Petersson K M, Nichols T E, Poline J B, Holmes A P
Department of Clinical Neuroscience, Karolinska Institute, Karolinska Hospital, Stockholm, Sweden.
Philos Trans R Soc Lond B Biol Sci. 1999 Jul 29;354(1387):1261-81. doi: 10.1098/rstb.1999.0478.
The field of functional neuroimaging (FNI) methodology has developed into a mature but evolving area of knowledge and its applications have been extensive. A general problem in the analysis of FNI data is finding a signal embedded in noise. This is sometimes called signal detection. Signal detection theory focuses in general on issues relating to the optimization of conditions for separating the signal from noise. When methods from probability theory and mathematical statistics are directly applied in this procedure it is also called statistical inference. In this paper we briefly discuss some aspects of signal detection theory relevant to FNI and, in addition, some common approaches to statistical inference used in FNI. Low-pass filtering in relation to functional-anatomical variability and some effects of filtering on signal detection of interest to FNI are discussed. Also, some general aspects of hypothesis testing and statistical inference are discussed. This includes the need for characterizing the signal in data when the null hypothesis is rejected, the problem of multiple comparisons that is central to FNI data analysis, omnibus tests and some issues related to statistical power in the context of FNI. In turn, random field, scale space, non-parametric and Monte Carlo approaches are reviewed, representing the most common approaches to statistical inference used in FNI. Complementary to these issues an overview and discussion of non-inferential descriptive methods, common statistical models and the problem of model selection is given in a companion paper. In general, model selection is an important prelude to subsequent statistical inference. The emphasis in both papers is on the assumptions and inherent limitations of the methods presented. Most of the methods described here generally serve their purposes well when the inherent assumptions and limitations are taken into account. Significant differences in results between different methods are most apparent in extreme parameter ranges, for example at low effective degrees of freedom or at small spatial autocorrelation. In such situations or in situations when assumptions and approximations are seriously violated it is of central importance to choose the most suitable method in order to obtain valid results.
功能神经成像（FNI）方法学领域已发展成为一个成熟但仍在不断演进的知识领域，其应用广泛。FNI数据分析中的一个普遍问题是在噪声中找到信号。这有时被称为信号检测。信号检测理论总体上关注与优化从噪声中分离信号条件相关的问题。当概率理论和数理统计方法直接应用于此过程时，它也被称为统计推断。在本文中，我们简要讨论与FNI相关的信号检测理论的一些方面，此外，还讨论FNI中使用的一些常见统计推断方法。讨论了与功能 - 解剖变异性相关的低通滤波以及滤波对FNI感兴趣的信号检测的一些影响。此外，还讨论了假设检验和统计推断的一些一般方面。这包括在原假设被拒绝时表征数据中信号的必要性、FNI数据分析核心的多重比较问题、综合检验以及FNI背景下与统计功效相关的一些问题。反过来，对随机场、尺度空间、非参数和蒙特卡罗方法进行了综述，这些代表了FNI中使用的最常见统计推断方法。作为这些问题的补充，在一篇配套论文中给出了非推断性描述方法、常见统计模型和模型选择问题的概述与讨论。一般来说，模型选择是后续统计推断的重要前奏。两篇论文的重点都在于所介绍方法的假设和固有局限性。当考虑到固有假设和局限性时，这里描述的大多数方法通常都能很好地实现其目的。不同方法之间结果的显著差异在极端参数范围内最为明显，例如在低有效自由度或小空间自相关时。在这种情况下，或者在假设和近似严重违反的情况下，选择最合适的方法以获得有效结果至关重要。
