Abstract
Functional-effective connectivity and network topology are nowadays key issues for studying brain physiological functions and pathologies. Inferring neuronal connectivity from electrophysiological recordings presents open challenges and unsolved problems. In this work, we present a cross-correlation based method for reliably estimating not only excitatory but also inhibitory links, by analyzing multi-unit spike activity from large-scale neuronal networks. The method is validated by means of realistic simulations of large-scale neuronal populations. New results related to functional connectivity estimation and network topology identification obtained by experimental electrophysiological recordings from high-density and large-scale (i.e., 4096 electrodes) microtransducer arrays coupled to in vitro neural populations are presented. Specifically, we show that: (i) functional inhibitory connections are accurately identified in in vitro cortical networks, providing that a reasonable firing rate and recording length are achieved; (ii) small-world topology, with scale-free and rich-club features are reliably obtained, on condition that a minimum number of active recording sites are available. The method and procedure can be directly extended and applied to in vivo multi-units brain activity recordings.
Highlights
Understanding the relationships between structure and function, dynamics and connectivity of neuronal circuits are a challenge of the modern neurosciences, especially as the characterization of neuronal interaction in terms of functional and effective connectivity [1,2,3] is concerned
The balance between excitation and inhibition is fundamental for proper brain functions and for this reason is precisely regulated in adult cortices
Effective connectivity refers explicitly to the influence that a neuron or neural system exerts on another one, either at synaptic or population level; it can be inferred by perturbing the activity of a neuron, and by measuring the other neurons activity changes.Structural or anatomical connectivity is related to the physical connections among neurons [2]
Summary
Understanding the relationships between structure and function, dynamics and connectivity of neuronal circuits are a challenge of the modern neurosciences, especially as the characterization of neuronal interaction in terms of functional and effective connectivity [1,2,3] is concerned. The availability of validated methods able of reliably inferring functional connections down to synaptic level is still limited To this end, we adopted a reductionist approach making use of in vitro experimental models coupled to Micro-Electrode Arrays (MEAs). Large-scale neural networks developing ex vivo and chronically coupled to MEAs [6], represent a well-established experimental system for studying the neuronal dynamics at population level [7] Despite their simplicity, they show recurrent synchronized periods of activity, as observed in vivo during sleep or anesthesia, and even quiet wakefulness [8, 9]. These methods are very attractive since they allow the detailed monitoring of the on-going electrophysiological spatiotemporal patterns of complex networks [11,12,13,14]
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