Details

Autor(en) / Beteiligte

• Hjelm, R. Devon
• Calhoun, Vince D.
• Salakhutdinov, Ruslan
• Allen, Elena A.
• Adali, Tulay
• Plis, Sergey M.

Titel

Restricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks

Ist Teil von

• NeuroImage (Orlando, Fla.), 2014-08, Vol.96, p.245-260

Ort / Verlag

Amsterdam: Elsevier Inc

Erscheinungsjahr

2014

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Matrix factorization models are the current dominant approach for resolving meaningful data-driven features in neuroimaging data. Among them, independent component analysis (ICA) is arguably the most widely used for identifying functional networks, and its success has led to a number of versatile extensions to group and multimodal data. However there are indications that ICA may have reached a limit in flexibility and representational capacity, as the majority of such extensions are case-driven, custom-made solutions that are still contained within the class of mixture models. In this work, we seek out a principled and naturally extensible approach and consider a probabilistic model known as a restricted Boltzmann machine (RBM). An RBM separates linear factors from functional brain imaging data by fitting a probability distribution model to the data. Importantly, the solution can be used as a building block for more complex (deep) models, making it naturally suitable for hierarchical and multimodal extensions that are not easily captured when using linear factorizations alone. We investigate the capability of RBMs to identify intrinsic networks and compare its performance to that of well-known linear mixture models, in particular ICA. Using synthetic and real task fMRI data, we show that RBMs can be used to identify networks and their temporal activations with accuracy that is equal or greater than that of factorization models. The demonstrated effectiveness of RBMs supports its use as a building block for deeper models, a significant prospect for future neuroimaging research. •A naturally extensible novel method for separating intrinsic networks from fMRI data•Effective performance of the method in comparison to state of the art in simulations•Effective intrinsic networks separation in task-related fMRI data•Enhanced time course and functional connectivity estimates•Overall competitive approach naturally extensible to group and multimodal settings