Neurological disorders dramatically impact patients of any age population, their families, and societies. Pediatrics are among vulnerable age populations who differently experience the devastating consequences of neurological conditions, such as attention-deficit hyperactivity disorders (ADHD), autism spectrum disorders (ASD), cerebral palsy, concussion, and epilepsy. System-level understanding of these neurological disorders, particularly from the brain networks' dynamic perspective, has led to the significant trend of recent scientific investigations. While a dramatic maturation in the network science application domain is evident, leading to a better understanding of neurological disorders, such rapid utilization for studying pediatric neurological disorders falls behind that of the adult population. Aside from the specific technological needs and constraints in studying neurological disorders in children, the concept of development introduces uncertainty and further complexity topping the existing neurologically driven processes caused by disorders. To unravel these complexities, indebted to the availability of high-dimensional data and computing capabilities, approaches based on machine learning have rapidly emerged a new trend to understand pathways better, accurately diagnose, and better manage the disorders. Deep learning has recently gained an ever-increasing role in the era of health and medical investigations. Thanks to its relatively more minor dependency on feature exploration and engineering, deep learning may overcome the challenges mentioned earlier in studying neurological disorders in children. The current scoping review aims to explore challenges concerning pediatric brain development studies under the constraints of neurological disorders and offer an insight into the potential role of deep learning methodology on such a task with varying and uncertain nature. Along with pinpointing recent advancements, possible research directions are highlighted where deep learning approaches can assist in computationally targeting neurological disorder-related processes and translating them into windows of opportunities for interventions in diagnosis, treatment, and management of neurological disorders in children.

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深度学习能击中移动目标吗？对其在儿童神经疾病研究中的作用进行范围审查

神经系统疾病极大地影响着任何年龄段的患者、他们的家庭和社会。儿科属于弱势年龄群体，他们因神经系统疾病而遭受不同程度的破坏性后果，例如注意力缺陷多动障碍 (ADHD)、自闭症谱系障碍 (ASD)、脑瘫、脑震荡和癫痫。对这些神经系统疾病的系统层面的理解，特别是从大脑网络的动态角度，已经导致了最近科学研究的显着趋势。虽然网络科学应用领域的显着成熟是显而易见的，可以更好地理解神经系统疾病，但在研究儿科神经系统疾病方面的快速利用落后于成年人群。除了研究儿童神经系统疾病的特定技术需求和限制之外，发展的概念还引入了不确定性和进一步的复杂性，超越了由疾病引起的现有神经驱动过程。为了解决这些复杂性，得益于高维数据和计算能力的可用性，基于机器学习的方法迅速出现了一种新趋势，以更好地理解通路、准确诊断和更好地管理疾病。最近，深度学习在健康和医学调查时代发挥着越来越重要的作用。由于深度学习对特征探索和工程的依赖性相对较小，因此它可以克服前面提到的研究儿童神经系统疾病的挑战。当前的范围审查旨在探索神经系统疾病限制下儿科大脑发育研究的挑战，并深入了解深度学习方法在此类具有变化和不确定性质的任务中的潜在作用。除了指出最近的进展外，还强调了可能的研究方向，其中深度学习方法可以帮助以计算方式针对神经障碍相关过程，并将其转化为干预儿童神经障碍的诊断、治疗和管理的机会之窗。