Quantifying person-level brain network functioning to facilitate clinical translation

T M Ball,J M Gatt,A N Goldstein-Piekarski,L M Williams

doi:10.1038/tp.2017.204

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.

Highlights

Intrinsic brain networks have been extensively examined in groundbreaking basic neuroscience research,[1,2,3] and have shown relevance to psychiatric disorders in case-control studies.[4,5]there has been no translation of these insights into clinical practice.[6]
Our aim was to establish the proof of principle for a reliable summary metric that can quantify the intrinsic network function of a single patient and allow it to be interpreted relative to healthy normative peers
We examined the reproducibility of the mean values, standard deviations, and internal consistency across three samples: Samples 1 and 2 consist of 255 healthy Caucasian adult twins with functional magnetic resonance imaging (fMRI) data (65% monozygotic, 33% dizygotic and 2% unknown zygosity), divided such that each twin pair was split across the two samples,[29] and Sample 3 consists of 67 healthy adults that were recruited as controls for an antidepressant medication trial.[30]

Summary

Introduction

Intrinsic brain networks have been extensively examined in groundbreaking basic neuroscience research,[1,2,3] and have shown relevance to psychiatric disorders in case-control studies.[4,5]there has been no translation of these insights into clinical practice.[6]. A major analytic approach to functional connectivity is to examine pairwise functional connectivity between a seed region of interest and the rest of the brain.[3] this process is not standardized, requiring each researcher to select and define the seed region and set statistical and spatial thresholds for significant connectivity. Each of these steps tends to differ from researcher to researcher and even study to study, making it difficult to compare across studies to understand normal or abnormal network function. This variability makes false-positive results more likely[9] and can result in over-fitting due to the high dimensionality of neuroimaging data.[10]

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Discussion

Conclusion