Combining EPI and motion correction for fMRI human brain images with big motion

Motion correction is an important component in fMRI brain image analysis. Linear registration technique is mostly used in the process based on the assumption that there is not any shape changes of human brain during imaging process. Echo planar imaging (EPI) technique has been widely adapted in fMRI imaging to shorten encoding duration and increase temporal resolution. However, due to the magnetic field inhomogeneity caused by tissues, shape distortion and signal intensity lose are brought into fMRI images by the technique. On the other hand, subject's pose in scanner has a effect on magnetic field inhomogeneity, so the EPI distortions are subject to head movement, especially when the movement is big. As a result, most current motion correction techniques, which are based on rigid registration, cannot handle the problem. In this paper, a technique that combines EPI distortion correction and motion correction to handle the above-mentioned problem is proposed. Since it is almost impossible to obtain ground truth at present, a task-related fMRI BOLD time course image with big motion is selected as experimental material to test its performance. The image is pre-processed with the proposed EPI-motion correction scheme then analyzed by FSL feat tool [1]. Compared with another process with only motion correction and FSL feat analysis, the experimental result using the proposed method has no false activation detection. It is suggested the proposed EPI-motion correction scheme has the ability to handle the fMRI human brain images with big motion

Real-time fMRI connectivity neurofeedback for modulation of the motor system

Advances in functional magnetic resonance imaging (fMRI) have enabled an understanding of the neural mechanisms underlying human brain functions such as motor functions. In recent decades fMRI, which is a non-invasive and highresolution technique, has been used to investigate the functions of the human brain using the blood oxygen level dependent (BOLD) response as an indirect measurement of brain neural activities. Real-time fMRI (rt-fMRI) has been used as neurofeedback to enable individuals to regulate their neural activity to achieve improvements in their health and performance, such as their motor performance. Neurofeedback can be defined as the measurement of the neural activity of a participant that is presented to them as visual or auditory signals that enable self-regulation of neural activity. Rt-fMRI has also been used to provide feedback about the connectivity between brain regions. Such connectivity neurofeedback can be a more effective feedback strategy than providing feedback from a single region. Recently, connectivity neurofeedback has been explored to examine how functional connectivity of cortical areas and subcortical areas of the brain can be modulated. Enhancing connectivity between cortical and subcortical regions holds promise for the improvement of performance, particularly motor function performance. The aim of this PhD research was to modulate connectivity neurofeedback by using real-time fMRI neurofeedback (rt-fMRI-NF) between brain regions and to investigate whether any possible enhancement in the activation due to a successful fMRI-NF will translate into changes in behavioural measures. The thesis research began with experimental work to establish the experimental paradigm. This included work, using fMRI, to develop and test localisers for different motor areas such as primary motor cortex (M1), supplementary motor cortex (SMA), the motor cerebellum and the motor thalamus. The results showed that the execution of actions, such as hand clenching, can be used to functionally activate many motor areas including M1, SMA and the cerebellum. The motor thalamus was localised using a motor thalamus mask that was created offline using the Talairach atlas. All localisers tested in this research were feasible and able to be used for applications such as rt-fMRI-NF research to define the regions of interest. The first rt-fMRI connectivity neurofeedback experimental study of this thesis was conducted to determine whether healthy participants can use neurofeedback to enhance the connectivity between M1 and the thalamus using rt-fMRI. It also aimed to investigate whether successful rt-fMRI-NF of M1- thalamus connectivity could translate into changes in behavioural measures. For this purpose, the behavioural tasks were conducted before and after each MRI session. Two behavioural tasks were used in this experiment: Go/No Go and switching tasks. The results of this experiment showed a significant increase in connectivity neurofeedback in the experimental group (M1-thalamus), hence, rt-fMRI-NF is a useful tool to modulate functional connectivity between M1 and the thalamus using motor imagery and it facilitates the learning by participants of new mental strategies to upregulate M1-thalamus connectivity. The behavioural tasks showed a significant reduction in the switching time in the experimental group while Go/No Go task did not show a significant reduction in the reaction time in the experimental group. The second rt-fMRI connectivity neurofeedback experimental study of this thesis was conducted to investigate the ability of neurofeedback to modulate M1-cerebellum connectivity using motor imagery based rt-fMRI-NF. The results of this research showed enhanced connectivity between M1 and the cerebellum in each participant. However, this enhancement was not statistically significant. In summary, this PhD thesis extends and validates the usefulness of connectivity neurofeedback using motor imagery based rt-fMRI to modulate the correlation between cortical and subcortical brain regions. Successful modulation using this technique has the potential to lead to an enhancement in motor functions. Thereby, the results of this PhD research may help to advance connectivity neurofeedback for use as a supplementary treatment for many brain disorders such as stroke recovery and Parkinson’s disease