The Multilayer Network Approach in the Study of Personality Neuroscience

Abstract

It has long been understood that a multitude of biological systems, from genetics, to brain networks, to psychological factors, all play a role in personality. Understanding how these systems interact with each other to form both relatively stable patterns of behaviour, cognition and emotion, but also vast individual differences and psychiatric disorders, however, requires new methodological insight. This article explores a way in which to integrate multiple levels of personality simultaneously, with particular focus on its neural and psychological constituents. It does so first by reviewing the current methodology of studies used to relate the two levels, where psychological traits, often defined with a latent variable model are used as higher-level concepts to identify the neural correlates of personality (NCPs). This is known as a top-down approach, which though useful in revealing correlations, is not able to include the fine-grained interactions that occur at both levels. As an alternative, we discuss the use of a novel complex system approach known as a multilayer network, a technique that has recently proved successful in revealing veracious interactions between networks at more than one level. The benefits of the multilayer approach to the study of personality neuroscience follow from its well-founded theoretical basis in network science. Its predictive and descriptive power may surpass that of statistical top-down and latent variable models alone, potentially allowing the discernment of more complete descriptions of individual differences, and psychiatric and neurological changes that accompany disease. Though in its infancy, and subject to a number of methodological unknowns, we argue that the multilayer network approach may contribute to an understanding of personality as a complex system comprised of interrelated psychological and neural features.

Keywords: five-factor model; individual differences; multilayer network; network neuroscience; personality neuroscience; symptom network.

Figure 1

Schematic demonstration of the top down approach to personality study. Arrows in this figure represent the direction by which lower level concepts, in this case brain connectivity [21], are defined through use of the higher-level concepts of the FFM trait measure. The direction of this definition means that brain regions and/or connectivity are dependent upon the higher-level concept of the personality trait.

Figure 2

Symptom Network Model of Psychological Features. This figure shows a hypothetical symptom network model of the symptoms: insomnia, fatigue, concentration and self-reproach. The symptoms are connected by edges that could represent causal relationships or co-occurrence. The edges further have weights which represent the magnitude of the relationship between two symptoms, in this example, insomnia and fatigue would be highly related, but self-reproach and concentration less so. Together, the symptom network approach endeavours to view symptoms in relation to each other, which removes the need for a unified underlying cause to higher-level psychological terms.

Figure 3

Methodology for multilayer approach without interlayer identities. (a) Normalised personality scores in every facet of a personality the FFM (Friendliness = Extraversion One (E1)) are collected from a sample of individuals and used to make a BCEC network by means of the Partial Correlation network methodology. This data is used to create the BCEC network layer in the multilayer network (d). (b) Brain network nodes are defined and network characteristics, such as degree, clustering and centrality, are quantified for the original sample of individuals. This is used to create a brain network layer in the multilayer network (d). (c) A matrix is created which quantifies the correlations between each node in the personality data and each node in the brain data, this matrix score is then used to define the weight of the intralayer connections (X_n,n).