BRAIN DECODING OF FMRI CONNECTIVITY GRAPHS USING DECISION TREE ENSEMBLES

被引：6

作者：

Richiardi, Jonas ^{[1
,2
]}

Eryilmaz, Hamdi ^{[3
]}

Schwartz, Sophie ^{[3
]}

Vuilleumier, Patrik ^{[3
]}

Van De Ville, Dimitri ^{[1
,2
]}

机构：

[1] Ecole Polytech Fed Lausanne, Med Image Proc Lab, CH-1015 Lausanne, Switzerland

[2] Univ Geneva, Med Image Proc Lab, CH-1211 Geneva, Switzerland

[3] Univ Geneva, Lab Neurol & Imaging Cognit, CH-1211 Geneva, Switzerland

来源：

2010 7TH IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO | 2010年

基金：

瑞士国家科学基金会;

关键词：

fMRI; brain decoding; functional connectivity; graphs; decision tree; FUNCTIONAL CONNECTIVITY; NETWORK; CORTEX; SINGLE; MRI;

D O I：

10.1109/ISBI.2010.5490194

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Functional connectivity analysis of fMRI data can reveal synchronized activity between anatomically distinct brain regions. Here, we exploit the characteristic connectivity graphs of task and resting epochs to perform classification between these conditions. Our approach is based on ensembles of decision trees, which combine powerful discriminative ability with interpretability of results. This makes it possible to extract discriminative graphs that represent a subset of the connections that distinguish best between the experimental conditions. Our experimental results also show that the method can be applied for group-level brain decoding.

引用

页码：1137 / 1140

页数：4

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