Deep Learning for Detecting Spaceflight-Induced Brain Changes
Abstract
Prolonged exposure to microgravity during spaceflight induces significant neuroplastic changes in the human brain, affecting cognitive and motor functions. Structural and functional MRI studies have demonstrated alterations in brain volume, cortical thickness, ventricular expansion, and white matter integrity post-mission. However, traditional neuroimaging analysis methods are time-consuming and require extensive manual processing. In this study, we employ Convolutional Neural Networks (CNNs) to automate the detection and classification of spaceflight-induced brain structural changes using pre- and post-flight MRI scans. Our deep learning pipeline consists of data preprocessing, brain region segmentation, and feature extraction to identify microgravity-related neuroplastic adaptations. The CNN model is initially trained on publicly available brain MRI datasets, including the Human Connectome Project (HCP) and IXI Dataset, and fine-tuned with astronaut MRI data and Earth-based microgravity analogs, such as head-down tilt bed rest studies. The model achieves high accuracy in detecting subtle structural alterations, providing an efficient and objective method for analyzing astronaut brain adaptation.This study offers a novel approach to understanding spaceflight's impact on brain structure and has implications for developing countermeasures to mitigate cognitive decline and neurophysiological risks during long-duration missions, such as those to Mars. The integration of deep learning in astronaut health monitoring enables faster, more reliable assessments, aiding space agencies in ensuring crew well-being. Future work will focus on extending the model to functional MRI (fMRI) for analyzing connectivity changes in microgravity.