Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder.
Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder.
复制标题
DOI：
10.1186/s12868-017-0373-0
复制
发表时间：
2017-07-11
期刊：
影响因子：
2.4
通讯作者：
Tanougast C
中科院分区：
文献类型：
--
作者：
Chaddad A;Desrosiers C;Hassan L;Tanougast C
新出现的证据表明，自闭症谱系障碍（ASD）患者存在神经解剖学异常。因此，识别解剖学相关因素可能对ASD的自动化诊断有用。基于磁共振成像（MRI）纹理特征的影像组学分析已显示出巨大潜力，可用于表征因组织异质性产生的差异以及识别与这些差异相关的异常。然而，只有少数研究探讨了图像纹理与ASD之间的联系。本文提出基于灰度共生矩阵（GLCM）对纹理特征进行研究，以此作为表征ASD患者和发育正常对照（DC）受试者之间差异的一种方法。我们的研究使用了从两组受试者获取的64次T1加权MRI扫描结果：28名4 - 15岁的典型年龄段受试者（14名ASD患者和14名DC受试者，年龄匹配），以及36名10 - 24岁的非典型年龄段受试者（20名ASD患者和16名DC受试者）。GLCM矩阵是从手动标记的海马体和杏仁核区域计算得出的，然后通过应用11种标准的Haralick量化函数将其编码为纹理特征。进行显著性检验以识别ASD患者和DC受试者之间的纹理差异。然后使用支持向量机（SVM）和随机森林分类器进行分析，以找出最具判别力的特征，并利用这些特征对ASD患者和DC受试者进行分类。
初步结果显示，从海马体（典型和非典型年龄）得出的所有11种特征以及从杏仁核（非典型年龄）提取的4种特征在ASD患者中的分布与DC受试者相比均有显著差异，经过Holm - Bonferroni校正后，显著性水平为p < 0.05。来自海马体区域的特征在区分ASD患者和DC受试者方面也显示出较高的判别能力，对于典型年龄段的年龄匹配受试者，分类器准确率为67.85%，敏感度为62.50%，特异度为71.42%，受试者工作特征曲线下面积（AUC）为76.80%。
结果表明，海马体纹理特征作为ASD诊断和表征的生物标志物具有潜力。
Emerging evidence suggests the presence of neuroanatomical abnormalities in subjects with autism spectrum disorder (ASD). Identifying anatomical correlates could thus prove useful for the automated diagnosis of ASD. Radiomic analyses based on MRI texture features have shown a great potential for characterizing differences occurring from tissue heterogeneity, and for identifying abnormalities related to these differences. However, only a limited number of studies have investigated the link between image texture and ASD. This paper proposes the study of texture features based on grey level co-occurrence matrix (GLCM) as a means for characterizing differences between ASD and development control (DC) subjects. Our study uses 64 T1-weighted MRI scans acquired from two groups of subjects: 28 typical age range subjects 4–15 years old (14 ASD and 14 DC, age-matched), and 36 non-typical age range subjects 10–24 years old (20 ASD and 16 DC). GLCM matrices are computed from manually labeled hippocampus and amygdala regions, and then encoded as texture features by applying 11 standard Haralick quantifier functions. Significance tests are performed to identify texture differences between ASD and DC subjects. An analysis using SVM and random forest classifiers is then carried out to find the most discriminative features, and use these features for classifying ASD from DC subjects. Preliminary results show that all 11 features derived from the hippocampus (typical and non-typical age) and 4 features extracted from the amygdala (non-typical age) have significantly different distributions in ASD subjects compared to DC subjects, with a significance of p < 0.05 following Holm–Bonferroni correction. Features derived from hippocampal regions also demonstrate high discriminative power for differentiating between ASD and DC subjects, with classifier accuracy of 67.85%, sensitivity of 62.50%, specificity of 71.42%, and the area under the ROC curve (AUC) of 76.80% for age-matched subjects with typical age range. Results demonstrate the potential of hippocampal texture features as a biomarker for the diagnosis and characterization of ASD.
登录
查看更多内容
影响因子：
11
作者：
Di Martino, A.;Yan, C-G;Li, Q.;Denio, E.;Castellanos, F. X.;Alaerts, K.;Anderson, J. S.;Assaf, M.;Bookheimer, S. Y.;Dapretto, M.;Deen, B.;Delmonte, S.;Dinstein, I.;Ertl-Wagner, B.;Fair, D. A.;Gallagher, L.;Kennedy, D. P.;Keown, C. L.;Keysers, C.;Lainhart, J. E.;Lord, C.;Luna, B.;Menon, V.;Minshew, N. J.;Monk, C. S.;Mueller, S.;Mueller, R. A.;Nebel, M. B.;Nigg, J. T.;O'Hearn, K.;Pelphrey, K. A.;Peltier, S. J.;Rudie, J. D.;Sunaert, S.;Thioux, M.;Tyszka, J. M.;Uddin, L. Q.;Verhoeven, J. S.;Wenderoth, N.;Wiggins, J. L.;Mostofsky, S. H.;Milham, M. P.
通讯作者：
Milham, M. P.
影响因子：
3.7
作者：
Bedford R;Pickles A;Gliga T;Elsabbagh M;Charman T;Johnson MH;BASIS Team
通讯作者：
BASIS Team
影响因子：
25.8
作者：
Ecker, Christine;Andrews, Derek S.;Murphy, Declan G. M.
通讯作者：
Murphy, Declan G. M.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
5.3
作者：
Ecker, Christine;Marquand, Andre;Murphy, Declan G. M.
通讯作者：
Murphy, Declan G. M.