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QDECR: A Flexible, Extensible Vertex-Wise Analysis Framework in R.

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  • 1Department of Clinical Genetics, Erasmus MC University Medical Center, Rotterdam, Netherlands.

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Summary
This summary is machine-generated.

We developed QDECR, an R package for analyzing brain cortex morphology in large studies. It enhances statistical modeling for neuroimaging data, accommodating big data and advanced methods.

Keywords:
cerebral cortexneuroimagingstatisticssurface-basedvertex-wise analysis

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Area of Science:

  • Neuroimaging and Computational Neuroscience
  • Statistical Genetics and Epidemiology

Background:

  • The cerebral cortex is crucial for cognitive and bodily functions.
  • Magnetic resonance imaging (MRI) allows in vivo study of cortical morphology via surface models.
  • Existing software struggles with big data, missing data imputation, bias correction, and non-linear modeling for cortical analysis.

Purpose of the Study:

  • To introduce QDECR, a flexible R package for group-level statistical analysis of cortical morphology.
  • To address limitations in current software for large-scale neuroimaging studies.
  • To enable advanced statistical methods in vertex-wise surface-based analyses.

Main Methods:

  • Developed QDECR, an extensible R package utilizing R's modeling capabilities.
  • Implemented vertex-wise linear regression with user-friendly R formula specification.
  • Designed to handle large datasets (thousands of participants) with unimputed or imputed data.

Main Results:

  • QDECR supports flexible statistical modeling, including polynomials, splines, and interactions.
  • The package accommodates large population-based epidemiological studies.
  • Modular design allows for the implementation of new statistical models.

Conclusions:

  • QDECR provides a powerful framework for vertex-wise surface-based analyses in neuroimaging.
  • Enables flexible statistical modeling previously unavailable for population-based and clinical studies.
  • Facilitates advanced analyses on large neuroimaging datasets.