Predicting Personality from Network-based Resting-State Functional Connectivity
Presented During:
Wednesday, June 28, 2017: 10:55 AM - 11:08 AM
Vancouver Convention Centre
Room:
Room 220-222
Submission No:
4258
Submission Type:
Abstract Submission
On Display:
Wednesday, June 28 & Thursday, June 29
Authors:
Alessandra Nostro1,2, Veronika Müller1,2, Deepthi Varikuti1,2, Rachel Pläschke1,2, Robert Langner1,2, Simon Eickhoff1,2
Institutions:
1Heinrich-Heine University, Düsseldorf, Germany, 2Research Center Jülich (INM-1), Jülich, Germany
First Author:
Alessandra Nostro
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Lecture Information
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Contact Me
Heinrich-Heine University|Research Center Jülich (INM-1)
Düsseldorf, Germany|Jülich, Germany
Heinrich-Heine University|Research Center Jülich (INM-1)
Düsseldorf, Germany|Jülich, Germany
Introduction:
Personality as a key feature of inter-individual differences affects all aspects of life, including affective, social, executive and memory functioning [3,4,6]. Task-based fMRI studies investigated personality and brain activity in association to each of these domains; however, since personality traits are enduring across situations [2], it is possible that they relate to many brain systems, not detected by task-based fMRI. The investigation of functional connectivity in resting state conditions might therefore help in capturing the intrinsic and complex neural architecture underlying personality [1]. A recent study [7] showed a sexual dimorphism in brain structure-personality relationships, with associations revealed only in males. In females, brain connectivity rather than structure, might thus play a stronger role in light of personality. Therefore, we aimed to predict scores of the five-factor personality model (openness, conscientiousness, extraversion, agreeableness, neuroticism) [2] from resting-state functional connectivity (RS-FC) in meta-analytically defined brain networks, and tested how these predictions are modulated by gender.
Methods:
We assessed 9 meta-analytic networks representing regions consistently activated by different social (empathy, face perception), affective (reward, pain, emotion perception), executive (working memory, vigilant attention) and mnemonic (autobiographic and semantic memory) functions. FIX-denoised RS fMRI data of 136 males and 137 matched females was downloaded from the HCP WU-Minn Consortium [10] and further preprocessed with SPM8 using standard procedures. Within each network, FC between all nodes was computed using their respective extracted time series. A relevance vector machine-learning algorithm [9] was used to predict NEO-FFI scores [2] based on FC between all nodes of each network, separately for males and females. Prediction performance was assessed by Pearson correlations between real and predicted scores (p<0.05, corrected for multiple comparisons) and compared between groups.
Results:
Personality traits were successfully predicted by FC within different networks in men and women (see Fig. 1 for a summary). Specifically, in men, conscientiousness was predicted by FC within networks of the affective system (e.g. r=.40 for the reward network; Fig. 2A), extraversion by networks related to social, memory and affective processing, and agreeableness by networks of affective and social domains. In women, openness was predicted by FC within affective and memory-related networks (e.g. r=.45 for the autobiographic memory network; Fig. 2B), conscientiousness by networks linked to executive functioning, and neuroticism by memory-related network. Significant gender differences in prediction performance were found for openness, conscientiousness and agreeableness (Fig. 1).
Conclusions:
Using machine-learning techniques the current study revealed substantial associations of personality with various brain networks related to affective, social, executive, and long-term memory functions, based on FC within these networks. These results indicate that RS connectivity patterns within meta-analytically defined functional brain systems provide information on the individual expression of specific personality traits. Indeed, they were not only predicted by networks already associated to them in the literature, but also not expected brain systems were found informative, with the exception of neuroticism which was not predicted by any expected affective networks. Additionally, FC patterns of different functional networks were shown to predict different personality traits in males and females, indicating gender-specific neural mechanisms associated with specific personality characteristics. This extends previous findings on relations between network-specific differences in gray-matter volume and personality [7] by demonstrating that RS-FC–personality relations should not be considered independent of gender.
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
fMRI Connectivity and Network Modeling
Multivariate modeling
Task-Independent and Resting-State Analysis
Social Neuroscience:
Social Neuroscience Other 1
Keywords:
Machine Learning
Meta- Analysis
Sexual Dimorphism
Other - Brain networks
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