Tracking recurrence of correlation structure in neuronal recordings
Neymotin, Samuel A.
Talbot, Zoe N.
Jung, Jeeyune Q.
Fenton, André A.
Lytton, William W.
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Correlated neuronal activity in the brain is hypothesized to contribute to information representation, and is important for gauging brain dynamics in health and disease. Due to high dimensional neural datasets, it is difficult to study temporal variations in correlation structure. We developed a multiscale method, Population Coordination (PCo), to assess neural population structure in multiunit single neuron ensemble and multi-site local field potential (LFP) recordings. PCo utilizes population correlation (PCorr) vectors, consisting of pair-wise correlations between neural elements. The PCo matrix contains the correlations between all PCorr vectors occurring at different times. We used PCo to interpret dynamics of two electrophysiological datasets: multisite LFP and single unit ensemble. In the LFP dataset from an animal model of medial temporal lobe epilepsy, PCo isolated anomalous brain states, where particular brain regions broke off from the rest of the brain's activity. In a dataset of rat hippocampal single-unit recordings, PCo enabled visualizing neuronal ensemble correlation structure changes associated with changes of animal environment (place-cell remapping). PCo allows directly visualizing high dimensional data. Dimensional reduction techniques could also be used to produce dynamical snippets that could be examined for recurrence. PCo allows intuitive, visual assessment of temporal recurrence in correlation structure directly in the high dimensionality dataset, allowing for immediate assessment of relevant dynamics at a single site. PCo can be used to investigate how neural correlation structure occurring at multiple temporal and spatial scales reflect underlying dynamical recurrence without intermediate reduction of dimensionality.
© The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Neuroscience Methods 275 (2017): 1-9, doi:10.1016/j.jneumeth.2016.10.009.
Suggested CitationJournal of Neuroscience Methods 275 (2017): 1-9
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