Local generators of spontaneous brain rhythms can be identified from the measured magnetic field pattern both in the time and frequency domains when the sources are dipolar. The dipole assumption is most efficient when only a few sources contribute to the signal at one time or frequency. We have quantified the effects of different filters and spectral transformation sizes in the analysis of spontaneous magnetic activity measured simultaneously over the whole head to determine the optimal values for maximum identification probability of localized (dipolar) activity.
We recorded somatosensory evoked magnetic fields from ten healthy, right-handed subjects with a 122-channel whole-scalp SQUID magnetometer. The stimuli, exceeding the motor threshold, were delivered alternately to the left and right median nerves at the wrists, with interstimulus intervals of 1, 3, and 5 s. The first responses, peaking around 20 and 35 ms, were explained by activation of the contralateral primary somatosensory cortex (SI) hand area.
When a visual scene allows multiple interpretations, the percepts may spontaneously alternate despite the stable retinal image and the invariant sensory input transmitted to the brain. To study the brain basis of such multi-stable percepts, we superimposed rapidly changing dynamic noise as regional tags to the Rubin vase-face figure and followed the corresponding tag-related cortical signals with magnetoencephalography. The activity already in the earliest visual cortical areas, the primary visual cortex included, varied with the perceptual states reported by the observers.
Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals originating from neural currents in the brain. Using these signals to characterize and locate brain activity is a challenging task, as evidenced by several decades of methodological contributions.
Magnetoencephalography and electroencephalography (M/EEG) measure the weak electromagnetic signals generated by neuronal activity in the brain. Using these signals to characterize and locate neural activation in the brain is a challenge that requires expertise in physics, signal processing, statistics, and numerical methods.
Functional connectivity between cortical areas may appear as correlated time behavior of neural activity. It has been suggested that merging of separate features into a single percept ("binding") is associated with coherent gamma band activity across the cortical areas involved. Therefore, it would be of utmost interest to image cortico-cortical coherence in the working human brain. The frequency specificity and transient nature of these interactions requires time-sensitive tools such as magneto- or electroencephalography (MEG/EEG).
Human perception, cognition, and action are supported by a complex network of interconnected brain regions. There is an increasing interest in measuring and characterizing these networks as a function of time and frequency, and inter-areal phase locking is often used to reveal these networks. This measure assesses the consistency of phase angles between the electrophysiological activity in two areas at a specific time and frequency. Non-invasively, the signals from which phase locking is computed can be measured with magnetoencephalography (MEG) and electroencephalography (EEG).
Dynamic estimation methods based on linear state-space models have been applied to the inverse problem of magnetoencephalography (MEG), and can improve source localization compared with static methods by incorporating temporal continuity as a constraint. The efficacy of these methods is influenced by how well the state-space model approximates the dynamics of the underlying brain current sources.
This paper investigates and characterizes sources of variability in MEG signals in multi-site, multi-subject studies. Understanding these sources will help to develop efficient strategies for comparing and pooling data across repetitions of an experiment, across subjects, and across sites. In this work, we investigated somatosensory MEG data collected at three different sites and applied variance component analysis and nonparametric KL divergence analysis in order to characterize the sources of variability.
