Sadeghi Fatemeh, Agua Banyeres Elvira Del, Pizzuti Alessandra, Okar Abdullah, Grimm Kai, Gerloff Christian, Kringelbach Morten L, Goebel Rainer, Zittel Simone, Deco Gustavo
Department of Neurology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany.
Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, 08005 Barcelona, Spain.
Neuroimage Clin. 2025 Jun 24;47:103834. doi: 10.1016/j.nicl.2025.103834.
Parkinson's disease (PD) is a system-level disorder that implicates brain network dynamics across multiple scales. Detecting the 'arrow of time', or temporal reversibility of the brain's information processing flow enables quantification of equilibrium in the brain and inferences on the hierarchical organization. Therefore we aimed to explore disturbances in resting-state equilibrium levels as well as changes in the hierarchical organization due to PD.
Structural and functional MRI of 29 PD patients and 19 healthy controls were acquired and analyzed. Empirical non-reversibility was computed as the distance between time-shifted forward- and artificially-reversed time series. Levels of equilibrium were subsequently assessed globally and within two cortico-subcortical motor networks implicated in PD. Moreover, whole-brain generative computational models consisting of 1051 Hopf oscillators were constructed to evaluate effective connectivities and alterations of the functional hierarchical organization.
We found that PD is characterized by disrupted equilibrium regimes, marked by distinct effective connectivity patterns, particularly within the motor networks. Additionally, we observed a flatter hierarchical organization in PD, with the cerebellum and thalamus exerting increased influence.
The arrow of time methodology effectively identifies distinct and informative characteristics of PD. Our analyses suggest that PD shifts the brain towards less efficient, non-equilibrium dynamics that impair intrinsic flexibility and disrupt motor coordination. Thus, these findings not only provide insight into widespread system alterations in PD that could serve as potential biomarkers, but also lay the groundwork for next-generation stimulation techniques aimed at restoring balance in the Parkinsonian brain.
帕金森病(PD)是一种涉及多个尺度脑网络动态的系统层面疾病。检测大脑信息处理流的“时间箭头”或时间可逆性,能够量化大脑中的平衡状态并推断其层次组织。因此,我们旨在探讨帕金森病导致的静息态平衡水平紊乱以及层次组织的变化。
对29例帕金森病患者和19名健康对照者进行了结构和功能磁共振成像检查并分析。经验性不可逆性通过时移正向和人工反向时间序列之间的距离来计算。随后在全局范围内以及与帕金森病相关的两个皮质 - 皮质下运动网络内评估平衡水平。此外,构建了由1051个霍普夫振荡器组成的全脑生成计算模型,以评估有效连接性和功能层次组织的改变。
我们发现帕金森病的特征是平衡状态被破坏,表现为独特的有效连接模式,特别是在运动网络内。此外,我们观察到帕金森病患者的层次组织较为扁平,小脑和丘脑的影响增加。
时间箭头方法有效地识别了帕金森病的独特且有信息量的特征。我们的分析表明,帕金森病使大脑向效率较低的非平衡动态转变,损害了内在灵活性并破坏了运动协调。因此,这些发现不仅为帕金森病中广泛的系统改变提供了见解,这些改变可能作为潜在的生物标志物,而且为旨在恢复帕金森病大脑平衡的下一代刺激技术奠定了基础。