Multimodal Brain Image Analysis
Multimodal Brain Image Analysis
复制标题
多模态脑图像分析
DOI：
10.1007/978-3-319-02126-3_21
复制
发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Deligianni F
中科院分区：
文献类型：
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作者：
Deligianni F
了解大脑功能和结构之间的联系在神经成像和心理学中至关重要。在实践中，恢复大脑网络的不准确性可能会混淆神经生理学因素，并降低检测统计上稳健的链接的灵敏度。因此，纤维束成像方法的可重复性和受试者间差异性目前正在进行广泛的研究。然而，可复制的网络不一定更准确。在这里，我们建立了一个统计框架来比较局部和全局纤维束描记术在预测功能性大脑网络方面的表现。我们使用基于稀疏典型相关分析的模型选择框架和适当的度量来评估预测和观察到的功能网络之间的相似性。我们证明了令人信服的证据，全球纤维束成像优于当地纤维束成像在健康成人队列。
Understanding the link between brain function and structure is of paramount importance in neuroimaging and psychology. In practice, inaccuracies in recovering brain networks may confound neurophysiological factors and reduce the sensitivity in detecting statistically robust links. Hence, reproducibility and inter-subject variability of tractography approaches is currently under extensive investigation. However, a reproducible network is not necessarily more accurate. Here, we build a statistical framework to compare the performance of local and global tractograpy in predicting functional brain networks. We use a model selection framework based on sparse canonical correlation analysis and an appropriate metric to evaluate the similarity between the predicted and the observed functional networks. We demonstrate compelling evidence that global tractography outperforms local tractography in a cohort of healthy adults.