Brain Controllability and Control Energy in Gray-White Matter Fusion Network

Understanding brain dynamics has long depended on models based on white-matter connectivity. However, these models might be missing out on the role gray-matter architecture plays in shaping brain networks. Researchers have now developed a new approach by merging diffusion tensor imaging-derived white-matter connectivity with gray-matter morphological similarity.

This fusion network was then analyzed for its controllability, biological links, genetic predispositions, predictions of various traits, and the energy required to control it.

The fusion network retained the key topological characteristics of the white-matter network. Interestingly, it was also linked to neurotransmitter systems and cerebral metabolism. Compared to the traditional white-matter connectivity-based network, the fusion-based network showed a consistent trend towards greater heritability, enhanced prediction of several individual attributes and cognitive functions, and required less energy for activating resting-state networks during modeling.

The insights from this study suggest that integrating gray-matter morphological data into networks supported by diffusion tensor imaging could offer a complementary structural viewpoint when examining brain network controllability and transitions between states. However, the lower control energy reported is a model-derived measure of transition cost and should not be misconstrued as a direct indicator of physiological energy consumption.