Parcellating an Individual Subject's Cortical and Subcortical Brain Structures Using Snowball Sampling of Resting-State Correlations

Abstract

We describe methods for parcellating an individual subject's cortical and subcortical brain structures using resting-state functional correlations (RSFCs). Inspired by approaches from social network analysis, we first describe the application of snowball sampling on RSFC data (RSFC-Snowballing) to identify the centers of cortical areas, subdivisions of subcortical nuclei, and the cerebellum. RSFC-Snowballing parcellation is then compared with parcellation derived from identifying locations where RSFC maps exhibit abrupt transitions (RSFC-Boundary Mapping). RSFC-Snowballing and RSFC-Boundary Mapping largely complement one another, but also provide unique parcellation information; together, the methods identify independent entities with distinct functional correlations across many cortical and subcortical locations in the brain. RSFC parcellation is relatively reliable within a subject scanned across multiple days, and while the locations of many area centers and boundaries appear to exhibit considerable overlap across subjects, there is also cross-subject variability—reinforcing the motivation to parcellate brains at the level of individuals. Finally, examination of a large meta-analysis of task-evoked functional magnetic resonance imaging data reveals that area centers defined by task-evoked activity exhibit correspondence with area centers defined by RSFC-Snowballing. This observation provides important evidence for the ability of RSFC to parcellate broad expanses of an individual's brain into functionally meaningful units.

Functional Connectivity Relationships Predict Similarities in Task Activation and Pattern Information during Associative Memory Encoding

“…RSFC-defined networks are being increasingly used to identify ROIs (e.g., Wig et al, 2013) and mark changes in neural processing because of experimental manipulations (e.g., Tambini, Ketz, & Davachi, 2010) or disease (Greicius & Kimmel, 2012;Greicius, 2008). Prior attempts to ascertain the cognitive relevance of RSFC-defined networks have focused on measuring the spatial overlap between these networks and task-related activation patterns collected in separate samples (e.g., Crossley et al, 2013;AndrewsHanna, Reidler, Sepulcre, Poulin, & Buckner, 2010;Nelson et al, 2010;Smith et al, 2009;Vincent et al, 2006).…”

Decreased segregation of brain systems across the healthy adult lifespan

“…17,67,68). Small fixed-size disks also are beneficial if the nodes are intended to sample information across individuals who may have variation in their area parcellation (69)(70)(71). This technique, by definition, limits use of information from the full extent of a given area, but the many advantages noted above outweighed this concern.…”

Functional Connectivity-Based Parcellation of the Thalamus: An Unsupervised Clustering Method and Its Validity Investigation

“…The first crucial step to delineate the topological properties of the human brain is node definition, or delineating how the brain is parcellated into individual subunits that can be assessed for functional or structural connections with each other (Fornito et al, 2013). As a result, more and more researchers have lent their attention to parcellation of the human brain, in both cortical and subcortical areas (Bzdok et al, 2013;Craddock et al, 2012;Kim et al, 2013;Shen et al, 2013;Wig et al, 2013).…”