Singh Matthew F, Braver Todd S, Cole Michael W, Ching ShiNung
Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, 63130, MO, USA.
Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, 07102, NJ, USA.
bioRxiv. 2023 Dec 2:2023.11.14.567088. doi: 10.1101/2023.11.14.567088.
Task-free brain activity affords unique insight into the functional structure of brain network dynamics and is a strong marker of individual differences. In this work, we present an algorithmic optimization framework that makes it possible to directly invert and parameterize brain-wide dynamical-systems models involving hundreds of interacting brain areas, from single-subject time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions. We extensively validate the models' performance in forecasting future brain activity and predicting individual variability in key M/EEG markers. Lastly, we demonstrate the power of our technique in resolving individual differences in the generation of alpha and beta-frequency oscillations. We characterize subjects based upon model attractor topology and a dynamical-systems mechanism by which these topologies generate individual variation in the expression of alpha vs. beta rhythms. We trace these phenomena back to global variation in excitation-inhibition balance, highlighting the explanatory power of our framework in generating mechanistic insights.
无任务脑活动为洞察脑网络动力学的功能结构提供了独特视角，并且是个体差异的有力标志。在这项工作中，我们提出了一种算法优化框架，该框架能够从单受试者时间序列记录中直接对涉及数百个相互作用脑区的全脑动力学系统模型进行反演和参数化。这项技术为探究个体脑动力学背后的机制（“精确脑模型”）并做出定量预测提供了一个强大的神经计算工具。我们广泛验证了模型在预测未来脑活动以及预测关键脑磁图/脑电图标记物个体变异性方面的性能。最后，我们展示了我们的技术在解析α和β频率振荡产生过程中的个体差异方面的能力。我们基于模型吸引子拓扑结构以及这些拓扑结构在α与β节律表达中产生个体差异的动力学系统机制对受试者进行特征描述。我们将这些现象追溯到兴奋 - 抑制平衡的全局变化，突出了我们的框架在产生机制性见解方面的解释力。
