北京生物醫(yī)學(xué)工程

基于描峰聚類的動(dòng)態(tài)腦功能網(wǎng)絡(luò)狀態(tài)劃分

Dynamic functional brain network divisionbased on density peak clustering

作者：崔園

單位：北京交通大學(xué)計(jì)算機(jī)與信息技術(shù)學(xué)院(北京 100044)

關(guān)鍵詞：動(dòng)態(tài)功能連接；靜息態(tài)功能磁共振成像；描峰聚類；腦功能網(wǎng)絡(luò)；降維

分類號(hào)：R318.04

出版年·卷·期（頁(yè)碼）：2019·38·6（575-582）

摘要：

目的近年來(lái)腦功能網(wǎng)絡(luò)的動(dòng)態(tài)屬性分析已經(jīng)成為腦功能研究的熱點(diǎn)，腦功能網(wǎng)絡(luò)狀態(tài)劃分則是腦功能網(wǎng)絡(luò)動(dòng)態(tài)屬性分析的重要方面，目前國(guó)際上廣泛采用的腦功能網(wǎng)絡(luò)狀態(tài)劃分策略是k-means聚類算法，而k-means聚類算法存在兩個(gè)缺陷。而描峰聚類（density peak clustering）算法能直觀展現(xiàn)合理的類別數(shù)，從而有效解決k-means聚類中k值難以確定的問(wèn)題。本文擬基于動(dòng)態(tài)功能連接（dynamic functional connectivity, DFC）的腦功能網(wǎng)絡(luò)狀態(tài)劃分，為腦功能網(wǎng)絡(luò)劃分探索新的模型。方法基于61位成年人靜息態(tài)功能磁共振成像(resting state functional magnetic resonance image, rs-fMRI)數(shù)據(jù)，采用滑動(dòng)窗口計(jì)算方法構(gòu)建功能連接矩陣。基于多種距離度量使用多維尺度分析算法對(duì)其進(jìn)行有效降維，通過(guò)描峰聚類算法進(jìn)行腦功能網(wǎng)絡(luò)狀態(tài)劃分，使用腦功能網(wǎng)絡(luò)劃分常用的狀態(tài)模式圖和聚類決策圖進(jìn)行結(jié)果的校驗(yàn)。結(jié)果基于余弦距離、相關(guān)系數(shù)以及Spearman等描述相似性的距離度量進(jìn)行降維，得到的結(jié)果生理意義較為明確，且有效功能網(wǎng)絡(luò)狀態(tài)數(shù)為3~5。另外，腦區(qū)之間松散聯(lián)系的網(wǎng)絡(luò)狀態(tài)比其他網(wǎng)絡(luò)狀態(tài)更頻繁地發(fā)生。結(jié)論描峰聚類算法足以對(duì)個(gè)體腦功能連接隨時(shí)間的動(dòng)態(tài)波動(dòng)進(jìn)行狀態(tài)劃分，這可為腦功能網(wǎng)絡(luò)劃分研究提供新的思路。

Objective In recent years, the dynamic attribute analysis of functional brain network has become a hot topic in functional brain research. The network state division is an important aspect of network dynamic attribute analysis. At present, the internationally widely used network state division strategy is k-means clustering algorithm which has two defects. Density peak clustering can visually display the reasonable number of categories, which can effectively solve the problem that k-values in k-means clustering are difficult to determine. We intend to explore a new model for network state partitioning based on dynamic functional connectivity (DFC). Methods Based on resting state functional magnetic resonance image (rs-fMRI) data of 61 adults, a sliding window calculation method is used to construct a functional connectivity matrix. Multidimensional scale analysis algorithm is used to effectively reduce dimension based on multiple distance metrics, and network state division is performed by density peak clustering. State pattern graph and cluster decision graph are used to verify the results. Results Dimension reduction based on cosine distance, correlation coefficient and distance metrics describing similarity such as Spearman, the physiological significance of the results are clear, and the number of effective functional network states is 3 to 5. In addition, network states that are loosely connected between brain regions occur more frequently than other network states. Conclusions Brain network state can be divided by DFC characteristics, which can provide new ideas for functional brain network partition.

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