Biology subjects

Gu, B.-M.

Publications and source records attributed to Gu, B.-M..

4 recordsLinked to original sources

bioRxiv2025

Dynamic respiration-neural coupling in substantia nigra across sleep and anesthesia

Respiration is increasingly recognized as a coordinator of neural activity across widespread brain regions and behavioral states. Even during sleep, respiration rhythms modulate sleep-related oscillations. While the basal ganglia are known to play roles in both sleep and respiratory regulation, their interaction with respiration rhythms remains poorly understood. Here, we examined respiration-neural couplings in the substantia nigra pars reticulata (SNr), a major output nucleus of the basal ganglia, and the primary motor cortex (M1) across multiple states in male and female mice, including non-rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep, quiet wakefulness, and anesthesia. Simultaneous recordings of local field potentials (LFPs) from M1 and SNr along with diaphragm muscle activities revealed state-dependent, region specific patterns of respiration-neural coupling. Coupling strength in both SNr and M1 was attenuated during NREM sleep compared to REM sleep and quiet wakefulness. However, under ketamine/xylazine anesthesia, coupling was markedly enhanced in the SNr, but not in M1, indicating region-specific sensitivity to arousal and anesthesia state. Notably, respiration-neural coupling was systematically related to delta sub-band power; coupling strength was reduced with increased slow delta (0.5-2 Hz) and decreased fast delta (2.5-4 Hz) powers. In addition, slow delta was associated with SNr-M1 synchronization, suggesting that inter-regional communication during deep sleep may suppress respiration locking. Together, these findings highlight dynamic, state- dependent modulation of respiration-neural couplings in cortico-basal ganglia circuits, underscoring its potential role in coordinating body-brain interactions during sleep and anesthesia. Significance StatementNeural oscillations are known to couple with respiration rhythms across various brain areas; however, this coupling in the SNr, a major output hub of the basal ganglia, remained unexplored. Current study fills this gap with several novel findings, revealing state-dependent coupling to respiration rhythms in the SNr and M1 of mice. In particular, the strength of respiration-neural coupling varied across multiple states, including NREM sleep, REM sleep, quiet wakefulness, and anesthesia, and was directly related to the delta power, a hallmark of NREM sleep. These findings provide new insight into how respiratory rhythms interact with cortico-basal ganglia circuits and demonstrate that coupling changes dynamically within and across multiple states.

neuroscience↗

bioRxiv2022

Altered basal ganglia output during self-restraint

Suppressing actions is essential for flexible behavior. Multiple neural circuits involved in behavioral inhibition converge upon a key basal ganglia output nucleus, the substantia nigra pars reticulata (SNr). To examine how changes in basal ganglia output contribute to self-restraint, we recorded SNr neurons during a proactive behavioral inhibition task. Rats responded to Go! cues with rapid leftward or rightward movements, but also prepared to cancel one of these movement directions on trials when a Stop! cue might occur. This action restraint - visible as direction-selective slowing of reaction times - altered both rates and patterns of SNr spiking. Overall firing rate was elevated before the Go! cue, and this effect was driven by a subpopulation of direction-selective SNr neurons. In neural state space, this corresponded to a shift away from the restrained movement. SNr neurons also showed more variable inter-spike-intervals during proactive inhibition. This corresponded to more variable state-space trajectories, which may slow reaction times via reduced preparation to move. These findings open new perspectives on how basal ganglia dynamics contribute to movement preparation and cognitive control.

neuroscience↗

bioRxiv2020

Accurate inference in parametric models reshapes neuroscientific interpretation and improves data-driven discovery

A central goal of systems neuroscience is to understand the relationships amongst constituent units in neural populations and their modulation by external factors using high-dimensional and stochastic neural recordings. Statistical models, particularly parametric models, play an instrumental role in accomplishing this goal, because their fitted parameters can provide insight into the underlying biological processes that generated the data. However, extracting conclusions from a parametric model requires that it is fit using an inference procedure capable of selecting the correct parameters and properly estimating their values. Traditional approaches to parameter inference have been shown to suffer from failures in both selection and estimation. Recent development of algorithms that ameliorate these deficiencies raises the question of whether past work relying on such inference procedures have produced inaccurate systems neuroscience models, thereby impairing their interpretation. Here, we used the Union of Intersections, a statistical inference framework capable of state-of-the-art selection and estimation performance, to fit functional coupling, encoding, and decoding models across a battery of neural datasets. We found that, compared to baseline procedures, UoI inferred models with increased sparsity, improved stability, and qualitatively different parameter distributions, while maintaining predictive performance across recording modality, brain region, and task. Specifically, we obtained highly sparse functional coupling networks with substantially different community structure, more parsimonious encoding models, and decoding models that rely on fewer single-units. Together, these results demonstrate that accurate parameter inference reshapes interpretation in diverse neuroscience contexts. The ubiquity of model-based data-driven discovery in biology suggests that analogous results would be seen in other fields.

neuroscience↗

bioRxiv2020

Globus pallidus dynamics reveal covert strategies for behavioral inhibition.

Flexible behavior requires restraint or cancellation of actions that are no longer appropriate. This behavioral inhibition critically relies on frontal cortex - basal ganglia circuits. A central node within the basal ganglia, the globus pallidus pars externa (GPe), has been hypothesized to mediate "proactive" inhibition: being prepared to stop an action if needed. Here we investigate the population dynamics of rat GPe neurons during preparation-to-stop, stopping, and going. Rats could selectively engage proactive inhibition towards one specific action, as shown by slowed reaction times (RTs) for that action. While proactive inhibition was engaged, GPe population activity occupied state-space locations farther from the trajectory followed during normal movement initiation. Furthermore, the specific state-space location was predictive of distinct types of errors: failures to stop, failures to go, and incorrect choices. The slowed RTs on correct proactive trials reflected a starting bias towards the alternative action, which was overcome before making progress towards action initiation. Our results demonstrate that rats can exert cognitive control via strategic positioning of their GPe network state.

neuroscience↗