title: Prediction of Biological Motion Perception Performance from Intrinsic Brain Network Regional Efficiency.
abstract: Biological motion perception (BMP) refers to the ability to perceive the moving form of a human figure from a limited amount of stimuli, such as from a few point lights located on the joints of a moving body. BMP is commonplace and important, but there is great inter-individual variability in this ability. This study used multiple regression model analysis to explore the association between BMP performance and intrinsic brain activity, in order to investigate the neural substrates underlying inter-individual variability of BMP performance. The resting-state functional magnetic resonance imaging (rs-fMRI) and BMP performance data were collected from 24 healthy participants, for whom intrinsic brain networks were constructed, and a graph-based network efficiency metric was measured. Then, a multiple linear regression model was used to explore the association between network regional efficiency and BMP performance. We found that the local and global network efficiency of many regions was significantly correlated with BMP performance. Further analysis showed that the local efficiency rather than global efficiency could be used to explain most of the BMP inter-individual variability, and the regions involved were predominately located in the Default Mode Network (DMN). Additionally, discrimination analysis showed that the local efficiency of certain regions such as the thalamus could be used to classify BMP performance across participants. Notably, the association pattern between network nodal efficiency and BMP was different from the association pattern of static directional/gender information perception. Overall, these findings show that intrinsic brain network efficiency may be considered a neural factor that explains BMP inter-individual variability.
authors.affiliation: Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Center for the Study of Applied Psychology, School of Psychology, South China Normal University Guangzhou, China.
authors.forename: Zengjian
authors.initials: Z
authors.lastname: Wang
authors.affiliation: Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Center for the Study of Applied Psychology, School of Psychology, South China Normal University Guangzhou, China.
authors.forename: Delong
authors.initials: D
authors.lastname: Zhang
authors.affiliation: Guangdong Polytechnic Normal University Guangzhou, China.
authors.forename: Bishan
authors.initials: B
authors.lastname: Liang
authors.affiliation: Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Center for the Study of Applied Psychology, School of Psychology, South China Normal University Guangzhou, China.
authors.forename: Song
authors.initials: S
authors.lastname: Chang
authors.affiliation: Guangzhou Library Guangzhou, China.
authors.forename: Jinghua
authors.initials: J
authors.lastname: Pan
authors.affiliation: Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Center for the Study of Applied Psychology, School of Psychology, South China Normal University Guangzhou, China.
authors.forename: Ruiwang
authors.initials: R
authors.lastname: Huang
authors.affiliation: Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Center for the Study of Applied Psychology, School of Psychology, South China Normal University Guangzhou, China.
authors.forename: Ming
authors.initials: M
authors.lastname: Liu
keywords: biological motion; brain-behavior analysis; multiple linear regression model; network efficiency; resting-state network