Humans have always strived for improving their functioning and facilitating their lifestyles, including improving their cognition. While training one's cognition with the help of mnemonics is an accepted and broadly used strategy, utilising drugs, e.g. metylphenidate for improving attention, has been criticised immensely. In the past years, real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback has been used in order to stabilise or regulate activation in certain brain areas or networks without any known damaging side effects. The research described in this dissertation aimed at paving the way for rt-fMRI neurofeedback to train cognition. For this, three important steps in research for improving cognition with this technique are described. First, it was investigated whether humans can self-regulate their brain activation to specific target levels and whether neurofeedback facilitates this. Second, it was researched whether it is possible to predict how people performed on a mental rotation task based on the state of one specific brain network, the default mode network (DMN), even before the task was initiated. This step was necessary to define a target network for the later neurofeedback investigation, thus steps three, which looked at whether it is at all possible to self-regulate this network to specific target levels. In conclusion, the application of neurofeedback for improving cognition is promising, but at the same time, there are many steps to be taken before this technique will be proven efficient, which are described in the current dissertation.
|Award date||30 Nov 2018|
|Place of Publication||Maastricht|
|Publication status||Published - 2018|