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GEMReg: a spatio-temporal grayordinate ensemble modelling framework for predicting task activation maps from

Sasideep Pasumarthi1, Satwik Bathula1, Nitya Tiwari1

  • 1School of Electrical and Computer Sciences, Indian Institute of Technology, Bhubaneswar, India.

Frontiers in Neuroscience
|December 19, 2025
PubMed
Summary

This study introduces a novel Grayordinate Ensemble Modeling for Regression (GEMReg) framework to predict brain activity maps using resting-state functional MRI (rs-fMRI) data. The spatio-temporal GEMReg achieves state-of-the-art performance by integrating temporal and spatial features from rs-fMRI.

Keywords:
activation map predictionfunctional MRI (fMRI)histogramtemporal feature extractiontime series regression

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Machine Learning

Background:

  • Resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly used as an alternative to task-based fMRI due to its efficiency and suitability for diverse populations.
  • Predicting task activation maps from rs-fMRI data remains a challenge, particularly in leveraging the rich temporal information inherent in rs-fMRI.

Purpose of the Study:

  • To introduce a novel framework, Grayordinate Ensemble Modeling for Regression (GEMReg), for predicting task activation maps solely from rs-fMRI data.
  • To uniquely leverage the temporal dynamics of rs-fMRI for task activation map prediction.
  • To develop a spatio-temporal approach integrating both temporal and spatial features for enhanced prediction accuracy.

Main Methods:

  • Developed a novel Grayordinate Ensemble Modeling for Regression (GEMReg) framework.
  • Formulated task activation map prediction as time series regression, exploiting diverse temporal features and representations from rs-fMRI, including novel histogram-based features.
  • Trained 59,412 individualized models per grayordinate and optimized predictions by selecting optimal feature-regressor combinations within the GEMReg framework.
  • Integrated temporal features with conventional functional connectivity maps-based spatial features to create a spatio-temporal GEMReg.

Main Results:

  • The proposed spatio-temporal GEMReg consistently outperformed existing methods in predicting task activation maps from rs-fMRI data.
  • Achieved a new state-of-the-art performance across standard evaluation metrics.
  • Demonstrated the effectiveness of integrating temporal rs-fMRI information with spatial features for improved prediction.

Conclusions:

  • The spatio-temporal GEMReg framework represents a significant advancement in predicting task activation maps using rs-fMRI.
  • This novel approach offers a more efficient and effective method for brain activity mapping, particularly beneficial for populations where task-based fMRI is challenging.
  • The study highlights the potential of leveraging temporal dynamics in rs-fMRI for predictive modeling in neuroscience.