N Heugel1, E Liebenthal2, S A Beardsley3. 1. Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA. 2. Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA; Department of Psychiatry, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA. 3. Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA; Clinical Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI, USA. Electronic address: scott.beardsley@marquette.edu.
Abstract
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.
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.
Authors: Einat Liebenthal; Michael L Ellingson; Marianna V Spanaki; Thomas E Prieto; Kristina M Ropella; Jeffrey R Binder Journal: Neuroimage Date: 2003-08 Impact factor: 6.556
Authors: Maria A Bobes; Agustin Lage-Castellanos; Ela I Olivares; Jhoanna Perez Hidalgo-Gato; Jaime Iglesias; Ana Maria Castro-Laguardia; Pedro Valdes-Sosa Journal: Brain Topogr Date: 2018-02-20 Impact factor: 3.020