Biology subjects

Szelenyi, E. R.

Publications and source records attributed to Szelenyi, E. R..

4 recordsLinked to original sources

bioRxiv2026

Subcortical recruitment dissociates isoflurane emergence from distinct wakeful states in mice

Emergence from general anesthesia, defined by a recovery of consciousness to the wakeful state, is a clinically consequential state transition that remains a passive process dependent on drug clearance. Despite the critical use of anesthesia, the neural circuitry underlying behavioral recovery remains poorly defined. Here, we map whole-brain neural activity during emergence from isoflurane anesthesia in mice using Fos immunolabeling, tissue clearing, and light-sheet microscopy. This approach enables unbiased quantification of neural activity at cellular resolution across the intact whole brain and supports subsequent network analysis. Rather than resembling wakefulness, emergence exhibits widespread cortical suppression alongside selective activation of discrete subcortical nuclei. This pattern of activity includes both previously implicated arousal-related regions and lesser-studied structures linked to respiratory, autonomic, interoceptive, and cerebellar function. By comparing emergence to two behaviorally distinct wakeful control states, we find that control state selection substantially shapes interpretation of whole-brain activity maps. This establishes dual-state comparisons as a broadly useful strategy for state-dependent circuit mapping. Functional network analysis further elucidates candidate central regions that strongly covary together during emergence, with the most integrated region being the ventral orbital cortex. This approach allows for targeted causal investigation, linking brain-wide circuit discovery with future hypothesis-driven mechanistic interrogation. Together, we find that emergence from isoflurane anesthesia reflects selective subcortical recruitment rather than broad global reactivation toward wakefulness. Significance StatementMillions of people undergo general anesthesia each year. While anesthetic unconsciousness is induced rapidly, emergence from altered consciousness is unpredictable. Neural mechanisms that underlie behavioral emergence remain poorly defined. Using whole-brain Fos mapping at cellular resolution, we found that emergence from isoflurane anesthesia is characterized by widespread cortical suppression alongside selective activation of discrete subcortical, autonomic, hindbrain, and cerebellar nuclei. This selective systems-level activity pattern identifies behavioral emergence as more than a simple global return toward wakefulness and highlights underappreciated neural circuitry involved in post-anesthetic recovery. Network analysis of the Fos maps further identifies candidate regions for targeted causal investigation of emergence-related regions.

neuroscience↗

bioRxiv2025

Brainwide genetic capture for conscious state transitions

Neural circuits underlying unconsciousness remain poorly defined. We test the hypothesis that unconsciousness arises from specific, distributed circuits using general anesthesia in mice as a reproducible model. We identify a cortical-to-subcortical shift in neural activity during isoflurane anesthesia that is organized into nine discrete functional communities mapped at single-cell resolution. The lateral parabrachial nucleus (LPB) emerges as a central hub, exhibiting high interconnectivity, spontaneous firing under anesthesia, and preferential recruitment during reactivation of the brain-wide ensemble confirmed by single unit recordings. Chemogenetic reactivation of the captured brain-wide ensemble induces sedation, slow wave oscillations, hypothermia, and analgesia, which are components of anesthesia-induced unconsciousness. Reactivation of the LPB ensemble alone recapitulates a subset of these effects. Together, we define a global neural substrate for unconsciousness and recapitulate its dissociable autonomic, neurophysiologic, and behavioral effects using brain-wide ensemble manipulations. These results establish a neural circuit framework for anesthesia-induced unconsciousness in the mammalian brain.

neuroscience↗

bioRxiv2024

Distinct dynamics and intrinsic properties in ventral tegmental area populations mediate reward association and motivation

Ventral tegmental area (VTA) dopamine neurons regulate reward-related associative learning and reward-driven motivated behaviors, but how these processes are coordinated by distinct VTA neuronal subpopulations remains unresolved. Here we examine the neural correlates of reward-related prediction-error, action, cue, and outcome encoding as well as effort exertion and reward anticipation during reward-seeking behaviors. We compare the contribution of two primarily dopaminergic and largely non-overlapping VTA subpopulations, all VTA dopamine neurons, and VTA GABAergic neurons of the mouse midbrain to these processes. The dopamine subpopulation that projects to the nucleus accumbens (NAc) core preferentially encodes prediction-error and reward-predictive cues. In contrast, the dopamine subpopulation that projects to the NAc shell preferentially encodes goal-directed actions and reflects relative reward anticipation. VTA GABA neuron activity strongly contrasts VTA dopamine population activity and preferentially encodes reward outcome and retrieval. Electrophysiology, targeted optogenetics, and whole-brain input mapping reveal heterogeneity among VTA dopamine subpopulations. Our results demonstrate that VTA subpopulations carry distinct reward-related learning and motivation signals and reveal a striking pattern of functional heterogeneity among projection-defined VTA dopamine neuron populations.

neuroscience↗

bioRxiv2023

An arginine-rich nuclear localization signal (ArgiNLS) strategy for streamlined image segmentation of single-cells

High-throughput volumetric fluorescent microscopy pipelines can spatially integrate whole-brain structure and function at the foundational level of single-cells. However, conventional fluorescent protein (FP) modifications used to discriminate single-cells possess limited efficacy or are detrimental to cellular health. Here, we introduce a synthetic and non-deleterious nuclear localization signal (NLS) tag strategy, called Arginine-rich NLS (ArgiNLS), that optimizes genetic labeling and downstream image segmentation of single-cells by restricting FP localization near-exclusively in the nucleus through a poly-arginine mechanism. A single N-terminal ArgiNLS tag provides modular nuclear restriction consistently across spectrally separate FP variants. ArgiNLS performance in vivo displays functional conservation across major cortical cell classes, and in response to both local and systemic brain wide AAV administration. Crucially, the high signal-to-noise ratio afforded by ArgiNLS enhances ML-automated segmentation of single-cells due to rapid classifier training and enrichment of labeled cell detection within 2D brain sections or 3D volumetric whole-brain image datasets, derived from both staining-amplified and native signal. This genetic strategy provides a simple and flexible basis for precise image segmentation of genetically labeled single-cells at scale and paired with behavioral procedures. Significance StatementQuantifying labeled cells in fluorescent microscopy is a fundamental aspect of modern biology. Critically, the use of short nuclear localization sequences (NLS) is a key genetic modification for discriminating single-cells labeled with fluorescent proteins (FPs). However, mainstay NLS approaches typically localize proteins to the nucleus with limited efficacy, while alternative non-NLS tag strategies can enhance efficacy at the cost of cellular health. Thus, quantitative cell counting using FP labels remains suboptimal or not compatible with health and behavior. Here, we present a novel genetic tagging strategy - named ArgiNLS - that flexibly and safely achieves FP nuclear restriction across the brain to facilitate machine learning-based segmentation of single-cells at scale, delivering a timely update to the behavioral neuroscientists toolkit.

neuroscience↗