Biology subjects

McGinley, M.

Publications and source records attributed to McGinley, M..

3 recordsLinked to original sources

bioRxiv2025

Learning and language in the unconscious human hippocampus

Consciousness is a fundamental component of cognition,1 but the degree to which higher-order pattern recognition relies on it remains disputed.2,3 Here we demonstrate the persistence of oddball discrimination, semantic processing, and online prediction in individuals under general anesthesia-induced loss of consciousness.4,5 Using high-density Neuropixels microelectrodes6 to record both single unit and local field potential neural activity in the human hippocampus while playing a series of tones to anesthetized patients, we found that hippocampal neurons and local oscillations retained some detection of oddball tones. This effect size grew over the course of the experiment ([~]10 minutes), demonstrating representational plasticity. A biologically plausible recurrent neural network model showed that learning and oddball representation are an emergent property of flexible tone discrimination. Moreover, when we played language stimuli, single units and local field potentials carried information about the semantic and grammatical features of natural speech, even predicting semantic information about upcoming words. Together these results indicate that in the hippocampus, which is anatomically and functionally distant from primary sensory cortices,7 complex processing of sensory stimuli occurs even in the unconscious state.

neuroscience↗

bioRxiv2024

Leveraging clinical sleep data across multiple pediatric cohorts for insights into neurodevelopment: the Retrospective Analysis of Sleep in Pediatric (RASP) cohorts study

Sleep disturbances are prominent across neurodevelopmental disorders (NDDs) and may reflect specific abnormalities in brain development and function. Overnight polysomnography (PSG) allows for detailed investigation of sleep architecture, offering a unique window into neurocircuit function. A better understanding of sleep in NDDs compared to typically developing children could therefore define mechanisms underlying abnormal development in NDDs and provide avenues for the development of therapeutic interventions to improve sleep quality and developmental outcomes. Here, we introduce and characterize a collection of 1527 pediatric overnight PSGs across five different sites. We first developed an automated stager trained on independent pediatric sleep data, which yielded better performance compared to a stager trained on adults. Using consistent staging across cohorts, we derived a panel of EEG micro-architectural features. This unbiased approach replicated broad trajectories previously described in typically developing sleep architecture. Further, we found sleep architecture disruptions in children with Downs Syndrome (DS) that were consistent across independent cohorts. Finally, we built and evaluated a model to predict age from sleep EEG metrics, which recapitulated our previous findings of younger predicted brain age in children with DS. Altogether, by creating a resource pooled from existing clinical data we expanded the available datasets and computational resources to study sleep in pediatric populations, specifically towards a better understanding of sleep in NDDs. This Retrospective Analysis of Sleep in Pediatric (RASP) cohorts dataset, including staging annotation derived from our automated stager, will be deposited at https://sleepdata.org. Statement of significanceWe introduce the RASP cohorts, a collection of 1527 clinical pediatric overnight polysomnographies that includes typically developing and neurodevelopmental disorder cases. As a first step towards addressing the analytic bottleneck inherent in manual sleep staging, we developed and validated a pediatric-specific sleep stager. Leveraging the retrospective RASP cohorts dataset, we redemonstrated known developmental trajectories in sleep architecture. To summarize changes in brain function reflected in sleep, we developed a model to predict brain age from sleep measures. We recapitulate younger predicted age in RASP Downs Syndrome cases. This work not only enhances our understanding of sleep disturbances in NDDs, but also provides a valuable resource for future research and underscores the utility of existing clinical polysomnography studies.

neuroscience↗

bioRxiv2024

Foraging Under Uncertainty Follows the Marginal Value Theorem with Bayesian Updating of Environment Representations

Foraging theory has been a remarkably successful approach to understanding the behavior of animals in many contexts. In patch-based foraging contexts, the marginal value theorem (MVT) shows that the optimal strategy is to leave a patch when the marginal rate of return declines to the average for the environment. However, the MVT is only valid in deterministic environments whose statistics are known to the forager; naturalistic environments seldom meet these strict requirements. As a result, the strategies used by foragers in naturalistic environments must be empirically investigated. We developed a novel behavioral task and a corresponding computational framework for studying patch-leaving decisions in head-fixed and freely moving mice. We varied between-patch travel time, as well as within-patch reward depletion rate, both deterministically and stochastically. We found that mice adopt patch residence times in a manner consistent with the MVT and not explainable by simple ethologically motivated heuristic strategies. Critically, behavior was best accounted for by a modified form of the MVT wherein environment representations were updated based on local variations in reward timing, captured by a Bayesian estimator and dynamic prior. Thus, we show that mice can strategically attend to, learn from, and exploit task structure on multiple timescales simultaneously, thereby efficiently foraging in volatile environments. The results provide a foundation for applying the systems neuroscience toolkit in freely moving and head-fixed mice to understand the neural basis of foraging under uncertainty.

neuroscience↗