Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease
Comparison of nine tractography algorithms for detecting abnormal structural brain networks in Alzheimer's disease
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DOI：
10.3389/frogi.2015.00048
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发表时间：
2015-04-14
影响因子：
4.8
通讯作者：
Thompson, Paul M.
中科院分区：
文献类型：
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作者：
Zhan, Liang;Zhou, Jiayu;Thompson, Paul M.
阿尔茨海默病 (AD) 涉及大脑连接的逐渐破坏，而网络分析为跟踪和了解疾病进展提供了一种有前途的新方法。即便如此，我们检测大脑网络退行性变化的能力取决于所使用的方法。在这里，我们比较了几种纤维束成像和特征提取方法，看看哪种方法可以为 202 名 AD 患者、轻度认知障碍或正常认知患者提供最佳诊断分类，这些患者使用 41 梯度扩散加权磁共振成像进行扫描，作为阿尔茨海默病神经影像计划 (ADNI) 项目的一部分。我们使用九种不同的方法计算基于全脑纤维束成像的大脑网络 - 其中四种基于张量的确定性方法（FACT、RK2、SL 和 TO）、基于双向分布函数 (ODF) 的确定性方法（FACT、RK2）、两种基于 ODF 的概率方法（Hough 和 PICo）以及一种“球棒”方法（Probtrackx）。源自不同纤维束成像算法的脑网络在 ADNI 上的分类性能方面没有差异，但在某些情况下，对网络进行主成分分析有助于在真正庞大的队列中检测到微小的差异，但这些实验有助于评估不同纤维束成像算法和不同后处理选择在用于分类时的相对优势。
Alzheimer's disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment or normal cognition, scanned with 41-gradient diffusion-weighted magnetic resonance imaging as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We computed brain networks based on whole brain tractography with nine different methods - four of them tensor based deterministic (FACT, RK2, SL, and TO, two orientation distribution function (ODF)-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo), and one "ball-and-stick" approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing principal components analysis on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post processing choices, when used for classification.