Abstract

Neuroimaging techniques allow to acquire images of the brain involved in cognitive tasks. In traditional neuroimaging studies, the brain response to external stimulation is investigated. Stimulation categories, the order they are presented to the subject and the presentation duration are dened in the stimulation protocol. The protocol is xed before the beginning of the study and does not change in the course of experiment. Recently, there has been a major rise in the number of real-time neuroscientic experiments where the incoming brain data is analysed in an online mode. Real-time neuroimaging studies open an avenue for approaching a whole new broad range of questions, like, for instance, how the outcome of the cognitive task depends on the current brain state. Real-time experiments need a dierent protocol type that can be exibly and interactively adjusted in line with the experimental scope, e.g. hypotheses testing or optimising design for individual subject's parameters. A plethora of methods is currently deployed for protocol adaptation: information theory, optimisation algorithms, genetic algorithms. What is lacking, however, is the paradigm for interacting with the subject's state, brain state in particular. I am addressing this problem in my research. I have concentrated on two types of real-time experiments: closed-loop stimulation experiments and brain-state dependent stimulation (BSDS). As the rst contribution, I put forward a method for closed-loop stimulation adaptation and apply it in a real-time Galvanic Skin Response (GSR) experimental setting. The second contribution is an unsupervised method for brain state detection and a real-time functional Magnetic Resonance Imaging (rtfMRI) setup making use of this method. In a neurofeedback setting the goal is for the subject to achieve a target state. Ideally, the stimulation protocol should be adapted to the subject to better guide them towards that state. One way to do this would be modelling the subject's activity in a way that we can evaluate the eect of various stimulation options and choose the optimised ones, maximising the reward or minimising the error. However, currently developing such models for neuroimaging neurofeedback experiments presents a number of challenges, namely: complex dynamics of a very noisy neural signal and non-trivial mapping of neural and cognitive processes. We designed a simpler experiment as a proof of concept using GSR signal. We showed that if it is possible to model the subject's state and the dynamics of the system, it is also possible to steer the subject towards the desired state. In BSDS, there is no target state, but the challenge lies in the most accurate identication of the subject state in any given moment. The reference, state-of-the-art method for determining the current brain state is the use of machine learning classiers, or multivariate decoding. However, running supervised machine learning classiers on neuroimaging data has a number of issues that might seriously limit their application, especially in real- time scenarios. For BSDS, we show how an unsupervised machine learning algorithm (clustering in real-time) can be employed with fMRI data to determine the onset of the activated brain state. We also developed a real-time fMRI setup for BSDS that uses this method. In an initial attempt to base BSDS on brain decoding, we encountered a set of issues related to classier use. These issues prompted us to developed a new set of methods based on statistical inference that help address fundamental neuroscientic questions. The methods are presented as the secondary contribution of the thesis.