Charité-Universitätsmedizin Berlin, Department of Neurology, Berlin, Germany.
Humboldt-Universität zu Berlin, Faculty of Philosophy, Berlin School of Mind and Brain, Berlin, Germany.
Commun Biol. 2022 Mar 24;5(1):261. doi: 10.1038/s42003-022-03185-3.
The prediction of inter-individual behavioural differences from neuroimaging data is a rapidly evolving field of research focusing on individualised methods to describe human brain organisation on the single-subject level. One method that harnesses such individual signatures is functional connectome fingerprinting, which can reliably identify individuals from large study populations. However, the precise relationship between functional signatures underlying fingerprinting and behavioural prediction remains unclear. Expanding on previous reports, here we systematically investigate the link between discrimination and prediction on different levels of brain network organisation (individual connections, network interactions, topographical organisation, and connection variability). Our analysis revealed a substantial divergence between discriminatory and predictive connectivity signatures on all levels of network organisation. Across different brain parcellations, thresholds, and prediction algorithms, we find discriminatory connections in higher-order multimodal association cortices, while neural correlates of behaviour display more variable distributions. Furthermore, we find the standard deviation of connections between participants to be significantly higher in fingerprinting than in prediction, making inter-individual connection variability a possible separating marker. These results demonstrate that participant identification and behavioural prediction involve highly distinct functional systems of the human connectome. The present study thus calls into question the direct functional relevance of connectome fingerprints.
从神经影像学数据预测个体间的行为差异是一个快速发展的研究领域，其重点是描述个体大脑组织的个体化方法。一种利用这种个体特征的方法是功能连接组指纹识别，它可以可靠地从大的研究人群中识别个体。然而，指纹识别和行为预测所依据的功能特征之间的确切关系尚不清楚。在之前报告的基础上，我们在这里系统地研究了不同脑网络组织层次（个体连接、网络相互作用、拓扑组织和连接可变性）上的判别和预测之间的联系。我们的分析揭示了所有网络组织层次上的判别和预测连接特征之间存在实质性的差异。在不同的脑区划分、阈值和预测算法中，我们发现判别连接存在于更高阶的多模态联合皮质中，而行为的神经相关性显示出更可变的分布。此外，我们发现指纹识别中的参与者之间的连接的标准偏差显著高于预测，使得个体间连接的可变性成为可能的分离标记。这些结果表明，参与者识别和行为预测涉及到人类连接组的高度不同的功能系统。因此，本研究质疑了连接组指纹的直接功能相关性。
