One-Class FMRI-Inspired EEG Model for Self-Regulation Training

Abstract

Recent evidence suggests that learned self-regulation of localized brain activity in deep limbic areas such as the amygdala, may alleviate symptoms of affective disturbances. Thus far self-regulation of amygdala activity could be obtained only via fMRI guided neurofeedback, an expensive and immobile procedure. EEG on the other hand is relatively inexpensive and can be easily implemented in any location. However the clinical utility of EEG neurofeedback for affective disturbances remains limited due to low spatial resolution, which hampers the targeting of deep limbic areas such as the amygdala. We introduce an EEG prediction model of amygdala activity from a single electrode. The gold standard used for training is the fMRI-BOLD signal in the amygdala during simultaneous EEG/fMRI recording. The suggested model is based on a time/frequency representation of the EEG data with varying time-delay. Previous work has shown a strong inhomogeneity among subjects as is reflected by the models created to predict the amygdala BOLD response from EEG data. In that work, different models were constructed for different subjects. In this work, we carefully analyzed the inhomogeneity among subjects and were able to construct a single model for the majority of the subjects. We introduce a method for inhomogeneity assessment. This enables us to demonstrate a choice of subjects for which a single model could be derived. We further demonstrate the ability to modulate brain-activity in a neurofeedback setting using feedback generated by the model. We tested the effect of the neurofeedback training by showing that new subjects can learn to down-regulate the signal amplitude compared to a sham group, which received a feedback obtained by a different participant. This EEG based model can overcome substantial limitations of fMRI-NF. It can enable investigation of NF training using multiple sessions and large samples in various locations.

Real‐time fMRI neurofeedback in adolescents with attention deficit hyperactivity disorder

“…If rtfMRI‐NF proves to be a successful treatment for ADHD, it could also be transferred to the cheaper NIRS‐NF which has already been piloted in ADHD [Marx et al, 2014] or to fMRI‐inspired EEG‐NF methods, which use concurrent fMRI‐EEG‐NF to establish the EEG correlates of localized fMRI BOLD activity for a better spatially informed EEG‐NF [Keynan et al, 2016; Meir‐Hasson et al, 2016]. …”

“…Although rtfMRI-NF is expensive, it could potentially achieve a high cost-benefit ratio over long-term pharmacotherapy for ADHD, which is costly, if it treats the underlying cause rather than symptoms of ADHD, reduces or eliminates medication treatment, and has longer-term efficacy and no side effects [Klora et al, 2016]. If rtfMRI-NF proves to be a successful treatment for ADHD, it could also be transferred to the cheaper NIRS-NF which has already been piloted in ADHD [Marx et al, 2014] or to fMRI-inspired EEG-NF methods, which use concurrent fMRI-EEG-NF to establish the EEG correlates of localized fMRI BOLD activity for a better spatially informed EEG-NF Meir-Hasson et al, 2016].…”

Quantifying and modelling non-local information processing of associative brain regions

“…As simulated ( Fig. 4c), spontaneous baseline activity of cortical areas organizes after several seconds to minutes without external stimuli 25,26 around ~8 Hz [27][28][29] . Brain coherence measurements without external stimuli 30,31 align well with the coherence patterns of simulated spontaneous activity in dependence of space and frequency ( Fig.…”

Multi-modal Virtual Scenario Enhances Neurofeedback Learning

“…It is, therefore, important to note that our previous studies Meir-Hasson et al, 2016) clearly showed that volitional regulation of the amyg-EFP could be learned only when veritable feedback and not sham-feedback is provided. Another possible limitation regards the interface chosen for the transferability testing (skateboard visualization; Figure 1C).…”