Functional connectivity subtypes associate robustly with ASD diagnosis | JoVE Visualize

Area of Science:

Neuroscience
Developmental Neuroscience
Computational Neuroscience

Background:

Autism spectrum disorder (ASD) is characterized by significant heterogeneity, complicating the study of functional brain organization.
Data-driven clustering methods offer a potential framework to identify subtypes within this heterogeneity based on brain connectivity patterns.
The reliability and clinical relevance of these identified subtypes remain largely unexamined.

Purpose of the Study:

To investigate the robustness and generalizability of functional connectivity subtypes in autism spectrum disorder (ASD).
To explore the association between identified connectivity subtypes and clinical diagnosis and behavioral symptoms.
To examine the topographical consistency of these subtypes across different brain networks.

Main Methods:

Hierarchical cluster analysis was applied to functional brain network data from individuals with and without ASD.
Continuous and discrete assignment methods were compared for robustness.
Associations between connectivity subtypes and clinical diagnosis were assessed, including replication in an independent dataset.
18 distinct brain networks were systematically analyzed.

Main Results:

Hierarchical clustering robustly assigned individuals to connectivity subtypes, with continuous assignments demonstrating greater reliability.
Functional connectivity subtypes exhibited moderate associations with the clinical diagnosis of ASD, which were reproducible in independent data.
Contrary to predictions, autism-related connectivity subtypes displayed a consistent topography across diverse brain networks.
This consistent topography aligns with a previously reported compression of the primary functional brain organization gradient.

Conclusions:

Data-driven clustering is a reliable dimensionality reduction technique for analyzing brain connectivity in ASD.
Functional connectivity subtypes in ASD are moderately associated with clinical diagnosis and generalize across datasets.
A common topographical pattern, indicative of a compressed organizational gradient, characterizes autism-related connectivity subtypes across networks.