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Sunday, July 19 • 8:00pm - 9:00pm
P108: Ensemble empirical mode decomposition of noisy local filed potentials from optogenetic data

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Sorinel Oprisan, Xandre Clementsmith, Tamas Tompa, Antonieta Lavin

Zoom meeting link:  https://cofc.zoom.us/j/4096287484
P108 from 2:00-2:30 PM
P16 from 2:30-3:00PM

Gamma rhythms with frequencies over 30 Hz are thought to reflect cortical information processing. However, signals associated with gamma rhythms are notoriously difficult to record due to their low energy and relatively short duration of the order of a few seconds. In our experiments, of particular interest was the 40 Hz synchronization of neurons, which is believed to be indicative of temporal binding. Temporal binding glues together spatially distributed representations of different features of sensory input, e.g., during the analysis of a visual stimulus, to produce a coherent description of constituent elements.

Our goal was to investigate the effect of systemic cocaine injection on the local field potentials (LFPs) recorded from the medial prefrontal cortex (mPFC) of mice. We used male PV-Cre mice infected with a viral vector [1] that makes some proteins sensitive to light, such as the members of the opsin family, including retinal pigments in visual systems. By genetic engineering, channelrhodopsins was coupled to sodium channels express in neurons and increase their excitability when exposed to blue light [1].

We previously used nonlinear dynamics tools, such as delay embedding and false nearest neighbors [2,3], to estimate the embedding dimension and the delay time for attractor reconstruction from LFPs [4]. While nonlinear dynamics is a powerful tool for data analysis, recent developments suggested that ensemble empirical mode decomposition (EEMD) could be better suited for short and noisy time series. The traditional EMD method is a data-driven decomposition of the original data into orthogonal Intrinsic Mode Functions (IMFs) [5]. In the presence of noise, the time scale separation is not perfect, and IMF mixing produces significant energy leaks between modes. The advantage of EEMD is that by adding a controlled amount of noise to the data leads to a between demixing of the IMFs. We also performed a Hilbert-Huang [5] transform to the demixed IMFs and computed the instantaneous frequency spectrum. Our results indicate that cocaine significantly shifts the energy distribution towards earlier durations during the trial compared to control. Our findings allow us to estimate the contribution of different spectral components quantitatively and develop a dynamical model of the data.


[1] Dilgen JE, Tompa T, Saggu S, Naselaris T, and Lavin A, Optogenetically evoked gamma oscillations are disturbed by cocaine administration, Frontiers in Cellular Neuroscience. 2013, 7:213.

[2] Oprisan SA, Lynn PE, Tompa T, and Lavin A, Low-dimensional attractor for neural activity from local field potentials in optogenetic mice. Frontiers in Computational Neuroscience. 2015, 9:125.

[3] Oprisan SA, Imperatore J, Helms J, Tompa T, and Lavin A, Cocaine-induced changes in low-dimensional attractors of local field potentials in optogenetic mice, Frontiers in Computational Neuroscience. 2018, 12:1.

[4] Oprisan SA, Clementsmith X, Tompa T, Lavin A. Dopamine receptor antagonists effects on low-dimensional attractors of local field potentials in optogenetic mice. PLoS ONE. 2019, 14(10): e0223469.

[5] Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, Yen NC, Tung CC, Liu H H, The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis, Proceedings of the Royal Society of London A. 1998, 454: 903-995.

avatar for Sorinel Oprisan

Sorinel Oprisan

Professor, Department of Physics and Astronomy, College of Charleston

Sunday July 19, 2020 8:00pm - 9:00pm CEST
Slot 17