Using multivariate pattern analysis (MVPA) to determine learning related changes in structural brain connectivity with diffusion MRI

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Memory depends on the interaction of brain-wide networks of memory systems. During learning, remodeling of synaptic connectivity is initiated. During following consolidation periods, further synaptic and systems modulations render the new memory stable and integrate it into existing networks. Recent developments in diffusion-weighted magnetic resonance imaging methods give indication that rapid structural changes induced by learning can be observed in humans in vivo. First studies and our own preliminary experiments have shown that changes in brain connectivity can be detected on a microstructural (grey matter) and macrostructural (white matter) level already after 60-90 min of learning. Studying learning-dependent changes thus provide a unique opportunity to investigate how brain structure gives rise to neural function and cognition. Because of the highly multivariate nature of connectivity data, we will adopt a machine learning approach (multivariate pattern analysis, MVPA) for statistical hypothesis testing. We already successfully implemented this method in high-density all-night sleep EEG data, and we will adapt experimental design, signal preprocessing, and feature selection to diffusion MRI data. We aim at investigating the local and network changes in brain connectivity following systems memory consolidation during the hours and days after learning. Particularly, we will look at the development of brain connectivity during sleep and wakefulness. Conversely, we will also investigate how the individual’s connectome predicts successful learning. Together, this project will allow us to determine the neurobiological relevance of brain structure for learning and memory, and at the same time advance the use of MVPA for hypothesis testing in highly multivariate data.