Resolving the Connectome, Spectrally-Specific Functional Connectivity Networks and Their Distinct Contributions to Behavior

Abstract

The resting human brain exhibits spontaneous patterns of activity that reflect features of the underlying neural substrate. Examination of interareal coupling of resting-state oscillatory activity has revealed that the brain's resting activity is composed of functional networks, whose topographies differ depending on oscillatory frequency, suggesting a role for carrier frequency as a means of creating multiplexed, or functionally segregated, communication channels between brain areas. Using canonical correlation analysis (CCA), we examined spectrally resolved resting-state connectivity patterns derived from magnetoencephalography (MEG) recordings to determine the relationship between connectivity intrinsic to different frequency channels and a battery of over a hundred behavioral and demographic indicators, in a group of 89 young healthy participants. We demonstrate that each of the classical frequency bands in the range 1-40 Hz (δ, θ, α, β, and γ) delineates a subnetwork that is behaviorally relevant, spatially distinct, and whose expression is either negatively or positively predictive of individual traits, with the strongest link in the α-band being negative and networks oscillating at different frequencies, such as θ, β, and γ carrying positive function.

Keywords: canonical correlation analysis; connectome; networks; oscillations; resting state; variability.

Figure 1.

CCA of spectrally resolved MEG data and a large set of behavioral and other subject measures results in one significant mode (r = 0.94, p < 10⁻⁴, corrected for multiple comparisons by permutation testing). For the used parcellation, see Extended Data Figure 1-1, with anatomic labels found in Extended Data Figure 1-2. A visualization of the analysis approach is shown in Extended Data Figure 1-3. A, The canonical mode arranges behavioral variables and subject measures on a positive-negative axis, similar to what has been previously reported for hemodynamic measures of brain connectivity (Smith et al., 2015; for comparison, see also Extended Data Fig. 1-4, showing the fMRI-based brain-behavior mode for the set of subjects used here). At maximum positive correlation, there are mostly subject measures indexing CP such as reading skills and vocabulary knowledge, while on the negative end of the spectrum are subject measures like somatic problems, and tobacco consumption (thresholded at a correlation coefficient of |r| > 0.25). B, Correlation of CCA-derived subject measure scores and connectivity scores for the first canonical variates identified, i.e., the first canonical mode. In color the behavioral score for the working memory test is shown per subject. For a detailed overview of how certain methodological choices impact results see Extended Data Figure 1-5. The first CCA mode as visualized here also explains a significant amount of variance in the data (Extended Data Fig. 1-6). Penn matr. = Penn matrix test; ASR = Achenbach Adult Self Report; DSM = Diagnostic and Statistical Manual; MMSE = mini mental state examination; SSAGA = Semi-Structured Assessment for the Genetics of Alcoholism; unadj = unadjusted for age effects. Please note that some secondary measures (e.g., similar metrics for tobacco consumption) are left out to avoid redundancy.

Figure 2.

A–E, MCC (i.e., edges whose connectivity on a subject-by-subject level covaries with the mode, and thus behavior, in short MCC) for each frequency band, from δ-band, θ-band, α-band, and β-band to γ-band. MCC values of frequency bands are color coded (two colors per band, positive and negative, encoding the correlation coefficients, in a range from –0.5 to.5 to the observed canonical mode). Edges rendered on the brain templates are thresholded at the 0.5th bottom and 99.5th top percentile (of the permutation-based null distribution) for visualization. Each of the bands shows a preference for either positive or negative relationship to the mode but not a mixture of both. This is also visible in the histograms in the bottom row that depict the distributions of all (i.e., unthresholded) correlation coefficients, with comparison to null distributions generated by a permutation test (n = 10,000, in gray). For additional control analyses demonstrating the robustness of results, see also Extended Data Figure 2-1.

Figure 3.

A, Connectogram-style visualization (Irimia et al., 2012) of the MCC edges presented in Figure 2. This figure shows the top 50 MCC edges (i.e., the top 50 positive and negative edges, respectively), across all five frequency bands (globally tresholded across all bands pooled). Nodes are represented on the outside of the ring, in an approximately anatomically-faithful anterior-posterior and left-right arrangement. Anatomically labeled parcel identities are listed in Extended Data Figure 1-2. Each parcel’s grayscale value indicates its average relationship to the CCA mode, i.e., its average accumulated MCC values across frequency bands, white indicates a positive relationship to the mode, and black indicates a negative relationship (averaged over bands and all edges, i.e., connections of that parcel). The five-band histograms on the outer ring indicate the band-specific accumulated MCC values for each node (same color coding as for the top edges in Fig. 1A and the maps in Fig. 1B, histograms pointing outwards are positive, inward histograms indicate negative accumulated MCC values). B, Maps of accumulated MCC values for all frequency bands. These represent the average MCC values, over all connections, for each parcel. Thus, these accumulated maps represent the overall involvement of each parcel in predicting behavior and provide complementary information to the MCC edges by showing the presence of connectivity foci, i.e., nodes of particular importance to the MCC network, acting as sinks or hubs. Thresholded at the 10th and 90th percentiles for negative and positive accumulated MCC values, respectively. NB, no suprathreshold accumulated MCC values are observed in the γ-band. L = left; R = right; pos. = positive; neg. = negative; SUBCORT = subcortical.