Exploring the potential of real-time fMRI to change thought trajectories

Abstract

Real-time functional magnetic resonance imaging (rt-fMRI) is a non-invasive imaging technique with the potential to help us understand and train both normal and abnormal brain functioning. As fMRI records whole-brain, spatially-localized, patterns of activity in response to stimuli, neurofeedback capitalizes on this data by adjusting stimuli to drive neural responses. When applied to the clinical domain, rt-fMRI can provide specific training to the region or cognitive process thought to be deviant from healthy subjects. Given the potential of rt-fMRI, exploration must be done to develop new methods and uncover inherent limitations of this technique. This thesis represents work done to realize the potential of rt-fMRI for driving specific cognitive changes. While the current application of rt-fMRI is primarily focused on the treatment of depression, the experimental techniques can similarly be applied to other clinical populations. Chapter 2 reviews differences in how depressed and control subjects allocate attention. Attention encapsulates many cognitive processes, from initial encoding to sustained maintenance of stimuli. The review highlights the variability in results that are dependent upon the task used. This motivates the exploration of different real-time experimental designs and tasks to improve mental health; two such projects are detailed in Chapters 3 and 4. The study described in Chapter 3 directly aims to reduce depression severity through attention training, while the experiments presented in Chapter 4 explore the possibility of modifying story interpretations. In parallel, we developed an open-source cloud-based software framework for real-time processing. Our pipeline and experiments can be deployed at other institutions, with the hope that this encourages growth of the rt-fMRI community. Altogether, the work presented in this thesis tests the feasibility of rt-fMRI to be used to alter thought through neurofeedback. The findings in the respective experiments help us to understand the capabilities of this technique, while the cloud software facilitates future use.