranial electrical stimulation of the human brain.

Designing a transcranial electrical stimulation (tES) strategy requires considering multiple objectives, such as intensity in the target area, focality, stimulation depth, and avoidance zone. These objectives are often mutually exclusive. In this paper, we propose a general framework, called multi-objective optimization via evolutionary algorithm (MOVEA), which solves the non-convex optimization problem in designing tES strategies without a predefined direction. MOVEA enables simultaneous optimization of multiple targets through Pareto optimization, generating a Pareto front after a single run without manual weight adjustment and allowing easy expansion to more targets. This Pareto front consists of optimal solutions that meet various requirements while respecting trade-off relationships between conflicting objectives such as intensity and focality. MOVEA is versatile and suitable for both transcranial alternating current stimulation (tACS) and transcranial temporal interference stimulation (tTIS) based on high definition (HD) and two-pair systems. We comprehensively compared tACS and tTIS in terms of intensity, focality, and steerability for targets at different depths. Our findings reveal that tTIS enhances focality by reducing activated volume outside the target by 60%. HD-tTIS and HD-tDCS can achieve equivalent maximum intensities, surpassing those of two-pair tTIS, such as 0.51 V/m under HD-tACS/HD-tTIS and 0.42 V/m under two-pair tTIS for the motor area as a target. Analysis of variance in eight subjects highlights individual differences in both optimal stimulation policies and outcomes for tACS and tTIS, emphasizing the need for personalized stimulation protocols. These findings provide guidance for designing appropriate stimulation strategies for tACS and tTIS. MOVEA facilitates the optimization of tES based on specific objectives and constraints, advancing tTIS and tACS-based neuromodulation in understanding the causal relationship between brain regions and cognitive functions and treating diseases. The code for MOVEA is available at https://github.com/ncclabsustech/MOVEA.

Explainable fMRI-based brain decoding via spatial temporal-pyramid graph convolutional network.

Brain decoding, aiming to identify the brain states using neural activity, is important for cognitive neuroscience and neural engineering. However, existing machine learning methods for fMRI-based brain decoding either suffer from low classification performance or poor explainability. Here, we address this issue by proposing a biologically inspired architecture, Spatial Temporal-pyramid Graph Convolutional Network (STpGCN), to capture the spatial-temporal graph representation of functional brain activities. By designing multi-scale spatial-temporal pathways and bottom-up pathways that mimic the information process and temporal integration in the brain, STpGCN is capable of explicitly utilizing the multi-scale temporal dependency of brain activities via graph, thereby achieving high brain decoding performance. Additionally, we propose a sensitivity analysis method called BrainNetX to better explain the decoding results by automatically annotating task-related brain regions from the brain-network standpoint. We conduct extensive experiments on fMRI data under 23 cognitive tasks from Human Connectome Project (HCP) S1200. The results show that STpGCN significantly improves brain-decoding performance compared to competing baseline models; BrainNetX successfully annotates task-relevant brain regions. Post hoc analysis based on these regions further validates that the hierarchical structure in STpGCN significantly contributes to the explainability, robustness and generalization of the model. Our methods not only provide insights into information representation in the brain under multiple cognitive tasks but also indicate a bright future for fMRI-based brain decoding.

Covalently Grafted Biomimetic Matrix Reconstructs the Regenerative Microenvironment of the Porous Gradient Polycaprolactone Scaffold to Accelerate Bone Remodeling.

Integrating a biomimetic extracellular matrix to improve the microenvironment of 3D printing scaffolds is an emerging strategy for bone substitute design. Here, a "soft-hard" bone implant (BM-g-DPCL) consisting of a bioactive matrix chemically integrated on a polydopamine (PDA)-coated porous gradient scaffold by polyphenol groups is constructed. The PDA-coated "hard" scaffolds promoted Ca2+ chelation and mineral deposition; the "soft" bioactive matrix is beneficial to the migration, proliferation, and osteogenic differentiation of stem cells in vitro, accelerated endogenous stem cell recruitment, and initiated rapid angiogenesis in vivo. The results of the rabbit cranial defect model (Φ = 10 mm) confirmed that BM-g-DPCL promoted the integration between bone tissue and implant and induced the deposition of bone matrix. Proteomics confirmed that cytokine adhesion, biomineralization, rapid vascularization, and extracellular matrix formation are major factors that accelerate bone defect healing. This strategy of highly chemically bonded soft-hard components guided the construction of the bioactive regenerative scaffold.

Resting-state functional connectivity of social brain regions predicts motivated dishonesty.

Motivated dishonesty is a typical social behavior varying from person to person. Resting-state fMRI (rsfMRI) is capable of identifying unique patterns from functional connectivity (FC) between brain regions. Recent work has built a link between brain networks in resting state to dishonesty in Western participants. To determine and reproduce the relevant neural patterns and build an interpretable model to predict dishonesty, we analyzed two conceptually similar datasets containing rsfMRI data with different dishonesty tasks. Both tasks implemented the information-passing paradigm, in which monetary rewards were employed to induce dishonesty. We applied connectome-based predictive modeling (CPM) to build a model among FC within and between four social brain networks (reward, self-referential, moral, and cognitive control). The CPM analysis indicated that FCs of social brain networks are predictive of dishonesty rate, especially FCs within reward network, and between self-referential and cognitive control networks. Our study offers an conceptual replication with integrated model to predict dishonesty with rsfMRI, and the results suggest that frequent motivated dishonest decisions may require the higher engagement of social brain regions.

Long-term atorvastatin improves cognitive decline by regulating gut function in naturally ageing rats.

BACKGROUND: Statins have been widely used to prevent cardiovascular disease in middle-aged and elderly populations; however, the effect of long-term treatment on cognitive function is controversial. To simulate clinical conditions, middle-aged rats were given atorvastatin for 9 consecutive months to investigate the effect on natural cognitive decline and the possible mechanisms. RESULTS: The results showed that compared with the control group, long-term atorvastatin treatment naturally improved cognitive decline. Furthermore, long-term treatment regulated intestinal retinoic acid (RA) metabolism and storage by altering retinol dehydrogenase 7 (Rdh7) expression in the intestine, while RA metabolism affected the proliferation of intestinal Treg cells and inhibited IL-17+γδ T-cell function. In addition, long-term atorvastatin increased intestinal flora richness and decreased IL-17 expression in hippocampal tissue. CONCLUSION: Collectively, these findings provide the first evidence that long-term atorvastatin intervention may prevent cognitive decline in naturally ageing rats by inhibiting neuroinflammation via the gut-brain axis.

Nonconformist tendencies related to risky choices in female methamphetamine abstainers.

Background: Many experimental studies and theoretical models have tried to explain the multifaceted formation of drug addiction. In most addiction models, social factors are an important component; however, few empirical studies have investigated the social influences on the safe or risky choices of drug-addicted individuals during the abstinence stage. Objectives: To investigate the behavioral patterns of female methamphetamine abstainers under social influence. Methods: Thirty-seven female methamphetamine abstainers (average abstinence time: 8.61 ± 4.75 months) and 40 matched controls performed a gambling task in the presence of peers' choices. We applied both model-free and computational model-based analysis to examine how the decision patterns differed with social influence between the two groups. Results: 1) the choice data from the two groups showed a social influence effect such that participants made more risky choices when others made risky choices; 2) overall, the female methamphetamine abstainers made more risky choices in the social influence task; and 3) in the computational model parameters, the female methamphetamine abstainers exhibited more nonconforming attitudes (with negative other-conferred utility) with respect to peer influence, whereas controls showed higher conformity to peers. Conclusion: Our findings provide the first objective evidence that female methamphetamine abstainers show peer nonconformity. This nonconformist tendency may be a potential behavioral marker to track drug addiction and help to elucidate the mechanisms of decisions made by female methamphetamine abstainers.

Fluorination Enhances NIR-II Fluorescence of Polymer Dots for Quantitative Brain Tumor Imaging.

Here, we describe a fluorination strategy for semiconducting polymers for the development of highly bright second near-infrared region (NIR-II) probes. Tetrafluorination yielded a fluorescence QY of 3.2 % for the polymer dots (Pdots), over a 3-fold enhancement compared to non-fluorinated counterparts. The fluorescence enhancement was attributable to a nanoscale fluorous effect in the Pdots that maintained the molecular planarity and minimized the structure distortion between the excited state and ground state, thus reducing the nonradiative relaxations. By performing through-skull and through-scalp imaging of the brain vasculature of live mice, we quantitatively analyzed the vascular morphology of transgenic brain tumors in terms of the vessel lengths, vessel branches, and vessel symmetry, which showed statistically significant differences from the wild type animals. The bright NIR-II Pdots obtained through fluorination chemistry provide insightful information for precise diagnosis of the malignancy of the brain tumor.

Shared and connection-specific intrinsic interactions in the default mode network.

Electrophysiological studies revealed that different neuronal oscillations, among which the alpha (8-13 Hz) rhythm in particular, but also the beta (13-30 Hz) and gamma (30-80 Hz) rhythms, are modulated during rest in the default mode network (DMN). Little is known, however, about the role of these rhythms in supporting DMN connectivity. Biophysical studies suggest that lower and higher frequencies mediate long- and short-range connectivity, respectively. Accordingly, we hypothesized that interactions between all DMN areas are supported by the alpha rhythm, and that the connectivity between specific DMN areas is established through other frequencies, mainly in the beta and/or gamma bands. To test this hypothesis, we used high-density electroencefalographic data collected in 19 healthy volunteers at rest. We analyzed frequency-dependent functional interactions between four main DMN nodes in a broad (1-80 Hz) frequency range. In line with our hypothesis, we found that the frequency-dependent connectivity profile between pairs of DMN nodes had a peak at 9-11 Hz. Also, the connectivity profile showed other peaks at higher frequencies, which depended on the specific connection. Overall, our findings suggest that frequency-dependent connectivity analysis may be a powerful tool to better understand how different neuronal oscillations support connectivity within and between brain networks.

Neuronal dynamics enable the functional differentiation of resting state networks in the human brain.

Intrinsic brain activity is organized in spatial-temporal patterns, called resting-state networks (RSNs), exhibiting specific structural-functional architecture. These networks presumably reflect complex neurophysiological processes and have a central role in distinct perceptual and cognitive functions. In this work, we propose an innovative approach for characterizing RSNs according to their underlying neural oscillations. We investigated specific electrophysiological properties, including spectral features, fractal dimension, and entropy, associated with eight core RSNs derived from high-density electroencephalography (EEG) source-reconstructed signals. Specifically, we found higher synchronization of the gamma-band activity and higher fractal dimension values in perceptual (PNs) compared with higher cognitive (HCNs) networks. The inspection of this underlying rapid activity becomes of utmost importance for assessing possible alterations related to specific brain disorders. The disruption of the coordinated activity of RSNs may result in altered behavioral and perceptual states. Thus, this approach could potentially be used for the early detection and treatment of neurological disorders.

Concurrent tACS-fMRI Reveals Causal Influence of Power Synchronized Neural Activity on Resting State fMRI Connectivity.

Resting state fMRI (rs-fMRI) is commonly used to study the brain's intrinsic neural coupling, which reveals specific spatiotemporal patterns in the form of resting state networks (RSNs). It has been hypothesized that slow rs-fMRI oscillations (<0.1 Hz) are driven by underlying electrophysiological rhythms that typically occur at much faster timescales (>5 Hz); however, causal evidence for this relationship is currently lacking. Here we measured rs-fMRI in humans while applying transcranial alternating current stimulation (tACS) to entrain brain rhythms in left and right sensorimotor cortices. The two driving tACS signals were tailored to the individual's α rhythm (8-12 Hz) and fluctuated in amplitude according to a 1 Hz power envelope. We entrained the left versus right hemisphere in accordance to two different coupling modes where either α oscillations were synchronized between hemispheres (phase-synchronized tACS) or the slower oscillating power envelopes (power-synchronized tACS). Power-synchronized tACS significantly increased rs-fMRI connectivity within the stimulated RSN compared with phase-synchronized or no tACS. This effect outlasted the stimulation period and tended to be more effective in individuals who exhibited a naturally weak interhemispheric coupling. Using this novel approach, our data provide causal evidence that synchronized power fluctuations contribute to the formation of fMRI-based RSNs. Moreover, our findings demonstrate that the brain's intrinsic coupling at rest can be selectively modulated by choosing appropriate tACS signals, which could lead to new interventions for patients with altered rs-fMRI connectivity.SIGNIFICANCE STATEMENT Resting state fMRI (rs-fMRI) has become an important tool to estimate brain connectivity. However, relatively little is known about how slow hemodynamic oscillations measured with fMRI relate to electrophysiological processes. It was suggested that slowly fluctuating power envelopes of electrophysiological signals synchronize across brain areas and that the topography of this activity is spatially correlated to resting state networks derived from rs-fMRI. Here we take a novel approach to address this problem and establish a causal link between the power fluctuations of electrophysiological signals and rs-fMRI via a new neuromodulation paradigm, which exploits these power synchronization mechanisms. These novel mechanistic insights bridge different scientific domains and are of broad interest to researchers in the fields of Medical Imaging, Neuroscience, Physiology, and Psychology.

Heart-Brain Interactions in the MR Environment: Characterization of the Ballistocardiogram in EEG Signals Collected During Simultaneous fMRI.

The ballistocardiographic (BCG) artifact is linked to cardiac activity and occurs in electroencephalographic (EEG) recordings acquired inside the magnetic resonance (MR) environment. Its variability in terms of amplitude, waveform shape and spatial distribution over subject's scalp makes its attenuation a challenging task. In this study, we aimed to provide a detailed characterization of the BCG properties, including its temporal dependency on cardiac events and its spatio-temporal dynamics. To this end, we used high-density EEG data acquired during simultaneous functional MR imaging in six healthy volunteers. First, we investigated the relationship between cardiac activity and BCG occurrences in the EEG recordings. We observed large variability in the delay between ECG and subsequent BCG events (ECG-BCG delay) across subjects and non-negligible epoch-by-epoch variations at the single subject level. The inspection of spatial-temporal variations revealed a prominent non-stationarity of the BCG signal. We identified five main BCG waves, which were common across subjects. Principal component analysis revealed two spatially distinct patterns to explain most of the variance (85% in total). These components are possibly related to head rotation and pulse-driven scalp expansion, respectively. Our results may inspire the development of novel, more effective methods for the removal of the BCG, capable of isolating and attenuating artifact occurrences while preserving true neuronal activity.

Detecting large-scale networks in the human brain using high-density electroencephalography.

High-density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256-channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12-layer head models and exact low-resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631-4643, 2017. © 2017 Wiley Periodicals, Inc.

Cognitive behavioural therapy attenuates the enhanced early facial stimuli processing in social anxiety disorders: an ERP investigation.

BACKGROUND: Previous studies of patients with social anxiety have demonstrated abnormal early processing of facial stimuli in social contexts. In other words, patients with social anxiety disorder (SAD) tend to exhibit enhanced early facial processing when compared to healthy controls. Few studies have examined the temporal electrophysiological event-related potential (ERP)-indexed profiles when an individual with SAD compares faces to objects in SAD. Systematic comparisons of ERPs to facial/object stimuli before and after therapy are also lacking. We used a passive visual detection paradigm with upright and inverted faces/objects, which are known to elicit early P1 and N170 components, to study abnormal early face processing and subsequent improvements in this measure in patients with SAD. METHODS: Seventeen patients with SAD and 17 matched control participants performed a passive visual detection paradigm task while undergoing EEG. The healthy controls were compared to patients with SAD pre-therapy to test the hypothesis that patients with SAD have early hypervigilance to facial cues. We compared patients with SAD before and after therapy to test the hypothesis that the early hypervigilance to facial cues in patients with SAD can be alleviated. RESULTS: Compared to healthy control (HC) participants, patients with SAD had more robust P1-N170 slope but no amplitude effects in response to both upright and inverted faces and objects. Interestingly, we found that patients with SAD had reduced P1 responses to all objects and faces after therapy, but had selectively reduced N170 responses to faces, and especially inverted faces. Interestingly, the slope from P1 to N170 in patients with SAD was flatter post-therapy than pre-therapy. Furthermore, the amplitude of N170 evoked by the facial stimuli was correlated with scores on the interaction anxiousness scale (IAS) after therapy. CONCLUSIONS: Our results did not provide electrophysiological support for the early hypervigilance hypothesis in SAD to faces, but confirm that cognitive-behavioural therapy can reduce the early visual processing of faces. These findings have potentially important therapeutic implications in the assessment and treatment of social anxiety. Trial registration HEBDQ2014021.

Large-scale analysis of neural activity and connectivity from high-density electroencephalographic data.

INTRODUCTION: High-density electroencephalography (hdEEG) is a technique used for the characterization of the neural activity and connectivity in the human brain. The analysis of EEG data involves several steps, including signal pre-processing, head modelling, source localization and activity/connectivity quantification. Visual check of the analysis steps is often necessary, making the process time- and resource-consuming and, therefore, not feasible for large datasets. FINDINGS: Here we present the Noninvasive Electrophysiology Toolbox (NET), an open-source software for large-scale analysis of hdEEG data, running on the cross-platform MATLAB environment. NET combines all the tools required for a complete hdEEG analysis workflow, from raw signals to final measured values. By relying on reconstructed neural signals in the brain, NET can perform traditional analyses of time-locked neural responses, as well as more advanced functional connectivity and brain mapping analyses. The extracted quantitative neural data can be exported to provide broad compatibility with other software. CONCLUSIONS: NET is freely available (https://github.com/bind-group-kul/net) under the GNU public license for non-commercial use and open-source development, together with a graphical user interface (GUI) and a user tutorial. While NET can be used interactively with the GUI, it is primarily aimed at unsupervised automation to process large hdEEG datasets efficiently. Its implementation creates indeed a highly customizable program suitable for analysis automation and tight integration into existing workflows.

Injectable Microgels with Hybrid Exosomes of Chondrocyte-Targeted FGF18 Gene-Editing and Self-Renewable Lubrication for Osteoarthritis Therapy.

Abnormal silencing of fibroblast growth factor (FGF) signaling significantly contributes to joint dysplasia and osteoarthritis (OA); However, the clinical translation of FGF18-based protein drugs is hindered by their short half-life, low delivery efficiency and the need for repeated articular injections. This study proposes a CRISPR/Cas9-based approach to effectively activate the FGF18 gene of OA chondrocytes at the genome level in vivo, using chondrocyte-affinity peptide (CAP) incorporated hybrid exosomes (CAP/FGF18-hyEXO) loaded with an FGF18-targeted gene-editing tool. Furthermore, CAP/FGF18-hyEXO are encapsulated in methacrylic anhydride-modified hyaluronic (HAMA) hydrogel microspheres via microfluidics and photopolymerization to create an injectable microgel system (CAP/FGF18-hyEXO@HMs) with self-renewable hydration layers to provide persistent lubrication in response to frictional wear. Together, the injectable CAP/FGF18-hyEXO@HMs, combined with in vivo FGF18 gene editing and continuous lubrication, have demonstrated their capacity to synergistically promote cartilage regeneration, decrease inflammation, and prevent ECM degradation both in vitro and in vivo, holding great potential for clinical translation.

A nomogram for predicting cerebral white matter lesions in elderly men.

Objective: This study aimed to develop a nomogram tool to predict cerebral white matter lesions (WMLs) in elderly men. Methods: Based on a retrospective cohort from January 2017 to December 2019, a multivariate logistic analysis was performed to construct a nomogram for predicting WMLs. The nomogram was further validated using a follow-up cohort between January 2020 and December 2022. The calibration curve, receiver operating characteristics (ROC) curves, and the decision curves analysis (DCA) were used to evaluate discrimination and calibration of this nomogram. Result: A total of 436 male patients were enrolled in this study, and all 436 patients were used as the training cohort and 163 follow-up patients as the validation cohort. A multivariate logistic analysis showed that age, cystatin C, uric acid, total cholesterol, platelet, and the use of antiplatelet drugs were independently associated with WMLs. Based on these variables, a nomogram was developed. The nomogram displayed excellent predictive power with the area under the ROC curve of 0.951 [95% confidence interval (CI), 0.929-0.972] in the training cohort and 0.915 (95% CI, 0.864-0.966) in the validation cohort. The calibration of the nomogram was also good, as indicated by the Hosmer-Lemeshow test with p-value of 0.594 in the training cohort and 0.178 in the validation cohort. The DCA showed that the nomogram holds good clinical application value. Conclusion: We have developed and validated a novel nomogram tool for identifying elderly men at high risk of WMLs, which exhibits excellent predictive power, discrimination, and calibration.

ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding.

An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 13 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model. As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain's mechanisms of language processing within the context of the Chinese natural language.

Integration of cognitive tasks into artificial general intelligence test for large models.

During the evolution of large models, performance evaluation is necessary for assessing their capabilities. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we advocate for a comprehensive framework of cognitive science-inspired artificial general intelligence (AGI) tests, including crystallized, fluid, social, and embodied intelligence. The AGI tests consist of well-designed cognitive tests adopted from human intelligence tests, and then naturally encapsulates into an immersive virtual community. We propose increasing the complexity of AGI testing tasks commensurate with advancements in large models and emphasizing the necessity for the interpretation of test results to avoid false negatives and false positives. We believe that cognitive science-inspired AGI tests will effectively guide the targeted improvement of large models in specific dimensions of intelligence and accelerate the integration of large models into human society.