A Network Object Method to Uncover Hidden Disorder-Related Brain Connectome

TL;DR

This paper introduces a novel network-object method for detecting hidden disorder-related brain connectivity patterns, improving reproducibility and reducing false discoveries in connectome studies.

Contribution

The paper proposes a new network-object approach that incorporates graph topology to better identify disorder-related brain networks, enhancing statistical power and reproducibility.

Findings

Reduces false positive and negative rates in connectome analysis.

Improves reproducibility of disorder-related network detection.

Provides insights into systematic brain impairments in neuropsychiatric disorders.

Abstract

Neuropsychiatric disorders impact functional connectivity of the brain at the network level. The identification and statistical testing of disorder-related networks remains challenging. We propose novel methods to streamline the detection and testing of the hidden, disorder-related connectivity patterns as network-objects. We define an abnormal connectome subnetwork as a network-object that includes three classes: nodes of brain areas, edges representing brain connectomic features, and an organized graph topology formed by these nodes and edges. Comparing to the conventional statistical methods, the proposed approach simultaneously reduces false positive and negative discovery rates by letting edges borrow strengths precisely with the guidance of graph topological information, which effectively improves the reproducibility of findings across brain connectome studies. The network-object analyses may provide insights into how brain connectome is systematically impaired by brain illnesses.