Automatic classification of brain resting states using fMRI temporal signals

Soldati, N; Robinson, S; Persello, C; Jovicich, J; Bruzzone, L

doi:10.1049/el:20092178

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Automatic classification of brain resting states using fMRI temporal signals

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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4733083
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Soldati, N., Robinson, S., Persello, C., Jovicich, J., & Bruzzone, L. (2009). Automatic classification of brain resting states using fMRI temporal signals. Electronics Letters, 45(1), 19-21. doi:10.1049/el:20092178.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-C5E1-5

Abstract

A novel technique is presented for the automatic discrimination between networks of dasiaresting statesdasia of the human brain and physiological fluctuations in functional magnetic resonance imaging (fMRI). The method is based on features identified via a statistical approach to group independent component analysis time courses, which may be extracted from fMRI data. This technique is entirely automatic and, unlike other approaches, uses temporal rather than spatial information. The method achieves 83 accuracy in the identification of resting state networks.