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Exploring the neurobiological basis of personality

Primary Supervisor: Dr Manousos Klados, Life & Health Sciences

Secondary supervisor: Dr Carl Senior

PhD project title: Exploring the neurobiological basis of personality

University of Registration: Aston University

Project outline:

The aim of this project is to apply a ‘big data’ connectivity analytic approach to identify the neural mechanisms of personality traits and characteristics. Personality is far from being consistent across different people, having been described as: “. . . an individual’s unique variation on the general evolutionary design for human nature” (McAdams and Pals 2006, p. 212). Personality, as the characterization of interindividual variance, thus offers a valuable point of entry to psychological investigations of the relationship between individuality and brain organization.

In the pursuit of understanding the unique composite of behaviours, emotions and motivations that describe each individual person, personality psychology has developed sophisticated taxonomies to describe individual differences. Classificatory systems such as the Big Five model/five-factor model, the six-dimensional HEXACO model, or multi-factor hierarchical models mostly utilize self-report data to model our personality characteristics. Although these approaches have been very successful in describing individual differences and also in predicting actual behaviour from individual personality scores, they are mostly non-explanatory thus provide limited insights into why people differ in a range of behavioural outcomes.

Moreover, it is known that several personality traits are established early and remain stable across the lifespan. Anxiety-related traits are associated with psychiatric disease and represent predisposing factors for various affective disorders, including depression and anxiety. On the other hand, emotional processing relies on the structural and functional integrity of distributed neuronal circuits. Therefore, some personality traits and associated increased risk of psychiatric disease are thought to be rooted in structural and functional variability in large-scale neuronal networks. However, most work on personality traits has thus far focused on the aforementioned psychological questionnaire-based approach.

The main aim of the herein presented project is to explore how personality is encoded inside the brain and to propose a neurobiologically informed model, by integrating large-scale multimodal brain data and personality measures through efficient mathematical modelling and deep learning techniques.

The aforementioned aim can be decomposed into two main objectives:

  1. To integrate the various ‘levels’ of personality characteristics (i.e., ‘traits’, ‘facets’, etc...) into a neurobiologically informed hierarchical model of personality.
  2. To identify lower-level characteristics of individual personality using a large scale connectivity analysis.

To achieve these, we are going to use brain connectivity analysis derived from resting state fMRIs that will be obtained from three big open source databases (HCP (N=1200), NKI (N=771), MPI (N=318)). Brain connectivity analysis represents an important step forward to a more comprehensive understanding of the neuroscientific basis of human personality, since early biologically oriented personality psychologists have noted that it is always several brain areas (and not a single one) that underlie fundamental personality traits. Thus this analytic approach benefits from a high degree of ecological validity. Traditional neuroimaging analytic approaches, which typically rely on correlational paradigms and group activation maps have so far failed to identify such nuanced connections. Multivariate analysis, latent variable modelling, data mining, deep learning classifiers and clustering algorithms, will be some of the tools that will be used for the purposes of the proposed project. While there is little work that has attempted to integrate large scale brain imaging data to converge on a greater understanding of personality measures our early studies have shown the validity and utility of this approach with structural MR (Nunez et al, 2018). No prior work has examined variation in structure and function in the human brain in relation to differences in personality traits.

References:

  1. Markett, C. Montag, and M. Reuter, “Network Neuroscience and Personality,” Personal. Neurosci., vol. 1, p. e14, Aug. 2018.
  2. S. M. Smith et al., “A positive-negative mode of population covariation links brain connectivity, demographics and behavior,” Nat. Neurosci., vol. 18, p. 1565, Sep. 2015.
  3. Toschi, R. Riccelli, I. Indovina, A. Terracciano, and L. Passamonti, “Functional Connectome of the Five-Factor Model of Personality,” Personal. Neurosci., vol. 1, p. e2, May 2018.
  4. Núñez, C. Theofanopoulou, C, Senior, C. Cambra, M.R, Usall, M. R. C. Stephan-Otto, and G. Brébion “A large-scale study on the effects of sex on gray matter asymmetry”. Brain Structure & Function, vol 223 (1), p. 183. May 2018

BBSRC Strategic Research Priority: Understanding the Rules of Life: Neuroscience and Behaviour

Techniques that will be undertaken during the project:

The proposed analysis is going to be performed in resting state fMRI networks obtained by 1200 participants of the Human Connectome Project, which also includes personality data recorded using NEO-FFI. Considering the massive amount of the data, we are going to apply dimensionality reduction techniques based on spectral graph theory, and more specifically diffusion embedding mapping. Then latent variable modelling (Partial Least Square Correlation) is going to be employed in order to analyse the relationship between diffusion maps and personality questionnaires (NEO-FFI). Considering also that a neurobiological model of personality should both relate brain structure and function to personality and also be a predictor of personality, we are going to predict personality using deep learning algorithms on features derived from both networks, and their diffusion maps. Networks, will be further analysed using graph theory in order to see if richer information about the encoding of personality in the brain could be revealed.

Contact: Dr Manousos Klados, Aston University