Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox

Authors

Karl M. Kuntzelman, University of Nebraska - Lincoln
Jacob M. Williams, University of Nebraska - LincolnFollow
Phui Cheng Lim, University of Nebraska - Lincoln
Ashtok Samal, University of Nebraska- LincolnFollow
Prahalada K. Rao, University of Nebraska - LincolnFollow
Matthew R. Johnson, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

3-2-2021

Citation

Kuntzelman KM, Williams JM, Lim PC, Samal A, Rao PK and Johnson MR (2021) Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox. Front. Hum. Neurosci. 15:638052. doi: 10.3389/fnhum.2021.638052

Comments

OPEN ACCESS

Abstract

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox (the “Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education” package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere.