Expanding the applicability of magnetoencephalography
Doctoral thesis (article-based)
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TKK dissertations, 173
AbstractMagnetoencephalography (MEG) offers a unique way to non-invasively monitor the neural activity in the human brain. MEG is based on measuring the very weak magnetic fields generated by the electric currents in the active neurons. Such measurements allow, with certain limitations, estimating the underlying current distribution and thus the locations and time courses of the neural generators with an excellent temporal resolution. The aim of this Thesis was to advance MEG to certain realms that have been considered difficult or even impossible for it. Specifically, the included studies contributed to the modelling of the neural generators, detection of activity in the deep brain areas, analysis of oscillatory activity, and characterisation of neural states related to bistable perception. Estimating the sources of MEG signals is non-trivial as multiple current constellations can give rise to the same observed magnetic fields. As a new solution to this problem, we introduced an automatic Bayesian tracking algorithm that recovers the locations and time courses of a set of focal neural current sources from MEG data. The majority of MEG experiments have concentrated on brain signals originating in the neocortex due to the rapid decrease of the MEG signals as a function increasing source depth. Here, we demonstrated that neural activity deep in the brainstem can be detected and accurately localised by MEG in favourable conditions. We also explored the utility of stochastic resonance in varying the salience of a cognitive stimulus, and showed that the detection accuracy of visually-presented words correlated better with the amplitudes of the late than early responses. The temporal resolution provided by MEG was exploited in novel ways. We showed that oscillatory 20-Hz signals from the primary and secondary somatosensory cortex were transiently phase-locked in response to a stimulus, possibly signifying functional connectivity. We also introduced a frequency-tagging method employing dynamical noise to separate brain activations elicited by different parts of a visual scene: monitoring these rhythmic signals with MEG enabled us to probe the neural engagement in the early visual brain areas during bistable perception and thus to link subjective perceptual states to brain states.
magnetoencephalography, signal processing, inverse modelling, brain, human sensory systems
- [Publication 1]: Alberto Sorrentino, Lauri Parkkonen, Annalisa Pascarella, Cristina Campi, and Michele Piana. 2009. Dynamical MEG source modeling with multi-target Bayesian filtering. Human Brain Mapping, volume 30, number 6, pages 1911-1921.
- [Publication 2]: Lauri Parkkonen, Nobuya Fujiki, and Jyrki P. Mäkelä. 2009. Sources of auditory brainstem responses revisited: Contribution by magnetoencephalography. Human Brain Mapping, volume 30, number 6, pages 1772-1782.
- [Publication 3]: Cristina Simões, Ole Jensen, Lauri Parkkonen, and Riitta Hari. 2003. Phase locking between human primary and secondary somatosensory cortices. Proceedings of the National Academy of Sciences of the United States of America (PNAS), volume 100, number 5, pages 2691-2694. © 2003 National Academy of Sciences of the United States of America. By permission.
- [Publication 4]: Alberto Sorrentino, Lauri Parkkonen, Michele Piana, Anna Maria Massone, Livio Narici, Simone Carozzo, Massimo Riani, and Walter G. Sannita. 2006. Modulation of brain and behavioural responses to cognitive visual stimuli with varying signal-to-noise ratios. Clinical Neurophysiology, volume 117, number 5, pages 1098-1105. © 2006 International Federation of Clinical Neurophysiology (IFCN) and © 2006 Elsevier Science. By permission.
- [Publication 5]: Lauri Parkkonen, Jesper Andersson, Matti Hämäläinen, and Riitta Hari. 2008. Early visual brain areas reflect the percept of an ambiguous scene. Proceedings of the National Academy of Sciences of the United States of America (PNAS), volume 105, number 51, pages 20500-20504. © 2008 National Academy of Sciences of the United States of America. By permission.