Quantifying person-level brain network functioning to facilitate clinical translation

Abstract

Although advances in neuroimaging have yielded insights into the intrinsic organization of human brain networks and their relevance to psychiatric and neurological disorders, there has been no translation of these insights into clinical practice. One necessary step toward clinical translation is identifying a summary metric of network function that is reproducible, reliable, and has known normative data, analogous to normed neuropsychological tests. Our aim was therefore to establish the proof of principle for such a metric, focusing on the default mode network (DMN). We compared three candidate summary metrics: global clustering coefficient, characteristic path length, and average connectivity. Across three samples totaling 322 healthy, mostly Caucasian adults, average connectivity performed best, with good internal consistency (Cronbach's α=0.69-0.70) and adequate eight-week test-retest reliability (intra-class coefficient=0.62 in a subsample N=65). We therefore present normative data for average connectivity of the DMN and its sub-networks. These proof of principle results are an important first step for the translation of neuroimaging to clinical practice. Ultimately, a normed summary metric will allow a single patient's DMN function to be quantified and interpreted relative to normative peers.

Figure 1

Illustration of candidate summary metrics. (a) Average connectivity is the average magnitude of all pairwise functional connectivity values (note that a subset of connections is shown in the figure for clarity). (b) Topological properties are derived from a representation of the network with a proportion of the weakest connections set to zero (represented by lines present in a and absent in b). The clustering coefficient of node 1 is the weighted proportion of connections among that node’s neighbors. Neighbors of node 1 are illustrated in the figure by red dashed lines, connections among those neighbors by red solid lines. The global clustering coefficient is the aggregate of the clustering coefficient for all nodes in the network and indicates the network’s short-range efficiency. The path length between nodes 2 and 3 is the weighted shortest distance between those nodes, illustrated in the figure by orange solid lines. The characteristic path length is the average shortest distance across all pairs of nodes in the network and indicates the network’s long-range efficiency.

Figure 2

Replicability, internal consistency, and test–retest reliability of average functional connectivity. Means and standard deviations are in units of Fisher’s Z. (a) Whole network: average of all pairwise connections between all DMN regions. (b) Anterior sub-network: average of all pairwise connections between medial PFC regions within the DMN. (c) Posterior-to-anterior sub-network: average of all connections between PCC regions and medial PFC regions within the DMN. CI, confidence interval; DMN, default mode network; ICC, intra-class correlation coefficient; PCC, posterior cingulate cortex; PFC, prefrontal cortex; SD, standard deviation. *indicates value outside 95% CI of Sample 1.