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present study, we investigated the conductivity dependence in a new approach that combines MEG and EEG to accurately obtain, not only the location and tangential components, but also the radial component of the source. In this approach, the source location and tangential components are obtained from MEG alone, and optimal conductivity values of the EEG model are estimated by best-fitting EEG signal, while precisely matching the tangential components of the source in EEG and MEG. Then, the radial components are obtained from EEG using the previously estimated optimal conductivity values. Computer simulations testing this integrated approach demonstrated two main findings. First, there are well-organized optimal combinations of the conductivity values that provide an accurate fit to the combined MEG and EEG data. Second, the radial component, in addition to the location and tangential components, can be obtained with high accuracy without needing to know the precise conductivity profile of the head. We then demonstrated that this new approach performed reliably in an analysis of the 20-ms component from human somatosensory responses elicited by electric median-nerve stimulation.
Jousmäki V, Nishitani N, and Hari R: Brush stimulator for functional brain imaging. Clin Neurophysiol 2007, 118: 2620–2624.
OBJECTIVE: To describe a novel non-magnetic hand-held device to stimulate various parts of the skin and to evaluate its performance in magnetoencephalographic (MEG) recordings. METHODS: The hand-held part of the device consists of an optic fiber bundle that forms a small brush. Half of the fibers emit modulated red light and the other half detect the reflected light from the skin so that the brush-to-skin contact is detected by means of reflectance. RESULTS: Light tapping of the back of the hand at the innervation area of the radial nerve elicited clear responses in all 10 subjects studied, with the main deflections peaking 40-70 ms after the stimulus. The earliest responses, obtained with a higher number of averaged trials, peaked 27-28 ms after the tap to the left hand dorsum. Source analysis of the MEG signals indicated neuronal sources at the primary somatosensory (SI) cortex, with a clear somatotopical order for face vs. hand. CONCLUSIONS: The device seems feasible for both MEG and functional magnetic resonance imaging experiments to address functional anatomy of the human somatosensory system with a real-life like stimulation. SIGNIFICANCE: Non-magnetic and artefact-free tactile stimulator with a selective stimulus offers new possibilities for experimental designs to study the human mechanoreceptor system.
Malinen S, Hlushchuk Y, and Hari R: Towards natural stimulation in fMRI—issues of data analysis. Neuroimage 2007, 25: 131–139.
In search for suitable tools to study brain activation in natural environments, where the stimuli are multimodal, poorly predictable and irregularly varying, we collected functional magnetic resonance imaging data from 6 subjects during a continuous 8-min stimulus sequence that comprised auditory (speech or tone pips), visual (video clips dominated by faces, hands, or buildings), and tactile finger stimuli in blocks of 6-33 s. Results obtained by independent component analysis (ICA) and general-linear-model-based analysis (GLM) were compared. ICA separated in the superior temporal gyrus one independent component (IC) that reacted to all auditory stimuli and in the superior temporal sulcus another IC responding only to speech. Several distinct and rather symmetric vision-sensitive ICs were found in the posterior brain.
Annual Report 2007