Multiple Brain Activation Patterns for the Same Perceptual Decision-Making Task.

Johan Nakuci, Jiwon Yeon, Nadia Haddara, Ji-Hyun Kim, Sung-Phil Kim, Dobromir Rahnev
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Abstract

Meaningful variation in internal states that impacts cognition and behavior remains challenging to discover and characterize. Here we leveraged 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 estimated the brain activations for each trial. We then clustered the trials based on their similarity using modularity-maximization, a data-driven classification method. In each experiment, we found 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 exhibited 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 were characterized by activations in different task-positive areas. The default mode network subtype was characterized by behavioral signatures that were 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.

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同一任务的多种大脑激活模式。
影响认知和行为的内在状态的有意义的变化仍然难以发现和表征。在这里,我们利用功能性MRI记录的全脑信号的试验间波动来测试在完成相同任务时,不同的大脑区域是否在不同的试验中被激活。受试者执行感知决策任务并提供信心。我们使用模块化最大化(一种数据驱动的分类方法),根据它们的相似性,估计了每个试验和集群试验的大脑激活。我们确定了三种亚型的试验,它们的激活特征和行为表现都不同。至关重要的是,亚型1和2的特征是在不同的任务积极区域激活。令人惊讶的是,子类型3在默认模式网络中表现出强烈的激活,这通常被认为会在任务期间减少活动。计算模型揭示了每种亚型的大脑活动模式是如何从大规模大脑网络内部和之间的相互作用中产生的。这些发现表明,在存在广泛变化的大脑激活模式的情况下,同样的任务也可以完成。
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