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计算机工程

• 人工智能及识别技术 • 上一篇    下一篇

基于相似度矩阵约减的仿射聚类fMRI数据分析

管秀英,曾卫明,王倪传   

  1. (上海海事大学 信息工程学院,上海 201306)
  • 收稿日期:2015-12-04 出版日期:2016-12-15 发布日期:2016-12-15
  • 作者简介:管秀英(1991—),女,硕士研究生,主研方向为模式识别、图像处理;曾卫明,教授、博士、博士生导师;王倪传,博士研究生。
  • 基金资助:

    国家自然科学基金(31170952,31470954);上海市教育委员会科研创新重点项目(11ZZ143)。

fMRI Data Analysis of Affinity Propagation Clustering Based on Similarity Matrix Reduction

GUAN Xiuying,ZENG Weiming,WANG Nizhuan   

  1. (College of Information Engineering,Shanghai Maritime University,Shanghai 201306,China)
  • Received:2015-12-04 Online:2016-12-15 Published:2016-12-15

摘要:

利用仿射聚类(APC)方法分析数据量庞大的功能磁共振成像(fMRI)数据时,在时间复杂度、数据存储和聚类效果等方面存在局限性。为此,提出一种融合稀疏仿射传播聚类(SAPC)和相似度矩阵约减的新方法(SDAPC)。对fMRI数据进行稀疏逼近后,结合高斯密度函数和欧式距离对稀疏数据进行密度分析,完成约减后fMRI数据的功能连通性检测。任务态数据实验结果表明,对于单被试,SDAPC的ROC曲线与SAPC接近,但运行速度比SAPC提高了约3倍;对于多被试,SDAPC和SAPC的ROC曲线效果均优于其单被试的ROC曲线。静息态数据实验结果进一步表明,SDAPC能成功提取出9个静息态脑网络。

关键词: 仿射传播聚类, 功能磁共振成像, 时间复杂度, 相似度矩阵约减, 高斯密度函数

Abstract:

Affinity Propagation Clustering(APC) method shows its limitations in time complexity,data storage and clustering results while handling massive functional Magnetic Resonance Imaging(fMRI) data.Aiming at these problems,this paper proposes a new method named SDAPC,which combines Sparse APC(SAPC) with similarity matrix reduction.It starts from sparse approximation on fMRI data,continues with the density analysis on sparse data by Gaussian density function and Euclidean distance,and finally realizes the detection on the functional connectivity of reduced fMRI data.The task-related data experiment gets the following results:SDAPC produces a fine ROC curve for single subject while running about three times faster than SAPC.SDAPC and SAPC both get better ROC curves for multiple subjects than single subject.The resting-state data experiment leads to the further finding that SDAPC can successfully identify nine resting-state networks.

Key words: Affinity Propagation Clustering(APC), functional Magnetic Resonance Imaging(fMRI), time complexity, similarity matrix reduction, Gaussian density function

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