On complex infomax applied to functional MRI data

V. Calhoun; T. Adali; G. Pearlson; J. Pekar

doi:10.1109/icassp.2002.5743965

On complex infomax applied to functional MRI data

V. Calhoun, T. Adali, G. Pearlson, J. Pekar

Kennedy Krieger Institute

Research output: Contribution to journal › Conference article › peer-review

11 Scopus citations

Abstract

Functional magnetic resonance imaging (fMRI) is a technique which produces complex data; however the vast majority of functional magnetic resonance imaging analyses utilize only magnitude images. In this paper, we derive a complex-valued independent component analysis (ICA) algorithm using the infomax approach which we then apply to fMRI analysis. Theoretical and empirical results demonstrate an improved sensitivity to functional changes when utilizing the complex data. Additionally, the complex infomax algorithm developed provides a powerful method for exploratory analysis of fMRI data.

Original language	English (US)
Pages (from-to)	I/1009-I/1012
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	1
DOIs	https://doi.org/10.1109/icassp.2002.5743965
State	Published - 2002
Event	2002 IEEE International Conference on Acustics, Speech, and Signal Processing - Orlando, FL, United States Duration: May 13 2002 → May 17 2002

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

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10.1109/icassp.2002.5743965

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T1 - On complex infomax applied to functional MRI data

AU - Calhoun, V.

AU - Adali, T.

AU - Pearlson, G.

AU - Pekar, J.

PY - 2002

Y1 - 2002

N2 - Functional magnetic resonance imaging (fMRI) is a technique which produces complex data; however the vast majority of functional magnetic resonance imaging analyses utilize only magnitude images. In this paper, we derive a complex-valued independent component analysis (ICA) algorithm using the infomax approach which we then apply to fMRI analysis. Theoretical and empirical results demonstrate an improved sensitivity to functional changes when utilizing the complex data. Additionally, the complex infomax algorithm developed provides a powerful method for exploratory analysis of fMRI data.

AB - Functional magnetic resonance imaging (fMRI) is a technique which produces complex data; however the vast majority of functional magnetic resonance imaging analyses utilize only magnitude images. In this paper, we derive a complex-valued independent component analysis (ICA) algorithm using the infomax approach which we then apply to fMRI analysis. Theoretical and empirical results demonstrate an improved sensitivity to functional changes when utilizing the complex data. Additionally, the complex infomax algorithm developed provides a powerful method for exploratory analysis of fMRI data.

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VL - 1

SP - I/1009-I/1012

JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

T2 - 2002 IEEE International Conference on Acustics, Speech, and Signal Processing

Y2 - 13 May 2002 through 17 May 2002

ER -

On complex infomax applied to functional MRI data

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