Biology subjects

Blount, C.

Publications and source records attributed to Blount, C..

5 recordsLinked to original sources

bioRxiv2026

A widespread internal brain state for fentanyl withdrawal

Opioid addiction is characterized by escalating drug use, driven in part by negative reinforcement from withdrawal, but the neural processes linking withdrawal to increased drug-taking remain poorly understood. Here, we use multisite local field potential recordings and interpretable machine learning to identify large-scale brain networks engaged by repeated opioid exposure and withdrawal. After discovering that repeated fentanyl exposure induces a progressively ramping network of widespread high beta and low gamma oscillations, we then identified a distinct brain network that selectively encodes the emergence and severity of opioid withdrawal. This network, termed EN-Withdrawal, is characterized by regional gamma oscillations and widely synchronized delta/theta oscillations. Its activity patterns predict the emergence of spontaneous and naloxone-precipitated withdrawal across multiple independent cohorts, generalizing across mice, sex, opioids, and dosing regimens, while persisting over multiple days of withdrawal. Using a novel, data-driven severity index, we find that network activity scales with individual behavioral severity without simply reflecting ongoing somatic behaviors or general aversion, suggesting that EN-Withdrawal underlies a withdrawal-induced internal state. Strikingly, network activity predicts the escalation of fentanyl self-administration on a mouse-by-mouse basis in experienced, but not drug-naive, animals. These findings reveal a neurophysiological substrate of the negative reinforcement cycle of addiction that shapes individual vulnerability.

neuroscience↗

bioRxiv2020

Prenatal Environmental Stressors Impair Postnatal Microglia Function and Adult Behavior in Males

Gestational exposure to environmental toxins and socioeconomic stressors are epidemiologically linked to neurodevelopmental disorders with strong male-bias, such as autism. We modeled these prenatal risk factors in mice, by co-exposing pregnant dams to an environmental pollutant and limited-resource stress, which robustly activated the maternal immune system. Only male offspring displayed long-lasting behavioral abnormalities and alterations in the activity of brain networks encoding social interactions. Cellularly, prenatal stressors diminished microglial function within the anterior cingulate cortex, a central node of the social coding network, in males during early postnatal development. Genetic ablation of microglia during the same critical period mimicked the impact of prenatal stressors on a male-specific behavior, indicating that environmental stressors alter neural circuit formation in males via impairing microglia function during development.

neuroscience↗

bioRxiv2020

Brain-wide electrical dynamics encode an appetitive socioemotional state

Many cortical and subcortical regions contribute to complex social behavior; nevertheless, the network level architecture whereby the brain integrates this information to encode appetitive socioemotional behavior remains unknown. Here we measure electrical activity from eight brain regions as mice engage in a social preference assay. We then use machine learning to discover an explainable brain network that encodes the extent to which mice chose to engage another mouse. This socioemotional network is organized by theta oscillations leading from prelimbic cortex and amygdala that converge on ventral tegmental area, and network activity is synchronized with brain-wide cellular firing. The network generalizes, on a mouse-by-mouse basis, to encode socioemotional behaviors in healthy animals, but fails to encode an appetitive socioemotional state in a high confidence genetic mouse model of autism. Thus, our findings reveal the architecture whereby the brain integrates spatially distributed activity across timescales to encode an appetitive socioemotional brain state in health and disease.

neuroscience↗

bioRxiv2019

Brain-wide electrical spatiotemporal dynamics encode reward anticipation

Anticipation of an upcoming stimulus induces neural activity across cortical and subcortical regions and influences subsequent behavior. Nevertheless, the network mechanism whereby the brain integrates this information to signal the anticipation of rewards remains relatively unexplored. Here we employ multi-circuit electrical recordings from six brain regions as mice perform a sample-to-match task in which reward anticipation is operationalized as their progress towards obtaining a potential reward. We then use machine learning to discover the naturally occurring network patterns that integrate this neural activity across timescales. Only one of the networks that we uncovered signals responses linked to reward anticipation, specifically relative proximity and reward magnitude. Activity in this Electome (electrical functional connectivity) network is dominated by theta oscillations leading from prelimbic cortex and striatum that converge on ventral tegmental area, and by beta oscillations leading from striatum that converge on prelimbic cortex. Network activity is also synchronized with brain-wide cellular firing. Critically, this network generalizes to new groups of healthy mice, as well as a mouse line that models aberrant neural circuitry observed in brain disorders that show altered reward anticipation. Thus, our findings reveal the network-level architecture whereby the brain integrates spatially distributed activity across timescales to signal reward anticipation.

neuroscience↗

bioRxiv2017

A convergent depression vulnerability pathway encoded by emergent spatiotemporal dynamics

Fluctuations in brain local field potential (LFP) oscillations reflect emergent network-level signals that mediate behavior. Cracking the code whereby these LFP oscillations coordinate in time and space (spatiotemporal dynamics) to represent complex behaviors would provide fundamental insights into how the brain signals emotional processes at the network level. Here we use machine learning to integrate LFP activity acquired concurrently from seven cortical and subcortical brain regions into an analytical model that predicts the emergence of depression-related behavioral dysfunction across individual mice subjected to chronic social defeat stress. We uncover a spatiotemporal dynamic network in which activity originates in prefrontal cortex (PFC) and nucleus accumbens (NAc, ventral striatum), relays through amygdala and ventral tegmental area (VTA), and converges in ventral hippocampus (VHip). The activity of this network correlates with acute threat responses and brain-wide cellular firing, and it is enhanced in three independent molecular-, physiological-, and behavioral-based models of depression vulnerability. Finally, we use two antidepressant manipulations to demonstrate that this vulnerability network is biologically distinct from the networks that signal behavioral dysfunction after stress. Thus, corticostriatal to VHip-directed spatiotemporal dynamics organized at the network level are a novel convergent depression vulnerability pathway.

neuroscience↗