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Reliability and predictability of phenotype information from functional connectivity in large imaging datasets
Jessica Dafflon1,2, Dustin Moraczewski1, Eric Earl1
1Data Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA.
Arxiv
|May 15, 2024
Summary
Predicting human traits from brain connectivity is challenging. This study shows that using key components of functional connectivity (latent phenotypes) improves prediction accuracy, especially the first few components.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Behavioral Genetics
Background:
- Predictive modeling of behavior from brain functional connectivity is a key goal in neuroimaging.
- Previous models show limited success in predicting behavioral traits from functional connectivity patterns.
Approach:
- Developed models to predict observable traits (phenotypes) and their latent representations from resting-state functional connectivity.
- Utilized data from the Human Connectome Project (HCP) and Philadelphia Neurodevelopmental Cohort (PNC).
- Emphasized the importance of confounding variable regression for accurate phenotype prediction.
Key Points:
- Both phenotypes and their latent phenotypes showed similar predictive performance.
- Only the first five latent phenotypes were reliably identified.
- Using only the top five latent phenotypes for prediction yielded performance comparable to using all latent phenotypes.
Conclusions:
- Predictable phenotypic information is concentrated in the leading latent phenotypes.
- Filtering out less reliable latent phenotypes does not harm predictive performance.
- Findings enhance understanding of functional connectivity's role in phenotypic predictability and reliability for neuroimaging research.
