第一次脱髓鞘发作后灰质网络的双相行为。

IF 4.5 Q1 CLINICAL NEUROLOGY
Brain communications Pub Date : 2025-09-23 eCollection Date: 2025-01-01 DOI:10.1093/braincomms/fcaf367
Sara Collorone, Giuseppe Pontillo, Michael A Foster, Ferran Prados, Baris Kanber, Marios C Yiannakas, Ailbhe Burke, Lola Ogunbowale, Indran Davagnanam, Claudia A M Gandini Wheeler-Kingshott, Frederik Barkhof, Olga Ciccarelli, Ahmed T Toosy
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引用次数: 0

摘要

多发性硬化症可以被认为是一种网络疾病。越来越多的证据表明,灰质网络的重要性如下:提取灰质网络只需要高分辨率的解剖扫描,它们捕捉到可检测到的萎缩之外的变化,它们的改变与残疾进展和认知障碍有关。因此,了解他们在患病最初几年的行为是至关重要的。这项观察性纵向研究旨在调查第一次脱髓鞘发作后灰质网络的变化,以及它们与3-5年内脑损伤、残疾和向多发性硬化症转化的关系。到目前为止,在多发性硬化症中,网络构建仅基于皮质灰质,而忽略了深部灰质的可能作用。我们应用了一种基于放射组学的网络方法,包括深部和皮质灰质。在发病3个月内招募的患者和健康对照者分别在6个月、1年、3年和5年参加研究访问。研究访问包括身体和认知量表以及脑部核磁共振扫描。通过计算从脑网络图谱的任意对区域提取的基于t1w的放射学特征之间的相关性,构建个体灰质网络,并通过网络整合(全局效率和特征路径长度)、隔离(聚类系数和模块化)、弹性(分类性)和小世界性等措施进行表征。此外,计算了所有大脑区域的特征向量中心性,作为节点影响的度量。我们招募了89名患者(中位随访7个月,范围0-75)和31名健康对照。与对照组相比,患者表现出更高的全局效率,更短的特征路径长度和更高的小世界性,这表明重组优先考虑更有效的全局通信而不是局部处理。随着时间的推移,患者的网络通过增加最短特征路径长度和减少小世界性向健康对照组的值趋同。与对照组相比,右侧腹内侧壳核的协调性和特征向量中心性下降。所有观察到的变化都是由未转化为多发性硬化症的人引起的。这项研究表明,灰质网络采用双相行为。他们对脱髓鞘事件的反应是增加节点整合,然后收敛到健康控制值。然而,在这个过程中,他们的网络弹性受到了损害。这表明,单次脱髓鞘事件对灰质网络具有更持久的影响,即使在非转换者中也是如此,研究这些网络可能会揭示在疾病早期常规MRI无法捕捉到的相关变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The bi-phasic behaviour of grey matter networks after the first demyelinating attack.

Multiple sclerosis can be considered a network disease. Accumulating evidence recognizes the following importance of grey matter networks: they only require high-resolution anatomical scans for their extraction, they capture changes beyond detectable atrophy and their alteration is associated with disability progression and cognitive impairment. Therefore, it is crucial to understand their behaviours over the initial years of the disease. This observational longitudinal study aimed to investigate changes in grey matter networks after the first demyelinating attack, and how they correlate with brain damage, disability, and conversion to multiple sclerosis over 3-5 years. So far, in multiple sclerosis, network construction has only been based on cortical grey matter, neglecting a possible role for deep grey matter. We applied a radiomics-based network methodology incorporating both deep and cortical grey matter. Patients recruited within 3 months of disease onset and healthy controls attended study visits at 6 months, 1 year, 3 years and 5 years. Study visits included physical and cognitive scales and brain MRI scans. Individual grey matter networks were constructed by computing the correlations between T1w-based radiomic features extracted from any pair of regions of the Brainnetome atlas and characterized with measures of network integration (global efficiency and characteristic path length), segregation (clustering coefficient and modularity), resilience (assortativity) and smallworldness. Additionally, eigenvector centrality was computed for all brain regions as a measure of nodal influence. We enrolled 89 patients (median follow-up 7 months, range 0-75) and 31 healthy controls. Patients showed higher global efficiency, lower shortest characteristic path length and higher smallworldness than controls suggesting a reorganization that prioritize more efficient global communication over local processing. Over time, patients' networks converged towards healthy controls' values by increasing the shortest characteristic path length and decreasing the smallworldness. Assortativity, and the eigenvector centrality in the right ventromedial putamen decreased compared with controls. All the observed changes were driven by non-converters to multiple sclerosis. This study shows that grey matter networks adopt a biphasic behaviour. They respond to the demyelinating event with an increase in nodal integration and then converge to healthy control values. In the process, however, their network resilience is compromised. This suggests that a single demyelinating event has longer-lasting effects on grey matter networks, even in non-converters, and that studying these networks may reveal relevant changes that are not captured by conventional MRI in the early years of the disease.

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