1,045 research outputs found
Brain circuits involved in self-paced motion: the influence of 0.1 Hz waves
The neural mechanisms behind human voluntary motion are not fully characterized
yet, in spite of numerous research studies. Slow ( 0.1 Hz) brain oscillations are known to have a powerful modulatory effect on several cognitive and physiological phenomena, including free movement.
This study is based on fMRI data acquired from 25 young, healthy subjects. The tasks
were: rest, self-paced motion, motion paced by a periodic 0.1 Hz stimulus. The temporal
resolution was finer than standard fMRI protocols (TR=871 ms). After preprocessing, the signal from brain regions of interest was extracted, and functional connectivity was computed between brain regions using wavelet phase coherence. Complementarily, effective connectivity was measured using Granger causality. The final output was Phase-Locking (PL) and Granger Causality (GC) matrices reflecting inter-regional phase coherence and causal interactions, respectively, around 0.1 Hz.
Using the GraphVar toolbox, inter-task and inter-group comparisons were performed.
In inter-task comparisons PL matrices showed encouraging results unlike GC matrices.
Pairs of regions for which PL differs significantly between rest and self-paced movement were identified. These include mainly the Postcentral gyrus, Putamen, the Anterior Cingulum, the Precentral gyrus, the Calcarine, the Lingual and the Insula (all in the left hemisphere). Topological changes in the brain wiring were identified across the tasks by computing the node degree and global efficiency. Inter-group comparisons took into account the inter movement interval and the coupling between BOLD and heart rate beatto-beat interval signals and showed changes in brain activity depending on the regularity of movement intervals and specific connectivity patterns for neural BOLD oscillations, respectively.
This methodological approach allowed to make a contribution towards the characterization of the functional connectivity of brain circuits related to voluntary motor behavior
Incorporation of phase changes in functional magnetic resonance imaging
Functional magnetic resonance imaging (fMRI) data is acquired as a complex image pair including magnitude and phase information. The vast majority of fMRI experiments do not attempt to take advantage of the time varying phase information. The phase of the MRI signal is related to the local magnetic field changes, suggesting it may contain useful information about the source of hemodynamic activity. Analysis of phase data acquired from different fMRI experiments has shown the presence of activity in response to various stimuli. However, there have been no studies which have examined phase data in a larger group of subjects for multiple types of fMRI tasks, nor have studies examined phase changes due to event-related stimuli. In this thesis, we examine the magnitude and phase changes in group data in a block-design motor tapping task and in an event-related auditory oddball task. We also look at any additional processing steps that might be required for phase. The results for both block-design and event-related tasks indicate the presence of task related information in the phase data with phase only and magnitude only approaches showing signal changes in the expected brain regions. Techniques like temporal smoothing and Gaussian smoothing seem to help improve the results. Although there is more overall activity detected with magnitude data, the phase only analysis also reveals activity in regions expected to be involved in the task, but not significantly activated in the magnitude only analysis, suggesting that the phase might provide some unique information. In addition, the phase can potentially increase sensitivity within regions also showing magnitude changes. Future work should focus on additional methods for combining the magnitude and phase data
Dynamic reconfiguration of human brain networks during learning
Human learning is a complex phenomenon requiring flexibility to adapt
existing brain function and precision in selecting new neurophysiological
activities to drive desired behavior. These two attributes -- flexibility and
selection -- must operate over multiple temporal scales as performance of a
skill changes from being slow and challenging to being fast and automatic. Such
selective adaptability is naturally provided by modular structure, which plays
a critical role in evolution, development, and optimal network function. Using
functional connectivity measurements of brain activity acquired from initial
training through mastery of a simple motor skill, we explore the role of
modularity in human learning by identifying dynamic changes of modular
organization spanning multiple temporal scales. Our results indicate that
flexibility, which we measure by the allegiance of nodes to modules, in one
experimental session predicts the relative amount of learning in a future
session. We also develop a general statistical framework for the identification
of modular architectures in evolving systems, which is broadly applicable to
disciplines where network adaptability is crucial to the understanding of
system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4
figures, 3 table
Wavelet statistics of functional MRI data and the general linear model
PURPOSE: To improve the signal-to-noise ratio (SNR) of functional magnetic resonance imaging (fMRI) data, an approach is developed that combines wavelet-based methods with the general linear model. MATERIALS AND METHODS: Ruttimann et al. (1) developed a wavelet-based statistical procedure to test wavelet-space partitions for significant wavelet coefficients. Their method is applicable for the detection of differences between images acquired under two experimental conditions using long blocks of stimulation. However, many neuropsychological questions require more complicated event-related paradigms and more experimental conditions. Therefore, in order to apply wavelet-based methods to a wide range of experiments, we present a new approach that is based on the general linear model and wavelet thresholding. RESULTS: In contrast to a monoresolution filter, the application of the wavelet method increased the SNR and showed a set of clearly dissociable activations. Furthermore, no relevant decrease of the local maxima was observed. CONCLUSION: Wavelet-based methods can increase the SNR without diminishing the signal amplitude, while preserving the spatial resolution of the image. The anatomical localization is strongly improved
Correlation between amygdala BOLD activity and frontal EEG asymmetry during real-time fMRI neurofeedback training in patients with depression
Real-time fMRI neurofeedback (rtfMRI-nf) is an emerging approach for studies
and novel treatments of major depressive disorder (MDD). EEG performed
simultaneously with an rtfMRI-nf procedure allows an independent evaluation of
rtfMRI-nf brain modulation effects. Frontal EEG asymmetry in the alpha band is
a widely used measure of emotion and motivation that shows profound changes in
depression. However, it has never been directly related to simultaneously
acquired fMRI data. We report the first study investigating
electrophysiological correlates of the rtfMRI-nf procedure, by combining
rtfMRI-nf with simultaneous and passive EEG recordings. In this pilot study,
MDD patients in the experimental group (n=13) learned to upregulate BOLD
activity of the left amygdala using an rtfMRI-nf during a happy emotion
induction task. MDD patients in the control group (n=11) were provided with a
sham rtfMRI-nf. Correlations between frontal EEG asymmetry in the upper alpha
band and BOLD activity across the brain were examined. Average individual
changes in frontal EEG asymmetry during the rtfMRI-nf task for the experimental
group showed a significant positive correlation with the MDD patients'
depression severity ratings, consistent with an inverse correlation between the
depression severity and frontal EEG asymmetry at rest. Temporal correlations
between frontal EEG asymmetry and BOLD activity were significantly enhanced,
during the rtfMRI-nf task, for the amygdala and many regions associated with
emotion regulation. Our findings demonstrate an important link between amygdala
BOLD activity and frontal EEG asymmetry. Our EEG asymmetry results suggest that
the rtfMRI-nf training targeting the amygdala is beneficial to MDD patients,
and that alpha-asymmetry EEG-nf would be compatible with the amygdala
rtfMRI-nf. Combination of the two could enhance emotion regulation training and
benefit MDD patients.Comment: 28 pages, 16 figures, to appear in NeuroImage: Clinica
A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data
In this paper we propose a unified, probabilistically coherent framework for the analysis of task-related brain activity in multi-subject fMRI experiments. This is distinct from two-stage “group analysis” approaches traditionally considered in the fMRI literature, which separate the inference on the individual fMRI time courses from the inference at the population level. In our modeling approach we consider a spatiotemporal linear regression model and specifically account for the between-subjects heterogeneity in neuronal activity via a spatially informed multi-subject nonparametric variable selection prior. For posterior inference, in addition to Markov chain Monte Carlo sampling algorithms, we develop suitable variational Bayes algorithms. We show on simulated data that variational Bayes inference achieves satisfactory results at more reduced computational costs than using MCMC, allowing scalability of our methods. In an application to data collected to assess brain responses to emotional stimuli our method correctly detects activation in visual areas when visual stimuli are presented
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