Eigenvector Centrality Characterization on fMRI Data: Gender and Node Differences in Normal and ASD Subjects : Author name.

Papri (2024) ran brain scans on people with and without autism. They used resting-state fMRI and a math tool called eigenvector centrality. The tool scores how “important” each brain node is in the whole network.

The team asked if these scores could tell ASD brains from typical ones. They also checked if boys and girls showed different hub patterns.

The eigenvector scores cleanly split ASD from control groups. Certain networks—like the default-mode and attention nets—drove the split. Sex mattered too; the hub maps looked different in males and females.

Lin et al. (2025) pooled 26 studies and saw under-connectivity in the same nets, so Papri’s 2024 hub score is a quick way to spot that low wiring.

Yang et al. (2018) and Li et al. (2023) also show sex matters, but they used different math; Papri confirms the sex split with a simpler centrality rule.

Bravo Balsa et al. (2024) found trait severity links to one small area (right temporoparietal junction), while Papri finds a whole-brain signature; together they suggest both wide and spot checks can track ASD.

You now have a fast, numbers-only flag that says “check for ASD” without long tasks. If you refer a teen for imaging, ask the lab to run eigenvector centrality; it may speed diagnosis and catch sex-specific profiles that shape your treatment plan.