Bo Hong

Generalizable movement decoding remains a central challenge for invasive brain–computer interfaces (BCIs), as decoders trained under limited calibration conditions often fail to generalize to unseen movement speeds, limbs, and subjects. Existing decoding methods are typically trained on paired data collected under restricted conditions. How to incorporate behavioral structure from unpaired data for robust out-of-distribution (OOD) decoding therefore remains unresolved. To address this, we propose LAND ( L atent A ligned N eural-behavioral D ynamics), a framework that aligns latent neural and behavioral dynamics through flow matching. By learning a neural-to-behavioral transport map and using behavioral-dynamics priors from unpaired data to encourage structured neural manifolds, LAND regularizes representation geometry to promote cross-domain generalization. We evaluate LAND on synthetic neural data, epidural BCI recordings from a tetraplegia participant, and multi-electrode array (MEA) recordings from nonhuman primates (NHPs). Across these settings, LAND improves zero-shot generalization to OOD movement speeds and yields speed-modulated manifolds. With limited target-domain fine-tuning, it further improves transfer across limbs and subjects. These results support flow-based neural–behavioral alignment with unpaired kinematic priors as an approach for learning transferable neural representations and robust movement decoding across behavioral and recording domains.

Burst suppression is widely observed across cortical regions during reversible or pathological unconsciousness, yet its neuronal organization remains incompletely understood. Here we present an integrated Cortex-wide Optical-electrical Dual-modal Explorer (CODE) system to examine neuronal dynamics during burst suppression under isoflurane anesthesia in the mouse. We identified distinct cortex-wide neuronal ensembles that alternately associate with burst or suppression events, exhibiting dynamic neuronal recruitment and reactivation during anesthesia. Burst events were marked by highly synchronized neuronal activity early in the burst phase with high functional connectivity, whereas suppression events displayed more asynchronous, temporally distributed activity with reduced connectivity. Transitions between these states involved sequential, directionally organized propagation across cortical regions. ECoG bursts propagated from bilateral sensory cortices to motor areas within tens of milliseconds with increasing synchrony with calcium activity. Furthermore, we established a robust metric linking ECoG and calcium signals, revealing state-dependent interpretability. These findings reveal the single-neuron-to-population architecture of burst suppression and illustrate how integrated optical–electrical measurements enable high-resolution, large-scale interrogation of cortical dynamics.

Purpose: Continuous electroencephalogram (cEEG) monitoring is an important technique used in detecting electroclinical seizures in the pediatric intensive care unit (PICU). This study developed an artificial intelligence method for the real‐time automatic detection of seizures in the PICU. Methods: We designed an artificial intelligence method to analyze EEG, electromyography (EMG), and electrocardiography (ECG) signals, detecting features from multiple dimensions, extracting candidate signal fragments in real time, and analyzing the relevant indicators of these fragments to determine whether they indicate electroclinical seizures. We tested the sensitivity and specificity of the detection system on patients with seizures who were hospitalized in the PICU of Peking University First Hospital and received cEEG monitoring. Results: A total of 28 PICU patients were collected, including 17 boys and 11 girls, with a median age of 4.30 (1.02–7.00) years. Sixteen patients had convulsive status epilepticus, of which eight were generalized and eight were focal. Twelve patients had cluster seizures, of which seven were generalized and five were focal. A total of 218.73 h of EEG data were collected from all 28 EEG records. The electroencephalography physician annotated a total of 1561 seizures, whereas the algorithm detected a total of 1095 seizures. The overall detection sensitivity was 94%, and the overall false detection rate was 0.18 (0.03–0.28)/h. There was no statistical difference in the sensitivity and false detection rate between focal and generalized seizures. Conclusion: The detection system has high sensitivity and specificity, suggesting great potential for future real‐time automatic detection of electroclinical seizures in the PICU. Plain Language Summary: We developed a computer program that helps doctors quickly detect seizures in critically ill children by analyzing brain activity, muscle movements, and heart rate signals. The system was designed specifically for the challenging environment of pediatric intensive care units, where timely seizure detection is particularly important but often difficult. The program reliably identified seizures that showed both brain activity and physical signs, matching doctors' diagnoses. This technology marks an important step toward better monitoring and care for critically ill children with seizures.

Brain–computer interface (BCI) can assist paralyzed patients in controlling external devices and improve their quality of life. However, existing intracortical BCIs entail risks of infection and challenges of long-term stability. In this study, we report on a tetraplegic patient implanted with our newly developed wireless minimally invasive BCI, NEO, in which eight Pt-Ir electrodes were placed epidurally over the hand area of the right sensorimotor cortex to record field potentials. We found that epidural neural signals from the hand area simultaneously represented both contralateral and ipsilateral movements. The spatio-spectral patterns of different movements exhibited prominent distinctions, revealing a bilateral representation structure of limb movements. Moreover, when two limb effectors (e.g., hand and elbow) moved simultaneously, their neural patterns exhibited non-additive changes relative to single movements. Based on these findings, we proposed a fine grained bilateral single/dual-movement decoding scheme for two-dimensional target control, thereby extending the degrees of freedom (DoF) of minimally invasive BCI systems and enhancing the information transfer rate (ITR). In two-dimensional center-out and web-grid tasks, the system achieved mean Fitts’ ITRs of 36.7 bpm and 30.0 bpm, respectively, with hit rates exceeding 91%. Neural recordings remained stable for over 18 months, and the decoder maintained stable performance for over 6 months without recalibration, demonstrating the safety and reliability of long-term home use.

Localized space coding and phase coding are two distinct strategies responsible, respectively, for representing abstract structure and sensory observations in neural cognitive maps. In spatial representation, localized space coding is implemented by place cells in the hippocampus (HPC), while phase coding is implemented by grid cells in the medial entorhinal cortex (MEC). Both strategies have their own advantages and disadvantages, and neither of them meets the requirement of representing space robustly and efficiently in the brain. Here, we show that through reciprocal connections between HPC and MEC, place and grid cells can complement each other to overcome their respective shortcomings. Specifically, we build a coupled network model, in which a continuous attractor neural network (CANN) with position coordinate models place cells, while multiple CANNs with phase coordinates model grid cell modules with varying spacings. The reciprocal connections between place and grid cells encode the correlation prior between the sensory cues processed by HPC and MEC, respectively. Using this model, we show that: 1) place and grid cells interact to integrate sensory cues in a Bayesian manner; 2) place cells complement grid cells in coding accuracy by eliminating non-local errors of the latter; 3) grid cells complement place cells in coding efficiency by enlarging the number of environmental maps stored stably by the latter. We demonstrate that the coupled network model explains the seemingly contradictory experimental findings about the remapping phenomena of place cells when grid cells are either inactivated or depolarized. This study gives us insight into understanding how the brain employs collaborative localized and phase coding to realize both robust and efficient information representation.

Whole brain neural oscillation activities exhibit multiple wave phase patterns and seem to be supported by the common circuit network structure. We proposed a Hopfield Kuramoto model based entirely on heterogeneous connectivity strength rather than phase delay. Multiple wave phase patterns can be encoded in heterogeneous connectivity networks via Hebbian rule and retrieved as attractors. We systematically investigated how the model dynamic landscape influenced by attractors and their corresponding eigenvalues, as well as how to control the stability of equilibrium points and the occurrence of high dimensional bifurcations. This framework enables us to reproduce the dominant wave activity components in human brain functional MRI signal, and provides a canonical model for the multi-body physical system spatio-temporal pattern attractor dynamics.