Multi-Study Integer Programming Methods for Human Voltammery

项目摘要/摘要 成瘾治疗方法的发展需要对潜在的神经机制进行描述。 奖励。研究人类的奖赏需要检测神经递质水平的变化 化学专一性。最近，快速扫描循环伏安法(FSCV)已在人体内实施，以 以高时间和空间分辨率测量多巴胺。这项技术成就是在 这在很大程度上得益于机器学习方法的新颖应用。自FSCV以来，FSCV依赖于统计工具 记录电化学响应，必须通过统计将其转换为浓度估计 模特。因此，来自人类FSCV研究的科学结论的有效性在很大程度上取决于 这些统计模型的可靠性，以生成准确的多巴胺浓度估计。 在人类FSCV中，模型适合于体外训练集，因为在人类体内制作训练集是不可行的。 因此，要想做出准确的估计，需要在体外训练集上训练的模型推广到体内的大脑 录音。组合来自多个训练集的数据是人类FSCV研究人员的标准方法 用来提高模型的泛化能力。这项提议扩展了表明多学习机器的工作 学习方法通过组合来自不同电极的训练集来改进多巴胺浓度估计 使得得到的平均信号(循环伏安图或CV)类似于电极的平均CV 在大脑中使用。但是，此方法依赖于随机重采样。这很有问题，因为 随机性限制了估计精度可以提高的程度和重采样的缓慢速度 该方法排除了在数据收集过程中产生估计的可能性，这是实验成功的关键。 该提案详细介绍了利用混合整数编程优化方法的开发 生成组合来自多个电极的数据的训练集。通过生成特定于 根据用于大脑测量的电极进行定制，可以极大地提高对多巴胺浓度的估计 精确度。整数编程方法的速度将使在数据期间使用该方法成为可能 收集。这项工作将包括对体外数据以及发表在 啮齿类动物的活体和切片实验。通过将方法应用于已发表的光遗传学实验，人们可以 比较建议方法和标准方法的估计值。该方程的渐近性质 提出的方法将在假设线性混合效应模型的情况下进行分析表征，并进行经验验证 通过将该方法应用于该模型下模拟的数据。 这项工作将在高度合作和创新的哈佛公共卫生学院进行。这个 奖学金将支持统计、计算和协作技能的发展，并为受训人员准备 作为一名开发神经科学和成瘾研究方法的生物统计学教授，他的职业生涯富有成效。

Project Summary/Abstract The development of treatments for addiction requires the characterization of neural mechanisms underlying reward. Studying reward in humans requires assays that can detect changes in neurotransmitter levels with high chemical specificity. Recently, fast-scan cyclic voltammetry (FSCV) has been implemented in humans to measure dopamine with high temporal and spatial resolution. This technological achievement was enabled in large part through the novel application of machine learning methods. FSCV relies on statistical tools since FSCV records an electrochemical response which must be converted into concentration estimates via a statistical model. The validity of the scientific conclusions from human FSCV studies therefore depends heavily on the reliability of these statistical models to generate accurate dopamine concentration estimates. In human FSCV, models are fit on in vitro training sets as making in vivo training sets in humans is infeasible. Producing accurate estimates thus requires that models trained on in vitro training sets generalize to in vivo brain recordings. Combining data from multiple training sets is the standard approach human FSCV researchers have employed to improve model generalizability. This proposal extends work that shows that multi-study machine learning methods improve dopamine concentration estimates by combining training sets from different electrodes such that the resulting average signal (“cyclic voltammogram” or CV) is similar to the average CV of the electrode used in the brain. However, this approach relies on random resampling. This is problematic because the randomness limits the extent to which estimate accuracy can be improved and the slow speed of the resampling approach precludes the generation of estimates during data collection, which is critical to experiment success. This proposal details the development of methods that leverage mixed integer programming to optimally generate training sets that combine data from multiple electrodes. By generating training sets that are specifically tailored to the electrode used for brain measurements, one can vastly improve dopamine concentration estimate accuracy. The speed of the integer programming methods will enable the use of this approach during data collection. This work will include validation of the methods on in vitro data as well as on data from published in vivo and slice experiments in rodents. By applying methods to published optogenetic experiments, one can compare estimates from the proposed methods and from standard methods. The asymptotic properties of the proposed methods will be characterized analytically assuming a linear mixed effects model and empirically through application of the methods to data simulated under this model. This work will be conducted at the highly collaborative and innovative Harvard School of Public Health. The fellowship will support growth in statistical, computing and collaborative skills, and prepare the trainee for a productive career as a biostatistics professor who develops methods for neuroscience and addiction research.