■疾病过程在描述精神疾病中起着至关重要的作用。然而,现有的nosologies只考虑其最基本的特征(例如,症状序列,持续时间)。我们开发了一种动态因果模型(DCM),该模型使用密集的时间序列数据更充分地表征了课程模式。这项基础研究介绍了新的建模方法,并使用经验和模拟数据评估了其有效性。
■构建了三级DCM来模拟潜在动力学如何产生抑郁症状,躁狂症,和精神病。该模型符合在四年内前瞻性收集的9名患者的症状评分,第一次住院后。基于这些经验数据的模拟受试者用于评估受试者水平的模型参数。在集团层面,我们使用参数经验贝叶斯(PEB)和留一交叉验证测试了DCM估计潜在病程模式的准确性.
■对经验数据的分析表明,DCM准确地捕获了所有9名受试者的症状轨迹。仿真结果表明,该方法可以准确估计参数(生成参数与估计参数的相关性>=0.76)。此外,该模型可以区分不同的潜在课程模式,PEB正确地将模拟患者分配给9个疗程中的8个。当使用留一交叉验证测试任何两个特定课程模式时,36对中的30对显示出真实组成员资格与估计的组成员资格值之间的中等或高样本外相关性。
■DCM已广泛用于神经科学中,以从神经影像学数据推断潜在的神经元过程。我们的发现强调了采用这种方法来建模症状轨迹以阐明病态实体的潜力,定义它们的时间模式,促进个性化治疗。
UNASSIGNED: Illness course plays a crucial role in delineating psychiatric disorders. However, existing nosologies consider only its most basic features (e.g., symptom sequence, duration). We developed a Dynamic Causal Model (DCM) that characterizes course patterns more fully using dense timeseries data. This foundational study introduces the new modeling approach and evaluates its validity using empirical and simulated data.
UNASSIGNED: A three-level DCM was constructed to model how latent dynamics produce symptoms of depression, mania, and psychosis. This model was fit to symptom scores of nine patients collected prospectively over four years, following first hospitalization. Simulated subjects based on these empirical data were used to evaluate model parameters at the subject-level. At the group-level, we tested the accuracy with which the DCM can estimate the latent course patterns using Parametric Empirical Bayes (PEB) and leave-one-out cross-validation.
UNASSIGNED: Analyses of empirical data showed that DCM accurately captured symptom trajectories for all nine subjects. Simulation results showed that parameters could be estimated accurately (correlations between generative and estimated parameters >= 0.76). Moreover, the model could distinguish different latent course patterns, with PEB correctly assigning simulated patients for eight of nine course patterns. When testing any pair of two specific course patterns using leave-one-out cross-validation, 30 out of 36 pairs showed a moderate or high out-of-samples correlation between the true group-membership and the estimated group-membership values.
UNASSIGNED: DCM has been widely used in neuroscience to infer latent neuronal processes from neuroimaging data. Our findings highlight the potential of adopting this methodology for modeling symptom trajectories to explicate nosologic entities, temporal patterns that define them, and facilitate personalized treatment.