Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden.
Department of Clinical Neuroscience, Karolinska Institutet, 17177 Stockholm, Sweden.
Neuroimage. 2021 May 1;231:117844. doi: 10.1016/j.neuroimage.2021.117844. Epub 2021 Feb 10.
Recent advances in non-linear computational and dynamical modelling have opened up the possibility to parametrize dynamic neural mechanisms that drive complex behavior. Importantly, building models of neuronal processes is of key importance to fully understand disorders of the brain as it may provide a quantitative platform that is capable of binding multiple neurophysiological processes to phenotype profiles. In this study, we apply a newly developed adaptive frequency-based model of whole-brain oscillations to resting-state fMRI data acquired from healthy controls and a cohort of attention deficit hyperactivity disorder (ADHD) subjects. As expected, we found that healthy control subjects differed from ADHD in terms of attractor dynamics. However, we also found a marked dichotomy in neural dynamics within the ADHD cohort. Next, we classified the ADHD group according to the level of distance of each individual's empirical network from the two model-based simulated networks. Critically, the model was mirrored in the empirical behavior data with the two ADHD subgroups displaying distinct behavioral phenotypes related to emotional instability (i.e., depression and hypomanic personality traits). Finally, we investigated the applicability and feasibility of our whole-brain model in a therapeutic setting by conducting in silico excitatory stimulations to parsimoniously mimic clinical neuro-stimulation paradigms in ADHD. We tested the effect of stimulating any individual brain region on the key network measures derived from the simulated brain network and its contribution in rectifying the brain dynamics to that of the healthy brain, separately for each ADHD subgroup. This showed that this was indeed possible for both subgroups. However, the current effect sizes were small suggesting that the stimulation protocol needs to be tailored at the individual level. These findings demonstrate the potential of this new modelling framework to unveil hidden neurophysiological profiles and establish tailored clinical interventions.
最近在非线性计算和动力建模方面的进展为参数化驱动复杂行为的动态神经机制提供了可能性。重要的是，构建神经元过程的模型对于充分理解大脑障碍至关重要，因为它可能提供一个能够将多个神经生理过程与表型谱联系起来的定量平台。在这项研究中，我们将一种新开发的基于自适应频率的全脑振荡模型应用于从健康对照组和注意力缺陷多动障碍（ADHD）患者队列中获得的静息状态 fMRI 数据。正如预期的那样，我们发现健康对照组在吸引子动力学方面与 ADHD 患者不同。然而，我们还发现 ADHD 患者队列中的神经动力学存在明显的二分法。接下来，我们根据每个个体的经验网络与两个基于模型的模拟网络之间的距离水平对 ADHD 组进行分类。关键的是，该模型与经验行为数据相吻合，ADHD 的两个亚组表现出与情绪不稳定相关的不同行为表型（即抑郁和轻躁狂人格特征）。最后，我们通过进行仿真兴奋性刺激来模拟 ADHD 中的临床神经刺激范式，在治疗环境中研究了我们的全脑模型的适用性和可行性。我们测试了刺激单个脑区对从模拟脑网络及其对健康脑网络动力学的校正中得出的关键网络测量值的影响，分别针对每个 ADHD 亚组。这表明这对于两个亚组都是可行的。然而，目前的效果量较小，表明需要针对个体进行刺激方案的调整。这些发现表明，这种新的建模框架具有揭示隐藏的神经生理特征和建立定制化临床干预的潜力。
