The doctoral dissertations of the former Helsinki University of Technology (TKK) and Aalto University Schools of Technology (CHEM, ELEC, ENG, SCI) published in electronic format are available in the electronic publications archive of Aalto University - Aaltodoc.
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Dissertation for the degree of Doctor of Science in Technology to be presented with due permission of the Faculty of Information and Natural Sciences for public examination and debate in Auditorium AS2 at Helsinki University of Technology (Espoo, Finland) on the 12th of June, 2009, at 12 noon.
Overview in PDF format (ISBN 978-951-22-9951-5) [1385 KB]
Dissertation is also available in print (ISBN 978-951-22-9950-8)
Magnetoencephalography (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.
This thesis consists of an overview and of the following 5 publications:
Keywords: magnetoencephalography, signal processing, inverse modelling, brain, human sensory systems
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© 2009 Helsinki University of Technology