title: Tracking recurrence of correlation structure in neuronal recordings.
abstract: 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.
authors.affiliation: Department of Physiology & Pharmacology, SUNY Downstate, United States. Electronic address: samn@neurosim.downstate.edu.
authors.forename: Samuel A
authors.initials: SA
authors.lastname: Neymotin
authors.affiliation: Center for Neural Science, New York University, United States; Graduate Program of Neuroscience and Physiology, NYU School of Medicine, United States.
authors.forename: Zoe N
authors.initials: ZN
authors.lastname: Talbot
authors.affiliation: Whitman Center, Marine Biological Laboratory, United States.
authors.forename: Jeeyune Q
authors.initials: JQ
authors.lastname: Jung
authors.affiliation: Department of Physiology & Pharmacology, SUNY Downstate, United States; Center for Neural Science, New York University, United States.
authors.forename: André A
authors.initials: AA
authors.lastname: Fenton
authors.affiliation: Department of Physiology & Pharmacology, SUNY Downstate, United States; Department of Neurology, SUNY Downstate, United States; Department of Neurology, Kings County Hospital Center, United States; Whitman Center, Marine Biological Laboratory, United States.
authors.forename: William W
authors.initials: WW
authors.lastname: Lytton
mesh_terms: D000200:Action Potentials; D000818:Animals; D001921:Brain / Q000502:physiology / Q000503:physiopathology; D004195:Disease Models, Animal; D000069280:Electrocorticography; D004833:Epilepsy, Temporal Lobe / Q000503:physiopathology; D005106:Exploratory Behavior / Q000502:physiology; D007051:Ibotenic Acid; D008297:Male; D008810:Mice, Inbred C57BL; D008839:Microelectrodes; D009474:Neurons / Q000502:physiology*; D010507:Periodicity; D020318:Rats, Long-Evans; D012815:Signal Processing, Computer-Assisted
chemical_list: D007051:Ibotenic Acid
keywords: Correlation structure; Local field potentials; Multiscale analysis; Neuronal ensembles; Temporal recurrence