Herzog Rubén, Mediano Pedro A M, Rosas Fernando E, Luppi Andrea I, Sanz-Perl Yonatan, Tagliazucchi Enzo, Kringelbach Morten L, Cofré Rodrigo, Deco Gustavo
Sorbonne Universite, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, Paris, France.
Department of Computing, Imperial College London, London, UK.
Netw Neurosci. 2024 Dec 10;8(4):1590-1612. doi: 10.1162/netn_a_00410. eCollection 2024.
Different whole-brain computational models have been recently developed to investigate hypotheses related to brain mechanisms. Among these, the Dynamic Mean Field (DMF) model is particularly attractive, combining a biophysically realistic model that is scaled up via a mean-field approach and multimodal imaging data. However, an important barrier to the widespread usage of the DMF model is that current implementations are computationally expensive, supporting only simulations on brain parcellations that consider less than 100 brain regions. Here, we introduce an efficient and accessible implementation of the DMF model: the FastDMF. By leveraging analytical and numerical advances-including a novel estimation of the feedback inhibition control parameter and a Bayesian optimization algorithm-the FastDMF circumvents various computational bottlenecks of previous implementations, improving interpretability, performance, and memory use. Furthermore, these advances allow the FastDMF to increase the number of simulated regions by one order of magnitude, as confirmed by the good fit to fMRI data parcellated at 90 and 1,000 regions. These advances open the way to the widespread use of biophysically grounded whole-brain models for investigating the interplay between anatomy, function, and brain dynamics and to identify mechanistic explanations of recent results obtained from fine-grained neuroimaging recordings.
最近已经开发出不同的全脑计算模型来研究与脑机制相关的假设。其中，动态平均场（DMF）模型特别具有吸引力，它结合了一个通过平均场方法进行扩展的生物物理现实模型和多模态成像数据。然而，DMF模型广泛应用的一个重要障碍是当前的实现方式计算成本高昂，仅支持对少于100个脑区的脑图谱进行模拟。在此，我们介绍一种高效且易于使用的DMF模型实现：FastDMF。通过利用分析和数值方面的进展——包括对反馈抑制控制参数的新颖估计和贝叶斯优化算法——FastDMF规避了先前实现方式的各种计算瓶颈，提高了可解释性、性能和内存使用效率。此外，这些进展使FastDMF能够将模拟区域的数量增加一个数量级，对90个和1000个区域进行脑功能磁共振成像（fMRI）数据分割后的良好拟合证实了这一点。这些进展为广泛使用基于生物物理的全脑模型来研究解剖结构、功能和脑动力学之间的相互作用以及确定从细粒度神经成像记录中获得的近期结果的机制性解释开辟了道路。
