ArticleFrontiers in neuroinformatics2022
PyMVPD: A Toolbox for Multivariate Pattern Dependence.
Article in Frontiers in neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Distinct Portions of Superior Temporal Sulcus Combine Auditory Representations with Different Visual Streams.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Intracranial Electroencephalography and Deep Neural Networks Reveal Shared Substrates for Representations of Face Identity and Expressions.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Improving the study of brain-behavior relationships by revisiting basic assumptions.Trends in cognitive sciences · 2023Review
- Multivariate connectivity: A brief introduction and an open question.Frontiers in neuroscience · 2022Article
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Authors and funding
3 authors.
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Abstract
Cognitive tasks engage multiple brain regions. Studying how these regions interact is key to understand the neural bases of cognition. Standard approaches to model the interactions between brain regions rely on univariate statistical dependence. However, newly developed methods can capture multivariate dependence. Multivariate pattern dependence (MVPD) is a powerful and flexible approach that trains and tests multivariate models of the interactions between brain regions using independent data. In this article, we introduce PyMVPD: an open source toolbox for multivariate pattern dependence. The toolbox includes linear regression models and artificial neural network models of the interactions between regions. It is designed to be easily customizable. We demonstrate example applications of PyMVPD using well-studied seed regions such as the fusiform face area (FFA) and the parahippocampal place area (PPA). Next, we compare the performance of different model architectures. Overall, artificial neural networks outperform linear regression. Importantly, the best performing architecture is region-dependent: MVPD subdivides cortex in distinct, contiguous regions whose interaction with FFA and PPA is best captured by different models.
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