Computational approaches to fMRI analysis

A revolution is underway in cognitive neuroscience, where tools and techniques from computer science and the tech industry are helping to extract more meaningful cognitive signals from noisy and increasingly large fMRI datasets. In this paper, the authors review the cutting edge of such computationa...

Full description

Saved in:
Bibliographic Details
Published in:Nature neuroscience Vol. 20; no. 3; pp. 304 - 313
Main Authors: Cohen, Jonathan D, Daw, Nathaniel, Engelhardt, Barbara, Hasson, Uri, Li, Kai, Niv, Yael, Norman, Kenneth A, Pillow, Jonathan, Ramadge, Peter J, Turk-Browne, Nicholas B, Willke, Theodore L
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.03.2017
Nature Publishing Group
Subjects:
59/36
631/378/2649
631/477
Analysis
Animal Genetics and Genomics
Behavioral Sciences
Biological Techniques
Biomedicine
Brain - physiology
Brain Mapping - methods
Cognition
Cognition - physiology
Computer engineering
Computer science
Control theory
Hemodynamics
Humans
Learning - physiology
Machine learning
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Medical imaging
Methods
Models, Neurological
Neurobiology
Neuroimaging
Neurosciences
perspective
Software
United States
United States > US
New Jersey
ISSN:1097-6256, 1546-1726, 1546-1726
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A revolution is underway in cognitive neuroscience, where tools and techniques from computer science and the tech industry are helping to extract more meaningful cognitive signals from noisy and increasingly large fMRI datasets. In this paper, the authors review the cutting edge of such computational analyses and discuss future opportunities and challenges. Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis. Several recent exploratory and theory-driven methods have begun to pursue these opportunities. These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing. Adoption of these techniques is enabling a new generation of experiments and analyses that could transform our understanding of some of the most complex—and distinctly human—signals in the brain: acts of cognition such as thoughts, intentions and memories.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Review-3
content type line 23
ISSN:1097-6256
1546-1726
1546-1726
DOI:10.1038/nn.4499