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Related Experiment Video

Updated: Sep 27, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Multivariate functional group sparse regression: Functional predictor selection.

Ali Mahzarnia1, Jun Song2

  • 1Department of Mathematics and Statistics, University of North Carolina at Charlotte, Charlotte, NC, United States of America.

Plos One
|April 7, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces new methods for selecting functional predictors and estimating coefficients in high-dimensional functional data. The techniques effectively identify brain regions associated with ADHD and IQ using functional magnetic resonance imaging data.

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Area of Science:

  • Statistics
  • Machine Learning
  • Neuroimaging Analysis

Background:

  • High-dimensional functional data presents challenges in predictor selection and coefficient estimation.
  • Scalar-on-function regression models are crucial for analyzing complex functional relationships.
  • Existing methods may not adequately handle the infinite-dimensional nature of functional data.

Purpose of the Study:

  • To develop simultaneous methods for functional predictor selection and smooth functional coefficient estimation.
  • To address high-dimensional multivariate functional data in a generic infinite-dimensional Hilbert space.
  • To apply these methods to functional magnetic resonance imaging (fMRI) data for neuroscientific insights.

Main Methods:

  • Proposed two novel methods for functional group-sparse regression.
  • Developed algorithms for simultaneous selection and estimation in infinite-dimensional Hilbert spaces.
  • Validated methods through simulation studies and real fMRI data analysis.

Main Results:

  • Demonstrated convergence of algorithms and consistency of estimation and selection (oracle property).
  • Simulation studies confirmed the effectiveness of the proposed methods.
  • Identified specific human brain regions associated with Attention-Deficit/Hyperactivity Disorder (ADHD) and Intelligence Quotient (IQ) using fMRI data.

Conclusions:

  • The developed methods provide a robust framework for high-dimensional functional data analysis.
  • The approach successfully integrates predictor selection and coefficient estimation.
  • The application to fMRI data highlights the potential for discovering neurobiological correlates of cognitive traits.