Deep learning algorithms for predicting neuroimaging data have shown considerable promise in various applications. Prior work has demonstrated that deep learning models that take advantage of the data's 3D structure can outperform standard machine learning on several learning tasks. However, most prior research in this area has focused on neuroimaging data from adults. Within the Adolescent Brain and Cognitive Development (ABCD) dataset, a large longitudinal development study, we examine structural MRI data to predict gender and identify gender-related changes in brain structure. Results demonstrate that gender prediction accuracy is exceptionally high (>97%) with training epochs >200 and that this accuracy increases with age. Brain regions identified as the most discriminative in the task under study include predominantly frontal areas and the temporal lobe. When evaluating gender predictive changes specific to a two-year increase in age, a broader set of visual, cingulate, and insular regions are revealed. Our findings show a robust gender-related structural brain change pattern, even over a small age range. This suggests that it might be possible to study how the brain changes during adolescence by looking at how these changes are related to different behavioral and environmental factors.
翻译:预测神经成像数据的深度学习算法在各种应用中显示出很大的希望。先前的工作表明,利用数据3D结构的深度学习模型可以优于若干学习任务的标准机器学习。然而,该领域以前的大部分研究侧重于成年人的神经成像数据。在青少年大脑和认知发展(ABCD)数据集(大型纵向发展研究)中,我们研究了结构性的MRI数据,以预测性别并确定大脑结构中与性别有关的变化。结果显示,性别预测准确性极高(>97%),培训时间 >200,而且这种精确性随着年龄的增长而提高。被确定为研究任务中最具歧视性的脑区域主要包括前方地区和时间圈。在评价与两年增长有关的性别预测变化时,将揭示出一系列更广泛的视觉、脉搏和岛屿区域。我们的调查结果显示,即使在小年龄范围,与性别有关的结构性脑变化模式也非常强劲。这表明,通过研究这些变化如何与不同的行为和环境因素相联系,可以研究青春期的大脑变化。