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.

Structural and effective brain connectivity underlying biological motion detection

“…Parallel processing, as demonstrated in the present study, would be consistent with reports of altered visual sensitivity to BM in neuropsychiatric conditions such as autism (88)(89)(90) or schizophrenia (91)(92)(93), which are associated with more distributed network alterations (94,95). Indeed, the local efficiency of intrinsic functional networks derived from restingstate fMRI data are related to behavioral variability in BM perception in typically developing participants (96).…”

Brain mechanisms involved in the perception of emotional gait: A combined magnetoencephalography and virtual reality study

“…Our results did reveal an interaction between gender and phase in pSTS and Occ across all emotional gait conditions. Both areas are thought to be involved, respectively, in face-body integration [86] and perception of gender information from biological motion [87]. We argue that the gender x phase interaction primarily results from differences in how brain activation unfolds over time between male and female stimuli rather than actual differences in activation amplitude between male vs. female stimuli.…”

Individual-specific networks for prediction modelling – A scoping review of methods

“…The implemented strategy of network sparsification across studies varied substantially in terms of: the sparsification method, the number or combination of investigated methods, the selected sparsification threshold(s) and the reasoning behind the chosen strategy. It was not uncommon for studies to combine several sparsification strategies [ 12 , 21 , 22 , 24 ] or to examine several strategies separately [ 33 , 34 , 37 ]. Further, all of the identified approaches to network sparsification were dependent on the careful selection of a thresholding parameter by the researcher.…”

“…For a complete list of the grouping of the identified methods, we refer to the supplementary material . In general, SVMs were the most popular approach ( N = 40, 45.0%) [ 27 ], followed by linear regression ( N = 25, 28.0%) [ 19 , 22 , 28 , 64 , 67 , 81 , 93 , 94 ], random forests ( N = 12, 13.5%) [ 15 , 41 , 58 , 72 , 95 , 96 ] and logistic regression ( N = 9, 10.1%) [ 41 , 61 , 65 , 97 , 98 ].

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“…The majority of the studies used only graph-theoretical features as independent variables and rarely adjusted for clinical characteristics (e.g. in [ 15 , 18 , 21 , 22 , 67 , 73 , 80 , 97 ]). Even fewer studies adjusted for network size and density within the regression model to account for their inter-subject differences affecting the extracted graph-theoretical features [ 79 , 81 ].…”