Neuroscience has been a driving force in improving artificial intelligence (AI). To understand how our brain recognizes object images, we focused on the underlying neuronal mechanisms of representation and identification in the inferotemporal cortex, which is repeatedly reported as a critical area for object recognition. In this study, we trained two types of classification models using the electrophysiological activity of cell populations in the inferotemporal cortex. The performance of the trained models was used to evaluate the feasibility of image identification by the neuronal activity of the inferotemporal cortex. Responses in individual trials were used in the present study, while responses averaged across trials were used in previous studies. We assessed the performance of models for distinguishing individual images as well as models for categorizing images into groups. The discrimination performance for each image showed consistently high hit rates between the two monkeys, but there was also a tendency for high false alarm rates. Interestingly, image classification by certain category types demonstrated high performance. For categories important to the monkeys’ daily lives, such as animals and fruits, the difference between hit rates and false alarm rates was as high as 50-60%. In particular, animals showed the highest average difference at 61.4%. These results suggest that information crucial for object recognition by category is represented by the activity patterns of numerous neurons in the inferotemporal cortex.

Many studies have reported inconsistent results when using kinesthetic motor imagery (KMI) to imagine the same movement. This inconsistency may be due to variations in how individuals perform KMI, which has not been thoroughly compared in previous research. In our previous letter, we reported the results of the initial experiment. Here, we categorized KMI into single-hand KMI (S-KMI) and double-hand KMI (D-KMI) and investigated potential differences using t-tests. To gain a clearer understanding of brain features, we conducted Event-Related Spectral Perturbation (ERSP) analysis and topographic mapping. Additionally, recognizing the importance of model classification accuracy for BCI applications, we performed classification evaluations. The results revealed significant differences between S-KMI and D-KMI, highlighting that different methods for performing KMI were crucial for BCI applications and significantly differed in brain features.

Stroke patients may have an abnormality in vertical perception of posture called Pusher syndrome. The mechanism of this symptom is thought to be that the patient’s subjective postural vertical is biased toward the paralyzed side due to somatosensory hemiplegia. Therefore, rehabilitation by presenting vertical positions with visual feedback is often performed. However, when combined with visual impairment such as unilateral spatial neglect, rehabilitation may not be as effective as it could be. Galvanic vestibular stimulation (GVS), a technique that uses vestibular sensation instead of vision, has been studied, and GVS improves anodal body tilting and postural sway with stimulation. However, the details of the correlation between the magnitude of body tilting and the intensity of stimulation are unknown. It is also not known how the change in vertical perception of the anode direction by stimulation affects the stability of the center of gravity after the stimulation. In this study, we tested the relationship between GVS stimulus intensity and the magnitude of body tilting, as well as the effect of the stimulus on the sway of the human center of gravity. The results showed a logarithmic relationship between the magnitude of body tilting and the transfer charge of the stimulus. In addition, a change in the anodal direction was observed in the center of gravity position before and after stimulation, suggesting that the change in vertical cognition in the anodal direction due to stimulation also affects the center of gravity position.

Sensory impairment arises from the compression or damage of sensory nerves, leading to difficulties in grip control as symptoms progress. To quantitatively assess peripheral nerve sensory function in patients with sensory impairment, we developed a vibration device that presents virtual haptic sensations using an external vibration actuator. This system generates virtual forces in the forward, backward, left, and right directions by stimulating the vibration sense with asymmetric waveforms while holding the device. However, the perceived direction of traction is determined subjectively by the participant, which poses a challenge for qualitative assessment. In this study, we aimed to clarify sensory responses independent of participant subjectivity by measuring and analyzing human cortical activity. We investigated the feasibility of estimating vibration stimulus of virtual forces using Electroencephalogram (EEG) through machine learning. Two types of motor tasks were performed: a high-stimulation condition and a low-stimulation condition. We found that the highest discrimination rate was observed in gamma waves under the high condition, suggesting that the direction of traction presented by the vibration device can be estimated from gamma waves and that the intensity of the haptic sensation depends on the discrimination rate.

Early detection and precise classification of ovarian cancer types remain significant challenges in clinical practice due to the complexity of MRI image interpretation and limited prior research specifically focused on type differentiation. To address this issue, we propose a Vision Transformer (ViT)-based framework capable of distinguishing ovarian cancer types by extracting both local and global features from MRI images.

Our approach uniquely leverages spatial relationships across image slices and incorporates multi-modal imaging features, facilitating a more comprehensive analysis. Unlike existing methods that focus primarily on binary classification of malignancy, our model provides fine-grained type discrimination. Experimental results demonstrate the effectiveness of our proposed method, with uni-modal (T2-weighted) imaging achieving an accuracy of 94.71%, while the multi-modal approach (combining T1- and T2-weighted images) achieves a remarkable 99.54% accuracy. This work represents a significant advancement in ovarian cancer diagnosis, with potential for improving early detection and personalized treatment planning.

In recent years, attempts have been made to develop human digital twins of the locomotorium for biomechanical simulations, highlighting the need for methods to capture these dynamics. Our group has proposed a simple method using functional electrical stimulation that emphasizes coordination among multiple muscles. This method uses the ratio and sum of the electrical stimulation intensities applied to the extensor and flexor muscles—known as the electrical agonist-antagonist muscle (EAA) ratio and EAA sum, respectively. By relating the force induced by electrical stimulation to the EAA ratio, we can capture the dynamics of the human neuromuscular system (NMS). In this study, we investigated how change in the EAA sum influence NMS dynamics.

In recent years, many studies have been conducted on engineering cultured muscle actuators that utilize muscle cell's contraction force. Cultured muscle actuators have unique advantages such as self-healing and high energy efficiency, but one of the challenges is that it is difficult to obtain sufficient contraction force. We have fabricated a donut-shaped actuator that contracts pores and improves contractility by controlling the orientation of muscle fibers in culturing muscle cells. However, when cells are seeded at high concentrations, they aggregate, making it impossible to maintain the actuator shape. In this study, an actuator was fabricated by placing the outer frame around the actuator to prevent aggregation. In addition, the contraction rate was measured by electrical stimulation. Based on the above, this study aims to investigate the cell seeding concentration and fabricate a cultured muscle actuator with higher contractility.

Nanotubes are nanostructures that mimic the mass transport properties of ion channels, which are membrane proteins found in cells. Among them, DNA nanotubes are nanotubes made of DNA, and by designing complex sequences, DNA nanotubes with various functions can be created. We aim to develop DNA nanotubes that can transport materials by ultrasound irradiation (US-DNA nanotubes). In this study, we attempted to confirm the synthesis of DNA nanotubes by electrophoresis. The results suggest the synthesis of DNA nanotubes.

Magnetoencephalography is an effective method for brain diagnosis due to its high temporal resolution and noninvasive nature. When estimating the current density of the cortex from magnetoencephalography, it is common to set an objective function that minimizes the total current density to avoid underdetermined solutions. However, due to the complex geometry of the cortical surface, steep current density changes can be estimated across the brain sulcus. In this study, we proposed a method to suppress steep current density changes by introducing smoothness using the graph Laplacian and validated it with simulated data.

In recent years, stiffness measurement of micro-order objects is required. For example, analysis of mechanical properties of a new material, such as soft crystal has been attracting attention. In the conventional methods to measure the stiffness, the grasping the object is performed without the force feedback. Therefore, there is a possibility to damage the objects. To solve this problem, this paper proposes stiffness measurement method based on micro-macro bilateral control for micro-order objects. Micro-macro bilateral system consists of the leader system that is easy for the operator to manipulate, and the follower system that is sized to operate in a small environment. For the precise measurement, grasping the object is achieved by the micro-macro bilateral control. In this grasping, the total stiffness including the object and the follower system is measured in real-time. The stiffness of the follower system is identified by the preliminary experiment. As a result, the stiffness of the object is estimated by subtracting total stiffness from the identified stiffness of the follower system. The validity of the proposed method was confirmed from the experimental results.