Computational Neuroscience Lab, Institute of Computer Science, University of Tartu, Tartu, Estonia.
UMRS 449, Université Catholique de Lyon/Ecole Pratique des Hautes Etudes, 10 Place des Archives, 69002, Lyon, France.
Sci Rep. 2020 May 12;10(1):7870. doi: 10.1038/s41598-020-64243-6.
Human brain has developed mechanisms to efficiently decode sensory information according to perceptual categories of high prevalence in the environment, such as faces, symbols, objects. Neural activity produced within localized brain networks has been associated with the process that integrates both sensory bottom-up and cognitive top-down information processing. Yet, how specifically the different types and components of neural responses reflect the local networks' selectivity for categorical information processing is still unknown. In this work we train Random Forest classification models to decode eight perceptual categories from broad spectrum of human intracranial signals (4-150 Hz, 100 subjects) obtained during a visual perception task. We then analyze which of the spectral features the algorithm deemed relevant to the perceptual decoding and gain the insights into which parts of the recorded activity are actually characteristic of the visual categorization process in the human brain. We show that network selectivity for a single or multiple categories in sensory and non-sensory cortices is related to specific patterns of power increases and decreases in both low (4-50 Hz) and high (50-150 Hz) frequency bands. By focusing on task-relevant neural activity and separating it into dissociated anatomical and spectrotemporal groups we uncover spectral signatures that characterize neural mechanisms of visual category perception in human brain that have not yet been reported in the literature.
人类大脑已经发展出了根据环境中高流行度的感知类别（例如人脸、符号、物体）高效解码感觉信息的机制。在局部脑网络中产生的神经活动与整合感觉自下而上和认知自上而下信息处理的过程有关。然而，神经反应的不同类型和组成部分如何具体反映局部网络对类别信息处理的选择性仍然未知。在这项工作中，我们使用随机森林分类模型，从在视觉感知任务中获得的广泛的人类颅内信号（4-150Hz，100 名受试者）中解码八个感知类别。然后，我们分析算法认为与感知解码相关的频谱特征，并深入了解记录的活动的哪些部分实际上是人类大脑视觉分类过程的特征。我们表明，单一或多个类别的网络选择性在感觉和非感觉皮层中与低频（4-50Hz）和高频（50-150Hz）带中功率增加和减少的特定模式有关。通过关注与任务相关的神经活动并将其分为分离的解剖和频谱时间组，我们揭示了特征谱，这些特征谱描述了人类大脑中视觉类别感知的神经机制，而这些机制在文献中尚未报道。
