Adolescence is the peak period for the incidence of anxiety disorders. Recent findings have revealed the immaturity of neural networks underlying emotional regulation in this population. Brain vulnerability to anxiety in adolescence is related to the unsynchronised development of anxiety-relevant brain functional systems. However, our current knowledge on brain deficits in adolescent anxiety is mainly borrowed from studies on adults. Understanding adolescent-specific brain deficits is essential for developing biomarkers and brain-based therapies targeting adolescent anxiety. This article reviews and compares recent neuroimaging literature on anxiety-related brain structural and functional deficits between adolescent and adult populations, and proposes a model highlighting the differences between adolescence and adulthood in anxiety-related brain networks. This model emphasises that in adolescence the emotional control system tends to be hypoactivated, the fear conditioning system is immature, and the reward and stress response systems are hypersensitive. Furthermore, the striatum’s functional links to the amygdala and the prefrontal cortex are strengthened, while the link between the prefrontal cortex and the amygdala is weakened in adolescence. This model helps to explain why adolescents are vulnerable to anxiety disorders and provides insights into potential brain-based approaches to intervene in adolescent anxiety disorders.

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BackgroundNeuroimaging techniques provide rich and accurate measures of brain structure and function, and have become one of the most popular methods in mental health and neuroscience research. Rapidly growing neuroimaging research generates massive amounts of data, bringing new challenges in data collection, large-scale data management, efficient computing requirements and data mining and analyses.AimsTo tackle the challenges and promote the application of neuroimaging technology in clinical practice, we developed an integrated neuroimaging cloud (INCloud). INCloud provides a full-stack solution for the entire process of large-scale neuroimaging data collection, management, analysis and clinical applications.MethodsINCloud consists of data acquisition systems, a data warehouse, automatic multimodal image quality check and processing systems, a brain feature library, a high-performance computing cluster and computer-aided diagnosis systems (CADS) for mental disorders. A unique design of INCloud is the brain feature library that converts the unit of data management from image to image features such as hippocampal volume. Connecting the CADS to the scientific database, INCloud allows the accumulation of scientific data to continuously improve the accuracy of objective diagnosis of mental disorders.ResultsUsers can manage and analyze neuroimaging data on INCloud, without the need to download them to the local device. INCloud users can query, manage, analyze and share image features based on customized criteria. Several examples of 'mega-analyses' based on the brain feature library are shown.ConclusionsCompared with traditional neuroimaging acquisition and analysis workflow, INCloud features safe and convenient data management and sharing, reduced technical requirements for researchers, high-efficiency computing and data mining, and straightforward translations to clinical service. The design and implementation of the system are also applicable to imaging research platforms in other fields.