Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.
Akili Interactive, Boston, MA 02110, USA.
Cereb Cortex. 2020 Sep 3;30(10):5333-5345. doi: 10.1093/cercor/bhaa115.
We present a model-based method for inferring full-brain neural activity at millimeter-scale spatial resolutions and millisecond-scale temporal resolutions using standard human intracranial recordings. Our approach makes the simplifying assumptions that different people's brains exhibit similar correlational structure, and that activity and correlation patterns vary smoothly over space. One can then ask, for an arbitrary individual's brain: given recordings from a limited set of locations in that individual's brain, along with the observed spatial correlations learned from other people's recordings, how much can be inferred about ongoing activity at other locations throughout that individual's brain? We show that our approach generalizes across people and tasks, thereby providing a person- and task-general means of inferring high spatiotemporal resolution full-brain neural dynamics from standard low-density intracranial recordings.
我们提出了一种基于模型的方法，使用标准的人类颅内记录来推断毫米级空间分辨率和毫秒级时间分辨率的全脑神经活动。我们的方法做出了简化的假设，即不同人的大脑表现出相似的相关性结构，并且活动和相关模式在空间上平滑变化。然后可以问，对于任意个体的大脑：给定该个体大脑中有限位置的记录，以及从其他人的记录中学习到的观察到的空间相关性，对于该个体大脑中其他位置的进行中的活动可以推断出多少？我们表明，我们的方法在人群和任务中具有通用性，从而提供了一种从标准的低密度颅内记录中推断高时空分辨率全脑神经动力学的个体和任务通用方法。
