EEG/ECoG/EMG publications

Neurexins are presynaptic transmembrane proteins crucial for synapse development and organization. Deletion and missense mutations in all three Neurexin genes have been identified in psychiatric disorders, with mutations in the NRXN1 gene most strongly linked to schizophrenia (SZ) and autism spectrum disorder (ASD). While the consequences of NRXN1 deletion have been extensively studied on the synaptic and behavioral levels, circuit endophenotypes that translate to the human condition have not been characterized yet. Therefore, we investigated the electrophysiology of cortico-striatal-thalamic circuits in Nrxn1α −/− rats and wildtype littermates focusing on a set of translational readouts, including spontaneous oscillatory activity, auditory-evoked oscillations and potentials, as well as mismatch negativity-like (MMN) responses and responses to social stimuli. On the behavioral level Nrxn1α −/− rats showed locomotor hyperactivity. In vivo freely moving electrophysiology revealed pronounced increases of spontaneous oscillatory power within the gamma band in all studied brain areas and elevation of gamma coherence in cortico-striatal and thalamocortical circuits of Nrxn1α −/− rats. In contrast, auditory-evoked oscillations driven by chirp-modulated tones showed reduced power in cortical areas confined to slower oscillations. Finally, Nrxn1α −/− rats exhibited altered auditory evoked-potentials and profound deficits in MMN-like responses, explained by reduced prediction error. Despite deficits for auditory stimuli, responses to social stimuli appeared intact. A central hypothesis for psychiatric and neurodevelopmental disorders is that a disbalance of excitation-to-inhibition is underlying oscillatory and sensory deficits. In a first attempt to explore the impact of inhibitory circuit modulation, we assessed the effects of enhancing tonic inhibition via δ-containing GABA A receptors (using Gaboxadol) on endophenotypes possibly associated with network hyperexcitability. Pharmacological experiments applying Gaboxadol showed genotype-specific differences, but failed to normalize oscillatory or sensory processing abnormalities. In conclusion, our study revealed endophenotypes in Nrxn1α −/− rats that could be used as translational biomarkers for drug development in psychiatric disorders.

Facial nerve (FN) injury can lead to debilitating and permanent facial paresis/paralysis (FP), where facial muscles progressively lose tone, atrophy, and ultimately reduce to scar tissue. Despite considerable efforts in the recent decades, therapies for FP still possess high failure rates and provide inadequate recovery of muscle function. In this pilot study, we used a feline model to demonstrate the potential for chronically implanted multichannel dual-cuff electrodes (MCE) to selectively stimulate injured facial nerves at low current intensities to avoid stimulus-induced neural injury. Selective facial muscle activation was achieved over six months after FN injury and MCE implantation in two domestic shorthaired cats (Felis catus). Through utilization of bipolar stimulation, specific muscles were activated at significantly lower electrical currents than was achievable with single channel stimulation. Moreover, interval increases in subthreshold current intensities using bipolar stimulation enabled a graded EMG voltage response while maintaining muscle selectivity. Histological examination of neural tissue at implant sites showed no appreciable signs of stimulation-induced nerve injury. Thus, by selectively activating facial musculature six months following initial FN injury and MCE implantation, we demonstrated the potential for our neural stimulator system to be safely and effectively applied to the chronic setting, with implications for FP treatment.

Objective: We investigated the electrophysiological relationships in the cortico-basal ganglia network on a sub-centimeter scale to increase our understanding of neural functional relationships in Parkinson's disease (PD). Methods: Data was intraoperatively recorded from 2 sources in the human brain-a microelectrode in the subthalamic nucleus (STN) and a micro-electrocorticography grid on the motor association cortex-during bilateral deep brain stimulation (DBS) electrode placement. STN neurons and local field potential (LFP) were defined as functionally connected when the 99.7% confidence intervals of the action potential (AP)-aligned average LFP and control did not overlap. Results: APs from STN neurons were functionally connected to the STN LFP for 18/46 STN neurons. This functional connection was observed between STN neuron APs and cortical LFP for 25/46 STN neurons. The cortical patterns of electrophysiological functional connectivity differed for each neuron. Conclusions: A subset of single neurons in the STN exhibited functional connectivity with electrophysiological activity in the STN and at a distance with the motor association cortex surveyed on a sub-centimeter spatial scale. These connections show a per neuron differential topography on the cortex. Significance: The cortico-basal ganglia circuit is organized on a sub-centimeter scale, and plays an important role in the mechanisms of PD and DBS.

The “cocktail party” problem—how a listener perceives speech in noisy environments—is typically studied using speech (multi-talker babble) or noise maskers. However, realistic cocktail party scenarios often include background music (e.g., coffee shops, concerts). Studies investigating music’s effects on concurrent speech perception have predominantly used highly controlled synthetic music or shaped noise, which do not reflect naturalistic listening environments. Behaviorally, familiar background music and songs with vocals/lyrics inhibit concurrent speech recognition. Here, we investigated the neural bases of these effects. While recording multichannel EEG, participants listened to an audiobook while popular songs (or silence) played in the background at a 0 dB signal-to-noise ratio. Songs were either familiar or unfamiliar to listeners and featured either vocals or isolated instrumentals from the original audio recordings. Comprehension questions probed task engagement. We used temporal response functions (TRFs) to isolate cortical tracking to the target speech envelope and analyzed neural responses around 100 ms (i.e., auditory N1 wave). We found that speech comprehension was, expectedly, impaired during background music compared to silence. Target speech tracking was further hindered by the presence of vocals. When masked by familiar music, response latencies to speech were less susceptible to informational masking, suggesting concurrent neural tracking of speech was easier during music known to the listener. These differential effects of music familiarity were further exacerbated in listeners with less musical ability. Our neuroimaging results and their dependence on listening skills are consistent with early attentional-gain mechanisms where familiar music is easier to tune out (listeners already know the song’s expectancies) and thus can allocate fewer attentional resources to the background music to better monitor concurrent speech material.

The impact of pulse timing is an important factor in our understanding of how to effectively modulate the basal ganglia thalamocortical (BGTC) circuit. Single pulse low-frequency DBS-evoked potentials generated through electrical stimulation of the subthalamic nucleus (STN) provide insight into circuit activation, but how the long-latency components change as a function of pulse timing is not well-understood. We investigated how timing between stimulation pulses delivered in the STN region influence the neural activity in the STN and cortex. DBS leads implanted in the STN of five patients with Parkinson's disease were temporarily externalized, allowing for the delivery of paired pulses with inter-pulse intervals (IPIs) ranging from 0.2 to 10 ms. Neural activation was measured through local field potential (LFP) recordings from the DBS lead and scalp EEG. DBS-evoked potentials were computed using contacts positioned in dorsolateral STN as determined through co-registered post-operative imaging. We quantified the degree to which distinct IPIs influenced the amplitude of evoked responses across frequencies and time using the wavelet transform and power spectral density curves. The beta frequency content of the DBS evoked responses in the STN and scalp EEG increased as a function of pulse-interval timing. Pulse intervals <1.0 ms apart were associated with minimal to no change in the evoked response. IPIs from 1.5 to 3.0 ms yielded a significant increase in the evoked response, while those >4 ms produced modest, but non-significant growth. Beta frequency activity in the scalp EEG and STN LFP response was maximal when IPIs were between 1.5 and 4.0 ms. These results demonstrate that long-latency components of DBS-evoked responses are pre-dominantly in the beta frequency range and that pulse interval timing impacts the level of BGTC circuit activation.

The contribution of different brain regions to movement abnormalities in children with dystonia is unknown. Three awake subjects undergoing depth electrode implantation for assessments of potential deep brain recording targets performed a rhythmic figure-8 drawing task. Two subjects had dystonia, one was undergoing testing for treatment of Tourette Syndrome and had neither dystonia nor abnormal movements during testing. Movement-related signals were evaluated by determining the magnitude of task-related frequency components. Brain signals were recorded in globus pallidus internus (GPi), the ventral oralis anterior/posterior (VoaVop) and the ventral intermediate (Vim) nuclei of the thalamus. In comparison to the subject without dystonia, both children with dystonia showed increased task-related activity in GPi and Vim. This finding is consistent with a role of both basal ganglia and cerebellar outputs in the pathogenesis of dystonia. Our results further suggest that frequency analysis of brain recordings during cyclic movements may be a useful tool for analysis of the presence of movement-related signals in various brain regions.

Deficits in auditory nerve (AN) function for older adults reduce afferent input to the cortex. The extent to which the cortex in older adults adapts to this loss of afferent input and the mechanisms underlying this adaptation are not well understood. We took a neural systems approach measuring AN and cortical evoked responses within 50 older and 27 younger human adults (59 female) to estimate central gain or increased cortical activity despite reduced AN activity. Relative to younger adults, older adults' AN response amplitudes were smaller, but cortical responses were not. We used the relationship between AN and cortical response amplitudes in younger adults to predict cortical response amplitudes for older adults from their AN responses. Central gain in older adults was thus defined as the difference between their observed cortical responses and those predicted from the parameter estimates of younger adults. In older adults, decreased afferent input contributed to lower cortical GABA levels, greater central gain, and poorer speech recognition in noise (SIN). These effects on SIN occur in addition to, and independent from, effects attributed to elevated hearing thresholds. Our results are consistent with animal models of central gain and suggest that reduced AN afferent input in some older adults may result in changes in cortical encoding and inhibitory neurotransmission, which contribute to reduced SIN. An advancement in our understanding of the changes that occur throughout the auditory system in response to the gradual loss of input with increasing age may provide potential therapeutic targets for intervention.SIGNIFICANCE STATEMENT Age-related hearing loss is one of the most common chronic conditions of aging, yet little is known about how the cortex adapts to this loss of sensory input. We measured AN and cortical responses to the same stimulus in younger and older adults. In older adults we found hyperexcitability in cortical activity relative to concomitant declines in afferent input that are consistent with central gain. Lower levels of cortical GABA, an inhibitory neurotransmitter, were associated with greater central gain, which predicted poorer SIN. The results suggest that the cortex in older adults may adapt to attenuated sensory input by reducing inhibition to amplify the cortical response, but this amplification may lead to poorer SIN.

The primate amygdala is a complex consisting of over a dozen nuclei that have been implicated in a host of cognitive functions, individual differences, and psychiatric illnesses. These functions are implemented through distinct connectivity profiles, which have been documented in animals but remain largely unknown in humans. Here we present results from 25 neurosurgical patients who had concurrent electrical stimulation of the amygdala with intracranial electroencephalography (electrical stimulation tract-tracing; es-TT), or fMRI (electrical stimulation fMRI; es-fMRI), methods providing strong inferences about effective connectivity of amygdala subdivisions with the rest of the brain. We quantified functional connectivity with medial and lateral amygdala, the temporal order of these connections on the timescale of milliseconds, and also detail second-order effective connectivity among the key nodes. These findings provide a uniquely detailed characterization of human amygdala functional connectivity that will inform functional neuroimaging studies in healthy and clinical populations.

The parasitoid wasp Ampulex compressa envenomates the brain of its host the American cockroach ( Periplaneta americana ), thereby making it a behaviorally compliant food supply for its offspring. The target of venom injection is a locomotory command center in the brain called the central complex. In this study, we investigate why stung cockroaches do not respond to injuries incurred during the manipulation process by the wasp. In particular, we examine how envenomation compromises nociceptive signaling pathways in the host. Noxious stimuli applied to the cuticle of stung cockroaches fail to evoke escape responses, even though nociceptive interneurons projecting to the brain respond normally. Hence, while nociceptive signals are carried forward to the brain, they fail to trigger robust nocifensive behavior. Electrophysiological recordings from the central complex of stung animals demonstrate decreases in peak firing rate, total firing, and duration of noxious-evoked activity. The single parameter best correlated with altered noxious-evoked behavioral responses of stung cockroaches is reduced duration of the evoked response in the central complex. Our findings demonstrate how the reproductive strategy of a parasitoid wasp is served by venom-mediated elimination of aversive, nocifensive behavior in its host.

Background: Sensory impairments commonly occur in patients with autism or intellectual disability. Fragile X syndrome (FXS) is one form of intellectual disability that is often comorbid with autism. In electroencephalographic (EEG) recordings obtained from humans with FXS, the ability of cortical regions to consistently synchronize, or "phase-lock", to modulated auditory stimuli is reduced compared to that of typically developing individuals. At the same time, less time-locked, "non-phase-locked" power induced by sounds is higher. The same changes occur in the Fmr1 knockout (KO) mouse - an animal model of FXS. We determined if Fmr1 deletion in a subset of brainstem auditory neurons plays any role in these EEG changes in the mouse. Methods: We reinstated FMRP expression in a subpopulation of brainstem auditory neurons in an otherwise Fmr1 KO control (conditional on; cON Fmr1) mouse and used EEG recordings to determine if reinstatement normalized, or "rescued", the phase-locking phenotype observed in the cON Fmr1 mouse. In determining rescue, this also meant that Fmr1 deletion in the same neuron population was necessary for the phenotype to occur. Results: We find that Fmr1 reinstatement in a subset of brainstem neurons rescues certain aspects of the phase-locking phenotype but does not rescue the increase in non-phase-locked power. Unexpectedly, not all electrophysiological phenotypes observed in the Fmr1 KO were observed in the cON Fmr1 mouse used for the reinstatement experiments, and this was likely due to residual expression of FMRP in these Fmr1 KO controls. Conclusions: Fmr1 deletion in brainstem neurons is necessary for certain aspects of the decreased phase-locking phenotype in the Fmr1 KO, but not necessary for the increase in non-phase-locked power induced by a sound. The most likely brainstem structure underlying these results is the inferior colliculus. We also demonstrate that low levels of FMRP can rescue some EEG phenotypes but not others. This latter finding provides a foundation for how symptoms in FXS individuals may vary due to FMRP levels and that reinstatement of low FMRP levels may be sufficient to alleviate particular symptoms.

Passive priming of prior knowledge to assimilate ongoing experiences underlies advanced cognitive processing. However, the necessary neural dynamics of memory assimilation remains elusive. Uninstructed brain could also show boosted creativity, particularly after idling states, yet it remains unclear whether the idling brain can spontaneously spark relevant knowledge assimilations. We established a paradigm that links/separates context-dependent memories according to geometrical similarities. Mice exploring one of four contexts 1 d before undergoing contextual fear conditioning in a square context showed a gradual fear transfer to preexposed geometrically relevant contexts the next day, but not after 15 min. Anterior cingulate cortex neurons representing relevant, rather than distinct, memories were significantly coreactivated during postconditioning sleep only, before their selective integration the next day during testing. Disrupting sleep coreactivations prevented assimilation while preserving recent memory consolidation. Thus, assimilating pertinent memories during sleep through coreactivation of their respective engrams represents the neural underpinnings of sleep-triggered implicit cortical learning.

Deep brain stimulation (DBS) of the deep cerebellar nuclei has been shown to enhance perilesional cortical excitability and promote motor rehabilitation in preclinical models of cortical ischemia and is currently being evaluated in patients with chronic, post-stroke deficits. Understanding the effects of cerebellar DBS on contralateral sensorimotor cortex may be key to developing approaches to optimize stimulation delivery and treatment outcomes. Using the naïve rat model, we characterized the effects of DBS of the lateral cerebellar nucleus (LCN) on somatosensory evoked potentials (SSEPs) and evaluated their potential use as a surrogate index of cortical excitability. SSEPs were recorded concurrently with continuous 30 Hz or 100 Hz LCN DBS and compared to the DBS OFF condition. Ratios of SSEP peak to peak amplitude during 100 Hz LCN DBS to DBS OFF at longer latency peaks were significantly>1, suggesting that cortical excitability was enhanced as a result of LCN DBS. Although changes in SSEP peak to peak amplitudes were observed, they were modest in relation to previously reported effects on motor cortical excitability. Overall, our findings suggest that LCN output influences thalamocortical somatosensory pathways, however further work is need to better understand the potential role of SSEPs in optimizing therapy.

Objectives: To characterize and compare the stability of cortical potentials evoked by deep brain stimulation (DBS) of the subthalamic nucleus (STN) across the naïve, parkinsonian, and pharmacologically treated parkinsonian states. To advance cortical potentials as possible biomarkers for DBS programming. Materials and methods: Serial electrocorticographic (ECoG) recordings were made more than nine months from a single non-human primate instrumented with bilateral ECoG grids spanning anterior parietal to prefrontal cortex. Cortical evoked potentials (CEPs) were generated through time-lock averaging of the ECoG recordings to DBS pulses delivered unilaterally in the STN region using a chronically implanted, six-contact, scaled DBS lead. Recordings were made across the naïve followed by mild and moderate parkinsonian conditions achieved by staged injections of the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) neurotoxin. In addition to characterizing the spatial distribution and stability of the response within each state, changes in the amplitude and latency of CEP components as well as in the frequency content were examined in relation to parkinsonian severity and dopamine replacement. Results: In the naïve state, the STN DBS CEP presented as a multiphase response maximal over M1 cortex, with components attributable to physiological activity distinguishable from stimulus artifact as early as 0.45-0.75 msec poststimulation. When delivered using therapeutically effective parameters in the parkinsonian state, the CEP was highly stable across multiple recording sessions within each behavioral state. Across states, significant differences were present with respect to both the latency and amplitude of individual response components, with greater differences present for longer-latency components (all p < 0.05). Power spectral density analysis revealed a high-beta peak within the evoked response, with significant changes in power between disease states across multiple frequency bands. Conclusions: Our findings underscore the spatiotemporal specificity and relative stability of the DBS-CEP associated with different disease states and with therapeutic benefit. DBS-CEP may be a viable biomarker for therapeutic programming.

A common challenge in neuroscience is how to decompose noisy, multi-source signals measured in experiments into biophysically interpretable components. Analysis of cortical surface electrical potentials (CSEPs) measured using electrocorticography arrays (ECoG) typifies this problem. We hypothesized that high frequency (70-1,000 Hz) CSEPs are composed of broadband (i.e., power-law) and bandlimited components with potentially differing biophysical origins. In particular, the high-gamma band (70-150 Hz) has been shown to be highly predictive for encoding and decoding behaviors and stimuli. Despite its demonstrated importance, whether high-gamma is composed of a bandlimited signal is poorly understood. To address this gap, we recorded CSEPs from rat auditory cortex and demonstrate that the evoked CSEPs are composed of multiple distinct frequency components, including high-gamma. We then show, using a novel robust regression method, that at fast timescales and on single trials during speech production, human high-gamma amplitude cannot be explained by a modulating power-law component; thus, high-gamma is band-limited. Furthermore, we show that the power-law component is less predictive of produced speech compared to the raw high-gamma amplitude. Finally, we show that the largest variance component of human ECoG signals is low-frequency and band-limited, not broadband. Together these results demonstrate that there are multiple, band-limited components of high frequency power in cortical surface electrical potentials, including the high-gamma band, which may have different biophysical origins. Significance Statement Electrocortigraphy (ECoG) records cortical surface electrical potentials (CSEPs). ECoG is utilized in both humans and animal models to understand distributed cortical processing and for brain machine interfaces. The spectral structure of evoked CSEPs is greatly debated. To address this issue, we recorded from rat auditory cortex using µ ECoG and human ventral sensory-motor cortex during speech production with high-density ECoG. Using novel analytic approaches, we found that evoked CSEPs are composed of multiple band-limited components, including high-gamma. These results contrast with the dominant thinking in the field that the high-frequency power in ECoG is broadband. Our results raise the possibility that distinct frequency components of ECoG are biomarkers of processing in different cortical layers.

The nucleus reuniens (RE) is necessary for memories dependent on the interaction between the medial prefrontal cortex (mPFC) and hippocampus (HPC). One example is trace eyeblink conditioning, in which the mPFC exhibits differential activity to neutral conditioned stimuli (CS) depending on their contingency with an aversive unconditioned stimulus (US). To test if this relevancy signal is routed to the RE, we photometrically recorded mPFC axon terminals within the RE and tracked their changes with learning. As a comparison, we measured prefrontal terminal activity in the mediodorsal thalamus (MD), which lacks connectivity with the HPC. In naïve male rats, prefrontal terminals within the RE were not strongly activated by tone or light. As the rats associated one of the stimuli (CS+) with the US, terminals gradually increased their response to the CS+ but not the other stimulus (CS-). In contrast, stimulus-evoked responses of prefrontal terminals within the MD were strong even before conditioning. They also became augmented only to the CS+ in the first conditioning session; however, the degree of activity differentiation did not improve with learning. These findings suggest that associative learning selectively increased mPFC output to the RE, signaling the behavioral relevance of sensory stimuli.

Electrophysiological oscillations in the brain have been shown to occur as multicycle events, with onset and offset dependent on behavioral and cognitive state. To provide a baseline for state-related and task-related events, we quantified oscillation features in resting-state recordings. We developed an open-source wavelet-based tool to detect and characterize such oscillation events (OEvents) and exemplify the use of this tool in both simulations and two invasively-recorded electrophysiology datasets: one from human, and one from nonhuman primate (NHP) auditory system. After removing incidentally occurring event-related potentials (ERPs), we used OEvents to quantify oscillation features. We identified ∼2 million oscillation events, classified within traditional frequency bands: δ, θ, α, β, low γ, γ, and high γ. Oscillation events of 1–44 cycles could be identified in at least one frequency band 90% of the time in human and NHP recordings. Individual oscillation events were characterized by nonconstant frequency and amplitude. This result necessarily contrasts with prior studies which assumed frequency constancy, but is consistent with evidence from event-associated oscillations. We measured oscillation event duration, frequency span, and waveform shape. Oscillations tended to exhibit multiple cycles per event, verifiable by comparing filtered to unfiltered waveforms. In addition to the clear intraevent rhythmicity, there was also evidence of interevent rhythmicity within bands, demonstrated by finding that coefficient of variation of interval distributions and Fano factor (FF) measures differed significantly from a Poisson distribution assumption. Overall, our study provides an easy-to-use tool to study oscillation events at the single-trial level or in ongoing recordings, and demonstrates that rhythmic, multicycle oscillation events dominate auditory cortical dynamics.

Fundamental to elucidating the functional organization of the brain is the assessment of causal interactions between different brain regions. Multivariate autoregressive (MVAR) modeling techniques applied to multisite electrophysiological recordings are a promising avenue for identifying such causal links. They estimate the degree to which past activity in one or more brain regions is predictive of another region's present activity, while simultaneously accounting for the mediating effects of other regions. Including as many mediating variables as possible in the model has the benefit of drastically reducing the odds of detecting spurious causal connectivity. However, effective bounds on the number of MVAR model coefficients that can be estimated reliably from limited data make exploiting the potential of MVAR models challenging for even modest numbers of recording sites. Here, we utilize well-established dimensionality-reduction techniques to fit MVAR models to human intracranial data from ∼100 - 200 recording sites spanning dozens of anatomically and functionally distinct cortical regions. First, we show that high-dimensional MVAR models can be successfully estimated from long segments of data and yield plausible connectivity profiles. Next, we use these models to generate synthetic data with known ground-truth connectivity to explore the utility of applying principal component analysis and group least absolute shrinkage and selection operator (gLASSO) to reduce the number of parameters (connections) during model fitting to shorter data segments. We show that gLASSO is highly effective for recovering ground-truth connectivity in the limited data regime, capturing important features of connectivity for high-dimensional models with as little as 10 seconds of data. The methods presented here have broad applicability to the analysis of high-dimensional time series data in neuroscience, facilitating the elucidation of the neural basis of sensation, cognition, and arousal.

This work aims to estimate severe fMRI scanning artifacts in extracellular neural recordings made at ultrahigh magnetic field strengths in order to remove the artifact interferences and uncover the complete neural electrophysiology signal. We build on previous work that used PCA to denoise EEG recorded during fMRI, adapting it to cover the much larger frequency range (1-6000 Hz) of the extracellular field potentials (EFPs) observed by extracellular neural recordings. We examine the singular value decomposition (SVD)-PCA singular value shrinkage (SVS) and compare two shrinkage rules and a sliding template subtraction approach. Additionally, we present a new technique for estimating the singular value upper bounds in spontaneous neural activity recorded in the isoflurane anesthetized rat that uses the temporal first difference of the neural signal. The approaches are tested on artificial datasets to examine their efficacy in detecting extracellular action potentials (EAPs: 300-6000 Hz) recorded during fMRI gradient interferences. Our results indicate that it is possible to uncover the EAPs recorded during gradient interferences. The methods are then tested on natural (non-artificial) datasets recorded from the cortex of isoflurane anesthetized rats, where both local field potential (LFP: 1-300 Hz) and EAP signals are analyzed. The SVS methods are shown to be advantageous compared to sliding template subtraction, especially in the high frequency range corresponding to EAPs. Our novel approach moves us towards simultaneous fMRI and completely sampled neural recording (1-6000Hz with no temporal gaps), providing the opportunity for further study of spontaneous brain function and neurovascular coupling at ultrahigh field in the isoflurane anesthetized rat.

We introduce two EEG techniques, one based on conventional monopolar electrodes and one based on a novel tripolar electrode, to record for the first time auditory brainstem responses (ABRs) from the scalp of unanesthetized, unrestrained big brown bats. Stimuli were frequency-modulated (FM) sweeps varying in sweep direction, sweep duration, and harmonic structure. As expected from previous invasive ABR recordings, upward-sweeping FM signals evoked larger amplitude responses (peak-to-trough amplitude in the latency range of 3–5 ms post-stimulus onset) than downward-sweeping FM signals. Scalp-recorded responses displayed amplitude-latency trading effects as expected from invasive recordings. These two findings validate the reliability of our noninvasive recording techniques. The feasibility of recording noninvasively in unanesthetized, unrestrained bats will energize future research uncovering electrophysiological signatures of perceptual and cognitive processing of biosonar signals in these animals, and allows for better comparison with ABR data from echolocating cetaceans, where invasive experiments are heavily restricted.

Categorizing sounds into meaningful groups helps listeners more efficiently process the auditory scene and is a foundational skill for speech perception and language development. Yet, how auditory categories develop in the brain through learning, particularly for non-speech sounds (e.g., music), is not well understood. Here, we asked musically naïve listeners to complete a brief (∼20 min) training session where they learned to identify sounds from a musical interval continuum (minor-major 3rds). We used multichannel EEG to track behaviorally relevant neuroplastic changes in the auditory event-related potentials (ERPs) pre- to post-training. To rule out mere exposure-induced changes, neural effects were evaluated against a control group of 14 non-musicians who did not undergo training. We also compared individual categorization performance with structural volumetrics of bilateral Heschl’s gyrus (HG) from MRI to evaluate neuroanatomical substrates of learning. Behavioral performance revealed steeper (i.e., more categorical) identification functions in the posttest that correlated with better training accuracy. At the neural level, improvement in learners’ behavioral identification was characterized by smaller P2 amplitudes at posttest, particularly over right hemisphere. Critically, learning-related changes in the ERPs were not observed in control listeners, ruling out mere exposure effects. Learners also showed smaller and thinner HG bilaterally, indicating superior categorization was associated with structural differences in primary auditory brain regions. Collectively, our data suggest successful auditory categorical learning of music sounds is characterized by short-term functional changes (i.e., greater post-training efficiency) in sensory coding processes superimposed on preexisting structural differences in bilateral auditory cortex.