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: 8841351
• 负责人: DIETMAR CORDES
• 金额: $ 27.58万
• 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 2018-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8841351
• 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(申请人提供)：这个项目的总体目标是开发一个用于功能磁共振数据分析的本地多变量分析软件包。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fMRI数据。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，它受到激活边缘模糊和弱激活区域检测的潜在消除的影响。局部多变量方法，如典型相关分析(CCA)及其变体，已被证明可以显著提高fMRI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核在噪声环境中更好地准确地提取信号。然而，该方法存在空间特异性低、计算时间长、单因素实验设计局限性等缺点。此外，还不存在确定家族错误率的参数估计方法，也没有扩展到组分析的研究，也没有使用核方法将局部CCA扩展到fMRI数据的非线性CCA的研究。所有这些缺点都阻碍了局部CCA方法在神经科学fMRI研究中的广泛接受。在这个提案中，我们的目标是使用新的局部多变量分析方法(基于CCA)消除这些缺陷，并开发一个软件工具来扩大其在神经科学研究领域的更广泛应用。我们希望这个软件工具对神经科学研究特别有价值，因为需要检测微弱的激活或空间定位的激活模式。随着高分辨率成像和计算机能力的提高，我们预计对这种软件工具的需求会增加，从而推动对大脑功能的新发现和更精确的激活空间定位。作为一种特殊情况 在高分辨率功能磁共振成像中，我们将重点研究记忆行为，使用一种新的事件相关识别范式来研究内侧颞叶(MTL)亚区的熟悉和回忆对高分辨率fMRI的影响。这项研究将促进我们对海马体/MTL对记忆的贡献的理解，这可以极大地促进我们对记忆缺陷的理解，这些记忆缺陷与一些衰弱的神经和精神疾病有关，这些疾病在这些区域显示出异常，包括轻度认知障碍、阿尔茨海默病、精神分裂症和严重的抑郁症。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fMRI数据。