TY - JOUR
T1 - Predicting narcissistic personality traits from brain and psychological features
T2 - A supervised machine learning approach
AU - Jornkokgoud, Khanitin
AU - Baggio, Teresa
AU - Faysal, Md
AU - Bakiaj, Richard
AU - Wongupparaj, Peera
AU - Job, Remo
AU - Grecucci, Alessandro
PY - 2023/7/31
Y1 - 2023/7/31
N2 - Narcissism is a multifaceted construct often linked to pathological conditions whose neural correlates are still poorly understood. Previous studies have reported inconsistent findings related to the neural underpinnings of narcissism, probably due to methodological limitations such as the low number of participants or the use of mass univariate methods. The present study aimed to overcome the previous methodological limitations and to build a predictive model of narcissistic traits based on neural and psychological features. In this respect, two machine learning-based methods (Kernel Ridge Regression and Support Vector Regression) were used to predict narcissistic traits from brain structural organization and from other relevant normal and abnormal personality features. Results showed that a circuit including the lateral and middle frontal gyri, the angular gyrus, Rolandic operculum, and Heschl's gyrus successfully predicted narcissistic personality traits (
p < 0.003). Moreover, narcissistic traits were predicted by normal (openness, agreeableness, conscientiousness) and abnormal (borderline, antisocial, insecure, addicted, negativistic, machiavellianism) personality traits. This study is the first to predict narcissistic personality traits via a supervised machine learning approach. As such, these results may expand the possibility of deriving personality traits from neural and psychological features.
AB - Narcissism is a multifaceted construct often linked to pathological conditions whose neural correlates are still poorly understood. Previous studies have reported inconsistent findings related to the neural underpinnings of narcissism, probably due to methodological limitations such as the low number of participants or the use of mass univariate methods. The present study aimed to overcome the previous methodological limitations and to build a predictive model of narcissistic traits based on neural and psychological features. In this respect, two machine learning-based methods (Kernel Ridge Regression and Support Vector Regression) were used to predict narcissistic traits from brain structural organization and from other relevant normal and abnormal personality features. Results showed that a circuit including the lateral and middle frontal gyri, the angular gyrus, Rolandic operculum, and Heschl's gyrus successfully predicted narcissistic personality traits (
p < 0.003). Moreover, narcissistic traits were predicted by normal (openness, agreeableness, conscientiousness) and abnormal (borderline, antisocial, insecure, addicted, negativistic, machiavellianism) personality traits. This study is the first to predict narcissistic personality traits via a supervised machine learning approach. As such, these results may expand the possibility of deriving personality traits from neural and psychological features.
KW - Kernel Ridge Regression
KW - Narcissism
KW - Narcissistic personality disorder
KW - Neuroimaging
KW - Support vector regression
KW - MRI
KW - sMRI
KW - Personality
KW - Machine learning
UR - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=plymouth_pure&SrcAuth=WosAPI&KeyUT=WOS:001038068700001&DestLinkType=FullRecord&DestApp=WOS_CPL
UR - https://pearl.plymouth.ac.uk/context/psy-research/article/1748/viewcontent/Predicting_narcissistic_personality_traits_from_brain_and_psychological_features_A_supervised_machine_learning_approach_6.pdf
U2 - 10.1080/17470919.2023.2242094
DO - 10.1080/17470919.2023.2242094
M3 - Article
C2 - 37497589
SN - 1747-0919
VL - 18
SP - 257
EP - 270
JO - Social Neuroscience
JF - Social Neuroscience
IS - 5
ER -