Computational NeuroscienceReproducible paired sources from concurrent EEG-fMRI data using BICAR

- We introduce BICAR, a new algorithm for subject-level EEG-fMRI data fusion.
- BICAR ranks each joint source by a task-independent measure of reproducibility.
- We derive an analytical reproducibility cutoff below which components are discarded.
- We apply BICAR to human subjects performing a visual search task.
- Among the most reproducible sources are visual, motor, and attentional components.

We introduce BICAR, an algorithm for obtaining robust, reproducible pairs of temporal and spatial components at the individual subject level from concurrent electroencephalographic and functional magnetic resonance imaging data. BICAR assigns a task-independent measure of component quality, reproducibility, to each paired source. Under BICAR a reproducibility cutoff is derived that can be used to objectively discard spuriously paired EEG-fMRI components. BICAR is run on minimally processed data: fMRI images undergo the standard preprocessing steps (alignment, motion correction, etc.) and EEG data, after scanner artifact removal, are simply bandpass filtered. This minimal processing allows the secondary scoring of the same set of BICAR components for a variety of different endpoint analyses; in this manuscript we propose a general method for scoring components for task event synchronization (evoked response analysis), but scoring using many other criteria, for example frequency content, are possible. BICAR is applied to five subjects performing a visual search task, and among the most reproducible components we find biologically relevant paired sources involved in visual processing, motor planning, execution, and attention.