Imokawa Tomoki, Maki Hiroyuki, Sone Daichi, Kagaya Risa, Shigemoto Yoko, Kimura Yukio, Matsuda Hiroshi, Takahashi Yuji, Tateishi Ukihide, Sato Noriko
Department of Radiology, National Centre Hospital, National Centre of Neurology and Psychiatry, 187-8551 Kodaira, Tokyo, Japan.
Department of Diagnostic Radiology, Institute of Science Tokyo, 113-8510 Bunkyo-ku, Tokyo, Japan.
Brain Commun. 2025 May 7;7(3):fcaf181. doi: 10.1093/braincomms/fcaf181. eCollection 2025.
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.
1型强直性肌营养不良是一种影响包括肌肉和中枢神经系统在内的多个系统的疾病。以往的研究主要使用基于体素的形态测量法来检查脑容量减少的区域及其与症状的相关性；然而，尚未获得一致的研究结果。亚型和阶段推断是一种无监督机器学习算法，可从横断面数据中阐明疾病进展和亚型。在本研究中，我们使用亚型和阶段推断来分析1型强直性肌营养不良患者的形态测量MRI数据，以揭示脑容量损失的详细轨迹，并探索形态测量MRI作为1型强直性肌营养不良生物标志物的潜力。我们检查了60例1型强直性肌营养不良患者和50例年龄及性别匹配的对照。1型强直性肌营养不良患者的中位年龄为44岁（范围20 - 67岁），其中包括32名男性。使用三维T1加权MRI图像，我们通过亚型和阶段推断分析了脑受累的亚型及其各自的脑容量损失轨迹。此外，我们检查了已识别亚型与对照之间临床和脑形态学指标的差异及相关性。亚型和阶段推断揭示了两种亚型：皮质型和皮质下型。在皮质型亚型中，体积减少始于中央前回，并主要扩散至大脑皮层。在皮质下型亚型中，体积减少早期发生在壳核、丘脑、海马体和杏仁核。临床指标检查显示，尽管皮质下型亚型患者的年龄比皮质型亚型患者年轻，但皮质下型亚型的简易精神状态检查得分显著更低，且与皮质下概率呈负相关。最大脑生长的标志物总颅内体积在皮质型亚型中显著更小；然而，皮质下型亚型的总颅内体积并不比对照组小。此外，皮质下型亚型的总脑室体积比对照组和皮质型亚型都更大。相比之下，其总脑实质体积低于对照组，与皮质型亚型相似。这些结果表明两种亚型在儿童早期脑发育方面存在差异。通过使用亚型和阶段推断，我们识别出了1型强直性肌营养不良的两种亚型，并证明了形态学MRI作为认知障碍和脑发育障碍生物标志物的潜力。机器学习有助于在临床环境中对1型强直性肌营养不良进行分层，并有助于阐明其复杂的病理生理学。
