Paladugu Phani, Kumar Rahul, Yelamanchi Jahnavi, Waisberg Ethan, Ong Joshua, Masalkhi Mouayad, Gowda Chirag, Lee Ryung, Amiri Dylan, Jagadeesan Ram, Zaman Nasif, Tavakkoli Alireza, Lee Andrew G
Sidney Kimmel Medical College, Philadelphia, PA, USA.
Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Neuroinformatics. 2025 Jul 10;23(3):38. doi: 10.1007/s12021-025-09729-2.
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.
无创颅内压（ICP）监测有助于临床医生安全、高效地监测宇航员的航天相关神经眼综合征（SANS）、特发性颅内高压和创伤性脑损伤。当前的有创ICP测量技术不适用于太空飞行等恶劣环境。在本研究中，我们探索了血浆来源的无细胞RNA（cfRNA）生物标志物作为脑脊液（CSF）标志物的无创替代品用于ICP评估的潜力。我们对美国国家航空航天局（NASA）开放科学数据存储库数据集363 - 364进行了二次分析，重点关注与ICP和神经血管健康相关的血浆和CSF生物标志物。开发了一种结合支持向量机、梯度提升回归器和岭回归的集成模型来捕捉血浆 - CSF生物标志物之间的关系。为了解决样本量有限的问题，我们采用生成对抗网络（GAN）来生成合成血浆 - CSF生物标志物对，将数据集从29个样本扩展到279个样本。使用均方误差（MSE）评估模型性能，并根据实际生物标志物数据进行验证。GAN增强的集成模型实现了高预测准确性，MSE为0.0044。合成血浆 - CSF对与实际生物标志物分布紧密对齐，证明了它们在减少过拟合和增强模型稳健性方面的有效性。血浆来源的RNA生物标志物与相应CSF指标之间的强相关性支持了它们作为ICP评估无创替代指标的潜力。本研究建立了一个使用富含GAN生成的合成数据的血浆cfRNA谱进行无创ICP监测的新框架。该方法在航天和临床应用中均显示出前景，可能拓宽与ICP相关病症的诊断能力。然而，需要在不同人群中进行进一步验证，同时要仔细考虑与临床诊断中使用合成数据相关的生物伦理和数据安全问题。
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