Abstract
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.
Highlights
Electrophysiological and hemodynamic measures of brain function vary in terms of their spatial and temporal resolution and the relation of the measured signals to the underlying neural activity
We examined the effect on within-subject Joint independent component analysis (jICA) performance of several variables relevant to the fusion of event-related potentials (ERP) and Functional magnetic resonance imaging (fMRI)
We used computer simulations to examine the performance of jICA as a function of imaging signal-to-noise ratio (SNR), number of independent representations of the ERP/fMRI data, the ERP temporal profile, and the relationship between the ERP and fMRI signals, for parametric and non-parametric experimental designs
Summary
Electrophysiological and hemodynamic measures of brain function vary in terms of their spatial and temporal resolution and the relation of the measured signals to the underlying neural activity (direct vs. indirect, respectively). FMRI measures brain function on a millimeter spatial scale and temporal scale of seconds in the form of slow hemodynamic responses in clusters of neighboring neurons. The complementary spatial and temporal scales of EEG and fMRI, and the possibility of acquiring the activity simultaneously, has been leveraged to examine brain function at a combined millisecond temporal and millimeter spatial scale (Bonmassar et al, 2001; Dale and Halgren, 2001; Horovitz et al, 2002; Liebenthal et al, 2003, 2010; Mulert et al, 2004; Debener et al, 2005, 2006; Bénar et al, 2007; Liu and He, 2008; Liu et al, 2010; Bridwell et al, 2013; Cottereau et al, 2015; Nguyen et al, 2016). Given the differences in the nature of the activity in each modality, an outstanding question is the degree to which they reflect the same neural activity
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