ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Brain Imaging Methods
GEMReg: A Spatio-Temporal Grayordinate Ensemble Modelling Framework for Predicting Task Activation Maps from Resting-State fMRI
Provisionally accepted
- Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Objective: Recent advances in neuroimaging have highlighted the growing utility of resting-state functional magnetic resonance imaging (rs-fMRI) as an alternative to task-based fMRI. In addition to being simpler, cost-effective, and time-efficient, rs-fMRI is particularly advantageous for non-compliant populations such as infants, elderly individuals, and patients with physical or cognitive impairments. Methods: Motivated by this, the present study introduces a novel Grayordinate Ensemble Modeling for Regression (GEMReg) framework for predicting task activation maps solely from rs-fMRI data, which, for the first time, leverages the rich temporal information of rs-fMRI for the task activation maps prediction. Specifically, the proposed approach uniquely formulates the task-activation map prediction as time series regression and exploits different temporal features and representations of the rs-fMRI for the same, including the proposed novel histogram-based features. Focusing on the individual characteristics of the grayordinates, 59412 individualized models (one per grayordinate) were trained by employing multiple univariate time series regressors. To optimize the prediction performance, a novel GEMReg framework is developed that selects the optimal feature–regressor combination for each grayordinate, exploiting the subtle variances in the individual grayordinate mapping. Furthermore, the temporal feature-based GEMReg is integrated with conventional functional connectivity maps-based spatial features, resulting in the spatio-temporal GEMReg, uniquely benefiting from both temporal and spatial features. Results and Conclusion: Comparative analyses demonstrate that the proposed spatio-temporal GEMReg consistently outperforms existing methods across standard evaluation metrics, thereby establishing a new state-of-the-art for task activation map prediction using rs-fMRI.
Keywords: Activation map prediction, functional MRI (fMRI), Histogram, Temporal feature extraction, Time series regression
Received: 30 Sep 2025; Accepted: 14 Nov 2025.
Copyright: © 2025 Pasumarthi, Bathula, Tiwari and Padole. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Himanshu Padole, himanshupadole@iitbbs.ac.in
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.