Johan Nakuci, Jiwon Yeon, Nadia Haddara, Ji-Hyun Kim, Sung-Phil Kim, Dobromir Rahnev
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引用次数: 0
Abstract
Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leverage trial-to-trial fluctuations in the brain-wide signal recorded using functional MRI to test if distinct sets of brain regions are activated on different trials when accomplishing the same task. Across three different perceptual decision-making experiments, we estimate the brain activations for each trial. We then cluster the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we find multiple distinct but stable subtypes of trials, suggesting that the same task can be accomplished in the presence of widely varying brain activation patterns. Surprisingly, in all experiments, one of the subtypes exhibits strong activation in the default mode network, which is typically thought to decrease in activity during tasks that require externally focused attention. The remaining subtypes are characterized by activations in different task-positive areas. The default mode network subtype is characterized by behavioral signatures that are similar to the other subtypes exhibiting activation with task-positive regions. These findings demonstrate that the same perceptual decision-making task is accomplished through multiple brain activation patterns.
期刊介绍:
Nature Communications, an open-access journal, publishes high-quality research spanning all areas of the natural sciences. Papers featured in the journal showcase significant advances relevant to specialists in each respective field. With a 2-year impact factor of 16.6 (2022) and a median time of 8 days from submission to the first editorial decision, Nature Communications is committed to rapid dissemination of research findings. As a multidisciplinary journal, it welcomes contributions from biological, health, physical, chemical, Earth, social, mathematical, applied, and engineering sciences, aiming to highlight important breakthroughs within each domain.