Zalesky Andrew, Sarwar Tabinda, Tian Ye, Liu Yuanzhe, Yeo B T Thomas, Ramamohanarao Kotagiri
Systems Lab, Department of Psychiatry, The University of Melbourne, Victoria, Australia.
Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia.
Netw Neurosci. 2024 Dec 10;8(4):1291-1309. doi: 10.1162/netn_a_00400. eCollection 2024.
Several recent studies have optimized deep neural networks to learn high-dimensional relationships linking structural and functional connectivity across the human connectome. However, the extent to which these models recapitulate individual-specific characteristics of resting-state functional brain networks remains unclear. A core concern relates to whether current individual predictions outperform simple benchmarks such as group averages and null conditions. Here, we consider two measures to statistically evaluate whether functional connectivity predictions capture individual effects. We revisit our previously published functional connectivity predictions for 1,000 healthy adults and provide multiple lines of evidence supporting that our predictions successfully capture subtle individual-specific variation in connectivity. While predicted individual effects are statistically significant and outperform several benchmarks, we find that effect sizes are small (i.e., 8%-11% improvement relative to group-average benchmarks). As such, initial expectations about individual prediction performance expressed by us and others may require moderation. We conclude that individual predictions can significantly outperform appropriate benchmark conditions and we provide several recommendations for future studies in this area. Future studies should statistically assess the individual prediction performance of their models using one of the measures and benchmarks provided here.
最近的几项研究对深度神经网络进行了优化，以学习连接人类大脑连接组中结构和功能连接性的高维关系。然而，这些模型在多大程度上概括了静息态功能性脑网络的个体特异性特征仍不清楚。一个核心问题是当前的个体预测是否优于简单的基准，如组平均值和零假设条件。在这里，我们考虑两种统计方法来评估功能连接性预测是否捕捉到了个体效应。我们重新审视了之前发表的针对1000名健康成年人的功能连接性预测，并提供了多条证据支持我们的预测成功捕捉到了连接性中细微的个体特异性变化。虽然预测的个体效应具有统计学意义且优于几个基准，但我们发现效应大小很小（即相对于组平均基准提高了8%-11%）。因此，我们和其他人对个体预测性能的最初期望可能需要调整。我们得出结论，个体预测可以显著优于适当的基准条件，并为该领域的未来研究提供了几条建议。未来的研究应该使用这里提供的方法和基准之一，对其模型的个体预测性能进行统计评估。
