Tracking the morphological evolution of neuronal dendrites by first-passage analysis

Abstract

A high degree of structural complexity arises in dynamic neuronal dendrites due to extensive branching patterns and diverse spine morphologies, which enable the nervous system to adjust function, construct complex input pathways, and thereby enhance the computational power of the system. Recognition of pathological changes due to neurodegenerative disorders is of crucial importance due to the determinant role of dendrite morphology in the functionality of the nervous system. Nevertheless, direct noninvasive measurements to collect adequate structural data in a reasonable time are currently not feasible. Here, we present a stochastic coarse-grained framework based on first-passage analysis to infer key dendritic morphological features affected by neurodegenerative diseases—including the density and size of spines, the extent of the tree, and the segmental increase of dendrite shaft diameter toward the soma—from the statistical characteristics of a measurable temporary signal generated by tracers that have diffusively passed through the complex dendritic structure. Thus, our theoretical approach can provide a noninvasive route to link dendritic morphology with possible accessible readouts in neurodegenerative disease monitoring. As a prospective application, we discuss how externally detectable signals could be realized in practice, suggesting potential pathways toward experimental implementation.