Abstract
It is widely agreed that the human brain is organized as a system of segregated modules that reside in separate regions and, through coordinated integration, support different cognitive functions. Through recent breakthroughs in modeling the activity of the brain, it has been demonstrated that each such module can participate in multiple so-called functional networks – networks of brain regions that activate in synchrony during specific types of cognition. If we model the brain as a temporal network, by representing brain regions as nodes and correlations within activity-windows at different times as links, we can formulate the task of finding functional networks as a community detection problem. In spite of this, however, relatively little attention has been given to solving this problem using recently developed techniques for temporal community detection. In this paper, as a proof-of-concept, we apply a novel technique for community detection in temporal networks to a dataset of fMRI measurements from 100 healthy subjects undertaking a working memory task with intermittent fixation (or resting-state) periods. We show that this method recovers two distinct communities that are shared between subjects: one that activates during the fixation period, and another that activates during a period associated with high cognitive load.
Highlights
Global brain activity exhibits a surprising level of organization in both space and time
The consilience of evidence from studies using various imaging technologies and computational methods establishes the existence of so-called “functional networks”: sets of brain regions that activate in synchrony and support a vast repertoire of brain functions (Gusnard and Raichle 2001; Fox et al 2005; Park and Friston 2013)
We use a dataset from the Human Connectome Project (HCP) (Barch et al 2013), with functional magnetic resonance imaging (fMRI) measurements from 100 subjects undertaking an n-back working memory task
Summary
Global brain activity exhibits a surprising level of organization in both space and time. There are many well studied methods for detecting these functional networks, involving non-invasive imaging techniques such as functional magnetic resonance imaging (fMRI), electro- and magnetoencephalography (EEG and MEG). Central to these methods is the inference of synchrony between different brain regions, commonly gauged by measuring mutual information or correlation strength between time-series representing activity in separate regions. (3) A researcher must make a design decision about which parcellation to use, a choice which can severely impact the results To remedy these limitations, various unsupervised parcellation methods exist, based on – but not limited to – mixture models, k-means clustering, hierarchical clustering, spectral clustering, principal component analysis (PCA) and independent component analysis (ICA) (Thirion et al 2014)
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