在计算精神病学中生成建模的贝叶斯工作流
Alexander J Hess1, Sandra Iglesias1, Laura Köchli1
1Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.
Computational psychiatry (Cambridge, Mass.)
|March 31, 2025
概括
贝叶斯工作流通过改进统计推理来增强临床应用的生成模型. 这种方法,使用层次高斯波器模型,确保在翻译神经建模和计算精神病学中获得可靠的结果.
科学领域:
- 计算神经科学是一种神经科学.
- 认知科学 认知科学
- 精神病学是一种精神病学.
背景情况:
- 生成模型具有显著的临床潜力,但需要可靠的统计推理.
- 贝叶斯工作流是一种在翻译神经建模和计算精神病学 (TN/CP) 中被建议但未得到充分利用的方法.
- 层级高斯波器 (HGF) 模型用于计算建模中的层级贝叶斯信念更新.
研究的目的:
- 在TN/CP中展示贝叶斯工作流的实际应用.
- 用单变量行为数据解决统计推理方面的挑战.
- 引入新的响应模型,从多变量数据同时推断.
主要方法:
- 将贝叶斯工作流应用于层次高斯波器 (HGF) 模型.
- 开发并利用多变量数据 (二进制响应和响应时间) 的新型响应模型.
- 使用模拟和经验数据从速度激发的协会奖励学习 (SPIRL) 任务的验证方法.
主要成果:
- 利用二进制响应和响应时间的模型确保了强大的统计推断和参数识别能力.
- 在SPIRL任务中确定了日志转换响应时间和结果不确定性之间的线性关系.
- 该研究说明了贝叶斯工作流对于TN/CP应用程序的好处.
结论:
- 贝叶斯工作流增加了TN/CP中生成建模的透明度和稳定性.
- 采用贝叶斯的工作流对于翻译神经建模和计算精神病学的长期成功至关重要.
- 开发的多变量响应模型改善了从有限的行为数据推断的结论.
更多相关视频
10:44Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
2.1K
10:50Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
Published on: June 21, 2022
1.6K
相关概念视频
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
19
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
19
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
34
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
34