Dr. Johan van der Meer | Johan.vanderMeer@qimrberghofer.edu.au
Electroencephalographic (EEG) neurofeedback (NF) is a technique by which subjects can learn to gain behavioural control over their brain signals. It is increasingly used as therapeutic intervention for various psychiatric disorders [1-4]. However, NF training has behavioural transfer effects associated with successful EEG regulation only in a subgroup of participants. EEG-NF has been proposed to rely on the dynamics of large-scale functional brain networks, and to normalize pathological network states . Although functional network analysis of EEG-NF based on magnetic resonance imaging (fMRI) would be ideal for testing this idea, direct investigation so far has not been performed, mainly due to large artefacts in the EEG co-registered with fMRI.
The aim of this project is to identify specific functional brain networks involved in NF learning and to characterize their plastic changes with respect to both learning efficiency and possible cognitive transfer effects of NF training. As a follow-up, we can exploit this knowledge to optimize the efficiency and the desired transfer effects of NF learning using combined EEG/fMRI-NF, for example by reinforcing recruitment of specific functional networks during learning. This project will provide grounds for improved NF based therapies and cognitive enhancement training applying to a wider range of patients. As a first step, we will focus on the up- and downregulation of global EEG alpha power by NF to address the following questions:
- Are there different functional networks associated with active alpha power regulation, as compared to spontaneous alpha fluctuations during rest?
- Are there configurations and plastic changes of functional networks related to NF learning of alpha power regulation?
- Are there inter-individual differences in the functional networks explaining differences in the efficiency of NF learning?
- Can we associate the recruitment of particular functional networks with transfer effects of NF training on cognitive performance?
To answer these questions, we are going to carry out EEG alpha-NF simultaneously with functional magnetic resonance imaging (fMRI) by employing a newly developed EEG artefact reduction technique using carbon wire loops (CWL). This technique will permit real-time EEG-NF in combination with MRI/EEG functional network analysis, which has not been achieved before. We will characterize functional networks involved in different stages of NF learning, and relate inter-individual differences of learning efficiency and transfer effects on working memory or mental rotation to different functional network characteristics.
- Niv et al. (2013) Efficacy and potential mechanisms of neurofeedback. Pers and Indiv Diff 54, 676-686.
- Birbaumer et al. (2009) Neurofeedback and brain-computer interface clinical applications. Int Rev Neuro Biol 85, 107-117.
- Arns et al. (2014) Evaluation of neurofeedback in ADHD: The long and winding road. Biol Psych 95, 108-115
- Choi et al. (2011) Is alpha wave neurofeedback effective with randomized clinical trails in depression? A pilot study. Neuropsychobiol 63, 43-51.
This project would suit an (international) Honors/Masters or PhD student who had as a part of their curriculum advanced programming, mathematics and/or signal analysis techniques (applied mathematics, physics, electrical engineering or similar curicullum).
Professor Michael Breakspear
Dr. Johan van der Meer: +61 7 3362 3009, Johan.vanderMeer@qimrberghofer.edu.au