The parcellation-based connectome: Limitations and extensions
Introduction
The brain is a complex network of structurally and functionally interlinked regions. Integrative brain function does not solely depend on the activation or influence of a single element of this network, but rather emerges from the system of interactions as a whole, the human connectome (Sporns et al., 2005). Inspired by pioneering work on the structure of animal connectomes, such as those of the nematode Caenorhabditis elegans (White et al., 1986), cat (Scannell et al., 1995, Scannell et al., 1999) and primate (Felleman and van Essen, 1991, Young, 1993), recent advances in neuroimaging techniques (Basser et al., 2000, Lowe et al., 2000, Tuch, 2004, Wedeen et al., 2005) are utilized to map the brain's functional and structural connections in ever increasing detail (Cammoun et al., 2012, Gong et al., 2009, Hagmann et al., 2007, Toga et al., 2012, Tuch, 2004, Wedeen et al., 2005). Studies examining the spatial and topological organization of these connectome maps have revealed several characteristics of an efficient network architecture, including aspects of economic wiring (Bassett et al., 2010, Bullmore and Sporns, 2012), the presence of clustered functional communities, indicative of information segregation (Bullmore and Sporns, 2009, Damoiseaux et al., 2006, Hilgetag and Kaiser, 2004, Hilgetag et al., 2000, Salvador et al., 2005, van den Heuvel et al., 2009a), short communication relays (Hagmann et al., 2007, Kaiser and Hilgetag, 2006, Sporns and Zwi, 2004, van den Heuvel et al., 2009b, van den Heuvel et al., 2012, Varshney et al., 2011) and the formation of a small set of densely connected and centrally placed hub nodes, playing a crucial role in global brain communication (Achard et al., 2006, Collin et al., 2013, Hagmann et al., 2008, Sporns et al., 2007, van den Heuvel and Sporns, 2011, van den Heuvel et al., 2012).
Due to the vast amounts of neurons and the small measurement scale, reconstructing an entire connectome on the neuronal level, be it of a simple life form as the Drosophila fly (Chiang et al., 2011, Chklovskii et al., 2010) or zebrafish (Stobb et al., 2012), let alone that of mammalian species, is an ambitious and challenging task. According to estimates, the human brain consists of circa 100 billion neurons, their soma varying between 4 and 100 μm in diameter, and a thousandfold connections (Azevedo et al., 2009, Williams and Herrup, 1988). For the mapping and analysis of this microscopic connectome, further advances in both imaging techniques (e.g., higher spatial resolutions and shorter acquisition times) and computer systems (increasing data storage and computational power) are required. Today, a complete connectome map on the microscopic scale is only available for the simple nematode C. elegans, with a nervous system consisting of just 302 neurons (Varshney et al., 2011, White et al., 1986). Connectome maps of the human brain are instead formed on a macroscopic scale, mapping structural and functional connections between large-scale brain regions rather than interactions between single neurons.
To allow a rescaling of the connectome from a microscopic to a macroscopic scale, a definition of the brain's large-scale regions and connections is required. The process of dividing the brain into such macroscopic regions is usually referred to as “parcellation”. In particular when it comes to the delineation of structurally or functionally distinct regions in the cerebral cortex, there is a long research tradition (see Zilles and Amunts (2010) and Geyer et al. (2011) for an overview), going back more than a century to the seminal work of Penfield (Penfield, 1950, Penfield and Boldrey, 1937) and Brodmann (Brodmann, 1909, Brodmann, 1914). Through the ages, cortical areas have been distinguished on basis of, e.g., cytoarchitecture (Brodmann, 1909, Schleicher et al., 1999, von Economo and Koskinas, 1925), myeloarchitecture (Vogt and Vogt, 1919), electrophysiological observations (Lim et al., 1994, Penfield and Boldrey, 1937), immunohistochemistry (Baleydier et al., 1997) and receptor autoradiography (Zilles et al., 2002). In recent years, novel techniques based on magnetic resonance imaging (MRI) have extended this list, exploiting cortical curvature patterns (Desikan et al., 2006, Van Essen, 2005), in vivo mappings of myelin content (Glasser and Van Essen, 2011) and similarities in structural and functional connectivity profiles (Craddock et al., 2012, Johansen-Berg et al., 2004). Results from these different parcellation criteria have been brought together in multiple three-dimensional brain atlases, also referred to as anatomical templates, which can be used to parcellate a subject's brain on basis of an MRI scan (Collins et al., 1995, Fischl et al., 2004, Tzourio-Mazoyer et al., 2002). Together with high-resolution parcellation approaches that subdivide the brain into voxels (Eguíluz et al., 2005, van den Heuvel et al., 2008a) or randomly generated parcels (Hagmann et al., 2007, van den Heuvel and Sporns, 2011), these brain atlases are at the heart of parcellation-based connectome analysis, providing the large-scale brain regions whose structural and functional connections are the topic of investigation.
For the examination of topological and spatial features of the brain's wiring pattern, a formal description of the system as a whole is needed. To this end, a connectome (map) is often represented as a graph; a mathematical notion of a network, describing elements (nodes) and their interactions (edges) in an abstract manner (Bullmore and Sporns, 2009), allowing a systematic study of the network's topology through computation of so-called graph metrics (Fig. 1) (Rubinov and Sporns, 2010, van den Heuvel et al., 2010). In the graph representation of a parcellation-based connectome, each node of the graph corresponds to a brain region from the template that was used to subdivide the brain. In turn, the edges of the graph correspond to structural or functional connections between these brain regions, depending on the utilized imaging modality. Since the boundaries and amount of distinguished regions, ranging in size from single voxels to large-scale parcels of hundreds of voxels, vary considerably between templates, the adopted parcellation approach directly implicates the (number of) nodes and therefore also the number and placement of edges. That is, the formal representation of a parcellation-based connectome depends on the choice of parcellation scheme.
Here, we review the application of parcellation templates for brain networks established by diffusion tractography (Basser et al., 2000, Hagmann et al., 2006, Mori et al., 1999, Wedeen et al., 2005) and resting-state functional magnetic resonance imaging (fMRI) (Biswal et al., 1995, Fox et al., 2005, Lowe et al., 2000) and discuss their impact on the estimation and examination of connectome characteristics. Special attention will be paid to the effect of parcellation resolution (i.e., the number of regions in a template), which has recently been suggested to play an important role in the assessment of network features. Following the main discussion on the use and impact of parcellation templates, we briefly touch upon the exciting field of connectivity-based parcellation, which aims to discern the fundamental functional and structural elements of the human brain on basis of the connectivity profile of brain regions (Anwander et al., 2007, Johansen-Berg et al., 2004).
Section snippets
Predefined anatomical templates
A common approach to select the nodes of a macroscopic brain network is to parcellate the brain of a given subject using a predefined anatomical template or brain atlas. Typically, the subject's anatomical images are registered to a template brain whose regions have been labeled using one or more manually parcellated subjects from a fixed training set. The labels of this template brain are then propagated onto the registered anatomical images, thus subdividing the subject's gray matter into
Influence on network metrics
The influence of spatial scale on macroscopic connectome characteristics has been addressed in several studies. Most importantly, all show a strong impact of network resolution on graph metrics. Bassett et al. (2011) repeatedly bisected the regions of the AAL, HO and LPBA40 atlases, obtaining templates of two, four and eight times the original resolutions. Similar to the comparison between anatomical templates, architectural principles were found to be robust across spatial scales, but network
Connectivity-based parcellation
Connectivity-based parcellation approaches are an exciting and upcoming class of parcellation techniques that aim to delineate large-scale brain regions by exploiting similarities in structural or functional connectivity patterns (Cloutman and Lambon Ralph, 2012). Similar to connectome research, connectivity-based parcellation is motivated by the idea that brain function may be understood from the underlying wiring pattern (Johansen-Berg et al., 2004, Sporns et al., 2005). Rather than
Conclusions
An important aspect of the mapping and analysis of parcellation-based connectomes is the choice of parcellation scheme, which directly implicates the nodes and edges of a macroscopic brain network. In this review, we have reflected upon the use and impact of several parcellation approaches, most prominently the application of predefined anatomical templates and random high-resolution templates. Studies examining the influence of parcellation schemes all support the same conclusion:
Conflict of interest
The authors have no conflict of interest to declare.
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