Varoquaux Gaël, Baronnet Flore, Kleinschmidt Andreas, Fillard Pierre, Thirion Bertrand
Parietal Project-Team, INRIA Saclay-ile de France.
Med Image Comput Comput Assist Interv. 2010;13(Pt 1):200-8. doi: 10.1007/978-3-642-15705-9_25.
Functional brain connectivity, as revealed through distant correlations in the signals measured by functional Magnetic Resonance Imaging (fMRI), is a promising source of biomarkers of brain pathologies. However, establishing and using diagnostic markers requires probabilistic inter-subject comparisons. Principled comparison of functional-connectivity structures is still a challenging issue. We give a new matrix-variate probabilistic model suitable for inter-subject comparison of functional connectivity matrices on the manifold of Symmetric Positive Definite (SPD) matrices. We show that this model leads to a new algorithm for principled comparison of connectivity coefficients between pairs of regions. We apply this model to comparing separately post-stroke patients to a group of healthy controls. We find neurologically-relevant connection differences and show that our model is more sensitive that the standard procedure. To the best of our knowledge, these results are the first report of functional connectivity differences between a single-patient and a group and thus establish an important step toward using functional connectivity as a diagnostic tool.
通过功能磁共振成像（fMRI）测量的信号中的远距离相关性所揭示的功能性脑连接性，是脑病理学有前景的生物标志物来源。然而，建立和使用诊断标志物需要进行概率性的受试者间比较。功能性连接结构的原则性比较仍然是一个具有挑战性的问题。我们给出了一个新的矩阵变量概率模型，适用于在对称正定（SPD）矩阵流形上对功能性连接矩阵进行受试者间比较。我们表明，该模型导致了一种用于对区域对之间的连接系数进行原则性比较的新算法。我们将此模型分别应用于比较中风后患者与一组健康对照。我们发现了与神经学相关的连接差异，并表明我们的模型比标准程序更敏感。据我们所知，这些结果是关于单例患者与一组患者之间功能性连接差异的首次报告，从而朝着将功能性连接用作诊断工具迈出了重要一步。
