Michael Trubshaw, Chetan Gohil, Evan Edmond, Malcolm Proudfoot, Katie Yoganathan, Joanne Wuu, Alicia Northall, Oliver Kohl, Charlotte J Stagg, Anna C Nobre, Kevin Talbot, Alexander G Thompson, Michael Benatar, Mark Woolrich, Martin R Turner
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Individuals with symptomatic ALS (symALS, n = 61), asymptomatic C9orf72 carriers (aC9, n = 16), or pathological SOD1 carriers (aSOD, n = 12), and healthy controls (n = 84) underwent resting-state MEG recordings. Extracted metrics included regional oscillatory power, connectivity, and spectral shape. 'DyNeMo' was trained to identify six functional dynamic brain networks. Metrics were compared between groups. A classifier was trained to distinguish asymptomatic gene carriers from controls. Compared to controls, beta frequency power was decreased in both symALS and aC9 groups. The aC9 group showed a marked slowing of frontal oscillatory activity, while the aSOD group showed a marked acceleration. Dynamic network coactivation was dramatically disrupted in aC9, more than in both symALS and aSOD. The classifier accurately distinguished genetically at-risk groups from controls (receiver-operator-characteristic area-under-curve 0.89). The cerebral network dynamics of aC9 are markedly different from both aSOD and symALS, supporting the concept of profoundly different upstream pathways in SOD1 ALS, sparing wider cortical pathology when compared to C9orf72 ALS. aC9 changes may reflect chronic adaptive changes relating to neurodevelopmental factors or underpin aspects of system vulnerability that define penetrance variability. 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引用次数: 0
摘要
了解肌萎缩性侧索硬化症(ALS)高危人群的症状前生物学对预防治疗干预的发展至关重要。大约10%的ALS患者与SOD1的C9orf72扩增或致病变异有关。脑磁图(MEG)与机器学习算法相结合,可以对这些高危人群的大脑网络动态进行建模,以开发致病生物标志物。对有症状的ALS (symALS, n = 61)、无症状的C9orf72携带者(aC9, n = 16)、病理性SOD1携带者(aSOD, n = 12)和健康对照(n = 84)进行静息状态MEG记录。提取的指标包括区域振荡功率、连通性和频谱形状。“DyNeMo”经过训练,可以识别六个功能动态的大脑网络。比较各组之间的指标。训练分类器以区分无症状基因携带者和对照组。与对照组相比,symALS组和aC9组的β频率功率均降低。aC9组脑额叶振荡活动明显减缓,aSOD组脑额叶振荡活动明显加速。动态网络共激活在aC9中被显著破坏,比在symALS和aSOD中更严重。分类器准确地将遗传风险组与对照组区分开来(接受者-操作者-特征曲线下面积0.89)。aC9的大脑网络动力学与aSOD和symALS明显不同,支持SOD1 ALS的上游通路的概念,与C9orf72 ALS相比,保留了更广泛的皮质病理。aC9的变化可能反映了与神经发育因素相关的慢性适应性变化或定义外显率变异性的系统脆弱性的基础方面。MEG指标可能为高危人群的预防治疗效果和表型转化提供重要的生物标志物。
Divergent Brain Network Activity in Asymptomatic C9orf72 and SOD1 Variant Carriers Compared With Established Amyotrophic Lateral Sclerosis.
Understanding the presymptomatic biology in those at high risk of developing amyotrophic lateral sclerosis (ALS) is essential for the development of preventative therapeutic interventions. Approximately 10% of ALS is associated with a C9orf72 expansion or pathogenic variants in SOD1. Magnetoencephalography (MEG), combined with machine learning algorithms, can model brain network dynamics in such at-risk populations to develop pathogenic biomarkers. Individuals with symptomatic ALS (symALS, n = 61), asymptomatic C9orf72 carriers (aC9, n = 16), or pathological SOD1 carriers (aSOD, n = 12), and healthy controls (n = 84) underwent resting-state MEG recordings. Extracted metrics included regional oscillatory power, connectivity, and spectral shape. 'DyNeMo' was trained to identify six functional dynamic brain networks. Metrics were compared between groups. A classifier was trained to distinguish asymptomatic gene carriers from controls. Compared to controls, beta frequency power was decreased in both symALS and aC9 groups. The aC9 group showed a marked slowing of frontal oscillatory activity, while the aSOD group showed a marked acceleration. Dynamic network coactivation was dramatically disrupted in aC9, more than in both symALS and aSOD. The classifier accurately distinguished genetically at-risk groups from controls (receiver-operator-characteristic area-under-curve 0.89). The cerebral network dynamics of aC9 are markedly different from both aSOD and symALS, supporting the concept of profoundly different upstream pathways in SOD1 ALS, sparing wider cortical pathology when compared to C9orf72 ALS. aC9 changes may reflect chronic adaptive changes relating to neurodevelopmental factors or underpin aspects of system vulnerability that define penetrance variability. MEG metrics might provide important biomarkers of prevention therapy efficacy and phenoconversion in at-risk populations.
期刊介绍:
Human Brain Mapping publishes peer-reviewed basic, clinical, technical, and theoretical research in the interdisciplinary and rapidly expanding field of human brain mapping. The journal features research derived from non-invasive brain imaging modalities used to explore the spatial and temporal organization of the neural systems supporting human behavior. Imaging modalities of interest include positron emission tomography, event-related potentials, electro-and magnetoencephalography, magnetic resonance imaging, and single-photon emission tomography. Brain mapping research in both normal and clinical populations is encouraged.
Article formats include Research Articles, Review Articles, Clinical Case Studies, and Technique, as well as Technological Developments, Theoretical Articles, and Synthetic Reviews. Technical advances, such as novel brain imaging methods, analyses for detecting or localizing neural activity, synergistic uses of multiple imaging modalities, and strategies for the design of behavioral paradigms and neural-systems modeling are of particular interest. The journal endorses the propagation of methodological standards and encourages database development in the field of human brain mapping.