Improving the Detection of Activation in High Resolution fMRI using Multivariate

使用多变量改进高分辨率 fMRI 中的激活检测

• 批准号: 8920855
• 负责人: DIETMAR CORDES
• 金额: $ 13.03万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-04 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8920855
• 关键词: Address; Algorithms; Alzheimer' s Disease; Brain; Communities; Computer software; Data; Data Analyses; Detection; Environment; Estimation Techniques; Event; Experimental Designs; Face; Familiarity; Family; Functional Magnetic Resonance Imaging; Goals; Health; Hemorrhage; Hippocampus (Brain); Image Analysis; Individual; Lead; Learning; Linear Models; Machine Learning; Major Depressive Disorder; Maps; Medial; Memory; Memory impairment; Methods; Modeling; Morphologic artifacts; Multivariate Analysis; Neighborhoods; Neurologic; Neurosciences Research; Occupations; Pattern; Problem Solving; Psychologist; Public Health; Research; Resolution; Schizophrenia; Shapes; Signal Transduction; Software Tools; Solutions; Specificity; Statistical Methods; Techniques; Temporal Lobe; Testing; Time; Variant; Weight; base; density; digital imaging; improved; interest; mathematical methods; memory recognition; mild cognitive impairment; novel; prevent; statistics; theories; tool; user friendly software

DESCRIPTION (provided by applicant): The overall goal of this project is to develop a local multivariate analysis software package for fMRI data analysis. It will provide psychologists and neuroscientists a more powerful tool to analyze their fMRI data using advanced multivariate methods. This project will lead to better brain activation maps and thus promote the discovery of currently unknown aspects of brain function. Mass-univariate analysis, such as the general linear model (GLM), is the prevailing fMRI data analysis method. However, it suffers from blurring of edges of activation and potential elimination of the detection of weak activated regions due to routinely applied fixed isotropic spatial Gaussian smoothing. Local multivariate methods such as canonical correlation analysis (CCA) and its variants have been shown to significantly increase the detection power of fMRI activations and improve activation maps. As an advantage, CCA uses adaptive spatial filtering kernels to accurately extract the signal better in a noisy environment. However, there are several drawbacks, particularly low spatial specificity, long computational time, and single-factor experimental design limitation. Furthermore, a parametric estimation method does not exist to determine the family-wise error rate, no extension to group analysis has been investigated, and no studies extending local CCA to nonlinear CCA for fMRI data using kernel methods have been systematically carried out. All these drawbacks prevent local CCA methods from being widely accepted in neuroscience research in fMRI. In this proposal, our goals are to eliminate these drawbacks using novel local multivariate analysis methods (based on CCA) and to develop a software tool to widen its broader application in the neuroscience research community. We expect this software tool to be particularly valuable for neuroscience research where detections of weak activations or spatially localized patterns of activations are desired. As high resolution imaging and computer power advance, we expect an increase in demand for this software tool, thus advancing new discoveries of brain function and more precise spatial localization of activations. As a particular application, we will focus on studying memory actions using a novel event-related recognition paradigm to investigate the effects of familiarity and recollection in subregions of the medial temporal lobes (MTL) for high resolution fMRI. This research will advance our understanding of hippocampal/MTL contributions to memory, which can substantially advance our understanding of the memory deficits associated with a number of debilitating neurological and psychiatric conditions that show abnormalities in these regions, including mild cognitive impairment (MCI), Alzheimer�s disease, schizophrenia, and major depression. More generally, it will provide psychologists and neuroscientists a more powerful tool to analyze their fMRI data using advanced multivariate methods.

描述（由适用提供）：该项目的总体目标是开发用于fMRI数据分析的本地多元分析软件包。它将为心理学家和神经科学家提供更强大的工具，可以使用高级多元方法分析其fMRI数据。该项目将导致更好的大脑激活图，从而促进目前未知的大脑功能方面的发现。群众分析（例如通用线性模型（GLM））是现行的fMRI数据分析方法。然而，由于常规应用固定的各向同性空间高斯平滑而导致弱活化区域检测的激活边缘的模糊和潜在的进化。局部多元方法（例如规范相关分析（CCA）及其变体）已被证明可显着提高fMRI激活的检测能力并改善激活图。作为优势，CCA使用自适应空间滤波内核来在嘈杂的环境中更好地提取信号。但是，有几个缺点，尤其是低空间特异性，较长的计算时间和单因素实验设计限制。此外，不存在一种参数估计方法来确定家庭错误率，未研究小组分析的扩展，并且没有系统地对fMRI数据扩展到非线性CCA将局部CCA扩展到非线性CCA，并使用核心方法进行了系统地进行了fMRI数据。所有这些缺点阻止局部CCA方法在fMRI的神经科学研究中被广泛接受。在此提案中，我们的目标是使用新型的本地多元分析方法（基于CCA）消除这些缺点，并开发一种软​​件工具来扩大其在神经科学研究社区中的广泛应用。我们希望该软件工具对于神经科学研究特别有价值，在这种研究中，需要检测弱激活或空间局部的激活模式。随着高分辨率成像和计算机功率提高，我们预计对该软件工具的需求会增加，从而提高了大脑功能的新发现以及更精确的激活空间定位。特别是 应用，我们将专注于使用新型事件相关的识别范式研究记忆动作，以研究媒体临时爱（MTL）对高分辨率fMRI的媒体临时爱情（MTL）中熟悉度和识别的影响。这项研究将提高我们对海马/MTL对记忆的贡献的理解，这可以大大提高我们对记忆的理解定义与许多衰弱的神经系统和精神病疾病相关的，这些神经系统和精神病疾病表明这些地区异常，包括轻度认知损害（MCI），阿尔茨海默氏病，精神分裂症，精神分裂症和严重抑郁症。更普遍地，它将为心理学家和神经科学家提供更强大的工具，可以使用高级多元方法分析其fMRI数据。