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Statistical source separation of rhythmic LFP patterns during sharp wave ripples in the macaque hippocampus

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Ramirez-Villegas,  JF
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Logothetis,  NK
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Besserve,  M
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Ramirez-Villegas, J., Logothetis, N., & Besserve, M. (2016). Statistical source separation of rhythmic LFP patterns during sharp wave ripples in the macaque hippocampus. Poster presented at 46th Annual Meeting of the Society for Neuroscience (Neuroscience 2016), San Diego, CA, USA.


Cite as: http://hdl.handle.net/21.11116/0000-0000-7AE2-8
Abstract
Sharp wave-ripples (SWR), episodes in the hippocampal CA1 local field potential (LFP) combining a low-frequency deflection (sharp wave) and a high-frequency oscillation (ripple), are thought to mediate memory consolidation. These events are paradigmatic episodes of the interaction between neuronal ensembles across distinct substructures of the hippocampal formation. However, the detailed neuronal ensemble mechanisms underlying this phenomenon remain largely unknown. This question arises partly due to inherent difficulties in inferring network-level dynamics from neuronal population measurements such as LFP. To address this question, we analysed in-vivo intracortical recordings of the CA1 of macaque monkeys. We devised a statistical source separation technique in order to disentangle the spatio-temporal signature of multichannel LFP in peri-SWR time windows of 1s. The first results of our study revealed that SWR complexes in CA1 can be approximated by a linear combination of four main oscillatory components with distinct spectral signatures. We found that SW (5.4-14.2 Hz) and gamma (31.7-68.1 Hz) components are expressed by stratum radiatum, while ripple (96.9-125.5 Hz) and supra-ripple (188.3-199.4 Hz, 95 confidence intervals) oscillations originate in stratum pyramidale. We then devised a model of the macaque’s CA3-CA1 network. The network consists of two layers, each with 200 pyramidal-neuron and 20 peri-somatic interneuron models of two compartments. The model was able to predict a large number of features of in-vivo SW episodes. In particular, we found that SW (5.6-7.5 Hz), gamma (23.2-34.0 Hz), ripple (155.8-167.4 Hz) and supra-ripple (174.4-194.7 Hz, 95 confidence intervals) are also the main oscillatory components of modelled SWR. Our model suggests that SW and gamma components arise from CA3 bursting in stratum radiatum, while ripple oscillations originate from local interactions between pyramidal cells and interneurons. Notably, CA1 interneurons, also entrained by CA3-gamma oscillations, are responsible for the high-frequency component of the LFP activity, thus establishing the population signature of supra-ripple LFP during SWR. Our experiments suggest that SW, gamma, ripple and supra-ripple rhythms are specific markers of the phenomena occurring in neuronal activity during SWR that can be automatically extracted from LFP data with our approach. Finally, this approach establishes a relationship between neuronal activity over meso- and microscopic scales that can be used to investigate network properties such as excitation-inhibition balance without resorting to single unit analysis.