Comparison of different group-level templates in gradient-based multimodal connectivity analysis

Area of Science:

• Neuroscience
• Brain Connectivity
• Computational Psychiatry

Background:

• Unsupervised gradient analysis is emerging for studying large-scale brain connectivity and interindividual variations.
• A key challenge is selecting an appropriate group-level template for aligning individual brain connectivity gradients.
• Autism spectrum disorder (ASD) research can benefit from refined methods for analyzing brain connectivity differences.

Purpose of the Study:

• To compare various group-level template construction strategies for gradient analysis of brain connectivity.
• To identify the optimal strategy for detecting between-group differences in functional and structural connectomes, particularly in individuals with ASD.
• To assess the generalizability of the chosen strategy across different datasets and neurological conditions.

Main Methods:

• Utilized multimodal magnetic resonance imaging data from the Autism Brain Imaging Data Exchange (ABIDE) Initiative II and the Human Connectome Project (HCP).
• Designed six template construction strategies, varying inclusion of control subjects and dataset mapping.
• Aligned individual functional and structural gradients to different group-level templates and evaluated effect sizes for identifying ASD-related differences.

Main Results:

• Aligning a combined template of ASD and control subjects from ABIDE onto the HCP template yielded the most significant effect size.
• This strategy robustly identified brain regions associated with ASD in both functional and structural gradients.
• The approach demonstrated generalizability through successful replication in studies of focal epilepsy.

Conclusions:

• The optimal template construction strategy involves combining ASD and control data from one dataset (ABIDE) and aligning it to a template from another (HCP).
• This method enhances the sensitivity of gradient analysis for detecting neurobiological differences in ASD.
• The findings provide a refined methodology for gradient-based brain connectivity research, applicable across various neurological conditions.