Previous experiments have shown that neural population structure in the cerebral cortex affects cognitive function. However, it is unclear how the activity of the neural population is involved between them. In this study, we compared the relationship between the structure and activity of neural populations in the primary visual cortex. Intracellular volume fraction obtained by NODDI analysis of diffusion-weighted MRI was used as an indicator of neural population structure. The temporal delay in change of alpha wave intensity on event-related desynchronization in response to light stimuli was also used as an indicator of neural population activity. Using the stochastic oscillation mathematical model, we simulated event-related desynchronization and measured delay in change of alpha wave intensity when the coupling probability is changed on the model. The results showed that delay in attenuation of alpha wave intensity was smaller and delay in recovery was greater in both cases measurement and simulation, given higher neuron density. In conclusion, it is clear that structural features of the neuronal population affect the collective activity and that such phenomenon can be explained by mathematical models.
For tissue engineering, it is important to understand the relationship between cells' behavior and the mechanical properties of extracellular matrix (ECM). We aim to develop a novel ECM taking the advantage of micro-sized polyacrylamide gel beads. The advantages of our ECM are creation of any elasticity at any position. Elasticity measurement experimental results by atomic force microscope (AFM) show that there is a significant difference in Young modulus between the place where gel beads exist and the place where gel beads do not exist. In addition, C2C12 cell culture experiments show that cell behavior changes with and without gel beads.
In the mammalian cerebral cortex, each sensory cortex plays a central role in the information processing of each sensory modality, yet the cross-modal interactions between sensory cortices remains to be elucidated. Herein, we designed a surface electrode array with 32 recording sites and established a novel electrophysiological technique to record neural activities simultaneously from the auditory and visual cortices of anesthetized rats. We evaluated our mapping in two ways; first, the middle-latency potentials in the auditory evoked potentials and visual evoked potentials were localized in the auditory and visual cortex, respectively. Second, the microelectrode array successfully recorded auditory and visual mismatch negativity (aMMN and vMMN), which is one of the neural activities reported to reflect cross-modal interactions in previous human studies. In addition, we demonstrated that MMN distributed beyond the border between these sensory cortices, suggesting cross-modal interactions between these areas. Taken together, the present recording technique will contribute to elucidating cross-modality information processing in sensory cortices.
Single-walled carbon nanotubes (SWNTs) are hollow cylinder-shaped carbon materials through which ions and some small molecules can pass with high permeability. Hence, SWNTs have attracted great interest for their potential application in spherical artificial biomembranes (liposomes) used to filter substances. Mass transport processes through SWNTs are currently measured based on changes in the fluorescence intensity of fluorescent probes in liposomes. However, it is difficult to determine whether the change in fluorescence intensity is due to SWNTs or to destruction of the liposome in small liposomes (<200 nm diameter). Accordingly, the mechanism of the transport process remains unclear. Therefore, in this study, we propose a new approach to observing the transport process through SWNTs by embedding SWNTs in liposomes over 10 µm in diameter (giant liposomes). The large size of the liposomes allows easy observation of their shape under a microscope. The experimental results indicate that the ion permeation and transport processes of SWNTs can be indirectly observed using giant liposomes and a fluorescence microscope.
Electrical stimulation of the auditory nerves is well known to provide auditory perception, while it remains to be elucidated whether the microstimulation of the auditory thalamus generate or modulate auditory perception. Herein, we established a novel experimental system to evaluate effects of thalamic microstimulation on auditory perception in both behavioral and electrophysiological manners; we conditioned rats to report their auditory perception by pulling a lever, then recorded their behavior and cortical activities when we provided sound stimuli, or thalamic microstimulation, or co-stimulation of them. Consequently, one of the three tested animals reported a putative auditory perception by lever-pulling behavior in response to the thalamic microstimulation with strong pulse current. In addition, co-stimulation using microstimulation with strong or weak pulse current increased or decreased its lever-pulling activity, respectively. Finally, the microelectrode mapping revealed that the microstimulation with strong pulse current induced positive shift of the surface potential inside the auditory cortex. These results suggest that the microstimulation to thalamus affect auditory perception by modulating cortical activity, and that the present experimental system will contribute to elucidating neural mechanisms of auditory prosthesis.
The intense drowsiness that occurs after lunch is called the post-lunch dip (PLD), and it is known to reduce alertness and work efficiency. Although studies aimed at elucidating the mechanism of PLD generation have been actively conducted, the cause has not yet been elucidated. For this, it is essential to establish an objective evaluation index based on the brain activity index. In this paper, we focused on P300 and contingent negative variation (CNV), which are a kind of event-related potentials (ERP), as quantitative evaluation indices, and conducted a verification experiment to verify its effectiveness. The subjects were 14 healthy young people, and two pieces of bread and 285 ml of water were used for the dietary load. P300 and CNV were measured four times before ingestion, immediately after ingestion, and 40 and 80 min after ingestion; furthermore, the Stanford Sleepiness Scale (SSS) was performed before each measurement. As a result, a significant increase in SSS, and a significant decrease in the early and late CNV areas, a significant increase in P300 latency prolongation were confirmed 40 minutes after eating (p<0.05). This suggests that P300 and CNV may be useful as an objective evaluation index for PLD.
Electrocorticography (ECoG) is one of the electrophysiological recording techniques, which has several beneficial features including low invasiveness, good signal quality, and long-term stability. We developed a novel high-density micro-ECoG array and evaluated it on the rat barrel cortex. Results of barrel-column mapping showed better performance of a high-density array on topographic delineation compared to a low-density array. Bending directions of a whisker could be estimated reliably using recorded ECoG signals.
Liposomes are spherical structures made from two membranes of phospholipid molecules enclosing an aqueous compartment. Since their components and structures are close to those of cell membranes, one can modify liposomes with membrane protein. This makes liposomes ideal candidate as biosensors. However, due to the absence of cytoskeleton, liposomes are very fragile. Its poor mechanical property greatly restricts their use. For this reason, encapsulation of cytoskeletal-mimic materials into liposomes is highly desirable. Here, we develop a method to encapsulate agarose as a cytoskeletal-mimic material into liposomes by using a polymer-assisted hydration approach. We show that by our method, a large number of agarose-supported liposomes can be formed without any complicated procedures. Experimental results indicate that the performed liposomes have a higher mechanical stability than non-supported liposomes. At the same time, we show that transmembrane proteins are still able to bind to the lipid membrane, and small molecules can diffuse through the membrane into the liposome interior.
High-frequency stimulation that induces long-term potentiation (LTP), rather than low-frequency stimulation that induces long-term depression (LTD), is usually used to induce stimulus-specific plasticity in neural systems. Here, we demonstrated in dissociated neuronal cultures that low-frequency stimulation induced stimulus-specific plasticity with a longer time scale than high-frequency stimulation. In the training session, 1-Hz low-frequency electrical stimulation with specific spatial patterns was applied for 1 h to induce plasticity in dissociated neurons on a high-density microelectrode array. Evoked discharges of neurons reduced immediately after the training session, corroborating that low-frequency stimulation induced LTD. However, the neuronal activity 24 hours after the training session exhibited higher discharge rates and became more stimulus specific than that in the pre-training session. These results imply that the spontaneous activity of dissociated neurons after LTD induces LTP and learning.
Evoked activities in the neural systems often depend on the event probabilities, suggesting that the neural systems have prediction capability based on the memory of stimulus history. Such prediction capability is often characterized as deviance detection property, which is not explained by the mere effect of stimulus specific adaptation. We here investigate whether neuronal dissociate culture, i.e., one of the most primitive neural circuits, exhibits deviance detection property as found in the higher-order sensory and association cortices. A high-dense CMOS electrode array measured neural activity patterns evoked by patterned electrical stimuli under either an oddball condition or many standards control (MSC) condition. Our results demonstrated the deviant-evoked neural activities were larger than the standard-evoked neural activities in the oddball condition and that the deviant stimulus evoked significantly larger activities in the oddball condition than in the MSC condition. Furthermore, these trends were more obvious in the late responses, which were mediated by a number of synapses, than in the early responses that were directly elicited by microstimulation. Thus, the present study showed the first evidence that the neuronal dissociate culture exhibits the deviance detection property, or the primitive intelligence.
AMPA-type glutamate receptor (AMPAR) is one of neurotransmitter receptors at excitatory synapses in neurons. For realizing the artificial control of synaptic transmission, we have applied optical trapping of quantum-dot (QD) conjugated AMPARs on neurons. Here, we evaluate optical trapping dynamics of QD-AMPARs on neurons by single particle tracking (SPT) analysis. The diffusion coefficient derived from mean square displacement (MSD) at laser focus of QD-AMPARs on neurons decreased with the laser irradiation and the culturing days, which suggests that the molecular dynamics of QD-AMPARs are constrained at the focal spot due to optical trapping forces.
Orthostatic dysregulation (OD) is an autonomic nervous disorder of the circulatory system that occurs in students in the higher grades of elementary school and in high school. It has been reported to affect approximately 700,000 Japanese children and 30%-40% of children out of school. OD is often neglected because the primary symptoms reported by parents of children with OD include difficulty in waking up in the morning, headache, dizziness, and tiredness, which are also considered as personality traits. OD diagnosis is performed by a specialist in accordance with the guidelines established by the Japanese Society of Pediatric Psychosomatic Medicine in 2006. The test classifies OD into four subtypes, and each subtype is differently treated. However, because there are few OD specialists, a doctor's experience is often used for subtype classification. We attempted to determine the reproducibility of the doctor's rule of thumb using multivariate analysis, which is the simplest available method using the blood pressure and heart rate data obtained from the test. We obtained a discriminant accuracy of approximately 92% for the two most common OD subtypes. Despite the primary subtypes and limited subject data, we obtained positive results, potentially reproducing the doctor's rule of thumb.
In this paper, we propose a novel energy management system (EMS) in which multiple homes cooperate. A buffer with a storage battery is needed to realize efficient power control in response to fluctuations in power demand and supply. Since there are problems such as economic cost and securing of installation place to apply the storage battery, it is conceivable to use electric vehicles (EV) instead. However, EVs need to be charged or discharged at home in preparation for traveling. The purpose of this paper is to propose a distributed cooperative energy management method for multiple homes based on EV state prediction and model predictive control (MPC). Finally, numerical simulation results show the effectiveness of the proposed algorithm.
We propose a new method for enhancing resiliency of power systems integrated with renewable power resources. In the proposed method, we design a resilience enhancer that shapes reference values, generated by existing any power flow computation scheme such as EDC, so that the influence of faults on power system dynamics can be appropriately mitigated. The design of the resilience enhancer is based on so-called gray-box modeling, which is a fusion of a few prior knowledge on the grid and data acquired in its healthy operation. The effectiveness of the proposed method is investigated through a three-machine system integrated with three solar farms.
Position information and time information provided by GNSS (Global Navigation Satellite System) are positively used. The accuracy of GNSS information is a very important factor for future ICT based systems such as an autonomous driving car, 5G wireless system etc. To meet such a demand, this work applies a machine learning technique to GNSS positioning and shows the feasibility of machine learning based GNSS positioning. As one of the advantages, our proposed system can make full use of current GNSS receiver system, that is, it does not need the modification of current device except for the signal processing architecture. Simulation results show that the proposed decision tree based GNSS positioning can enhance both accuracy and continutity of positioning compared to the conventional technique and the random forest based GNSS positioning can further improve both accuracy and continutity of positioning.
We study a technique of predictive maintenance for systems with solid oxide fuel cells (SOFCs). SOFC-based systems are a digital ecosystem that utilizes energy efficiently because they generate power using oxygen in the air and hydrogen taken out from gas and also make hot water using heat generated during the power generation. Such systems are complicated as they are equipped with many sensors to control fuel flows (i.e., gas and air) and power generation according to power demands. For predictive maintenance of such complicated systems, developing individual algorithms for each individual failure sign is infeasible because of the variety of possible failure signs. Thus, it is necessary to develop a novel algorithm capable of grasping various failure signs. In this work, we develop a method to detect failure signs by performing change-point detection as well as classification analysis of change points to understand the cause of detected change points. In the change-point detection technique, failure signs are detected based on the reconstruction errors calculated with principal component analysis. In the change-point analysis technique, we solve a classification problem on the detected failure signs. Moreover, we propose a method to tell a failure sign that may correspond to unknown types of faults. We show experimental results with data obtained from real SOFC-based systems and confirm the validity of the proposed method.