Advances in modeling and characterization of human neuromagnetic oscillations

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School of Science | Doctoral thesis (article-based) | Defence date: 2012-05-07
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Aalto University publication series DOCTORAL DISSERTATIONS, 56/2012
Intracranial electrophysiological measurements as well as electromagnetic recordings from the scalp have shown that oscillatory activity in the human brain plays an important role in sensory and cognitive processing. Communication between distant brain regions seems to be mediated by oscillatory coherence and synchrony. Our brain is both reactive and reflexive: it reacts to changes in the external environment, but it is also influenced by its past and present internal state. On the one hand, task-related or induced modulations of oscillatory activity provide an important marker for cortical excitability and information processing of the reactive brain. On the other hand, spontaneous oscillatory dynamics subserves information processing of the reflexive brain. In this thesis, methods were developed to model and characterize task-related oscillatory changes, as well as spontaneous oscillatory activity measured using magnetoencephalography (MEG). In Publication I, we developed a predictive model to capture the suppression-rebound reactivity of the ~20 Hz mu rhythm originating in the sensorimotor cortex and applied this model to locate the cortical generators of the rhythm from independent measurements. In Publications II and III, we developed temporal and spatial variants of a data-driven method to characterize spatial, temporal, and spectral aspects of spontaneous MEG oscillations. Analysis of complex-valued Fourier coefficients identified well-known rhythms, such as the parieto-occipital ~10-Hz and the rolandic ~20-Hz rhythms consistently across subjects. In Publication IV, we applied independent component analysis to time-frequency representations of cortical current estimates computed from simulated as well as resting-state and naturalistic stimulation data. Group-level analysis of Fourier envelopes also identified the ~20-Hz bilateral sensorimotor network, a subset of the default-mode network at ~8 and ~15 Hz, and lateralized temporal-lobe sources at ~8 Hz. The methods developed here represent important advances in the modeling and characterization of the brain's oscillatory activity measured using non-invasive electrophysiological methods in healthy humans.
Supervising professor
Kaski, Samuel, Prof., Department of Information and Computer Science, School of Science, Aalto University, Finland
Thesis advisor
Hari, Riitta, Acad. Prof., Brain Research Unit, O.V. Lounasmaa Laboratory, School of Science, Aalto University, Finland
Hyvärinen, Aapo, Prof., Department of Computer Science and Department of Mathematics and Statistics, University of Helsinki, Finland
magnetoencephalography, oscillations, oscillatory response, mu rhythm, resting-state, naturalistic stimulation, independent component analysis, time-frequency representation
Other note
  • [Publication 1]: Ramkumar P, Parkkonen L, Hari R. Oscillatory response function: Towards a parametric model of rhythmic brain activity. Human Brain Mapping, 31, 820-834, 2010.
  • [Publication 2]: Hyvärinen A, Ramkumar P, Parkkonen L, Hari R. Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis. NeuroImage, 49, 257-271, 2010.
  • [Publication 3]: Ramkumar P, Parkkonen L, Hari R, Hyvärinen A. Characterization of neuromagnetic brain rhythms over time scales of minutes using spatial independent component analysis. Human Brain Mapping, In Press, 2011.
  • [Publication 4]: Ramkumar P, Parkkonen L, Hyvärinen A. Group-level spatial independent component analysis of Fourier envelopes of resting-state MEG data. Under Revision, 2011.