Toward discovery science of human brain function

Abstract

Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.

Fig. 1.

Independent center-, age-, and sex-related variations detected in R-fMRI measures of functional connectivity and amplitude fluctuation. The first row depicts group-level maps for representative seed-based (column 1) and ICA-based (column 3) functional connectivity analyses (SI Results), as well as fALFF (column 2). Group-level maps were derived from one-way ANOVA across 1,093 participants from 24 centers (factor: center; covariates: age and sex). All group-level maps depicted were corrected for multiple comparisons at the cluster level using Gaussian random-field theory (Z > 2.3; P < 0.05, corrected). For each measure, the second row shows robust between-center concordances (Kendall's W), with the voxelwise coefficients of variation above the diagonal and the voxelwise means below the diagonal. Kendall's W concordance between any two centers was calculated across all voxels in the brain mask for the mean (or coefficient of variation) connectivity map across all participants included in each center. Rows 3, 4, and 5 depict voxels exhibiting significant effects of center, age, and sex, respectively, as detected by one-way ANOVA. “Male” refers to significantly greater connectivity (or amplitude, i.e., fALFF) in males; similarly, “female” refers to significantly greater connectivity (or amplitude) in females. “Older” refers to significantly increasing connectivity (or amplitude) with increasing age, whereas “younger” refers to significantly increasing connectivity (or amplitude) with decreasing age. “Pos” refers to positive functional connectivity, and “neg” refers to negative functional connectivity. The PCC seed region is indicated by a white dot. (Top Left) Surface map legend: LL, left lateral; RL, right lateral; LM, left medial; RM, right medial. All surface maps are rendered on the PALS-B12 atlas in CARET (http://brainvis.wustl.edu).

Fig. 2.

Illustrative areas exhibiting age- and sex-related variation in R-fMRI properties. Significant group-level variance in functional connectivity maps was explained by age and sex (cluster-based Gaussian random-field corrected: Z > 2.3; P < 0.05). For each of three methods (seed-based, fALFF, and ICA), variance in connectivity strength explained by age (Left) and sex (Right) is illustrated both anatomically and graphically. Age-related differences are represented as scatterplots. Sex-related differences are represented as histograms depicting the distributions of resting-state functional connectivity (RSFC) values for males and females separately. Vertical lines indicate peak values. Corresponding topographical brain areas are indicated with dots. “Male” refers to significantly greater connectivity (or amplitude, i.e., fALFF) in males; similarly, “female” refers to significantly greater connectivity (or amplitude) in females. “Older” refers to significantly increasing connectivity (or amplitude) with increasing age, whereas “younger” refers to significantly increasing connectivity (or amplitude) with decreasing age.

Fig. 3.

Variation across individuals reveals functional boundaries. Previous work has noted that functionally segregated regions are frequently characterized by well-demarcated boundaries for an individual (45). As such, variability in boundary areas is detectable across participants. Here we detect functional boundaries via examination of voxelwise coefficients of variation (absolute value) for fALFF and selected seed-based [intraparietal sulcus (IPS), posterior cingulate/precuneus (PCC)] and ICA-based (IC13) functional connectivity maps. For the purpose of visualization, coefficients of variation were rank-ordered, whereby the relative degree of variation across participants at a given voxel, rather than the actual value, was plotted to better contrast brain regions. Ranking coefficients of variation efficiently identified regions of greatest interindividual variability, thus delineating putative functional boundaries.