System-level matching of structural and functional connectomes in the human brain

Abstract

The brain can be considered as an information processing network, where complex behavior manifests as a result of communication between large-scale functional systems such as visual and default mode networks. As the communication between brain regions occurs through underlying anatomical pathways, it is important to define a "traffic pattern" that properly describes how the regions exchange information. Empirically, the choice of the traffic pattern can be made based on how well the functional connectivity between regions matches the structural pathways equipped with that traffic pattern. In this paper, we present a multimodal connectomics paradigm utilizing graph matching to measure similarity between structural and functional connectomes (derived from dMRI and fMRI data) at node, system, and connectome level. Through an investigation of the brain's structure-function relationship over a large cohort of 641 healthy developmental participants aged 8-22 years, we demonstrate that communicability as the traffic pattern describes the functional connectivity of the brain best, with large-scale systems having significant agreement between their structural and functional connectivity patterns. Notably, matching between structural and functional connectivity for the functionally specialized modular systems such as visual and motor networks are higher as compared to other more integrated systems. Additionally, we show that the negative functional connectivity between the default mode network (DMN) and motor, frontoparietal, attention, and visual networks is significantly associated with its underlying structural connectivity, highlighting the counterbalance between functional activation patterns of DMN and other systems. Finally, we investigated sex difference and developmental changes in brain and observed that similarity between structure and function changes with development.

Keywords: Connectomics; Large-scale systems; MRI; Network analysis; Structure-function matching.

Figure 1

Overview of study of structure-function relationship using graph matching. (a) (top) Separate connectomes corresponding to various traffic patterns are derived from the weighted structural connectome (i.e., direct connections, unweighted and weighted shortest path, search information, path transitivity, unweighted and weighted communicability). (bottom) Positive and negative functional connectivities for full and partial correlation-based functional connectomes are considered separately in order to investigate structural underpinnings of positive and negative functional connectivity independently. (b) Taking one structure and one function derived connectome, the similarity between their graph representations is calculated using graph matching, yielding a binary matching matrix (c) (top) which contains ones to indicate nodes of a structure-based graph (rows) mapped to those of a function-based graph (columns) with the most similar connectivity patterns, and zeros elsewhere. (Note the difference between a matching matrix and a connectome: a cell of the matching matrix corresponds to a node of structure-based graph matching to a node of function-based graph, whereas a cell in the connectome indicates two regions of the brain being structurally or functionally connected.) (c) (bottom) Averaging the resulting binary matching matrices for all subjects, we obtain the likelihood of accurately matching each structural region with its functional counterpart.

Figure 2

Determining the traffic pattern of human brain. (a) Average of the structure-positive function matching matrix across subjects in the Lausanne 234 atlas, where diagonal entries are deemed accurate matches. As neighboring regions within the same hemisphere are ordered consecutively in the matching matrix, we observe that most of the mismatches occur between either neighboring or contralateral regions. (b) Matching various structure-based connectomes with full positive functional connectomes, we observe that weighted communicability achieves the highest matching accuracy. (* indicates a significant group difference, obtained by a paired t-test with p≤0.05 after FDR correction)

Figure 3

Structure-function matching at system level. (a) By investigating node level matching accuracies over the brain, we found that regions of some of the systems achieved higher accuracies than that of others. (b) Regions belonging to the same functional systems in the average matching matrix were grouped together, demonstrating that most of the structure-function matchings occurred within systems. (c) Matching of structure and positive-function-based graphs at system level using weighted communicability as the traffic pattern. (d) Matching results obtained by repeating the same experiment with negative-function-based graph.

Figure 4

Relationship between system modularity and structure-function matching. Average participation coefficient of large-scale systems in (a) positive full functional and (b) structural connectomes. (c) Structure-positive function matching accuracies at large-scale system level (* indicates p≤0.05 after FDR correction). Correlation between structure-function matching accuracy and participation coefficient for individual regions in (d) positive functional and (e) structural connectomes.

Figure 5

Effect of parcellation on structure-function matching at system level. We evaluated (left and middle columns) the structure-function matching at a low (129 ROIs) and a high (463 ROIs) resolution over the Lausanne atlas in order to study the effect of parcellation resolution on the matching, and (right column) the effect of parcellation scheme by carrying out the matching experiments using the Schaefer atlas consisting of 400 regions of the brain cortex only. Top and bottom rows show matching of structural connectivity with positive and negative functional connectivity, respectively.

Figure 6

Relationship between structure-function matching and (a) sex and (b) age.