The brain consists of functional units with more-or-less specific information processing capabilities, yet cognitive functions require the co-ordinated activity of these spatially separated units. Magnetoencephalography (MEG) has the temporal resolution to capture these frequency-dependent interactions, although, due to volume conduction and field spread, spurious estimates may be obtained when functional connectivity is estimated on the basis of the extra-cranial recordings directly. Connectivity estimates on the basis of reconstructed sources may similarly be affected by biases introduced by the source reconstruction approach.Here we propose an analysis framework to reliably determine functional connectivity that is based around two main ideas: (i) functional connectivity is computed for a set of atlas-based ROIs in anatomical space that covers almost the entire brain, aiding the interpretation of MEG functional connectivity/network studies, as well as the comparison with other modalities; (ii) volume conduction and similar bias effects are removed by using a functional connectivity estimator that is insensitive to these effects, namely the Phase Lag Index (PLI).Our analysis approach was applied to eyes-closed resting-state MEG data for thirteen healthy participants. We first demonstrate that functional connectivity estimates based on phase coherence, even at the source-level, are biased due to the effects of volume conduction and field spread. In contrast, functional connectivity estimates based on PLI are not affected by these biases. We then looked at mean PLI, or weighted degree, over areas and subjects and found significant mean connectivity in three (alpha, beta, gamma) of the five (including theta and delta) classical frequency bands tested. These frequency-band dependent patterns of resting-state functional connectivity were distinctive; with the alpha and beta band connectivity confined to posterior and sensorimotor areas respectively, and with a generally more dispersed pattern for the gamma band. Generally, these patterns corresponded closely to patterns of relative source power, suggesting that the most active brain regions are also the ones that are most-densely connected.Our results reveal for the first time, using an analysis framework that enables the reliable characterisation of resting-state dynamics in the human brain, how resting-state networks of functionally connected regions vary in a frequency-dependent manner across the cortex.

Extensive changes in resting-state oscillatory brain activity have recently been demonstrated using magnetoencephalography (MEG) in moderately advanced, non-demented Parkinson's disease patients relative to age-matched controls. The aim of the present study was to determine the onset and evolution of these changes over the disease course and their relationship with clinical parameters. In addition, we evaluated the effects of dopaminomimetics on resting-state oscillatory brain activity in levodopa-treated patients. MEG background oscillatory activity was studied in a group of 70 Parkinson's disease patients with varying disease duration and severity (including 18 de novo patients) as well as in 21 controls that were age-matched to the de novo patients. Whole head 151-channel MEG recordings were obtained in an eyes-closed resting-state condition. Levodopa-treated patients (N = 37) were examined both in a practically defined 'OFF' as well as in the 'ON' state. Relative spectral power was calculated for delta, theta, low alpha, high alpha, beta and gamma frequency bands and averaged for 10 cortical regions of interest (ROIs). Additionally, extensive clinical and neuropsychological testing was performed in all subjects. De novo Parkinson's disease patients showed widespread slowing of background MEG activity relative to controls. Changes included a widespread increase in theta and low alpha power, as well as a loss of beta power over all but the frontal ROIs and a loss of gamma power over all but the right occipital ROI. Neuropsychological assessment revealed abnormal perseveration in de novo patients, which was associated with increased low alpha power in centroparietal ROIs. In the whole group of Parkinson's disease patients, longer disease duration was associated with reduced low alpha power in the right temporal and right occipital ROI, but not with any other spectral power measure. No association was found between spectral power and disease stage, disease severity or dose of dopaminomimetics. In patients on levodopa therapy, a change from the 'OFF' to the 'ON' state was associated with decreases in right frontal theta, left occipital beta and left temporal gamma power and an increase in right parietal gamma power. Widespread slowing of oscillatory brain activity is a characteristic of non-demented Parkinson's disease patients from the earliest clinical stages onwards that is (largely) independent of disease duration, stage and severity and hardly influenced by dopaminomimetic treatment. Some early cognitive deficits in Parkinson's disease appear to be associated with increased low alpha power. We postulate a role for hypofunctional non-dopaminergic ascending neurotransmitter systems in spectral power changes in non-demented Parkinson's disease patients.

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We set out to determine whether changes in resting-state corticocortical functional connectivity are a feature of early-stage Parkinson's disease (PD), explore how functional coupling might evolve over the course of the disease and establish its relationship with clinical deficits.Whole-head magnetoencephalography was performed in an eyesclosed resting-state condition in 70 PD patients with varying disease duration (including 18 recently diagnosed, drug-naive patients) in an "OFF" medication state and 21 controls. Neuropsychological testing was performed in all subjects. Data analysis involved calculation of three synchronization likelihood (SL, a general measure of linear and non-linear temporal correlations between time series) measures which reflect functional connectivity within (local) and between (intrahemispheric and interhemispheric) ten major cortical regions in five frequency bands.Recently diagnosed, drug-naive patients showed an overall increase in alpha1 SL relative to controls. Cross-sectional analysis in all patients revealed that disease duration was positively associated with alpha2 and beta SL measures, while severity of parkinsonism was positively associated with theta and beta SL measures. Moderately advanced patients had increases in theta, alpha1, alpha2 and beta SL, particularly with regard to local SL. In recently diagnosed patients, cognitive perseveration was associated with increased interhemispheric alpha1 SL.Increased resting-state cortico-cortical functional connectivity in the 8-10 Hz alpha range is a feature of PD from the earliest clinical stages onward. With disease progression, neighboring frequency bands become increasingly involved. These findings suggest that changes in functional coupling over the course of PD may be linked to the topographical progression of pathology over the brain.

Parkinson's disease (PD) related dementia (PDD) develops in up to 60% of patients, but the pathophysiology is far from being elucidated. Abnormalities of resting state functional connectivity have been reported in Alzheimer's disease (AD). The present study was performed to determine whether PDD is likewise characterized by changes in resting state functional connectivity. MEG recordings were obtained in 13 demented and 13 non-demented PD patients. The synchronization likelihood (SL) was calculated within and between cortical areas in six frequency bands. Compared to non-demented PD, PDD was characterized by lower fronto-temporal SL in the alpha range, lower intertemporal SL in delta, theta and alpha1 bands as well as decreased centro-parietal gamma band synchronization. In addition, higher parieto-occipital synchronization in the alpha2 and beta bands was found in PDD. The observed changes in functional connectivity are reminiscent of changes in AD, and may reflect reduced cholinergic activity and/or loss of cortico-cortical anatomical connections in PDD.