High-density EEG of auditory steady-state responses during stimulation of basal forebrain parvalbumin neurons

Recent advances in neurotechnology-based biohybrid robots

Biohybrid robots retain the innate biological characteristics and behavioral traits of animals, making them valuable in applications such as disaster relief, exploration of unknown terrains, and medical care. This review aims to comprehensively discuss the evolution of biohybrid robots, their key technologies and applications, and the challenges they face. By analyzing studies conducted on terrestrial, aquatic, and aerial biohybrid robots, we gain a deeper understanding of how these technologies have made significant progress in simulating natural organisms, improving mechanical performance, and intelligent control. Additionally, we address challenges associated with the application of electrical stimulation technology, the precision of neural signal monitoring, and the ethical considerations for biohybrid robots. We highlight the importance of future research focusing on developing more sophisticated and biocompatible control methods while prioritizing animal welfare. We believe that exploring multimodal monitoring and stimulation technologies holds the potential to enhance the performance of biohybrid robots. These efforts are expected to pave the way for biohybrid robotics technology to introduce greater innovation and well-being to human society in the future.

Developmental delays in cortical auditory temporal processing in a mouse model of Fragile X syndrome

BACKGROUND

Autism spectrum disorders (ASD) encompass a wide array of debilitating symptoms, including sensory dysfunction and delayed language development. Auditory temporal processing is crucial for speech perception and language development. Abnormal development of temporal processing may account for the language impairments associated with ASD. Very little is known about the development of temporal processing in any animal model of ASD.

METHODS

In the current study, we quantify auditory temporal processing throughout development in the Fmr1 knock-out (KO) mouse model of Fragile X Syndrome (FXS), a leading genetic cause of intellectual disability and ASD-associated behaviors. Using epidural electrodes in awake and freely moving wildtype (WT) and KO mice, we recorded auditory event related potentials (ERP) and auditory temporal processing with a gap-in-noise auditory steady state response (gap-ASSR) paradigm. Mice were recorded at three different ages in a cross sectional design: postnatal (p)21, p30 and p60. Recordings were obtained from both auditory and frontal cortices. The gap-ASSR requires underlying neural generators to synchronize responses to gaps of different widths embedded in noise, providing an objective measure of temporal processing across genotypes and age groups.

RESULTS

We present evidence that the frontal, but not auditory, cortex shows significant temporal processing deficits at p21 and p30, with poor ability to phase lock to rapid gaps in noise. Temporal processing was similar in both genotypes in adult mice. ERP amplitudes were larger in Fmr1 KO mice in both auditory and frontal cortex, consistent with ERP data in humans with FXS.

CONCLUSIONS

These data indicate cortical region-specific delays in temporal processing development in Fmr1 KO mice. Developmental delays in the ability of frontal cortex to follow rapid changes in sounds may shape language delays in FXS, and more broadly in ASD.

Hybrid Genetic Algorithm for Clustering IC Topographies of EEGs

Clustering of independent component (IC) topographies of Electroencephalograms (EEG) is an effective way to find brain-generated IC processes associated with a population of interest, particularly for those cases where event-related potential features are not available. This paper proposes a novel algorithm for the clustering of these IC topographies and compares its results with the most currently used clustering algorithms. In this study, 32-electrode EEG signals were recorded at a sampling rate of 500 Hz for 48 participants. EEG signals were pre-processed and IC topographies computed using the AMICA algorithm. The algorithm implements a hybrid approach where genetic algorithms are used to compute more accurate versions of the centroids and the final clusters after a pre-clustering phase based on spectral clustering. The algorithm automatically selects the optimum number of clusters by using a fitness function that involves local-density along with compactness and separation criteria. Specific internal validation metrics adapted to the use of the absolute correlation coefficient as the similarity measure are defined for the benchmarking process. Assessed results across different ICA decompositions and groups of subjects show that the proposed clustering algorithm significantly outperforms the (baseline) clustering algorithms provided by the software EEGLAB, including CORRMAP.

Nine-day continuous recording of EEG and 2-hour of high-density EEG under chronic sleep restriction in mice

This work provides an EEG dataset collected from nine mice during the sleep deprivation (SD) paradigm for the sleep science community. It includes 9-day of continuous recording of the frontal and parietal EEG, accelerometer, and 2-hour of high-density EEG (HD-EEG) under SD and SD-free conditions. Eighteen hours of SD were conducted on 5 consecutive days. The HD-EEG data were saved in the EEGLAB format and stored as the brain imaging data structure (BIDS). These datasets can be used to (i) compare mouse HD-EEG to human HD-EEG, (ii) track oscillatory activities of the sleep EEG (e.g., slow waves, spindles) across the cortical regions under different conditions of sleep pressure, and (iii) investigate the cortical traveling waves in the mouse brain. We also provided Python code for basic analyses of this dataset, including the detection of slow waves and sleep spindles. We hope that our dataset will reveal hidden activities during sleep and lead to a better understanding of the functions and mechanisms of sleep.

Age-related changes in event related potentials, steady state responses and temporal processing in the auditory cortex of mice with severe or mild hearing loss

Age-related changes in auditory processing affect the quality of life of older adults with and without hearing loss. To distinguish between the effects of sensorineural hearing loss and aging on cortical processing, the main goal of the present study was to compare cortical responses using the same stimulus paradigms and recording conditions in two strains of mice (C57BL/6J and FVB) that differ in the degree of age-related hearing loss. Electroencephalogram (EEG) recordings were obtained from freely moving young and old mice using epidural screw electrodes. We measured event related potentials (ERP) and 40 Hz auditory steady-state responses (ASSR). We used a novel stimulus, termed the gap-ASSR stimulus, which elicits an ASSR by rapidly presenting short gaps in continuous noise. By varying the gap widths and modulation depths, we probed the limits of temporal processing in young and old mice. Temporal fidelity of ASSR and gap-ASSR responses were measured as phase consistency across trials (inter-trial phase clustering; ITPC). The old C57 mice, which show severe hearing loss, produced larger ERP amplitudes compared to young mice. Despite robust ERPs, the old C57 mice showed significantly diminished ITPC in the ASSR and gap-ASSR responses, even with 100% modulation depth. The FVB mice, which show mild hearing loss with age, generated similar ERP amplitudes and ASSR ITPC across the age groups tested. However, the old FVB mice showed decreased gap-ASSR responses compared to young mice, particularly for modulation depths <100%. The C57 mice data suggest that severe presbycusis leads to increased gain in the auditory cortex, but with reduced temporal fidelity. The FVB mice data suggest that with mild hearing loss, age-related changes in temporal processing become apparent only when tested with more challenging sounds (shorter gaps and shallower modulation).

Comparing Methods of Feature Extraction of Brain Activities for Octave Illusion Classification Using Machine Learning

The aim of this study was to find an efficient method to determine features that characterize octave illusion data. Specifically, this study compared the efficiency of several automatic feature selection methods for automatic feature extraction of the auditory steady-state responses (ASSR) data in brain activities to distinguish auditory octave illusion and nonillusion groups by the difference in ASSR amplitudes using machine learning. We compared univariate selection, recursive feature elimination, principal component analysis, and feature importance by testifying the results of feature selection methods by using several machine learning algorithms: linear regression, random forest, and support vector machine. The univariate selection with the SVM as the classification method showed the highest accuracy result, 75%, compared to 66.6% without using feature selection. The received results will be used for future work on the explanation of the mechanism behind the octave illusion phenomenon and creating an algorithm for automatic octave illusion classification.