Studying depression using imaging and machine learning methods.
Studying depression using imaging and machine learning methods.
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DOI：
10.1016/j.nicl.2015.11.003
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Aizenstein HJ
中科院分区：
文献类型：
--
作者：
Patel MJ;Khalaf A;Aizenstein HJ
抑郁症是一个复杂的临床实体，可能对临床医生提出准确诊断和有效及时治疗的挑战。这些挑战促使开发多种机器学习方法，以帮助改善这种疾病的管理。这些方法利用从神经成像获得的解剖和生理数据来创建模型，可以识别抑郁症患者与非抑郁症患者并预测治疗结果。本文（1）介绍了抑郁症、成像和机器学习方法的背景;（2）回顾了过去使用成像和机器学习研究抑郁症的研究方法;（3）为未来抑郁症相关研究提出了方向。过去的研究使用机器学习和成像成功地研究了抑郁症。过去的研究在方法上有局限性。未来研究的方法可以改进。未来的研究可能会产生更强大的模型来诊断和治疗抑郁症。
Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies. Past studies successfully studied depression using machine learning and imaging. Past studies have limitations in their methods. Methods for future studies can be improved. Future studies could yield more robust models to diagnosis and treat depression.