Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex

Highlights

• We estimated overlapping cortical networks from resting fMRI data (N = 1012).

• Many association regions involved in multiple interdigitated, segregated networks.

• Many sensory-motor regions involved in single, preferentially local, networks.

• Architecture converges across two datasets and three methods.

• Results replicable across sessions in individual Human Connectome Project subjects.

Abstract

The organization of the human cerebral cortex has recently been explored using techniques for parcellating the cortex into distinct functionally coupled networks. The divergent and convergent nature of cortico-cortical anatomic connections suggests the need to consider the possibility of regions belonging to multiple networks and hierarchies among networks. Here we applied the Latent Dirichlet Allocation (LDA) model and spatial independent component analysis (ICA) to solve for functionally coupled cerebral networks without assuming that cortical regions belong to a single network. Data analyzed included 1000 subjects from the Brain Genomics Superstruct Project (GSP) and 12 high quality individual subjects from the Human Connectome Project (HCP). The organization of the cerebral cortex was similar regardless of whether a winner-take-all approach or the more relaxed constraints of LDA (or ICA) were imposed. This suggests that large-scale networks may function as partially isolated modules. Several notable interactions among networks were uncovered by the LDA analysis. Many association regions belong to at least two networks, while somatomotor and early visual cortices are especially isolated. As examples of interaction, the precuneus, lateral temporal cortex, medial prefrontal cortex and posterior parietal cortex participate in multiple paralimbic networks that together comprise subsystems of the default network. In addition, regions at or near the frontal eye field and human lateral intraparietal area homologue participate in multiple hierarchically organized networks. These observations were replicated in both datasets and could be detected (and replicated) in individual subjects from the HCP.

Introduction

Distributed neocortical brain areas form large-scale networks that exhibit complex patterns of divergent and convergent connectivity (e.g., Felleman and Van Essen, 1991, Goldman-Rakic, 1988, Jones and Powell, 1970, Mesulam, 1981, Pandya and Kuypers, 1969, Ungerleider and Desimone, 1986). A major challenge in systems neuroscience is to make sense of these connectivity patterns to infer functional organization. In the visual system, connectivity patterns suggest a separation of processing into largely parallel, but interacting, hierarchical pathways (Felleman and Van Essen, 1991, Ungerleider and Desimone, 1986). In contrast, the association cortex comprises networks of widely distributed and densely interconnected areas without rigid hierarchical organization (Goldman-Rakic, 1988, Selemon and Goldman-Rakic, 1988; but see Badre and D'Esposito, 2009).

Resting-state functional connectivity MRI (rs-fcMRI) provides a powerful, albeit indirect, approach to make inferences about human cortical organization (Biswal et al., 1995). Despite its limitations (Buckner et al., 2013), we and others have used functional connectivity to estimate cortical network patterns (e.g., Bellec et al., 2010, Damoiseaux et al., 2006, He et al., 2009, Margulies et al., 2007, Power et al., 2011, Smith et al., 2009, van den Heuvel et al., 2009, Yeo et al., 2011).

The majority of functional connectivity studies have focused on dissociating functionally distinct networks or modules (Beckmann et al., 2005, Calhoun et al., 2008, Craddock et al., 2012, Damoiseaux et al., 2006, De Luca et al., 2006, Dosenbach et al., 2007, Doucet et al., 2011, Fox et al., 2006, Greicius et al., 2003, Margulies et al., 2007, Rubinov and Sporns, 2011, Salvador et al., 2005, Seeley et al., 2007, Smith et al., 2009, van den Heuvel et al., 2009, Varoquaux et al., 2011). Fewer studies have examined the relationships among different functional networks (Sepulcre et al., 2012a, Sporns, 2013). For example, Fox et al. (2005) and Fransson (2005) have investigated the antagonistic relationship between the default and task-positive networks. Others (Doucet et al., 2011, Lee et al., 2012, Meunier et al., 2009) have investigated the (spatial) hierarchical relationship across functional networks.

We previously employed a mixture model that relied on a winner-takes-all assumption to map network topography in the human cerebral cortex (Yeo et al., 2011). Each brain region was assigned to a single, best-fit network allowing us to derive connectivity maps that emphasize the interdigitation of parallel, distributed association networks. The key features of this parallel organization are that (1) each association network consists of strongly coupled brain regions spanning frontal, parietal, temporal, and cingulate cortices, and (2) the components of multiple networks are spatially adjacent (Yeo et al., 2011; also see Vincent et al., 2008, Power et al., 2011).

However, it is unlikely that the brain is simply parcellated into a discrete number of nonoverlapping networks (Mesulam, 1998). Interactions across networks, as well as the existence of ‘convergence zones’ of regions that participate in multiple networks, are likely important features of brain organization (Beckmann et al., 2005, Bullmore and Sporns, 2009, Fornito et al., 2012, Jones and Powell, 1970, Mesulam, 1998, Pandya and Kuypers, 1969, Power et al., 2013, Sepulcre et al., 2012b, Spreng et al., 2010). Relevant to this point, we have observed variability in the goodness of fit of certain regions to their winner-takes-all network (Figs. 8 and 10 of Yeo et al., 2011), consistent with the notion that certain brain regions might participate in multiple networks (Andrews-Hanna et al., 2010, Beckmann et al., 2005, Leech et al., 2011, Rubinov and Sporns, 2011, Sporns et al., 2007).

Here, we address the possibility of multiple network membership by applying latent Dirichlet allocation (LDA; Blei et al., 2003) and spatial Independent Component Analysis (ICA; Calhoun et al., 2001, Beckmann and Smith, 2004) to examine the topography of overlapping networks. This is an important consideration because network topography may change substantially from our original estimates (Yeo et al., 2011) if constraints are relaxed to permit overlapping networks. Conversely, unbiased estimation of network topography may broadly confirm previous estimates and allow us to investigate the interactions and overlaps among networks.