Research data supporting "Recurrent processing drives perceptual plasticity"

Learning and experience are critical for translating ambiguous sensory information from our environments to perceptual decisions. Yet, evidence on how training molds the adult human brain remains controversial, as fMRI at standard resolution does not allow us to discern the finer-scale mechanisms that underlie sensory plasticity. Here, we combine ultra-high field (7T) functional imaging at sub-millimetre resolution with orientation discrimination training to interrogate experience-dependent plasticity across cortical depths that are known to support dissociable brain computations. Our results provide evidence for recurrent plasticity, by contrast to sensory encoding vs. feedback mechanisms. We demonstrate that learning alters orientation-specific representations in superficial rather than middle V1 layers, suggesting changes in read-out rather than input signals. Further, learning increases feedforward rather than feedback layer-to-layer connectivity in occipito-parietal regions, suggesting that sensory plasticity gates perceptual decisions. Our findings reveal finer-scale plasticity mechanisms that re-weight sensory signals to inform improved decisions, bridging the gap between micro- and macro- circuits of experience-dependent plasticity.
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See the file 'Description of uploaded data' for a detailed description of the dataset.

学习和经验对于将环境中模糊的感官信息转化为知觉决策至关重要。然而，关于训练如何塑造成人人类大脑的证据仍存在争议，因为标准分辨率的fMRI无法让我们辨识出感官可塑性的底层精细机制。在本研究中，我们结合了亚毫米级分辨率的超高速场（7T）功能性成像与方向辨别训练，以探究支持可分离脑计算的皮质深部依赖经验的可塑性的变化。我们的研究结果提供了关于循环可塑性的证据，与感官编码与反馈机制相对立。我们证明，学习改变了浅层而非中间V1层中的方向特异性表征，这表明读出信号而非输入信号发生了变化。此外，学习增加了枕顶区域的正向而非反馈层间连接，这表明感官可塑性调控知觉决策。我们的发现揭示了更精细的可塑性机制，这些机制重新加权感官信号以提供更优决策的信息，弥合了经验依赖性可塑性的微-宏电路之间的差距。