Distance disintegration characterizes node-level topological dysfunctions in cocaine addiction

Area of Science:

• Neuroscience
• Addiction Research
• Network Science

Background:

• Previous studies on cocaine use disorder (CUD) have utilized global graph theory measures to understand neural reorganization.
• However, the specific mapping of these global topological alterations to individual brain network areas in CUD remains unclear.

Purpose of the Study:

• To investigate node-level topological dysfunctions in the brain networks of individuals with CUD using resting-state functional magnetic resonance imaging (fMRI).
• To identify specific brain regions affected by topological alterations and their relationship with clinical measures of addiction severity and abstinence.

Main Methods:

• Utilized resting-state fMRI data from 32 individuals with CUD and 32 matched healthy controls.
• Applied graph theory measures, including optimal connectivity distance, node strength, nodal efficiency, and clustering coefficient, to voxel-wise functional connectivity connectomes.
• Analyzed relationships between topological measures and clinical indicators like dependence severity and abstinence length.

Main Results:

• Individuals with CUD exhibited significantly higher optimal connectivity distances in several brain regions, including the ventral striatum, insula, cerebellum, temporal cortex, lateral orbitofrontal cortex, middle frontal cortex, and left hippocampus, compared to controls.
• Positive correlations were found between dependence severity and optimal connectivity distances in the right rolandic operculum and right lateral orbitofrontal cortex.
• Negative associations were observed between abstinence length and optimal connectivity distances in the right temporal pole and left insula.

Conclusions:

• The findings indicate a 'topological distancing' of brain regions associated with cognitive and affective functions in individuals with CUD.
• This topological alteration suggests a general reduction in the communication capacity of these critical brain regions in the context of addiction.
• Node-level network analysis provides a more granular understanding of brain alterations in CUD, complementing global network studies.