Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94304, USA.
Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.
Neuroimage. 2023 Oct 1;279:120302. doi: 10.1016/j.neuroimage.2023.120302. Epub 2023 Aug 12.
Resting-state functional connectivity (RSFC) is altered across various psychiatric disorders. Brain network modeling (BNM) has the potential to reveal the neurobiological underpinnings of such abnormalities by dynamically modeling the structure-function relationship and examining biologically relevant parameters after fitting the models with real data. Although innovative BNM approaches have been developed, two main issues need to be further addressed. First, previous BNM approaches are primarily limited to simulating noise-driven dynamics near a chosen attractor (or a stable brain state). An alternative approach is to examine multi(or cross)-attractor dynamics, which can be used to better capture non-stationarity and switching between states in the resting brain. Second, previous BNM work is limited to characterizing one disorder at a time. Given the large degree of co-morbidity across psychiatric disorders, comparing BNMs across disorders might provide a novel avenue to generate insights regarding the dynamical features that are common across (vs. specific to) disorders. Here, we address these issues by (1) examining the layout of the attractor repertoire over the entire multi-attractor landscape using a recently developed cross-attractor BNM approach; and (2) characterizing and comparing multiple disorders (schizophrenia, bipolar, and ADHD) with healthy controls using an openly available and moderately large multimodal dataset from the UCLA Consortium for Neuropsychiatric Phenomics. Both global and local differences were observed across disorders. Specifically, the global coupling between regions was significantly decreased in schizophrenia patients relative to healthy controls. At the same time, the ratio between local excitation and inhibition was significantly higher in the schizophrenia group than the ADHD group. In line with these results, the schizophrenia group had the lowest switching costs (energy gaps) across groups for several networks including the default mode network. Paired comparison also showed that schizophrenia patients had significantly lower energy gaps than healthy controls for the somatomotor and visual networks. Overall, this study provides preliminary evidence supporting transdiagnostic multi-attractor BNM approaches to better understand psychiatric disorders' pathophysiology.
静息态功能连接（RSFC）在各种精神障碍中发生改变。脑网络建模（BNM）通过动态建模结构-功能关系并在使用真实数据拟合模型后检查生物相关参数，具有揭示此类异常的神经生物学基础的潜力。尽管已经开发出创新的 BNM 方法，但仍需要进一步解决两个主要问题。首先，以前的 BNM 方法主要局限于模拟在选定吸引子（或稳定的大脑状态）附近的噪声驱动动力学。另一种方法是检查多（或交叉）吸引子动力学，这可以用于更好地捕获静息状态下大脑的非平稳性和状态之间的切换。其次，以前的 BNM 工作仅限于一次描述一种疾病。鉴于精神障碍之间存在很大程度的共病，比较跨障碍的 BNM 可能为生成有关跨（与特定于）障碍的常见动力学特征的见解提供新途径。在这里，我们通过（1）使用最近开发的跨吸引子 BNM 方法检查整个多吸引子景观上吸引子库的布局；（2）使用来自 UCLA 神经精神病学表型联盟的公开可用且中等规模的多模态数据集，对多种疾病（精神分裂症、双相情感障碍和 ADHD）与健康对照组进行特征描述和比较。在跨障碍的情况下观察到全局和局部差异。具体而言，与健康对照组相比，精神分裂症患者的区域间全局耦合显著降低。同时，与 ADHD 组相比，精神分裂症组的局部兴奋与抑制的比值显著更高。与这些结果一致，对于包括默认模式网络在内的几个网络，精神分裂症组的切换成本（能量间隙）明显低于其他组。配对比较还表明，与健康对照组相比，精神分裂症患者的躯体运动和视觉网络的能量间隙明显更低。总的来说，这项研究提供了初步证据，支持使用跨诊断多吸引子 BNM 方法来更好地理解精神障碍的病理生理学。
