Developmental stability and segregation of Theory of Mind and Pain networks carry distinct temporal signatures during naturalistic viewing
Bhavna, K.; Ghosh, N.; Banerjee, R.; Roy, D.
Show abstract
Temporally stable large-scale functional brain connectivity among distributed brain regions is crucial during brain development. Recently, many studies highlighted an association between temporal dynamics during development and their alterations across various time scales. However, systematic characterization of temporal stability patterns of brain networks that represent the bodies and minds of others in children remains unexplored. To address this, we apply an unsupervised approach to reduce high-dimensional dynamic functional connectivity (dFC) features via low-dimensional patterns and characterize temporal stability using quantitative metrics across neurodevelopment. This study characterizes the development of temporal stability of the Theory of Mind (ToM) and Pain networks to address the functional maturation of these networks. The dataset used for this investigation comprised 155 subjects (children (n=122, 3-12 years) and adults (n=33)) watching engaging movie clips while undergoing fMRI data acquisition. The movie clips highlighted cartoon characters and their bodily sensations (often pain) and mental states (beliefs, desires, emotions) of others, activating ToM and Pain network regions of young children. Our findings demonstrate that ToM and pain networks display distinct temporal stability patterns by age 3 years. Finally, the temporal stability and specialization of the two functional networks increase with age and predict ToM behavior.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Age-related changes in the motor planning strategy slow down motor initiation in elderly adults 95%
- Multiclass Classification of Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, and Typically Developed Individuals Using fMRI Functional Connectivity Analysis 93%
- Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments 93%
Similar papers in this journal
Similar papers in this journal
- Developmental trajectories and differences in functional brain network properties of preterm and at-term neonates 93%
- Time-varying Spatial Propagation of Brain Networks in fMRI data 93%
- Bootstrapping promotes the RSFC-behavior associations: an application of individual cognitive traits prediction 92%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.