Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches

TL;DR

This paper introduces a new algorithm that combines graph theory and statistical methods to identify the core structural brain connectivity network shared across a population, aiding neuro-scientific analysis.

Contribution

The paper presents a novel algorithm for extracting the core brain connectivity network by integrating graph-theoretical and statistical approaches, supported by theoretical analysis and empirical validation.

Findings

Outperforms existing methods in feature selection for connectivity analysis

Proven to be effective on a sample of 309 subjects

Provides a computational framework aligned with brain topology evidence

Abstract

Finding the common structural brain connectivity network for a given population is an open problem, crucial for current neuro-science. Recent evidence suggests there's a tightly connected network shared between humans. Obtaining this network will, among many advantages , allow us to focus cognitive and clinical analyses on common connections, thus increasing their statistical power. In turn, knowledge about the common network will facilitate novel analyses to understand the structure-function relationship in the brain. In this work, we present a new algorithm for computing the core structural connectivity network of a subject sample combining graph theory and statistics. Our algorithm works in accordance with novel evidence on brain topology. We analyze the problem theoretically and prove its complexity. Using 309 subjects, we show its advantages when used as a feature selection for connectivity analysis on populations, outperforming the current approaches.