Biology subjects

Hafizi, H.

Publications and source records attributed to Hafizi, H..

2 recordsLinked to original sources

bioRxiv2021

Inhibition-Dominated Rich-Club Shapes Dynamics in Cortical Microcircuits of Awake Behaving Mice

Functional networks of cortical neurons contain highly interconnected hubs, forming a rich-club structure. However, the cell type composition within this distinct subnetwork and how it influences large-scale network dynamics is unclear. Using spontaneous activity recorded from hundreds of cortical neurons in orbitofrontal cortex of awake behaving mice and from organotypic cultures, we show that the rich-club is disproportionately composed of inhibitory neurons, and that inhibitory neurons within the rich-club are significantly more synchronous than other neurons. At the population level, neurons in the rich-club exert higher than expected Granger causal influence on overall population activity at a broad range of frequencies compared to other neurons. Finally, neuronal avalanche duration is significantly correlated with the fraction of rich neurons that participate in the avalanche. Together, these results suggest an unexpected role of a highly connected, inhibition-rich subnetwork in driving and sustaining activity in local cortical networks. SIGNIFICANCE STATEMENTIt is widely believed that the relative abundance of excitatory and inhibitory neurons in cortical circuits is roughly 4:1. This relative abundance has been widely used to construct numerous cortical network models. Here we show that contrary to this notion, a sub-network of highly connected hub neurons (rich-club) consists of a higher abundance of inhibitory neurons compared to that found in the entire network or the non-rich subnetwork. Inhibitory hub neurons contribute to higher synchrony within the rich club compared to the rest of the network. Strikingly, higher activation of the inhibition-dominated rich club strongly correlates with longer avalanches in cortical circuits. Our findings reveal how network topology combined with cell-type specificity orchestrates population wide activity in cortical microcircuits.

neuroscience↗

bioRxiv2020

Model-based detection of putative synaptic connections from spike recordings with latency and type constraints

Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. While previous methods often treat the detection of each putative connection as a separate hypothesis test, here we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned from the whole network. We use an extension of the Generalized Linear Model framework to describe the cross-correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the presynaptic neurons type, synaptic latencies, and time constants improves synapse detection. In data from simulated networks, this model outperforms two previously developed synapse detection methods, especially on the weak connections. We also apply our model to in vitro multielectrode array recordings from mouse somatosensory cortex. Here our model automatically recovers plausible connections from hundreds of neurons, and the properties of the putative connections are largely consistent with previous research. New & NoteworthyDetecting synaptic connections using large-scale extracellular spike recordings is a difficult statistical problem. Here we develop an extension of a Generalized Linear Model that explicitly separates fast synaptic effects and slow background fluctuations in cross-correlograms between pairs of neurons while incorporating circuit properties learned from the whole network. This model outperforms two previously developed synapse detection methods in the simulated networks, and recovers plausible connections from hundreds of neurons in in vitro multielectrode array data.

neuroscience↗