Stratification and Augmentation of EEG-Neurofeedback in MDD by Monitoring of Dynamic Brain States via Simultaneous Electroencephalography and Magnetic Resonance (EEG-fMRI)

• Applicant: Professor Dr. Martin Walter
• Subject Area: Clinical Psychiatry, Psychotherapy, Child and Adolescent Psychiatry ; Cognitive, Systems and Behavioural Neurobiology
• Term: from 2017 to 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 388570319

EEG Neurofeedback (NF) is a computer guided training method, which enables to train for a voluntary control over ones own brain signals. Because these are often abnormal in neuropsychiatric disorders, EEG NF poses a promising alternative or supplementary treatment option for neuropsychiatric disorders, including Major Depressive Disorder (MDD) as a common disorder affecting almost 20% of the population (Wittchen et al 2010).However, EEG NF efficacy is still low. There has always been a considerable proportion (30 to 40%) of people in which NF learning is ineffective that are therefore NF illiterate (Arns et al., 2014; Birbaumer et al., 2009). In MDD this also holds true. This illiteracy is not specific for MDD - it is a long existing issue that has been prevalent in the entire field of EEG Neurofeedback. Apart from one animal study showing that corticostriatal plasticity is important, the underlying neurophysiological mechanisms of EEG-NF illiteracy, especially in humans, have yet to be elucidated.Due to our recent methodological developments in simultaneous EEG/fMRI and previous investigatoins of dynamic network disturbances in MDD, we are now in a unique position to examine the underlying neurophysiological brain mechanisms of successful and insufficient EEG NF learning and exploit this understanding for improving individual NF success rates.In the proposed project, we aim to:1) Gain insights on the underlying neurophysiological mechanisms of EEG NF learning and related illiteracy with a simultaneous EEG/fMRI setup that allows for EEG NF training during fMRI scanning.2) Transfer knowledge of the detected mechanisms to accomplish our second aim: to augment EEG NF learning performance by using functional brain state information from the MRI scanner derived in real time during EEG NF training. We here will establish and validate a new method: simultaneous EEG/fMRI compound neurofeedback.We will accomplish this aim by 1) methodological work on the real time integration of multimodal neurofeedback and 2) dynamic analysis of newly acquired and preexisting datasets. Secondary exploitation of the generated data aims at definition of 3) individual fMRI predictors of illiteracy, 4) functional network definition during successful neurofeedback and 5) brain networks associated to transfer effects on functional targets for depression.

• DFG Programme: Research Grants