Independent Component Analysis Based Support Vector Machine Classification Method

描述（由申请人提供）：功能性磁共振成像（fMRI）技术为成瘾领域提供了一种独特而有价值的能力，可以探索人类受试者的大脑机制。尽管我们对神经机制的理解取得了进展，但神经影像学方法的诊断效用尚未实现。此外，我们评估神经网络随时间变化的反应能力仍然有限。为了探索新的方法来进一步理解和诊断物质使用障碍，该提案将利用新颖而强大的分析方法来评估可卡因成瘾受试者和健康对照者的现有功能磁共振成像（fMRI）数据集。PI是药物滥用领域的新成员，将把在其他调查领域开发的分析方法带到成瘾领域。该项目的主要目标是使用fMRI数据检查可卡因成瘾受试者的大脑功能差异，并揭示涉及去抑制和决策任务的区域之间的功能连接。这项研究将利用正在进行的NIDA资助的研究“冲动，神经缺陷和可卡因成瘾”的数据。“这项研究评估了BOLD反应在运动抑制解除任务和决策任务的反应逆转以及基本措施的互连措施在休息。将使用数据驱动的基于熵的算法独立成分分析（伊卡）来提取空间独立区域，这些区域在成瘾和对照受试者中显示出差异（目标1）。我们假设，所提出的方法将确定某些组的体素（不一定是空间连接）服务分离的两组谁是下去抑制和决策任务。为了揭示这些差异化区域之间的功能连接如何随时间变化，我们将使用时间过程中的各种窗口大小进行动态（短时间内的变化）动态功能网络连接（DFNC）分析（目标2）。这些发现有望揭示可卡因成瘾和健康对照人群中ICA衍生网络变化之间的时间差异。由于第一个目标将找到明显有区别的体素云，我们将使用机器学习算法，支持向量机（SVM），通过使用受试者的fMRI数据（目标3），自动将受试者映射到可卡因成瘾或对照组中的一组。我们假设基于SVM的主题分类工具将在两组之间的选择中提供高灵敏度。该框架潜在地提供了一种有用的临床诊断工具，并展示了各组之间的关键脑神经差异。最后一个目标旨在揭示给定相关解剖区域对之间的动态连接性，例如内侧前额叶皮质（PFC）、下额叶皮质、岛叶皮质和前扣带回（用于去抑制任务）以及眶额皮质、背外侧前额叶皮质和前扣带回（用于决策任务）。我们假设，一个明确的边界将观察到两组关于两个任务的互动模式。可卡因成瘾者和对照者的fMRI数据中的脑区之间的对比将首次使用非常强大的数据分析工具（伊卡）进行研究，并将用于SVM的分类框架。该提案将汇集两种极具创新性的技术（DFNC和SVM）和一名有前途的初级研究人员进入成瘾领域。利用预先存在的数据库提供了一个非常具有成本效益的方法，临床上重要的，令人困惑的和持久的问题，在该领域。 公共卫生相关性：兴奋剂的滥用和依赖是一个重大的公共卫生问题，在健康、法律的、社会和职业方面造成了巨大的相关成本。虽然以生物学为导向的研究，特别是那些使用成像技术的研究，在我们对与成瘾的发展和持续相关的大脑活动的理解方面取得了重大进展，但这些知识对这些疾病的诊断几乎没有帮助。这项研究将把其他研究领域中使用的统计程序应用于可卡因成瘾受试者的成像数据，以评估大脑网络的变化，并确定可卡因成瘾患者是否可以与非可卡因使用者区分开来。

DESCRIPTION (provided by applicant): Functional MRI (fMRI) techniques have provided the addiction field with a unique and profoundly valuable ability to explore brain mechanisms in human subjects. Despite the resultant advances in our understanding of neural mechanisms, the diagnostic utility of neuroimaging methods has not been realized. In addition, our ability to assess the reactivity of neural networks over time remains limited. To explore new approaches to further our understanding and diagnosis of substance use disorders, this proposal will utilize novel and powerful analytic methods to assess an existing functional magnetic resonance imaging (fMRI) data set in cocaine-addicted subjects and healthy controls. The PI, new to the substance abuse field, will bring analytic approaches developed in other areas of investigation to the field of addiction. The broad goal of this project is to examine differentiations of brain functions in cocaine-addicted subjects using fMRI data and to reveal functional connectivity between regions involving disinhibition and decision-making tasks. This study will exploit data from an ongoing NIDA-funded study, "Impulsivity, Neural Deficits and Cocaine Addiction." This study assesses BOLD response during a motor disinhibition task and a decision- making task of response reversal as well as basal measures of interconnectivity measures during rest. A data driven entropy-based algorithm, independent component analysis (ICA), will be used to extract spatially independent regions that show differences in addicted and control subjects (Aim 1). We hypothesize that the proposed method will identify certain groups of voxels (not necessarily spatially connected) serving separation of two groups who are under disinhibition and decision-making tasks. To uncover how the functional connectivity changes over time between those differentiated regions, we will perform dynamic (changes over short time) dynamic functional network connectivity (DFNC) analysis using various window sizes on time course (Aim 2). These findings are expected to reveal a temporal distinction between ICA-derived network changes in cocaine-addicted and healthy control populations. Since the first aim will find significantly discriminative clouds of voxels, we will use a machine-learning algorithm, the support vector machines (SVM), to automatically map a subject into one of the groups, cocaine-addicted or control, by using subject's fMRI data (Aim 3). We hypothesize that SVM based subject classification tool will provide high sensitivity in selection between the two groups. The framework potentially offers a useful clinical diagnostic instrument as well as demonstrating key brain neural differences between the groups. The last aim is designed to reveal dynamic connectivity between pairs of given anatomic regions of relevance, such as the mesial prefrontal cortex (PFC), inferior PFC, insular cortex, and anterior cingulate (for the disinhibition task) and the orbitofrontal cortex, dorsolateral prefrontal cortex, and anterior cingulate (for the decision-making task). We hypothesize that a clear boundary will be observed in interaction patterns of two groups regarding two tasks. The contrast between brain regions in fMRI data of cocaine-addicted and control subjects will be investigated for the first time using very powerful data analysis tool (ICA) and will be used in classification framework of SVM. The proposal will bring together two extremely innovative techniques (DFNC and SVM) and a promising junior investigator into the field of addiction. The utilization of a pre-existing database offers a highly cost-effective approach to a clinically important, perplexing and persistent problem in the field. PUBLIC HEALTH RELEVANCE: The abuse and dependence of stimulants is a major public health problem with significant health, legal, social and occupational associated costs. While biologically-oriented studies, particularly those using imaging techniques, have produced significant advances in our understanding of brain activity relevant to the development and persistence of addiction, this knowledge has done little to assist in the diagnosis of these disorders. This study will bring statistical procedures used in other fields of research to imaging data obtained from cocaine-addicted subjects to assess changes in brain networks and determine whether the cocaine-addicted patients can be distinguished from non-cocaine using subjects.