Applying fully tensorial ICA to fMRI data
Virta, J., Taskinen, S., & Nordhausen, K. (2016). Applying fully tensorial ICA to fMRI data. In Proceedings of 2016 IEEE Signal Processing in Medicine and Biology Symposium (pp. 1-6). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SPMB.2016.7846858
Date
2016Copyright
© IEEE, 2016. This is a final draft version of an article whose final and definitive form has been published by IEEE. Published in this repository with the kind permission of the publisher.
There are two aspects in functional magnetic resonance
imaging (fMRI) data that make them awkward to analyse
with traditional multivariate methods - high order and high
dimension. The first of these refers to the tensorial nature
of observations as array-valued elements instead of vectors.
Although this can be circumvented by vectorizing the array,
doing so simultaneously loses all the structural information in
the original observations. The second aspect refers to the high
dimensionality along each dimension making the concept of dimension
reduction a valuable tool in the processing of fMRI data.
Different methods of tensor dimension reduction are currently
gaining popUlarity in literature, and in this paper we apply two
recently proposed methods of tensorial independent component
analysis to simulated task-based fMRI data. Additionally, as a
preprocessing step we introduce a novel extension of PCA for
tensors. The simulations show that when extracting a sufficiently
large number of principal components, the tensor methods find
the task signals very reliably, something the standard temporal
independent component analysis (tICA) fails in.
...
Publisher
Institute of Electrical and Electronics EngineersParent publication ISBN
978-1-5090-6713-8Conference
IEEE Signal Processing in Medicine and Biology SymposiumIs part of publication
Proceedings of 2016 IEEE Signal Processing in Medicine and Biology SymposiumPublication in research information system
https://converis.jyu.fi/converis/portal/detail/Publication/26549595
Metadata
Show full item recordCollections
Related items
Showing items with similar title or keywords.
-
Calculation of magnetic coupling constants with hybrid density functionals
Mansikkamäki, Akseli (2013)The currently available computational methods for the calculation of magnetic coupling constants with density functional theory have been reviewed. These methods include modern approximations to the exchangecorrelation ... -
Brain integrative function driven by musical training during real-world music listening
Burunat Pérez, Iballa (University of Jyväskylä, 2017)The present research investigated differences in the brain dynamics of continuous, real-world music listening between listeners with and without professional musical training, using functional magnetic resonance imaging ... -
Harmonization of multi-site functional MRI data with dual-projection based ICA model
Xu, Huashuai; Hao, Yuxing; Zhang, Yunge; Zhou, Dongyue; Kärkkäinen, Tommi; Nickerson, Lisa D.; Li, Huanjie; Cong, Fengyu (Frontiers Media SA, 2023)Modern neuroimaging studies frequently merge magnetic resonance imaging (MRI) data from multiple sites. A larger and more diverse group of participants can increase the statistical power, enhance the reliability and ... -
Effects of caffeine on neuromuscular function in a non‐fatigued state and during fatiguing exercise
Mesquita, Ricardo N. O.; Cronin, Neil J.; Kyröläinen, Heikki; Hintikka, Jukka; Avela, Janna (Cambridge University Press, 2020)Purpose Caffeine enhances exercise performance but its mechanisms of action remain unclear. This study investigated its effects on neuromuscular function in a non‐fatigued state and during fatiguing exercise. Methods Eighteen ... -
Snowball ICA : A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data
Hu, Guoqiang; Waters, Abigail B.; Aslan, Serdar; Frederick, Blaise; Cong, Fengyu; Nickerson, Lisa D. (Frontiers Media, 2020)In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis. Model order ...