| Paper: | PS-2A.50 | ||
| Session: | Poster Session 2A | ||
| Location: | H Lichthof | ||
| Session Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation Time: | Sunday, September 15, 17:15 - 20:15 | ||
| Presentation: | Poster | ||
| Publication: | 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany | ||
| Paper Title: | A Simulation-Based Comparison of Methods for Analyzing Aperiodic Neural Activity | ||
| Manuscript: | Click here to view manuscript | ||
| License: |  This work is licensed under a Creative Commons Attribution 3.0 Unported License. |
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| DOI: | https://doi.org/10.32470/CCN.2019.1394-0 | ||
| Authors: | Thomas Donoghue, Richard Gao, University of California, San Diego, United States; Leonhard Waschke, University of Lübeck, Germany; Bradley Voytek, University of California, San Diego, United States | ||
| Abstract: | Electrophysiological field data is comprised of both periodic components — neural oscillations — and aperiodic activity, sometimes called scale-free or 1/f activity. Investigations of aperiodic activity have established that it is dynamic and systematically varies within and between individuals, and relates to aging, and task performance. Currently, however, there are a wide variety of conceptual frameworks and methods for interpreting and analyzing aperiodic activity, the relationships between which are unclear. Here, we evaluate extant methods for measuring aperiodic activity in neural data. We briefly summarize available methods, focusing on spectral fitting approaches. We introduce simulation procedures for creating statistically representative neural time series and power spectra. Using simulations with known parameters, we systematically compare available methods, testing those that measure aperiodic activity by fitting 1/f properties in neural power spectra. We find that the most accurate approach is one that explicitly parameterizes neural power spectra. We highlight future plans for extending this framework to explore other available methods, aimed at defining best practices for measuring aperiodic neural activity, and seek to consolidate across currently disparate approaches. | ||