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Activity Number: 403 - SPAAC Poster Competition
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #304223
Title: Bootstrap-Based Inference Method for Time-Dependent Dual-Frequency Coherence
Author(s): Kamila Kazimierska* and Ania Dudek and Hernando Ombao
Companies: KAUST, Saudi Arabia and AGH, Poland and King Abdullah University of Science and Technology (KAUST)
Keywords: non-stationary; bootstrap; dual-frequency coherence; time-dependent; multivariate time-series

Neuroscientists ask how different regions of the brain communicate with each other. We are interested in activity during resting state or a cognitive task. Measures of connectivity (cross-correlation or coherence) do not capture complex types of dependence. We model on the lead-lag relationships between different oscillations. The goal of this project is to develop inference methods for assessing the significance of these interactions. More specifically, the different oscillatory activities at a pair of brain regions. We will develop a bootstrap-based method for time-dependent dual-frequency coherence. We will take into account potential non-stationarity within an epoch. We want to include a variation of the strength and nature of dependence between epochs. We will investigate auto-correlation across epochs over the entire recording. Simulation studies based on realistic electrophysiological settings will be conducted. We will analyze local field potential data in a macaque monkey. Recordings were collected during an association-learning experiment. This is joint work with Anna Dudek (AGH, Krakow, Poland) and Hernando Ombao (KAUST, Saudi Arabia).

Authors who are presenting talks have a * after their name.

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