Combined machine learning and diffusion tensor imaging reveals altered anatomic fiber connectivity of the brain in primary open-angle glaucoma
Combined machine learning and diffusion tensor imaging reveals altered anatomic fiber connectivity of the brain in primary open-angle glaucoma
复制标题
机器学习和扩散张量成像相结合揭示了原发性开角型青光眼大脑解剖纤维连接的改变
DOI：
10.1016/j.brainres.2019.05.006
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发表时间：
2019-09-01
期刊：
影响因子：
2.9
通讯作者：
Xian, Junfang
中科院分区：
文献类型：
--
作者：
Qu, Xiaoxia;Wang, Qian;Xian, Junfang
原发性开角型青光眼（POAG）患者的视束、视神经和视光辐射的弥散张量成像（DTI）参数发生了显著改变。在本研究中，dti衍生的参数进一步构建为纤维连通性，我们研究了POAG患者视觉通路内外的解剖纤维连通性变化。对18例POAG患者和26例健康对照（HC）进行DTI和ti加权磁共振成像。基于Brodmann图谱（BA），采用确定性纤维跟踪方法构建了脑白质束。测量平均分数各向异性（FA）、纤维数（FN）和平均纤维长度（FL），然后使用POAG和HC之间的两次样本t检验进行评估。将区域间的纤维连通性作为HC和POAG分类的特征，使用一种称为朴素贝叶斯分类的机器学习方法。视觉皮质BA17/BA18与BA23/BA25/BA35/BA36连接的平均FA降低，而视觉皮质BA3/BA7/BA9与BA5/BA6/BA4S/BA2S连接的平均FA升高。使用纤维分类，其中FN的显著差异已经确定产生更好的准确性（ACC = 0.89）比使用FA或FL （ACC分别= 0.77和0.75）。单个纤维连接的FN具有较高的准确性和POAG的显著变化，涉及与视觉（BA19），抑郁（BA10/BA46/BA25）和记忆（BA29）相关的大脑区域。这些发现加强了POAG涉及视觉通路内外解剖连通性变化的假设。使用机器学习方法进行分类表明，平均FN具有作为检测POAG白质微观结构变化的生物标志物的潜力。
Parameters derived from diffusion tensor imaging (DTI) have been found to be significantly altered in the optic tracts, optic nerves, and optic radiations in patients with primary open-angle glaucoma (POAG). In this study, DTI-derived parameters were further constructed into fiber connectivity, and we investigated anatomical fiber connectivity changes within and beyond the visual pathway in POAG patients. DTI and TI-weighted magnetic resonance images were acquired in 18 POAG patients and 26 healthy controls (HC). White matter tracts based on the Brodmann atlases (BA) were constructed using the deterministic fiber tracking method. The mean fractional anisotropy (FA), fiber number (FN), and mean fiber length (FL) were measured and then evaluated using two sample t-tests between POAG and HC. The fiber connectivity between regions was taken as the features for classifying HC and POAG using a machine learning method known as na ve Bayesian classification. The mean FA decreased in connections between visual cortex BA17/BA18 and cortex BA23/BA25/BA35/BA36, while it increased in the connections between cortex BA3/BA7/BA9 and BA5/BA6/BA4S/BA2S in POAG. Classification using fibers where a significant difference in FN had been identified produced better accuracy (ACC = 0.89) than using FA or FL (ACC = 0.77 and 0.75, respectively). The FN of individual fiber connections with higher accuracy and significant changes in POAG involved brain regions associated with vision (BA19), depression (BA10/BA46/BA25), and memory (BA29). These findings strengthen the hypothesis that POAG involves changes in anatomical connectivity within and beyond the visual pathway. Classification using the machine learning method reveals that mean FN has the potential to be used as a biomarker for detecting white matter microstructure changes in POAG.