Cognitive relevance of the community structure of the human brain functional coactivation network

Abstract

There is growing interest in the complex topology of human brain functional networks, often measured using resting-state functional MRI (fMRI). Here, we used a meta-analysis of the large primary literature that used fMRI or PET to measure task-related activation (>1,600 studies; 1985–2010). We estimated the similarity (Jaccard index) of the activation patterns across experimental tasks between each pair of 638 brain regions. This continuous coactivation matrix was used to build a weighted graph to characterize network topology. The coactivation network was modular, with occipital, central, and default-mode modules predominantly coactivated by specific cognitive domains (perception, action, and emotion, respectively). It also included a rich club of hub nodes, located in parietal and prefrontal cortex and often connected over long distances, which were coactivated by a diverse range of experimental tasks. Investigating the topological role of edges between a deactivated and an activated node, we found that such competitive interactions were most frequent between nodes in different modules or between an activated rich-club node and a deactivated peripheral node. Many aspects of the coactivation network were convergent with a connectivity network derived from resting state fMRI data ( n = 27, healthy volunteers); although the connectivity network was more parsimoniously connected and differed in the anatomical locations of some hubs. We conclude that the community structure of human brain networks is relevant to cognitive function. Deactivations may play a role in flexible reconfiguration of the network according to cognitive demand, varying the integration between modules, and between the periphery and a central rich club.

Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia

“…It has been shown that higher-IQ individuals tend to have more-efficient structural and functional networks (24,25), that more-difficult cognitive tasks demand more-integrated or efficient functional network topology (26,27), and that a pharmacological challenge (acute nicotine replacement in abstinent cigarette smokers) that enhanced attention also increased efficiency and connection distance of fMRI networks (28). These observations are consistent with earlier theoretical claims that higher-order conscious processing depends on access to a "global workspace" rather than a segregated, modular architecture (29)(30)(31).…”

The modular and integrative functional architecture of the human brain

“…Importantly, the brain's network architecture during task performance is shaped primarily by the network architecture present during resting state (i.e., spontaneous neural activity), as spontaneous neural activity is likely a prior or constraint on task activity (31,32). This has been demonstrated in humans using fMRI (33)(34)(35)(36)(37), in monkeys using multielectrode recordings (38), and in zebrafish using two-photon Ca 2+ imaging (39). Thus, predictions regarding the brain's network structure-and potentially nodes' activity magnitudes-during tasks can be made based on the brain's network structure during spontaneous neural activity.…”

Time‐resolved detection of stimulus/task‐related networks, via clustering of transient intersubject synchronization

“…However, their approach focused on distinguishing cognitive states across fMRI‐sessions [see also Shirer et al, 2012], rather than detecting state‐changes within a single session. Crossley et al [Crossley et al, 2013] used graph analysis to examine a large dataset of task‐related PET and fMRI experiments, which revealed the recurrent coactivation of distinct areas across experiments/tasks. These results confirmed the possibility of using network configurations to track cognitive function in a data‐driven manner, but again without providing us any time‐resolved information for event‐categorization within a fMRI time‐series/experiment.…”

“…Because the identification of network configurations via coactivation does not rely directly on the BOLD signal, the areas belonging to the same network can have markedly different signal [cf., also CAPs, above; and “competitive interactions,” in Crossley et al, 2013]. Here, the characteristics of the BOLD signal within each area were examined further with the second clustering step (BOLD‐clustering).…”

“…Finally, the main purpose/application of the method is investigate network dynamics involved in the processing of complex and naturalistic stimuli; that is, when the construction of predictors for GLM analyses is particularly challenging. Because each bMode includes a set of relevant time‐windows (when), future work will focus on re‐examining the stimuli at the relevant time‐points with the aim of associating each bMode with specific aspects of the stimuli [cf., Hasson et al, 2004; see also Crossley et al, 2013, who combined data‐driven network identification with database‐derived “functional labels”]. This will add an additional dimension to the output of the algorithm, which would then include a specific “functionality” for each bMode.…”