Kleinerova Jana, Querin Giorgia, Pradat Pierre-Francois, Siah We Fong, Bede Peter
Computational Neuroimaging Group (CNG), School of Medicine, Trinity College Dublin, Room 5.43, Pearse Street, Dublin 2, Dublin, Ireland.
Biomedical Imaging Laboratory, CNRS, INSERM, Sorbonne University, Paris, France.
J Neurol. 2025 May 12;272(6):392. doi: 10.1007/s00415-025-13143-8.
Neuroimaging in ALS has contributed considerable academic insights in recent years demonstrating genotype-specific topological changes decades before phenoconversion and characterising longitudinal propagation patterns in specific phenotypes. It has elucidated the radiological underpinnings of specific clinical phenomena such as pseudobulbar affect, apathy, behavioural change, spasticity, and language deficits. Academic concepts such as sexual dimorphism, motor reserve, cognitive reserve, adaptive changes, connectivity-based propagation, pathological stages, and compensatory mechanisms have also been evaluated by imaging. The underpinnings of extra-motor manifestations such as cerebellar, sensory, extrapyramidal and cognitive symptoms have been studied by purpose-designed imaging protocols. Clustering approaches have been implemented to uncover radiologically distinct disease subtypes and machine-learning models have been piloted to accurately classify individual patients into relevant diagnostic, phenotypic, and prognostic categories. Prediction models have been developed for survival in symptomatic patients and phenoconversion in asymptomatic mutation carriers. A range of novel imaging modalities have been implemented and 7 Tesla MRI platforms are increasingly being used in ALS studies. Non-ALS MND conditions, such as PLS, SBMA, and SMA, are now also being increasingly studied by quantitative neuroimaging approaches. A unifying theme of recent imaging papers is the departure from describing focal brain changes to focusing on dynamic structural and functional connectivity alterations. Progressive cortico-cortical, cortico-basal, cortico-cerebellar, cortico-bulbar, and cortico-spinal disconnection has been consistently demonstrated by recent studies and recognised as the primary driver of clinical decline. These studies have led the reconceptualisation of ALS as a "network" or "circuitry disease".
近年来，肌萎缩侧索硬化症（ALS）的神经影像学研究带来了相当多的学术见解，揭示了在表型转换前数十年的基因型特异性拓扑变化，并描绘了特定表型中的纵向传播模式。它阐明了诸如假性球麻痹、冷漠、行为改变、痉挛和语言缺陷等特定临床现象的放射学基础。诸如性别二态性、运动储备、认知储备、适应性变化、基于连接性的传播、病理阶段和代偿机制等学术概念也已通过影像学进行评估。通过专门设计的成像方案研究了诸如小脑、感觉、锥体外系和认知症状等运动外表现的基础。已采用聚类方法来发现放射学上不同的疾病亚型，并试点使用机器学习模型将个体患者准确分类到相关的诊断、表型和预后类别中。已开发出有症状患者生存和无症状突变携带者表型转换的预测模型。一系列新型成像方式已得到应用，7特斯拉磁共振成像（MRI）平台在ALS研究中的使用也越来越多。现在，诸如原发性侧索硬化（PLS）、脊髓性肌萎缩症2型（SBMA）和脊髓性肌萎缩症（SMA）等非ALS运动神经元病（MND）状况也越来越多地通过定量神经影像学方法进行研究。近期成像论文的一个统一主题是从描述局灶性脑变化转向关注动态结构和功能连接改变。近期研究一致证明了进行性皮质 - 皮质、皮质 - 基底节、皮质 - 小脑、皮质 - 延髓和皮质 - 脊髓连接中断，并将其视为临床衰退的主要驱动因素。这些研究促使人们将ALS重新概念化为一种“网络”或“回路疾病”。
