Normative pathways in the functional connectome.

Area of Science:

• Neuroscience
• Graph Theory
• Medical Imaging

Background:

• Functional connectivity derived from fMRI data creates a functional connectome, often analyzed using shortest-path algorithms.
• Previous research identified differences in semi-metric pathways in major depressive disorder (MDD), autism spectrum disorder (ASD), and Alzheimer's disease.
• Limited research has investigated the composition of these pathways or inter-individual differences.

Purpose of the Study:

• To develop a novel measurement for pathway consistency across functional connectomes.
• To introduce an algorithm for improving pathway analysis by identifying "normative pathways" from k-shortest paths.
• To investigate the impact of this algorithm on graph measurements and its application to resting-state fMRI data.

Main Methods:

• Developed a novel algorithm to identify "normative pathways" by selecting frequently occurring paths from k-shortest paths.
• Utilized randomized matrix simulations to validate the algorithm's efficacy.
• Applied the algorithm to resting-state fMRI (rs-fMRI) data from adolescent control and MDD participant groups.

Main Results:

• The algorithm identified disrupted normative pathways in MDD, particularly affecting the default mode, affective, and ventral attention networks.
• Normative pathways, especially with partial correlation, showed increased utilization of striatal, cingulate, and cerebellar pathways.
• MDD is characterized by disrupted ventral attention network pathways and increased alternative frontoparietal network pathways.

Conclusions:

• The novel "normative pathway" approach offers a more robust analysis of functional connectomes compared to traditional shortest-path methods.
• Findings highlight specific pathway disruptions in MDD, suggesting potential biomarkers.
• The choice of connectivity estimation method significantly influences group comparison findings.