The modular and integrative functional architecture of the human brain

Abstract

Network-based analyses of brain imaging data consistently reveal distinct modules and connector nodes with diverse global connectivity across the modules. How discrete the functions of modules are, how dependent the computational load of each module is to the other modules' processing, and what the precise role of connector nodes is for between-module communication remains underspecified. Here, we use a network model of the brain derived from resting-state functional MRI (rs-fMRI) data and investigate the modular functional architecture of the human brain by analyzing activity at different types of nodes in the network across 9,208 experiments of 77 cognitive tasks in the BrainMap database. Using an authortopic model of cognitive functions, we find a strong spatial correspondence between the cognitive functions and the network's modules, suggesting that each module performs a discrete cognitive function. Crucially, activity at local nodes within the modules does not increase in tasks that require more cognitive functions, demonstrating the autonomy of modules' functions. However, connector nodes do exhibit increased activity when more cognitive functions are engaged in a task. Moreover, connector nodes are located where brain activity is associated with many different cognitive functions. Connector nodes potentially play a role in betweenmodule communication that maintains the modular function of the brain. Together, these findings provide a network account of the brain's modular yet integrated implementation of cognitive functions. modularity | hubs | cognition | graph theory | network

Strategy‐based reasoning training modulates cortical thickness and resting‐state functional connectivity in adults with chronic traumatic brain injury

“…Long‐range connections are fewer in number, but are important for efficient global neural communications (Achard, Salvador, Whitcher, Suckling, & Bullmore, 2006). The strength of brain activity in regions with high between‐network connectivity is proportional to the number of cognitive functions engaged in a task (Bertolero, Yeo, & D'Esposito, 2015). Furthermore, damage to these regions disrupts the brain's modular organization (Gratton, Nomura, Pérez, & D'Esposito, 2012) and yields widespread deficits in neuropsychological measures (Warren et al., 2014).…”

Data-Driven Extraction of a Nested Model of Human Brain Function

“…From a path diagram perspective, this can be conceptualized as specifying a priori links from the factors to the task contrasts. The overall fit of the a priori factor models to the data is assessed using a variety of global fit indices (Fan et al, 1999;Bentler, 2007;Iacobucci, 2010) that all measure the discrepancy between the covariance matrix implied by the factor model and the sample covariance matrix (covariance between the observed variables). Four global fit indices were used to compare the overall fit of each model to the data: root mean square error of approximation (RMSEA), comparative fit index (CFI), Tucker-Lewis index (TLI), and the standardized root mean square residual (SRMR).…”

“…1). The use of unthresholded brain activation maps provides an advantage over previous studies (Toro et al, 2008;Smith et al, 2009;Lenartowicz et al, 2010;Bertolero et al, 2015;Yeo et al, 2015) examining task-general brain activation patterns that have used activation-coordinate databases, such as the BrainMap database (Fox and Lancaster, 2002) or Neurosynth (Yarkoni et al, 2011). Coordinate-based analyses are inherently limited due to their reduction of full statistic images to peak-activation coordinates (SalimiKhorshidi et al, 2009;Poldrack and Yarkoni, 2016); and at present, there are no universal standards for reporting activation coordinates (Wager et al, 2007).…”

Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease

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