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A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

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Predicting infant brain connectivity with federated multi-trajectory GNNs using scarce data.

Michalis Pistos¹, Gang Li², Weili Lin²

¹BASIRA Lab, Imperial-X and Department of Computing, Imperial College London, London, UK.

Medical Image Analysis

|March 19, 2025

Summary

This study introduces FedGmTE-Net++, a novel federated learning framework for predicting infant brain network evolution. It effectively handles data scarcity and incomplete data, improving multi-trajectory prediction accuracy while preserving privacy.

Keywords:

Data scarcity Federated learning Graph neural networks Longitudinal connectomic datasets Multimodal brain graph evolution prediction

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Area of Science:

Neuroscience
Artificial Intelligence
Medical Imaging

Background:

Understanding infant brain network evolution is crucial for developmental neuroscience.
Current deep learning models predict brain evolution but struggle with multi-trajectory generalization, data scarcity, and incomplete time-series data.

Purpose of the Study:

To introduce FedGmTE-Net++, a federated graph-based network for multi-trajectory brain evolution prediction.
To address limitations of existing models in generalization, data requirements, and utilization of incomplete data.

Main Methods:

Developed FedGmTE-Net++, a federated learning framework for data-scarce environments.
Incorporated an auxiliary regularizer to maximize utilization of longitudinal brain connectivity data.
Implemented a two-step imputation process (K-Nearest Neighbours and regressor refinement) for incomplete time-series data.

Main Results:

FedGmTE-Net++ demonstrates superior performance in multi-trajectory brain prediction from a single baseline graph compared to benchmark methods.
The federated approach enhances local model performance across diverse hospitals while ensuring data privacy.
The auxiliary regularizer and two-step imputation effectively leverage longitudinal and incomplete data.

Conclusions:

FedGmTE-Net++ offers a robust solution for predicting infant brain network evolution, particularly in data-scarce settings.
The framework advances multi-trajectory prediction by effectively handling data limitations and privacy concerns.
This approach has significant implications for understanding early brain development and identifying potential abnormalities.