Brain recordings in large animal models and humans typically rely on a tethered connection, which has restricted the spectrum of accessible experimental and clinical applications. To overcome this limitation, we have engineered a compact, lightweight, high data rate wireless neurosensor capable of recording the full spectrum of electrophysiological signals from the cortex of mobile subjects. The wireless communication system exploits a spatially distributed network of synchronized receivers that is scalable to hundreds of channels and vast environments. To demonstrate the versatility of our wireless neurosensor, we monitored cortical neuron populations in freely behaving nonhuman primates during natural locomotion and sleep-wake transitions in ecologically equivalent settings. The interface is electrically safe and compatible with the majority of existing neural probes, which may support previously inaccessible experimental and clinical research.

The hippocampal CA3 contributes to spatial working memory (SWM), but which stage of SWM the CA3 neurons act on and whether the lateralization of CA3 function occurs in SWM is also unknown. Here, we reveal increased neural activity in both sample and choice phases of SWM. Left CA3 (LCA3) neurons show higher sensitivity in the choice phase during the correct versus error trials compared with right CA3 (RCA3) neurons. LCA3 initiates firing prior to RCA3 in the choice phase. Optogenetic suppression of pyramidal neurons in LCA3 disrupts SWM only in the choice phase. Furthermore, we discover that parvalbumin (PV) neurons, rather than cholinergic neurons in the medial septum (DB were cholinergic neurons), can project directly to unilateral CA3. Selective suppression of PV neurons in the MS projecting to LCA3 impairs SWM. The findings suggest that MS PV-LCA3 projection plays a crucial role in manipulating the lateralization of LCA3 in the retrieval of SWM.

(1) Background: Ultrasound has been used for noninvasive stimulation and is a promising technique for treating neurological diseases. Epilepsy is a common neurological disorder, that is attributed to uncontrollable abnormal neuronal hyperexcitability. Abnormal synchronized activities can be observed across multiple brain regions during a seizure. (2) Methods: we used low-intensity focused ultrasound (LIFU) to sonicate the brains of epileptic rats, analyzed the EEG functional brain network to explore the effect of LIFU on the epileptic brain network, and continued to explore the mechanism of ultrasound neuromodulation. LIFU was used in the hippocampus of epileptic rats in which a seizure was induced by kainic acid. (3) Results: By comparing the brain network characteristics before and after sonication, we found that LIFU significantly impacted the functional brain network, especially in the low-frequency band. The brain network connection strength across multiple brain regions significantly decreased after sonication compared to the connection strength in the control group. The brain network indicators (the path length, clustering coefficient, small-worldness, local efficiency and global efficiency) all changed significantly in the low-frequency. (4) Conclusions: These results revealed that LIFU could reduce the network connections of epilepsy circuits and change the structure of the brain network at the whole-brain level.

The development of coating materials for neural interfaces has been a pursued to improve the electrical, mechanical and biological performances. For these goals, a bioactive coating was developed in this work featuring a poly(3,4-ethylenedioxythiophene) (PEDOT)/carbon nanotube (CNT) composite and covalently bonded YIGSR and RGD. Its biological effect and electrical characteristics were assessed in vivo on microwire arrays (MWA). The coated electrodes exhibited a significantly higher charge storage capacity (CSC) and lower electrochemical impedance at 1 kHz which are desired to improve the stimulating and recording performances, respectively. Acute neural recording experiments revealed that coated MWA possess a higher signal/noise ratio capturing spikes undetected by uncoated electrodes. Moreover, coated MWA possessed more active sites and single units, and the noise floor of coated electrodes was lower than that of uncoated electrodes. There is little information in the literature concerning the chronic performance of bioactively modified neural interfaces in vivo. Therefore in this work, chronic in vivo tests were conducted and the PEDOT/PSS/MWCNT-polypeptide coated arrays exhibited excellent performances with the highest mean maximal amplitude from day 4 to day 12 during which the acute response severely compromised the performance of the electrodes. In brief, we developed a simple method of covalently bonding YIGSR and RGD to a PEDOT/PSS/MWCNT-COOH composite improving both the biocompatibility and electrical performance of the neural interface. Our findings suggest that YIGSR and RGD modified PEDOT/PSS/MWCNT is a promising bioactivated composite coating for neural recording and stimulating.

Epilepsy is common brain dysfunction, where abnormal synchronized activities can be observed across multiple brain regions. Low-frequency focused pulsed ultrasound has been proven to modulate the epileptic brain network. In this study, we used two modes of low-intensity focused ultrasound (pulsed-wave and continuous-wave) to sonicate the brains of KA-induced epileptic rats, analyzed the EEG functional brain connections to explore their respective effect on the epileptic brain network, and discuss the mechanism of ultrasound neuromodulation. By comparing the brain network characteristics before and after sonication, we found that two modes of ultrasound both significantly affected the functional brain network, especially in the low-frequency band below 12 Hz. After two modes of sonication, the power spectral density of the EEG signals and the connection strength of the brain network were significantly reduced, but there was no significant difference between the two modes. Our results indicated that the ultrasound neuromodulation could effectively regulate the epileptic brain connections. The ultrasound-mediated attenuation of epilepsy was independent of modes of ultrasound.

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Conventional pattern-recognition algorithms for surface electromyography (sEMG)-based hand-gesture classification have difficulties in capturing the complexity and variability of sEMG. The deep structures of deep learning enable the method to learn high-level features of data to improve both accuracy and robustness of a classification. However, the features learned through deep learning are incomprehensible, and this issue has precluded the use of deep learning in clinical applications where model comprehension is required. In this paper, a generative flow model (GFM), which is a recent flourishing branch of deep learning, is used with a SoftMax classifier for hand-gesture classification. The proposed approach achieves 63 . 86 ± 5 . 12 % accuracy in classifying 53 different hand gestures from the NinaPro database 5. The distribution of all 53 hand gestures is modelled by the GFM, and each dimension of the feature learned by the GFM is comprehensible using the reverse flow of the GFM. Moreover, the feature appears to be related to muscle synergy to some extent.