Faisan S, Thoraval L, Armspach J-P, Heitz F
Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection, UMR CNRS-ULP 7005, Strasbourg I University, France.
Med Image Anal. 2007 Feb;11(1):1-20. doi: 10.1016/j.media.2006.09.003. Epub 2006 Nov 9.
This paper presents a novel, completely unsupervised fMRI brain mapping method that addresses the three problems of hemodynamic response function (HRF) variability, hemodynamic event timing, and fMRI response non-linearity. Spatial and temporal information are directly taken into account into the core of the activation detection process. In practice, activation detection at voxel v is formulated in terms of temporal alignment between sequences of hemodynamic response onsets (HROs) detected in the fMRI signal at v and in the spatial neighborhood of v, and the input sequence of stimuli or stimulus onsets. Event-related and epoch paradigms are considered. The multiple event sequence alignment problem is solved within the probabilistic framework of hidden Markov multiple event sequence models (HMMESMs), a new class of hidden Markov models. Results obtained on real and synthetic data significantly outperform those obtained with the popular statistical parametric mapping (SPM2) method without requiring any prior definition of the expected activation patterns, the HMMESM mapping approach being completely unsupervised.
本文提出了一种全新的、完全无监督的功能磁共振成像（fMRI）脑图谱绘制方法，该方法解决了血液动力学响应函数（HRF）变异性、血液动力学事件时间以及fMRI响应非线性这三个问题。空间和时间信息被直接纳入激活检测过程的核心。在实际应用中，体素v处的激活检测是根据在v处及其空间邻域的fMRI信号中检测到的血液动力学响应起始（HRO）序列与刺激或刺激起始的输入序列之间的时间对齐来制定的。考虑了事件相关范式和时段范式。在隐马尔可夫多事件序列模型（HMMESMs）这一新型隐马尔可夫模型的概率框架内解决了多事件序列对齐问题。在真实数据和合成数据上获得的结果显著优于使用流行的统计参数映射（SPM2）方法获得的结果，且无需对预期激活模式进行任何先验定义，HMMESM映射方法是完全无监督的。
