Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, London, UK; Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany.
Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, London, UK; Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany.
Neuroimage. 2020 Aug 15;217:116836. doi: 10.1016/j.neuroimage.2020.116836. Epub 2020 Apr 10.
The extent to which brain responses differ across varying cognitive demands is referred to as "neural differentiation," and greater neural differentiation has been associated with better cognitive performance in older adults. An emerging approach has examined within-person neural differentiation using moment-to-moment brain signal variability. A number of studies have found that brain signal variability differs by cognitive state; however, the factors that cause signal variability to rise or fall on a given task remain understudied. We hypothesized that top performers would modulate signal variability according to the complexity of sensory input, upregulating variability when processing more feature-rich stimuli. In the current study, 46 older adults passively viewed face and house stimuli during fMRI. Low-level analyses showed that house images were more feature-rich than faces, and subsequent computational modelling of ventral visual stream responses (HMAX) revealed that houses were more feature-rich especially in V1/V2-like model layers. Notably, we then found that participants exhibiting greater face-to-house upregulation of brain signal variability in V1/V2 (higher for house relative to face stimuli) also exhibited more accurate, faster, and more consistent behavioral performance on a battery of offline visuo-cognitive tasks. Further, control models revealed that face-house modulation of mean brain signal was relatively insensitive to offline cognition, providing further evidence for the importance of brain signal variability for understanding human behavior. We conclude that the ability to align brain signal variability to the richness of perceptual input may mark heightened trait-level behavioral performance in older adults.
大脑对不同认知需求的反应程度差异被称为“神经分化”，而更强的神经分化与老年人更好的认知表现相关。一种新兴的方法使用即时脑信号变异性来检查个体内的神经分化。许多研究发现，脑信号变异性因认知状态而异；然而，导致信号变异性在给定任务中上升或下降的因素仍未得到充分研究。我们假设表现优异者会根据感觉输入的复杂性来调节信号变异性，在处理更具特征的刺激时增加变异性。在当前的研究中，46 名老年人在 fMRI 中被动观看人脸和房屋刺激。低水平分析表明，房屋图像比人脸更具特征丰富性，随后对腹侧视觉流反应（HMAX）的计算模型表明，房屋尤其在 V1/V2 样模型层中更具特征丰富性。值得注意的是，我们发现，在 V1/V2 中大脑信号变异性的面孔到房屋上调幅度更大（相对于面孔刺激，房屋刺激的信号变异性更高）的参与者，在一系列离线视觉认知任务中的表现也更加准确、快速和一致。此外，控制模型表明，大脑信号的平均调制对离线认知相对不敏感，这为理解人类行为的大脑信号变异性的重要性提供了进一步的证据。我们得出结论，将大脑信号变异性与感知输入的丰富度对齐的能力可能标志着老年人在行为表现上的特质水平提高。
