Samuel A Neymotin1, Zoe N Talbot2, Jeeyune Q Jung3, André A Fenton4, William W Lytton5. 1. Department of Physiology & Pharmacology, SUNY Downstate, United States. Electronic address: samn@neurosim.downstate.edu. 2. Center for Neural Science, New York University, United States; Graduate Program of Neuroscience and Physiology, NYU School of Medicine, United States. 3. Whitman Center, Marine Biological Laboratory, United States. 4. Department of Physiology & Pharmacology, SUNY Downstate, United States; Center for Neural Science, New York University, United States. 5. 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.
Abstract
BACKGROUND: 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. NEW METHOD: 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. RESULTS: 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). COMPARISON WITH EXISTING METHOD(S): 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. CONCLUSIONS: 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. Copyright Â
BACKGROUND: 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. NEW METHOD: 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. RESULTS: 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). COMPARISON WITH EXISTING METHOD(S): 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. CONCLUSIONS: 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. Copyright Â
Authors: Makoto Fukushima; Richard C Saunders; David A Leopold; Mortimer Mishkin; Bruno B Averbeck Journal: Neuron Date: 2012-06-07 Impact factor: 17.173
Authors: André A Fenton; Hsin-Yi Kao; Samuel A Neymotin; Andrey Olypher; Yevgeniy Vayntrub; William W Lytton; Nandor Ludvig Journal: J Neurosci Date: 2008-10-29 Impact factor: 6.167