Prediction of Cerebrospinal Fluid (CSF) Pressure with Generative Adversarial Network Synthetic Plasma-CSF Biomarker Pairing.

Non-invasive intracranial pressure (ICP) monitoring can help clinicians safely and efficiently monitor spaceflight-associated neuro-ocular syndrome (SANS), idiopathic intracranial hypertension, and traumatic brain injury in astronauts. Current invasive ICP measurement techniques are unsuitable for austere environments like spaceflight. In this study, we explore the potential of plasma-derived cell-free RNA (cfRNA) biomarkers as non-invasive alternatives to cerebrospinal fluid (CSF) markers for ICP assessment. We conducted a secondary analysis of NASA's Open Science Data Repository datasets 363-364, focusing on plasma and CSF biomarkers related to ICP and neurovascular health. An ensemble model combining Support Vector Machine, Gradient Boosting Regressor, and Ridge Regression was developed to capture plasma-CSF biomarker relationships. To address limited sample size, we employed a Generative Adversarial Network (GAN) to generate synthetic plasma-CSF biomarker pairs, expanding the dataset from 29 to 279 samples. The model's performance was evaluated using Mean Squared Error (MSE) and validated against real biomarker data. The GAN-augmented ensemble model achieved high predictive accuracy with an MSE of 0.0044. Synthetic plasma-CSF pairs closely aligned with actual biomarker distributions, demonstrating their effectiveness in reducing overfitting and enhancing model robustness. Strong correlations between plasma-derived RNA biomarkers and corresponding CSF indicators support their potential as non-invasive proxies for ICP assessment. This study establishes a novel framework for non-invasive ICP monitoring using plasma cfRNA profiles enriched with GAN-generated synthetic data. The approach shows promise for both spaceflight and clinical applications, potentially broadening diagnostic capabilities for ICP-related conditions. However, further validation across diverse populations is necessary, along with careful consideration of bioethical and data security issues associated with synthetic data use in clinical diagnostics.