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Efficient Deep Learning Models for Predicting Individualized Task Activation From Resting-State Functional

Soren J Madsen¹, Young-Eun Lee¹, Shaun K L Quah¹

¹Department of Psychiatry, Stanford University, Stanford, California, USA.

Human Brain Mapping

|June 18, 2026

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This summary is machine-generated.

Deep learning models can predict brain activity from resting-state fMRI, enabling personalized brain mapping. New models improve efficiency without sacrificing accuracy, though prediction is limited by signal reliability.

Area of Science:

Neuroimaging
Machine Learning
Computational Neuroscience

Background:

Predicting task-evoked brain activation from resting-state fMRI (rs-fMRI) offers a non-invasive approach to individualized brain mapping.
Deep learning models show promise in this area, but efficiency and scalability require further investigation.

Purpose of the Study:

To systematically evaluate and enhance deep learning architectures for predicting brain activation from rs-fMRI.
To introduce and assess novel models, BrainSERF and BrainSurfGCN, focusing on efficiency and performance.

Main Methods:

Replication of the BrainSurfCNN framework using Human Connectome Project data.
Development of BrainSERF with channel-wise attention and BrainSurfGCN utilizing graph convolutional networks.
Evaluation using spatial correlation, Dice score, Dice AUC, and subject identification accuracy.

Keywords:

deep learning functional MRI resting‐state task contrast

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Main Results:

All evaluated models demonstrated comparable predictive performance.
BrainSERF offered marginal improvements in individual-specific feature capture.
BrainSurfGCN significantly reduced model size and training time, demonstrating improved computational efficiency.
Prediction accuracy was found to be constrained by resting-state data quality, behavioral task performance, and inter-subject variability.

Conclusions:

Incorporating topological and functional priors enhances deep learning model efficiency for brain activation prediction without compromising accuracy.
The reliability of neural signals fundamentally limits prediction performance.
Graph-based approaches like BrainSurfGCN offer a promising balance of accuracy and computational efficiency.