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

Meir-Hasson Y, Keynan JN, Kinreich S, Jackont G, Cohen A, Podlipsky-Klovatch I, Hendler T, Intrator N - PLoS ONE (2016)

Bottom Line: In that work, different models were constructed for different subjects.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.

View Article: PubMed Central - PubMed

Affiliation: The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.

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.

No MeSH data available.


Related in: MedlinePlus

Common EFP characteristics.a) Individual EFP prediction correlation coefficient on the validation set using different electrodes on the back of the brain, averaged over all 'successful' sessions (i.e., those with prediction correlation coefficients greater than 0.6 on the validation set using any electrode, n = 26). The electrodes are sorted according to their signal-to-noise ratio (μ∕σ). b) The dendrogram of the clustering results and the EFPs’ coefficients in the leaves. The different clusters are marked in different colors. The 10 selected sessions, belonging to the biggest cluster, are marked in red. c) The cEFP frequency bands divide the averaged spectral logarithmic mean of the EEG data across the 10 selected sessions to 10 equal areas.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4862623&req=5

pone.0154968.g004: Common EFP characteristics.a) Individual EFP prediction correlation coefficient on the validation set using different electrodes on the back of the brain, averaged over all 'successful' sessions (i.e., those with prediction correlation coefficients greater than 0.6 on the validation set using any electrode, n = 26). The electrodes are sorted according to their signal-to-noise ratio (μ∕σ). b) The dendrogram of the clustering results and the EFPs’ coefficients in the leaves. The different clusters are marked in different colors. The 10 selected sessions, belonging to the biggest cluster, are marked in red. c) The cEFP frequency bands divide the averaged spectral logarithmic mean of the EEG data across the 10 selected sessions to 10 equal areas.

Mentions: The common EFP for the amygdala was based on electrode Pz. Fig 4a shows the performance of different electrodes using an individual model for this task. While electrode P3 appears slightly better than the others, adjacent electrodes in more posterior regions, achieved roughly similar results (i.e., an insignificant difference at *p<0.05). The chosen electrode was, nevertheless, Pz, which is adjacent to electrode P3. Therefore, both are less sensitive to eye movements and to the "Berger effect" [39]. However, unlike electrode P3, electrode Pz is closer to the medial temporal cortex. Due to its medial location, it might be more sensitive in detecting amygdala activity in both hemispheres. In addition, recent papers dealing with T/A training using a single electrode have chosen Pz as their NF electrode [40,41].


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

Meir-Hasson Y, Keynan JN, Kinreich S, Jackont G, Cohen A, Podlipsky-Klovatch I, Hendler T, Intrator N - PLoS ONE (2016)

Common EFP characteristics.a) Individual EFP prediction correlation coefficient on the validation set using different electrodes on the back of the brain, averaged over all 'successful' sessions (i.e., those with prediction correlation coefficients greater than 0.6 on the validation set using any electrode, n = 26). The electrodes are sorted according to their signal-to-noise ratio (μ∕σ). b) The dendrogram of the clustering results and the EFPs’ coefficients in the leaves. The different clusters are marked in different colors. The 10 selected sessions, belonging to the biggest cluster, are marked in red. c) The cEFP frequency bands divide the averaged spectral logarithmic mean of the EEG data across the 10 selected sessions to 10 equal areas.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4862623&req=5

pone.0154968.g004: Common EFP characteristics.a) Individual EFP prediction correlation coefficient on the validation set using different electrodes on the back of the brain, averaged over all 'successful' sessions (i.e., those with prediction correlation coefficients greater than 0.6 on the validation set using any electrode, n = 26). The electrodes are sorted according to their signal-to-noise ratio (μ∕σ). b) The dendrogram of the clustering results and the EFPs’ coefficients in the leaves. The different clusters are marked in different colors. The 10 selected sessions, belonging to the biggest cluster, are marked in red. c) The cEFP frequency bands divide the averaged spectral logarithmic mean of the EEG data across the 10 selected sessions to 10 equal areas.
Mentions: The common EFP for the amygdala was based on electrode Pz. Fig 4a shows the performance of different electrodes using an individual model for this task. While electrode P3 appears slightly better than the others, adjacent electrodes in more posterior regions, achieved roughly similar results (i.e., an insignificant difference at *p<0.05). The chosen electrode was, nevertheless, Pz, which is adjacent to electrode P3. Therefore, both are less sensitive to eye movements and to the "Berger effect" [39]. However, unlike electrode P3, electrode Pz is closer to the medial temporal cortex. Due to its medial location, it might be more sensitive in detecting amygdala activity in both hemispheres. In addition, recent papers dealing with T/A training using a single electrode have chosen Pz as their NF electrode [40,41].

Bottom Line: In that work, different models were constructed for different subjects.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.

View Article: PubMed Central - PubMed

Affiliation: The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.

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.

No MeSH data available.


Related in: MedlinePlus