Nisar Sabah, Haris Mohammad
Laboratory of Molecular and Metabolic Imaging, Sidra Medicine, Doha, Qatar.
Department of Diagnostic Imaging, St Jude Children's Research Hospital, Memphis, TN, USA.
Mol Psychiatry. 2023 Dec;28(12):4995-5008. doi: 10.1038/s41380-023-02060-9. Epub 2023 Apr 17.
Autism-spectrum disorders (ASDs) are developmental disabilities that manifest in early childhood and are characterized by qualitative abnormalities in social behaviors, communication skills, and restrictive or repetitive behaviors. To explore the neurobiological mechanisms in ASD, extensive research has been done to identify potential diagnostic biomarkers through a neuroimaging genetics approach. Neuroimaging genetics helps to identify ASD-risk genes that contribute to structural and functional variations in brain circuitry and validate biological changes by elucidating the mechanisms and pathways that confer genetic risk. Integrating artificial intelligence models with neuroimaging data lays the groundwork for accurate diagnosis and facilitates the identification of early diagnostic biomarkers for ASD. This review discusses the significance of neuroimaging genetics approaches to gaining a better understanding of the perturbed neurochemical system and molecular pathways in ASD and how these approaches can detect structural, functional, and metabolic changes and lead to the discovery of novel biomarkers for the early diagnosis of ASD.
自闭症谱系障碍(ASD)是一种在幼儿期出现的发育障碍,其特征是社交行为、沟通技能以及局限或重复行为存在质性异常。为了探索ASD的神经生物学机制,人们通过神经影像遗传学方法进行了广泛研究,以识别潜在的诊断生物标志物。神经影像遗传学有助于识别导致脑回路结构和功能变化的ASD风险基因,并通过阐明赋予遗传风险的机制和途径来验证生物学变化。将人工智能模型与神经影像数据相结合,为准确诊断奠定了基础,并有助于识别ASD的早期诊断生物标志物。本综述讨论了神经影像遗传学方法对于更好地理解ASD中受干扰的神经化学系统和分子途径的重要性,以及这些方法如何检测结构、功能和代谢变化,并导致发现用于ASD早期诊断的新型生物标志物。