Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy

Abstract

The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.

Keywords: CSD, constrained spherical deconvolution; CSF, cerebrospinal fluid; FA, fractional anisotropy; FCA, analytic functional connectivity; FCD, functional connectivity dynamics; FOD, fiber orientation distribution; Functional connectivity; NBS, network based statistics; Network based statistics; Network communication; Rich club; Structural connectivity; Temporal lobe epilepsy; dMRI, diffusion magnetic resonance imaging; rTLE, right temporal lobe epilepsy; rsfMRI, resting state functional magnetic resonance imaging.

Fig. 1

Illustration of graph metrics to characterize features of the shortest path from A to B: a) weighted path length (summed connection strengths); b) search information (weight path length by paths branching off in one of the shortest path’s nodes) and c) path transitivity (path lengths weighted by additional triangle detours which can be used to arrive at B).

Fig. 2

Network based statistics (NBS) on absolute functional connectivity (t-test rTLE > controls, subnetwork of modified edges threshold T > = 5, p (NBS corrected) < 0.05). Yellow nodes represent a subnetwork hub at nodal degree k > = 5 (considering only subnetwork connections), red nodes represent a degree k < 5.

Fig. 3

a) High degree rich club nodes and their intra-connections at a degree threshold of k = 250, 300 and 330 connections for controls and rTLE patients forming a rich club at different levels (for better visualization 512 regions are projected on the 90 regions of the AAL atlas); b) Rich club coefficient for averaged connectomes (connectome sparsity = 30%, rich club coefficient weighted by streamline counts); c) Subnetwork size (number of nodes) of the largest component as a function of the nodal degree threshold (connectome sparsity = 30%), grey background marks significant difference between the two groups (p < 0.05, two-sided t-test, 10000 permutations of group labels to build the averaged connectome).

Fig. 4

Prediction of functional connectivity a) using a combined model of euclidian distance, path length, path transitivity and search information (contols R = 0.575, rTLE patients R = 0.656, rTLE > controls: p < 0.05, 10000 permutations ) and b) using group averaged search information (controls R = −0.46, rTLE patients R = −0.535, rTLE > controls: p < 0.05, 10000 permutations).

Fig. 5

Prediction of functional connectivity for controls and rTLE patients using a) Euclidian distance, b) search information and c) streamline count based on group averaged connectomes for connections in the altered NBS subnetwork of functional connectivity; Euclidian distance (middle row) and search information (lower row) vs. functional connectivity in three subnetworks at a degree threshold k > 250 splitting the brain in d) Rich club connections, e) Feeder connections and f) Local connections. For detailed correlation values and slope differences see Table 1, Table 2.