分层地形因子分析

2014 International Workshop on Pattern Recognition in Neuroimaging Pub Date : 2014-06-04 DOI:10.1109/PRNI.2014.6858530

Jeremy R. Manning, R. Ranganath, Waitsang Keung, N. Turk-Browne, J. Cohen, K. Norman, D. Blei

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引用次数: 12

摘要

最近的研究表明，认知过程通常反映在整个大脑的功能连接模式中(详见[16])。然而，使用传统方法检查功能连接模式会带来大量的计算负担(计算时间和内存)。在这里，我们提出了一种称为分层地形因子分析的技术，用于有效地发现大型多学科神经成像数据集中的大脑网络。

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Hierarchical topographic factor analysis

Recent work has revealed that cognitive processes are often reflected in patterns of functional connectivity throughout the brain (for review see [16]). However, examining functional connectivity patterns using traditional methods carries a substantial computational burden (of computing time and memory). Here we present a technique, termed Hierarchical topographic factor analysis, for efficiently discovering brain networks in large multi-subject neuroimaging datasets.

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2014 International Workshop on Pattern Recognition in Neuroimaging

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