Jee Hyun Choi

Oxytocin modulates social information processing by altering excitatory-inhibitory balance at the microcircuit level, but how such local modulation gives rise to selective processing at the level of distributed brain systems remains unclear. Here, we investigated the effects of oxytocin on large-scale neurodynamics across cortico-limbic network in the mouse brain using multisite local field potential recordings. Oxytocin selectively enhanced neural responses to infant calls in the auditory cortex (AC) and medial prefrontal cortex (mPFC). These enhancements occurred while baseline activity was reduced, indicating increased signal-to-noise ratio rather than a global increase in excitability. During auditory steady-state responses (ASSRs), oxytocin increased prefrontal phase coherence without altering ASSR power. During rest, oxytocin induced a transient, broadband reduction in spontaneous spectral power across regions. Despite this reduction in activity, analyses of interregional interactions revealed a selective increase in low-theta phase coupling and directional connectivity of AC→mPFC. Session-level analyses showed that stronger bottom–up AC→mPFC coupling was associated with lower prefrontal power, consistent with a gating or disinhibitory network regime favoring sensory-to-prefrontal information transfer. Multivariate analyses showed that oxytocin/saline conditions were reliably discriminable using supervised classification models, with specific contributions from spectral power, phase-locking, and Granger-causal connectivity features. Conversely, unsupervised dimensionality reduction did not identify a distinct low-dimensional manifold separating conditions, although a modest shift in the centroid of neural state space was observed. Together, these results indicate that oxytocin reduces background neural activity while selectively enhancing sensory–prefrontal network interactions, providing a systems-level account linking local inhibitory modulation to selective processing of socially salient infant cues.

Adaptive brain function requires communication that is selective in both space and time, yet the circuit mechanisms that transiently favor specific routes of information transfer remain unclear. Here we show that, when coupled, spiking populations with distinct inhibitory timescales can reprogram one another’s intrinsic burst preferences, generating irregular multi-frequency bursts that self-organize into Multi-Frequency Oscillatory Patterns (MFOPs). State-resolved transfer entropy revealed that different MFOPs were associated with distinct Information Routing Patterns (IRPs), defining transient windows of enhanced directed transfer with specific delays and directions. At the microscopic level, the same oscillatory states selectively increased or decreased delayed spike transmission across monosynaptic pathways, yielding transmission barcodes that significantly resembled the corresponding IRPs. This repertoire was markedly reduced when inhibitory diversity was removed. These results identify coordinated multi-frequency bursting as a mechanism by which recurrent circuits can dynamically filter inputs according to source and latency.

Neural oscillations at distinct frequency bands facilitate communication within and between neural populations. While single-frequency oscillations are well-characterized, the simultaneous emergence of slow (beta) and fast (gamma) oscillations within the same network remains unclear. Here, we demonstrate that multi-frequency oscillations naturally arise when the ratio of inhibitory-to-excitatory synaptic strength falls within a specific regime using a biologically plausible Izhikevich model. We show that this regime maximizes both information capacity and transmission efficiency, suggesting an optimal balance for neural communication. Deviations from this range lead to single-frequency oscillations and reduced communication efficiency, mirroring disruptions observed in neurological disorders. These findings provide mechanistic insight into how the brain leverages multiple oscillatory frequencies for efficient information processing and suggest a potential biomarker for impaired neural communication.

Neural oscillations at distinct frequency bands facilitate communication within and between neural populations. While single-frequency oscillations are well-characterized, the simultaneous emergence of slow (beta) and fast (gamma) oscillations within the same network remains unclear. Here, we demon-strate that multi-frequency oscillations naturally arise when the ratio of inhibitory-to-excitatory synaptic strength falls within a specific regime using a biologically plausible Izhikevich model. We show that this regime maximizes both information capacity and transmission efficiency, suggesting an optimal balance for neural communication. Deviations from this range lead to single-frequency oscillations and reduced communication efficiency, mirroring disruptions observed in neurological disorders. These findings provide mechanistic insight into how the brain leverages multiple oscillatory frequencies for efficient information processing and suggest a potential biomarker for impaired neural communication. SIGNIFICANCE STATEMENT Beta (slow) and gamma (fast) oscillations often coexist in the brain, yet their origin and functional role remain unclear. Our study reveals that the inhibitory-to-excitatory synaptic strength ratio governs the emergence of this multifrequency state. Furthermore, we demonstrate that information capacity and transmission efficiency are maximized in this regime, leading to significantly enhanced neural communication. These findings provide mechanistic insight into how multiple oscillatory frequencies support efficient brain function and offer a potential framework for understanding disruptions in neural communication associated with neurological disorders.

We investigated how neural dynamics support role differentiation in group foraging by mice cohabiting with a predator-like spider robot serving as a mobile food platform. While solitary foraging behaviors were consistent among mice, group foraging revealed distinct roles–working, participating, and freeriding–that solidified over time, leading to an imbalance (Gini = 0.5) across five groups. Recordings from the medial prefrontal cortex (mPFC), nucleus accumbens (NAc), and basolateral amygdala (BLA) revealed enhanced beta power in active foragers. Workers exhibited a stronger fast beta drive from mPFC to BLA compared to participants, and role prediction accuracy based on spectral features improved over trials. Our findings highlight how neural dynamics underlie the emergence of distinct roles in collective action by mice. One-Sentence Summary: Distinct foraging roles emerge in group-housed mice, driven by fast beta (24–32 Hz) activity in the mPFC-NAc-BLA circuit.

Responding to threats in the real world demands a sophisticated orchestration of freeze and flight behaviors dynamically modulated by the neural activity. While the medial prefrontal cortex-basolateral amygdala (mPFC-BLA) network is known to play a pivotal role in coordinating these responses, the mechanisms underlying its population dynamics remain vague. As traditional Pavlovian fear conditioning models fall short in encapsulating the breadth of natural escape behaviors, we introduce a novel dataset to bridge this gap, capturing the defensive strategies of mice against a spider robot in a natural-like environment. The adaptive escape behaviors and concurrent mPFC-BLA activity in eight mice were monitored using wireless local field potential (LFP) and video recordings, both individually and in groups. Our data offers a unique avenue to explore the neural dynamics that govern fear- and vigilance-induced threat responses in isolated and social contexts. Supplemented by detailed methodologies and validation, the dataset allows for the analysis of the transient neural oscillatory dynamics, with prospective implications for the fields of neuroscience, robotics, and artificial intelligence.

Chronic pain is highly prevalent and is linked to a broad range of comorbidities, including sleep disorders. Epidemiological and clinical evidence suggests that chronic sleep disruption (CSD) leads to heightened pain sensitivity, referred to as CSD-induced hyperalgesia. However, the underlying mechanisms are unclear. The thalamic reticular nucleus (TRN) has unique integrative functions in sensory processing, attention/arousal and sleep spindle generation. We report that the TRN played an important role in CSD-induced hyperalgesia in mice, through its projections to the ventroposterior region of the thalamus. Metabolomics revealed that the level of N-arachidonoyl dopamine (NADA), an endocannabinoid, was decreased in the TRN after CSD. Using a recently developed CB1 receptor (cannabinoid receptor 1) activity sensor with spatiotemporal resolution, CB1 receptor activity in the TRN was found to be decreased after CSD. Moreover, CSD-induced hyperalgesia was attenuated by local NADA administration to the TRN. Taken together, these results suggest that TRN NADA signaling is critical for CSD-induced hyperalgesia.

The medial prefrontal cortex (mPFC) and basolateral amygdala (BLA) are involved in the regulation of defensive behavior under threat, but their engagement in flexible behavior shifts remains unclear. Here, we report the oscillatory activities of mPFC–BLA circuit in reaction to a naturalistic threat, created by a predatory robot in mice. Specifically, we found dynamic frequency tuning among two different theta rhythms (~5 or ~10 Hz) was accompanied by agile changes of two different defensive behaviors (freeze-or-flight). By analyzing flight trajectories, we also found that high beta (~30 Hz) is engaged in the top–down process for goal-directed flights and accompanied by a reduction in fast gamma (60 to 120 Hz, peak near 70 Hz). The elevated beta nested the fast gamma activity by its phase more strongly. Our results suggest that the mPFC–BLA circuit has a potential role in oscillatory gear shifting allowing flexible information routing for behavior switches.