Network-Level Associations in Nonlinear Brain Dynamics Predict Transcendent Thinking in a Diverse Adolescent Sample

Abstract

Transcendent thinking (TT) is an enduring affective and cognitive process characterized by abstract meaning-making, moral reflection, self-referential integration, and strong emotional engagement. Despite growing interest in its developmental and affective significance, the intrinsic neural dynamics that predict individual differences in disposition to TT remain poorly understood. Most prior work has relied on linear functional connectivity measures, which may be insufficient to capture the nonlinear and multiscale nature of brain dynamics underlying higher-order affective dispositions like TT. Here, we introduce a nonlinear functional brain network (FBN) framework based on multiscale entropy (MSE) to investigate whether intrinsic resting-state nonlinear brain dynamics predict disposition to TT in adolescents. Functional connectivity was defined as inter-regional similarity in MSE profiles derived from resting-state fMRI, yielding weighted networks that capture scale-dependent dynamical correspondence rather than linear synchrony. Graph-theoretical, spectral, and information-theoretic measures were computed and evaluated against signal-level and network-level null models. Predictive performance was assessed using machine-learning models and compared with conventional time series-based FBNs. Global intelligence (IQ) was examined as a control cognitive variable. MSE-based network features, particularly spectral energy and Shannon entropy, showed significant associations with TT and enabled reliable prediction of individual differences, whereas time series-based network measures failed to predict TT. No network measures reliably predicted IQ. Overall, these results indicate that intrinsic nonlinear brain dynamics carry predictive information about affective dispositions, rather than domainspecific or network-localized cognitive abilities such as IQ. This work demonstrates that nonlinear, multiscale network representations of resting-state brain activity provide a principled and predictive framework for modeling individual differences in enduring affective dispositions.