The reproducibility of functional magnetic resonance imaging (fMRI) is important for fMRI-based neuroscience research and clinical applications. Previous studies show considerable variation in amplitude and spatial extent of fMRI activation across repeated sessions on individual subjects even using identical experimental paradigms and imaging conditions. Most existing fMRI reproducibility studies were typically limited by time duration and data analysis techniques. Particularly, the assessment of reproducibility is complicated by a fact that fMRI results may depend on data analysis techniques used in reproducibility studies. In this work, the long-term fMRI reproducibility was investigated with a focus on the data analysis methods. Two spatial smoothing techniques, including a wavelet-domain Bayesian method and the Gaussian smoothing, were evaluated in terms of their effects on the long-term reproducibility. A multivariate support vector machine (SVM)-based method was used to identify active voxels, and compared to a widely used general linear model (GLM)-based method at the group level. The reproducibility study was performed using multisession fMRI data acquired from eight healthy adults over 1.5 years' period of time. Three regions-of-interest (ROI) related to a motor task were defined based upon which the long-term reproducibility were examined. Experimental results indicate that different spatial smoothing techniques may lead to different reproducibility measures, and the wavelet-based spatial smoothing and SVM-based activation detection is a good combination for reproducibility studies. On the basis of the ROIs and multiple numerical criteria, we observed a moderate to substantial within-subject long-term reproducibility. A reasonable long-term reproducibility was also observed from the inter-subject study. It was found that the short-term reproducibility is usually higher than the long-term reproducibility. Furthermore, the results indicate that brain regions with high contrast-to-noise ratio do not necessarily exhibit high reproducibility. These findings may provide supportive information for optimal design/implementation of fMRI studies and data interpretation.
A line-scan echo planar spectroscopic imaging (LSEPSI) sequence was used to serially acquire spectra from 4,096 voxels every 6.4 s throughout the breasts of nine female subjects in vivo. Data from the serial acquisitions were analyzed to determine the potential of the technique to characterize temperature changes using either the water frequency alone or the water-methylene frequency difference. Fluctuations of the apparent temperature change under these conditions of no heating were smallest using the water-methylene frequency difference, most probably due to a substantial reduction of motion effects both within and without the imaged plane. The approach offers considerable advantages over other methods for temperature change monitoring in the breast with magnetic resonance but suffers from some limitations, including the unavailability of lipid and water resonances in some voxels as well as a surprisingly large distribution of water-methylene frequency differences, which may preclude absolute temperature measurement.
The impact of voxel geometry on the blood oxygenation level-dependent (BOLD) signal detectability in the presence of field inhomogeneity is assessed and a quantitative approach to selecting appropriate voxel geometry is developed in this report. Application of the developed technique to BOLD sensitivity improvement of the human amygdala is presented. Field inhomogeneity was measured experimentally at 1.5 T and 3 T and the dominant susceptibility field gradient in the human amygdala was observed approximately along the superior-inferior direction. Based on the field mapping studies, an optimal selection for the slice orientation would be an oblique pseudo-coronal plane with its frequency-encoding direction parallel to the field gradient measured from each subject. Experimentally this was confirmed by comparing the normalized standard deviation of time-series echo-planar imaging signals acquired with different slice orientations, in the absence of a functional stimulus. A further confirmation with a carefully designed functional magnetic resonance imaging study is needed. Although the BOLD sensitivity may generally be improved by a voxel size commensurable with the activation volume, our quantitative analysis shows that the optimal voxel size also depends on the susceptibility field gradient and is usually smaller than the activation volume. The predicted phenomenon is confirmed with a hybrid simulation, in which the functional activation was mathematically added to the experimentally acquired rest-period echo-planar imaging data.
Functional magnetic resonance imaging (fMRI) is a non-invasive and powerful imaging tool for detecting brain activities. The majority of fMRI studies are performed with single-shot echo-planar imaging (EPI) due to its high temporal resolution. Recent studies have demonstrated that, by increasing the spatial-resolution of fMRI, previously unidentified neuronal networks can be measured. However, it is challenging to improve the spatial resolution of conventional single-shot EPI based fMRI. Although multi-shot interleaved EPI is superior to single-shot EPI in terms of the improved spatial-resolution, reduced geometric distortions, and sharper point spread function (PSF), interleaved EPI based fMRI has two main limitations: 1) the imaging throughput is lower in interleaved EPI; 2) the magnitude and phase signal variations among EPI segments (due to physiological noise, subject motion, and B0 drift) are translated to significant in-plane aliasing artifact across the field of view (FOV). Here we report a method that integrates multiple approaches to address the technical limitations of interleaved EPI-based fMRI. Firstly, the multiplexed sensitivity-encoding (MUSE) post-processing algorithm is used to suppress in-plane aliasing artifacts resulting from time-domain signal instabilities during dynamic scans. Secondly, a simultaneous multi-band interleaved EPI pulse sequence, with a controlled aliasing scheme incorporated, is implemented to increase the imaging throughput. Thirdly, the MUSE algorithm is then generalized to accommodate fMRI data obtained with our multi-band interleaved EPI pulse sequence, suppressing both in-plane and through-plane aliasing artifacts. The blood-oxygenation-level-dependent (BOLD) signal detectability and the scan throughput can be significantly improved for interleaved EPI-based fMRI. Our human fMRI data obtained from 3 Tesla systems demonstrate the effectiveness of the developed methods. It is expected that future fMRI studies requiring high spatial-resolvability and fidelity will largely benefit from the reported techniques.