Machine learning in resting-state fMRI analysis

被引：136

作者：

Khosla, Meenakshi ^{[1
]}

Jamison, Keith ^{[2
]}

Ngo, Gia H. ^{[1
]}

Kuceyeski, Amy ^{[2
,3
]}

Sabuncu, Mert R. ^{[1
,4
]}

机构：

[1] Cornell Univ, Sch Elect & Comp Engn, 300 Frank HT Rhodes Hall, Ithaca, NY 14853 USA

[2] Weill Cornell Med Coll, Radiol, New York, NY USA

[3] Weill Cornell Med Coll, Brain & Mind Res Inst, New York, NY USA

[4] Cornell Univ, Nancy E & Peter C Meinig Sch Biomed Engn, Ithaca, NY 14853 USA

来源：

MAGNETIC RESONANCE IMAGING | 2019年 / 64卷

关键词：

Machine learning; Resting-state; Functional MRI; Intrinsic networks; Brain connectivity; FUNCTIONAL CONNECTIVITY PATTERNS; INDEPENDENT COMPONENT ANALYSIS; BRAIN CONNECTIVITY; ALZHEIMERS-DISEASE; MAJOR DEPRESSION; DYNAMIC CONNECTIVITY; SUSTAINED ATTENTION; SLEEP-DEPRIVATION; CEREBRAL-CORTEX; NETWORKS;

D O I：

10.1016/j.mri.2019.05.031

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

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 offer a methodical taxonomy of machine learning methods in restingstate 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 subjectlevel predictions. The goal is to provide a high-level overview of the burgeoning field of rs-fMRI from the perspective of machine learning applications.

引用

页码：101 / 121

页数：21

共 169 条

[1] Network diffusion accurately models the relationship between structural and functional brain connectivity networks [J].