Characterizing variation in the functional connectome: promise and pitfalls

Abstract

The functional MRI (fMRI) community has zealously embraced resting state or intrinsic functional connectivity approaches to mapping brain organization. Having demonstrated their utility for charting the large-scale functional architecture of the brain, the field is now leveraging task-independent methods for the investigation of phenotypic variation and the identification of biomarkers for clinical conditions. Enthusiasm aside, questions regarding the significance and validity of intrinsic brain phenomena remain. Here, we discuss these challenges and outline current developments that, in moving the field toward discovery science, permit a shift from cartography toward a mechanistic understanding of the neural bases of variation in cognition, emotion and behavior.

Figure 1

Effect of sample size on the group-level correlation between age and posterior cingulate cortex (PCC) iFC (adapted from [24]). The correlation between age and PCC iFC was computed for each of a set of randomly sampled subgroups, ranging in size from 10 to 1,090. The plot shows the mean correlation ± 2 times the standard deviation (SD), computed across 10,000 iterations. The plot demonstrates that sample sizes less than 100 produce wildly varying estimates of the ‘true’ effect (the observed correlation between iFC and age, computed on the basis of 1093 participants and indicated by the solid horizontal line).

Figure 2

Interindividual variation in functional boundaries or transition zones (adapted from [24]). The figure shows the spatial distribution of voxel-wise coefficients of variation (absolute value) for (a) fractional Amplitude of Low Frequency Fluctuations (fALFF: a frequency domain-based measure of intrinsic activity); (b) iFC associated with a seed placed in the intraparietal sulcus (IPS); (c) iFC associated with a seed placed in posterior cingulate cortex (PCC); and (d) iFC within an ICA-based network identified as the default network. For the purpose of visualization, coefficients of variation (CV) were rank-ordered, so that the relative degree of variation across participants at a given voxel is shown. Ranking coefficients of variation in this way clearly delineates regions of greatest interindividual variability, thus demarcating putative functional boundaries or transition zones.