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: 8656325
• 负责人: DIETMAR CORDES
• 金额: $ 6.51万
• 依托单位: RYERSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2015-01-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8656325
• 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; 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; public health relevance; 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（由申请人提供）：本项目的总体目标是开发一个本地多变量分析软件包，用于功能磁共振成像数据分析。它将为心理学家和神经科学家提供一个更强大的工具来分析他们的功能磁共振成像数据使用先进的多变量方法。该项目将导致更好的大脑激活图，从而促进发现目前未知的大脑功能方面。质量单变量分析，如一般线性模型（GLM），是目前流行的fMRI数据分析方法。然而，由于常规应用的固定各向同性空间高斯平滑，它遭受激活边缘的模糊和弱激活区域的检测的潜在消除。局部多变量方法，如典型相关分析（CCA）及其变体已被证明可以显着提高功能磁共振成像激活的检测能力，并改善激活图。作为一个优点，CCA使用自适应空间滤波内核来在噪声环境中更好地准确提取信号。然而，有几个缺点，特别是空间特异性低，计算时间长，和单因素实验设计的限制。此外，参数估计方法不存在，以确定家庭明智的错误率，没有扩展到组分析进行了研究，并没有研究扩展本地CCA非线性CCA的功能磁共振成像数据使用核方法已系统地进行。这些缺点阻碍了局部CCA方法在fMRI神经科学研究中的广泛应用。在这个建议中，我们的目标是消除这些缺点，使用新的本地多变量分析方法（基于CCA），并开发一个软件工具，以扩大其在神经科学研究界的更广泛的应用。我们希望这个软件工具是特别有价值的神经科学研究中，检测弱激活或空间定位模式的激活是必要的。随着高分辨率成像和计算机能力的发展，我们预计对这种软件工具的需求会增加，从而推动大脑功能的新发现和更精确的激活空间定位。作为特定 应用，我们将专注于研究记忆行动，使用一种新的事件相关的识别范式，探讨熟悉和回忆的影响，内侧颞叶（MTL）的高分辨率功能磁共振成像。这项研究将促进我们对海马/MTL对记忆的贡献的理解，这可以大大促进我们对与这些区域显示异常的许多衰弱性神经和精神疾病相关的记忆缺陷的理解，包括轻度认知障碍（MCI），阿尔茨海默病，精神分裂症和重度抑郁症。更广泛地说，它将为心理学家和神经科学家提供一个更强大的工具，使用先进的多变量方法分析他们的功能磁共振成像数据。