Biology subjects

Adamson, E.

Publications and source records attributed to Adamson, E..

5 recordsLinked to original sources

bioRxiv2025

Long-term editing of brain circuits in mice using an engineered electrical synapse

Electrical signaling across distinct populations of brain cells underpins cognitive and emotional function; however, approaches that selectively regulate electrical signaling between two cellular components of a mammalian neural circuit remain sparse. Here, we engineered an electrical synapse composed of two connexin proteins found in Morone americana (white perch fish) - connexin34.7 and connexin35 - to accomplish mammalian circuit modulation. By exploiting protein mutagenesis, devising a new in vitro system for assaying connexin hemichannel docking, and performing computational modeling of hemichannel interactions, we uncovered a structural motif that contributes to electrical synapse formation. Targeting these motifs, we designed connexin34.7 and connexin35 hemichannels that dock with each other to form an electrical synapse, but not with other major connexins expressed in the mammalian central nervous system. We validated this electrical synapse in vivo using C. elegans and mice, demonstrating that it can strengthen communication across neural circuits composed of pairs of distinct cell types and modify behavior accordingly. Thus, we establish Long-term integration of Circuits using connexins (LinCx) for precision circuit-editing in mammals.

neuroscience↗

bioRxiv2024

Sleep-wake states are encoded across emotion-regulation regions of the mouse brain

Emotional dysregulation is highly comorbid with sleep disturbances. Sleep is comprised of unique physiological states that are reflected by conserved brain oscillations. Though the role of these state-dependent oscillations in cognitive function has been well established, less is known regarding the nature of state-dependent oscillations across brain regions that strongly contribute to emotional function. To characterize these dynamics, we recorded local field potentials simultaneously from multiple cortical and subcortical regions implicated in sleep and emotion-regulation and characterize widespread patterns of spectral power and synchrony between brain regions during sleep/wake states. First, we showed that these brain regions encode sleep state, albeit to various degrees of accuracy. We then identified network-based classifiers of sleep based on the combination of features from all recorded brain regions. Spectral power and synchrony from brain networks allowed for automatic, accurate and rapid discrimination of wake, non-REM sleep (NREM) and rapid eye movement (REM) sleep. When we examined the impact of commonly prescribed sleep promoting medications on neural dynamics across these regions, we found disparate alterations to both cortical and subcortical activity across all three states. Finally, a we found that a stress manipulation that disrupts circadian rhythm produced increased sleep fragmentation without altering the underlying average brain dynamics across sleep-wake states. Thus, we characterized state dependent brain dynamics across regions canonically associated with emotions. Significance StatementSleep and emotion regulation are known to be intertwined at the level of behavior and in neuropsychiatric illnesses. Here, we examined how brain regions involved in emotion regulation encode wake and sleep states by performing multi-site electrophysiological recordings in mice. We developed sleep-wake state classifiers that rapidly labeled sleep-wake states from brain activity alone. We then identified how commonly prescribed sleep-inducing medications have unique impacts on brain activity throughout these emotion-regulation regions. Finally, we explored the impact of circadian rhythm disruption on sleep architecture and brain activity. Together, these data shed light on how brain regions which regulate emotion behave during sleep so that one day, treatments to improve both sleep and emotional well-being may be developed.

neuroscience↗

bioRxiv2021

Long-term precision editing of neural circuits using engineered gap junction hemichannels

The coordination of activity between brain cells is a key determinant of neural circuit function; nevertheless, approaches that selectively regulate communication between two distinct cellular components of a mammalian circuit remain sparse. To address this gap, we developed a novel class of gap junctions by selectively engineering two connexin proteins found in Morone americana (white perch fish): connexin34.7 (Cx34.7) and connexin35 (Cx35). By iteratively exploiting protein mutagenesis, a novel in vitro assay of connexin docking, and computational modeling of connexin hemichannel interactions, we uncovered a pattern of structural motifs that contribute to hemichannel docking compatibility. Targeting these motifs, we designed Cx34.7 and Cx35 hemichannels that dock with each other, but not with themselves, nor with other major connexins expressed in the mammalian central nervous system. We validated these hemichannels in vivo using C. elegans and mice, demonstrating that they can facilitate communication across neural circuits composed of pairs of distinct cell types and modify behavior accordingly. Thus, we establish a potentially translational approach, Long-term integration of Circuits using connexins (LinCx), for context-precise circuit-editing with unprecedented spatiotemporal specificity in mammals.

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↗