The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move toward a translational neuroscience era. Regression-based multivariate models (hereafter "predictive modeling") provide a powerful and widely used approach to predict human behavior with neuroimaging features. These studies maintain a focus on decoding individual differences in a continuously behavioral phenotype from neuroimaging data, opening up an exciting opportunity to describe the human brain at the single-subject level. In this survey, we provide an overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade. We first review regression-based approaches and highlight connectome-based predictive modeling, which has grown in popularity in recent years. Next, we systematically describe recent representative studies using these tools in the context of cognitive function, symptom severity, personality traits, and emotion processing. Finally, we highlight a few challenges related to combining multimodal data, longitudinal prediction, external validations, and the employment of deep learning methods that have emerged from our review of the existing literature, as well as present some promising and challenging future directions.

中文翻译：

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基于神经影像学的精神障碍和健康认知和行为个体化预测：方法和承诺

神经影像学界见证了生物标志物发现的范式转变，从使用传统的单变量大脑映射方法到多变量预测模型，使该领域进入了转化神经科学时代。基于回归的多变量模型（以下简称“预测建模”）提供了一种强大且广泛使用的方法来预测具有神经影像特征的人类行为。这些研究始终专注于从神经影像数据中解码连续行为表型中的个体差异，这为在单个受试者水平上描述人类大脑开辟了令人兴奋的机会。在本次调查中，我们概述了过去十年中利用机器学习方法来识别神经影像预测因子的近期研究。我们首先回顾基于回归的方法并重点介绍近年来越来越流行的基于连接组的预测建模。接下来，我们系统地描述了最近在认知功能、症状严重程度、个性特征和情绪处理方面使用这些工具的代表性研究。最后，我们强调了与结合多模态数据、纵向预测、外部验证和使用深度学习方法相关的一些挑战，这些挑战来自我们对现有文献的回顾，并提出了一些有前途和具有挑战性的未来方向。性格特征和情绪处理。最后，我们强调了与结合多模态数据、纵向预测、外部验证和使用深度学习方法相关的一些挑战，这些挑战来自我们对现有文献的回顾，并提出了一些有前途和具有挑战性的未来方向。性格特征和情绪处理。最后，我们强调了与结合多模态数据、纵向预测、外部验证和使用深度学习方法相关的一些挑战，这些挑战来自我们对现有文献的回顾，并提出了一些有前途和具有挑战性的未来方向。