神经科学的研究已由传统的单变量分析进入到以多元个体预测方法为主要手段的转化神经科学阶段，该类研究致力于利用先进的模式回归算法，开发应用能够在单个样本水平对连续变量进行精准预测的机器学习模型，寻找稳健可靠的客观影像学标记物。目前，基于机器学习算法的预测模型在包括认知能力、精神疾病严重程度、性格特质、情绪感受等在内的多种行为变量的预测中展现了巨大潜力，是探索人类个体认知能力、个性发展的一项有力工具。目前，国际上主流用于个体化预测的回归分析方法主要包括多元线性回归、最小绝对收缩和选择算子回归、弹性网、岭回归、支持向量回归、关联向量回归以及偏最小二乘回归。在未来研究中，需要结合多中心大样本影像数据，充分利用多模态影像特征在挖掘互补信息上的优势，开发能够对未来行为进行预测的纵向分析模型，并通过独立数据集检验模型的泛化能力。个体化预测为深入理解认知功能及精神疾病的脑机制提供了新的参考。

The neuroimaging researches are moving towards a translational neuroscience era that is characterized by the use of multivariate predictive modeling, which is distinct from traditional univariate brain mapping studies. These studies maintain a focus on decoding individual differences in a continuously behavioural phenotype from neuroimaging data using regression-based methods, with an ultimate goal of identifying reliable and objective neuromarkers that can aid in clinical practice at the individual level. The machine learning algorithms-based predictive modeling has been successfully applied in the prediction of multiple important behavioural aspects including cognitive abilities, symptom severity for psychiatric patients, personality, and emotion feeling. The methods used for predictive modeling primarily involve multiple linear regression, least absolute shrinkage and selection operator regression, elastic net, ridge regression, support vector regression, relevance vector regression and partial least square regression. Promisingly, studies that are performed large neuroimaging dataset with rigorously external validation focusing on predicting future behavioural outcomes are encouraged. Moreover, multimodal data can be leveraged to extract the complementary information for investigating individual differences. Overall, predictive modelling opens up novel opportunities to better understand the neurobiological substrates of cognitive abilities and brain disorders.