The utility of independent component analysis and machine learning in the identification of the amyotrophic lateral sclerosis diseased brain.
The utility of independent component analysis and machine learning in the identification of the amyotrophic lateral sclerosis diseased brain.
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DOI：
10.3389/fnhum.2013.00251
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发表时间：
2013
影响因子：
2.9
通讯作者：
Foerster BR
中科院分区：
文献类型：
--
作者：
Welsh RC;Jelsone-Swain LM;Foerster BR
肌萎缩侧索硬化症是一种终生风险为∼1的破坏性疾病。目前，ALS的诊断依赖于多个身体节段的上运动神经元和下运动神经元缺陷的临床评估，以及症状的进展史。此外，肌电图法评价ALS患者下运动神经元的病理改变也是很常见的。然而，上运动神经元的病理仅根据临床进行评估，因此阻碍了诊断。在过去的十年中，磁共振方法被证明对ALS的疾病过程很敏感，即：用功能MRI测量静息状态的连通性，用高分辨率成像测量皮质厚度，扩散张量成像(DTI)测量分数各向异性和径向扩散系数，以及最近的磁共振波谱(MRS)测量伽马-氨基丁酸浓度。在这项工作中，我们利用独立分量分析得到基于静息状态功能磁共振成像的大脑网络，并使用这些得到的网络来构建使用机器学习(支持向量机)的疾病状态分类器。我们表明，对于疾病状态分类，可以达到71%以上的准确率。这些结果对临床相关疾病状态分类器的开发是有希望的。未来纳入其他MR模式，如高分辨率结构成像、DTI和MRS，应该会提高总体准确性。
Amyotrophic lateral sclerosis (ALS) is a devastating disease with a lifetime risk of ∼1 in 2000. Presently, diagnosis of ALS relies on clinical assessments for upper motor neuron and lower motor neuron deficits in multiple body segments together with a history of progression of symptoms. In addition, it is common to evaluate lower motor neuron pathology in ALS by electromyography. However, upper motor neuron pathology is solely assessed on clinical grounds, thus hindering diagnosis. In the past decade magnetic resonance methods have been shown to be sensitive to the ALS disease process, namely: resting-state connectivity measured with functional MRI, cortical thickness measured by high-resolution imaging, diffusion tensor imaging (DTI) metrics such as fractional anisotropy and radial diffusivity, and more recently magnetic resonance spectroscopy (MRS) measures of gamma-aminobutyric acid concentration. In this present work we utilize independent component analysis to derive brain networks based on resting-state functional magnetic resonance imaging and use those derived networks to build a disease state classifier using machine learning (support-vector machine). We show that it is possible to achieve over 71% accuracy for disease state classification. These results are promising for the development of a clinically relevant disease state classifier. Future inclusion of other MR modalities such as high-resolution structural imaging, DTI and MRS should improve this overall accuracy.