基于多尺度多模态数据的脑疾病研究进展——遗传影像大数据研究

Exploring Brain Diseases with Multi-scale,Multi-modality Data and Computational Approach

脑疾病是当前世界各国面临的共同挑战,给个人、家庭及社会都带来沉重的负担。随着各种高通量技术和无线远程获取技术的迅速发展,为脑科学与脑疾病的研究提供了前所未有的契机。借助多尺度多模态的脑科学大数据,我们能够从不同尺度、不同角度对人脑功能及脑疾病展开研究。本文将以遗传影像大数据为例,首先介绍国内外遗传影像大数据的数据库建设进展与挑战。进一步我们将讨论当前遗传影像数据挖掘分析的主要数学理论和方法,以及遗传影像大数据与智能算法在揭示脑疾病病理机制和辅助临床诊断治疗中的应用。最后,我们就多尺度多模态大数据与脑疾病未来发展方向——疾病分型、计算神经科学、计算精神病学进行总结与展望。

It is challenging in diagnosis and treatment of brain diseases across the whole world,which places heavy burden on individuals,families and society.The rapid development of various high-throughput and remote healthcare technologies provides unprecedented opportunities for advancing the research of brain sciences and brain diseases.The multiscale and multimodalilty data of the brain structure and functions enables us to explore the mechanisms of brain diseases and develop novel approaches for precision diagnosis and treatment.In this paper,we firstly reviewed the progress and challenges in developing genetic-imaging database in China and worldwide.Furthermore,we discussed current mathematical theories and methods for analyzing genetic imaging data,as well as the translational research in application of genetic imaging big data analysis to revealing the pathological mechanisms of brain diseases and assisting clinical diagnosis and treatment.Finally,we looked into perspectives on future research directions in multiscale and multimodality big data,including the phenotypes of brain diseases,computational neuroscience,and computational psychiatry.