Single-unit Recordings Research Articles

Inference technique for the synaptic conductances in rhythmically active networks and application to respiratory central pattern generation circuits.

Unraveling synaptic interactions between excitatory and inhibitory interneurons within rhythmic neural circuits, such as central pattern generation (CPG) circuits for rhythmic motor behaviors, is critical for deciphering circuit interactions and functional architecture, which is a major problem for understanding how neural circuits operate. Here, we present a general method for extracting and separating patterns of inhibitory and excitatory synaptic conductances at high temporal resolution from single neuronal intracellular recordings in rhythmically active networks. These post-synaptic conductances reflect the combined synaptic inputs from the key interacting neuronal populations and can reveal the functional connectome of the active circuits. To illustrate the applicability of our analytic technique, we employ our method to infer the synaptic conductance profiles in identified rhythmically active interneurons within key microcircuits of the mammalian (mature rat) brainstem respiratory CPG and provide a perspective on how our approach can resolve the functional interactions and circuit organization of these interneuron populations. We demonstrate the versatility of our approach, which can be applied to any other rhythmic circuits where conditions allow for neuronal intracellular recordings.

Inference technique for the synaptic conductances in rhythmically active networks and application to respiratory central pattern generation circuits

Unraveling synaptic interactions between excitatory and inhibitory interneurons within rhythmic neural circuits, such as central pattern generation (CPG) circuits for rhythmic motor behaviors, is critical for deciphering circuit interactions and functional architecture, which is a major problem for understanding how neural circuits operate. Here, we present a general method for extracting and separating patterns of inhibitory and excitatory synaptic conductances at high temporal resolution from single neuronal intracellular recordings in rhythmically active networks. These post-synaptic conductances reflect the combined synaptic inputs from the key interacting neuronal populations and can reveal the functional connectome of the active circuits. To illustrate the applicability of our analytic technique, we employ our method to infer the synaptic conductance profiles in identified rhythmically active interneurons within key microcircuits of the mammalian (mature rat) brainstem respiratory CPG and provide a perspective on how our approach can resolve the functional interactions and circuit organization of these interneuron populations. We demonstrate the versatility of our approach, which can be applied to any other rhythmic circuits where conditions allow for neuronal intracellular recordings.

CalTrig: A GUI-based Machine Learning Approach for Decoding Neuronal Calcium Transients in Freely Moving Rodents.

Advances in in vivo Ca2+ imaging using miniature microscopes have enabled researchers to study single-neuron activity in freely-moving animals. Tools such as MiniAN and CalmAn have been developed to convert Ca 2+ visual signals to numerical data, collectively referred to as CalV2N. However, substantial challenges remain in analyzing the large datasets generated by CalV2N, particularly in integrating data streams, evaluating CalV2N output quality, and reliably and efficiently identifying Ca2+ transients. In this study, we introduce CalTrig, an open-source graphical user interface (GUI) tool designed to address these challenges at the post-CalV2N stage of data processing collected from C57BL/6J mice. CalTrig integrates multiple data streams, including Ca2+ imaging, neuronal footprints, Ca2+ traces, and behavioral tracking, and offers capabilities for evaluating the quality of CalV2N outputs. It enables synchronized visualization and efficient Ca2+ transient identification. We evaluated four machine learning models (i.e., GRU, LSTM, Transformer, and Local Transformer) for Ca2+ transient detection. Our results indicate that the GRU model offers the highest predictability and computational efficiency, achieving stable performance across training sessions, different animals and even among different brain regions. The integration of manual, parameter-based, and machine learning-based detection methods in CalTrig provides flexibility and accuracy for various research applications. The user-friendly interface and low computing demands of CalTrig make it accessible to neuroscientists without programming expertise. We further conclude that CalTrig enables deeper exploration of brain function, supports hypothesis generation about neuronal mechanisms, and opens new avenues for understanding neurological disorders and developing treatments.Significance statement Understanding neuronal activity at the single-cell level is critical for unraveling brain mechanisms underlying behavior and neurological disorders. CalTrig, a novel GUI-based tool, integrates machine learning with Ca2+ imaging analysis to address key challenges in processing complex, large-scale neural data from freely moving rodents. By providing synchronized visualization and flexible detection of Ca2+ transients, CalTrig enables researchers without programming expertise to extract meaningful insights into brain function. This tool's adaptability and computational efficiency support diverse research applications, from behavioral neuroscience to translational studies. In brief, CalTrig enhances the precision and scalability of Ca2+ transient analysis, paving the way for deeper exploration of neuronal dynamics and facilitating advancements in neuroscience research.

(-)-Epigallocatechin-3-Gallate Suppresses Hyperexcitability in Rat Primary Nociceptive Neurons Innervating Inflamed Tissues: A Comparison with Lidocaine

Objective: Given the side effects and reduced efficacy of conventional local anesthetics in inflammatory conditions, there is a compelling need for complementary alternative medicine (CAM), particularly those based on phytochemicals. While a previous study showed that in vivo local injection of (-)-epigallocatechin-3-gallate (EGCG) into the peripheral receptive field suppresses the excitability of rat trigeminal ganglion (TG) neurons in the absence of inflammation, the acute effects of EGCG in vivo, especially on TG neurons under inflammatory conditions, are still unknown. We aimed to determine if acute local EGCG administration into inflamed tissue effectively attenuates the excitability of nociceptive TG neurons evoked by mechanical stimulation. Methods: The escape reflex threshold was measured to assess hyperalgesia caused by complete Freund’s adjuvant (CFA)-induced inflammation. To assess neuronal activity, extracellular single-unit recordings were performed on TG neurons in anesthetized CFA-inflamed rats in response to orofacial mechanical stimulation. Results: The mechanical escape threshold was significantly lower in CFA-inflamed rats compared to before CFA injection. EGCG (1–10 mM) reversibly and dose-dependently inhibited the mean firing frequency of TG neurons evoked by both non-noxious and noxious mechanical stimuli (p < 0.05). For comparison, 1% lidocaine (37 mM), a local anesthetic, also caused reversible inhibition of the mean firing frequency in inflamed TG neurons responding to mechanical stimuli. Importantly, 10 mM EGCG produced a significantly greater magnitude of inhibition on TG neuronal discharge frequency than 1% lidocaine (noxious, lidocaine vs. EGCG, 19.7 ± 3.3% vs. 42.3 ± 3.4%, p < 0.05). Conclusions: Local injection of EGCG into inflamed tissue effectively suppresses the excitability of nociceptive primary sensory TG neurons, as indicated by these findings. Significantly, locally administered EGCG exerted a more potent local analgesic action compared to conventional voltage-gated sodium channel blockers. This heightened efficacy originates from EGCG’s ability to inhibit both generator potentials and action potentials directly at nociceptive primary nerve terminals. As a result, EGCG stands out as a compelling candidate for novel analgesic development, holding particular relevance for CAM strategies.

Differential Actions of Ketamine on CA3-Prelimbic and CA3-Infralimbic Connection Responsivity Depend on Prior Exposure to Stress.

Differential Actions of Ketamine on CA3-Prelimbic and CA3-Infralimbic Connection Responsivity Depend on Prior Exposure to Stress.

Synchronous rhythmic activity in area V4 can impair shape detection and neuronal reliability.

Rhythms at a population level are a defining characteristic of both normal and pathological cortical activity, but it is unclear howsuch rhythms interactwith activity of specific neurons to impact task performance on a trial-by-trial basis. We address this by employing a challenging visual detection task in which male rhesus macaques must signal the presentation of a shape embedded in a noisy background. We analyzed the rhythmic activity in the local field potential (LFP) and single neuron activity in area V4, a brain area strongly implicated in shape perception, prior to such presentations and focused on two different frequency ranges: alpha/beta (10-30 Hz), in which coherence was particularly strong and spatially extensive, and gamma (50-70 Hz), which has traditionally been strongly associated with single unit activity. We find that within sessions there were periods of time during which successful detection was associated with the absence of rhythmic activity prior to shape presentation in either frequency range. During these periods, rhythmic activity in both frequency bands could predict whether the shape would be detected by the animal at the time of, as well as before, shape presentation on a trial-to-trial basis with high accuracy. Importantly, for both frequency ranges, the individual neurons carrying the most relevant information with regard to the task had the weakest coupling to the LFP rhythms. These results are consistent with spatially-distributed rhythmic activity acting as a source of decision noise in the context of rapid visual detection by reducing the moment-to-moment reliability of task-relevant information carried by individual neurons.Significance Statement Although rhythmic activity in the brain has been studied for over 100 years, its relevance to information processing remains unresolved. In this study we show for the first time that, in the context of a challenging visual detection task, rhythmic activity in local populations of neurons prior to appearance of the visual stimulus can predict mistakes on a trial-by-trial basis. Furthermore, this activity is linked to task-relevant signals at a neuronal level because the individual neurons with the weakest coupling to these rhythms are the most reliable.

Predictive goal coding by dentate gyrus somatostatin-expressing interneurons in male mice

To select appropriate behaviour, individuals must rely on encoding of relevant features within their environment in the context of current and past experiences. This function has been linked to goal-associated activity patterns of hippocampal principal cells. Using single-unit recordings from optogenetically identified somatostatin-expressing interneurons (SOMIs) in the dentate gyrus of head-fixed mice trained in a spatial goal-oriented reward-learning task in virtual realities, we show that SOMI activity temporally precedes reward-locations in expert mice characterized by goal-anticipatory behaviour. Predictive goal-encoding by SOMIs is lost after translocation of learned goals to novel previously unrewarded sites leading to rapid reductions in anticipatory behaviour and fast reconfiguration of SOMI activity to times after reward onset in association with reward consumption at novel goal-sites. Chemogenetic silencing of SOMIs caused a loss of memory that trained goal-sites were no longer available. Thus, our data reveal the ability of SOMIs to flexibly encode goal-locations depending on current and past experiences to bias behavioral outcomes.

Auditory cortex neurons that encode negative prediction errors respond to omissions of sounds in a predictable sequence.

Predictive coding posits the brain predicts incoming sensory information and signals a positive prediction error when the actual input exceeds what was predicted, and a negative prediction error when it falls short of the prediction. It is theorized that specific neurons encode the negative prediction error, distinct from those for the positive prediction error, and are linked to responses to omitted expected inputs. However, what information is actually encoded by omission responses remains unclear. This information is essential to confirm their role as negative prediction errors. Here, we record single-unit activity in the rat auditory cortex during an omission paradigm where tone probabilities are manipulated to vary the prediction content. We identify neurons that robustly respond to omissions, with responses that increase with evidence accumulation and directly correlate with tone predictability-key characteristics suggesting their role as negative prediction-error neurons. Interestingly, these neurons showed selective omission responses but broad tone responses, revealing an asymmetry in error signaling. To capture this asymmetry, we propose a circuit model composed of laterally interconnected prediction-error neurons that qualitatively reproduce the observed asymmetry. Furthermore, we demonstrate that these lateral connections enhance the precision and efficiency of prediction encoding across receptive fields, and that their validity is supported by the free energy principle.

Brain implantation of soft bioelectronics via embryonic development.

Developing bioelectronics capable of stably tracking brain-wide, single-cell, millisecond-resolved neural activity in the developing brain is critical for advancing neuroscience and understanding neurodevelopmental disorders. During development, the three-dimensional structure of the vertebrate brain arises from a two-dimensional neural plate1,2. These large morphological changes have previously posed a challenge for implantable bioelectronics to reliably track neural activity throughout brain development3-9. Here we introduce a tissue-level-soft, submicrometre-thick mesh microelectrode array that integrates into the embryonic neural plate by leveraging the tissue's natural two-dimensional-to-three-dimensional reconfiguration. As organogenesis progresses, the mesh deforms, stretches and distributes throughout the brain, seamlessly integrating with neural tissue. Immunostaining, gene expression analysis and behavioural testing confirm no adverse effects on brain development or function. This embedded electrode array enables long-term, stable mapping of how single-neuron activity and population dynamics emerge and evolve during brain development. In axolotl models, it not only records neural electrical activity during regeneration but also modulates the process through electrical stimulation.

Neural control of human inspiratory muscles. What have we learnt from the study of single motor units?

Neural control of human inspiratory muscles. What have we learnt from the study of single motor units?

Rhythmic Quakes Shock the Cortical Focus Theory.

Objectives: Absence seizures impair psychosocial function, yet their detailed neuronal basis remains unknown. Recent work in a rat model suggests that cortical arousal state changes prior to seizures and that single neurons show diverse firing patterns during seizures. Our aim was to extend these investigations to a mouse model with studies of neuronal activity and arousal state to facilitate future fundamental investigations of absence epilepsy. Methods: We performed in vivo extracellular single-unit recordings on awake head-fixed C3H/HeJ mice. Mice were implanted with tripolar electrodes for cortical electroencephalography (EEG). Extracellular single-unit recordings were obtained with glass micropipettes in the somatosensory barrel cortex, while animals ambulated freely on a running wheel. Signals were digitized and analyzed during seizures and at baseline. Results: Neuronal activity was recorded from 36 cortical neurons in 19 mice while EEG showed characteristic 7–8 Hz spike-wave discharges. Different single neurons showed distinct firing patterns during seizures, but the overall mean population neuronal firing rate during seizures was no different from preseizure baseline. However, the rhythmicity of neuronal firing during seizures was significantly increased ( p < .001). In addition, beginning 10 s prior to seizure initiation, we observed a progressive decrease in cortical high-frequency (>40 Hz) EEG and an increase in lower-frequency (1–39 Hz) activity suggesting a decreased arousal state. Significance: We found that the awake head-fixed C3H/HeJ mouse model demonstrated rhythmic neuronal firing during seizures, and a decreased cortical arousal state prior to seizure onset. Unlike the rat model, we did not observe an overall decrease in neuronal firing during seizures. Similarities and differences across species strengthen the ability to investigate fundamental key mechanisms. Future work in the mouse model will identify the molecular basis of neurons with different firing patterns, their role in seizure initiation, and behavioral deficits, with ultimate translation to human absence epilepsy.

Normalization of Prefrontal Network Dynamics Prevents Cognitive Impairments After Developmental Insult.

The neurodevelopmental period is highly sensitive; insults during this period impair neural network connectivity, causing lasting cognitive deficits associated with many neuropsychiatric disorders. Medial prefrontal cortex (mPFC) networks subserve flexible behavior, but the mechanisms underlying their disruption after developmental insults remain unclear. We used an early-life seizure (ELS) model to investigate how mPFC networks become impaired and tested whether adrenocorticotropic hormone (ACTH), a clinically relevant neuroprotective peptide, could restore network function. Using in-vivo single-unit recordings during baseline and fear extinction learning, we found ELS-induced dysfunction was characterized by reduced neuronal firing, rigid spike-timing, and weakened functional connectivity, all predicting impaired extinction learning. ACTH treatment prevented these deficits, preserving dynamic spike-timing, flexible connectivity, and network organization. Advanced graph neural network modeling identified neuronal features predictive of cognitive outcomes, revealing potential biomarkers broadly relevant to other developmental disorders. These findings highlight fundamental mechanisms of mPFC network dysfunction and emphasize the translational potential of targeting network dynamics to restore cognition in neurodevelopmental disorders.

Tactile stimulation and its impact on barrel cortex neuron receptive fields in whisker-deprived male rats.

Tactile stimulation and its impact on barrel cortex neuron receptive fields in whisker-deprived male rats.

Photoreceptor degeneration induces homeostatic rewiring of rod bipolar cells.

Photoreceptor degeneration induces homeostatic rewiring of rod bipolar cells.

A local de-insulation method and its application in neural microneedle array

Silicon-based neural microneedle arrays, such as the Utah Array, have demonstrated excellent performance in chronic recordings from the cerebral cortex. Unlike planar thin-film electrodes with recording sites arranged on the surface of a silicon film, the recording sites of microneedle arrays are located at the tips of three-dimensional needles, which significantly complicates the fabrication process required for single-neuron recordings. To address this challenge, we develop a local de-insulation method for microneedle recording electrodes that eliminates the need for etching: the microneedle tips are encapsulated in a controllable-thickness protective layer, followed by deposition of a Parylene-C insulation layer. By optimizing the elasticity of the protection material, as well as its adhesion and shape on both the protective layer and the electrode shaft, we were able to precisely control the area of the removed insulated layers, resulting in consistent tip exposure. Experimental results show that the non-uniformity of the exposed microneedle recording sites in the silicon-based neural microelectrode arrays (each has 10 × 10 array) fabricated using this method is 3.32 ± 1.02%. Furthermore, the arrays exhibited high stability and reliability in both mechanical performance and electrical characteristics. They achieved an average spike signal-to-noise ratio of 12.63 ± 6.64 during in vivo testing. This fabrication technique provides a valuable method for the development of high-performance neural microelectrode array.

A complex acoustical environment is necessary for maintenance and development in the zebra finch auditory pallium

Postnatal experience is critical to auditory development in vertebrates. The zebra finch (Taeniopygia castanotis) provides a valuable model for understanding how complex social-acoustical environments influence development of the neural circuits that support perception of vocal communication signals. We previously showed that zebra finches raised in the rich acoustical environment of a breeding colony (colony-reared, CR) perform twice as well in an operant discrimination task as birds raised with only their families (pair-reared, PR), and we identified deficits in functional properties within the auditory pallium of PR birds that could explain this behavioral difference. Here, using single-unit extracellular recordings from the L3 subdivision of field L and caudomedial nidopallium (NCM) at three developmental timepoints (18–20, 30–35, and 90–110 days post hatch), we tracked how experience affects the emergence of these functional properties. Whereas CR birds showed stable single-unit response properties from fledging to adulthood alongside improvements in population-level encoding, PR birds exhibited progressive deterioration in neural function. Deficits in PR birds began emerging at 18 days for population metrics and by 30 days for single-unit properties, worsening into adulthood. These included altered spike waveforms, firing rates, selectivity, discriminability, coding efficiency, and noise invariance. Notably, these deficits occurred despite PR birds receiving normal exposure to the song of a male tutor, suggesting that learning to sing is robust enough to compensate for impaired auditory processing. Our findings demonstrate that a complex acoustical environment is necessary for both maintenance and development of the cortical-level auditory circuits that decode conspecific vocalizations.

Subcortical correlates of consciousness with human single neuron recordings

Subcortical brain structures such as the subthalamic nucleus or the thalamus are involved in regulating motor and cognitive behavior. However, their contribution to perceptual consciousness remains unclear, due to the inherent difficulties of recording subcortical neuronal activity in humans. Here, we asked neurological patients undergoing surgery for deep brain stimulation to detect weak vibrotactile stimuli applied on their hand while recording single neuron activity from the tip of a microelectrode. We isolated putative single neurons in the subthalamic nucleus and thalamus. A significant proportion of neurons modulated their activity while participants were expecting a stimulus. We found that the firing rate of 23% of these neurons differed between detected and undetected stimuli. Our results provide direct neurophysiological evidence of the involvement of the subthalamic nucleus and the thalamus for the detection of vibrotactile stimuli, thereby calling for a less cortico-centric view of the neural correlates of consciousness.

Subcortical correlates of consciousness with human single neuron recordings.

Subcortical brain structures such as the subthalamic nucleus or the thalamus are involved in regulating motor and cognitive behavior. However, their contribution to perceptual consciousness remains unclear, due to the inherent difficulties of recording subcortical neuronal activity in humans. Here, we asked neurological patients undergoing surgery for deep brain stimulation to detect weak vibrotactile stimuli applied on their hand while recording single neuron activity from the tip of a microelectrode. We isolated putative single neurons in the subthalamic nucleus and thalamus. A significant proportion of neurons modulated their activity while participants were expecting a stimulus. We found that the firing rate of 23% of these neurons differed between detected and undetected stimuli. Our results provide direct neurophysiological evidence of the involvement of the subthalamic nucleus and the thalamus for the detection of vibrotactile stimuli, thereby calling for a less cortico-centric view of the neural correlates of consciousness.

The control of overt and covert attention across two nodes of the attention-control network.

A central theory of attention poses that the brain computes a priority map to highlight spatial locations of relevance for behavior. Here we tested the hypothesis and its key predictions about how priority signals are assembled and used, through electrophysiological single-unit recordings from two nodes of the attention control network, localized by functional magnetic resonance imaging. We found both areas to highlight locations even in the absence of a stimulus, and that each assembled spatial signals differently and provided spatial information in different forms. These findings force a revision of how and where spatial attention is controlled in the brain.

Graphene-Integrated Ultrathin Neural Probe for Multiregional Cortical Recordings.

Electrophysiological measurement techniques are essential for understanding the functions of the central and peripheral nervous systems. Specifically, noninvasive neural probes, such as surface electrode arrays, provide stable electrophysiological recordings without eliciting an immunological response. However, the ability to capture complex interactions across multiple brain regions is limited by their localized recording site. Here, we present the "large-area NeuroWeb (LNW)", an ultrathin, minimally invasive neural probe designed for extensive cortical recording and stimulation. LNW consists of four recording areas, each containing 16-channel platinum electrodes interconnected by graphene networks. In vivo experiments of the mouse brain exhibit stable, high-quality single-unit spike recordings for up to 7 days post-surgery. Simultaneous high-resolution neural activity recordings are performed across left/right somatosensory cortex and cerebellum, simplifying the experimental procedure by eliminating the necessity for multiple synchronized probes, thus reducing tissue displacement and inflammation. Furthermore, whisker and electrical stimulations demonstrate that the LNW has precise and bidirectional connections with neurons for reliable, region-specific signal acquisition and activation. These findings highlight the capability of LNW to facilitate comprehensive and accurate mapping of neuronal dynamics, thereby advancing brain-machine interfaces and neural prostheses.