Stork: Brain AnalyzIR: A software platform for improving scientific rigor in functional NIRS statistical analysis

THEODORE JAMES HUPPERT (2019-08-01 to 2023-04-30) Brain AnalyzIR: A software platform for improving scientific rigor in functional NIRS statistical analysis. Amount: $672991

Brain AnalyzIR:一种软件平台,用于提高功能性NIRS统计分析的科学严谨性


Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging modality that uses low-levels of light to measure evoked hemodynamic changes in the brain. This technique has been growing in popularity over the last several decades due its versatility and portability and the applicability of this technique in unique experimental situations and subject populations, such as studies on children, infants, or using ecologically valid experimental designs (walking, social interaction, etc). As the number of end-users in this field grows, it is important to establish scientifically rigorous best practices for analysis and interpretation of these studies. A fallacy of the fNIRS field has been the direct import of methods and interpretations from other modalities (e.g. functional MRI) without proper adaptation and generalization for the fNIRS-specific noise and signal properties of the data. Furthermore, to date, the development of many fNIRS methods has been based on ad-hoc observations of these algorithms under specific datasets. As a result, end-users often use methods designed for statistical assumptions that do not match their own data. Failure to use proper statistical models or unmet assumptions often results in high false-positive rates and poor scientific rigor and this has been the case in many prior fNIRS studies. The goal of this Biomedical Research Group (BRG-R01) project is to establish current best practices for fNIRS analysis and an infrastructure for future development based on quantitative comparisons of methodologies via receiver operator characteristics analysis, quantification of bias, etc. This project will also establish an open-source fNIRS database to allow characterization and classification of the various properties of fNIRS signals and to quantify their effect on statistical models. Our group has a long history of fNIRS analysis and open-source software development over the last 15 years and is considered one of the top labs in fNIRS analysis. The specific aims of this project are: Aim 1. Development of an open fNIRS database and benchmarking platform for testing and characterizing the development of new algorithms and statistical methods. Aim 2. Determination of best practices for fNIRS analysis under general and categorized noise models. Aim 3. Continued development and improvement of fNIRS-specific analysis models with focus on end-user needs and feedback. Aim4. Dissemination and training of methods.

功能性近红外光谱(fNIRS)是一种非侵入性神经影像学方法,它使用低水平的光来测量大脑中诱发的血液动力学变化。由于其多功能性和便携性以及该技术在独特实验情况和受试者群体中的适用性,例如对儿童,婴儿的研究或使用生态有效的实验设计(步行,社交互动),这种技术在过去几十年中越来越受欢迎。等)。随着该领域最终用户数量的增长,建立科学严谨的最佳实践以分析和解释这些研究非常重要。 fNIRS字段的谬误是直接导入其他模态(例如功能性MRI)的方法和解释,而没有对fNIRS特定的噪声和数据信号属性进行适当的调整和推广。此外,迄今为止,许多fNIRS方法的开发基于在特定数据集下对这些算法的特别观察。因此,最终用户经常使用专为统计假设而设计的方法,这些方法与自己的数据不匹配。未能使用适当的统计模型或未满足的假设通常会导致高假阳性率和较差的科学严谨性,并且在许多之前的fNIRS研究中已经出现这种情况。该生物医学研究小组(BRG-R01)项目的目标是通过接收器操作员特征分析,偏差量化等方法对方法进行定量比较,建立当前fNIRS分析的最佳实践和未来发展的基础设施。该项目还将建立一个开源fNIRS数据库,以便对fNIRS信号的各种属性进行表征和分类,并量化它们对统计模型的影响。我们的团队在过去15年中拥有fNIRS分析和开源软件开发的悠久历史,被认为是fNIRS分析的顶级实验室之一。该项目的具体目标是:目标1.开发一个开放的fNIRS数据库和基准平台,用于测试和表征新算法和统计方法的开发。目标2.确定一般和分类噪声模型下fNIRS分析的最佳实践。目标3.继续开发和改进fNIRS特定的分析模型,重点关注最终用户的需求和反馈。 Aim4。传播和培训方法。

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