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Extraction of Neural Activation from Biological Spatio-temporal Imaging Data using Autoregressive Model-based Filtering Technique

Authors:
Fumikazu Miwakeichi

Keywords: Spatio-temporal filtering; Innovation approach; Brain functional imaing; Optical imaging

Abstract:
Regression and cross-correlation analyses have been widely used to detect neural activation in the dynamic brain imaging data. These analyses require a preliminarily assumed reference function, which reflects temporal changes in neural activation. In other words, only the neural activations, whose temporal patterns resemble to the reference function, can be detected. In cases which reference functions are hardly defined, these analyses are not applicable. In our previous study, we have proposed a method of spatio-temporal filtering to overcome these disadvantages. This method enables us to detect the time and region when and where dynamical state transition according to neural activation arises in repeatedly recorded data (multiple trial data). Moreover, we showed the capability to detect neural activation in single-trial data, such as recording of spontaneous brain activity, using sliding time window.

Pages: 63 to 69

Copyright: Copyright (c) IARIA, 2013

Publication date: November 17, 2013

Published in: conference

ISSN: 2308-4553

ISBN: 978-1-61208-314-8

Location: Lisbon, Portugal

Dates: from November 17, 2013 to November 21, 2013