Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders.
Graph Matching Based Connectomic Biomarker with Learning for Brain Disorders.
复制标题
DOI：
10.1007/978-3-030-60365-6_13
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Osmanlıoğlu Y
中科院分区：
文献类型：
--
作者：
Shen RS;Alappatt JA;Parker D;Kim J;Verma R;Osmanlıoğlu Y
神经成像技术的进步，如扩散MRI和功能MRI，使大脑作为一个信息处理网络，被称为连接体的评估。连接组学分析已经导致了许多关于大脑组织及其疾病病理变化的发现，提供了有助于诊断和预后的基于成像的生物标志物。绝大多数连接组学生物标志物受益于评估大脑网络结构的图论测量，或使用标准度量，如欧几里得距离或皮尔逊相关性来显示连接组之间的关系。然而，这些方法在疾病的诊断评估中受到限制，因为它们不能同时测量个体连接体之间的差异，结合疾病特异性模式，并利用网络结构信息。为了解决这些局限性，我们提出了一种基于图匹配的方法来量化连接组相似性，该方法可以在功能系统水平上针对疾病进行训练，以提供评估疾病的受试者特异性生物标志物。我们在创伤性脑损伤患者的数据集上验证了我们的测量，并证明与常用的连接相似性测量相比，我们的测量实现了患者和对照之间更好的分离。我们进一步评估的脆弱性的功能系统的疾病，利用我们的方法的参数调整方面。我们最终表明，我们的相似性评分与临床评分相关，突出了其作为疾病的受试者特异性生物标志物的潜力。
Advances in neuroimaging techniques such as diffusion MRI and functional MRI enabled evaluation of the brain as an information processing network that is called connectome. Connectomic analysis has led to numerous findings on the organization of the brain its pathological changes with diseases, providing imaging-based biomarkers that help in diagnosis and prognosis. A large majority of connectomic biomarkers benefit either from graph-theoretical measures that evaluate brain’s network structure, or use standard metrics such as Euclidean distance or Pearson’s correlation to show between-connectomes relations. However, such methods are limited in diagnostic evaluation of diseases, because they do not simultaneously measure the difference between individual connectomes, incorporate disease-specific patterns, and utilize network structure information. To address these limitations, we propose a graph matching based method to quantify connectomic similarity, which can be trained for diseases at functional systems level to provide a subject-specific biomarker assessing the disease. We validate our measure on a dataset of patients with traumatic brain injury and demonstrate that our measure achieves better separation between patients and controls compared to commonly used connectomic similarity measures. We further evaluate the vulnerability of the functional systems to the disease by utilizing the parameter tuning aspect of our method. We finally show that our similarity score correlates with clinical scores, highlighting its potential as a subject-specific biomarker for the disease.
登录
查看更多内容
影响因子：
9.2
作者：
Preston, Alison R.;Eichenbaum, Howard
通讯作者：
Eichenbaum, Howard
影响因子：
5.3
作者：
Bonnelle, Valerie;Leech, Robert;Sharp, David J.
通讯作者：
Sharp, David J.
影响因子：
81.5
作者：
Blennow, Kaj;Brody, David L.;Zetterberg, Henrik
通讯作者：
Zetterberg, Henrik
影响因子：
5.7
作者：
Osmanhoglu, Yusuf;Tunc, Birkan;Verma, Ragini
通讯作者：
Verma, Ragini
图形编辑距离的调查
影响因子：
3.9
作者：
Gao, Xinbo;Xiao, Bing;Li, Xuelong
通讯作者：
Li, Xuelong