A Unified Framework for Reliability Analysis in Neuroimaging With Krippendorff's α

IF 2.5 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Imaging Systems and Technology Pub Date : 2025-09-02 DOI:10.1002/ima.70192

Mikkel C. Vinding

引用次数: 0

Abstract

This paper proposes Krippendorff's α for reliability analysis of neuroimaging data. Reliability analysis quantifies the robustness of data and is crucial for ensuring consistent results across different analysis pipelines or methods. It measures the ratio between observed and expected agreement among raters using coincidence matrices. The paper explains how to calculate α and provides MATLAB code for implementation, along with examples of use on neuroimaging data. It includes a computationally efficient method for calculating α and a faster approximation method that maintains the logic of the exact test, making it suitable for large datasets typically found in neuroimaging. The uncertainty of the test statistic is estimated by bootstrapping. This work aims to simplify reliability analysis in neuroimaging, making it accessible for researchers.

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基于Krippendorff α的神经影像学可靠性分析的统一框架

本文提出Krippendorff α用于神经影像学数据的信度分析。可靠性分析量化了数据的健壮性，对于确保跨不同分析管道或方法的一致结果至关重要。它使用符合矩阵测量评分者之间观察到的和期望的一致性之间的比率。本文解释了如何计算α，并提供了MATLAB代码来实现，以及在神经成像数据上的使用示例。它包括计算α的高效方法和保持精确测试逻辑的更快的近似方法，使其适用于通常在神经成像中发现的大型数据集。检验统计量的不确定度由自举估计。这项工作旨在简化神经成像的可靠性分析，使其易于研究人员使用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术

CiteScore

6.90

自引率

6.10%

发文量

138

审稿时长

3 months

期刊介绍： The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.