Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Dublin, Ireland.
Department of Neurology, University Medical Centre Utrecht Brain Centre, Utrecht University, Utrecht, The Netherlands.
Brain. 2022 Apr 18;145(2):621-631. doi: 10.1093/brain/awab322.
Amyotrophic lateral sclerosis is a devastating disease characterized primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. Amyotrophic lateral sclerosis is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis. Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in amyotrophic lateral sclerosis. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (comodulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localized brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome. We show that amyotrophic lateral sclerosis patients (n = 95) can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterized by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (β-band neural activity and γl-band synchrony) and frontoparietal (γl-band comodulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after reassessment using a follow-up EEG session, and continue to predict the clinical trajectory and disease outcome. Our data demonstrate that novel phenotyping using neuroelectric signal analysis can distinguish disease subtypes based exclusively on different patterns of network disturbances. These patterns may reflect underlying disease neurobiology. The identification of amyotrophic lateral sclerosis subtypes based on profiles of differential impairment in neuronal networks has clear potential in future stratification for clinical trials. Advanced network profiling in amyotrophic lateral sclerosis can also underpin new therapeutic strategies that are based on principles of neurobiology and designed to modulate network disruption.
肌萎缩侧索硬化症是一种毁灭性疾病，主要表现为运动系统退化，多达 50%的病例有认知和行为改变的临床证据。肌萎缩侧索硬化症在临床上和生物学上都是异质的。目前，通过临床参数对其进行分组，例如症状起始部位（球部或脊髓）、疾病负担（基于改良埃尔塞罗尔研究标准）以及家族性疾病中的基因组学。然而，除了基因组学之外，这些亚类并没有考虑到潜在的疾病病理生理学，并且不能完全预测疾病过程或预后。最近，我们已经证明，静息状态脑电图可以可靠地定量捕获肌萎缩侧索硬化症中运动和认知网络中断的异常模式。这些网络中断已经在多个频带中被识别出来，并且使用源定位脑电图的脑振荡的神经活动（频谱功率）和连接性（通过幅度包络相关和同步性的实部相干性来调制活动）的测量。使用数据驱动方法（相似性网络融合和谱聚类），我们现在已经进行了聚类分析，以确定疾病亚型，并确定不同的破坏模式是否可以预测疾病结果。我们发现，肌萎缩侧索硬化症患者（n=95）可以分为四个具有不同神经生理学特征的表型。这些聚类的特点是躯体运动（α波段同步性）、额颞（β波段神经活动和γl 波段同步性）和额顶（γl 波段调制）网络的破坏程度不同，这些网络与不同的临床特征和不同的疾病轨迹可靠相关。通过深入的稳定性分析，我们发现这些聚类在统计学上是可重复和稳健的，在使用后续脑电图会议重新评估后仍然稳定，并且继续预测临床轨迹和疾病结果。我们的数据表明，使用神经电信号分析进行的新型表型分析可以仅根据网络干扰的不同模式来区分疾病亚型。这些模式可能反映了潜在的疾病神经生物学。基于神经元网络不同损伤模式的肌萎缩侧索硬化症亚型的识别在未来的临床试验分层中具有明显的潜力。肌萎缩侧索硬化症的高级网络分析也可以为基于神经生物学原理并旨在调节网络干扰的新治疗策略提供支持。
