Cyclotron Research Centre, University of Liège, Liège, Belgium.
PLoS One. 2012;7(4):e35860. doi: 10.1371/journal.pone.0035860. Epub 2012 Apr 26.
Predicting a particular cognitive state from a specific pattern of fMRI voxel values is still a methodological challenge. Decoding brain activity is usually performed in highly controlled experimental paradigms characterized by a series of distinct states induced by a temporally constrained experimental design. In more realistic conditions, the number, sequence and duration of mental states are unpredictably generated by the individual, resulting in complex and imbalanced fMRI data sets. This study tests the classification of brain activity, acquired on 16 volunteers using fMRI, during mental imagery, a condition in which the number and duration of mental events were not externally imposed but self-generated. To deal with these issues, two classification techniques were considered (Support Vector Machines, SVM, and Gaussian Processes, GP), as well as different feature extraction methods (General Linear Model, GLM and SVM). These techniques were combined in order to identify the procedures leading to the highest accuracy measures. Our results showed that 12 data sets out of 16 could be significantly modeled by either SVM or GP. Model accuracies tended to be related to the degree of imbalance between classes and to task performance of the volunteers. We also conclude that the GP technique tends to be more robust than SVM to model unbalanced data sets.
从特定的 fMRI 体素值模式预测特定的认知状态仍然是一个方法学挑战。解码大脑活动通常在高度受控的实验范式中进行，这些范式具有一系列由时间受限的实验设计引起的不同状态。在更现实的条件下，个体产生的心理状态的数量、顺序和持续时间是不可预测的，导致 fMRI 数据集复杂且不平衡。本研究测试了使用 fMRI 在 16 名志愿者进行心理意象时的大脑活动分类，在这种情况下，心理事件的数量和持续时间不是外部强加的，而是自我产生的。为了解决这些问题，考虑了两种分类技术（支持向量机 (SVM) 和高斯过程 (GP)），以及不同的特征提取方法（广义线性模型 (GLM) 和 SVM）。这些技术被组合在一起，以确定导致最高精度测量的程序。我们的结果表明，在 16 个数据集中，有 12 个可以通过 SVM 或 GP 显著建模。模型精度往往与类之间的不平衡程度和志愿者的任务表现有关。我们还得出结论，与 SVM 相比，GP 技术更能稳健地对不平衡数据集进行建模。
