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Event-related potentials (ERPs) are widely used in basic neuroscience and in clinical diagnostic procedures. In contrast, neurophysiological insights from ERPs have been limited, as several different mechanisms lead to ERPs. Apart from stereotypically repeated responses (additive evoked responses), these mechanisms are asymmetric amplitude modulations and phase-resetting of ongoing oscillatory activity. Therefore, a method is needed that differentiates between these mechanisms and moreover quantifies the stability of a response. We propose a constrained subspace independent component analysis that exploits the multivariate information present in the all-to-all relationship of recordings over trials. Our method identifies additive evoked activity and quantifies its stability over trials. We evaluate identification performance for biologically plausible simulation data and two neurophysiological test cases: Local field potential (LFP) recordings from a visuo-motor-integration task in the awake behaving macaque and magnetoencephalography (MEG) recordings of steady-state visual evoked fields (SSVEFs). In the LFPs we find additive evoked response contributions in visual areas V2/4 but not in primary motor cortex A4, although visually triggered ERPs were also observed in area A4. MEG-SSVEFs were mainly created by additive evoked response contributions. Our results demonstrate that the identification of additive evoked response contributions is possible both in invasive and in non-invasive electrophysiological recordings.
Understanding causal relationships, or effective connectivity, between parts of the brain is of utmost importance because a large part of the brain’s activity is thought to be internally generated and, hence, quantifying stimulus response relationships alone does not fully describe brain dynamics. Past efforts to determine effective connectivity mostly relied on model based approaches such as Granger causality or dynamic causal modeling. Transfer entropy (TE) is an alternative measure of effective connectivity based on information theory. TE does not require a model of the interaction and is inherently non-linear. We investigated the applicability of TE as a metric in a test for effective connectivity to electrophysiological data based on simulations and magnetoencephalography (MEG) recordings in a simple motor task. In particular, we demonstrate that TE improved the detectability of effective connectivity for non-linear interactions, and for sensor level MEG signals where linear methods are hampered by signal-cross-talk due to volume conduction.