Neurofeedback Preventive Intervention for PTSD: a Method to Strengthen Mental and Emotional Resilience

The most significant disadvantage of the current EEG-NF method is the low spatial resolution of the EEG, which does not allow for the localization of site activity within deep brain regions known to be associated with the development of PTSD, such as the amygdala, the hippocampus, and the mPFC. The aim of the present project is to overcome this drawback by integrating EEG and fMRI imaging methods, such that the patient will receive real-time feedback regarding the activation of emotional target areas located deep within the brain. FMRI is based on the measurement of metabolic changes following electrical activity in the brain, thus non-invasively representing the neuronal activity of various brain regions when performing emotional and/or cognitive tasks. fMRI has high spatial resolution, and in contrast to EEG - it allows for the localization of regions deep within the brain, such as those related to trauma responses within the limbic system and prefrontal cortex. Recently, real-time fMRI (rt fMRI) has been used to train subjects to modulate activity within deep brain regions , . Some studies have even shown that subjects can be trained to regulate activity within a network of regions including the mPFC and limbic regions, resulting in a positive effect on patients suffering from chronic pain and depression . Moreover, it has also been found that via real-time fMRI feedback subjects can be trained to regulate activity within networks that have been linked to emotional regulation; a skill critical for dealing with traumatic events . The major disadvantage of this method is that fMRI tests are expensive and inconvenient for the patient, and the MRI machine required for this method are stationary and not easily accessible.

To combine the advantages of both methods the investigators performed simultaneous testing of both EEG and fMRI. Using special algorithms, with high reliability, the investigators studied the electrical signals that represent activity related to emotional regulation within deep brain regions . This combination allowed for a significant improvement in the spatial resolution of the EEG device and added significant temporal information taken from the fMRI signal. The combined recording of EEG and fMRI with advanced computational methods, such as cross correlation and machine learning, provide significant improvement in the attribution of EEG signal localization, which until now could not be achieved with adequate reliability. In other words, the EEG pattern of activity reliably represents activation of deep limbic regions providing an "electrical fingerprint" (EFP) of these areas.

Accordingly, the investigators developed an innovative treatment protocol in which subjects are asked to control either visual or auditory stimuli, determined by feedback from the brain, based on the extent of the EFP . In a pilot study on a group of civilians the investigators showed that subjects successfully learned to modify their electrical brain signal based on the EFP . In an additional study, simultaneous recordings of EEG and fMRI showed that success was indeed related to changes in activity within deep brain regions involved in emotional regulation; such as the mPFC and hippocampus. Furthermore, our results indicate that following training to volitionally regulate the EFP via EEG-NF, participants exhibited improved emotion regulation .

Rationale for current study:

Based on the EFP model and the NF literature in the treatment of PTSD this study aims to examine the efficiency of an fMRI-guided-EEG-protocol for self-regulation through NF for reducing stress vulnerability.