Pipeline validation for connectivity-based cortex parcellation

Abstract

Structural connectivity plays a dominant role in brain function and arguably lies at the core of understanding the structure-function relationship in the cerebral cortex. Connectivity-based cortex parcellation (CCP), a framework to process structural connectivity information gained from diffusion MRI and diffusion tractography, identifies cortical subunits that furnish functional inference. The underlying pipeline of algorithms interprets similarity in structural connectivity as a segregation criterion. Validation of the CCP-pipeline is critical to gain scientific reliability of the algorithmic processing steps from dMRI data to voxel grouping. In this paper we provide a proof of concept based upon a novel model validation principle that characterizes the trade-off between informativeness and robustness to assess the validity of the CCP pipeline, including diffusion tractography and clustering. We ultimately identify a pipeline of algorithms and parameter settings that tolerate more noise and extract more information from the data than their alternatives.