本文關(guān)鍵詞： 靜息態(tài)fMRI 動態(tài)功能腦網(wǎng)絡(luò) 功能連接 多任務(wù)融合Lasso 稀疏 分類 阿爾茲海默癥　出處：《中國圖象圖形學(xué)報》2017年07期 　論文類型：期刊論文

【摘要】：目的傳統(tǒng)的靜息態(tài)功能性磁共振成像(f MRI)的功能腦網(wǎng)絡(luò)(FBN)研究是基于在整個掃描過程中FBN固定不變的假設(shè)。但是,最近的研究表明FBN是動態(tài)變化的,而且其中蘊含著豐富的信息。本文提出一種多任務(wù)融合最小絕對值收縮和選擇算子(Lasso)方法來構(gòu)建靜息態(tài)f MRI的動態(tài)FBN。方法提出的多任務(wù)融合Lasso方法可以在構(gòu)建動態(tài)FBN時,保留網(wǎng)絡(luò)的稀疏性及子序列的時間平滑性。具體來說,首先用滑動窗方法得到交疊的靜息態(tài)f MRI子序列;然后用多任務(wù)融合Lasso方法聯(lián)合地估計一個樣本的所有子序列的功能連接從而構(gòu)建動態(tài)FBN,用k均值聚類算法得到每類樣本子序列的功能連接的聚類中心,并將所有類的聚類中心組成回歸矩陣;最后根據(jù)回歸矩陣求樣本的回歸系數(shù),將其作為特征進(jìn)行分類,驗證多任務(wù)融合Lasso方法對動態(tài)FBN建模的有效性。結(jié)果采用公開的f MRI數(shù)據(jù)集來驗證多任務(wù)融合Lasso模型構(gòu)建動態(tài)FBN的分類效果。實驗使用阿爾茲海默癥神經(jīng)影像學(xué)計劃(ADNI)公開的f MRI數(shù)據(jù)集中的阿爾茲海默癥患者、早期輕度認(rèn)知功能障礙患者和健康被試3組數(shù)據(jù),并用準(zhǔn)確率、靈敏度和特異度來評估算法的分類性能。在3組二分類實驗中,本文方法分別達(dá)到了92.31%、80.00%和84.00%的準(zhǔn)確率。實驗結(jié)果表明,與靜態(tài)FBN模型和其他傳統(tǒng)的動態(tài)FBN模型相比,本文方法能取得更好的分類效果。結(jié)論本文提出的多任務(wù)融合Lasso構(gòu)建動態(tài)FBN的方法,能有效地保留網(wǎng)絡(luò)的稀疏性和子序列的時間平滑性,同時提高算法的分類效果,在一定程度上為腦部疾病的診斷提供幫助。多任務(wù)融合Lasso模型可以用于動態(tài)FBN的構(gòu)建,挖掘功能連接的動態(tài)信息,同時整個算法可以用于基于f MRI數(shù)據(jù)的腦部疾病的分類研究中。
[Abstract]:Objective the traditional resting functional magnetic resonance imaging (fMRI) study is based on the assumption that FBN is fixed throughout the scanning process. Recent studies have shown that FBN is dynamic. And it contains abundant information. In this paper, we propose a multitask fusion minimum absolute contraction and selection operator Lasso). Methods to construct the dynamic MRI of resting f MRI. The multitask fusion Lasso method proposed by the method can be used to construct dynamic FBN. The sparsity of the network and the temporal smoothness of the subsequences are preserved. Firstly, the overlapping resting f MRI subsequences are obtained by the sliding window method. Then the multitask fusion Lasso method is used to estimate the functional connections of all subsequences of a sample to construct a dynamic FBN. By using k-means clustering algorithm, the cluster center of functional connection of each subsequence of samples is obtained, and the cluster center of all classes is formed into a regression matrix. Finally, the regression coefficient of the sample is calculated according to the regression matrix, and it is classified as a feature. The validity of multitask fusion Lasso method for dynamic FBN modeling is verified. MRI data set was used to verify the classification effect of multitask fusion Lasso model to construct dynamic FBN. ADNI) exposes the f MRI dataset to Alzheimer's patients. The classification performance of the algorithm was evaluated with accuracy, sensitivity and specificity in three groups of data from patients with early mild cognitive impairment and healthy subjects. The accuracy of this method is 92.31% and 84.00%, respectively. The experimental results show that this method is compared with the static FBN model and other traditional dynamic FBN models. Conclusion the proposed multi-task fusion Lasso method for constructing dynamic FBN can effectively preserve the sparsity of the network and the temporal smoothness of the sub-sequences. The multi-task fusion Lasso model can be used to construct dynamic FBN and mine the dynamic information of functional connection. At the same time, the whole algorithm can be used in the classification of brain diseases based on f MRI data.
【作者單位】： 中國科學(xué)院自動化研究所;中國中醫(yī)科學(xué)院廣安門醫(yī)院;
【基金】：國家自然科學(xué)基金項目(61305018,61432008,61472423,61532006)~~
【分類號】：R445.2
【正文快照】： 第22卷/第7期/2017年7月王鑫,任燕雙,張文生/動態(tài)功能腦網(wǎng)絡(luò)模型的多任務(wù)融合Lasso方法brain region is the node,and a functional connectivity between each pair of brain regions is an edge.The functional con-nectivity between the brain regions can reveal disease，

本文編號：1487512