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

Brain AnalyzIR：用于提高功能 NIRS 统计分析科学严谨性的软件平台

• 批准号: 9797359
• 负责人: Theodore James Huppert
• 金额: $ 33.67万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9797359
• 关键词: Algorithms; Award; Benchmarking; Biomedical Research; Blood Vessels; Brain; Brain imaging; Child; Childhood; Classification; Computer software; Data; Data Analyses; Data Set; Databases; Development; Electroencephalography; Excision; Experimental Designs; Failure; Feedback; Functional Magnetic Resonance Imaging; Future; Goals; Imaging technology; Individual; Infant; Infrastructure; Institutes; Lead; Libraries; Light; Magnetic Resonance Imaging; Measures; Metabolic; Methodology; Methods; Modality; Modeling; Morphologic artifacts; Motion; National Institute of Child Health and Human Development; National Institute of Neurological Disorders and Stroke; Near-Infrared Spectroscopy; Noise; Performance; Population; Property; Publications; Quantitative Evaluations; Receiver Operating Characteristics; Recommendation; Recording of previous events; Regression Analysis; Reporting; Resources; Rest; Scanning; Sensitivity and Specificity; Signal Transduction; Social Interaction; Source; Standardization; Statistical Data Interpretation; Statistical Methods; Statistical Models; Structure; Tail; Techniques; Testing; Time Series Analysis; Training; United States National Institutes of Health; Update; Variant; Walking; Work; algorithmic methodologies; base; blood oxygen level dependent; cerebral hemodynamics; design; experience; functional improvement; hemodynamics; image reconstruction; improved; innovation; interest; neuroimaging; novel; open source; portability; relating to nervous system; software development

Abstract 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.

摘要 功能性近红外光谱（fNIRS）是一种非侵入性神经成像模式，其使用低水平的 光来测量大脑中诱发的血液动力学变化。这项技术越来越受欢迎 在过去的几十年中，由于其多功能性和便携性以及该技术在独特领域的适用性， 实验情况和受试人群，如对儿童，婴儿的研究，或使用生态有效的 实验设计（步行，社会互动等）。随着该领域最终用户数量的增长， 重要的是建立科学严谨的最佳做法，以分析和解释这些研究。一 fNIRS领域的谬误是直接从其他模式（例如， 功能性MRI），而没有对fNIRS特异性噪声和信号特性进行适当的适应和概括 的数据。此外，到目前为止，许多fNIRS方法的开发已经基于ad-hoc方法。 这些算法在特定数据集下的观察。因此，最终用户经常使用设计的方法， 因为统计假设与他们自己的数据不符。未使用适当的统计模型或未满足 假设往往导致高假阳性率和科学严谨性差， 许多先前的FNIRS研究。本生物医学研究组（BRG-R 01）项目的目标是建立 fNIRS分析的当前最佳实践和基于定量分析的未来发展的基础设施 通过接收器操作员特征分析、偏差量化等对方法进行比较。 该项目还将建立一个开源的fNIRS数据库，以便对 fNIRS信号的各种特性，并量化其对统计模型的影响。我们组有一个很长的 fNIRS分析和开源软件开发在过去15年的历史，被认为是一个 fNIRS分析的顶尖实验室该项目的具体目标是： 目标1。开发开放式fNIRS数据库和基准平台，用于测试和表征 开发新的算法和统计方法。 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.持续开发和改进fNIRS特定分析模型，重点关注最终用户 需求和反馈。 目标4。方法的传播和培训。