Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, Oxford, UK.
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, Oxford, UK.
Neuroimage. 2022 Oct 1;259:119418. doi: 10.1016/j.neuroimage.2022.119418. Epub 2022 Jun 28.
Modelling and predicting individual differences in task-fMRI activity can have a wide range of applications from basic to clinical neuroscience. It has been shown that models based on resting-state activity can have high predictive accuracy. Here we propose several improvements to such models. Using a sparse ensemble learner, we show that (i) features extracted using Stochastic Probabilistic Functional Modes (sPROFUMO) outperform the previously proposed dual-regression approach, (ii) that the shape and overall intensity of individualised task activations can be modelled separately and explicitly, (iii) training the model on predicting residual differences in brain activity further boosts individualised predictions. These results hold for both surface-based analyses of the Human Connectome Project data as well as volumetric analyses of UK-biobank data. Overall, our model achieves state of the art prediction accuracy on par with the test-retest reliability of task-fMRI scans, suggesting that it has potential to supplement traditional task localisers.
基于静息态活动的模型可以具有较高的预测准确性，对任务 fMRI 活动中的个体差异进行建模和预测可以在基础和临床神经科学领域有广泛的应用。我们提出了几种改进这些模型的方法。使用稀疏集成学习器，我们表明：（i）使用 Stochastic Probabilistic Functional Modes (sPROFUMO) 提取的特征优于之前提出的双回归方法，（ii）可以分别且明确地对个体任务激活的形状和整体强度进行建模，（iii）在预测大脑活动的剩余差异上训练模型进一步提高了个体预测的准确性。这些结果对于人类连接组计划数据的基于表面的分析以及 UK-biobank 数据的体积分析都是适用的。总的来说，我们的模型在预测准确性方面达到了与任务 fMRI 扫描的测试-重测可靠性相当的水平，这表明它有可能补充传统的任务定位器。
