Frontiers in Neuroscience

We completed a video-based, four-night, in-home, level 3 sleep apnea study of healthy, low-risk pregnant participants and their bed partners in order to characterize sleep physiology in the third trimester of pregnancy. Demographic, anthropometric, and baseline sleep health characteristics were recorded, and the NightOwl home sleep apnea test device was used to measure sleep breathing, posture, and architecture parameters. Symptoms of restless legs syndrome were elicited in the exit interview. Forty-one pregnant participants and 36 bed partners completed the study. Bed partners had a significantly higher prevalence of sleep apnea than their pregnant co-sleepers (31% vs. 5.9%). Bed partners also had more severe sleep apnea than their pregnant co-sleepers, and this persisted on an adjusted analysis for baseline differences in factors known to increase risk of sleep apnea. In pregnant participants, increasing gestational age was found to be protective against mild respiratory events but not more severe events. While the correlation between STOP-Bang score and measures of sleep apnea severity was weak, an affirmative response to the witnessed apneas item on the STOP-Bang questionnaire was a strong predictor of more severe sleep apnea for all participants. Smoking history also increased sleep apnea risk. Pregnant participants had lower sleep efficiency and longer self-reported sleep onset latency. Restless legs syndrome was experienced by 39.5% of the pregnant participants but no bed partners. From a sleep breathing perspective, people with healthy, low-risk pregnancies have better sleep than their bed partners despite lower sleep efficiency and higher rates of restless legs syndrome.

We investigated the anatomical localization of the frontal eye field (FEF) and its relationship to the eyelid motor area (EMA) and precentral motor cortex. We performed functional mapping using electrical cortical stimulation (ECS) and correlated electrode position by non-linear co-registration techniques using postoperative MRI. We studied 22 patients who underwent chronic implantation of subdural electrodes for epilepsy surgery. Eye movements were elicited at 52 electrodes overall. The majority of the movements were conjugated, saccadic eye deviation contralateral to the side of ECS. Head turning and non-saccadic eye deviation more frequently occurred in the vicinity of the precentral sulcus. Anatomically, FEF was located at Brodmanns area 6 in the most-caudal region of the middle frontal gyrus and in the adjacent part of the superior frontal sulcus and precentral sulcus. Functionally, FEF was situated at the level of the hand motor area, more dorsal than was described in Penfields motor homunculus. The FEF is situated anteriorly from the precentral motor cortex. The EMA was situated within the precentral motor cortex, partially overlapping with but distinctly ventral and caudal to FEF, and dorsal to the lower face motor area. A standardized map of the FEF and precentral motor homunculus is provided as a reference for human system neuroscience research.

Motor imagery (MI) brain-computer interfaces (BCIs) are promising technologies for neurorehabilitation. In this context, deep learning (DL) models are increasingly being used to decode the mental imagination of movement. However, countless studies across multiple domains have shown that DL models are susceptible to bias, which can lead to disparate performance across subpopulations in terms of protected attributes, such as sex. The reported presence of sex-related information in electroencephalography (EEG) signals, widely used for MI-BCI, further raises warnings in this regard. For this reason, we conducted an in-depth analysis of the performance of DL in terms of the sex and other potential confounding factors. While an initial basic stratified analysis in terms of sex showed differences in favor of the female population, further analysis revealed that performance disparities were actually primarily driven by the discriminability of EEG patterns themselves, and not by the DL model. Moreover, DL models improve overall performance as well as per-group performance, particularly helping subjects with less discriminable EEG patterns. Our work highlights the benefits of DL methods for MI-BCI as well as the need for careful analysis when it comes to bias assessment in complex settings where multiple variables interact. We argue that in-depth studies of model behavior beyond standard performance metrics, should become widespread in the community in order to ensure the development and later deployment of fair BCI systems.

We propose a quality measure for spatio-temporal spike patterns (STPs) in multiple-neuron recordings. In such recordings, repeating STPs or pattern repetitions (PRs) are often found, with many of these generated by chance. To rule those out, statistical tests have been developed to discriminate the unlikely from the more likely PRs. This statistical problem is complicated by the fact that there are several obvious quality criteria for a PR, such as the size (the number of spikes) of the pattern and the number of its occurrences. Here, we propose a canonical way of combining several criteria (which we collect in the so-called signature of the pattern) into a single quality measure, based on the unlikeliness of the pattern. This measure is defined mathematically, and a formula for its computation is derived for stationary spike trains. It can be used to compare PRs. Since spike trains are not stationary in practice, we discuss, for two experimental data sets, how well the stationary formula correlates with the defined quality measure as determined from simulations. The results encourage the use of the stationary formula or also some simpler, related formulas as proxies for the quality, for the comparison of PRs and also for statistical tests that avoid the multiple testing problem incurred by using several quality criteria. Based on our results, we propose a few test statistics, i.e., random variables on the space of multi-unit spike trains with an appropriate null-hypothesis distribution, to evaluate STPs with less computational and sampling efforts.

Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG) has strong potential for recording cortical reactivity and connectivity. However, this promise is hampered by TMS-induced EEG artifacts. Here, we examine the origins of these artifacts with phantom TMS-EEG recordings and simulations. We focus on two major types of artifacts: (1) the TMS pulse artifact during each [~]0.2 ms TMS pulse and (2) the decay artifact that may last tens of milliseconds. We examine how these artifacts change as a function of the relative position between TMS coil windings and EEG electrode leads. We also examine the hypothesis that certain EEG lead configurations may reduce or even cancel out these artifacts. In experimental results across 23 different TMS coil / EEG lead configurations, the amplitudes between the TMS pulse artifact and the decay artifact were highly correlated (Spearman {rho} = 0.86, p < 0.001), suggesting that the decay artifact is caused by the TMS pulse artifact. As predicted, in certain EEG lead configurations, both the TMS pulse and decay artifacts were minimized. The simulations confirmed that the TMS pulse artifacts depended on the electromagnetic induction from the TMS coil windings to the EEG leads. These results illuminate the generator mechanisms of--and possible means to reduce--both artifacts.

Ferroptosis is a form of non-apoptotic cell death in which iron catalyzes the formation of reactive oxygen species, leading to lipid peroxidation. Experimentally, this process has recently been associated with seizures based on the increased levels of specific markers (4-hydroxynonenal and malondialdehyde) in the brain and plasma. Clinically, iron deposits have been identified in resected tissue from patients with refractory temporal lobe epilepsy. Quantitative susceptibility mapping (QSM) offers an opportunity to detect these accumulations in vivo. In this study, we investigated how pilocarpine-induced status epilepticus contributes to the generation of iron deposits in diverse cerebral regions and whether QSM can detect these deposits longitudinally. We scanned 14 animals (n=10 experimental; n=4 control) at five different time points (pre-status epilepticus induction and 1, 7, 14, 21 days post-induction) using QSM. We identified iron deposits in the caudate putamen, hippocampus, thalamus, and primary somatosensory cortex of experimental animals, which is consistent with histological findings. The initial size of the hippocampal iron deposits significantly increased over the following weeks. None of these effects was observed in the control animals. The presence of cerebral iron depositions in an animal model of pilocarpine-induced status epilepticus suggests that ferroptosis may be involved in the onset, development, and progression of spontaneous recurrent seizures. Furthermore, non-invasive, longitudinal in vivo mapping of brain iron deposits could be a potential imaging marker in neurological disorders such as epilepsy. Future experiments will be required to determine the origin of the iron and avoid its progressive accumulation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/712677v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@14abf67org.highwire.dtl.DTLVardef@5c08fborg.highwire.dtl.DTLVardef@51c40forg.highwire.dtl.DTLVardef@1eb5f9_HPS_FORMAT_FIGEXP M_FIG C_FIG

Flexible and biocompatible neurointerfaces are crucial elements for intraoperative monitoring and chronic neural recordings. However, existing fabrication methods often involve complex cleanroom processes, limiting rapid prototyping and customization. In this study, we present a fast, low-cost method for manufacturing a flexible subdural electrode array based on polydimethylsiloxane (PDMS) and gold conductive layer. The fabrication process utilizes a laser cutter for both mask generation and direct patterning of metal traces on a PDMS substrate, achieving a resolution of up to 30 {micro}m. A detachable interface was developed for reliable connectivity during testing. The electrochemical and mechanical properties of the array were characterized, demonstrating Ohmic behavior and stable conductivity after 50 cycles of mechanical bending, with a degradation of less than 10%. Electrochemical impedance spectroscopy (EIS) confirmed the viability of the electrodes for recording physiological signals. The functionality of the array was validated in vivo by performing simultaneous recordings of local field potentials (LFPs) and electrocorticography (ECoG) in the rat somatosensory cortex. The signals from the flexible subdural array showed a statistically significant (p < 0.001) median cross-correlation of 0.35 with LFPs recorded at a depth of 600-800 {micro}m by industrial electrode. We demonstrate here a robust and accessible approach for producing functional neural interfaces, suitable for rapid iteration and customization in research and clinical applications.

This study assessed neural responses to continuous speech to classify listening state, cognitive load, and selective auditory attention in complex acoustic environments. EEG was recorded while participants listened to concurrent male and female talkers under two conditions: active listening, where attention was directed to one of two competing speakers (target vs. masker), or passive listening, where attention was diverted to a visual task. Cognitive load was varied by manipulating target-to-masker (TMR) ratio (TMR: +7 dB, -7 dB), with lower TMR representing more demanding listening conditions. Spectral EEG features across frequency bands were ranked with univariate statistics and used to classify listening state (active vs passive) and cognitive load (low vs. high TMR). Auditory attention decoding (AAD) was performed using linear stimulus reconstruction to identify the target talker during active listening. Classification of listening state achieved 90.3% accuracy, and AAD reached 84.4% accuracy, demonstrating robust tracking of attentional engagement. In contrast, classification of cognitive load was near chance, suggesting that more extreme acoustic manipulations may be required to elicit distinct neural signatures. Comparable performance using a reduced set of electrodes near the ear indicates the potential for integration with wearable hearing devices. Overall, these results demonstrate that EEG can distinguish attentional states and selectively track target speech in realistic auditory scenarios. The findings provide a foundation for future applications in monitoring listening behavior, supporting auditory processing, and improving brain-controlled hearing aids in complex acoustic environments. HighlightsO_LIListening state (active vs. passive) can be classified from EEG spectral features. C_LIO_LIAttended speech can be decoded by reconstructing speech envelopes from EEG. C_LIO_LIComparable accuracy is achieved using only electrodes placed around the ears. C_LIO_LIEEG can monitor listening state and track auditory attention in two-speaker settings. C_LI Graphical AbstractEEG signals were recorded while participants listened to two concurrent speech streams, either by actively attending to one speaker or by focusing on an unrelated visual task. Spectral features of the EEG were used to classify listening state (active vs. passive) and cognitive load (low vs. high TMR). Auditory attention decoding (AAD) was performed by reconstructing the speech envelope from the EEG time signal. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/711289v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1079628org.highwire.dtl.DTLVardef@1135404org.highwire.dtl.DTLVardef@1f0d950org.highwire.dtl.DTLVardef@14b4c9a_HPS_FORMAT_FIGEXP M_FIG C_FIG Classification of listening state (active vs. passive): 90.3% accuracy. EEG difference between active and passive listening. Left, power spectrum, right, topographic map (alpha band 8-12 Hz). Classification of cognitive load (low vs high TMR): near chance level. EEG difference between low and high TMR. Left, power spectrum, right, topographic map (alpha band 8-12 Hz). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/711289v1_ufig2.gif" ALT="Figure 2"> View larger version (34K): org.highwire.dtl.DTLVardef@9229b1org.highwire.dtl.DTLVardef@1ef394corg.highwire.dtl.DTLVardef@9adecforg.highwire.dtl.DTLVardef@199f8c2_HPS_FORMAT_FIGEXP M_FIG C_FIG AAD achieved 84.4% accuracy, indicating robust decoding of the attended speaker during active listening, while performance dropped to near chance during passive listening.

An increasingly popular approach to investigating the neural bases of speech processing is forward modeling via a multivariate temporal-response function (mTRF). This approach uses stimulus characteristics to predict neural responses, especially in EEG and MEG. A central question in this regard is how best to represent the input stimulus. In the case of speech processing, established representations include the speech envelope or spectrogram, as well as feature-based linguistic representations of phonetic content. However, when multiple representations are used as input, a key challenge is how best to isolate their relative effects. This is particularly challenging because such representations have nonvanishing mutual information. To address this problem, we propose optimizations to the mTRF framework via a novel statistical approach of cyclic permutation. Additionally, we propose methodological improvements to the mTRF model targeting three key challenges: effectively managing spatial and temporal autocorrelations endemic to multi-sensor EEG data; mitigating the effects of endogenous drift; and introducing robust artifact rejection to enhance data quality. To demonstrate the effectiveness of this approach, the novel method was applied to a novel EEG data set of natural language listening in 27 adults with normal hearing. Our data showed that including ICA decomposition, artifact rejection, and cyclic permutations in an mTRF analysis improves the isolation of neural responses specific to phonetic and acoustic input variables. Author SummarySpeech processing happens in different stages. It starts with recognizing basic sounds, then categorizes them into discrete categories called phonemes, and goes on to understanding words and sentences. The multivariate temporal response function (mTRF) is a method for predicting brain activity from different features of the speech stimulus. Features that can be used as input to the mTRF model include acoustic features, such as sound envelopes, as well as more abstract language features, such as phonemes, which are a fundamental building block of words. One problem in speech research is distinguishing neural responses to different features. This is challenging because knowing one feature of the speech stimulus enables educated guesses about others and educated predictions about how this feature will behave in the future. Both of these properties of speech make multivariate temporal statistical analysis more difficult. To address this, we propose changes to the preprocessing of the EEG recordings and a new mathematical model that uses a partially rearranged version of the features of the speech stimulus to isolate the predictive power of a particular type of speech feature.

Our ability to recognize sound sources in the world is critical to daily life, but is not well documented or understood in computational terms. We developed a large-scale behavioral benchmark of human environmental sound recognition, built stimulus-computable models of sound recognition, and used the benchmark to compare models to humans. The behavioral benchmark measured how sound recognition varied across source categories, audio distortions, and concurrent sound sources, all of which influenced recognition performance in humans. Artificial neural network models trained to recognize sounds in multi-source scenes reached near-human accuracy and qualitatively matched human patterns of performance in many conditions. By contrast, traditional models of the cochlea and auditory cortex that were trained to recognize sounds produced worse matches to human performance. Models trained on larger datasets exhibited stronger alignment with both human behavior and brain responses. The results suggest that many aspects of human sound recognition emerge in systems optimized for the problem of real-world recognition. The benchmark results set the stage for future explorations of auditory scene perception involving salience and attention.

The main challenges in the life of a child with autism are difficulties in communication, behavior, and social interaction. Early diagnosis of this neurodevelopmental disorder improves patient outcomes by enabling more effective, personalized interventions. This diagnosis can sometimes be difficult, especially in very young children. Non-invasive, relatively accessible, and able to reflect neural function in real time, electroencephalography (EEG) shows promise in the detection of Autism spectrum disorders (ASD). However, because EEG data is still difficult for experts to understand, machine learning and artificial intelligence (AI) are beginning to be used in this field as well. In this paper, a ResNet+BiLSTM hybrid deep network was applied and achieved high accuracy in distinguishing individuals with autism from neurotypical subjects. Since AI models typically provide predictions without clear explanations, this study employs explainable AI (XAI) methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to clarify their decision-making.Delta, theta, alpha, beta, and gamma waves, as well as ERP components P100, N100, P200, MMN, and P600, were analyzed in the two neurotypical and autistic groups that were compared in this study using EEG recordings. By integrating SHAP and LIME, the system achieved both accurate classification and transparent explanations, pointing to EEG- and ERP-based features as reliable biomarkers for ASD.

The Drosophila adult central brain contains 240 circadian neurons, of which there are more than 25 different neuron subtypes based on connectomic data. Recent single cell RNA-seq (scRNAseq) characterization of these neurons "around the clock" also indicates a similar number of molecular subtypes of circadian neurons, but other conclusions from these transcriptomic studies warranted verifying and extending with other approaches. To this end: 1) We used a genetic multiplexing strategy to profile the transcriptomes of circadian neurons from multiple time points in a single experiment, reducing confounding technical variation between timepoints; 2) Large numbers of single nuclei were sequenced (snRNA-seq), which was enabled because the new method EL-INTACT purifies nuclei from frozen heads; 3) We assayed 12 time points under both light-dark (LD) and constant darkness (DD) conditions. These approaches showed dramatic transcriptional differences between time points in many circadian neuron types and enhanced time-of-day gene expression analysis. The data indicate that most of this regulation is transcriptional and circadian. There were however a small number of light-dependent transcripts, including a few that correspond to mammalian immediate-early genes. They probably play a role in the light-regulation of gene expression and behavior in specific neurons, perhaps circadian entrainment or phase-shifting. The results taken together provide a more comprehensive picture of gene expression heterogeneity within adult Drosophila circadian neurons including how intrinsic clock mechanisms and light cues are integrated across circadian neuron subtypes.

Sensory organization at the spinal segment level is commonly inferred from dermatomal maps that assume a fixed correspondence between cutaneous regions and spinal segments. However, based on the complexities of spinal neuroanatomy and neurophysiology, the distribution of sensory signals within the cord may be broader and less segment-specific than dermatomal maps suggest, leaving the segment-level localization of sensory-evoked activity in humans uncertain. Spinal cord functional magnetic resonance imaging (fMRI) is currently the only technique capable of noninvasively mapping sensory activity with high spatial resolution in the human spinal cord. However, its application remains technically challenging and is limited by the uncertainty in segmental localization. In this study, we leveraged recent advancements in spinal cord fMRI, including spinal nerve rootlet-based spatial normalization, to investigate how sensory information is represented and distributed within the human spinal cord during electrocutaneous stimulation of the third digit of the right hand (i.e., C7 dermatome). Forty healthy adults were scanned with electrocutaneous stimulation at four individualized intensities across multiple runs to quantify (i) the rostrocaudal distribution of sensory-evoked activity, (ii) intensity-dependent changes in detectability and localization, and (iii) the effect of normalization strategy on segmental localization. Across participants, stimulation produced activation localized in the lower cervical cord (e.g., C6-C8), with the most consistent segmental localization near C7. Stronger stimulation increased detectability and produced more consistent segmental localization across participants. Importantly, normalization that incorporated nerve rootlet landmarks sharpened localization and improved sensitivity relative to conventional intervertebral disc-based alignment. This highlights the value of functionally relevant anatomical landmarks for group inference in the spinal cord. Responses were strongest in the initial run and attenuated with repetition, suggesting habituation or adaptation that can bias multi-run paradigms if unmodeled. Together, our results define practical acquisition and analysis conditions (e.g., stimulation strength, anatomical alignment strategy, and run structure) under which segment-level spinal sensory responses can be detected, thereby supporting more reliable studies of human spinal cord future basic and translational studies, including pain mechanisms, sensory function, and spinal injury.

The 40-Hz auditory steady-state response (40-Hz ASSR) is a sensitive marker of changes in arousal level, which has been reported to decrease during slow-wave sleep. However, sleep-related changes in directional connectivity during 40-Hz ASSR across cortical networks remain underexplored. In this study, we examined how wakefulness, NREM (N1, N2, N3) and REM sleep affect the direction and extent of neural signal propagation. EEG data during periodic 40-Hz auditory stimulation were collected during an overnight study from 29 normal-hearing human subjects (including 16 females). A source analysis was implemented to locate cortical activity, and effective connectivity was assessed with the Directed Transfer Function (DTF) in the low-gamma band (37-43 Hz). We focused on the connections between auditory cortical regions, prefrontal and temporo-parietal associative cortices. We hypothesized that: 1) feedback connections from associative to primary auditory areas will be the most affected by the arousal state changes; 2) associative reciprocal connectivity between prefrontal and temporo-parietal regions will display gradual connectivity reduction with increasing NREM sleep depth, with partial restoration during REM sleep. Our results showed that feedforward rather than feedback connectivity was most strongly disrupted during sleep, particularly in NREM N2 and N3 stages, contradicting our first hypothesis. The second hypothesis was supported: reciprocal connectivity between prefrontal and parietal associative cortices significantly decreased with sleep depth. Overall, our findings suggest that reduced cortical propagation of 40-Hz ASSR related neuronal signals during sleep primarily reflects a breakdown in bottom-up signal transmission, and a parallel weakening of reciprocal prefrontal-parietal coupling.

Signal detection theory posits that subjects in two-stimulus, two-choice discrimination tasks decide by comparing random samples of an evidence variable to a static decision criterion. While the core assumptions of the theory have received ample experimental support, it has become evident that the decision criterion is not static but subject to trial-by-trial fluctuations and can be influenced by experimental manipulations. The mechanisms governing the trial-by-trial criterion changes are however not well understood. Here, we report results from five experiments in which we subjected rats to a two-stimulus, two-choice auditory discrimination task. In the first three experiments, we investigated the effects of stimulus presentation ratios and reward ratios and provide clear evidence that the effects of changing reward ratios are more pronounced than those of stimulus presentation ratios. A model-based analysis revealed that this effect was due to more than tenfold higher learning rates when reward ratios were manipulated. In two separate experiments, we investigated the effect of reward density (i.e., global reward rate) on criterion learning but failed to find consistent effects. A systematic comparison of three different trial-by-trial criterion learning models based on detection theory, the matching law, and reinforcement learning showed that no model was able to capture the differential effects of stimulus presentation and reward ratios. We conclude that subjects explicitly represent either prior stimulus probabilities or entire stimulus distributions, and accordingly future models need to represent these factors as well.

Identifying the location of a sound source in a complex environment and assessing its importance can be crucial for survival. The superior colliculus (SC), a midbrain structure involved in sensorimotor functions, contributes to sound localization and contains auditory responsive neurons that have spatially restricted receptive fields (RFs) that are organized into a topographic map along the azimuth. However, individual auditory SC neurons have large spatial RFs, are noisy, and do not respond to the same stimulus at each trial. Therefore, when an animal is presented with a "single trial" sound, and it needs to rely on a single neuron to locate the sound source direction, the location measurement may be erroneous, missing, or have poor spatial resolution. It is expected that a more reliable and accurate determination of the sound source location will come from a population of neurons. We therefore built a population pattern Maximum Likelihood Estimation (MLE) decoder to build a model that can accurately predict the location of a stimulus given the population response. We compared three models that use either strict firing rate (FR), weighting based on equal (EW) or mutual information (MIW) and show that the MIW model works best, needing only 92 neurons to localize a stimulus with behaviorally relevant precision. Furthermore, by comparing the models fit using the responses from non-RF and RF auditory neurons, we show that only RF neurons contain the information needed to localize a sound source. These results are consistent with the hypothesis that the SC uses a population of RF neurons to determine sound source location. Author SummaryBeing able to tell where a sound is coming from and how important it is can be critical for survival. The superior colliculus, a midbrain region involved in orienting behaviors, contains neurons that respond best to sounds coming from specific locations. This suggests that the combined activity of many neurons in the SC is used to determine sound location from a single sound event. To test this idea, we modeled responses from mouse SC neurons while sounds were played from different positions in space, both along the elevation and horizon. A model that weighted the most informative neurons performed best in both directions needing only 92 neurons to localize a stimulus with behaviorally relevant precision along the azimuth. Comparing the models fit using the responses from non-RF and RF auditory neurons, we show that only RF neurons contain the information needed to localize a sound source Overall, our findings show that the SC can accurately locate sounds in both horizontal and vertical space using a population-based strategy, providing a simple and effective solution for rapid sound localization.

Prolonged exposure to loud and moderate noise impairs hearing; the lower the noise level, the lower risk of hearing loss is. To date, little is known about how low the noise level can be safe to hearing. This study investigated the risk of exposure to tone at typical conversational levels by measuring auditory brainstem response (ABR). We show that exposing C57 mice to continuous pure tone at 65 dB SPL for 1 hour (TE65) leads to an increase in ABR threshold that is specific to the exposure frequency. Tone exposure also increased the latencies and decreased the amplitude in Waves I and II but not in Waves III and V. Significantly, the changes in amplitude and latency were highly correlated in Wave I and such correlation gradually degraded from Wave I through to Wave V. Our findings suggest that exposure to low level sound can impair hearing and alter the auditory information process in the brain if it is persistent and presented over a sufficient period of time. Significant StatementOur findings established the risk of hearing impairment following the exposure to continuous tone at normal or conversational voice levels. This finding challenges current public health guidelines for hearing protection. Although further clarification is required, our studies prompt that the regular use of ABR testing is a potential protocol for diagnosing hearing impairment in patients experiencing hidden hearing loss (HHL).

Research in cognitive neuroscience has relied on simple, highly controlled stimuli due to the difficulty in developing standardized, ecologically valid stimulus sets. However, there is a consensus that using ecologically valid stimuli is imperative to generalize results beyond controlled laboratory settings. The current study introduces a naturalistic audio stimulus database, consisting of short, recognizable, and emotionally rated stimuli. To create such a database, the current study collected 291 audio files from a wide range of sources. 361 participants rated the audio clips on emotionality, arousal, and recognizability, and subsequently freely described the audios by typing what they believed the sound to be. The text responses of the participants were embedded and clustered using an unsupervised machine-learning algorithm to derive a participant-grounded organization of auditory object categories. The results indicate audio clips were easily recognizable, while emotionality and arousal ratings showed broad variability, making the database suitable for diverse experimental needs. Furthermore, the final database comprises 10 distinct semantic categories, providing a diverse set of auditory stimuli.

Creativity is one of the unique cognitive constructs in human beings and its neurobiological correlates are one of the current hot topics in neuroscience. The "Different Hearing" program (DHP) is an educational activity aimed at stimulating musical creativity by means of group composing in the classroom, alternative to the mainstream model of music education in Czechia. In our previous study, the data from task-related functional MRI with passive listening was analyzed. The results suggested that DHP training modified the response to diverse sound samples, differentially changing the engagement of functional networks known to be related to creative thinking, namely, increasing default mode network activation and decreasing activation of executive and salience networks. In the present study, we hypothesized that the DHP short-term (2 days) intense workshop would also induce changes in the resting-state networks that were significantly modified during task. To investigate it, seed-based, ROI-to-ROI resting-state functional connectivity and degree centrality analysis were performed on the acquired resting-state fMRI data. The results showed no significant group-by-time interaction.

Traumatic brain injury (TBI) induces a series of neuropathological changes in the brain including neurogenesis, an important cellular response involved in brain repair and regeneration. TBI-enhanced neurogenesis in the dentate gyrus (DG) of the hippocampus is of particular importance due its contribution to learning and memory functions. In the neurogenic process, proliferation and differentiation of neural stem cells (NSCs) follow a well-characterized sequence controlled by many factors including Notch1, which plays essential roles in regulating NSC fate determination under physiological conditions in both developing and adult brains. Following TBI, the dynamic changes of NSCs and the involvement of Notch1 on their development at different stages post-injury are not fully characterized. In the current study, we examined the impact of TBI and Notch1 on NSCs proliferation, survival and neuronal differentiation. Utilizing transgenic mice with tamoxifen-induced GFP expression and Notch1 knock-out in nestin+ NSCs, we examined DG neurogenic response at acute, subacute and chronic stages following a moderate lateral fluid percussion injury. We found that TBI enhanced a proliferative response in the DG at the acute stage following injury; however, this injury response was abolished when Notch1 was conditionally deleted from nestin+ NSCs. We also found that injury and Notch1 deletion drove NSCs committing fate choice towards neuronal differentiation. The results of this study provides further knowledge regarding TBI-induced neurogenic response and Notch1 as the key regulating mechanism.