Multimodal monitoring and high-dimensional data for episode prediction in bipolar disorder

总结 双相情感障碍（BD）是一种复发率和致残率高，经济负担重， 估计自杀风险比普通人群高20倍。虽然有效的治疗是 然而，BD患者一生中很大一部分时间都是有症状的。预测发作的开始 是降低自杀率和残疾率以及优化医疗保健成本的重要策略。 本（R21）探索性/开发性研究的总体目标是获得初步数据，以支持 一种新方法预测稳定成人患者情绪发作的可行性和潜在价值 BD.该提案旨在开发新的数据建模和推理技术， 定制的临床信号检测：检查每个个体随时间的变化。为此，我们 建议整合多模式，高维远程监护数据，非线性技术和人工 情报分类系统这种方法建立在我们的初步工作：（i）非线性 BD情绪调节研究的技术;（ii）使用机器的获奖模拟 学习技术（马尔可夫大脑）用于BD中的发作预测。 目标1（可行性）：获取和整合多模态数据，以进行时间序列分析， 计算90名正常BD成人的熵水平。探索性目标2（潜在价值）：使用 机器学习技术（马尔可夫大脑），以区分参与者在抑郁或 躁狂复发的基础上，他们的时间序列和熵水平（从目标1）。 假设：H1：我们将能够在80%的参与者中收集足够的数据， 多模态数据来执行时间序列分析和计算熵水平。H2：马尔可夫大脑将 基于高（相对于低）自相关时间序列来识别情绪发作风险较高的参与者 和低（对高）熵水平。 意义：R21应用通过以下方式挑战了传统的预测模型： 将个体间和个体内的变异性概念化为生物系统的动态特性。通过 利用密集采样的客观和主观数据、自主、临床和人口统计数据， 该提案旨在开发推理技术，以检查每个人的变化， 时间，以提高估计性能。最终，如果我们发展出预测 情绪发作我们应该能预防

SUMMARY Bipolar disorder (BD) is a mood disorder with high recurrence and disability rates, high economic burden, and an estimated suicide risk 20 times higher than the general population. While efficacious treatment is available, BD patients spend a large proportion of their life symptomatic. Predicting the onset of episodes is a valuable strategy to decrease suicide and disability rates and to optimize healthcare costs. The overall objective of this (R21) Exploratory/Developmental study is to obtain pilot data to support the feasibility and potential value of a new approach to predict mood episodes in stable adult patients with BD. This proposal aims to develop new data modeling and inference techniques that will enable more tailored clinical signal detection: examining changes within each individual, over time. To do so, we propose integrating multimodal, high-dimensional telemonitoring data, nonlinear techniques and artificial intelligence classification systems. This approach builds on our preliminary work on: (i) nonlinear techniques for the study of mood regulation in BD; (ii) an award-winning simulation using a machine learning technique (Markov Brains) for episode prediction in BD. AIMS: Aim 1 (feasibility): To obtain and integrate multimodal data to perform time-series analysis and to calculate entropy levels in 90 euthymic BD adults. Exploratory Aim 2 (potential value): To use machine learning techniques (Markov Brains) to distinguish participants at higher risk for a depressive or manic relapse based on their time-series and entropy levels (from Aim 1). HYPOTHESES: H1: We will be able to collect enough data in 80% of our participants and to integrate multimodal data to perform time-series analysis and to calculate entropy levels. H2: Markov Brains will identify participants at higher risk for a mood episode based on high (vs. low) auto-correlated time-series and low (vs. high) entropy levels. SIGNIFICANCE: This R21 application challenges more traditional prediction models by conceptualizing inter- and intra-individual variability as a dynamic property of biological systems. By leveraging densely-sampled objective and subjective data, autonomic, clinical and demographic data, this proposal aims to develop inference techniques that will examine changes within each individual, over time, in order to enhance the estimation performance. Ultimately, if we develop the capacity to predict mood episodes, we should be able to prevent them.