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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

基本信息

批准号：
10436947
负责人：
Theodore James Huppert
金额：
$ 33.53万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-08-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10436947
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 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 functional near infrared spectroscopy hemodynamics image reconstruction improved innovation interest neuroimaging novel open source portability relating to nervous system software development

项目摘要

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