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Editorial: Towards an integrated approach to measurement, analysis and modeling of cortical networks

M. Malmierca,H. Read,A. R. Rao,Rao Ar,Cecchi Ga,K. E.,G. Cecchi,E. Kaplan

Published 2015 in Front. Neural Circuits

ABSTRACT

Recent technological advances have led to an unprecedented increase in the volume and detail of neuroscientific data, creating significant challenges for their processing and interpretation. We approach this challenge through a network-centric perspective, as we believe that brain function is fundamentally determined by patterns of connectivity between neurons, and the resulting dynamics. This is in contrast to traditional computational neuroscience techniques that focus on models of individual neurons and compartments. Progress, in consequence, is essential on (at least) three major fronts: measurement of neural activity, analysis of network structures deduced from this activity, and modeling of network function, leading to theoretical insights. The measurement front spans the range from multi-electrode recordings to whole-brain measurements using imaging. Several basic scientific questions arise: What do we need to measure in brain networks? Are there theoretical constraints that would dictate this? How do we design our experiments to generate the most meaningful data? How do we record from awake/behaving animals, or even from multiple animals interacting socially? The analysis front consists of creating network models from the measurements. Some promising techniques explore the estimation of networks using causality. However, several open questions remain: How do we define the fundamental units within the network? Are these units fixed or do they evolve dynamically? How do we infer connectivity between network elements? How do we identify functional clustering, based on the individual neuronal features? How do we quantify and interpret the activity of multiple neurons via multi-unit recordings, especially when there is no stimulus-response paradigm? The modeling front can proceed in several directions. From the extracted network we can identify topological regularities, such as motifs and cycles. An interesting research direction is to analyze the relationship between the structure of the network, as represented by its motifs, and its function. A growing body of work is examining the relationship between network structure and phenomena such as stability and synchrony. For instance, neurons in the hippocampus could be modeled as a network wherein hubs consisting of hub neurons promote synchrony, while cycles in this network may cause instability. The theme of synchrony as an important network phenomenon emerges in several articles in this research topic (Canavier et al., 2013; Latorre et al., 2013; Tibau et al., 2013; Vardi et al., 2013; Cavallari et al., 2014; Chary and Kaplan, 2014; Konstantoudaki et al., 2014; Ratnadurai-Giridharan et al., 2014). We emphasize that the three fronts consisting of measurement, analysis and modeling are interdependent, but must evolve synergistically. The model and theoretical understanding need to be grounded in constraints produced by the measurement process. Insights derived from modeling can be used to drive novel experiments and measurement techniques. An emerging trend deploys active probing and network manipulation through viral vectors and optogenetic methods. We expect that by aligning existing and future research along these fronts, we will be able to answer questions at the system level. We can view this development as a generalization of the Hubel-Wiesel approach which characterizes feed-forward sensory coding to approaches that characterize dynamic network-level interactions with the input signals. We can derive value from our understanding of network function by applying it to brain-related disorders, such as schizophrenia, drug addiction, or autism. For instance, differences between default mode networks of ASD (autism spectrum disorder) subjects and normals have been reported, among other psychiatric and neurodegenerative conditions. Cortical network properties ultimately determine how different network oscillation states are established and maintained and defining these principles could explain why there is impaired synchronization between different brain areas in schizophrenics and Parkinson's patients. Overall, network-based measures capture better the dynamics of brain processes, and provide features with greater discriminative power than point-based measures. The articles in this research topic cover these different aspects of cortical networks. To guide the reader, we provide below a brief summary of each article, and relate it to the overall theme of the research topic.

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