Lifespan Models of White Matter in Early Psychosis
Author Information
Author(s): Cirstian Ramona, Forde Natalie J., Zhang Gary, Hellemann Gerhard S., Beckmann Christian F., Kraguljac Nina V., Marquand Andre F.
Primary Institution: Radboud University, Nijmegen, the Netherlands
Hypothesis
Can normative models of white matter fractional anisotropy provide insights into individual variations in early psychosis?
Conclusion
The study reveals significant inter-individual variability in white matter integrity among individuals with early psychosis, which is not captured by traditional group-level analyses.
Supporting Evidence
- The study utilized diffusion MRI data from over 25,000 healthy individuals to create normative models.
- Results indicated that individuals with early psychosis exhibited greater variability in white matter integrity compared to controls.
- Normative models provided insights into individual deviations that traditional group analyses missed.
- Models are publicly available for community use, enhancing research in psychiatric assessments.
- Findings suggest that normative modeling can bridge gaps in understanding psychiatric disorders.
Takeaway
This study looks at how white matter in the brain changes as people grow up and how these changes can help us understand mental health issues like early psychosis.
Methodology
The study used diffusion MRI data from over 25,000 healthy individuals to develop normative models of white matter fractional anisotropy across the lifespan and applied these models to a clinical dataset of individuals with early psychosis.
Potential Biases
The datasets used for training the models are not representative of the wider population and may introduce bias towards Western populations.
Limitations
The age distribution in the dataset is skewed, with fewer data points for very young children and older adults, which may limit the generalizability of the models.
Participant Demographics
The clinical dataset included 173 individuals with early psychosis, comprising 37% females and 63% males, with a racial composition of 58% White, 28% Black, 9% Asian, 1% Mixed, and 3% Other.
Statistical Information
P-Value
0.0036
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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