fMRI Time Series (fmri + time_series)

Distribution by Scientific Domains

Selected Abstracts

Characterizing instantaneous phase relationships in whole-brain fMRI activation data

Angela R. Laird
Abstract Typically, fMRI data is processed in the time domain with linear methods such as regression and correlation analysis. We propose that the theory of phase synchronization may be used to more completely understand the dynamics of interacting systems, and can be applied to fMRI data as a novel method of detecting activation. Generalized synchronization is a phenomenon that occurs when there is a nonlinear functional relationship present between two or more coupled, oscillatory systems, whereas phase synchronization is defined as the locking of the phases while the amplitudes may vary. In this study, we developed an application of phase synchronization analysis that is appropriate for fMRI data, in which the phase locking condition is investigated between a voxel time series and the reference function of the task performed. A synchronization index is calculated to quantify the level of phase locking, and a nonparametric permutation test is used to determine the statistical significance of the results. We performed the phase synchronization analysis on the data from five volunteers for an event-related finger-tapping task. Functional maps were created that provide information on the interrelations between the instantaneous phases of the reference function and the voxel time series in a whole-brain fMRI activation data set. We conclude that this method of analysis is useful for revealing additional information on the complex nature of the fMRI time series. Hum. Brain Mapping 16:71,80, 2002. 2002 Wiley-Liss, Inc. [source]

Comparison of TCA and ICA techniques in fMRI data processing

Xia Zhao MS
Abstract Purpose To make a quantitative comparison of temporal cluster analysis (TCA) and independent component analysis (ICA) techniques in detecting brain activation by using simulated data and in vivo event-related functional MRI (fMRI) experiments. Materials and Methods A single-slice MRI image was replicated 150 times to simulate an fMRI time series. An event-related brain activation pattern with five different levels of intensity and Gaussian noise was superimposed on these images. Maximum contrast-to-noise ratio (CNR) of the signal change ranged from 1.0 to 2.0 by 0.25 increments. In vivo visual stimulation fMRI experiments were performed on a 1.9 T magnet. Six human volunteers participated in this study. All imaging data were analyzed using both TCA and ICA methods. Results Both simulated and in vivo data have shown that no statistically significant difference exists in the activation areas detected by both ICA and TCA techniques when CNR of fMRI signal is larger than 1.75. Conclusion TCA and ICA techniques are comparable in generating functional brain maps in event-related fMRI experiments. Although ICA has richer features in exploring the spatial and temporal information of the functional images, the TCA method has advantages in its computational efficiency, repeatability, and readiness to average data from group subjects. J. Magn. Reson. Imaging 2004;19:397,402. 2004 Wiley-Liss, Inc. [source]

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

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]

Cluster analysis of BOLD fMRI time series in tumors to study the heterogeneity of hemodynamic response to treatment

Christine Baudelet
Abstract BOLD-contrast functional MRI (fMRI) has been used to assess the evolution of tumor oxygenation and blood flow after treatment. The aim of this study was to evaluate K-means-based cluster analysis as a exploratory, data-driven method. The advantage of this approach is that it can be used to extract information without the need for prior knowledge concerning the hemodynamic response function. Two data sets were acquired to illustrate different types of BOLD fMRI response inside tumors: the first set following a respiratory challenge with carbogen, and the second after pharmacological modulation of tumor blood flow using flunarizine. To improve the efficiency of the clustering, a power density spectrum analysis was first used to isolate voxels for which signal changes did not originate from noise or linear drift. The technique presented here can be used to assess hemodynamic response to treatment, and especially to display areas of the tumor with heterogeneous responses. Magn Reson Med 49:985,990, 2003. 2003 Wiley-Liss, Inc. [source]