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This paper introduces a self-supervised method to measure dependencies between biological signals and highlights its limitations in capturing extraneous dependencies.

This paper develops a harmonization framework for structural connectomes to reduce site-related biases, showing that gamma-GLM improves generalizability and statistical power in multi-site analyses.

The paper introduces subnet communicability to model diffusive communication in the brain through a backbone subnetwork, showing it better couples structural and functional connectomes than previous models.

The paper identifies clinically relevant genetic dysfunction in ASD risk genes by analyzing whole-exome data from 2,381 individuals, finding associations between specific variants and cognitive symptoms.

The paper constructs a multifactorial model of white matter connectivity during adolescence using Bayesian networks and diffusion imaging data, showing that architectural specifications become more influential with age.

This paper evaluates how in-scanner head motion affects structural connectivity estimates from diffusion tractography in a developmental cohort, showing motion biases connectivity strength in a consistency and length-dependent manner.

This paper shows that diffusion tensor imaging data are site-specific and demonstrates that ComBat effectively harmonizes them while preserving biological variability.