Optimizing Within-Subject Experimental Designs for jICA of Multi-Channel ERP and fMRI

• Published in: Frontiers in neuroscience Vol. 12; p. 13
• Main Authors: Mangalathu-Arumana, Jain, Liebenthal, Einat, Beardsley, Scott A.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 23.01.2018; Frontiers Media S.A
• Subjects: Brain mapping; Cross-modal; EEG; Electroencephalography; ERP; Event-related potentials; fMRI; Functional magnetic resonance imaging; independent component analysis (ICA); Mathematical models; Medical imaging; modeling; multimodal neuroimaging; Neuroimaging; Neuroscience; Noise; Physiology; Principal components analysis; United States > US; data fusion; fMRI; ERP; modeling; independent component analysis (ICA); EEG; simulation; multimodal neuroimaging
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2018.00013

Joint independent component analysis (jICA) can be applied within subject for fusion of multi-channel event-related potentials (ERP) and functional magnetic resonance imaging (fMRI), to measure brain function at high spatiotemporal resolution (Mangalathu-Arumana et al., 2012). However, the impact of experimental design choices on jICA performance has not been systematically studied. Here, the sensitivity of jICA for recovering neural sources in individual data was evaluated as a function of imaging SNR, number of independent representations of the ERP/fMRI data, relationship between instantiations of the joint ERP/fMRI activity (linear, non-linear, uncoupled), and type of sources (varying parametrically and non-parametrically across representations of the data), using computer simulations. Neural sources were simulated with spatiotemporal and noise attributes derived from experimental data. The best performance, maximizing both cross-modal data fusion and the separation of brain sources, occurred with a moderate number of representations of the ERP/fMRI data (10-30), as in a mixed block/event related experimental design. Importantly, the type of relationship between instantiations of the ERP/fMRI activity, whether linear, non-linear or uncoupled, did not in itself impact jICA performance, and was accurately recovered in the common profiles (i.e., mixing coefficients). Thus, jICA provides an unbiased way to characterize the relationship between ERP and fMRI activity across brain regions, in individual data, rendering it potentially useful for characterizing pathological conditions in which neurovascular coupling is adversely affected.