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One-Class FMRI-Inspired EEG Model for Self-Regulation Training
- Source :
- PLoS ONE, Vol 11, Iss 5, p e0154968 (2016), PLoS ONE
- Publication Year :
- 2016
- Publisher :
- Public Library of Science (PLoS), 2016.
-
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.
- Subjects :
- Male
Research Validity
Time Factors
Physiology
Computer science
lcsh:Medicine
Electroencephalography
Diagnostic Radiology
0302 clinical medicine
Functional Magnetic Resonance Imaging
Medicine and Health Sciences
lcsh:Science
Clinical Neurophysiology
Brain Mapping
Multidisciplinary
medicine.diagnostic_test
Radiology and Imaging
Physics
Mechanisms of Signal Transduction
Post-Traumatic Stress Disorder
05 social sciences
Brain
Software Engineering
Neurofeedback
Research Assessment
Amygdala
Magnetic Resonance Imaging
Anxiety Disorders
Healthy Volunteers
Electrophysiology
Bioassays and Physiological Analysis
medicine.anatomical_structure
Brain Electrophysiology
Physical Sciences
Engineering and Technology
Female
Anatomy
psychological phenomena and processes
Research Article
Signal Transduction
Adult
Computer and Information Sciences
Feedback Regulation
Imaging Techniques
Models, Neurological
Neurophysiology
Neuroimaging
Neuropsychiatric Disorders
Research and Analysis Methods
Neuroses
EEG-fMRI
050105 experimental psychology
03 medical and health sciences
Diagnostic Medicine
Acoustic Signals
Mental Health and Psychiatry
medicine
Humans
0501 psychology and cognitive sciences
Electrodes
Preprocessing
Mood Disorders
Electrophysiological Techniques
lcsh:R
Biology and Life Sciences
Acoustics
Cell Biology
Class (biology)
nervous system
lcsh:Q
Functional magnetic resonance imaging
Neuroscience
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 11
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....51d720357b651da03accd68f413a70f2