Tibial nerve stimulation (TNS) is one of the neuromodulation methods used to treat an overactive bladder (OAB). However, the treatment mechanism is not accurately understood owing to significant differences in the results obtained from animal and clinical studies. Thus, this study was aimed to confirm the response of bladder activity to the different stimulation frequencies and to observe the duration of prolonged post-stimulation inhibitory effects following TNS. This study used unanesthetized rats to provide a closer approximation of the clinical setting and evaluated the changes in bladder activity in response to 30 min of TNS at different frequencies. Moreover, we observed the long-term changes of post-stimulation inhibitory effects. Our results showed that bladder response was immediately inhibited after 30 min of 10 Hz TNS, whereas it was excited at 50 Hz TNS. We also used the implantable stimulator to observe a change in duration of the prolonged post-stimulation inhibitory effects of the TNS and found large discrepancies in the time that the inhibitory effect lasted after stimulation between individual animals. This study provides important evidence that can be used to understand the neurophysiological mechanisms underlying the bladder inhibitory response induced by TNS as well as the long-lasting prolonged post-stimulation effect.
Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements in transforearm amputees. A feature extraction method in the frequency domain of the EEG and EMG signals was adopted to establish a 2D image. The input images were used for training on a model based on the convolutional neural network algorithm and TL, which requires 2D images as input data. For the purpose of data acquisition, five transforearm amputees and nine healthy controls were recruited. Compared with the conventional single-modal EEG signal trained models, the proposed multimodal fusion method significantly improved classification accuracy in both the control and patient groups. When the two signals were combined and used in the pretrained model for EEG TL, the classification accuracy increased by 4.18–4.35% in the control group, and by 2.51–3.00% in the patient group.
Treatments for epilepsy include pharmacotherapy or surgery. Recently, focused ultrasound stimulation has been investigated as a promising non-invasive neuromodulation tool for neurological disorders, including epilepsy. To investigate the neuronal dynamics in epilepsy, we acutely stimulated 29 Sprague-Dawley rats to low-intensity focused ultrasound stimulation (LIFUS) three times for 3 minutes. Pentylenetetrazol (PTZ) was injected into the abdominal cavity of the anesthetized rats to induce epilepsy. During the anesthesia, the electroencephalography (EEG) signal was measured and analyzed for 1 h in groups of animals untreated (sham) and treated with LIFUS. The EEG signal was quantitatively processed to show the different characteristics of the frequency change over time and of the band power between sham and treated groups. Histological analyses (Nissl, Iba1, c-Fos, and GAD65) measured the degree of staining of expression factors to confirm the effect of the stimulation on seizure suppression. These results suggest that repetitive LIFUS can effectively reduce epileptic seizure activity by attenuating theta and betaband oscillation in a PTZ-induced rat model. LIFUS can potentially facilitate hippocampal and cortical cellular recovery by augmenting GABAergic inhibitory neurons via its anti-seizure effect.
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