Neuroimaging‐based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term “predictome” to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network‐based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject‐level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention‐deficit hyperactivity disorder, obsessive–compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging‐based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.

中文翻译：

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迈向基于大脑的精神疾病预测。


近年来，基于神经影像的方法已广泛应用于精神疾病的研究，并加深了我们对认知健康和紊乱的大脑结构和功能的理解。机器学习技术的最新进展在精神疾病患者的个体化预测和表征方面显示出了有希望的结果。研究利用了各种神经影像学模式的特征，包括结构、功能和扩散磁共振成像数据，以及多种模式的联合估计特征，来评估患有异质性精神障碍的患者，例如精神分裂症和自闭症。我们使用术语“预测组”来描述使用一种或多种神经影像模式的多元大脑网络特征来预测精神疾病。在预测组中，多个基于大脑网络的特征（来自相同模态或多种模态）被纳入预测模型中，以共同估计疾病特有的特征并相应地预测受试者。迄今为止，已发表超过 650 项针对精神疾病的受试者水平预测的研究。我们调查了大约 250 项研究，包括精神分裂症、重度抑郁症、双相情感障碍、自闭症谱系障碍、注意力缺陷多动障碍、强迫症、社交焦虑症、创伤后应激障碍和物质依赖。在这篇综述中，我们对最近基于神经影像的预测方法、当前趋势和常见缺点进行了全面回顾，并分享了我们对未来方向的愿景。