GEMReg: a spatio-temporal grayordinate ensemble modelling framework for predicting task activation maps from resting-state fMRI - Pasumarthi, Sasideep; Bathula, Satwik; Tiwari, Nitya; Padole, Himanshu (Frontiers in Neuroscience, 2025)
ObjectiveRecent 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.MethodsMotivated 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 conclusionComparative 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.