Data-analysis perspectives on naturalistic stimulation in functional magnetic resonance imaging
Aalto-yliopiston teknillinen korkeakoulu | Doctoral thesis (article-based)
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Verkkokirja (1284 KB, 42 s.)
TKK dissertations, 230
AbstractModern 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.
Supervising professorIlmoniemi, Risto, Prof.
Thesis advisorHari, Riitta, Academy Prof.
functional magnetic resonance imaging, statistical parametric mapping, independent component analysis, correlation analysis, brain
- [Publication 1]: Sanna Malinen, Martin Schürmann, Yevhen Hlushchuk, Nina Forss, and Riitta Hari. 2006. Improved differentiation of tactile activations in human secondary somatosensory cortex and thalamus using cardiac-triggered fMRI. Experimental Brain Research, volume 174, number 2, pages 297-303.
- [Publication 2]: Sanna Malinen, Yevhen Hlushchuk, and Riitta Hari. 2007. Towards natural stimulation in fMRI—Issues of data analysis. NeuroImage, volume 35, number 1, pages 131-139. © 2006 Elsevier Science. By permission.
- [Publication 3]: Sanna Malinen and Riitta Hari. 2010. Comprehension of audiovisual speech: Data-based sorting of independent components of fMRI activity. Espoo, Finland: Aalto University School of Science and Technology. 14 pages. Helsinki University of Technology, Low Temperature Laboratory Publications, Report TKK-KYL-023. ISBN 978-952-60-3170-5. © 2010 by authors.
- [Publication 4]: Sanna Malinen, Nuutti Vartiainen, Yevhen Hlushchuk, Miika Koskinen, Pavan Ramkumar, Nina Forss, Eija Kalso, and Riitta Hari. 2010. Aberrant temporal and spatial brain activity during rest in patients with chronic pain. Proceedings of the National Academy of Sciences of the United States of America, volume 107, number 14, pages 6493-6497. © 2010 by authors.