The potential of MR-Encephalography for BCI/Neurofeedback applications with high temporal resolution

Autor: Benedikt A. Poser, Florian Krause, Rainer Goebel, Bettina Sorger, Juergen Hennig, Armin Heinecke, Bruno Riemenschneider, Amaia Benitez Andonegui, Michael Lührs, Fabrizio Esposito, Judith Eck
Přispěvatelé: Vision, RS: FPN CN 1, MRI, RS: FPN CN 5, Luhrs, M., Riemenschneider, B., Eck, J., Andonegui, A. B., Poser, B. A., Heinecke, A., Krause, F., Esposito, F., Sorger, B., Hennig, J., Goebel, R., Netherlands Institute for Neuroscience (NIN)
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Male
ECHO-PLANAR
Computer science
Stress-related disorders Donders Center for Medical Neuroscience [Radboudumc 13]
STATISTICAL-ANALYSIS
NEUROFEEDBACK
Signal
0302 clinical medicine
CONNECTIVITY
130 000 Cognitive Neurology & Memory
Image Processing
Computer-Assisted
MR-Encephalography
GLM
MREG
Real-time
Neurology
Cognitive Neuroscience
Echo-planar imaging
Brain Mapping
medicine.diagnostic_test
Functional connectivity
05 social sciences
Brain
Electroencephalography
Human brain
FUNCTIONAL MRI
HUMAN BRAIN
Magnetic Resonance Imaging
medicine.anatomical_structure
PROSPECTIVE MOTION CORRECTION
Brain-Computer Interfaces
Brain size
Artifact
Female
Artifacts
Human
Adult
Heartbeat
050105 experimental psychology
03 medical and health sciences
Young Adult
Motor imagery
ENHANCEMENT
medicine
Humans
0501 psychology and cognitive sciences
Brain–computer interface
business.industry
Pattern recognition
Independent component analysis
SIGNAL
Temporal resolution
Artificial intelligence
Neurofeedback
Functional magnetic resonance imaging
business
FMRI TIME-SERIES
030217 neurology & neurosurgery
Zdroj: Neuroimage, 194, 228-243. Elsevier Science
NeuroImage, 194, 228-243
NeuroImage, 194, 228-243. Academic Press
NeuroImage, 194, pp. 228-243
ISSN: 1053-8119
Popis: Item does not contain fulltext Real-time functional magnetic resonance imaging (rt-fMRI) enables the update of various brain-activity measures during an ongoing experiment as soon as a new brain volume is acquired. However, the recorded Blood-oxygen-level dependent (BOLD) signal also contains physiological artifacts such as breathing and heartbeat, which potentially cause misleading false positive effects especially problematic in brain computer interface (BCI) and neurofeedback (NF) setups. The low temporal resolution of echo planar imaging (EPI) sequences (which is in the range of seconds) prevents a proper separation of these artifacts from the BOLD signal. MR-Encephalography (MREG) has been shown to provide the high temporal resolution required to unalias and correct for physiological fluctuations and leads to increased specificity and sensitivity for mapping task-based activation and functional connectivity as well as for detecting dynamic changes in connectivity over time. By comparing a simultaneous multislice echo planar imaging (SMS-EPI) sequence and an MREG sequence using the same nominal spatial resolution in an offline analysis for three different experimental fMRI paradigms (perception of house and face stimuli, motor imagery, Stroop task), the potential of this novel technique for future BCI and NF applications was investigated. First, adapted general linear model pre-whitening which accounts for the high temporal resolution in MREG was implemented to calculate proper statistical results and be able to compare these with the SMS-EPI sequence. Furthermore, the respiration- and cardiac pulsation-related signals were successfully separated from the MREG signal using independent component analysis which were then included as regressors for a GLM analysis. Only the MREG sequence allowed to clearly separate cardiac pulsation and respiration components from the signal time course. It could be shown that these components highly correlate with the recorded respiration and cardiac pulsation signal using a respiratory belt and fingertip pulse plethysmograph. Temporal signal-to-noise ratios of SMS-EPI and MREG were comparable. Functional connectivity analysis using partial correlation showed a reduced standard error in MREG compared to SMS-EPI. Also, direct time course comparisons by down-sampling the MREG signal to the SMS-EPI temporal resolution showed lower variance in MREG. In general, we show that the higher temporal resolution is beneficial for fMRI time course modeling and this aspect can be exploited in offline application but also, is especially attractive, for real-time BCI and NF applications.
Databáze: OpenAIRE
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