Analysis of very low frequency oscillations in electromagnetic brain signal recordings

Abstract

Spontaneous very low frequency oscillations (<0.5 Hz), previously regarded as physiological noise, have of late been increasingly analysed in neuroimaging studies. These slow oscillations, which occur within widely distributed neuroanatomical systems and are unrelated to cardiac and respiratory events, are thought to arise from variations in metabolic demands in the resting brain. However, they also persist during active goal-directed processing, where they predict inter-trial variability in evoked responses and may present a potential source of attention deficit during task performance. This work presents a series of new approaches for investigating: (i) the slow waves in electromagnetic (EM) brain signal recordings, (ii) their contribution in brain function, and (iii) the changes that the slow wave mechanisms undergo during cognitive processing versus resting states. State-of-the-art blind source separation methodologies, including single-channel and spacetime independent component analysis (SC-ICA and ST-ICA), are employed for denoising and dimensionality reduction of multi-channel EM data, and to extract neurophysiologically meaningful brain sources from the recordings. Particularly, magnetoencephalographic (MEG) data of attention-deficit/hyperactivity disorder (ADHD) and control children, and electroencephalographic (EEG) data recorded from healthy adult controls, are analysed. The key analytical challenges and techniques available for the analysis of the slow waves in EM brain signal recordings are discussed, and specific solutions proposed.

Core results demonstrate that the inter-trial variability in the amplitude and latency of the eventrelated fields sensory component, the M100 (in MEG), exhibits a slow wave pattern, which is indicative of the intrinsic slow waves modulating underlying brain processes. In a separate study, phase synchronisation in the slow wave band was observed between fronto-central, central and parietal brain regions, and the level of synchrony varied between rest and task conditions, and as a function of ADHD. Furthermore, a new EEG experimental framework and a multistage signal processing methodology have been designed and implemented in order to investigate brain activity during task performance in contrast with that during rest. Here, the brain has been envisaged as an oscillatory system onto which a graded load was imposed to yield a variable output response – the P300. Specifically, results show that the amplitude and phase of the brain sources in the slow wave band share essential similarities during rest and task conditions, but are distinct enough to be classified separately. This is in keeping with the view that the intrinsic slow waves are continuously influencing active brain sources and they are in turn affected by external stimulation. These slow wave variations are also significantly correlated with the level of cognitive attention assessed by performance measures (such as reaction time and error rates). Moreover, the power of
the sources in the slow wave band is attenuated during task, and the level of attenuation drops as the task difficulty level is increased, whilst their phase undergoes a change in structure (measured through entropy).

These new methodologies, developed for gaining insight into the neurophysiological role of the slow waves, could be used for assessing changes in the brain electrical oscillators as a function of various psychiatric and/or neurobehavioural disorders such as ADHD. This could ultimately lead towards a more scientific (and accurate) approach for the prognosis and diagnosis of these disorders.

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