Dynamic core-periphery structure of information sharing networks in entorhinal cortex and hippocampus

Abstract

Neural computation is associated with the emergence, reconfiguration and dissolution of cell assemblies in the context of varying oscillatory states. Here, we describe the complex spatio-temporal dynamics of cell assemblies through temporal network formalism. We use a sliding window approach to extract sequences of networks of information sharing among single units in hippocampus and enthorinal cortex during anesthesia and study how global and node-wise functional connectivity properties evolve along time and as a function of changing global brain state (theta vs slow-wave oscillations). First, we find that information sharing networks display, at any time, a core-periphery structure in which an integrated core of more tightly functionally interconnected units link to more loosely connected network leaves. However the units participating to the core or to the periphery substantially change across time-windows, with units entering and leaving the core in a smooth way. Second, we find that discrete network states can be defined on top of this continuously ongoing liquid core-periphery reorganization. Switching between network states results in a more abrupt modification of the units belonging to the core and is only loosely linked to transitions between global oscillatory states. Third, we characterize different styles of temporal connectivity that cells can exhibit within each state of the sharing network. While inhibitory cells tend to be central, we show that, otherwise, anatomical localization only poorly influences the patterns of temporal connectivity of the different cells. Furthermore, cells can change temporal connectivity style when the network changes state. Altogether, these findings reveal that the sharing of information mediated by the intrinsic dynamics of hippocampal and enthorinal cortex cell assemblies have a rich spatiotemporal structure, which could not have been identified by more conventional time-or state-averaged analyses of functional connectivity. arXiv:2001.06371v1 [q-bio.NC] 17 Jan 2020 between individuals 16, 17 , multiple temporal and structural scales [19][20][21] , and a rich array of intrinsically dynamical structures that could not be unveiled within a static network framework [22][23][24][25] . Taking into account temporality has moreover been shown to have a strong impact in processes taking place on networks, in particular the propagation of diseases or of information 18, 26, 27 . In the neuroscience field, emphasis have been recently put on the need to upgrade "connectomics" into "chronnectomics", to disentangle temporal variability from inter-subject and intercohort differences and thus achieve a superior biomarking performance 28, 29 . However a majority of dynamic network studies have been so far considering large-scale brain-wide networks of interregional connectivity --see, e.g. 30 for a study of link burstiness-and only fewer have addressed the dynamics of information sharing networks at the level of micro-circuits, often in vitro or in silico [31][32][33] and even mor...

A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse

“…There have been recent attempts to ameliorate this problem by examining functional connectivity over time. These approaches often, though not always (W. H. Thompson, Richter, Plavén-Sigray, & Fransson, 2018), consisted of choosing a window of time for analysis and sliding this window along the period of data acquisition (V. D. Calhoun, Miller, Pearlson, & Adalı, 2014;Pedreschi et al, 2020; G. J. Thompson et al, 2013).…”

Disordered information processing dynamics in experimental epilepsy

“…The partners from whom a given neuron receives or to whom it sends information are continuously changing (Clawson et al, 2019). At each time step, the instantaneous sharing networks can be seen as having a dynamic core-periphery structure (Pedreschi et al, 2020), with a core of tightly integrated neurons, surrounded by lightly connected periphery neurons. Two key measures of the core-periphery structure are the coreness, how central or well-integrated within a dense subnetwork -how "core"-a given neuron is, and the Jaccard index, a measure indicating how similar (or, conversely, liquid) the connections are between the recorded neurons between two time steps.…”

“…The values for coreness & Jaccard were computed using the methods presented in Pedreschi et al (2020). These were then analyzed using the same sliding window technique as presented in 'Feature Computation'.…”

Inferring cell cycle phases from a partially temporal network of protein interactions

“…Since biological phases and processes are driven by specific protein-protein interactions, it is expected that each phase could be related to a specific structure or "state" (Masuda and Holme 2019;Pedreschi et al 2020) of the temporal PPI network. In particular, a large similarity between snapshots of the temporal network at different times could indicate that the system is in the same phase at these times, and low similarity between successive times could indicate a change of phase (Masuda and Holme 2019;Gelardi et al 2020;Pedreschi et al 2020). This idea can be taken further and formalised to infer phases by performing a clustering of the snapshots of the temporal PPI network.…”

“…In a temporal network of PPIs, nodes represent proteins, and interactions between them are represented by time-varying edges. Temporal network theory has been used successfully in areas ranging from social interactions (Miritello et al 2011; Saramäki and Moro 2015; Gelardi et al 2020) to neuroscience (Pedreschi et al 2020; Lopes et al 2021). However, despite calls to use it more in biology (Przytycka et al 2010), it is still underused to study, e.g., PPI networks.…”