Dukic Stefan, van Veenhuijzen Kevin, Westeneng Henk-Jan, McMackin Roisin, van Eijk Ruben P A, Sleutjes Boudewijn T H M, Nasseroleslami Bahman, Hardiman Orla, van den Berg Leonard H
Department of Neurology, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands.
Academic Unit of Neurology, School of Medicine, Trinity College Dublin, University of Dublin, Dublin, Ireland.
Hum Brain Mapp. 2025 Aug 1;46(11):e70275. doi: 10.1002/hbm.70275.
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by motor neuron degeneration. Around 10% of cases have a genetic basis, with the C9orf72 hexanucleotide repeat expansion being the most common cause in individuals of European ancestry. Detecting early alterations in at-risk individuals could aid in identifying biomarkers for timely diagnosis and intervention. In this study, we investigated electrophysiological changes in asymptomatic C9orf72 mutation carriers using EEG, focusing on cognitive and motor networks, as these individuals are at risk of developing impairments in both domains. This study included 87 asymptomatic family members (AFM) of patients with familial C9orf72 ALS, comprising 37 individuals carrying the pathological repeat expansion (C9+) and 50 without it (C9-). High-density EEG was recorded during the sustained attention to response task (SART), which is a Go/NoGo paradigm that engages the frontoparietal and motor networks. Task performance was recorded and six behavioral measures were extracted: NoGo accuracy, Go accuracy, total accuracy, anticipation error, average response time, and response time variability. Analyses were conducted on EEG data in both sensor- and source-space, using stimulus- and response-locked data. The stimulus-locked Go and NoGo data were analysed within two time windows: 180-350 ms (N2) and 300-600 ms (P3), while response-locked Go data were analysed within a -100 to 100 ms time window. Linear mixed models were used to quantify differences between groups, incorporating familial pedigree to control for between-subject dependencies. While the two groups did not significantly differ in any SART performance measures, EEG analyses revealed differences. During the stimulus-locked N2, significant differences were observed in sensor-space, primarily in central electrodes during both NoGo and Go conditions, with C9+ AFM exhibiting an increased negative potential. Source analysis confirmed these findings and localized the increased activity in the bilateral precuneus and superior parietal regions. Further analysis of the response-locked data supported the involvement of the same posterior regions. No significant relationships were found between these EEG observations and SART performance. These findings provide the first evidence of EEG changes in AFM carrying the C9orf72 repeat expansion. The observed functional changes in the parietal regions may reflect genotype-related effects on the motor control network, potentially contributing to early pathophysiology. In contrast, clinical assessments and task performance did not differ between groups, suggesting that our EEG findings may hold promise as biomarkers for monitoring the risk of conversion to symptomatic disease and warrant further exploration to assess their predictive value for future symptom onset.
肌萎缩侧索硬化症(ALS)是一种以运动神经元变性为特征的神经退行性疾病。约10%的病例有遗传基础,C9orf72六核苷酸重复序列扩增是欧洲血统个体中最常见的病因。检测高危个体的早期改变有助于识别生物标志物,以便及时诊断和干预。在本研究中,我们使用脑电图(EEG)研究了无症状C9orf72突变携带者的电生理变化,重点关注认知和运动网络,因为这些个体在这两个领域都有出现功能障碍的风险。本研究纳入了87名家族性C9orf72 ALS患者的无症状家庭成员(AFM),其中37人携带病理性重复扩增(C9+),50人未携带(C9-)。在持续注意力反应任务(SART)期间记录高密度脑电图,这是一种涉及额顶叶和运动网络的Go/NoGo范式。记录任务表现并提取六项行为指标:NoGo准确率、Go准确率、总准确率、预期误差、平均反应时间和反应时间变异性。使用刺激锁定和反应锁定数据对传感器空间和源空间中的EEG数据进行分析。刺激锁定的Go和NoGo数据在两个时间窗口内进行分析:180-350毫秒(N2)和300-600毫秒(P3),而反应锁定的Go数据在-100至100毫秒的时间窗口内进行分析。使用线性混合模型量化组间差异,并纳入家族谱系以控制个体间的依赖性。虽然两组在任何SART表现指标上均无显著差异,但EEG分析显示存在差异。在刺激锁定的N2期间,在传感器空间中观察到显著差异,主要在NoGo和Go条件下的中央电极,C9+ AFM表现出负电位增加。源分析证实了这些发现,并将双侧楔前叶和顶上叶区域的活动增加定位。对反应锁定数据的进一步分析支持了相同后叶区域的参与。这些EEG观察结果与SART表现之间未发现显著关系。这些发现首次证明了携带C9orf72重复扩增的AFM中EEG变化。顶叶区域观察到的功能变化可能反映了基因型对运动控制网络的影响,可能有助于早期病理生理学。相比之下,两组之间的临床评估和任务表现没有差异,这表明我们的EEG发现有望作为监测转化为症状性疾病风险的生物标志物,值得进一步探索以评估其对未来症状发作的预测价值。