Abstract
Intracranial recordings have opened a unique opportunity to simultaneously measure activity across multiregional networks in the human brain. Recent works have focused on developing transformer-based neurofoundation models of such recordings that can generalize across subjects and datasets. However, these recordings exhibit highly complex spatiotemporal interactions across diverse spatial scales, from the single-channel scale to the scale of brain regions. As such, there remain critical open questions regarding how best to encode spatial information and how to design self-supervision tasks that enable the learning of brain network patterns and enhance downstream decoding performance using such high-dimensional, multiregional recordings. To allow for exploring these questions, we propose a new spatiotemporal transformer model of multiregional neural activity and a corresponding self-supervised masked latent reconstruction task, designed to enable flexibility in the spatial scale used for token encoding and masking. Applying this model on publicly available multiregional intracranial electrophysiology (iEEG) data, we demonstrate that adjusting the spatial scale for both token encoding and masked reconstruction significantly impacts downstream decoding. Further, we find that spatial encoding at larger scales than channel-level encoding, which is commonly used in existing iEEG transformer models, improves downstream decoding performance. Finally, we demonstrate that our method allows for region-level token encoding while also maintaining accurate channel-level neural reconstruction. Taken together, our modeling framework enables exploration of the spatial scales used for token encoding and masking, reveals their importance towards self-supervised pretraining of neurofoundation models of multiregional human brain activity, and enhances downstream decoding performance.
Abstract (translated)
颅内记录为同时测量人类大脑多区域网络的活动提供了一个独特的机会。近期的研究集中在开发能够跨不同受试者和数据集泛化的基于变压器的基础神经模型上，这些模型处理这样的记录。然而，这种记录展示了高度复杂的时空相互作用，跨越从单通道尺度到脑区尺度的不同空间尺度。因此，关于如何最有效地编码空间信息以及如何设计自我监督任务以利用此类高维、多区域记录学习大脑网络模式并提高下游解码性能，仍存在关键的未解决的问题。 为了探索这些问题，我们提出了一种新的时空变压器模型来描述多区域神经活动，并相应地提出了一个自监督掩码潜在重构任务。该任务旨在使令牌编码和掩码的空间尺度使用具有灵活性。在公开可用的多区域颅内电生理学（iEEG）数据上应用这种模型时，我们发现调整令牌编码和掩码重建的空间尺度对下游解码有显著影响。 此外，我们发现以大于通道级别编码的大规模空间进行空间编码，这是现有iEEG变压器模型中常用的通道级编码方式，可以提高下游的解码性能。最后，我们证明了我们的方法可以在区域级进行令牌编码的同时保持准确的通道级神经重构。 总的来说，我们的建模框架使探索用于令牌编码和掩码的空间尺度成为可能，并揭示了这些空间尺度对于多区域人类大脑活动的基础神经模型的自我监督预训练的重要性，同时提高了下游解码性能。
URL
https://arxiv.org/abs/2512.12135
