Publications using the OpenEx

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.

Closed-loop neural control is a powerful tool for both the scientific exploration of neural function and for mitigating deficiencies found in open-loop deep brain stimulation (DBS). Here, we present a protocol for artificial intelligence-guided neural control in rats using deep reinforcement learning (RL) and infrared neural stimulation (INS). We describe steps for integrating RL closed-loop control into neuroscience and neuromodulation studies. We then detail procedures for using and deploying infrared INS in chronic DBS applications. For complete details on the use and execution of this protocol, please refer to Coventry et al.1 and Coventry and Bartlett.2.

Mammillary body (MB) is traditionally viewed as a structural node of an anatomic circuit for emotion and memory. However, little is known about its molecular and cellular organizations. Here, a discovery that MB contains four subtypes of neurons that occupy different spatial subregions is reported. Of these, two subtypes of neurons are tagged by parvalbumin (PV) and dopamine receptor‐D2 (Drd2) markers. PV neurons are spontaneously active, whereas Drd2 neurons are inactive at rest and generate rebound bursts. These two distinct electrophysiological properties are encoded by Kcnn4 and Cacna1h . PV and Drd2 neurons generate two distinct cell‐type specific circuits by receiving inputs from two discrete subiculum neuronal classes. Gain‐ and loss‐of‐function studies on these cortical‐subcortical circuits demonstrate their differential roles for place and object recognition memory. This finding provides a comprehensive molecular and structural atlas of MB neurons at single‐cell resolution and reveals that MB contains molecularly, structurally, and functionally dissociable streams within its serial architecture.

High-frequency stimulation (HFS)-induced long–term potentiation (LTP) is generally regarded as a homosynaptic Hebbian-type LTP, where synaptic changes are thought to occur at the synapses that project from the stimulation site and terminate onto the neurons at the recording site. In this study, we first investigated HFS-induced LTP on urethane-anesthetized rats and found that cortical HFS enhances neural responses at the recording site through the strengthening of local connectivity with nearby neurons at the stimulation site rather than through synaptic strengthening at the recording site. This enhanced local connectivity at the stimulation site leads to increased output propagation, resulting in signal potentiation at the recording site. Additionally, we discovered that HFS can also nonspecifically strengthen distant afferent synapses at the HFS site, thereby expanding its impact beyond local neural connections. This form of plasticity exhibits a neo-Hebbian characteristic as it exclusively manifests in the presence of cholecystokinin release, induced by HFS. The cortical HFS-induced local LTP was further supported by a behavioral task, providing additional evidence. Our results unveil a previously overlooked mechanism underlying cortical plasticity: synaptic plasticity is more likely to occur around the soma site of strongly activated cortical neurons rather than solely at their projection terminals.

Background Fragile X syndrome (FXS) is a leading known genetic cause of intellectual disability and autism spectrum disorders (ASD)-associated behaviors. A consistent and debilitating phenotype of FXS is auditory hypersensitivity that may lead to delayed language and high anxiety. Consistent with findings in FXS human studies, the mouse model of FXS, the Fmr1 knock out (KO) mouse, shows auditory hypersensitivity and temporal processing deficits. In electroencephalograph (EEG) recordings from humans and mice, these deficits manifest as increased N1 amplitudes in event-related potentials (ERP), increased gamma band single trial power (STP) and reduced phase locking to rapid temporal modulations of sound. In our previous study, we found that administration of the selective serotonin-1 A (5-HT 1A )receptor biased agonist, NLX-101, protected Fmr1 KO mice from auditory hypersensitivity-associated seizures. Here we tested the hypothesis that NLX-101 will normalize EEG phenotypes in developing Fmr1 KO mice. Methods To test this hypothesis, we examined the effect of NLX-101 on EEG phenotypes in male and female wildtype (WT) and Fmr1 KO mice. Using epidural electrodes, we recorded auditory event related potentials (ERP) and auditory temporal processing with a gap-in-noise auditory steady state response (ASSR) paradigm at two ages, postnatal (P) 21 and 30 days, from both auditory and frontal cortices of awake, freely moving mice, following NLX-101 (at 1.8 mg/kg i.p.) or saline administration. Results Saline-injected Fmr1 KO mice showed increased N1 amplitudes, increased STP and reduced phase locking to auditory gap-in-noise stimuli versus wild-type mice, reproducing previously published EEG phenotypes. An acute injection of NLX-101 did not alter ERP amplitudes at either P21 or P30, but significantly reduces STP at P30. Inter-trial phase clustering was significantly increased in both age groups with NLX-101, indicating improved temporal processing. The differential effects of serotonin modulation on ERP, background power and temporal processing suggest different developmental mechanisms leading to these phenotypes. Conclusions These results suggest that NLX-101 could constitute a promising treatment option for targeting post-synaptic 5-HT 1A receptors to improve auditory temporal processing, which in turn may improve speech and language function in FXS.

Noise exposure is one of the most common causes of hearing loss and hyperacusis. Studies have shown that noise exposure can induce a cortical gain to compensate for reduced input of the cochlea, which may contribute to the increased sound sensitivity. However, many people with hyperacusis have no measurable cochlear lesion after being exposed to loud sound. In this experiment, we studied the neurological alterations in the cortical and subcortical areas following a prolonged moderate level of noise exposure (84 dB SPL, 8 h/day for 4 weeks) in the laboratory mice. The cochlear function was monitored by auditory brainstem responses (ABRs). The behavioral auditory sensitivity and temporal processing were evaluated using the acoustic startle response (ASR) and gap‐induced prepulse inhibition (gap‐PPI). The central auditory functions were determined by electrophysiological recordings of the inferior colliculus (IC) and the auditory cortex (AC). Our results showed that although there was no significant difference in the ABR thresholds, the noise group showed enhanced ASR and gap‐PPI compared to the control group. Increased neural activity in both the IC and the AC was recorded in the noise‐exposed mice compared to the control group, suggesting a central gain in both the subcortical and cortical regions. The current source density (CSD) analysis of the AC response revealed an increased columnar excitation and reduced corticocortical projection in the noise group, different from the central gain model of noise‐induced hearing loss. Our results suggest that chronic “nondestructive” noise can increase the gain of the central auditory system by altering the balance of auditory thalamocortical and intracortical inputs, which may contribute to the increased sound sensitivity in people with normal hearing.

Reconstructing perceived speech stimuli from neural recordings is not only advancing the understanding of the neural coding underlying speech processing but also an important building block for brain-computer interfaces and neuroprosthetics. However, previous attempts to directly re-synthesize speech from neural decoding suffer from low re-synthesis quality. With the limited neural data and complex speech representation space, it is hard to build decoding model that directly map neural signal into high-fidelity speech. In this work, we proposed a pre-trained encoder-decoder framework to address these problems. We recorded high-density electrocorticography (ECoG) signals when participants listening to natural speech. We built a pre-trained speech re-synthesizing network that consists of a context-dependent speech encoding network and a generative adversarial network (GAN) for high-fidelity speech synthesis. This model was pre-trained on a large naturalistic speech corpus and can extract critical features for speech re-synthesize. We then built a light-weight neural decoding network that mapped the ECoG signal into the latent space of the pre-trained network, and used the GAN decoder to synthesize natural speech. Using only 20 minutes of intracranial neural data, our neural-driven speech re-synthesis model demonstrated promising performance, with phoneme error rate (PER) at 28.6%, and human listeners were able to recognize 71.6% of the words in the re-synthesized speech. This work demonstrates the feasibility of using pre-trained self-supervised model and feature alignment to build efficient neural-to-speech decoding model.

This study involves longitudinal neuro-electrophysiological analysis using motor-evoked potentials (MEP) and the Basso, Beattie, and Bresnahan behavioral examinations (BBB) to evaluate moderate mid-thoracic contusive spinal cord injury (SCI) in a rat model. Objectives/Background: The objective of the study is to characterize the onset and progression of contusive SCI over an eight-week period using a clinically applicable tool in an in vivo model. The background highlights the importance of a reliable and reproducible injury model and assessment tools for SCI. Methods: The methods section describes the experimental setup, including randomly assigned rats in three groups: Sham, Control, and Injury (undergoing a moderate contusive SCI using the NYU-Impactor). MEP monitoring and BBB examinations are conducted at baseline and weekly for eight weeks post-injury. Results: The results indicate that the relative MEP power spectral decreased to 11% and 22% in the left and right hindlimbs, respectively, during the first week post-SCI. In the second week, a slight spontaneous recovery was observed, reaching 17% in the left and 31% in the right hindlimbs. Over the following four weeks post-SCI, continuing deterioration of MEP signal power was observed with no detectable recovery. Conclusions: SCI attenuates hindlimb MEP power spectral and reduces locomotion, though the changes in MEP and locomotion exhibit distinct temporal patterns. The MEP monitoring provides valuable insights into the functional integrity of motor pathways following SCI and offer a sensitive and reliable assessment. By implementing MEP monitoring, researchers can track the progression of SCI and evaluate the efficacy of therapeutic interventions quantitatively.

Autism Spectrum Disorders (ASD) encompass a wide array of debilitating symptoms, including severe sensory deficits and abnormal language development. Sensory deficits early in development may lead to broader symptomatology in adolescents and adults. The mechanistic links between ASD risk genes, sensory processing and language impairment are unclear. There is also a sex bias in ASD diagnosis and symptomatology. The current study aims to identify the developmental trajectory and genotype- and sex-dependent differences in auditory sensitivity and temporal processing in a Pten-deletion (phosphatase and tensin homolog missing on chromosome 10) mouse model of ASD. Auditory temporal processing is crucial for speech recognition and language development and deficits will cause language impairments. However, very little is known about the development of temporal processing in ASD animal models, and if there are sex differences. To address this major gap, we recorded epidural electroencephalography (EEG) signals from the frontal (FC) and auditory (AC) cortex in developing and adult Nse-cre PTEN mice, in which Pten is deleted in specific cortical layers (layers III-V) (PTEN conditional knock-out (cKO). We quantified resting EEG spectral power distribution, auditory event related potentials (ERP) and temporal processing from awake and freely moving male and female mice. Temporal processing is measured using a gap-in-noise-ASSR (auditory steady state response) stimulus paradigm. The experimental manipulation of gap duration and modulation depth allows us to measure cortical entrainment to rapid gaps in sounds. Temporal processing was quantified using inter-trial phase clustering (ITPC) values that account for phase consistency across trials. The results show genotype differences in resting power distribution in PTEN cKO mice throughout development. Male and female cKO mice have significantly increased beta power but decreased high frequency oscillations in the AC and FC. Both male and female PTEN cKO mice show diminished ITPC in their gap-ASSR responses in the AC and FC compared to control mice. Overall, deficits become more prominent in adult (p60) mice, with cKO mice having significantly increased sound evoked power and decreased ITPC compared to controls. While both male and female cKO mice demonstrated severe temporal processing deficits across development, female cKO mice showed increased hypersensitivity compared to males, reflected as increased N1 and P2 amplitudes. These data identify a number of novel sensory processing deficits in a PTEN-ASD mouse model that are present from an early age. Abnormal temporal processing and hypersensitive responses may contribute to abnormal development of language function in ASD.

The detection of novel, low probability events in the environment is critical for survival. To perform this vital task, our brain is continuously building and updating a model of the outside world; an extensively studied phenomenon commonly referred to as predictive coding. Predictive coding posits that the brain is continuously extracting regularities from the environment to generate predictions. These predictions are then used to supress neuronal responses to redundant information, filtering those inputs, which then automatically enhances the remaining, unexpected inputs. We have recently described the ability of auditory neurons to generate predictions about expected sensory inputs by detecting their absence in an oddball paradigm using omitted tones as deviants. Here, we studied the responses of individual neurons to omitted tones by presenting individual sequences of repetitive pure tones, using both random and periodic omissions, presented at both fast and slow rates in the inferior colliculus and auditory cortex neurons of anesthetized rats. Our goal was to determine whether feature-specific dependence of these predictions exists. Results showed that omitted tones could be detected at both high (8 Hz) and slow repetition rates (2 Hz), with detection being more robust at the non-lemniscal auditory pathway.

The neural mechanisms of novelty detection, especially in relation to behavior, are currently poorly understood. Here, we present a protocol for recording neuronal activity in macaque auditory cortex during novelty detection tasks. We describe steps for behavioral training, surgical headpost implantation, MRI-based electrode targeting, and electrophysiological recording. These steps allow direct assessment of the correlation between novelty detection behavior and neuronal activity. For complete details on the use and execution of this protocol, please refer to Gong et al.1.

Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p -values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies in both rat electrophysiological and computational modeling data. We first start with an intuitive discussion of Bayes' rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools.

Idling brain activity has been proposed to facilitate inference, insight, and innovative problem-solving. However, it remains unclear how and when the idling brain can create novel ideas. Here, we show that cortical offline activity is both necessary and sufficient for building unlearned inferential knowledge from previously acquired information. In a transitive inference paradigm, male C57BL/6J mice gained the inference 1 day after, but not shortly after, complete training. Inhibiting the neuronal computations in the anterior cingulate cortex (ACC) during post-learning either non-rapid eye movement (NREM) or rapid eye movement (REM) sleep, but not wakefulness, disrupted the inference without affecting the learned knowledge. In vivo Ca 2+ imaging suggests that NREM sleep organizes the scattered learned knowledge in a complete hierarchy, while REM sleep computes the inferential information from the organized hierarchy. Furthermore, after insufficient learning, artificial activation of medial entorhinal cortex-ACC dialog during only REM sleep created inferential knowledge. Collectively, our study provides a mechanistic insight on NREM and REM coordination in weaving inferential knowledge, thus highlighting the power of idling brain in cognitive flexibility.

The cerebral cortex is widely considered part of the neural substrate of consciousness, but direct causal evidence is missing. Here, we tested in mice whether optogenetic activation of cortical neurons in posterior parietal cortex (PtA) or medial prefrontal cortex (mPFC) is sufficient for arousal from three behavioral states characterized by progressively deeper unresponsiveness: sleep, a coma-like state induced by muscimol injection in the midbrain, and deep sevoflurane-dexmedetomidine anesthesia. We find that cortical stimulation always awakens the mice from both NREM sleep and REM sleep, with PtA requiring weaker/shorter light pulses than mPFC. Moreover, in most cases light pulses produce both cortical activation (decrease in low frequencies) and behavioral arousal (recovery of the righting reflex) from brainstem coma, as well as cortical activation from anesthesia. These findings provide evidence that direct activation of cortical neurons is sufficient for behavioral and/or cortical arousal from sleep, brainstem coma, and anesthesia.

Background Autism spectrum disorder (ASD) is currently diagnosed in approximately 1 in 44 children in the United States, based on a wide array of symptoms, including sensory dysfunction and abnormal language development. Boys are diagnosed ~ 3.8 times more frequently than girls. Auditory temporal processing is crucial for speech recognition and language development. Abnormal development of temporal processing may account for ASD language impairments. Sex differences in the development of temporal processing may underlie the differences in language outcomes in male and female children with ASD. To understand mechanisms of potential sex differences in temporal processing requires a preclinical model. However, there are no studies that have addressed sex differences in temporal processing across development in any animal model of ASD. Methods To fill this major gap, we compared the development of auditory temporal processing in male and female wildtype (WT) and Fmr1 knock-out (KO) mice, a model of Fragile X Syndrome (FXS), a leading genetic cause of ASD-associated behaviors. Using epidural screw electrodes, we recorded auditory event related potentials (ERP) and auditory temporal processing with a gap-in-noise auditory steady state response (ASSR) paradigm at young (postnatal (p)21 and p30) and adult (p60) ages from both auditory and frontal cortices of awake, freely moving mice. Results The results show that ERP amplitudes were enhanced in both sexes of Fmr1 KO mice across development compared to WT counterparts, with greater enhancement in adult female than adult male KO mice. Gap-ASSR deficits were seen in the frontal, but not auditory, cortex in early development (p21) in female KO mice. Unlike male KO mice, female KO mice show WT-like temporal processing at p30. There were no temporal processing deficits in the adult mice of both sexes. Conclusions These results show a sex difference in the developmental trajectories of temporal processing and hypersensitive responses in Fmr1 KO mice. Male KO mice show slower maturation of temporal processing than females. Female KO mice show stronger hypersensitive responses than males later in development. The differences in maturation rates of temporal processing and hypersensitive responses during various critical periods of development may lead to sex differences in language function, arousal and anxiety in FXS.

Animal psychophysics can generate rich behavioral datasets, often comprised of many 1000s of trials for an individual subject. Gradient-boosted models are a promising machine learning approach for analyzing such data, partly due to the tools that allow users to gain insight into how the model makes predictions. We trained ferrets to report a target word’s presence, timing, and lateralization within a stream of consecutively presented non-target words. To assess the animals’ ability to generalize across pitch, we manipulated the fundamental frequency (F0) of the speech stimuli across trials, and to assess the contribution of pitch to streaming, we roved the F0 from word token to token. We then implemented gradient-boosted regression and decision trees on the trial outcome and reaction time data to understand the behavioral factors behind the ferrets’ decision-making. We visualized model contributions by implementing SHAPs feature importance and partial dependency plots. While ferrets could accurately perform the task across all pitch-shifted conditions, our models reveal subtle effects of shifting F0 on performance, with within-trial pitch shifting elevating false alarms and extending reaction times. Our models identified a subset of non-target words that animals commonly false alarmed to. Follow-up analysis demonstrated that the spectrotemporal similarity of target and non-target words rather than similarity in duration or amplitude waveform was the strongest predictor of the likelihood of false alarming. Finally, we compared the results with those obtained with traditional mixed effects models, revealing equivalent or better performance for the gradient-boosted models over these approaches.

In order to investigate the involvement of the primary visual cortex (V1) in working memory (WM), parallel, multisite recordings of multi-unit activity were obtained from monkey V1 while the animals performed a delayed match-to-sample (DMS) task. During the delay period, V1 population firing rate vectors maintained a lingering trace of the sample stimulus that could be reactivated by intervening impulse stimuli that enhanced neuronal firing. This fading trace of the sample did not require active engagement of the monkeys in the DMS task and likely reflects the intrinsic dynamics of recurrent cortical networks in lower visual areas. This renders an active, attention-dependent involvement of V1 in the maintenance of WM contents unlikely. By contrast, population responses to the test stimulus depended on the probabilistic contingencies between sample and test stimuli. Responses to tests that matched expectations were reduced which agrees with concepts of predictive coding.

A fundamental property of sensory systems is their ability to detect novel stimuli in the ambient environment. The auditory brain contains neurons that decrease their response to repetitive sounds but increase their firing rate to novel or deviant stimuli; the difference between both responses is known as stimulus-specific adaptation or neuronal mismatch (nMM). Here, we tested the effect of microiontophoretic applications of ACh on the neuronal responses in the auditory cortex (AC) of anesthetized rats during an auditory oddball paradigm, including cascade controls. Results indicate that ACh modulates the nMM, affecting prediction error responses but not repetition suppression, and this effect is manifested predominantly in infragranular cortical layers. The differential effect of ACh on responses to standards, relative to deviants (in terms of averages and variances), was consistent with the representational sharpening that accompanies an increase in the precision of prediction errors. These findings suggest that ACh plays an important role in modulating prediction error signaling in the AC and gating the access of these signals to higher cognitive levels.

We investigated whether neurons in monkey primary visual cortex (V1) exhibit mixed selectivity for sensory input and behavioral choice. Parallel multisite spiking activity was recorded from area V1 of awake monkeys performing a delayed match-to-sample task. The monkeys had to make a forced choice decision of whether the test stimulus matched the preceding sample stimulus. The population responses evoked by the test stimulus contained information about both the identity of the stimulus and with some delay but before the onset of the motor response the forthcoming choice. The results of subspace identification analysis indicate that stimulus-specific and decision-related information coexists in separate subspaces of the high-dimensional population activity, and latency considerations suggest that the decision-related information is conveyed by top-down projections.