发帖
雷达卡
摘要翻译：
基于标准通用线性模型(GLM)和谱聚类的功能性磁共振成像(fMRI)数据的主体间分割是近年来提出的一种缓解fMRI空间归一化相关问题的方法。然而，尽管基于GLM的解析方法很有吸引力，但它引入了自己的偏见，即关于血流动力学响应函数(HRF)和任务相关信号变化的先验知识，或关于任务期间受试者行为的先验知识。本文提出了一种基于独立分量分析(ICA)和偏最小二乘法(PLS)的数据驱动谱聚类分解方法，以取代GLM。首先，自动选择若干独立的组件。然后从相关的ICA图中获得种子体素，并计算种子体素的fMRI信号（包括HRF的区域变化）与所有体素信号的主成分之间的PLS潜在变量。最后，我们用PLS潜在变量的谱聚类对所有被试数据进行了Parcellated。我们给出了该方法在单目标和多目标fMRI数据集上的应用结果。初步的实验结果表明，与基于GLM的方法相比，这种数据驱动的方法在解析精度方面有了很大的提高，并用GLM的T值和PLS导出的T值的包内方差进行了评估。
---
英文标题：
《Parcellation of fMRI Datasets with ICA and PLS-A Data Driven Approach》
---
作者：
Yongnan Ji, Pierre-Yves Herve, Uwe Aickelin, Alain Pitiot
---
最新提交年份：
2010
---
分类信息：
一级分类：Computer Science 计算机科学
二级分类：Computer Vision and Pattern Recognition 计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
--
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--
一级分类：Computer Science 计算机科学
二级分类：Neural and Evolutionary Computing 神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
--
---
英文摘要：
Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM)and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. Finally, we parcellate all subjects data with a spectral clustering of the PLS latent variables. We present results of the application of the proposed method on both single-subject and multi-subject fMRI datasets. Preliminary experimental results, evaluated with intra-parcel variance of GLM t-values and PLS derived t-values, indicate that this data-driven approach offers improvement in terms of parcellation accuracy over GLM based techniques.
---
PDF链接：
https://arxiv.org/pdf/1004.3708
京公网安备 11010802022788号
