Today, diagnosis of attention deficit hyperactivity disorder (ADHD) still primarily relies on a series of subjective evaluations that highly rely on a doctor's experiences and intuitions from diagnostic interviews and observed behavior measures. An accurate and objective diagnosis of ADHD is still a challenge and leaves much to be desired. Many children and adults are inappropriately labeled with ADHD conditions, whereas many are left undiagnosed and untreated. Recent advances in neuroimaging studies have enabled us to search for both structural (e.g., cortical thickness, brain volume) and functional (functional connectivity) abnormalities that can potentially be used as new biomarkers of ADHD. However, structural and functional characteristics of neuroimaging data, especially magnetic resonance imaging (MRI), usually generate a large number of features. With a limited sample size, traditional machine learning techniques can be problematic to discover the true characteristic features of ADHD due to the significant issues of overfitting, computational burden, and interpretability of the model. There is an urgent need of efficient approaches to identify meaningful discriminative variables from a higher dimensional feature space when sample size is small compared with the number of features. To tackle this problem, this paper proposes a novel integrated feature ranking and selection framework that utilizes normalized brain cortical thickness features extracted from MRI data to discriminate ADHD subjects against healthy controls. The proposed framework combines information theoretic criteria and the least absolute shrinkage and selection operator (Lasso) method into a two-step feature selection process which is capable of selecting a sparse model while preserving the most informative features. The experimental results showed that the proposed framework generated the highest/comparable ADHD prediction accuracy compared with the state-of-the-art feature selection approaches with minimum number of features in the final model. The selected regions of interest in our model were consistent with recent brain-behavior studies of ADHD development, and thus confirmed the validity of the selected features by the proposed approach.

中文翻译：

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用于ADHD表征的集成功能分级和选择框架。

如今，注意力缺陷多动障碍（ADHD）的诊断仍主要依靠一系列主观评估，这些评估高度依赖于医生的经验和诊断性访谈以及观察到的行为测量的直觉。准确，客观地诊断多动症仍然是一个挑战，任重而道远。许多儿童和成人在ADHD条件下贴上了不适当的标签，而许多人则没有得到诊断和治疗。神经影像学研究的最新进展使我们能够搜索结构异常（例如，皮质厚度，大脑体积）和功能异常（功能连接性），这些异常可能被用作ADHD的新生物标记。但是，神经影像数据，尤其是磁共振成像（MRI）的结构和功能特征，通常会生成大量功能。在有限的样本量下，由于模型的过度拟合，计算负担和可解释性等重大问题，传统的机器学习技术可能难以发现ADHD的真实特征。当特征量与样本数量相比较小时，迫切需要一种有效的方法来从高维特征空间中识别出有意义的判别变量。为了解决这个问题，本文提出了一种新颖的综合特征分级和选择框架，该框架利用从MRI数据中提取的归一化大脑皮层厚度特征来区分ADHD受试者与健康对照。所提出的框架将信息理论标准和最小绝对收缩与选择算子（Lasso）方法结合到两步特征选择过程中，该过程能够选择稀疏模型，同时保留最多信息量的特征。实验结果表明，与最终模型中具有最少特征数的最新特征选择方法相比，该框架产生了最高/可比的ADHD预测精度。在我们的模型中选定的感兴趣区域与ADHD发展的近期脑行为研究一致，因此通过提出的方法证实了选定特征的有效性。实验结果表明，与最终模型中具有最少特征数的最新特征选择方法相比，该框架产生了最高/可比的ADHD预测精度。在我们的模型中选定的感兴趣区域与ADHD发展的近期脑行为研究一致，因此通过提出的方法证实了选定特征的有效性。实验结果表明，与最终模型中具有最少特征数的最新特征选择方法相比，该框架产生了最高/可比的ADHD预测精度。在我们的模型中选定的感兴趣区域与ADHD发展的近期脑行为研究一致，因此通过提出的方法证实了选定特征的有效性。