An Exploration of Real-Time Functional Magnetic Resonance Imaging Neurofeedback in Cognition
by Wan Ilma Dewiputri
Date of Examination:2014-10-14
Date of issue:2014-10-31
Advisor:Prof. Dr. Jens Frahm
Referee:Prof. Dr. Jens Frahm
Referee:Prof. Dr. Stefan Treue
Referee:Prof. Dr. Michael Waldmann
Referee:PD Dr. Peter Dechent
Referee:Prof. Hansjörg Scherberger
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Abstract
English
This thesis deals with an extensive exploration of the use of rt-fMRI neurofeedback in cognition. Specifically, the work presented here comprises the major steps in the development of a suitable experimental framework for rt-fMRI NF in a cognitive brain region: defining a target region, defining a strategy and protocol for training the target region by NF, and testing the cognitive and behavioral effects of NF training. The target region, the anterior mid-cingulate cortex was defined based on its function in the cognitive process of anticipation, which was probed in the Continuous Performance Task (CPT). Two different methods were compared to analyze fMRI data of the CPT: the classical GLM approach and the combination of ICA with GLM. The next part of this work then details the extensive rt-fMRI neurofeedback training on the aMCC, which had to meet various challenges, which are outlined in detail. To resolve the challenges, this work introduced a novel Serial neurofeedback paradigm, which temporally uncouples self-regulation and feedback appraisal. Compared to the conventional Parallel paradigm, the Serial paradigm emerges as a promising new tool in facilitating learning in rt-fMRI neurofeedback. Finally, this work employed two behavioral tests, the CPT and Flanker task, as measures to investigate the effects of successful self-regulation of the aMCC on the processes of anticipation and conflict resolution, respectively. Overall, the work presented in this thesis is the first extensive rt-fMRI neurofeedback study of the aMCC and provides insights into neurofeedback learning.
Keywords: fMRI; neurofeedback; cognition; anterior mid-cingulate cortex; operant learning