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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

The transformation steps before applying the metric.a) The original EFP. b) Expanding y-axes to a minimum resolution of 1Hz. c) Collapsing y-axes to a uniform frequency band division. d) Reshaping EFP to a vector.
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pone.0154968.g001: The transformation steps before applying the metric.a) The original EFP. b) Expanding y-axes to a minimum resolution of 1Hz. c) Collapsing y-axes to a uniform frequency band division. d) Reshaping EFP to a vector.

Mentions: The transformation steps before applying the metric are shown in Fig 1. These include: converting their frequency bands on the y-axis to a single frequency band division by expanding their y-axes to a minimum resolution of 1Hz and collapsing this back to a uniform frequency band division. The uniform frequency bands used for comparing two EFPs divided into 10 equal areas the averaged spectral logarithmic mean of the EEG data across all sessions (instead of a single session as in [25]). Then, the resultant EFP matrix coefficients were converted to a vector of size m*n, where m and n are the width and the height of the matrix, respectively.


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)

The transformation steps before applying the metric.a) The original EFP. b) Expanding y-axes to a minimum resolution of 1Hz. c) Collapsing y-axes to a uniform frequency band division. d) Reshaping EFP to a vector.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0154968.g001: The transformation steps before applying the metric.a) The original EFP. b) Expanding y-axes to a minimum resolution of 1Hz. c) Collapsing y-axes to a uniform frequency band division. d) Reshaping EFP to a vector.
Mentions: The transformation steps before applying the metric are shown in Fig 1. These include: converting their frequency bands on the y-axis to a single frequency band division by expanding their y-axes to a minimum resolution of 1Hz and collapsing this back to a uniform frequency band division. The uniform frequency bands used for comparing two EFPs divided into 10 equal areas the averaged spectral logarithmic mean of the EEG data across all sessions (instead of a single session as in [25]). Then, the resultant EFP matrix coefficients were converted to a vector of size m*n, where m and n are the width and the height of the matrix, respectively.

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