Spatial inference methods for image analysis

从生物医学到环境研究，图像分析中的一个中心问题是识别和 找出重要的影响。一个典型的例子是3D脑体积或2D皮质的图像分析 表面，使用解剖和功能成像。1D功能数据(EEG)的例子也比比皆是 模式或解剖神经纤维)、2D图像(显微镜)和2D空间数据(气候图)。这些 问题共享一个共同的数据结构，其中平滑的函数或图像被重复观察，并且 在一个精细的网格上对齐。定位的目标是识别信号较强的区域或 不同条件或不同组的受试者之间存在差异。 虽然有丰富的工具集合来分析成像数据，但重点主要放在 零假设下的显著性检验和错误率控制 但对噪声场的不切实际的假设，损害了差错控制和统计能力。论 另一方面，函数数据分析方法在非零均值模型下正确地工作，但忽略了 光滑随机场理论的分析能力，它在图像分析和 可以为功能数据带来类似的收益。 这项提议的主要目标是开发新的空间推理方法，直接解决 估计非稀疏信号并量化其空间不确定性，以增加统计 功率，控制错误率，并获得适当的可解释结果。在这笔赠款的前一个周期中， 我们建立了峰值检测中正式误差控制的方法学。这一更新开发了位置 峰的不确定度和探测能力(目标1)，并进一步发展置信带和 整个信号的空间置信域(目标2)和信号超过 阈值(目标3)。提出了以均值(效应值)和信噪比为目标的方法 比率(标准化平均值或效果大小)，允许在存在空间上非 恒方差，神经成像数据的特征。该提案提供了对空间的明确定义 推论，并支持光滑高斯随机场理论的方法论，放弃平稳性 和零均值假设。这些方法都经过了严格的验证，并被用来绘制认知效果图 美国国立卫生研究院资助的大型青少年大脑认知发展(ABCD)研究中的成瘾物质使用情况。 该提案独特地将图像和功能数据分析的想法结合在一起，以提供更多 对平滑信号和图像中的重要影响进行准确和可解释的空间定位。这个 在这项提案中开发的方法提供了更准确的大脑和其他领域的映射，以及 更高的统计能力，以确定发生重要影响的位置，增强科学 了解和指导更有针对性的后续研究。

From biomedical to environmental research, a central problem in image analysis is to recognize and locate important effects. An archetypal example is image analysis of the 3D brain volume or the 2D cortical surface, using both anatomical and functional imaging. Examples also abound in 1D functional data (EEG patterns or anatomical neural fibers), 2D images (microscopy) and 2D spatial data (climate maps). These problems share a common data structure in which smooth functions or images are observed repeatedly and aligned on a fine grid. The goal of localization is to identify regions where the signal is strong or where differences exist between conditions or groups of subjects. While there is a rich collection of tools to analyze imaging data, the focus has been mainly on significance testing and controlling error rates under the null hypothesis and has been limited by practical but unrealistic assumptions about the noise field, compromising error control and statistical power. On the other hand, the functional data analysis approach rightly works under the non-zero mean model but ignores the analytical power of smooth random field theory, which has been so successful in image analysis and could enable similar gains for functional data. The main goal of this proposal is to develop new spatial inference methods that directly address the estimation of non-sparse signals and quantification of their spatial uncertainty, in order to increase statistical power, control error rates and obtain appropriately interpretable results. In the previous cycle of this grant, we established methodology for formal error control in peak detection. This renewal develops location uncertainty and detection power for peaks (Aim 1), and moves further to develop confidence bands and spatial confidence regions for the entire signal (Aim 2) and for excursion sets where the signal exceeds a threshold (Aim 3). Methods are proposed to target both the mean (effect magnitude) and the signal-to-noise ratio (standardized mean or effect size), allowing interpretable inference in the presence of spatially non- constant variance, characteristic of neuroimaging data. The proposal offers clear definitions of spatial inference, and supports the methodology with smooth Gaussian random field theory, forgoing the stationarity and zero-mean assumptions. These methods are rigorously validated and used to map the cognitive effects of addictive substance use in the large NIH-funded Adolescent Brain Cognitive Development (ABCD) study. This proposal uniquely brings together ideas from image and functional data analysis to provide more accurate and interpretable spatial localization of important effects in smooth signals and images. The methods developed in this proposal offer more accurate mapping of the brain and other domains, and higher statistical power to identify locations where important effects occur, enhancing scientific understanding and guiding better targeted follow-up studies.