A bird's-eye view of brain activity in socially interacting mice through mobile edge computing (MEC)

Social context modulates multibrain broadband dynamics and functional brain-to-brain coupling in the group of mice

Although mice are social, multiple animals' neural activities are rarely explored. To characterise the neural activities during multi-brain interaction, we simultaneously recorded local field potentials (LFP) in the prefrontal cortex of four mice. The social context and locomotive states predominately modulated the entire LFP structure. The power of lower frequency bands-delta to alpha-were correlated with each other and anti-correlated with gamma power. The high-to-low-power ratio (HLR) provided a useful measure to understand LFP changes along the change of behavioural and locomotive states. The HLR during huddled conditions was lower than that during non-huddled conditions, dividing the social context into two. Multi-brain analyses of HLR indicated that the mice in the group displayed high cross-correlation. The mice in the group often showed unilateral precedence of HLR by Granger causality analysis, possibly comprising a hierarchical social structure. Overall, this study shows the importance of the social environment in brain dynamics and emphasises the simultaneous multi-brain recordings in social neuroscience.

Simultaneous two-photon imaging and wireless EEG recording in mice

Background

In vivo two-photon imaging is a reliable method with high spatial resolution that allows observation of individual neuron and dendritic activity longitudinally. Neurons in local brain regions can be influenced by global brain states such as levels of arousal and attention that change over relatively short time scales, such as minutes. As such, the scientific rigor of investigating regional neuronal activities could be enhanced by considering the global brain state.

New method

In order to assess the global brain state during in vivo two-photon imaging, CBRAIN (collective brain research platform aided by illuminating neural activity), a wireless EEG collecting and labeling device, was controlled by the same computer of two-photon microscope. In an experiment to explore neuronal responses to isoflurane anesthesia through two-photon imaging, we investigated whether the response of individual cells correlated with concurrent EEG changes induced by anesthesia.

Results

In two-photon imaging, calcium activities of the excitatory neurons in the primary somatosensory cortex disappeared in about 30s after to the initiation of isoflurane anesthesia. The simultaneously recorded EEG showed various transitional activity for about 7 min from the initiation of anesthesia and continued with burst and suppression alternating pattern thereafter. As such, there was a dissociation between excitatory neuron activity of the primary somatosensory cortex and the global brain activity under anesthesia.

Comparison with existing methods

Existing methods to combine two-photon and EEG recording used wired EEG recording. In this study, wireless EEG was used in conjunction with two-photon imaging, facilitated by CBRAIN. More importantly, built-in algorithms of the CBRAIN can automatically detect brain state such as sleep. The codes used for EEG classification are easy to use, with no prior experience required.

Conclusion

Simultaneous recording of wireless EEG and two-photon imaging provides a practical way to capture individual neuronal activities with respect to global brain state in an experimental set-up.

The endocannabinoid N-arachidonoyl dopamine is critical for hyperalgesia induced by chronic sleep disruption

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.

Dynamic switching of neural oscillations in the prefrontal-amygdala circuit for naturalistic freeze-or-flight

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.

A guide towards optimal detection of transient oscillatory bursts with unknown parameters

Objectives. Recent event-based analyses of transient neural activities have characterized the oscillatory bursts as a neural signature that bridges dynamic neural states to cognition and behaviors. Following this insight, our study aimed to (1) compare the efficacy of common burst detection algorithms under varying signal-to-noise ratios and event durations using synthetic signals and (2) establish a strategic guideline for selecting the optimal algorithm for real datasets with undefined properties.Approach.We tested the robustness of burst detection algorithms using a simulation dataset comprising bursts of multiple frequencies. To systematically assess their performance, we used a metric called 'detection confidence', quantifying classification accuracy and temporal precision in a balanced manner. Given that burst properties in empirical data are often unknown in advance, we then proposed a selection rule to identify an optimal algorithm for a given dataset and validated its application on local field potentials of basolateral amygdala recorded from male mice (n=8) exposed to a natural threat.Main Results.Our simulation-based evaluation demonstrated that burst detection is contingent upon event duration, whereas accurately pinpointing burst onsets is more susceptible to noise level. For real data, the algorithm chosen based on the selection rule exhibited superior detection and temporal accuracy, although its statistical significance differed across frequency bands. Notably, the algorithm chosen by human visual screening differed from the one recommended by the rule, implying a potential misalignment between human priors and mathematical assumptions of the algorithms.Significance.Therefore, our findings underscore that the precise detection of transient bursts is fundamentally influenced by the chosen algorithm. The proposed algorithm-selection rule suggests a potentially viable solution, while also emphasizing the inherent limitations originating from algorithmic design and volatile performances across datasets. Consequently, this study cautions against relying solely on heuristic-based approaches, advocating for a careful algorithm selection in burst detection studies.

Social Synchronization of Conditioned Fear in Mice Requires Ventral Hippocampus Input to the Amygdala

BACKGROUND

Social organisms synchronize behaviors as an evolutionary-conserved means of thriving. Synchronization under threat, in particular, benefits survival and occurs across species, including humans, but the underlying mechanisms remain unknown because of the scarcity of relevant animal models. Here, we developed a rodent paradigm in which mice synchronized a classically conditioned fear response and identified an underlying neuronal circuit.

METHODS

Male and female mice were trained individually using auditory fear conditioning and then tested 24 hours later as dyads while allowing unrestricted social interaction during exposure to the conditioned stimulus under visible or infrared illumination to eliminate visual cues. The synchronization of the immobility or freezing bouts was quantified by calculating the effect size Cohen's d for the difference between the actual freezing time overlap and the overlap by chance. The inactivation of the dorsomedial prefrontal cortex, dorsal hippocampus, or ventral hippocampus was achieved by local infusions of muscimol. The chemogenetic disconnection of the hippocampus-amygdala pathway was performed by expressing hM4D(Gi) in the ventral hippocampal neurons and infusing clozapine N-oxide in the amygdala.

RESULTS

Mice synchronized cued but not contextual fear. It was higher in males than in females and attenuated in the absence of visible light. Inactivation of the ventral but not dorsal hippocampus or dorsomedial prefrontal cortex abolished fear synchronization. Finally, the disconnection of the hippocampus-amygdala pathway diminished fear synchronization.

CONCLUSIONS

Mice synchronize expression of conditioned fear relying on the ventral hippocampus-amygdala pathway, suggesting that the hippocampus transmits social information to the amygdala to synchronize threat response.