Magnetic Resonance Connectome Automated Pipeline

TL;DR

This paper introduces MRCAP, an efficient, modular pipeline for automated estimation of human brain connectomes from MRI data, facilitating rapid analysis and discovery of neural structure-function relationships.

Contribution

The paper presents a novel, integrated pipeline for MRI-based connectome estimation that is efficient, customizable, and validated on over 200 datasets.

Findings

Validated pipeline on 200+ connectomes

Enables rapid generation of brain structural connectivity maps

Supports analysis of cognitive covariates

Abstract

This manuscript presents a novel, tightly integrated pipeline for estimating a connectome, which is a comprehensive description of the neural circuits in the brain. The pipeline utilizes magnetic resonance imaging (MRI) data to produce a high-level estimate of the structural connectivity in the human brain. The Magnetic Resonance Connectome Automated Pipeline (MRCAP) is efficient and its modular construction allows researchers to modify algorithms to meet their specific requirements. The pipeline has been validated and over 200 connectomes have been processed and analyzed to date. This tool enables the prediction and assessment of various cognitive covariates, and this research is applicable to a variety of domains and applications. MRCAP will enable MR connectomes to be rapidly generated to ultimately help spur discoveries about the structure and function of the human brain.