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...

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Podrobná bibliografia
Vydané v:	Nature neuroscience Ročník 20; číslo 3; s. 304 - 313
Hlavní autori:	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
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Nature Publishing Group US 01.03.2017 Nature Publishing Group
Predmet:	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
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Popis
Shrnutí:	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.
Bibliografia:	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