From the MS Center Amsterdam (T.A.A.B., M.v.D., V.R., L.D., Y.D.v.d.W., C.H.V., M.M.S.), Anatomy & Neurosciences, and MS Center Amsterdam (G.P., F.B.), Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, the Netherlands; Queen Square Institute of Neurology and Centre for Medical Image Computing (G.P., F.B.), University College London, United Kingdom; Departments of Advanced Biomedical Sciences and Electrical Engineering and Information Technology (G.P.), University of Naples "Federico II," Italy; MS Center Amsterdam (J.K.), Neurology, and MS Center Amsterdam (C.H.V.), Psychiatry, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc; Amsterdam Public Health (C.H.V.), Mental Health Program; and GGZ inGeest Mental Health Care (C.H.V.), Amsterdam, the Netherlands.
Neurology. 2024 Nov 12;103(9):e209952. doi: 10.1212/WNL.0000000000209952. Epub 2024 Oct 11.
Patients with multiple sclerosis (MS) often experience cognitive impairment, and this is related to structural disconnection and subsequent functional reorganization. It is unclear how specific patterns of functional reorganization might make it harder for cognitively impaired (CI) patients with MS to dynamically adapt how brain regions communicate, which is crucial for normal cognition. We aimed to identify dynamic functional network patterns that are relevant to cognitive impairment in MS and investigate whether these patterns can be explained by altered energy costs.
Resting-state functional and diffusion MRI was acquired in a cross-sectional design, as part of the Amsterdam MS cohort. Patients with clinically definitive MS (relapse-free) were classified as CI (≥2/7 domains < -2), mildly CI (MCI) (≥2/7 domains < -1.5), or cognitively preserved (CP) based on an expanded Brief Repeatable Battery of Neuropsychological Tests. Functional connectivity states were determined using -means clustering of moment-to-moment cofluctuations (i.e., edge time series), and the resulting state sequence was used to characterize the frequency of transitions. Control energy of the state transitions was calculated using the structural network with network control theory.
Imaging and cognitive data were available for 95 controls and 330 patients (disease duration: 15 years; 179 CP, 65 MCI, and 86 CI). We identified a "visual network state," "sensorimotor network state," "ventral attention network state," and "default mode network state." CI patients transitioned less frequently between connectivity states compared with CP (β = -5.78; = 0.038). Relative to the time spent in a state, CI patients transitioned less from a "default mode network state" to a "visual network state" (β = -0.02; = 0.004). The CI patients required more control energy to transition between states (β = 0.32; = 0.007), particularly for the same transition (β = 0.34; = 0.049).
This study showed that it costs more energy for MS patients with cognitive impairment to dynamically change the functional network, possibly explaining why these transitions occur less frequently. In particular, transitions from a default mode network state to a visual network state were relevant for cognition in these patients. To further study the order of events leading to these network disturbances, future work should include longitudinal data across different disease stages.
多发性硬化症（MS）患者常伴有认知障碍，这与结构连接中断及随后的功能重组有关。目前尚不清楚特定的功能重组模式如何使认知障碍（CI）的 MS 患者更难动态地适应大脑区域之间的通讯方式，而这对于正常认知至关重要。我们旨在确定与 MS 认知障碍相关的动态功能网络模式，并探讨这些模式是否可以用能量成本的改变来解释。
该研究采用横断面设计，在阿姆斯特丹 MS 队列中采集静息态功能磁共振成像和弥散张量成像数据。根据扩展的简明重复神经心理测试评估结果，将临床确诊的缓解期 MS 患者（复发缓解期）分为认知障碍（≥2/7 个域＜-2）、轻度认知障碍（MCI）（≥2/7 个域＜-1.5）或认知正常（CP）。使用边缘时间序列的均值聚类确定功能连接状态，并用状态序列来描述状态转移的频率。使用网络控制理论的结构网络来计算状态转移的控制能量。
共有 95 名对照和 330 名患者（病程：15 年；179 名 CP、65 名 MCI 和 86 名 CI）完成了影像学和认知评估。我们确定了“视觉网络状态”、“感觉运动网络状态”、“腹侧注意网络状态”和“默认模式网络状态”。与 CP 相比，CI 患者的连接状态之间的转换频率更低（β=-5.78；=0.038）。与状态持续时间相比，CI 患者从“默认模式网络状态”向“视觉网络状态”的转换频率更低（β=-0.02；=0.004）。CI 患者在状态之间转换时需要更多的控制能量（β=0.32；=0.007），特别是对于相同的转换（β=0.34；=0.049）。
本研究表明，认知障碍的 MS 患者动态改变功能网络需要更多的能量，这可能解释了为什么这些转换发生的频率较低。特别是，从默认模式网络状态到视觉网络状态的转换与这些患者的认知有关。为了进一步研究导致这些网络紊乱的事件顺序，未来的研究应包括不同疾病阶段的纵向数据。
