Detecting social (in)stability in primates from their temporal co-presence network

Gelardi, Valeria; Fagot, Joël; Barrat, Alain; Claidière, Nicolas

doi:10.1016/j.anbehav.2019.09.011

Cited by 33 publications

(37 citation statements)

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“…First: are the connections of the network stable in time, or rapidly changing? Second: does the network have a clear and specific structural organization, and if so, is it persistent in time or unstable and only transient?In order to answer the first question, we quantified, for each neuron i, how much its neighborhood changed between successive time windows 37,38,42,43 . To this aim we computed for each i and at each time t the cosine similarity Θ i (t) between the neighborhoods of i (the subgraphs composed only by the edges involving i) at time t − 1 and at time t. To analyze the unweighted temporal networks, we instead used the Jaccard index J i (t) between these successive neighborhoods (see Methods for precise definitions).…”

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confidence: 99%

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

Pedreschi

Bernard

Clawson

et al. 2020

Network Neuroscience

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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...

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

Pedreschi

Bernard

Clawson

et al. 2020

Network Neuroscience

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“…In order to answer the first question, we quantified, for each neuron i, how much its neighborhood changed between successive time windows 37,38,42,43 . To this aim we computed for each i and at each time t the cosine 3/33 19/33 28/33…”

Section: Information Sharing Dynamics Can Be Described As a Temporal mentioning

confidence: 99%

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

Pedreschi

Bernard

Clawson

et al. 2020

Preprint

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ABSTRACTNeural 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.AUTHOR SUMMARYIt is generally thought that computations performed by local brain circuits rely on complex neural processes, associated to the flexible waxing and waning of cell assemblies, i.e. ensemble of cells firing in tight synchrony. Although cell assembly formation is inherently and unavoidably dynamical, it is still common to find studies in which essentially “static” approaches are used to characterize this process. In the present study, we adopt instead a temporal network approach. Avoiding usual time averaging procedures, we reveal that hub neurons are not hardwired but that cells vary smoothly their degree of integration within the assembly core. Furthermore, our temporal network framework enables the definition of alternative possible styles of “hubness”. Some cells may share information with a multitude of other units but only in an intermittent manner, as “activists” in a flash mob. In contrast, some other cells may share information in a steadier manner, as resolute “lobbyists”. Finally, by avoiding averages over pre-imposed states, we show that within each global oscillatory state a rich switching dynamics can take place between a repertoire of many available network states. We thus show that the temporal network framework provides a natural and effective language to rigorously describe the rich spatiotemporal patterns of information sharing instantiated by cell assembly evolution.

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“…Comparison between 70 sensors and direct observations or videos have yielded mixed results [46,47]. Among 71 animals, different types of networks built from the same data set of direct observations 72 have been shown to differ [41,42], while a social network deduced from co-presence in 73 cognitive testing booths has been shown to correlate with the one obtained from 74 directly observed interactions [48,49]. However, we are not aware of studies using data 75 collected in the same population with on the one hand wearable sensors and on the 76 other hand direct observations.…”

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“…Second, all individuals 47 equipped with a sensor are monitored together, continuously and potentially for a long 48 time without the need for constant human supervision. This enables in principle the 49 collection of large data sets covering long periods of times and, consequently, makes it 50 possible to investigate the evolution and stability of social relationships and social 51 groups on long timescales. On the other hand, wearable sensors do not yield 52 information on the type of behavioral interactions and they do not register contacts 53 with individuals not wearing any sensor, such as very young individuals or out-group 54 members for instance.…”

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Measuring social networks in primates: wearable sensors vs. direct observations

Gelardi

Godard

Paleressompoulle

et al. 2020

Preprint

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Network analysis represents a valuable and flexible framework to understand the structure of individual interactions at the population level in animal societies. The versatility of network representations is moreover suited to different types of datasets describing these interactions. However, depending on the data collection method, different pictures of the social bonds between individuals could a priori emerge. Understanding how the data collection method influences the description of the social structure of a group is thus essential to assess the reliability of social studies based on different types of data. This is however rarely feasible, especially for animal groups, where data collection is often challenging. Here, we address this issue by comparing datasets of interactions between primates collected through two different methods: behavioral observations and wearable proximity sensors. We show that, although many directly observed interactions are not detected by the sensors, the global pictures obtained when aggregating the data to build interaction networks turn out to be remarkably similar. Sensors data yield moreover a reliable social network already over short timescales and can be used for long term campaigns, showing their important potential for detailed studies of the evolution of animal social groups. Introduction 1 Interactions between individuals are the foundation of complex social structures in 2 human and other animal societies. Network analysis represents a valuable framework to 3 understand the structure and evolution of these interactions, as it encodes a whole 4 hierarchy of patterns, from individual-level interactions to complex population-level 5 social structures. [1-3, 3-8].6 With the increasing deployment of digital devices, new ways of collecting data, 7 combined with new network analysis tools, have made possible the development of 8 quantitative measures of these relationships and patterns in modern human societies, 9 January 17, 2020 1/20leading to the emergence of computational social science in the last decades [9]. For 10 instance, social relationships have been inferred and studied using various data sources 11 ranging from phone calls [10], e-mails [11,12], online interactions [13], to face-to-face 12 interactions measured by wearable sensors [14][15][16][17][18][19][20]. 13The availability of large volumes of data with high temporal resolution has thus 14 contributed to the rapid expansion of data-driven computational studies of human 15 relationships and human social networks. On the contrary, the data collection remains 16 more challenging in the field of animal studies, because the data on animal interactions 17 are still largely obtained from direct observations [6,21]. Data resulting from such 18 observations are extremely valuable as they often include detailed information about the 19 nature, duration and location of the interactions between individuals. They thus allow 20 researchers to grasp and investigate complex social patterns in animal groups. 21 Unfortunat...

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Detecting social (in)stability in primates from their temporal co-presence network

Cited by 33 publications

References 71 publications

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

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

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

Measuring social networks in primates: wearable sensors vs. direct observations

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