Mapping brain activity using event-related independent components analysis (eICA): specific advantages for EEG-fMRI.

Abstract

Event-related analyses of functional MRI (fMRI) typically assume that the onset and offset of neuronal activity match stimuli onset and offset, and that evoked fMRI signal changes follow the canonical haemodynamic response function (HRF). Some event types, however, may be unsuited to this approach: brief stimuli might elicit an extended neuronal response; anticipatory effects might result in activity preceding the event; or altered neurovascular coupling may result in a non-canonical HRF. An example is interictal epileptiform discharges (IEDs), which may show a non-canonical HRF and fMRI signal changes preceding their onset as detected on EEG. In such cases, less constrained analyses - capable of detecting early, non-canonical responses - may be necessary. A consequence of less constrained analyses, however, is that artefactual sources of signal change - motion or physiological noise for example - may also be detected and mixed with the neuronally-generated signals. In this paper, to address this issue, we describe an event-related independent components analysis (eICA) that identifies different sources of event-related signal change that can then be separately assessed to identify likely artefacts and separate primary from propagated activity. We also describe a group analysis that identifies eICA components that are spatially and temporally consistent across subjects and provides an objective approach for selecting group-specific components likely to be of neural origin. We apply eICA to patients with rolandic epilepsy - with stereotypical IEDs arising from a focus in the rolandic fissure - and demonstrate that a single event-related component, concordant with this source location, is detected.