Youssofzadeh Vahab, McGuinness Bernadette, Maguire Liam P, Wong-Lin KongFatt
Computational Neuroscience Research Team, Intelligent Systems Research Centre, School of Computing and Intelligent Systems, Faculty of Computing and Engineering, Ulster UniversityLondonderry, United Kingdom.
Division of Neurology, Cincinnati Children's Hospital Medical CenterCincinnati, OH, United States.
Front Hum Neurosci. 2017 Jul 25;11:380. doi: 10.3389/fnhum.2017.00380. eCollection 2017.
Magnetic resonance imaging (MRI) and positron emission tomography (PET) are neuroimaging modalities typically used for evaluating brain changes in Alzheimer's disease (AD). Due to their complementary nature, their combination can provide more accurate AD diagnosis or prognosis. In this work, we apply a multi-modal imaging machine-learning framework to enhance AD classification and prediction of diagnosis of subject-matched gray matter MRI and Pittsburgh compound B (PiB)-PET data related to 58 AD, 108 mild cognitive impairment (MCI) and 120 healthy elderly (HE) subjects from the Australian imaging, biomarkers and lifestyle (AIBL) dataset. Specifically, we combined a Dartel algorithm to enhance anatomical registration with multi-kernel learning (MKL) technique, yielding an average of >95% accuracy for three binary classification problems: AD-vs.-HE, MCI-vs.-HE and AD-vs.-MCI, a considerable improvement from individual modality approach. Consistent with -contrasts, the MKL weight maps revealed known brain regions associated with AD, i.e., (para)hippocampus, posterior cingulate cortex and bilateral temporal gyrus. Importantly, MKL regression analysis provided excellent predictions of diagnosis of individuals by = 0.86. In addition, we found significant correlations between the MKL classification and delayed memory recall scores with = 0.62 ( < 0.01). Interestingly, outliers in the regression model for diagnosis were mainly converter samples with a higher likelihood of converting to the inclined diagnostic category. Overall, our work demonstrates the successful application of MKL with Dartel on combined neuromarkers from different neuroimaging modalities in the AIBL data. This lends further support in favor of machine learning approach in improving the diagnosis and risk prediction of AD.
磁共振成像（MRI）和正电子发射断层扫描（PET）是常用于评估阿尔茨海默病（AD）脑部变化的神经成像方式。由于它们具有互补性，两者结合可以提供更准确的AD诊断或预后评估。在这项研究中，我们应用了一种多模态成像机器学习框架，以增强对58例AD患者、108例轻度认知障碍（MCI）患者和120例健康老年人（HE）的受试者匹配灰质MRI和匹兹堡化合物B（PiB）-PET数据的AD分类及诊断预测，这些数据来自澳大利亚成像、生物标志物和生活方式（AIBL）数据集。具体而言，我们将Dartel算法与多核学习（MKL）技术相结合以增强解剖配准，对于AD与HE、MCI与HE以及AD与MCI这三个二分类问题，平均准确率超过95%，相较于单一模态方法有显著提升。与对照一致，MKL权重图显示了与AD相关的已知脑区，即（旁）海马体、后扣带回皮质和双侧颞叶回。重要的是，MKL回归分析对个体诊断的预测效果极佳，相关系数为0.86。此外，我们发现MKL分类与延迟记忆回忆分数之间存在显著相关性，相关系数为0.62（P<0.01）。有趣的是，诊断回归模型中的异常值主要是更有可能转变为倾向诊断类别的转化样本。总体而言，我们的工作证明了在AIBL数据中，MKL与Dartel成功应用于来自不同神经成像模态的联合神经标志物。这进一步支持了机器学习方法在改善AD诊断和风险预测方面的应用。
