Biology subjects

Handy, G.

Publications and source records attributed to Handy, G..

7 recordsLinked to original sources

bioRxiv2025

The synaptic vesicle priming protein Munc13 mediates evoked somatodendritic dopamine release

Midbrain dopamine neurons secrete dopamine from their somata and dendrites in addition to their axonal release. Shared and distinct properties have been proposed for somatodendritic and axonal release, but the mechanisms of somatodendritic release remain unclear. We here used mouse genetics, electrophysiology, and imaging to define roles of the synaptic vesicle priming protein Munc13 in somatodendritic release in comparison to axonal secretion. Munc13 ablation in dopamine neurons decreased evoked but not spontaneous somatodendritic dopamine transmission measured as D2 receptor-mediated currents. Imaging with a fluorescent dopamine sensor confirmed this finding and established comparable importance for Munc13 in somatodendritic and axonal secretion. Pharmacological experiments revealed that release from adrenergic terminals contributes to D2 receptor-mediated currents, and their relative contribution was enhanced after Munc13 knockout. Altogether, these data establish important roles for Munc13 in evoked somatodendritic release. These roles are similar to Munc13 functions in axonal dopamine release and at fast synapses. Spontaneous midbrain dopamine release does not necessitate Munc13 in dopamine neurons and may rely on a release pathway that is independent of the prototypical release machinery employed at synapses.

neuroscience↗

bioRxiv2025

Boundary homogenization and numerical modeling of solute transport across the blood-brain barrier

Effective clearance of amyloid-{beta} (A{beta}) from the brain is essential for preventing neurodegenerative diseases such as Alzheimers. A significant portion of this clearance occurs through the blood-brain barrier (BBB) via receptor-mediated transport. However, current models fail to capture the complex kinetics and spatial heterogeneity of receptors at the BBB. In this study, we derive a novel boundary condition that accounts for finite receptor kinetics, receptor density, and bidirectional transport across the BBB. Specifically, we develop a nonlinear homogenized boundary condition that ensures mass conservation and incorporates receptor-mediated Michaelis-Menten kinetics. We then implement this boundary condition in a cylindrical geometry representing a capillary surrounded by brain tissue. After verifying that the model matches an analytical steady state solution that we derive and that it yields realistic blood A{beta} concentrations, we explore how realistic variations in parameter values drive changes in both steady state A{beta} concentration and transient dynamics. Simulations and analytical results reveal that A{beta} concentrations in the brain are sensitive to receptor number ratios, while concentrations in the blood are primarily affected by the blood clearance rate. Additionally, we use the model to investigate A{beta} clearance during sequential sleep cycles and due to a pathological phenomenon, spreading depolarization. This work presents the first biophysically consistent boundary condition for A{beta} transport across the BBB, offering a powerful tool for studying brain waste clearance under both physiological and pathological conditions.

neuroscience↗

bioRxiv2024

Extending mathematical frameworks to investigate neuronal dynamics in the presence of microglial ensheathment

Recent experimental evidence has shown that glial cells, including microglia and astrocytes, can ensheathe synapses, positioning them to disrupt neurotransmitter flow between pre- and post-synaptic terminals. This study extends micro- and network-scale theoretical models to explore how varying degrees of synaptic ensheathment affect synaptic communication and network dynamics. Consistent with previous studies, our microscale model shows that ensheathment accelerates synaptic transmission while reducing its strength and reliability, with the potential to effectively switch off synaptic connections. Building on these findings, we integrate an "effective" glial cell model into a large-scale neuronal network. Specifically, we analyze a network with highly heterogeneous synaptic strengths and time constants, where glial proximity parametrizes synaptic parameters. Unlike previous models that assumed normal parameter distributions, our model uses parameters drawn from distinct distributions. This framework is applied to large networks of exponential integrate-and-fire neurons, extending linear response theory to analyze not only firing rate distributions but also noise correlations across the network. Despite the significant heterogeneity in the system, a mean-field approximation accurately captures network statistics. We demonstrate the utility of our model by reproducing experimental findings, showing that microglial ensheathment leads to post-anesthesia hyperactivity in excitatory neurons of mice. Furthermore, we explore how glial ensheathment may be used in the visual cortex to target specific neuronal subclasses, tuning higher-order network statistics.

neuroscience↗

bioRxiv2023

The mechanics of correlated variability in segregated cortical excitatory subnetworks

Understanding the genesis of shared trial-to-trial variability in neural activity within sensory cortex is critical to uncovering the biological basis of information processing in the brain. Shared variability is often a reflection of the structure of cortical connectivity since this variability likely arises, in part, from local circuit inputs. A series of experiments from segregated networks of (excitatory) pyramidal neurons in mouse primary visual cortex challenge this view. Specifically, the across-network correlations were found to be larger than predicted given the known weak cross-network connectivity. We aim to uncover the circuit mechanisms responsible for these enhanced correlations through biologically motivated cortical circuit models. Our central finding is that coupling each excitatory subpopulation with a specific inhibitory subpopulation provides the most robust network-intrinsic solution in shaping these enhanced correlations. This result argues for the existence of excitatory-inhibitory functional assemblies in early sensory areas which mirror not just response properties but also connectivity between pyramidal cells.

neuroscience↗

bioRxiv2022

Investigating the ability of astrocytes to drive neural network synchrony

Recent experimental works have implicated astrocytes as a significant cell type underlying several neuronal processes in the mammalian brain, from encoding sensory information to neurological disorders. Despite this progress, it is still unclear how astrocytes are communicating with and driving their neuronal neighbors. While previous computational modeling works have helped propose mechanisms responsible for driving these interactions, they have primarily focused on interactions at the synaptic level, with microscale models of calcium dynamics and neurotransmitter diffusion. Since it is computationally infeasible to include the intricate microscale details in a network-scale model, little computational work has been done to understand how astrocytes may be influencing spiking patterns and synchronization of large networks. We overcome this issue by first developing an "effective" astrocyte that can be easily implemented to already established network frameworks. We do this by showing that the astrocyte proximity to a synapse makes synaptic transmission faster, weaker, and less reliable. Thus, our "effective" astrocytes can be incorporated by considering heterogeneous synaptic time constants, which are parametrized only by the degree of astrocytic proximity at that synapse. We then apply our framework to large networks of exponential integrate-and-fire neurons with various spatial structures. Depending on key parameters, such as the number of synapses ensheathed and the strength of this ensheathment, we show that astrocytes can push the network to a synchronous state and exhibit spatially correlated patterns. Author summaryIn many areas of the brain, glial cells called astrocytes wrap their processes around synapses - the points of contact between neurons. The number of wrapped synapses and the tightness of wrapping varies between brain areas and changes during some diseases, such as epilepsy. We investigate the effect that this synaptic ensheathment has on communication between neurons and the resulting collective dynamics of the neuronal network. We present a general, computationally-efficient way to include astrocytes in neuronal networks using an "effective astrocyte" representation derived from detailed microscopic scale models. The resulting hybrid networks allow us to emulate and observe the effect of ensheathment conditions corresponding to different brain areas and disease states. In particular, we find that it makes the networks more likely to switch into a highly correlated regime, contrary to predictions from the traditional neurons-only view. These results open a new perspective on neural network dynamics, where our understanding of conditions for generating correlated brain activity (e.g., rhythms associated with various brain functions, epileptic seizures) needs to be reevaluated.

neuroscience↗

bioRxiv2022

The logic of recurrent circuits in the primary visual cortex

Recurrent cortical activity sculpts visual perception by refining, amplifying, or suppressing incoming visual signals. Despite the importance of recurrent circuits for cortical processing, the basic rules that govern how nearby cortical neurons influence each other remains enigmatic. We used two-photon holographic optogenetics to activate ensembles of neurons in Layer 2/3 of the primary visual cortex (V1) in the absence of external stimuli to isolate the impact of local recurrence from external inputs. We find that the spatial arrangement and the stimulus feature preference of both the stimulated and the target ensemble jointly determine the net effect of recurrent activity, defining the cortical activity patterns that drive competition versus facilitation in L2/3 circuits. Computational modeling suggests that a combination of highly local recurrent excitatory connectivity and selective convergence onto inhibitory neurons give rise to these principles of recurrent activity. Our data and modeling reveal that recurrent activity can have varied impact, but a logic emerges through an understanding of the precise spatial distribution and feature preference of the multicellular pattern of activity.

neuroscience↗

bioRxiv2022

Cell-type-specific roles of inhibitory interneurons in the rehabilitation of auditory cortex after peripheral damage

Peripheral sensory organ damage leads to compensatory cortical plasticity that supports a remarkable recovery of perceptual capabilities. A major knowledge gap is the lack of precise mechanisms that explain how this plasticity is implemented and distributed over a diverse collection of excitatory and inhibitory cortical neurons. Here, we explored these mechanisms in mouse A1. After peripheral damage, we found recovered sound-evoked activity of excitatory principal neurons (PNs) and parvalbumin (PVs) interneurons (INs), reduced activity in somatostatin-INs (SOMs), and recovered activity in vasoactive intestinal peptide-INs (VIPs). Given the sequentially organized cortical network where VIPs inhibit INs, SOMs inhibit PVs and PNs, and PVs inhibit PNs, our results suggest that PVs contribute to PN stability, SOMs allow for increased PN and PV activity, and VIPs enable the PN and PV recovery by inhibiting SOMs. These results highlight a strategic, cooperative, and cell-type-specific plasticity program that restores cortical sound processing after peripheral damage.

neuroscience↗