Abstract
Neural computations are often fast and anatomically localized. Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both. Of particular relevance, fast neural replay is known to occur throughout the brain in a coordinated fashion about which little is known. We develop a multivariate analysis method for functional magnetic resonance imaging that makes it possible to study sequentially activated neural patterns separated by less than 100 ms with precise spatial resolution. Human participants viewed five images individually and sequentially with speeds up to 32 ms between items. Probabilistic pattern classifiers were trained on activation patterns in visual and ventrotemporal cortex during individual image trials. Applied to sequence trials, probabilistic classifier time courses allow the detection of neural representations and their order. Order detection remains possible at speeds up to 32 ms between items (plus 100 ms per item). The frequency spectrum of the sequentiality metric distinguishes between sub- versus supra-second sequences. Importantly, applied to resting-state data our method reveals fast replay of task-related stimuli in visual cortex. This indicates that non-hippocampal replay occurs even after tasks without memory requirements and shows that our method can be used to detect such spontaneously occurring replay.
Highlights
Neural computations are often fast and anatomically localized
We investigated the possibility that functional magnetic resonance imaging (fMRI) can be used to address two cornerstones of understanding signals resulting from fast activation sequences: order detection and element detection
While event detection is a standard problem in fMRI, we focused on the special case relevant to our question: detecting neural patterns of brief events that are affected by patterns from other sequence elements occurring only tens of milliseconds before or afterwards, causing backward and forward interference, respectively
Summary
Neural computations are often fast and anatomically localized. Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both. Investigating fast-paced representational dynamics within specific brain areas promises important insights into a variety of cognitive processes Such investigations could be crucial for understanding replay, which is characterized by a widespread co-occurrence of neural reactivation events throughout the brain of mostly unknown functional significance, in particular outside of the hippocampus, see, e.g.17,22. The present experiment tests this idea and our results confirm that logistic regression classifier time courses reveal the content and order of fast sequential neural events using fMRI. We use this method to ask whether sequential reactivations of sensory events occur outside of the hippocampus, even if task experiences did not require memorization or involve repeated sequential structure. Our results have implications for the interpretation of our own previous results in Schuck and Niv[54] and future fMRI studies investigating fast neural event sequences, like hippocampal replay
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