SVM based group data analysis for drug abuse disorder ASL perfusion study

描述（由申请人提供）：动脉自旋标记（ASL）灌注MRI为药物滥用/成瘾研究提供了一种潜在的非常有用的工具，这是由于无创脑血流量（CBF）测量和对低频MR信号漂移效应的免疫力，低频MR信号漂移效应会随着时间的推移降低基于BOLD对比度的功能性MRI。然而，通过常规的基于单变量一般线性模型（GLM）的方法进行的数据处理已经证明对于ASL数据是极其具有挑战性的，这是由于ASL数据固有的低SNR、在药物渴求状态期间通常存在的增加的运动和激动以及在具有某些类型的慢性物质滥用（例如，可卡因）。假设大脑对功能刺激的反应是线性的，忽略了大量的空间脑活动耦合，标准的GLM最初是次优的fMRI数据分析。用规范的血液动力学响应函数（HRF）对每个体素的时间序列进行建模，它可能进一步不适定到药物滥用研究，因为HRF的实际形状在成瘾患者的大脑中可能与规范的HRF显著不同，并且可能在患者与患者之间、在体素与体素之间显著不同。药物滥用/成瘾ASL灌注研究从根本上需要更强大的数据分析方法。本项目的目标是开发一种多变量和无脑反应建模的fMRI数据处理方法，并使用它来揭示现有药物滥用/成瘾ASL灌注fMRI数据中与药物渴求相关的脑激活模式。一种机器学习算法，支持向量机（SVM），将被用来提取不同的实验条件之间的空间判别图为每个主题，并提供一个统计框架，以给出一个人口推断提取的判别（目标1）。我们假设，这种基于机器学习的数据处理将增加ASL灌注fMRI的检测灵敏度相比，传统的GLM方法，因为数据驱动的性质和多变量处理的SVM。为了验证这一假设，并验证所提出的方法的灵敏度，特异性和可靠性，我们建议获得40名正常对照者的空假设ASL灌注fMRI数据和感觉运动任务数据的目的2。最后，但最重要的目的（目的3）是应用所提出的方法来分析现有的药物滥用/成瘾ASL灌注fMRI数据在我们的中心。除了ASL灌注fMRI和BOLD fMRI的基础科学意义外，我们相信这一系列工作将提供关键信息，例如，更精确的诊断测量，以及对目前可卡因和尼古丁成瘾/复发脆弱性研究的治疗或药物反应的预测。这些更强大的ASL数据分析方法的输出也将为ASL灌注fMRI方法在多种脑疾病中的普遍应用以及正常大脑中的持续状态铺平道路。

DESCRIPTION (provided by applicant): Arterial spin labeling (ASL) perfusion MRI provides a potentially extremely useful tool for drug abuse/addiction studies due to the noninvasive cerebral blood flow (CBF) measurement and immunity to low frequency MR signal drift effects that degrade functional MRI based on BOLD contrast over time. However, data processing through conventional univariate general linear model (GLM) based methods has proved extremely challenging for ASL data due to its intrinsic low SNR, the increased motion and agitation typically present during drug craving states, and the "patchy flow" defects commonly found in the brains of patients with certain types of chronic substance abuse (e.g., cocaine). Assuming a linear brain response to the functional stimuli and ignoring the abundant spatial brain activity coupling, the standard GLM is initially sub-optimal for fMRI data analysis. Modeling each voxel's time series with a canonical hemodynamic response function (HRF), it may be further ill-posed to drug abuse studies since the actual shape of HRF may differ significantly in the addicted patient's brain from the canonical one and may differ significantly from patient to patient, from voxel to voxel. A more powerful data analysis method is fundamentally demanded for drug abuse/addiction ASL perfusion studies. The goal of this project is to develop a multivariate and brain response modeling-free fMRI data processing method and to use it for revealing the drug craving related brain activation patterns within existing drug abuse/addiction ASL perfusion fMRI data in our center. A machine-learning algorithm, the support vector machine (SVM), will be used to extract a spatial discriminance map between different experimental conditions for each subject, and a statistic framework will be provided to give a population inference about the extracted discriminance (Aim 1). We hypothesize that this machine-learning based data processing will increase the detection sensitivity of ASL perfusion fMRI as compared to the conventional GLM approach because of the data driven nature and multivariate processing of SVM. To verify this hypothesis and to validate the sensitivity, specificity and reliability of the proposed methods, we propose to acquire 40 normal controls' null-hypothesis ASL perfusion fMRI data and sensory-motor task data in Aim 2. The last but the most important aim (Aim 3) is to apply the proposed method to analyze the existing drug abuse/addiction ASL perfusion fMRI data in our center. In addition to the basic science implications for ASL perfusion fMRI and BOLD fMRI, we believe this line of work will provide critical information, e.g., more precise diagnostic measurement, and prediction of treatment or medication response for the current cocaine and nicotine addiction/relapse vulnerability studies. The output of these more powerful ASL data analysis methods will also pave the way for general application of ASL perfusion fMRI methods across multiple brain disorders, and for sustained states within the normal brain.