Defining functional areas in individual human brains using resting functional connectivity MRI

Abstract

The cerebral cortex is anatomically organized at many physical scales starting at the level of single neurons and extending up to functional systems. Current functional magnetic resonance imaging (fMRI) studies often focus at the level of areas, networks, and systems. Except in restricted domains, (e.g., topographically-organized sensory regions), it is difficult to determine area boundaries in the human brain using fMRI. The ability to delineate functional areas non-invasively would enhance the quality of many experimental analyses allowing more accurate across-subject comparisons of independently identified functional areas. Correlations in spontaneous BOLD activity, often referred to as resting state functional connectivity (rs-fcMRI), are especially promising as a way to accurately localize differences in patterns of activity across large expanses of cortex. In the current report, we applied a novel set of image analysis tools to explore the utility of rs-fcMRI for defining wide-ranging functional area boundaries. We find that rs-fcMRI patterns show sharp transitions in correlation patterns and that these putative areal boundaries can be reliably detected in individual subjects as well as in group data. Additionally, combining surface-based analysis techniques with image processing algorithms allows automated mapping of putative areal boundaries across large expanses of cortex without the need for prior information about a region's function or topography. Our approach reliably produces maps of bounded regions appropriate in size and number for putative functional areas. These findings will hopefully stimulate further methodological refinements and validations.

Figure 1

Flowchart outlining the analysis stream presented here and the techniques involved. Bolded letters refer to specific portions of the Methods section that describe each procedural operation. Examples of several steps in the procedure are denoted by the Figure where they can be found.

Figure 2

Shown are transverse sections and lateral hemispheric views, mapped to the PALS human cortical atlas (Van Essen, 2005), showing the functional connectivity patterns of regions of interest in the angular (upper slice and lateral view) and supramarginal gyrus (lower slice and lateral view). Highlighted (circles) are a few of the salient differences. Seed regions are indicated with filled dark blue circles. The strength of positive and negative correlations is shown in warm and cool colors respectively.

Figure 3

Panels A–C show the locations of the angular (blue) and supramarginal (red) regions. Black dots in C indicate the seed regions used. Panel D (upper panel) shows some of the connectivity maps derived from the series of seed regions. Encircled are particular differences that highlight the changing connectivity patterns. Panel D (lower panel) represents the eta² values derived when comparing the AG map with all other maps (first blue line), the SMG map with all other maps (last red line), and so forth for all maps …

Figure 4

Panel A displays the eta² coefficients between each seed point’s correlation maps, as in Figure 3D. Triangle and circle designate locations of rapid change. Panel B shows the location of the line of seed points on the left hemisphere, as well as the nearby artificial ‘cuts’ created during the process of flattening the cortex. The medial wall hole is shown in orange, with ‘a’ and ‘p’ designating anterior and posterior ends of the anterior medial wall cut. The cingulate cut is shown in purple on both the inflated medial view and the flattened view. Panel C shows the results of hierarchical clustering the eta² profiles shown in Panel A.

Figure 5

Panel A shows the 2D patch of seed regions on this subject’s flattened cortex (Note: The line of points and boundary locations from Figure 4 are plotted in panels A and D for comparison). The eta² profile for one of the seeds (blue circle) is shown in panel B. Each of these eta² maps are then analyzed with an automated edge-detection algorithm that generates borders, blue overlay in panel C. Averaging all of the detected edge maps (binary blue overlay in panel C) results in the putative edge map shown in panel D, where intensity of each location reflects the fraction of maps in which that location was considered an edge.

Figure 6

Panel A shows the original patch of the left cingulate cortex shown in Figure 5. Panel B demonstrates that edge locations identified in the neighboring posterior patch align with those in the original patch, even though the two datasets do not share seed points or correlation maps. Panel C shows that the independently analyzed overlapping patch is consistent with the matching regions of A and B.

Figure 7

Panel A shows the rs-fcMRI derived boundaries generated above. Applying a watershed image segmentation algorithm parses the patch into contiguous non-overlapping regions least likely to be edges (i.e. most likely to be areas) shown in panel B, which can then be individually identified and labeled for investigation and validation as shown in panel C.