Bamicha Victoria, Pergantis Pantelis, Skianis Charalabos, Drigas Athanasios
Net Media Lab & Mind & Brain R&D, Institute of Informatics & Telecommunications, National Centre of Scientific Research 'Demokritos', 153 41 Agia Paraskevi, Greece.
Department of Information & Communication Systems Engineering, University of the Aegean, 832 00 Karlovasi, Greece.
Biomedicines. 2025 Jun 10;13(6):1420. doi: 10.3390/biomedicines13061420.
Autism spectrum disorder is a complex and diverse neurobiological condition. Understanding the mechanisms and causes of the disorder requires an in-depth study and modeling of the immune, mitochondrial, and neurological systems. Computational neuroscience enhances psychiatric science by employing machine learning techniques on neural networks, combining data on brain activity with the pathophysiological and biological characteristics of psychiatric-neurobiological disorders. The research explores the integration of neurotransmitter activity into computational models and their potential roles in diagnosing and treating autism using computational methods. This research employs a narrative review that focuses on four neurotransmitter systems directly related to the manifestation of autism, specifically the following neurotransmitters: serotonin, dopamine, glutamate, and gamma-aminobutyric acid (GABA). This study reveals that computational neuroscience advances autism diagnosis and treatment by identifying genetic factors and improving the efficiency of diagnosis. Neurotransmitters play a crucial role in the function of brain cells, enhancing synaptic conduction and signal transmission. However, the interaction of chemical compounds with genetic factors and network alterations influences the pathophysiology of autism. This study integrates the investigation of computational approaches in four neurotransmitter systems associated with ASD. It improves our understanding of the disorder and provides insights that could stimulate further research, thereby contributing to the development of effective treatments.
自闭症谱系障碍是一种复杂多样的神经生物学病症。要了解该病症的机制和病因，需要对免疫系统、线粒体系统和神经系统进行深入研究和建模。计算神经科学通过在神经网络上运用机器学习技术，将大脑活动数据与精神神经生物学病症的病理生理和生物学特征相结合，从而推动精神病学发展。该研究探讨了将神经递质活动整合到计算模型中，以及它们在使用计算方法诊断和治疗自闭症方面的潜在作用。本研究采用叙述性综述，重点关注与自闭症表现直接相关的四个神经递质系统，具体为以下神经递质：血清素、多巴胺、谷氨酸和γ-氨基丁酸（GABA）。这项研究表明，计算神经科学通过识别遗传因素和提高诊断效率，推动了自闭症的诊断和治疗。神经递质在脑细胞功能中起着至关重要的作用，可增强突触传导和信号传递。然而，化合物与遗传因素的相互作用以及网络改变会影响自闭症的病理生理学。本研究整合了对与自闭症谱系障碍相关的四个神经递质系统中计算方法的研究。它增进了我们对该病症的理解，并提供了可激发进一步研究的见解，从而有助于开发有效的治疗方法。
