4,829 research outputs found
Experimental Design Modulates Variance in BOLD Activation: The Variance Design General Linear Model
Typical fMRI studies have focused on either the mean trend in the
blood-oxygen-level-dependent (BOLD) time course or functional connectivity
(FC). However, other statistics of the neuroimaging data may contain important
information. Despite studies showing links between the variance in the BOLD
time series (BV) and age and cognitive performance, a formal framework for
testing these effects has not yet been developed. We introduce the Variance
Design General Linear Model (VDGLM), a novel framework that facilitates the
detection of variance effects. We designed the framework for general use in any
fMRI study by modeling both mean and variance in BOLD activation as a function
of experimental design. The flexibility of this approach allows the VDGLM to i)
simultaneously make inferences about a mean or variance effect while
controlling for the other and ii) test for variance effects that could be
associated with multiple conditions and/or noise regressors. We demonstrate the
use of the VDGLM in a working memory application and show that engagement in a
working memory task is associated with whole-brain decreases in BOLD variance.Comment: 18 pages, 7 figure
Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI
Parallel MRI is a fast imaging technique that enables the acquisition of
highly resolved images in space or/and in time. The performance of parallel
imaging strongly depends on the reconstruction algorithm, which can proceed
either in the original k-space (GRAPPA, SMASH) or in the image domain
(SENSE-like methods). To improve the performance of the widely used SENSE
algorithm, 2D- or slice-specific regularization in the wavelet domain has been
deeply investigated. In this paper, we extend this approach using 3D-wavelet
representations in order to handle all slices together and address
reconstruction artifacts which propagate across adjacent slices. The gain
induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE:
3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal
acquisition is considered. Another important extension accounts for temporal
correlations that exist between successive scans in functional MRI (fMRI). In
addition to the case of 2D+t acquisition schemes addressed by some other
methods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisition
schemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and
4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that
all regularization parameters are estimated in the maximum likelihood sense on
a reference scan. The gain induced by such extensions is illustrated on both
anatomical and functional image reconstruction, and also measured in terms of
statistical sensitivity for the 4D-UWR-SENSE approach during a fast
event-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSE
reconstruction at the subject and group levels (15 subjects) for different
contrasts of interest (eg, motor or computation tasks) and using different
parallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.Comment: arXiv admin note: substantial text overlap with arXiv:1103.353
Cortical lamina-dependent blood volume changes in human brain at 7T
Cortical layer-dependent high (sub-millimeter) resolution functional magnetic resonance imaging (fMRI) in human or animal brain can be used to address questions regarding the functioning of cortical circuits, such as the effect of different afferent and efferent connectivities on activity in specific cortical layers. The sensitivity of gradient echo (GE) blood oxygenation level-dependent (BOLD) responses to large draining veins reduces its local specificity and can render the interpretation of the underlying laminar neural activity impossible. The application of the more spatially specific cerebral blood volume (CBV)-based fMRI in humans has been hindered by the low sensitivity of the noninvasive modalities available. Here, a vascular space occupancy (VASO) variant, adapted for use at high field, is further optimized to capture layer-dependent activity changes in human motor cortex at sub-millimeter resolution. Acquired activation maps and cortical profiles show that the VASO signal peaks in gray matter at 0.8–1.6 mm depth, and deeper compared to the superficial and vein-dominated GE-BOLD responses. Validation of the VASO signal change versus well-established iron-oxide contrast agent based fMRI methods in animals showed the same cortical profiles of CBV change, after normalization for lamina-dependent baseline CBV. In order to evaluate its potential of revealing small lamina-dependent signal differences due to modulations of the input-output characteristics, layer-dependent VASO responses were investigated in the ipsilateral hemisphere during unilateral finger tapping. Positive activation in ipsilateral primary motor cortex and negative activation in ipsilateral primary sensory cortex were observed. This feature is only visible in high-resolution fMRI where opposing sides of a sulcus can be investigated independently because of a lack of partial volume effects. Based on the results presented here, we conclude that VASO offers good reproducibility, high sensitivity and lower sensitivity than GE-BOLD to changes in larger vessels, making it a valuable tool for layer-dependent fMRI studies in humans
Serial Correlations in Single-Subject fMRI with Sub-Second TR
When performing statistical analysis of single-subject fMRI data, serial
correlations need to be taken into account to allow for valid inference.
Otherwise, the variability in the parameter estimates might be under-estimated
resulting in increased false-positive rates. Serial correlations in fMRI data
are commonly characterized in terms of a first-order autoregressive (AR)
process and then removed via pre-whitening. The required noise model for the
pre-whitening depends on a number of parameters, particularly the repetition
time (TR). Here we investigate how the sub-second temporal resolution provided
by simultaneous multislice (SMS) imaging changes the noise structure in fMRI
time series. We fit a higher-order AR model and then estimate the optimal AR
model order for a sequence with a TR of less than 600 ms providing whole brain
coverage. We show that physiological noise modelling successfully reduces the
required AR model order, but remaining serial correlations necessitate an
advanced noise model. We conclude that commonly used noise models, such as the
AR(1) model, are inadequate for modelling serial correlations in fMRI using
sub-second TRs. Rather, physiological noise modelling in combination with
advanced pre-whitening schemes enable valid inference in single-subject
analysis using fast fMRI sequences
Cluster Failure Revisited: Impact of First Level Design and Data Quality on Cluster False Positive Rates
Methodological research rarely generates a broad interest, yet our work on
the validity of cluster inference methods for functional magnetic resonance
imaging (fMRI) created intense discussion on both the minutia of our approach
and its implications for the discipline. In the present work, we take on
various critiques of our work and further explore the limitations of our
original work. We address issues about the particular event-related designs we
used, considering multiple event types and randomisation of events between
subjects. We consider the lack of validity found with one-sample permutation
(sign flipping) tests, investigating a number of approaches to improve the
false positive control of this widely used procedure. We found that the
combination of a two-sided test and cleaning the data using ICA FIX resulted in
nominal false positive rates for all datasets, meaning that data cleaning is
not only important for resting state fMRI, but also for task fMRI. Finally, we
discuss the implications of our work on the fMRI literature as a whole,
estimating that at least 10% of the fMRI studies have used the most problematic
cluster inference method (P = 0.01 cluster defining threshold), and how
individual studies can be interpreted in light of our findings. These
additional results underscore our original conclusions, on the importance of
data sharing and thorough evaluation of statistical methods on realistic null
data
The quest for the best: The impact of different EPI sequences on the sensitivity of random effect fMRI group analyses.
We compared the sensitivity of standard single-shot 2D echo planar imaging (EPI) to three advanced EPI sequences, i.e., 2D multi-echo EPI, 3D high resolution EPI and 3D dual-echo fast EPI in fixed effect and random effects group level fMRI analyses at 3T. The study focused on how well the variance reduction in fixed effect analyses achieved by advanced EPI sequences translates into increased sensitivity in the random effects group level analysis. The sensitivity was estimated in a functional MRI experiment of an emotional learning and a reward based learning tasks in a group of 24 volunteers. Each experiment was acquired with the four different sequences. The task-related response amplitude, contrast level and respective t-value were proxies for the functional sensitivity across the brain. All three advanced EPI methods increased the sensitivity in the fixed effects analyses, but standard single-shot 2D EPI provided a comparable performance in random effects group analysis when whole brain coverage and moderate resolution are required. In this experiment inter-subject variability determined the sensitivity of the random effects analysis for most brain regions, making the impact of EPI pulse sequence improvements less relevant or even negligible for random effects analyses. An exception concerns the optimization of EPI reducing susceptibility-related signal loss that translates into an enhanced sensitivity e.g. in the orbitofrontal cortex for multi-echo EPI. Thus, future optimization strategies may best aim at reducing inter-subject variability for higher sensitivity in standard fMRI group studies at moderate spatial resolution
Temporal SNR characteristics in segmented 3D-EPI at 7T.
Three-dimensional segmented echo planar imaging (3D-EPI) is a promising approach for high-resolution functional magnetic resonance imaging, as it provides an increased signal-to-noise ratio (SNR) at similar temporal resolution to traditional multislice 2D-EPI readouts. Recently, the 3D-EPI technique has become more frequently used and it is important to better understand its implications for fMRI. In this study, the temporal SNR characteristics of 3D-EPI with varying numbers of segments are studied. It is shown that, in humans, the temporal variance increases with the number of segments used to form the EPI acquisition and that for segmented acquisitions, the maximum available temporal SNR is reduced compared to single shot acquisitions. This reduction with increased segmentation is not found in phantom data and thus likely due to physiological processes. When operating in the thermal noise dominated regime, fMRI experiments with a motor task revealed that the 3D variant outperforms the 2D-EPI in terms of temporal SNR and sensitivity to detect activated brain regions. Thus, the theoretical SNR advantage of a segmented 3D-EPI sequence for fMRI only exists in a low SNR situation. However, other advantages of 3D-EPI, such as the application of parallel imaging techniques in two dimensions and the low specific absorption rate requirements, may encourage the use of the 3D-EPI sequence for fMRI in situations with higher SNR
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