A novel ANCOVA design for analysis of MEG data with application to a visual attention study

Statistical inference from MEG-based distributed activation maps is well suited to the general linear modeling framework, a standard approach to the analysis of fMRI and PET neuroimaging studies. However, there are important differences from the other neuroimaging modalities related to how observations are created and fitted in GLM models, as well as how subsequent statistical inference is performed. In this paper, we demonstrate how MEG oscillatory components can be analyzed in this framework based on a custom ANCOVA modeling that takes into account baseline and inter-hemispheric effects, rather than a simpler ANOVA design. We present the methodology using as an example an MEG study of visual spatial attention, since the model design depends on the specific experiment and neuroscience hypotheses being tested. However, the techniques presented here can be readily adapted to accommodate other experimental paradigms. We create statistics that estimate the temporal evolution of attention effects on alpha power in several cortical regions. We present evidence for direction specific attention effects on alpha activity in occipital and parietal regions and demonstrate the sub-second timing of these effects in each region. The results support a mechanism for anticipatory attentional deployment that dynamically modulates the local alpha synchrony in a network of parietal control and occipital sensory regions

Dynamic correlations in ongoing neuronal oscillations in humans - perspectives on brain function and its disorders

This Thesis is involved with neuronal oscillations in the human brain and their coordination across time, space and frequency. The aim of the Thesis was to quantify correlations in neuronal oscillations over these dimensions, and to elucidate their significance in cognitive processing and brain disorders. Magnetoencephalographic (MEG) recordings of major depression patients revealed that long-range temporal correlations (LRTC) were decreased, compared to control subjects, in the 5 Hz oscillations in a manner that was dependent on the degree of the disorder. While studying epileptic patients, on the other hand, it was found that the LRTC in neuronal oscillations recorded intracranially with electroencephalography (EEG) were strengthened in the seizure initiation region. A novel approach to map spatial correlations between cortical regions was developed. The method is based on parcellating the cortex to patches and estimating phase synchrony between all patches. Mapping synchrony from inverse-modelled MEG / EEG data revealed wide-spread phase synchronization during a visual working memory task. Furthermore, the network architectures of task-related synchrony were found to be segregated over frequency. Cross-frequency interactions were investigated with analyses of nested brain activity in data recorded with full-bandwidth EEG during a somatosensory detection task. According to these data, the phase of ongoing infra-slow fluctuations (ISF), which were discovered in the frequency band of 0.01-0.1 Hz, was correlated with the amplitude of faster > 1 Hz neuronal oscillations. Strikingly, the behavioral detection performance displayed similar dependency on the ISFs as the > 1 Hz neuronal oscillations. The studies composing this Thesis showed that correlations in neuronal oscillations are functionally related to brain disorders and cognitive processing. Such correlations are suggested to reveal the coordination of neuronal oscillations across time, space and frequency. The results contribute to system-level understanding of brain function