Ensemble Tractography.
Ensemble Tractography.
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DOI：
10.1371/journal.pcbi.1004692
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发表时间：
2016-02
影响因子：
4.3
通讯作者：
Pestilli F
中科院分区：
文献类型：
--
作者：
Takemura H;Caiafa CF;Wandell BA;Pestilli F
纤维束成像使用扩散MRI来估计活体人脑中白色物质束的轨迹和皮质投射区。有许多不同的纤维束成像算法，每种算法都需要用户设置几个参数，如曲率阈值。选择具有特定参数的单一算法带来了两个挑战。首先，不同的算法和参数值产生不同的结果。第二，算法和参数值的最佳选择在不同的白色物质区域或不同的神经束、对象和采集参数之间可能不同。我们建议使用集成方法来减少算法和参数的依赖性。为此，我们将分册生成和评估的过程分开。具体来说，我们分析了创建优化的连接体的价值，通过系统地结合候选流线从一个合奏的算法（确定性和概率）和系统地改变参数（曲率和停止标准）。集成方法导致优化的连接体，其提供比使用单一算法或参数集生成的优化的连接体更好的扩散MRI数据的交叉验证的预测误差。此外，集成方法产生的连接体包含短距离和长距离的纤维束，而单参数连接体偏向于一个或另一个。总之，系统的整体纤维束成像方法可以产生上级于标准单参数估计的连接体，用于预测扩散测量和估计白色物质束。弥散磁共振成像和纤维束成像为研究活体人脑中的白色物质束及其组织特性开辟了新的途径。有许多不同的纤维束成像方法，每种方法都需要用户设置多个参数。纤维束成像的局限性在于其结果取决于算法和参数的选择。在这里，我们分析了一个集成的方法，Ensemble Tractography（ET），减少了算法和参数选择的影响。ET使用算法和参数值的集合创建一个大的候选流线集合，然后使用全局分册评估方法从数据中选择具有强有力支持的流线。与单参数连接体相比，ET连接体更好地预测弥散MRI信号，并覆盖更宽范围的白色物质体积。重要的是，ET连接体包括短关联和长关联束，它们通常不会在单参数连接体中一起发现。
Tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with specific parameters poses two challenges. First, different algorithms and parameter values produce different results. Second, the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles, subjects, and acquisition parameters. We propose using ensemble methods to reduce algorithm and parameter dependencies. To do so we separate the processes of fascicle generation and evaluation. Specifically, we analyze the value of creating optimized connectomes by systematically combining candidate streamlines from an ensemble of algorithms (deterministic and probabilistic) and systematically varying parameters (curvature and stopping criterion). The ensemble approach leads to optimized connectomes that provide better cross-validated prediction error of the diffusion MRI data than optimized connectomes generated using a single-algorithm or parameter set. Furthermore, the ensemble approach produces connectomes that contain both short- and long-range fascicles, whereas single-parameter connectomes are biased towards one or the other. In summary, a systematic ensemble tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles. Diffusion MRI and tractography opened a new avenue for studying white matter fascicles and their tissue properties in the living human brain. There are many different tractography methods, and each requires the user to set several parameters. A limitation of tractography is that the results depend on the selection of algorithms and parameters. Here, we analyze an ensemble method, Ensemble Tractography (ET), that reduces the effect of algorithm and parameter selection. ET creates a large set of candidate streamlines using an ensemble of algorithms and parameter values and then selects the streamlines with strong support from the data using a global fascicle evaluation method. Compared to single parameter connectomes, ET connectomes predict diffusion MRI signals better and cover a wider range of white matter volume. Importantly, ET connectomes include both short- and long-association fascicles, which are not typically found together in single-parameter connectomes.