Biology subjects

Hai, A.

Publications and source records attributed to Hai, A..

7 recordsLinked to original sources

bioRxiv2026

Acute DOI exposure drives cortical hyperexcitability and functional network remodeling

Serotonergic psychoplastogens can produce durable cortical remodeling, but how a brief exposure to the 5-HT2A agonist 2,5-dimethoxy-4-iodoamphetamine (DOI) reshapes population activity and functional connectivity remains unclear. We recorded primary rat cortical cultures on spatially defined microelectrode arrays before and after acute DOI exposure using a within-culture repeated-measures design, with a separate ketanserin + DOI arm to probe 5-HT2A receptor involvement. Network activity was summarized from spikes, bursts, and functional connectivity estimated with Pearson cross-correlation and the rate-corrected spike-time tiling coefficient. Following DOI exposure, mean firing rate increased across all six wells, burst timing accelerated, and functional-network metrics showed a convergent but sensitivity-limited shift toward shorter characteristic path length. Ketanserin + DOI exposure reduced population bursting and prolonged inter-burst intervals, while descriptive path-length shortening persisted. Path-length shortening persisted under the rate-corrected STTC estimator, suggesting that the connectivity shift was not simply a firing-rate artifact. These findings show that acute DOI exposure can move dissociated cortical cultures into a hyperexcitable population state with altered functional-network dynamics. Multiplexed cortical network recordings therefore provide a tractable bridge between molecular psychoplastogen biology and systems-level circuit outcomes relevant to durable therapeutic plasticity. HighlightsO_LIMEA recordings reveal post-acute DOI responses in cortical cultures C_LIO_LIDOI increases spontaneous firing and accelerates burst timing C_LIO_LIDOI shifts functional network structure toward integration C_LIO_LIKetanserin + DOI suppresses population bursting while preserving path-length shortening C_LI

neuroscience↗

bioRxiv2026

Pharmacological recruitment of a VTA glutamatergic arousal circuit by the natural product BN3 drives broad-spectrum emergence from general anesthesia

Pharmacological control of emergence from general anesthesia remains strikingly limited. Clinically available antagonists are largely drug-class specific, leaving recovery dependent on anesthetic clearance rather than to an actively programmable switch in brain state. Through phenotypic screening of a structurally diverse natural-product library, we identified BN3 (L-(-)-camphor) as a broad-spectrum emergence agent. BN3 robustly shortened recovery from propofol anesthesia across species--from mice and rats to rabbits, dogs, and rhesus monkeys--and remained effective after continuous infusion. In rodents, BN3 also accelerated emergence from mechanistically distinct anesthetics, including etomidate, remimazolam, sodium {gamma} - hydroxybutyrate, midazolam, ketamine, sodium pentobarbital, and isoflurane. EEG recordings revealed a rapid cortical shift from slow-wave dominance to a wake-like high-frequency spectrum. Mechanistically, BN3 preferentially recruited ventral tegmental area (VTA) glutamatergic neurons: chemogenetic silencing abolished the BN3-evoked EEG spectral shift and strongly attenuated behavioral acceleration of emergence, whereas optogenetic activation of these neurons was sufficient to promote recovery. BN3 increased glutamate release in the nucleus accumbens and medial prefrontal cortex, and projection-specific inhibition of VTA[->]NAc or VTA[->]mPFC outputs blunted BN3-induced emergence. These findings define a VTA-centered glutamatergic arousal circuit that can be pharmacologically recruited as a common gateway for anesthetic-independent emergence.

neuroscience↗

bioRxiv2025

TI-Toolbox: An Open-Source Software for Temporal Interference Stimulation Research

BackgroundTemporal interference stimulation is a novel non-invasive brain stimulation approach that promises selective targeting of deep brain structures while minimizing off-target cortical stimulation. Despite a growing interest in temporal interference applications, there is a need for integrated computational tools that seamlessly connect neuroimaging data preprocessing through montage optimization, field simulation, and analysis within a unified framework designed for translational and clinical research. MethodsWe developed TI-Toolbox, an open-source software platform that integrates established neuroimaging tools (dcm2niix, SimNIBS, FreeSurfer) with specialized algorithms for temporal interference research. The platform provides end-to-end workflows encompassing structural MRI preprocessing, volume conduction modeling, montage optimization, electric field simulation, and region-of-interest analysis. Both graphical user interface and command-line interface implementations ensure accessibility across user expertise levels. The platform employs containerized deployment via Docker to ensure reproducibility and cross-platform compatibility. ResultsTI-Toolbox successfully automates the complete temporal interference research pipeline, from DICOM conversion through final field analysis. The platform demonstrates robust performance across operating systems and provides standardized workflows that enhance reproducibility. Furthermore, our case studies support the validity of our HD-EEG mapping approach for montage standardization and the need for individualized modeling for exposure assessment. ConclusionsTI-Toolbox addresses critical infrastructure gaps in temporal interference research by providing researchers with a unified, validated platform that reduces technical barriers and accelerates translational research in non-invasive deep brain stimulation. HighlightsO_LIOpen-source platform unifying TI stimulation workflow end-to-end C_LIO_LIDocker deployment ensures reproducibility across operating systems C_LIO_LIHD-EEG electrode mapping preserves field characteristics C_LIO_LIStandardized model is sufficient for montage optimization C_LIO_LIAnatomical differences explain >40% of inter-individual variability C_LI

neuroscience↗

bioRxiv2025

A Semi-Automated MEA Spike sorting (SAMS) method for high throughput assessment of cultured neurons

Neurons derived from human pluripotent stem cells (hPSCs) are valuable models for studying brain development and developing therapies for brain disorders. Evaluating human-derived neurons requires assessing their electrical activity, which can be achieved using multi-electrode arrays (MEAs) for extracellular recordings. Because each electrode channel generally detects activity from multiple neurons, resolving the activity of single neurons requires a process called spike sorting. However, currently available spike sorting methods are not optimized for the analysis of hPSC-derived neurons, and require complex workflows and time-consuming manual intervention. Here, we introduce a Semi-Automated MEA Spike sorting software (SAMS) designed specifically for low-density MEA recordings of cultured neurons. SAMS outperforms commercially available automated spike sorting algorithms in terms of accuracy and greatly reduces computational and human processing time. By providing an accessible, efficient, and integrated platform for spike sorting, SAMS enhances the resolution and utility of MEA in disease modeling and drug development using human-derived neurons. HighlightsO_LISAMS is designed and optimized for high throughput analysis of hPSC-derived neurons. C_LIO_LISAMS is more efficient and accurate compared to recommended spike-sorting software. C_LIO_LISAMS resolves phenotypic differences previously not observed without spike sorting. C_LIO_LISAMS is an open-source software. C_LI

neuroscience↗

bioRxiv2023

Wireless in vivo Recording of Cortical Activity by an Ion-Sensitive Field Effect Transistor

Wireless brain technologies are empowering basic neuroscience and clinical neurology by offering new platforms that minimize invasiveness and refine possibilities during electrophysiological recording and stimulation. Despite their advantages, most systems require on-board power supply and sizeable transmission circuitry, enforcing a lower bound for miniaturization. Designing new minimalistic architectures that can efficiently sense neurophysiological events will open the door to standalone microscale sensors and minimally invasive delivery of multiple sensors. Here we present a circuit for sensing ionic fluctuations in the brain by an ion-sensitive field effect transistor that detunes a single radiofrequency resonator in parallel. We establish sensitivity of the sensor by electromagnetic analysis and quantify response to ionic fluctuations in vitro. We validate this new architecture in vivo during hindpaw stimulation in rodents and verify correlation with local field potential recordings. This new approach can be implemented as an integrated circuit for wireless in situ recording of brain electrophysiology.

neuroscience↗

bioRxiv2022

Inference of Presynaptic Connectivity from Temporally Blurry Spike Trains by Supervised Learning

Reconstruction of neural network connectivity is a central focus of neuroscience. The ability to use neuronal connection information to predict activity at single unit resolution and decipher its effect on whole systems can provide critical information about behavior and cognitive processing. Neuronal sensing modalities come in varying forms, but there is yet to exist a modality that can deliver readouts that sufficiently address the spatiotemporal constraints of biological nervous systems. This necessitates supplementary approaches that rely on mathematical models to mitigate physical limitations and decode network features. Here, we introduce a simple proof-of-concept model that addresses temporal constraints by reconstructing presynaptic connections from temporally blurry data. We use a variation of the perceptron algorithm to process firing rate information at multiple time constraints for a heterogenous feed-forward network of excitatory, inhibitory, and unconnected presynaptic units. We evaluate the performance of the algorithm under these conditions and determine the optimal learning rate, firing rate, and the ability to reconstruct single unit spikes for a given degree of temporal blur. We then test our method on a physiologically relevant configuration by sampling network subpopulations of leaky integrate-and-fire neuronal models displaying bursting firing patterns and find comparable learning rates for optimized reconstruction of network connectivity. Our method provides a recipe for reverse engineering neural networks based on limited data quality that can be extended to more complicated readouts and connectivity distributions relevant to multiple brain circuits.

neuroscience↗

bioRxiv2021

Stimulation-mediated reverse engineering of silent neural networks

Reconstructing connectivity of neuronal networks from single cell activity is essential to understanding brain function, but the challenge of deciphering connections from populations of silent neurons has been largely unmet. We demonstrate a protocol for deriving connectivity of realistic silent neuronal networks using stimulation combined with a supervised learning algorithm, that enables inferring connection weights with high fidelity and predicting spike trains at the single-spike and single-cell level with high accuracy. These testable predictions about the number and protocol of the required stimulations is expected to enhance future efforts for deriving neuronal connectivity and drive new experiments to better understand brain function.

neuroscience↗