Myotonic dystrophy Type 1 is a disorder that affects multiple systems, including the muscles and the CNS. Previous studies have primarily used voxel-based morphometry to examine areas of brain volume reduction and their correlation with symptoms; however, consistent findings have not been obtained. Subtype and stage inference is an unsupervised machine learning algorithm that elucidates disease progression and subtypes from cross-sectional data. In this study, we used Subtype and Stage Inference to analyse the morphometric MRI data of patients with myotonic dystrophy Type 1 to reveal the detailed trajectories of brain volume loss and to explore the potential of morphometric MRI as a biomarker for myotonic dystrophy Type 1. We examined 60 patients with myotonic dystrophy Type 1 and 50 age- and sex-matched controls. The patients with myotonic dystrophy Type 1 had a median age of 44 years (range 20-67 years) and included 32 males. Using three-dimensional T1-weighted MRI images, we analysed the subtypes of brain involvement and their respective trajectories of brain volume loss with subtype and stage inference. Additionally, we examined the differences and correlations in clinical and brain morphological indicators between the identified subtypes and controls. Subtype and stage inference revealed two subtypes: cortical and subcortical. In the cortical subtype, volume reduction began in the precentral gyrus and spread primarily to the cerebral cortex. In the subcortical subtype, it progressed early in the putamen, thalamus, hippocampus and amygdala. Examination of clinical indicators showed that despite the younger age of the subcortical subtype compared to the cortical subtype, mini-mental state examination scores were significantly lower in the subcortical subtype and negatively correlated with subcortical probability. The total intracranial volume, a marker of maximal brain growth, was significantly smaller in the cortical subtype; however, it was not smaller in the subcortical subtype than in controls. Furthermore, the subcortical subtype showed a larger total ventricle volume than both the controls and the cortical subtype. In contrast, its total brain parenchymal volume was lower than that of the controls, similar to the cortical subtype. These results suggest early childhood brain development differences between the two subtypes. Using Subtype and Stage Inference, we identified two subtypes of myotonic dystrophy Type 1 and demonstrated the potential of morphological MRI as a biomarker for cognitive impairment and brain developmental disorders. Machine learning can aid in stratifying myotonic dystrophy Type 1 in clinical settings and contribute to the elucidation of its complex pathophysiology.

Keywords: SuStaIn; dementia; muscular dystrophy; neurodegenerative disease; tauopathy.

© The Author(s) 2025. Published by Oxford University Press on behalf of the Guarantors of Brain.

肌张力障碍营养不良1型是一种影响多个系统的疾病，包括肌肉和中枢神经系统。以往的研究主要使用体素形态计量法来检查脑体积减少的区域及其与症状的相关性；然而，并未获得一致的结果。亚型和阶段推断是一种无监督机器学习算法，可以从横断面数据中阐明疾病的进展和亚型。在这项研究中，我们使用了亚型和阶段推断分析肌张力障碍营养不良1型患者的形态学MRI数据，揭示脑体积减少的详细轨迹，并探索形态学MRI作为肌张力障碍营养不良1型生物标志物的潜力。我们检查了60名肌张力障碍营养不良1型患者和50名年龄、性别匹配的对照组。这些肌张力障碍营养不良1型患者的中位年龄为44岁（范围20-67岁），其中32名为男性。使用三维T1加权MRI图像，我们分析了脑参与的亚型及其各自的脑体积减少轨迹，并研究了所识别的亚型与对照组在临床和脑形态指标上的差异及相关性。亚型和阶段推断揭示了两种亚型：皮质型和皮质下型。在皮质型中，体积减少首先出现在中央前回，并主要向大脑皮层扩散。而在皮质下型中，则早期在壳核、丘脑、海马和杏仁体进展。临床指标的检查显示，尽管皮质下亚型比皮质亚型年轻，但简易精神状态检查得分显著较低且与皮质下概率呈负相关。作为最大脑生长标志物的总颅内体积，在皮质亚型中明显较小；然而，在皮质下亚型中并未小于对照组。此外，皮质下亚型显示比对照组和皮质亚型更大的总脑室体积。相反，其总体脑实质体积低于对照组，类似于皮质亚型。这些结果表明了两种亚型在早期儿童大脑发育方面的差异。使用亚型和阶段推断，我们确定了肌张力障碍营养不良1型的两个亚型，并展示了形态学MRI作为认知功能障碍及大脑发育异常生物标志物的潜力。机器学习可以帮助临床环境中划分肌张力障碍营养不良1型并有助于阐明其复杂的病理生理机制。

关键词：SuStaIn；痴呆症；肌肉萎缩病；神经退行性疾病；tau蛋白病变。

© 作者保留版权，2025年。牛津大学出版社为Brain杂志的保证人代为出版。