注意力不足過動症(attention-deficit/hyperactivity disorder, ADHD)是目前學齡兒童最普遍的發展性神經行為障礙之一，也是兒童精神科常見的疾患，通常會持續到青春期或成年。患有ADHD之病患在學業或社會功能上常會出現問題，並可能同時伴隨情緒障礙、學習障礙、強迫、焦慮等等問題，若不加以治療，未來可能導致自卑、人格異常、人際障礙，甚至反社會行為，因此若能夠早期發現早期治療，相信對ADHD患者而言絕對是一大幫助。目前ADHD的診斷主要於臨床透過精神疾病診斷與手冊，結合父母或老師的主觀意見來診斷；另外，根據神經造影技術所提供的客觀結果顯示ADHD患者及正常人之大腦結構存在相當的差異性；而在各種大腦功能性研究上也在特定區域呈現活動異常。

在此篇論文中，我們經由受試者在靜止狀態下之功能性磁振造影，分析其大腦功能性之相關性，並以統計方法發現ADHD患者及正常人間在特定大腦區域上之差異，利用其特徵結合機器學習，發展出可有效分辨ADHD患者及正常人之系統。本實驗已實際應用在七十三位ADHD病患與七十六位健康受試者上，並發現在前扣帶皮層、後扣帶皮層、腹內側額葉、小腦、動作皮質區及顳葉與全腦之大腦功能性相關性可提供有效特徵於區分ADHD患者及正常人，測試各式分類器後在非線性分類器可達到87%之準確率。

Attention-deficit/hyperactivity disorder (ADHD) is a kind of neuropsychiatric disorder which is quite common in childhood, with an estimated prevalence of 5-8%, and often persists into adolescence and adulthood. It is characterized by developmentally inappropriate symptoms of inattention, impulsively, motor over-activity and restlessness that interfere with normal functioning in various settings.

In this thesis, we analyze the functional connectivity of resting state fMRI and used statistical techniques to discover the differences in some brain areas between ADHD and normal subjects. Combine the functional connectivity and machine learning methods to identify the two groups. This study has applied to the datasets of 73 children with ADHD and 76 normal children. Based on prior researches, anterior cingulate cortex (ACC), posterior cingulated cortex (PCC) and ventromedial prefrontal cortex (vmPFC) have been well confirmed. Moreover, we have also found differences in cerebellum, motor cortex and temporal lobe between ADHD and normal humans. These 7 brain areas truly supply useful features in ADHD classification. Experimental results showed a correct classification rate of 87% by using nonlinear classifier with the validation which is half for training and half for testing. As a result, using functional connectivity of resting-state fMRI as features in classifiers do has the ability to evaluate ADHD.

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