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Latent source mining in FMRI via restricted Boltzmann machine
- Source :
- Hum Brain Mapp
- Publication Year :
- 2017
-
Abstract
- Blind source separation (BSS) is commonly used in functional magnetic resonance imaging (fMRI) data analysis. Recently, BSS models based on restricted Boltzmann machine (RBM), one of the building blocks of deep learning models, have been shown to improve brain network identification compared to conventional single matrix factorization models such as independent component analysis (ICA). These models, however, trained RBM on fMRI volumes, and are hence challenged by model complexity and limited training set. In this article, we propose to apply RBM to fMRI time courses instead of volumes for BSS. The proposed method not only interprets fMRI time courses explicitly to take advantages of deep learning models in latent feature learning but also substantially reduces model complexity and increases the scale of training set to improve training efficiency. Our experimental results based on Human Connectome Project (HCP) datasets demonstrated the superiority of the proposed method over ICA and the one that applied RBM to fMRI volumes in identifying task-related components, resulted in more accurate and specific representations of task-related activations. Moreover, our method separated out components representing intermixed effects between task events, which could reflect inherent interactions among functionally connected brain regions. Our study demonstrates the value of RBM in mining complex structures embedded in large-scale fMRI data and its potential as a building block for deeper models in fMRI data analysis.
- Subjects :
- Computer science
Models, Neurological
Datasets as Topic
Blind signal separation
030218 nuclear medicine & medical imaging
Matrix decomposition
03 medical and health sciences
0302 clinical medicine
Task Performance and Analysis
Image Processing, Computer-Assisted
Humans
Radiology, Nuclear Medicine and imaging
Research Articles
Block (data storage)
Restricted Boltzmann machine
Brain Mapping
Human Connectome Project
Radiological and Ultrasound Technology
business.industry
Deep learning
Brain
Pattern recognition
Independent component analysis
Magnetic Resonance Imaging
Oxygen
Neurology
Neurology (clinical)
Artificial intelligence
Anatomy
business
Feature learning
030217 neurology & neurosurgery
Algorithms
Subjects
Details
- ISSN :
- 10970193
- Volume :
- 39
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- Human brain mapping
- Accession number :
- edsair.doi.dedup.....eee2c12e0661417402a837836c8f0bb1