Electrophysiology publications

The inferior colliculus (IC) is a major midbrain convergence site critical for processing complex sounds and undergoes fundamental changes with age-related hearing loss (ARHL). Local field potentials (LFPs) represent pre-synaptic integration of excitatory and inhibitory signals in local neural populations. Here, we assessed age-related changes in sound-evoked IC LFPs in the CBA/CaJ mouse model of ARHL across the tonotopic axis. We recorded from 495 sites across four age groups (young: 4-6 months; middle: 8-14 months; old: 24-25 months; “oldest old”: 27-31 months), examining LFP responses from dorsal (low-frequency), medial (mid-frequency), and ventral (high-frequency) IC regions. Analysis of the onset depolarization (N1 component) in response to broadband noise bursts revealed significant age-related hyperexcitability in medial and dorsal regions, with oldest old animals showing enhanced responses while ventral regions were unaffected. Amplitude-intensity functions demonstrated significant age × level interactions across all regions, with oldest old animals exhibiting level-dependent hyperexcitability most pronounced at suprathreshold intensities. Time-frequency analysis of LFP spectral content (30-100 Hz) revealed a crossover pattern across medial and dorsal regions, with oldest old animals showing reduced power at low-to-moderate stimulus levels but enhanced power at high stimulus levels. Ventral regions showed a low-level reduction without suprathreshold enhancement. These findings reveal paradoxical enhancement of neural activity in the aging IC, with the most pronounced changes in dorsal and medial regions rather than ventral regions most affected by peripheral hearing loss, suggesting that central hyperexcitability depends on residual afferent drive and implicating region-specific alterations in excitatory-inhibitory balance underlying central presbycusis.

In nature, animals learn to replace predisposed behaviors with new strategies, yet the neural constraints on these transitions are unclear. Using an ethological search task in mice, we reveal medial prefrontal cortical (mPFC) neural correlates of a predisposed win-stay strategy that decays as animals learn to follow a more reliable auditory cue. Auditory cortex (ACx) activity predicts correct trial-by-trial sound-guided search, even on day one of training. This prognostic coding strengthens with learning and emerges from suppressed spiking, most pronounced in neurons tuned laterally to the cue’s spectrum. Chemogenetic disruption reveals ACx contributions to improving performance. Unexpectedly, the global silencing of mPFC accelerates successful usage of sound-tracking, contrary to its canonical role in flexible or stimulus-dependent behavior. Instead, a decentralized multiexpert competition model best predicts behavior and causal perturbations. These findings suggest that mPFC implements a default strategy based on prior knowledge, which actively hinders the expression of more efficient strategies.

Brain function relies on coordinated activity across spatial and temporal scales. Single neurons integrate local and long-range connectivity and reflect activity across brain-wide networks. Understanding integrated brain function requires tools capable of recording from anatomically connected populations in distributed brain areas to bridge local and global dynamics. Here, we present high-density, micro-electrocorticography arrays that facilitate multi-scale studies of brain activity. The hybrid arrays integrate the desirable features of silicone elastomers and polyimide films: silicone provides optical transparency and permits repeated penetration with intracortical arrays, while polyimide enables fine photolithographic feature definition. This combination facilitates high-throughput functional mapping to identify targets and insertion of intracortical arrays for dense local sampling. We demonstrate functional mapping in rats, cats, and marmosets, showing how functional maps guide multi-area laminar recordings. Finally, we demonstrate local and feedforward optogenetic stimulation to investigate cortico-cortical interactions. These capabilities establish the hybrid μECoG as a compelling tool for systems neuroscience.

Platinum electrodes are used in many bionic devices; however, they have limited charge injection capacity (CIC) and signal‐to‐noise ratio (SNR), restricting performance. Iridium oxide has substantially larger CIC and reduced impedance compared to platinum. While these types of electrochemical measurements are typically used to infer improved electrophysiological performance, there are no studies demonstrating this correlation. This article compares the electrochemical and acute electrophysiological recording performance of iridium and electrochemically activated IrOx. Increased iridium activation reduced total impedance at low‐intermediate frequencies and steady‐state electroactive area but had no impact on linear diffusion electroactive area. The total impedance had a poor correlation with background noise, SNR and spike count. The results contribute to the previous literature indicating linear diffusion electroactive area determines the number of neurons within recording distance of the electrode, impacting SNR and spike count. Measurements of impedance are mainly of benefit as an indirect measure of linear diffusion electroactive area, in which case the measurement and analysis must be performed appropriately. A wide range of IrOx activation states can be produced with increased CIC, without concern of neural recording performance impacts. However, higher oxidation states resulting in electrode dissolution or increase in electrode resistance may limit IrOx chronic performance.

Tissue inflammation or nerve injury at the periphery can cause chronic pain. Although the spinal-cord-projecting neurons in the rostral ventromedial medulla (RVMSC neurons) can promote pain chronification1-4, the pathway by which peripheral injury signals drive these neurons is poorly understood1-3,5. Here we report a circuit loop that extends from the spinal cord to the ventral posterolateral thalamus and posterior complex of the thalamus, proceeds to the primary somatosensory cortex and returns to the spinal cord via the lateral superior colliculus, which in turn connects to μ-opioid-receptor-expressing RVMSC neurons. Silencing any node along this multisynaptic circuit has minimal effects on nociception in healthy mice, but can eliminate mechanical hypersensitization and restore normal nociceptive response thresholds in mouse models of inflammatory and neuropathic pain. In healthy mice, repetitive-but not acute-activation of each node in this circuit is sufficient to cause robust chronic mechanical hypersensitization. Our findings reveal a spino-brain-spinal cord circuit loop that links ascending and descending pathways and specifically drives chronic mechanical pain. This could enable the identification of cellular targets for treating chronic pain.

Reconstructing speech from neural recordings is crucial for understanding human speech coding and developing brain-computer interfaces (BCIs). However, existing methods trade off acoustic richness (pitch, prosody) for linguistic intelligibility (words, phonemes). To overcome this limitation, we propose a dual-path framework to concurrently decode acoustic and linguistic representations. The acoustic pathway uses a long-short term memory (LSTM) decoder and a high-fidelity generative adversarial network (HiFi-GAN) to reconstruct spectrotemporal features. The linguistic pathway employs a transformer adaptor and text-to-speech (TTS) generator for word tokens. These two pathways merge via voice cloning to combine both acoustic and linguistic validity. Using only 20 min of electrocorticography (ECoG) data per human subject, our approach achieves highly intelligible synthesized speech (mean opinion score = 4.0/5.0, word error rate = 18.9%). Our dual-path framework reconstructs natural and intelligible speech from ECoG, resolving the acoustic-linguistic trade-off.

Atypical sensory processing is a common feature of autism, yet the neural computations that give rise to these differences, particularly in relation to biological sex and etiological origin, remain unclear. Here we examine predictive auditory processing at the single-neuron level in the inferior colliculus of two adult rat models of autism: a genetic model with a heterozygous Grin2b deletion (Grin2b + /-) and an environmental model based on prenatal valproic acid exposure. We recorded neuronal responses to an auditory oddball paradigm and a cascade control sequence across lemniscal and non-lemniscal IC divisions under high-intensity stimulation, allowing us to derive indices of repetition suppression, prediction error and neuronal mismatch. Using generalized linear mixed-effects models that accounted for animal identity, inferior colliculus division, sex, and rat model, followed by hierarchical group-level comparisons, we identified robust alterations in predictive processing in both autism-like models. These effects varied across inferior colliculus divisions and differed between sexes, revealing distinct phenotype-specific signatures. The results indicate that sex and etiology jointly modulate early auditory computations in autism. More broadly, our findings highlight the translational value of predictive coding frameworks and support the use of complementary animal models to capture neurobiological heterogeneity across the autism spectrum.

This work presents three open datasets featuring various levels of processing, containing neural recordings from the auditory cortex of rats. These recordings were obtained during experiments using the auditory oddball paradigm before, during and after the local microiontophoretic application of acetylcholine. The primary objective of these datasets is to investigate how the brain processes predictable versus unexpected auditory stimuli, and the role of cholinergic inputs during such processing. The data include multi-unit recordings of neuronal activity during the presentation of standard and deviant tones, classified by stimulus type and cortical sub-region. These resources enable quantitative investigations of deviance detection, stimulus-specific adaptation, cholinergic modulation and predictive-coding mechanisms at multiple temporal scales.

The mammillary body (MB) has traditionally been regarded as a relay station for the hippocampus and plays a pivotal role in the Papez circuit. However, its molecular and cellular organization remains inadequately characterized. This study focuses on the horizontally symmetrically distributed neurotensin (Nts)-expressing and nitric oxide synthase 1 (Nos1)-expressing neurons in the MB, demonstrating that Grik4 (encoding a high-affinity kainate receptor subunit) underlies their distinct electrophysiological properties. Within neural circuits, Nts and Nos1 neurons receive excitatory inputs from the ventral subiculum and send parallel excitatory projections to the dorsomedial and ventrolateral subdivisions of the anteroventral thalamus (AV). These 2 cell type-specific circuits are essential for working memory and exhibit selective activation during the maintenance phase with a marked temporal difference. Together, our findings establish a direct link from molecular identity to circuit architecture and cognitive processing by demonstrating that molecularly distinct Nts and Nos1 neurons constitute differential circuits with convergent inputs, divergent outputs, and dissociable roles in working memory maintenance. This work thus reveals a fundamental cross-scale organizational principle—molecule, cell, circuit, function—within the MB.

The hippocampus is classically linked to memory, yet increasing evidence points to a broader role in perceptual inference and deviance detection. Predictive coding theories propose that perception minimizes mismatches between expected and actual sensory input, expressed in neural signatures such as mismatch negativity (MMN) and P300. Although MMN arises mainly from sensory and prefrontal cortices, the hippocampus is anatomically interconnected with both and may also contribute to prediction error processing. We recorded single- and multi-unit activity and local field potentials (LFPs) from DG and CA1 in urethane-anesthetized rats during an auditory oddball paradigm and a no-repetition control sequence to dissociate prediction error from repetition suppression. Approximately 20% of hippocampal neurons were sound responsive, and a subset showed deviant selectivity. Spiking activity predominantly reflected prediction errors, while LFPs revealed complementary contributions from repetition suppression and prediction error. Early LFP components were enhanced for randomly presented deviants, whereas later components within the P300 latency range were stronger for predictable deviants, indicating temporally distinct phases of error signaling and top-down modulation. These findings identify the hippocampus as an active contributor to auditory deviance detection and support a hierarchical model in which hippocampal circuits participate in predictive sensory processing beyond memory.

Objective. Infrared neural stimulation (INS) has emerged as a potent neuromodulation technology, offering safe and focal stimulation with superior spatial recruitment profiles compared to conventional electrical methods. However, the neural dynamics induced by INS stimulation remain poorly understood. Elucidating these dynamics will help develop new INS stimulation paradigms and advance its clinical application. Approach. In this study, we assessed the local network dynamics of INS entrainment in the auditory thalamocortical circuit using the chronically implanted rat model. Our approach focused on measuring INS energy-based local field potential (LFP) recruitment induced by focal thalamocortical stimulation. We further characterized linear and nonlinear oscillatory LFP activity in response to single-pulse and periodic INS and performed spectral decomposition to uncover specific LFP band entrainment to INS. Finally, we examined spike-field transformations across the thalamocortical synapse using spike-LFP coherence coupling measures. Main results. We found that INS significantly increases LFP amplitude as a log-linear function of INS energy per pulse, primarily entraining to LFP β and γ bands with synchrony extending to 200 Hz in some cases. A subset of neurons demonstrated nonlinear, chaotic oscillations linked to information transfer across cortical circuits. Finally, we utilized spike-field coherences to correlate spike coupling to LFP frequency band activity and suggest an energy-dependent model of network activation resulting from INS stimulation. Significance. We show that INS reliably drives robust network activity and can potently modulate cortical field potentials across a wide range of frequencies in a stimulus parameter-dependent manner. Based on these results, we propose design principles for developing full coverage, all-optical thalamocortical auditory neuroprostheses.

The thalamocortical pathways exhibit neuroplasticity not only during the critical period but also in adulthood. In this study, we investigated how cholecystokinin (CCK) modulates age-dependent thalamocortical plasticity. Our findings demonstrated that CCK is expressed in thalamocortical neurons and that high-frequency stimulation (HFS) of the thalamocortical pathway triggers the release of CCK in auditory cortex (ACx), as detected by a CCK sensor. HFS of the medial geniculate body (MGB) induced thalamocortical long-term potentiation (LTP) in wild-type young adult mice. However, knockdown of CCK expression in MGB neurons or blockade of the CCK-B receptor (CCKBR) in the ACx abolished HFS-induced LTP. Interestingly, this LTP could not be elicited in juvenile (3-week-old) or aged mice (over 18-month-old) due to distinct mechanisms: the absence of CCKBR in juveniles and the inability to release CCK in aged mice. Notably, exogenous administration of CCK into the ACx rescued LTP in aged mice and significantly improved frequency discrimination. These findings highlight the potential of CCK as a therapeutic intervention for ameliorating neuroplasticity deficits associated with thalamocortical connectivity.

Most spinal cord injuries (SCI) spare descending and sublesional circuits that can be targeted through motor cortex and spinal cord stimulation to mitigate locomotor deficits. However, the potential synergy between cortical and spinal stimulation remains unknown. Here, we first investigated the phase-locked electrical stimulation of the motor cortex and lumbar spinal cord at 40 Hz in a rat model of unilateral SCI. Combining cortical and lumbar stimulation around the anticipated lift synergistically enhanced leg movements. When integrated into rehabilitation training, cortical stimulation proved essential for the recovery of skilled locomotion. We next investigated the effects of high-frequency (330 Hz) lumbar and sacral stimulation combined with cortical stimulation. Timely integration during the swing phase showed that cortical and lumbar stimulations enhance the initial and mid-swing phases, while sacral stimulation improves extension velocity in the late swing. These findings indicate that supraspinal and sublesional neuromodulation offer complementary neuroprosthetic effects in targeted SCI gait rehabilitation.

Diverse biological factors, such as sex and age, confer heterogeneity on sensory processing challenges in autism. These factors result in major difficulties in the processing of contextual information in social and non-social situations. To assess divergence in autistic traits, it is critical to consider sex- and age-related variability. Nevertheless, these differences remain largely elusive. Animal models of autism offer the possibility to examine contextual processing at the single-neuron level. Here, we investigated predictive processing of contextual auditory cues in the auditory midbrain of control and prenatally valproic acid-induced rats, a well-established animal model of autism. The rats were prepubertal and adult female and male animals. We performed single-unit recordings in the inferior colliculus of control and prenatally, or in utero , exposed rats under the classical oddball paradigm and non-repetitive cascade control sequences to study neuronal mismatch. This is the neuronal correlate of mismatch negativity, the brain’s automatic response to interruptions in environmental regularity. When comparing control and exposed rats, our results demonstrated a reduction in neuronal mismatch in rats exposed to valproic acid. However, exposed adult females exhibited an increased neuronal mismatch compared to their control counterparts. With respect to sex distinctions, valproic acid induced sex differences in neuronal mismatch of prepubertal and adult rats that are not observable in control animals. Moreover, we detected an age-dependent refinement in prediction error that is not affected by the drug. But valproic acid altered typical developmental trajectory of neuronal mismatch in both sexes. Such observations support sex- and age-related effects of in utero valproic acid exposure in contextual auditory processing at the neural level of the inferior colliculus. In autism, atypical predictive processing of environmental regularities underlies unusual responses to novel experiences. The present study highlights the importance of sex and age, that confer heterogeneity to these challenges.

The first-spike latency (FSL) is an important temporal characteristic of a neurophysiological response. We study the FSLs of sound-driven responses in individual neurons in the rat’s auditory midbrain. Responses were elicited by a train of stimuli created using multiple presentations of two tone bursts with different frequencies. Presentations of the two sounds were interleaved temporally in a random order. We found that the mean and the temporal variation of FSL of the response elicited by a sound was increased when the sound was presented more frequently or when it was moved from the ear that drove an excitatory response to the ear that drove an inhibitory effect. Furthermore, the FSL of response to one sound was dependent on the spatial location of the other sound. Results suggested that the timing of the first spike could be used by midbrain neurons to encode information related to the probability of occurrence and spatial location of a sound. It could also be used to gauge how the sound was related to the other sound in spatial location. These results enhance our understanding of neural bases of binaural hearing, especially in an environment with temporally separated competing sounds.

The prefrontal cortex plays a crucial role in procedural rule learning; however, the specific neuronal mechanism through which it represents rules is unknown. We hypothesized that sequential neuronal activities in the prefrontal cortex encode these rules. To investigate this, we recorded neuronal activities in the medial prefrontal cortex of mice during rule learning using Ca 2+ imaging. We utilized a method based on convolutional negative matrix factorization, iSeq, to automatically detect temporal neuronal sequences in the recorded data. As rule learning advanced, these neuronal sequences began to encode critical information for rule execution. In mice that had mastered the rule, the dynamics of neuronal sequences could predict success and failure of reward acquisition. Furthermore, the composition of cell populations within the neuronal sequences was rearranged throughout the learning process. These findings suggest that as animals learn a rule, the medial prefrontal cortex continually updates its neuronal sequences to assign significance to behavioural actions crucial for reward acquisition.

The mediodorsal thalamic nucleus (MD) forms neural circuits with various brain regions, including the prefrontal cortex (PFC), the reticular thalamic nucleus (TRN), and the midbrain reticular nucleus (MRN). However, the specific roles and underlying mechanisms in auditory information processing remain unclear. Here, we perform multi-channel electrophysiological recordings in awake mice to investigate the response patterns of the MD to auditory stimuli, as well as the regulatory effects of PFC, MRN, and TRN inputs. We identify two distinct types of sound-evoked responses. The Phasic-response features a transient burst firing to the stimulus with short latency, rapidly adapting to baseline and corresponding to the onset fluctuation of the local field potential. The Sustained-response is marked by prolonged firing with longer latency and is accompanied by persistent enhancement of oscillatory power following stimulus offset. The response patterns of MD neurons remain consistent across different types of auditory stimuli. Optogenetic inactivation of the MRN suppresses both response types in the MD. The Sustained-response is attenuated by PFC inactivation but enhanced by TRN inactivation, while the Phasic-response remains unaffected by inactivation of either the PFC or TRN. Our findings expand the understanding of the MD's role in sound information integration and auditory cognitive regulation.

Electroanalytical methods are used to understand, modify, and control bionic devices. Bionic devices can record or stimulate cells to understand and/or control normal or abnormal biological processes. These devices contain electrodes that transduce electrical current within the electrical circuit into ionic current within a tissue. Despite the similarity between electroanalysis and electrophysiology, there remains a poor understanding of the relationship between the two techniques, including their methodology and theory. This paper investigates the electrochemical and acute electrophysiological recording performance of neural electrodes. A range of behaviors is achieved by modifying electrodes with the conducting polymer poly(3,4‐ethylenedioxythiophene) (PEDOT) doped with chondroitin sulfate, dextran sulfate, or para ‐toluene sulfonate. The results support previous studies showing that increased electrode area reduced total impedance below the Maxwell–Wagner relaxation frequency and thermal noise while increasing the signal‐to‐noise ratio and neural spike count. The results allowed novel investigation of relative contributions of biological and electrode properties to electrophysiological performance, with increased electrode area having a larger impact on neural population within recording range rather than reducing thermal noise. The utility of measuring electrode impedance for predicting electrophysiological performance is mainly for an indirect measure of electrode area. The results provide insight into noise sources from electrophysiological recordings and limitations in cable theory in neuroscience.

Although control of movement involves many cortical association areas, bidirectional brain-machine interfaces (BMIs) typically decode movement intent from the motor cortex and deliver feedback information to the primary somatosensory cortex (S1). Compared to the S1, the parietal and premotor areas encode more complex information about object properties, hand pre-shaping, and reach trajectories. BMIs therefore might deliver richer information to those cortical association areas than to primary areas. Here, we investigated whether instructions for a center-out task could be delivered via intracortical microstimulation (ICMS) in the anterior intraparietal area (AIP), dorsal posterior parietal cortex (dPPC), or dorsal premotor cortex (PMd) as well as the ventral premotor cortex (PMv) and S1. Two monkeys successfully learned to use AIP, PMv, or S1-ICMS instructions, but neither learned to use dPPC- or PMd-ICMS instructions. The AIP, PMv, and S1 may thus be effective cortical territory for delivering information to the brain, whereas the dPPC or PMd may be comparatively ineffective.

Neurostimulation for cardiovascular control faces challenges due to the lack of predictive modeling for stimulus-driven dynamic responses, which is crucial for precise neuromodulation via quality feedback. We address this by employing a digital twin approach that leverages computational mechanisms underlying neuro-hemodynamic responses during neurostimulation. Our results emphasize the computational role of the nucleus tractus solitarius (NTS) in the brainstem in determining these responses. The intrinsic neural circuit within the NTS harbors collective dynamics residing in a low-dimensional latent space, which effectively captures stimulus-driven hemodynamic perturbations. Building on this, we developed a digital twin framework for individually optimized predictive modeling of neuromodulatory outcomes. This framework potentially enables the design of closed-loop neurostimulation systems for precise hemodynamic control. Consequently, our digital twin based on neural computation mechanisms marks an advancement in the artificial regulation of internal organs, paving the way for precise translational medicine to treat chronic diseases.