A symmetry-based method to infer structural brain networks from tractography data

TL;DR

This paper introduces MANIA, a novel symmetry-based algorithm for inferring brain networks from tractography data that avoids arbitrary thresholds by leveraging the inherent symmetry limitation of tractography.

Contribution

The paper presents MANIA, a new method that infers structural brain networks by minimizing asymmetry, differing from prior threshold-based approaches, and evaluates it on synthetic and real data.

Findings

MANIA effectively infers networks from noisy synthetic data.

Applied to 28 subjects, it revealed consistent corticolimbic connectivity patterns.

The method shows robustness to data corruption and potential clinical relevance.

Abstract

Recent progress in diffusion MRI and tractography algorithms as well as the launch of the Human Connectome Project (HCP) have provided brain research with an abundance of structural connectivity data. In this work, we describe and evaluate a method that can infer the structural brain network that interconnects a given set of Regions of Interest (ROIs) from tractography data. The proposed method, referred to as Minimum Asymmetry Network Inference Algorithm (MANIA), differs from prior work because it does not determine the connectivity between two ROIs based on an arbitrary connectivity threshold. Instead, we exploit a basic limitation of the tractography process: the observed streamlines from a source to a target do not provide any information about the polarity of the underlying white matter, and so if there are some fibers connecting two voxels (or two ROIs) X and Y tractography should be able in principle to follow this connection in both directions, from X to Y and from Y to X. We leverage this limitation to formulate the network inference process as an optimization problem that minimizes the (appropriately normalized) asymmetry of the observed network. We evaluate the proposed method on a noise model that randomly corrupts the observed connectivity of synthetic networks. As a case-study, we apply MANIA on diffusion MRI data from 28 healthy subjects to infer the structural network between 18 corticolimbic ROIs that are associated with various neuropsychiatric conditions including depression, anxiety and addiction.