Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer’s disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.

中文翻译：

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维度神经影像内表型：通过机器学习表达疾病异质性的神经生物学表征


机器学习越来越多地用于获取个性化的神经影像特征，用于神经精神和神经退行性疾病的疾病诊断、预后和治疗反应。因此，通过识别具有不同大脑表型测量的疾病亚型，有助于更好地理解疾病异质性。在这篇综述中，我们首先对使用机器学习和多模态磁共振成像来揭示各种神经精神和神经退行性疾病的疾病异质性的研究进行了系统的文献综述，包括阿尔茨海默病、精神分裂症、重度抑郁症、自闭症谱系障碍和多发性硬化症，以及它们在跨诊断框架中的潜力，其中跨诊断边界评估神经解剖学和神经生物学的共性。随后，我们总结了相关的机器学习方法及其临床可解释性。我们讨论当前研究结果的潜在临床意义并展望未来的研究途径。最后，我们讨论一种称为维度神经影像内表型的新兴范例。维度神经影像内表型将神经精神和神经退行性疾病的神经生物学异质性剖析成低维但信息丰富的定量脑表型表征，作为稳健的中间表型（即内表型），大概反映了与神经精神疾病和神经退行性疾病相关的潜在遗传、生活方式和环境过程的相互作用。疾病病因学。