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Cited by 2,715

The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.

Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging

Beckmann, C.F., Smith, S.M.·2004·IEEE Transactions on Medical Imaging
DOI 10.1109/tmi.2003.822821
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Abstract

We present an integrated approach to probabilistic independent component analysis (ICA) for functional MRI (FMRI) data that allows for nonsquare mixing in the presence of Gaussian noise. In order to avoid overfitting, we employ objective estimation of the amount of Gaussian noise through Bayesian analysis of the true dimensionality of the data, i.e., the number of activation and non-Gaussian noise sources. This enables us to carry out probabilistic modeling and achieves an asymptotically unique decomposition of the data. It reduces problems of interpretation, as each final independent component is now much more likely to be due to only one physical or physiological process. We also describe other improvements to standard ICA, such as temporal prewhitening and variance normalization of timeseries, the latter being particularly useful in the context of dimensionality reduction when weak activation is present. We discuss the use of prior information about the spatiotemporal nature of the source processes, and an alternative-hypothesis testing approach for inference, using Gaussian mixture models. The performance of our approach is illustrated and evaluated on real and artificial FMRI data, and compared to the spatio-temporal accuracy of results obtained from classical ICA and GLM analyses.

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Research works printed but not linked: 8

  1. 10.1016/S1053-8119(00)91573-8 · doi:10.1016/s1053-8119(00)91573-8
  2. 10.1016/0893-6080(95)00015-R · doi:10.1016/0893-6080(95)00015-r
  3. Baram, 1994, Density Shaping by Neural Networks with Application to Calssification, Estimation, and Forecasting
  4. 10.1214/aoms/1177697400 · doi:10.1214/aoms/1177697400
  5. Rao, 1966, Characterization of the distribution of random variables in linear structural relations, Synkhya, 252
  6. Johnstone, 2000, On the Distribution of the Largest Principal Component
  7. Kass, 1993, Bayes Factors and Model Uncertainty
  8. 10.1016/S1053-8119(01)91462-4 · doi:10.1016/s1053-8119(01)91462-4

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Metadata

Authors
Beckmann, C. F., & Smith, S. M.
Venue
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2004-02
Volume
23
Issue
2
Pages
137-152
ISSN
0278-0062 (ISSN-L)
DOI
10.1109/tmi.2003.822821
OpenAlex
W2117621792
Cited by
2,715
References
53
FWCI
45.10
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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CoChat. (2026). Citation check: Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging. https://app.cochat.ai/papers/10.1109/tmi.2003.822821
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Beckmann, C. F., & Smith, S. M. (2004). Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2003.822821

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