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Deep learning for neuroimaging: a validation study
Journal article   Open access   Peer reviewed

Deep learning for neuroimaging: a validation study

Sergey M Plis, Devon R Hjelm, Ruslan Salakhutdinov, Elena A Allen, Henry J Bockholt, Jeffrey D Long, Hans J Johnson, Jane S Paulsen, Jessica A Turner and Vince D Calhoun
Frontiers in neuroscience, Vol.8(8), pp.229-229
2014
DOI: 10.3389/fnins.2014.00229
PMCID: PMC4138493
PMID: 25191215
url
https://doi.org/10.3389/fnins.2014.00229View
Published (Version of record) Open Access

Abstract

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager's toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data.
 Neuroscience classification fMRI intrinsic networks MRI unsupervised learning

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