注意力不足過動症是一種常好發於孩童及青少年的精神疾病，主要症狀包含容易分心及無法專注在特定的事物上，同時也對患者的日常生活造成極大的影響，然而這些源自於異常的腦部活動所造成的症狀，其實也是近年來研究的主要方向之一，在功能性磁振造影的技術日益成熟下，我們能夠透過更完善的腦部神經成像來辨識及分析過動症的腦部訊號，同時透過深度學習的方式來加強辨識的結果，在這項基礎上，我們從兩個不同的角度提出一個可以改善過動症在功能性磁振造影中的辨識結果，其一，是整合每一筆資料的個人特質(例如:年齡、性別)，來給予我們的模型額外的資訊進行辨識，其二，是增加訓練的資料，對我們的資料集進行資料增強的方法，將額外的資料集進行轉化合併後，進而加強我們的模型表現，在實驗一中我們提出了以表型特徵計算為主的一種注意力機制，並結合在腦部嵌入式特徵模型中做辨識，而實驗二則是透過訓練一個轉化自閉症資料集的模型，並將轉化後的假資料合併至原本的訓練集，兩項實驗在ADHD-200這份資料集中都得到很顯著的改善，並結合這兩項實驗，可以在改善過動症的預測方面有很明顯的進步。

Attention Deficit/Hyperactivity Disorder is one of the common disease prevalent in adolescents and child. Main symptoms include impulsiveness, distractibility, and deficient concentration which causes delayed social relationship. However, these symptoms caused by abnormal brain activity are actually one of the main research directions in recent years. Within the technology of functional magnetic resonance imaging, we are able to identify and analyze brain signal in ADHD. At the same time, deep learning algorithm also enhance the classification performance. On this basis, we propose two algorithm strategy from different kinds of aspect to improve ADHD classification in fMRI. First, integrating personal attribute to our modality in order to obtain extra reliable information to assist the classification. Second, we implement a data augmentation to increase the amount of training data also enhance our model’s performance. Overall, we propose a CVAE-based brain embedding network with an attention mechanism derived from phenotypic attributes and data augmentation strategy.
Summarizing all the strategy we propose demonstrated a significant improvement in ADHD classification.

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