The computational human brain model with voxel-wise assimilation method is established based on individual structural and functional imaging data. We found that the more resembling the brain was in both scale and architecture, the more similarity was found between the assimilated model and the biological brain both in resting states and during tasks by quantitative metrics. The hypothesis that resting state activity reflects internal body states was validated by the interoceptive circuit's capability to enhance the similarity between the simulation model and the biological brain. We identified that the removal of connections from the primary visual cortex (V1) to downstream visual pathways significantly decreased the similarity at the hippocampus between the model and its biological counterpart, despite a slight influence on the whole brain. In conclusion, the model and methodology present a solid quantitative framework of digital twin brain (DTB) for discovering the relationship between brain architecture and functions, and for digitally trying and testing diverse cognitive, medical, and lesioning approaches that could be otherwise infeasible to real subjects.

中文翻译：

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通过类脑计算来模仿和探索人脑的休息和执行任务的状态：尺度和架构

基于个体结构和功能成像数据，建立了采用体素同化方法的计算人脑模型。我们发现，通过定量指标，大脑在规模和结构上越相似，同化模型与生物大脑在静息状态和任务期间的相似性就越大。静息状态活动反映内部身体状态的假设得到了内感受回路增强模拟模型和生物大脑之间相似性的能力的验证。我们发现，移除初级视觉皮层（V1）与下游视觉通路的连接显着降低了模型与其生物对应物之间海马体的相似性，尽管对整个大脑影响很小。总之，该模型和方法提出了数字孪生大脑（DTB）的可靠定量框架，用于发现大脑结构和功能之间的关系，并以数字方式尝试和测试各种认知、医学和损伤方法，否则这些方法可能无法实现科目。