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Measurement of dynamic task related functional networks using MEG.

ABSTRACT: The characterisation of dynamic electrophysiological brain networks, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method for measuring such networks in the human brain using magnetoencephalography (MEG). Previous network analyses look for brain regions that share a common temporal profile of activity. Here distinctly, we exploit the high spatio-temporal resolution of MEG to measure the temporal evolution of connectivity between pairs of parcellated brain regions. We then use an ICA based procedure to identify networks of connections whose temporal dynamics covary. We validate our method using MEG data recorded during a finger movement task, identifying a transient network of connections linking somatosensory and primary motor regions, which modulates during the task. Next, we use our method to image the networks which support cognition during a Sternberg working memory task. We generate a novel neuroscientific picture of cognitive processing, showing the formation and dissolution of multiple networks which relate to semantic processing, pattern recognition and language as well as vision and movement. Our method tracks the dynamics of functional connectivity in the brain on a timescale commensurate to the task they are undertaking.

SUBMITTER: O'Neill GC

PROVIDER: S-EPMC5312793 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Measurement of dynamic task related functional networks using MEG.

O'Neill George C GC Tewarie Prejaas K PK Colclough Giles L GL Gascoyne Lauren E LE Hunt Benjamin A E BAE Morris Peter G PG Woolrich Mark W MW Brookes Matthew J MJ

NeuroImage 20160914

The characterisation of dynamic electrophysiological brain networks, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method for measuring such networks in the human brain using magnetoencephalography (MEG). Previous network analyses look for brain regions that share a common temporal profile of activity. Here distinctly, we exploit the high spatio-temporal resolution of MEG to measure the temporal ev ...[more]

PMID: 27639354

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Project description:Prospective Memory (PM) is the ability to encode an intention in memory and retrieve it at the right time in the future. After the intention is formed, it must be maintained in memory while simultaneously monitoring the environment until the occurrence of the stimulus associated with its retrieval. Therefore, monitoring and maintenance processes must work in conjunction to subserve PM processing (monitoring/maintenance phase). Several brain regions play a role in PM, such as the anterior prefrontal cortex, inferior parietal lobules, and precuneus. Notably, these regions belong to different brain networks and are differently involved depending on the memory and attentional requests of the PM task. In this study, we investigate the neural bases of PM from a network perspective, using functional connectivity (FC) analysis to identify the networks involved in the attentional and memory mechanisms underlying PM. To this end, we analyzed MEG data collected in two different PM conditions, enhancing either the monitoring (i.e., attention) or the maintenance (i.e., memory) loads of the PM task. To disentangle the neural correlates of these mechanisms from other processes occurring after stimulus presentation, the analysis focused on the prestimulus time window (monitoring/maintenance phase). The monitoring-load condition was characterized by increased inter-network FC of the Dorsal Attention Network (DAN) in the alpha band, a marker of increased top-down monitoring. In contrast, the maintenance-load condition was associated with increased connectivity of the Ventral Attention Network (VAN) with the FrontoParietal Control and the Default-Mode Networks (FPCN and DMN, respectively). Additionally, response times were found to correlate with prestimulus alpha connectivity of different networks in the two conditions. These differences in connectivity within and between networks support the hypothesis that different networks (DAN, or VAN and DMN) and mechanisms (top-down or bottom-up, respectively) are involved in PM processing depending on the features of the PM task.

Dataset Information

Measurement of dynamic task related functional networks using MEG.

Publications

Measurement of dynamic task related functional networks using MEG.

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