Beyond Noise: Using Temporal ICA to Extract Meaningful Information from High-Frequency fMRI Signal Fluctuations during Rest
Open Access
- 1 January 2013
- journal article
- research article
- Published by Frontiers Media SA in Frontiers in Human Neuroscience
- Vol. 7, 168
- https://doi.org/10.3389/fnhum.2013.00168
Abstract
Analysis of resting-state networks using fMRI usually ignores high-frequency fluctuations in the BOLD signal – be it because of low TR prohibiting the analysis of fluctuations with frequencies higher than 0.25 Hz (for a typical TR of 2 s), or because of the application of a bandpass filter (commonly restricting the signal to frequencies lower than 0.1 Hz). While the standard model of convolving neuronal activity with a hemodynamic response function suggests that the signal of interest in fMRI is characterized by slow fluctuation, it is in fact unclear whether the high-frequency dynamics of the signal consists of noise only. In this study, 10 subjects were scanned at 3 T during 6 min of rest using a multiband EPI sequence with a TR of 354 ms to critically sample fluctuations of up to 1.4 Hz. Preprocessed data were high-pass filtered to include only frequencies above 0.25 Hz, and voxelwise whole-brain temporal ICA (tICA) was used to identify consistent high-frequency signals. The resulting components include physiological background signal sources, most notably pulsation and heart-beat components, that can be specifically identified and localized with the method presented here. Perhaps more surprisingly, common resting-state networks like the default-mode network also emerge as separate tICA components. This means that high-frequency oscillations sampled with a rather T1-weighted contrast still contain specific information on these resting-state networks to consistently identify them, not consistent with the commonly held view that these networks operate on low-frequency fluctuations alone. Consequently, the use of bandpass filters in resting-state data analysis should be reconsidered, since this step eliminates potentially relevant information. Instead, more specific methods for the elimination of physiological background signals, for example by regression of physiological noise components, might prove to be viable alternatives.Keywords
This publication has 53 references indexed in Scilit:
- RESCALE: Voxel-specific task-fMRI scaling using resting state fluctuation amplitudeNeuroImage, 2013
- How language production shapes language form and comprehensionFrontiers in Psychology, 2013
- What's learned together stays together: Speakers' choice of referring expression reflects shared experience.Journal of Experimental Psychology: Learning, Memory, and Cognition, 2013
- Temporally-independent functional modes of spontaneous brain activityProceedings of the National Academy of Sciences of the United States of America, 2012
- Spontaneous BOLD event triggered averages for estimating functional connectivity at resting stateNeuroscience Letters, 2011
- Multiband multislice GE‐EPI at 7 tesla, with 16‐fold acceleration using partial parallel imaging with application to high spatial and temporal whole‐brain fMRIMagnetic Resonance in Medicine, 2010
- Toward discovery science of human brain functionProceedings of the National Academy of Sciences of the United States of America, 2010
- Language processing in the natural worldPhilosophical Transactions Of The Royal Society B-Biological Sciences, 2007
- Consistent resting-state networks across healthy subjectsProceedings of the National Academy of Sciences of the United States of America, 2006
- Fast and robust fixed-point algorithms for independent component analysisIEEE Transactions on Neural Networks, 1999