Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

Abstract

Intrinsic timescales of activity fluctuations vary hierarchically across the brain. This variation reflects a broad gradient of functional specialization in information storage and processing, with integrative association areas displaying slower timescales that are thought to reflect longer temporal processing windows. The organization of timescales is associated with cognitive function, distinctive between individuals, and disrupted in disease, but we do not yet understand how the temporal properties of activity dynamics are shaped by the brain's underlying structural connectivity network. Using resting-state fMRI and diffusion MRI data from 100 healthy individuals from the Human Connectome Project, here we show that the timescale of resting-state fMRI dynamics increases with structural connectivity strength, matching recent results in the mouse brain. Our results hold at the level of individuals, are robust to parcellation schemes, and are conserved across a range of different timescale- related statistics. We establish a comprehensive BOLD dynamical signature of structural connectivity strength by comparing over 6,000 time series features, highlighting a range of new temporal features for characterizing BOLD dynamics, including measures of stationarity and symbolic motif frequencies. Our findings indicate a conserved property of mouse and human brain organization in which a brain region's spontaneous activity fluctuations are closely related to their surrounding structural scaffold.

Keywords: Interspecies comparison; Resting-state fMRI; Structural connectivity; Structure–function relationship; Time series analysis.

Figure 1.

Schematic showing how we investigate the relationship between a region’s structural connectivity properties to their resting-state dynamics. We summarize each cortical region as its structural connectivity strength, s, and its relative low-frequency power, RLFP (f < 0.14 Hz). Note that, for the purposes of schematic visualization, edge weights and node strength colors represent relative strength, from low (blue) to high (red).

Figure 2.

Group-level connectivity strength, s, is positively correlated with relative low-frequency power of BOLD dynamics, RLFP (f < 0.14 Hz) after correcting for region volume. (A) Rank residuals of relative low-frequency power (RLFP) and node strength, s, across 34 left-hemisphere cortical regions of the Desikan-Killiany atlas (Desikan et al., 2006), after regressing out region volume. The plot reveals a positive relationship, partial Spearman’s ρ_V = 0.53 (p = 2 × 10⁻³). (B) The group-averaged Fourier power spectra for three colored brain areas in A are plotted: medial orbitofrontal area (low s, blue), pars triangularis (moderate s, red), and superior parietal (high s, green), shown up to a maximum of 0.3 Hz. RLFP corresponds to the shaded area under the curve below 0.14 Hz. (C) As A, but for 100 left-hemisphere cortical regions from a custom 200-region parcellation generated by randomly dividing each hemisphere into 100 approximately equal-sized regions (Fornito et al., 2011). (D) Spatial maps of node strength and low-frequency power across 180 left-hemisphere cortical areas of the Glasser et al. (2016) parcellation, with the relative variation of each metric shown using color, from low (blue) to high (red).

Figure 3.

Many individuals exhibit a significant relationship between node strength, s, and relative low-frequency power, RLFP. The histogram of partial Spearman correlation coefficients, ρ_V, between RLFP and s (correcting for variations in region volume) computed separately for each of 100 individuals. The group-level result, ρ_V = 0.54, is shown as a vertical red line.

Figure 4.

RLFP has amongst the strongest correlations to connectivity strength, s, in a comparison to 6,062 time series features. We plot a histogram of absolute partial Spearman correlation coefficients, |ρ_V|, between each of 6,062 rs-fMRI time-series features and connectivity strength s (controlling for region volume). The features were computed using the hctsa toolbox (Fulcher & Jones, ; Fulcher et al., 2013). RLFP (|ρ_V| = 0.53) is shown in red, and the 5% FDR-corrected statistical-significance threshold (|ρ_V| > 0.39) is shown in green.