The human mediodorsal thalamic nucleus (MD) is crucial for higher cognitive functions, while the fine anatomical organization of the MD and the function of each subregion remain elusive. In this study, using high-resolution data provided by the Human Connectome Project, an anatomical connectivity-based method was adopted to unveil the topographic organization of the MD. Four fine-grained subregions were identified in each hemisphere, including the medial (MDm), central (MDc), dorsal (MDd), and lateral (MDl), which recapitulated previous cytoarchitectonic boundaries from histological studies. The subsequent connectivity analysis of the subregions also demonstrated distinct anatomical and functional connectivity patterns, especially with the prefrontal cortex. To further evaluate the function of MD subregions, partial least squares analysis was performed to examine the relationship between different prefrontal-subregion connectivity and behavioral measures in 1012 subjects. The results showed subregion-specific involvement in a range of cognitive functions. Specifically, the MDm predominantly subserved emotional-cognition domains, while the MDl was involved in multiple cognitive functions especially cognitive flexibility and inhibition. The MDc and MDd were correlated with fluid intelligence, processing speed, and emotional cognition. In conclusion, our work provides new insights into the anatomical and functional organization of the MD and highlights the various roles of the prefrontal-thalamic circuitry in human cognition.

The increasing application requirements of multilegged walking robots in outdoor environments pose new challenges regarding the design of their teleoperation systems. Some of these challenges arise from the multiple degrees of freedom of the telerobotic system and nonpassive exogenous disturbance. Herein, a novel control system based on a dual-master/single-slave bilateral haptic teleoperation framework using a semiautonomous strategy for hexapod robots walking on deformable rough terrains is proposed. In this teleoperation system, the body velocities and postures of the hexapod robot are determined according to the positions of two haptic master robots. The proposed teleoperator includes a time-domain passivity control approach to compensate for the system’s potential nonpassivity induced by the contact slippage between the foot and the ground. Furthermore, a posture-level bilateral controller is designed to overcome the unpredictable posture vibration. Information about the velocity loss and posture error is displayed to the human operator in the form of haptic force. In the underlying controller of the slave robot, a foot-force optimization algorithm is developed to improve the local autonomy of the teleoperation system. Furthermore, the stability of the system is demonstrated by its passivity. Experimental results indicate that the proposed controllers can provide a stable and transparent bilateral haptic teleoperation system for a hexapod robot under environmental perturbations.

Benign epilepsy with centrotemporal spikes (BECTS), the most common type of epilepsy among children, is considered a network disorder. Both fMRI and EEG source imaging (ESI) studies have indicated that BECTS is associated with static resting-state functional network (SFN) alterations (e.g., decreased global efficiency) in source space. However, we find that the abovementioned alterations are not significant when the SFN calculations are performed in the scalp space using only clinical routine low-density (e.g., 19 channels) EEG recordings (shown in our results). In the context of EEG microstates, it is clear that networks in the scalp space with resting-state EEG recordings dynamically reconfigure in a well-organized way based on different functional states. We are therefore inspired to propose a whole-brain dynamic resting-state functional network (DFN) computation method based on resting-state low-density EEG recordings with four classical microstates in scalp space. Notably, on the one hand, this approach is suitable for clinical conditions, and, on the other hand, the dynamic alternations calculated with a DFN may promote our understanding of how the networks change in BECTS. We analysed the changes in a DFN in six frequency bands (δ, θ, α _low , α _high , β, and γ) in patients with BECTS compared to those for healthy controls. Superior to traditional SFNs, the proposed DFN can reveal significant differences between individuals with BECTS and healthy controls (e.g., lower global efficiency), thus matching traditional fMRI and ESI methods in the source space. Our method directly performs DFN computations from low-density EEG recordings and avoids complex ESI computations, making it promising for clinical applications, especially in the outpatient diagnosis stage.

Artifacts rejection is crucial to electroencephalogram (EEG) application. Short-term few-channel EEG (e.g., in real-time detection of stress level and motor imagery) brings new challenges for removing artifacts due to less data. The existing artifact removal methods cannot guarantee both effectiveness and efficiency for removing artifacts from short-term few-channel EEG recordings. Consequently, we propose a fast adaptive sub-bands’ blind source separation method to remove artifacts from short-term few-channel EEG recordings effectively and efficiently. First, noise-assisted fast multivariate empirical mode decomposition (NA-FMEMD), as a fast adaptive multidimensional sub-bands’ decomposition method, is used to decompose short-term few-channel EEG recordings into multidimensional sub-bands. Then canonical correlation analysis (CCA), as a blind source separation method, is used to estimate artifact-related and EEG-related sources. Finally, EEG-related sources are intelligently selected and reconstructed as clean EEG recordings. The results demonstrate that our method takes at least five times less computing time for 2-s few-channel EEG recordings than state-of-the-art methods with similar effectiveness, using the same computer and software. Therefore, our method enhances the efficiency of removing artifacts from short-term few-channel EEG recordings while ensuring effectiveness, and it is more suitable for real-time processing.

Thermal fatigue is the main failure mode for chromium hot-work steels. In this study, pre-alloyed chromium hot-work steel powders with three different Cr, Mo, and V addition levels (low content (LH), medium content (MH), and high content (HH)) were used for selective laser melting (SLM). The microstructure and thermal fatigue properties of these SLM-processed materials were investigated. After thermal fatigue tests, LH possessed the lowest hardness (approximately 573 HV₅) and longest crack length, MH possessed the highest hardness (approximately 688 HV₅) and HH (with the hardness of approximately 675 HV₅) possessed the shortest crack length. It can be concluded that the increase of V content in MH is the main reason for the refined grains which result in an enhanced hardness and thermal fatigue resistance compared to LH. The further increase of the Cr and Mo content in HH leads to the grain coarsening and hardness decreasing, which is supposed to degrade the thermal fatigue resistant properties according to the conventional theory. However, HH exhibited an enhanced thermal fatigue resistance compared to MH. That is because the higher stored energy in MH deteriorated its thermal fatigue resistance compared to HH.