北京生物醫(yī)學(xué)工程

不同大腦分區(qū)下注意缺陷多動障礙分類研究

Classification of ADHD children under different kinds of brain partition

作者：葉子駿黃惠芳劉杰

單位：北京交通大學(xué)(北京100044)

關(guān)鍵詞：注意缺陷障礙；功能磁共振成像；大腦分區(qū)；功能連接；分類性能

分類號：R318.04

出版年·卷·期（頁碼）：2016·35·4（389-395）

摘要：

目的注意缺陷多動障礙(attention deficit hyperactivity disorder, ADHD)是一種常見的影響兒童行為能力的精神疾病，但由于缺乏有力的客觀依據(jù)判斷發(fā)病機制，因而臨床上診斷與治療存在一定難度。從腦功能研究ADHD的發(fā)病機制是一個熱點，特別是從基于感興趣區(qū)域提取特征進行分類研究。為方便找出感興趣區(qū)域，研究者們根據(jù)解剖或功能將大腦進行區(qū)域劃分，常用的腦分區(qū)有6種，但還沒有研究證明哪一種腦分區(qū)最適用于ADHD分類研究。因此本文將對不同大腦分區(qū)下注意缺陷多動障礙分類研究，以判斷哪種分類效果好。方法本實驗采用ADHD-200大賽的數(shù)據(jù)樣本，將數(shù)據(jù)分為12組，每種大腦分區(qū)下包含一組訓(xùn)練集和一組測試集。首先基于靜息態(tài)fMRI對6種不同大腦分區(qū)的訓(xùn)練集進行功能連接計算、特征分類、特征選擇，然后利用測試者檢驗得到6種分類模型的分類性能并做統(tǒng)計分析。結(jié)果實驗結(jié)果顯示每種大腦分區(qū)下的訓(xùn)練集得到的分類準確率，并比較每種大腦分區(qū)下的測試集的分類性能，綜合分析總結(jié)出AAL大腦分區(qū)的分類性能最好，分類準確率達到63.16%。結(jié)論在6種大腦分區(qū)下，AAL大腦分區(qū)是目前最適合用于研究ADHD的大腦分區(qū)方法。

Objective Attention deficit hyperactivity disorder (ADHD) is one of the most common diseases in school-age children, yet it is difficult to diagnose and treat because of lacking objective evidence to judge the pathogenesis. To study the pathogenesis of ADHD from the aspect of brain function is a hot spot, especially the research of classification based on the region of interest. In order to find useful region of interest, researchers divide the brain into certain regions based on anatomy or function. There are six kinds of common brain regions, yet no one proves which method is the best for ADHD classification. So this paper studies on ADHD classification under the six brain partitions in order to find the best. Methods The data come from the ADHD-200 competition including train data and test data. Data were divided into twelve groups, each containing a set of training sets and a set of test sets. Firstly, based on the resting state fMRI we make functional connectivity analysis, feature classification and feature selection under six brain partitions by using train data. Then we use the test data to test the classification effect of the classification models and analyze the results. Results The experimental results show that the classification accuracy of the training set is obtained under different brain regions, and then the classification performance of the test sets is compared. In the overall analysis of the classification performance of the six sets data, AAL brain regions has the best performance, and the accuracy is 63.16%. Conclusions In all the six kinds of brain partition, AAL brain partition is the most suitable method for the study of ADHD classification.

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