On the relation between cortical information processing and geometrical adaptation during a learning task: A conceptual mechanical feedback loop framework.

The expansion and gyrification of the human cerebral cortex support the topologicallycomplex connectome required for brain-wide cognitive tasks. While macroscopicalcortical curvature is largely stable in adulthood, learning can induce observablestructural alterations. These manifest as a predominant type characterized by transientchanges mainly in cortical thickness, and a sporadic, long-term, cortical surface areabased type.In this paper, we propose a unified physical mechanism for both. Integrating principlesfrom tissue mechanobiology, we describe a multi-staged conceptual framework. Twomain premises are necessary: first, observable structural adaptation evolves after thefunctional capacity of a task-related domain is surpassed; second, both changes aregoverned by the same mechanism—compressive stress induced by neuropil growth orpruning—developed in different dominant axes.We propose a vertical, dendrite-guided adaptation in the case of cortical thicknesschange and a second, horizontal, dendrite-guided adaptation in the case of corticalsurface area change: The first is guided by radial growth of dendrites and supportivestructures, generates vertical stress (relieved by elastic deformation), resulting intransient cortical thickening described by the expansion-renormalization model ofcortical change during learning.The second is guided by horizontal dendritic growth, generates in-plane compressivestress (relieved by a buckling instability), and results in long-term changes of surfacearea and gyrification. During both types, mechanobiological feedback loops contributeto the temporal and energetic optimization of these processes.Overall, the proposed framework connects information processing and adaptivenetwork growth with the resultant mechanical phenomena, linking microscale plasticityto macroscale morphological change. Finally, we discuss the model's testablepredictions and its implications for the neurobiology of knowledge acquisition.