Evaluation and calibration of functional network modeling methods based on known anatomical connections
Evaluation and calibration of functional network modeling methods based on known anatomical connections
复制标题
DOI：
10.1016/j.neuroimage.2012.11.006
复制
发表时间：
2013-02-15
期刊：
影响因子：
5.7
通讯作者：
Shmuel, Amir
中科院分区：
文献类型：
--
作者：
Dawson, Debra Ann;Cha, Kuwook;Shmuel, Amir
最近的研究基于静息状态fMRI数据的自发波动的时空结构确定了大规模的大脑网络。预计基于静息状态数据的功能连通性反映-但不等同于-潜在的解剖连通性。然而，哪些功能连通性分析方法能够可靠地预测网络结构，目前尚不清楚。在这里，我们通过将网络连接分析方法应用于从人类视觉皮层获得的fMRI静息状态时间序列来测试和比较网络连接分析方法。这里评估的方法是Smith等人之前针对模拟数据进行的测试（Neuroimage, 2011）。为此，我们根据视界内的偏心率定义了视网膜定位视觉区域V1、V2和V3内的区域，划定了感兴趣的中心、中间和外围偏心率区域（roi）。这些roi在我们研究的模型中充当节点。我们的评估基于“基本事实”，深入研究了猴子视觉皮层中视网膜组织的解剖连接。对于每种评估方法，我们计算了已知存在的连接的检测分数率（“c敏感度”），同时使用那些不期望存在的连接的相互作用幅度分布的第95个百分位数的阈值。在最佳条件下——包括会话持续时间为68分钟，一个由9个节点组成的相对较小的网络，以及对全局效应的无人工回归——每种顶级方法预测的预期连接的c灵敏度为67-85%。相关性方法表现最好，包括相关性（Corr; 85%）、正则化逆协方差（ICOV; 84%）和偏相关性（PCorr; 81%），其次是帕特尔卡帕法（80%）、贝叶斯网络方法PC （BayesNet; 77%）、一般同步测量（67-77%）和相干性（CohB; 74%）。随着会话持续时间的减少，这些顶级方法的c敏感度降低，在17分钟的会话中达到59-76%。在8.5分钟的短静息状态fMIR扫描中，没有一种方法能很好地预测真实网络，Corr（65%）表现最好。随着网络复杂度从9个节点增加到36个节点，包括PCorr和BayesNet在内的多变量方法的性能下降。全局效应的无伪影回归增加了表现最好的方法的c敏感性。在我们执行的所有测试的总体评估中，相关方法（Corr， ICOV和PCorr）， Patel的Kappa和BayesNet方法PC将自己置于所有其他方法之上。我们建议将基于已知解剖连接的基于数据的校准整合到未来的网络研究中，以最大限度地提高灵敏度并减少误报。(c) 2012 Elsevier Inc.版权所有。
Recent studies have identified large scale brain networks based on the spatio-temporal structure of spontaneous fluctuations in resting-state fMRI data. It is expected that functional connectivity based on resting-state data is reflective of - but not identical to - the underlying anatomical connectivity. However, which functional connectivity analysis methods reliably predict the network structure remains unclear. Here we tested and compared network connectivity analysis methods by applying them to fMRI resting-state time-series obtained from the human visual cortex. The methods evaluated here are those previously tested against simulated data in Smith et al. (Neuroimage, 2011).To this end, we defined regions within retinotopic visual areas V1, V2, and V3 according to their eccentricity in the visual field, delineating central, intermediate, and peripheral eccentricity regions of interest (ROIs). These ROIs served as nodes in the models we study. We based our evaluation on the "ground-truth", thoroughly studied retinotopically-organized anatomical connectivity in the monkey visual cortex. For each evaluated method, we computed the fractional rate of detecting connections known to exist ("c-sensitivity"), while using a threshold of the 95th percentile of the distribution of interaction magnitudes of those connections not expected to exist.Under optimal conditions - including session duration of 68 min, a relatively small network consisting of 9 nodes and artifact-free regression of the global effect - each of the top methods predicted the expected connections with 67-85% c-sensitivity. Correlation methods, including Correlation (Corr; 85%), Regularized Inverse Covariance (ICOV; 84%) and Partial Correlation (PCorr; 81%) performed best, followed by Patel's Kappa (80%), Bayesian Network method PC (BayesNet; 77%), General Synchronization measures (67-77%), and Coherence (CohB; 74%). With decreased session duration, these top methods saw decreases in c-sensitivities, achieving 59-76% for 17 min sessions. With a short resting-state fMIR scan of 8.5 min, none of the methods predicted the real network well, with Corr (65%) performing best. With increased complexity of the network from 9 to 36 nodes, multivariate methods including PCorr and BayesNet saw a decrease in performance. Artifact-free regression of the global effect increased the c-sensitivity of the top-performing methods. In an overall evaluation across all tests we performed, correlation methods (Corr, ICOV, and PCorr), Patel's Kappa, and BayesNet method PC set themselves somewhat above all other methods.We propose that data-based calibration based on known anatomical connections be integrated into future network studies, in order to maximize sensitivity and reduce false positives. (c) 2012 Elsevier Inc. All rights reserved.