北京生物醫(yī)學工程

基于靜息態(tài)fMRI的動態(tài)功能連接特征的智商預(yù)測

Prediction of intelligence quotient based on the characteristics of dynamic functional connectivity in resting state fMRI

作者：朱鴻睿

單位：北京交通大學計算機與信息技術(shù)學院（北京100044）

關(guān)鍵詞：動態(tài)功能連接；智商；靜息態(tài)fMRI ；回歸

分類號：

出版年·卷·期（頁碼）：2020·39·1（62-68）

摘要：

目的近年來越來越多的研究表明大腦不同腦區(qū)間的功能連接的動態(tài)波動具有生理意義，但關(guān)于智商(intelligence quotient，IQ)的相關(guān)研究較少。本文基于動態(tài)功能連接（dynamic functional connections, DFC）提取動態(tài)特征對智商進行評估，為智商預(yù)測探索新的特征參數(shù)和預(yù)測模型。方法基于97個兒童靜息態(tài)功能磁共振圖像（resting state functional magnetic resonance image,RS-fMRI），采用滑動窗相關(guān)計算方法構(gòu)建DFC。基于DFC提取相應(yīng)時域、頻域特征，通過彈性網(wǎng)（elastic-net，E-Net）和最小角回歸（least angle regression，LAR）算法建立智商回歸模型進行個體智商預(yù)測，并通過置換檢驗驗證其顯著性。結(jié)果基于動態(tài)功能連接的特定頻段（0.075-0.1Hz）頻域特征和波動均值(Mean)特征，可以實現(xiàn)對智商的基本預(yù)測，且頻域特征的表現(xiàn)優(yōu)于時域特征。另外，基于LAR算法構(gòu)建的預(yù)測模型的表現(xiàn)優(yōu)于E-Net算法。結(jié)論個體腦功能連接隨時間的動態(tài)波動足以預(yù)測個體智商，且特定頻段的頻域特征和LAR算法能夠提高預(yù)測準確率，這可為個體智商評估研究和動態(tài)功能連接的應(yīng)用提供新的思路。

Objective In recent years, studies have shown that the dynamic fluctuations of functional connectivity can reflect physiological information, but few studies about intelligence quotient (IQ). This study evaluated IQ based on features extracted from dynamic functional connectivity (DFC), and new feature parameters and prediction models are explored for IQ prediction. Methods In the study resting state functional magnetic resonance imaging (RS-fMRI) data of 97 children subjects were selected, and the sliding window technique was used to constructed DFC. Based on DFC, the time domain and frequency domain characteristics were extracted, IQ regression models were established with the use of elastic net (E-Net) and least angle regression (LAR) algorithm, and its significance was verified by Permutation test. Results Individuals’ IQ could be predicted by frequency domain of 0.075-0.01Hz and the mean strength of dynamic fluctuation, the performance of frequency domain was better than that of time domain characteristics. Besides, the performance of the model based on the LAR algorithm was better than that of the E-Net algorithm. Conclusions IQ can be predicted by DFC characteristics, the frequency domain characteristics of specific frequency band and LAR algorithm can improve the prediction accuracy. This result provides new ideas for further research about IQ prediction and dynamic functional connectivity.

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