Average is optimal: an inverted-U relationship between trial-to-trial brain activity and behavioral performance.
Average is optimal: an inverted-U relationship between trial-to-trial brain activity and behavioral performance.
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DOI：
10.1371/journal.pcbi.1003348
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发表时间：
2013
影响因子：
4.3
通讯作者：
Zempel JM
中科院分区：
文献类型：
--
作者：
He BJ;Zempel JM
众所周知，即使在相同的任务条件下，大脑活动和行为输出也存在巨大的试验差异。迄今为止，绝大多数调查大脑活动的试验间波动与行为表现之间关系的事件相关电位（ERP）研究仅测试了它们之间的单调关系。然而，最近发现跨试验变异性可以与独立于试验平均活动的行为表现相关。这一发现预测试验间大脑活动和行为输出之间存在 U 形或倒 U 形关系，具体取决于较大的大脑变异性是否与更好或更差的行为相关。使用视觉刺激检测任务，我们通过人类皮质电图 (ECoG) 提供倒 U 型大脑行为关系的证据：当宽带 ECoG 活动的原始波动更接近跨试验平均值时，命中率更高，反应时间更快。重要的是，我们表明这种关系不仅存在于刺激后任务诱发的大脑活动中，而且还存在于刺激前的自发大脑活动中，这表明预期的大脑动力学。我们的发现与大脑中随机噪声的存在是一致的。他们进一步支持吸引子网络理论，该理论假设大脑在任务表现下会陷入更受限的状态空间，并且接近目标轨迹与更好的表现相关。人类的大脑是出了名的“吵闹”。即使具有相同的物理感官输入和任务要求，大脑反应和行为输出在每次试验中也有很大差异。几十年来，这种大脑和行为的变异性以及它们之间的关系一直是神经科学研究的焦点。传统上，人们认为试验之间的大脑活动和行为表现之间的关系是单调的：最高或最低的大脑活动水平与最佳行为表现相关。通过对神经外科患者进行侵入性录音，我们证明了大脑与行为变异之间的倒 U 型关系。在这种关系下，适度的大脑活动与最佳表现相关，而非常低和非常高的大脑活动水平都预示着表现不佳。这些结果对于我们理解大脑功能具有重要意义。他们进一步支持了最新的理论框架，该框架将大脑视为主动非线性动力系统，而不是被动信号处理设备。
It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations in brain activity and behavioral performance have only tested a monotonic relationship between them. However, it was recently found that across-trial variability can correlate with behavioral performance independent of trial-averaged activity. This finding predicts a U- or inverted-U- shaped relationship between trial-to-trial brain activity and behavioral output, depending on whether larger brain variability is associated with better or worse behavior, respectively. Using a visual stimulus detection task, we provide evidence from human electrocorticography (ECoG) for an inverted-U brain-behavior relationship: When the raw fluctuation in broadband ECoG activity is closer to the across-trial mean, hit rate is higher and reaction times faster. Importantly, we show that this relationship is present not only in the post-stimulus task-evoked brain activity, but also in the pre-stimulus spontaneous brain activity, suggesting anticipatory brain dynamics. Our findings are consistent with the presence of stochastic noise in the brain. They further support attractor network theories, which postulate that the brain settles into a more confined state space under task performance, and proximity to the targeted trajectory is associated with better performance. The human brain is notoriously “noisy”. Even with identical physical sensory inputs and task demands, brain responses and behavioral output vary tremendously from trial to trial. Such brain and behavioral variability and the relationship between them have been the focus of intense neuroscience research for decades. Traditionally, it is thought that the relationship between trial-to-trial brain activity and behavioral performance is monotonic: the highest or lowest brain activity levels are associated with the best behavioral performance. Using invasive recordings in neurosurgical patients, we demonstrate an inverted-U relationship between brain and behavioral variability. Under such a relationship, moderate brain activity is associated with the best performance, while both very low and very high brain activity levels are predictive of compromised performance. These results have significant implications for our understanding of brain functioning. They further support recent theoretical frameworks that view the brain as an active nonlinear dynamical system instead of a passive signal-processing device.
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16.2
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