Method for spatial overlap estimation of electroencephalography and functional magnetic resonance imaging responses.
Adult
Auditory Perception
/ physiology
Brain Mapping
/ methods
Cerebral Cortex
/ diagnostic imaging
Electroencephalography
/ methods
Event-Related Potentials, P300
/ physiology
Evoked Potentials
/ physiology
Humans
Magnetic Resonance Imaging
/ methods
Neurosciences
/ methods
Signal Processing, Computer-Assisted
Auditory
EEG
ERP
Joint independent component analysis (jICA)
Oddball
P300
fMRI
Journal
Journal of neuroscience methods
ISSN: 1872-678X
Titre abrégé: J Neurosci Methods
Pays: Netherlands
ID NLM: 7905558
Informations de publication
Date de publication:
01 12 2019
01 12 2019
Historique:
received:
27
03
2019
revised:
19
07
2019
accepted:
20
08
2019
pubmed:
25
8
2019
medline:
24
10
2020
entrez:
25
8
2019
Statut:
ppublish
Résumé
Simultaneous functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) measurements may represent activity from partially divergent neural sources, but this factor is seldom modeled in fMRI-EEG data integration. This paper proposes an approach to estimate the spatial overlap between sources of activity measured simultaneously with fMRI and EEG. Following the extraction of task-related activity, the key steps include, 1) distributed source reconstruction of the task-related ERP activity (ERP source model), 2) transformation of fMRI activity to the ERP spatial scale by forward modelling of the scalp potential field distribution and backward source reconstruction (fMRI source simulation), and 3) optimization of fMRI and ERP thresholds to maximize spatial overlap without a priori constraints of coupling (overlap calculation). FMRI and ERP responses were recorded simultaneously in 15 subjects performing an auditory oddball task. A high degree of spatial overlap between sources of fMRI and ERP responses (in 9 or more of 15 subjects) was found specifically within temporoparietal areas associated with the task. Areas of non-overlap in fMRI and ERP sources were relatively small and inconsistent across subjects. The ERP and fMRI sources estimated with solely jICA overlapped in just 4 of 15 subjects, and strictly in the parietal cortex. The study demonstrates that the new fMRI-ERP spatial overlap estimation method provides greater spatiotemporal detail of the cortical dynamics than solely jICA. As such, we propose that it is a superior method for the integration of fMRI and EEG to study brain function.
Sections du résumé
BACKGROUND
Simultaneous functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) measurements may represent activity from partially divergent neural sources, but this factor is seldom modeled in fMRI-EEG data integration.
NEW METHOD
This paper proposes an approach to estimate the spatial overlap between sources of activity measured simultaneously with fMRI and EEG. Following the extraction of task-related activity, the key steps include, 1) distributed source reconstruction of the task-related ERP activity (ERP source model), 2) transformation of fMRI activity to the ERP spatial scale by forward modelling of the scalp potential field distribution and backward source reconstruction (fMRI source simulation), and 3) optimization of fMRI and ERP thresholds to maximize spatial overlap without a priori constraints of coupling (overlap calculation).
RESULTS
FMRI and ERP responses were recorded simultaneously in 15 subjects performing an auditory oddball task. A high degree of spatial overlap between sources of fMRI and ERP responses (in 9 or more of 15 subjects) was found specifically within temporoparietal areas associated with the task. Areas of non-overlap in fMRI and ERP sources were relatively small and inconsistent across subjects.
COMPARISON WITH EXISTING METHOD
The ERP and fMRI sources estimated with solely jICA overlapped in just 4 of 15 subjects, and strictly in the parietal cortex.
CONCLUSION
The study demonstrates that the new fMRI-ERP spatial overlap estimation method provides greater spatiotemporal detail of the cortical dynamics than solely jICA. As such, we propose that it is a superior method for the integration of fMRI and EEG to study brain function.
Identifiants
pubmed: 31445115
pii: S0165-0270(19)30258-4
doi: 10.1016/j.jneumeth.2019.108401
pmc: PMC6810902
mid: NIHMS1538914
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
108401Subventions
Organisme : NIDCD NIH HHS
ID : R01 DC006287
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001436
Pays : United States
Informations de copyright
Copyright © 2019 Elsevier B.V. All rights reserved.
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