Vivian L. Smith Department of Neurosurgery, McGovern Medical School at UT Health Houston, Houston, TX, 77030, United States of America.
Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, 77030, United States of America.
Sci Data. 2022 Jan 31;9(1):28. doi: 10.1038/s41597-022-01125-8.
For most people, recalling information about familiar items in a visual scene is an effortless task, but it is one that depends on coordinated interactions of multiple, distributed neural components. We leveraged the high spatiotemporal resolution of direct intracranial recordings to better delineate the network dynamics underpinning visual scene recognition. We present a dataset of recordings from a large cohort of humans while they identified images of famous landmarks (50 individuals, 52 recording sessions, 6,775 electrodes, 6,541 trials). This dataset contains local field potential recordings derived from subdural and penetrating electrodes covering broad areas of cortex across both hemispheres. We provide this pre-processed data with behavioural metrics (correct/incorrect, response times) and electrode localisation in a population-normalised cortical surface space. This rich dataset will allow further investigation into the spatiotemporal progression of multiple neural processes underlying visual processing, scene recognition and cued memory recall.
对于大多数人来说，回忆视觉场景中熟悉物品的信息是一项毫不费力的任务，但这需要多个分布式神经组件的协调交互。我们利用直接颅内记录的高时空分辨率来更好地描绘视觉场景识别的基础网络动态。我们展示了一组来自大量人类的记录数据集，这些人在识别著名地标图像时（50 名个体，52 次记录会议，6775 个电极，6541 次试验）。该数据集包含源自硬脑膜和穿透电极的局部场电位记录，这些电极覆盖了两个半球的广泛皮层区域。我们提供了经过预处理的数据，其中包含行为指标（正确/错误，反应时间）和在人群归一化皮质表面空间中的电极定位。这个丰富的数据集将允许进一步研究视觉处理、场景识别和提示记忆回忆背后的多个神经过程的时空进展。
