probabilistic multivariate classifiers

Distributed replay in the human brain, and how to find it

I will present work on a novel fMRI analysis method that allows us to investigate sequential reactivation in the hippocampus. Our method focuses on analysing the time courses of probabilistic multivariate classifiers and allows us to infer the presence and frequency of fast sequential reactivation events. Using a paradigm in which we controlled the speed of sequential visually elicited activations, we validated the method in visual cortex for event sequences with only 32 ms between items. We show that detectability remains possible if low signal-to-noise ratio and when sequence events occur at unknown times. In a preliminary analysis, we show that even the exposure to our visual paradigm elicits reactivations in visual cortex at rest following the task. I then present work in which we tested how representations influence replay by asking whether transitions between task-state representations are reactivated at rest during hippocampal replay events. Participants learned to make decisions about ambiguous stimuli that depended on past events and attentionally filtered stimulus processing. FMRI signals during rest periods following this task indicated sequential reactivation of task states. These results indicate that adaptive task state representations are computed and replayed at different cortical sites. In combination with other methods, fMRI may allow us to unravel this coordinated nature of replay.