Removing motion and physiological artifacts from intrinsic BOLD fluctuations using short echo data
| Autor: | Molly G. Bright, Kevin Murphy |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Male
Noise correction Movement Speech recognition Cognitive Neuroscience Sensitivity and Specificity 030218 nuclear medicine & medical imaging Correlation Motion 03 medical and health sciences 0302 clinical medicine Image Interpretation Computer-Assisted Humans Resting state Short echo time Intrinsic fluctuations Default mode network Brain Mapping Connectivity Resting state fMRI business.industry fMRI Brain Reproducibility of Results Pattern recognition Image Enhancement Explained variation Magnetic Resonance Imaging Regression Communication noise Noise Neurology Temporal resolution Evoked Potentials Visual Full Length Articles Female Artificial intelligence Artifacts Psychology business Algorithms 030217 neurology & neurosurgery |
| Zdroj: | Neuroimage |
| ISSN: | 1053-8119 |
| DOI: | 10.1016/j.neuroimage.2012.09.043 |
| Popis: | Differing noise variance across study populations has been shown to cause artifactual group differences in functional connectivity measures. In this study, we investigate the use of short echo time functional MRI data to correct for these noise sources in blood oxygenation level dependent (BOLD)-weighted time series. A dual‐echo sequence was used to simultaneously acquire data at both a short (TE = 3.3 ms) and a BOLD-weighted (TE = 35 ms) echo time. This approach is effectively “free,” using dead-time in the pulse sequence to collect an additional echo without affecting overall scan time or temporal resolution. The proposed correction method uses voxelwise regression of the short TE data from the BOLD-weighted data to remove noise variance. In addition to a typical resting state scan, non-compliant behavior associated with patient groups was simulated via increased head motion or physiological fluctuations in 10 subjects. Short TE data showed significant correlations with the traditional motion-related and physiological noise regressors used in current connectivity analyses. Following traditional preprocessing, the extent of significant additional variance explained by the short TE data regressors was significantly correlated with the average head motion across the scan in the resting data (r2 = 0.93, p Highlights ► Short echo time fMRI data reflect head motion and physiological noise sources. ► Variance removed by short TE regressors correlates with total scan motion. ► Regressing short TE data from resting state BOLD data reduces artifactual correlations. ► This method could improve group comparisons of resting state data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect