静息状态功能磁共振成像
计算机科学
人工智能
机器学习
无监督学习
功能磁共振成像
透视图(图形)
人口
模式识别(心理学)
心理学
神经科学
社会学
人口学
作者
Meenakshi Khosla,Keith Jamison,Gia H. Ngo,Amy Kuceyeski,Mert R. Sabuncu
出处
期刊:Cornell University - arXiv
日期:2018-12-30
被引量:2
摘要
Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We present a methodical taxonomy of machine learning methods in resting-state fMRI. We identify three major divisions of unsupervised learning methods with regard to their applications to rs-fMRI, based on whether they discover principal modes of variation across space, time or population. Next, we survey the algorithms and rs-fMRI feature representations that have driven the success of supervised subject-level predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.
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