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.
Highlights
Measurement of statistical interdependencies between neuroimaging signals has revealed a number of robust networks of functional connectivity in the brain (Beckmann et al, 2005; Corbetta, 1998; Fox and Raichle, 2007; Fox et al, 2005; Friston, 1994; Raichle et al, 2001; Smith et al, 2009)
We introduce a method which tracks the formation of functional electrophysiological networks, and use it to image the network formations associated with self-initiated movement, and working memory
Previous MEG-independent component analysis (ICA) network analyses look for brain regions that share a common temporal profile of activity
Summary
Measurement of statistical interdependencies between neuroimaging signals has revealed a number of robust networks of functional connectivity in the brain (Beckmann et al, 2005; Corbetta, 1998; Fox and Raichle, 2007; Fox et al, 2005; Friston, 1994; Raichle et al, 2001; Smith et al, 2009) These networks, each with their own characteristic spatial signature, are thought to govern core mental processes with some supporting sensory integration and others associated with cognition or attention. The brain is a dynamic system and efficient function likely relies on the formation and dissolution of many hierarchical networks at rapid time scales, which support ongoing cognition The characterisation of such transient networks represents a key goal in neuroscience. We introduce a method which tracks the formation of functional electrophysiological networks, and use it to image the network formations associated with self-initiated movement, and working memory
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