White Matter Network Alterations in Female Abdominal Obesity by Appetite Subtype

A neuroimaging study analyzes whole-brain white matter structural network topological properties in 60 female abdominal obesity patients stratified by appetite subtypes and 30 healthy controls. Graph theoretical approaches were used to analyze global and nodal topological properties, correlating network metrics with clinical appetite scores and waist circumference. The dataset includes findings on degree centrality, nodal local efficiency, and betweenness centrality in specific brain regions.

Use Cases

• Correlate degree centrality in the right temporal pole (TPOsup.R) with clinical appetite scores to investigate neural substrates of appetite regulation.
• Compare nodal local efficiency in the right amygdala (AMYG.R) between strong appetite (SA) patients and healthy controls to identify localized disruptions in prefrontal-limbic circuits.
• Analyze betweenness centrality in the right rectus gyrus (REC.R) across appetite subtypes to differentiate SA and MA patient neural patterns.
• Identify hypo-connected subnetworks involving 23 nodes and 24 edges specific to the moderate appetite (MA) subtype to study widespread structural disconnectivity.
• Examine correlations between weakened white matter connections (e.g., AMYG.R–SOG.R) and waist circumference to link neuroanatomy with adiposity severity.

Strengths

• Includes data from 90 total participants (60 patients and 30 controls), providing a defined cohort for analysis.
• Stratifies patients into two distinct appetite subtypes (30 SA and 30 MA), enabling comparative analysis.
• Employs graph theoretical analysis on diffusion tensor imaging (DTI) data, a systematic method for studying brain networks.
• Reports specific, statistically significant correlations (e.g., r=0.408, p=0.025) between network metrics and clinical measures.

Limitations

• The dataset is very small (13.8 KB), contained within a DOCX file, suggesting it is a summary of results rather than raw tabular data.
• Sample size is limited to 90 female participants, which may restrict the generalizability of findings.
• Specific column definitions and raw data rows are unavailable, limiting direct computational analysis.

Provenance

Source

Siwen Zhao via figshare.

Collection Method

Diffusion tensor imaging (DTI) and graph theoretical analysis of whole-brain white matter structural networks.

Freshness

Last updated on 2026-03-24.