Data-analysis perspectives on naturalistic stimulation in functional magnetic resonance imaging

Abstract

Modern brain imaging allows to study human brain function during naturalistic stimulus conditions, which entail specific challenges for the analysis of the brain signals. The conventional analysis of data obtained by functional magnetic resonance imaging (fMRI) is based on user-specified models of the temporal behavior of the signals (general linear model, GLM). Alongside these approaches, data-based methods can be applied to model the signal behavior either on the basis of the measured data, as in seed-point correlations or inter-subject correlations (ISC), or alternatively the temporal behavior is not modeled, but spatial signal sources and related time courses are estimated directly from the measured data (independent component analysis, ICA). In this Thesis, fMRI data-analysis methods were studied and compared in experiments that gradually proceeded towards more naturalistic and complex stimuli. ICA showed superior performance compared with GLM-based method in the analysis of naturalistic situations. The particular strengths of the ICA were its capability to reveal activations when signal behavior deviated from an expected model, and to show similarities between signals of different brain areas and of different individuals. The practical difficulty of ICA in naturalistic conditions is that the user may not be able to determine, purely on the basis of the components' spatial distribution or temporal behavior, the brain networks that are related to the given stimuli. In this Thesis, a new solution to sort the components was proposed that ordered the components according to the ISC map, and thereby facilitated the selection of stimulus-related components. The method prioritized brain areas closely related to sensory processing, but it also revealed circuitries of intrinsic processing if they were affected similarly across individuals by external stimulation. Analysis issues related to the impact of physiological noise in fMRI signals were also considered. Cardiac-triggered fMRI improved detection of touch-related activation both in the thalamus and in the secondary somatosensory cortex. The most common way to eliminate noise is to filter the data. In this Thesis, however, aberrations in temporal behavior, as well as in functional connectivities in chronic pain patients were observed, which likely could not have been revealed with conventional temporal filtering.