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

Karl M. Kuntzelman, Jacob M. Williams, Phui Cheng Lim, Ashok Samal, Prahalada K. Rao, Matthew R. Johnson

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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.

Original languageEnglish (US)
Article number638052
JournalFrontiers in Human Neuroscience
StatePublished - Mar 2 2021


  • EEG
  • MVPA
  • Python
  • cognitive neuroscience
  • deep learning
  • fMRI
  • machine learning
  • neural networks

ASJC Scopus subject areas

  • Neuropsychology and Physiological Psychology
  • Neurology
  • Psychiatry and Mental health
  • Biological Psychiatry
  • Behavioral Neuroscience


Dive into the research topics of 'Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox'. Together they form a unique fingerprint.

Cite this