Duff Eugene P, Cunnington Ross, Egan Gary F
The Howard Florey Institute and Centre for Neuroscience, The University of Melbourne, Parkville, Melbourne, Victoria 3010, Australia.
Neuroinformatics. 2007 Winter;5(4):223-34. doi: 10.1007/s12021-007-9001-y. Epub 2007 Nov 6.
Neuroimaging technologies produce large and complex datasets. The challenge of comprehensively analysing the recorded dynamics remains an important field of research. The whole-brain linear modelling of hypothesised response dynamics and experimental effects must utilise simple basis sets, which may not detect unexpected or complex signal effects. These unmodelled effects can influence statistical mapping results, and provide important additional clues to the underlying neural dynamics. They can be detected via exploration of the raw signal, however this can be difficult. Specialised visualisation tools are required to manage the huge number of voxels, events and scans. Many effects can be occluded by noise in individual voxel time-series. This paper describes a visualisation framework developed for the assessment of entire neuroimaging datasets. While currently available tools tend to be tied to a specific model of experimental effects, this framework includes a novel metadata schema that enables the rapid selection and processing of responses based on easily-adjusted classifications of scans, brain regions, and events. Flexible event-related averaging and process pipelining capabilities enable users to investigate the effects of preprocessing algorithms and to visualise power spectra and other transformations of the data. The framework has been implemented as a MATLAB package, REX (Response Exploration), which has been utilised within our lab and is now publicly available for download. Its interface enables the real-time control of data selection and processing, for very rapid visualisation. The concepts outlined in this paper have general applicability, and could provide significant further functionality to neuroimaging databasing and process pipeline environments.
神经成像技术会产生大量复杂的数据集。全面分析记录的动态变化所面临的挑战仍然是一个重要的研究领域。对假设的响应动态和实验效应进行全脑线性建模必须使用简单的基集，而这些基集可能无法检测到意外的或复杂的信号效应。这些未建模的效应会影响统计映射结果，并为潜在的神经动力学提供重要的额外线索。然而，通过对原始信号的探索来检测这些效应可能很困难。需要专门的可视化工具来管理大量的体素、事件和扫描数据。许多效应可能会被单个体素时间序列中的噪声掩盖。本文描述了一个为评估整个神经成像数据集而开发的可视化框架。虽然目前可用的工具往往与特定的实验效应模型相关联，但这个框架包括一个新颖的元数据模式，该模式能够基于对扫描、脑区和事件的易于调整的分类快速选择和处理响应。灵活的事件相关平均和处理流水线功能使用户能够研究预处理算法效应，并可视化数据的功率谱和其他变换。该框架已作为一个MATLAB包REX（响应探索）实现，我们实验室已经使用了它，现在可以公开下载。它的界面能够实时控制数据选择和处理，以实现非常快速的可视化。本文概述的概念具有普遍适用性，并且可以为神经成像数据库和处理流水线环境提供显著的进一步功能。
