Role of white-matter pathways in coordinating alpha oscillations in resting visual cortex.

In the absence of cognitive tasks and external stimuli, strong rhythmic fluctuations with a frequency ≈ 10 Hz emerge from posterior regions of human neocortex. These posterior α-oscillations can be recorded throughout the visual cortex and are particularly strong in the calcarine sulcus, where the primary visual cortex is located. The mechanisms and anatomical pathways through which local \alpha-oscillations are coordinated however, are not fully understood. In this study, we used a combination of magnetoencephalography (MEG), diffusion tensor imaging (DTI), and biophysical modeling to assess the role of white-matter pathways in coordinating cortical α-oscillations. Our findings suggest that primary visual cortex plays a special role in coordinating α-oscillations in higher-order visual regions. Specifically, the amplitudes of α-sources throughout visual cortex could be explained by propagation of α-oscillations from primary visual cortex through white-matter pathways. In particular, α-amplitudes within visual cortex correlated with both the anatomical and functional connection strengths to primary visual cortex. These findings reinforce the notion of posterior α-oscillations as intrinsic oscillations of the visual system. We speculate that they might reflect a default-mode of the visual system during which higher-order visual regions are rhythmically primed for expected visual stimuli by α-oscillations in primary visual cortex.

Inferring task-related networks using independent component analysis in magnetoencephalography.

A novel framework for analysing task-positive data in magnetoencephalography (MEG) is presented that can identify task-related networks. Techniques that combine beamforming, the Hilbert transform and temporal independent component analysis (ICA) have recently been applied to resting-state MEG data and have been shown to extract resting-state networks similar to those found in fMRI. Here we extend this approach in two ways. First, we systematically investigate optimisation of time-frequency windows for connectivity measurement. This is achieved by estimating the distribution of functional connectivity scores between nodes of known resting-state networks and contrasting it with a distribution of artefactual scores that are entirely due to spatial leakage caused by the inverse problem. We find that functional connectivity, both in the resting-state and during a cognitive task, is best estimated via correlations in the oscillatory envelope in the 8-20 Hz frequency range, temporally down-sampled with windows of 1-4s. Second, we combine ICA with the general linear model (GLM) to incorporate knowledge of task structure into our connectivity analysis. The combination of ICA with the GLM helps overcome problems of these techniques when used independently: namely, the interpretation and separation of interesting independent components from those that represent noise in ICA and the correction for multiple comparisons when applying the GLM. We demonstrate the approach on a 2-back working memory task and show that this novel analysis framework is able to elucidate the functional networks involved in the task beyond that which is achieved using the GLM alone. We find evidence of localised task-related activity in the area of the hippocampus, which is difficult to detect reliably using standard methods. Task-positive ICA, coupled with the GLM, has the potential to be a powerful tool in the analysis of MEG data.

Task induced modulation of neural oscillations in electrophysiological brain networks.

In recent years, one of the most important findings in systems neuroscience has been the identification of large scale distributed brain networks. These networks support healthy brain function and are perturbed in a number of neurological disorders (e.g. schizophrenia). Their study is therefore an important and evolving focus for neuroscience research. The majority of network studies are conducted using functional magnetic resonance imaging (fMRI) which relies on changes in blood oxygenation induced by neural activity. However recently, a small number of studies have begun to elucidate the electrical origin of fMRI networks by searching for correlations between neural oscillatory signals from spatially separate brain areas in magnetoencephalography (MEG) data. Here we advance this research area. We introduce two methodological extensions to previous independent component analysis (ICA) approaches to MEG network characterisation: 1) we show how to derive pan-spectral networks that combine independent components computed within individual frequency bands. 2) We show how to measure the temporal evolution of each network with millisecond temporal resolution. We apply our approach to ~10h of MEG data recorded in 28 experimental sessions during 3 separate cognitive tasks showing that a number of networks could be identified and were robust across time, task, subject and recording session. Further, we show that neural oscillations in those networks are modulated by memory load, and task relevance. This study furthers recent findings on electrodynamic brain networks and paves the way for future clinical studies in patients in which abnormal connectivity is thought to underlie core symptoms.