Morphometric Latent Factors in Autism and Their Association with Receptor Profiles and Behavior

Area of Science:

• Neuroscience
• Autism Spectrum Disorder (ASD) research
• Biomarker discovery

Background:

• Autism Spectrum Disorder (ASD) is highly heterogeneous, hindering targeted therapies.
• Functional connectivity (FC) subtyping shows promise but suffers from instability, limiting biomarker potential.
• Stable structural measures are needed for reliable ASD trait identification.

Purpose of the Study:

• Directly compare latent factors from stable morphometric measures (MIND) and unstable FC in ASD.
• Hypothesize that stable structural factors better explain ASD heterogeneity and behavior.
• Investigate structure-function correspondence and neurodevelopmental links in ASD versus controls.

Main Methods:

• Utilized Morphometric Inverse Divergence (MIND) for structural analysis.
• Analyzed functional connectivity (FC) data within the same ASD cohort.
• Correlated latent factors from MIND and FC with behavioral measures (SCQ, SRS).
• Examined structure-function correspondence and receptor associations (CB1, NET) in ASD and healthy controls (HC).

Main Results:

• Latent factors from MIND, but not FC, significantly correlated with core ASD behavioral traits.
• Reduced structure-function correspondence observed in the ASD group compared to HC.
• Stable structural factors linked to neurodevelopmental mechanisms (CB1 receptor), while FC linked to arousal systems (NET).

Conclusions:

• Stable morphometric factors (MIND) are more predictive of behavioral traits in ASD than dynamic FC factors.
• MIND is a validated, robust approach for identifying stable ASD biomarkers.
• The link between structural organization and neurodevelopmental underpinnings (CB1) is a promising target for autism biomarker development.