Afshani Mortaza, Mahmoudi-Aznaveh Ahmad, Noori Khadijeh, Rostampour Masoumeh, Zarei Mojtaba, Spiegelhalder Kai, Khazaie Habibolah, Tahmasian Masoud
Institute of Medical Science and Technology, Shahid Beheshti University, Tehran 1983969411, Iran.
Cyberspace Research Institute, Shahid Beheshti University, Tehran 1983969411, Iran.
Brain Sci. 2023 Apr 17;13(4):672. doi: 10.3390/brainsci13040672.
Insomnia disorder (ID) is a prevalent mental illness. Several behavioral and neuroimaging studies suggested that ID is a heterogenous condition with various subtypes. However, neurobiological alterations in different subtypes of ID are poorly understood. We aimed to assess whether unimodal and multimodal whole-brain neuroimaging measurements can discriminate two commonly described ID subtypes (i.e., paradoxical and psychophysiological insomnia) from each other and healthy subjects. We obtained T1-weighted images and resting-state fMRI from 34 patients with ID and 48 healthy controls. The outcome measures were grey matter volume, cortical thickness, amplitude of low-frequency fluctuation, degree centrality, and regional homogeneity. Subsequently, we applied support vector machines to classify subjects via unimodal and multimodal measures. The results of the multimodal classification were superior to those of unimodal approaches, i.e., we achieved 81% accuracy in separating psychophysiological vs. control, 87% for paradoxical vs. control, and 89% for paradoxical vs. psychophysiological insomnia. This preliminary study provides evidence that structural and functional brain data can help to distinguish two common subtypes of ID from each other and healthy subjects. These initial findings may stimulate further research to identify the underlying mechanism of each subtype and develop personalized treatments for ID in the future.
失眠症（ID）是一种常见的精神疾病。多项行为学和神经影像学研究表明，失眠症是一种具有多种亚型的异质性疾病。然而，人们对失眠症不同亚型的神经生物学改变了解甚少。我们旨在评估单模态和多模态全脑神经影像学测量能否区分两种常见的失眠症亚型（即矛盾性失眠和心理生理性失眠）以及健康受试者。我们获取了34例失眠症患者和48例健康对照者的T1加权图像和静息态功能磁共振成像。结果测量指标包括灰质体积、皮质厚度、低频波动幅度、度中心性和局部一致性。随后，我们应用支持向量机通过单模态和多模态测量对受试者进行分类。多模态分类的结果优于单模态方法，即我们在区分心理生理性失眠与对照组时准确率达到81%，区分矛盾性失眠与对照组时准确率为87%，区分矛盾性失眠与心理生理性失眠时准确率为89%。这项初步研究提供了证据，表明大脑的结构和功能数据有助于区分失眠症的两种常见亚型以及健康受试者。这些初步发现可能会激发进一步的研究，以确定每种亚型的潜在机制，并在未来开发针对失眠症的个性化治疗方法。
