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Signal Fluctuations (signal + fluctuation)

Distribution by Scientific Domains

Medical Sciences	75%

Selected Abstracts

Signal fluctuations induced by non-T1 -related confounds in variable TR fMRI experiments

JOURNAL OF MAGNETIC RESONANCE IMAGING, Issue 5 2009
Shuowen Hu BS
Abstract Purpose To assess and model signal fluctuations induced by non-T1 -related confounds in variable repetition time (TR) functional magnetic resonance imaging (fMRI) and to develop a compensation procedure to correct for the non-T1 -related artifacts. Materials and Methods Radiofrequency disabled volume gradient sequences were effected at variable offsets between actual image acquisitions, enabling perturbation of the measurement system without perturbing longitudinal magnetization, allowing the study of non-T1 -related confounds that may arise in variable TR experiments. Three imaging sessions utilizing a daily quality assurance (DQA) phantom were conducted to assess the signal fluctuations, which were then modeled as a second-order system. A modified projection procedure was implemented to correct for signal fluctuations arising from non-T1 -related confounds, and statistical analysis was performed to assess the significance of the artifacts with and without compensation. Results Assessment using phantom data reveals that the signal fluctuations induced by non-T1 -related confounds was consistent in shape across the phantom and well-modeled by a second-order system. The phantom exhibited significant spurious detections (at P < 0.01) almost uniformly across the central slices of the phantom. Conclusion Second-order system modeling and compensation of non-T1 -related confounds achieves significant reduction of spurious detection of fMRI activity in a phantom. J. Magn. Reson. Imaging 2009;29:1234,1239. © 2009 Wiley-Liss, Inc. [source]

The effect of respiration variations on independent component analysis results of resting state functional connectivity

HUMAN BRAIN MAPPING, Issue 7 2008
Rasmus M. Birn
Abstract The analysis of functional connectivity in fMRI can be severely affected by cardiac and respiratory fluctuations. While some of these artifactual signal changes can be reduced by physiological noise correction routines, signal fluctuations induced by slower breath-to-breath changes in the depth and rate of breathing are typically not removed. These slower respiration-induced signal changes occur at low frequencies and spatial locations similar to the fluctuations used to infer functional connectivity, and have been shown to significantly affect seed-ROI or seed-voxel based functional connectivity analysis, particularly in the default mode network. In this study, we investigate the effect of respiration variations on functional connectivity maps derived from independent component analysis (ICA) of resting-state data. Regions of the default mode network were identified by deactivations during a lexical decision task. Variations in respiration were measured independently and correlated with the MRI time series data. ICA appears to separate the default mode network and the respiration-related changes in most cases. In some cases, however, the component automatically identified as the default mode network was the same as the component identified as respiration-related. Furthermore, in most cases the time series associated with the default mode network component was still significantly correlated with changes in respiration volume per time, suggesting that current methods of ICA may not completely separate respiration from the default mode network. An independent measure of the respiration provides valuable information to help distinguish the default mode network from respiration-related signal changes, and to assess the degree of residual respiration related effects. Hum Brain Mapp 2008. © 2008 Wiley-Liss, Inc. [source]

Signal fluctuations induced by non-T1 -related confounds in variable TR fMRI experiments

Application of parallel imaging to fMRI at 7 Tesla utilizing a high 1D reduction factor

MAGNETIC RESONANCE IN MEDICINE, Issue 1 2006
Steen Moeller
Abstract Gradient-echo EPI, blood oxygenation level-dependent (BOLD) functional MRI (fMRI) using parallel imaging (PI) is demonstrated at 7 Tesla with 16 channels, a fourfold 1D reduction factor (R), and fourfold maximal aliasing. The resultant activation detection in finger-tapping fMRI studies was robust, in full agreement with expected activation patterns based on prior knowledge, and with functional maps generated from full field of view (FOV) coverage of k -space using segmented acquisition. In all aspects the functional maps acquired with PI outperformed segmented coverage of full k -space. With a 1D R of 4, fMRI activation based on PI had higher statistical significance, up to 1.6-fold in an individual case and 1.25 ± .25 (SD) fold when averaged over six studies, compared to four-segment/full-FOV data in which the reduction in the image signal-to-noise ratio (SNR) due to k -space undersampling was compensated for by acquiring additional repetitions of the undersampled k -space. When this compensation for loss in SNR was not performed, the effect of PI was determined by the ratio of physiologically induced vs. intrinsic (thermal) noise in the fMRI time series and the extent to which physiological "noise" was amplified by the use of segmentation in the full-FOV data. The results demonstrate that PI is particularly beneficial at this ultrahigh field strength, where both the intrinsic image SNR and temporal signal fluctuations due to physiological processes are large. Magn Reson Med, 2006. © 2006 Wiley-Liss, Inc. [source]