Behavioral interpretations of intrinsic connectivity networks

Abstract

An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.

Figure 1

The data processing pipeline included four steps. Step 1: Peak coordinates in BrainMap were smoothed (12 mm FWHM) to generate 8637 modeled activation images. Step 2: ICA was applied to this 4D data using FSL’s MELODIC to decompose the experiment images into 20 spatially independent components. Step 3: The matrix that quantifies the relationship between components and BrainMap experiments was utilized to compute a set of matrices that corresponded to 14 independent metadata fields, each with n classes. The relative salience as computed for each field and the two fields with the highest salience were selected for further analysis: behavioral domain and paradigm. Step 4: HCA was performed on the concatenated behavioral domain and paradigm matrix (125 metadata classes × 20 networks). Clustering was first performed on the combined matrix to determine groupings across metadata classes; subsequently, the matrix was transposed and the analysis repeated to quantify similarity across networks.

Figure 2

ICA was used to decompose 8637 experiment images extracted from the BrainMap database into 20 spatially co-occurring maps of ICNs. ICA maps were converted to z statistic images via a normalized mixture model fit, thresholded at z > 4, and viewed in standard (Talairach) brain space. Orthogonal slices of the most representative point in space are shown.

Figure 3

The maximum metadata loading value across components was computed for every metadata class and averaged across all classes within a given field, as an approach to determine which fields captured a large amount of functional information. We hence identified the behavioral domain and paradigm as the two fields that provided the highest degree of network explanatory power.

Figure 4

Behavioral-driven HCA was carried out on the metadata matrix of concatenated behavioral domains and paradigms to generate clusters of metadata classes with congruous themes. HCA yielded a complex, well-organized dendrogram associating specific cognitive operations with corresponding experimental paradigms.

Figure 5

Network-driven HCA was carried out on the intrinsic connectivity metadata matrix of concatenated behavioral domains and paradigms to generate clusters of networks with function behavioral characterizations. HCA yielded three clusters of networks displaying high similarity (blue, green, red), along with a divergent set of dissimilar networks (black), which included a weakly coupled network pairing (light blue).

Figure 6

The concatenated metadata matrix for BrainMap behavioral domains and paradigms provides a per-network mapping of the functional properties of each ICN, ordered to reflect the groupings set forth by the behavioral- and network-driven HCA results.

Figure 7

The metadata matrix of behavioral domains and paradigms was extracted directly from experiments archived in BrainMap (8637 experiments × 125 metadata classes), without performing ICA on these data. HCA yielded a uniform and highly dissimilar characterized by very little branching and minimal organizational structure. Inspection of this dendrogram revealed a composition based upon simple paradigm–domain pairs that merely reflect trends in experimental design (e.g., n-back tasks elicit working memory).