Chen Haifeng, Hu Zheqi, Ke Zhihong, Xu Yun, Bai Feng, Liu Zhuo
Department of Neurology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China.
Nanjing Drum Tower Hospital Clinical College of Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing 210008, China.
Brain Sci. 2023 May 15;13(5):803. doi: 10.3390/brainsci13050803.
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder that progressively affects bulbar and limb function. Despite increasing recognition of the disease as a multinetwork disorder characterized by aberrant structural and functional connectivity, its integrity agreement and its predictive value for disease diagnosis remain to be fully elucidated. In this study, we recruited 37 ALS patients and 25 healthy controls (HCs). High-resolution 3D T1-weighted imaging and resting-state functional magnetic resonance imaging were, respectively, applied to construct multimodal connectomes. Following strict neuroimaging selection criteria, 18 ALS and 25 HC patients were included. Network-based statistic (NBS) and the coupling of grey matter structural-functional connectivity (SC-FC coupling) were performed. Finally, the support vector machine (SVM) method was used to distinguish the ALS patients from HCs. Results showed that, compared with HCs, ALS individuals exhibited a significantly increased functional network, predominantly encompassing the connections between the default mode network (DMN) and the frontoparietal network (FPN). The increased structural connections predominantly involved the inter-regional connections between the limbic network (LN) and the DMN, the salience/ventral attention network (SVAN) and FPN, while the decreased structural connections mainly involved connections between the LN and the subcortical network (SN). We also found increased SC-FC coupling in DMN-related brain regions and decoupling in LN-related brain regions in ALS, which could differentiate ALS from HCs with promising capacity based on SVM. Our findings highlight that DMN and LN may play a vital role in the pathophysiological mechanism of ALS. Additionally, SC-FC coupling could be regarded as a promising neuroimaging biomarker for ALS and shows important clinical potential for early recognition of ALS individuals.
肌萎缩侧索硬化症(ALS)是一种神经退行性疾病,会逐渐影响延髓和肢体功能。尽管越来越多的人认识到该疾病是一种以结构和功能连接异常为特征的多网络疾病,但其完整性一致性及其对疾病诊断的预测价值仍有待充分阐明。在本研究中,我们招募了37名ALS患者和25名健康对照者(HCs)。分别应用高分辨率3D T1加权成像和静息态功能磁共振成像来构建多模态连接组。遵循严格的神经影像学选择标准,纳入了18名ALS患者和25名HC患者。进行了基于网络的统计分析(NBS)以及灰质结构-功能连接耦合分析(SC-FC耦合)。最后,使用支持向量机(SVM)方法区分ALS患者和HCs。结果显示,与HCs相比,ALS患者的功能网络显著增加,主要包括默认模式网络(DMN)和额顶叶网络(FPN)之间的连接。结构连接增加主要涉及边缘网络(LN)与DMN、突显/腹侧注意网络(SVAN)与FPN之间的区域间连接,而结构连接减少主要涉及LN与皮质下网络(SN)之间的连接。我们还发现,在ALS患者中,DMN相关脑区的SC-FC耦合增加,而LN相关脑区的耦合解耦,基于SVM,这有望将ALS与HCs区分开来。我们的研究结果表明,DMN和LN可能在ALS的病理生理机制中起重要作用。此外,SC-FC耦合可被视为一种有前景的ALS神经影像学生物标志物,在早期识别ALS患者方面具有重要的临床潜力。