Gori Ilaria, Giuliano Alessia, Muratori Filippo, Saviozzi Irene, Oliva Piernicola, Tancredi Raffaella, Cosenza Angela, Tosetti Michela, Calderoni Sara, Retico Alessandra
Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, Italy.
Dipartimento di Chimica e Farmacia, Università di Sassari, Italy.
J Neuroimaging. 2015 Nov-Dec;25(6):866-74. doi: 10.1111/jon.12280. Epub 2015 Jul 27.
Sophisticated algorithms to infer disease diagnosis, pathology progression and patient outcome are increasingly being developed to analyze brain MRI data. They have been successfully implemented in a variety of diseases and are currently investigated in the field of neuropsychiatric disorders, including autism spectrum disorder (ASD). We aim to test the ability to predict ASD from subtle morphological changes in structural magnetic resonance imaging (sMRI).
The analysis of sMRI of a cohort of male ASD children and controls matched for age and nonverbal intelligence quotient (NVIQ) has been carried out with two widely used preprocessing software packages (SPM and Freesurfer) to extract brain morphometric information at different spatial scales. Then, support vector machines have been implemented to classify the brain features and to localize which brain regions contribute most to the ASD-control separation.
The features extracted from the gray matter subregions provide the best classification performance, reaching an area under the receiver operating characteristic curve (AUC) of 74%. This value is enhanced to 80% when considering only subjects with NVIQ over 70.
Despite the subtle impact of ASD on brain morphology and a limited cohort size, results from sMRI-based classifiers suggest a consistent network of altered brain regions.
用于推断疾病诊断、病理进展和患者预后的复杂算法正越来越多地被开发出来以分析脑磁共振成像（MRI）数据。它们已在多种疾病中成功应用，目前正在神经精神疾病领域进行研究，包括自闭症谱系障碍（ASD）。我们旨在测试从结构磁共振成像（sMRI）的细微形态变化预测ASD的能力。
使用两个广泛使用的预处理软件包（SPM和FreeSurfer）对一组年龄和非语言智商（NVIQ）匹配的男性ASD儿童和对照的sMRI进行分析，以提取不同空间尺度下的脑形态测量信息。然后，实施支持向量机对脑特征进行分类，并确定哪些脑区对ASD与对照的区分贡献最大。
从灰质亚区域提取的特征提供了最佳分类性能，受试者工作特征曲线（AUC）下面积达到74%。仅考虑NVIQ超过70的受试者时，该值提高到80%。
尽管ASD对脑形态的影响细微且队列规模有限，但基于sMRI的分类器结果表明存在一个一致的脑区改变网络。
