Griffa Alessandra, Amico Enrico, Liégeois Raphaël, Van De Ville Dimitri, Preti Maria Giulia
Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland; Center of Neuroprosthetics, Ecole Polytechnique Fédérale De Lausanne (EPFL), Institute of Bioengineering, Geneva, Switzerland.
Center of Neuroprosthetics, Ecole Polytechnique Fédérale De Lausanne (EPFL), Institute of Bioengineering, Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
Neuroimage. 2022 Apr 15;250:118970. doi: 10.1016/j.neuroimage.2022.118970. Epub 2022 Feb 4.
Brain signatures of functional activity have shown promising results in both decoding brain states, meaning distinguishing between different tasks, and fingerprinting, that is identifying individuals within a large group. Importantly, these brain signatures do not account for the underlying brain anatomy on which brain function takes place. Structure-function coupling based on graph signal processing (GSP) has recently revealed a meaningful spatial gradient from unimodal to transmodal regions, on average in healthy subjects during resting-state. Here, we explore the specificity of structure-function coupling to distinct brain states (tasks) and to individual subjects. We used multimodal magnetic resonance imaging of 100 unrelated healthy subjects from the Human Connectome Project both during rest and seven different tasks and adopted a support vector machine classification approach for both decoding and fingerprinting, with various cross-validation settings. We found that structure-function coupling measures allow accurate classifications for both task decoding and fingerprinting. In particular, key information for fingerprinting is found in the more liberal portion of functional signals, with contributions strikingly localized to the fronto-parietal network. Moreover, the liberal portion of functional signals showed a strong correlation with cognitive traits, assessed with partial least square analysis, corroborating its relevance for fingerprinting. By introducing a new perspective on GSP-based signal filtering and FC decomposition, these results show that brain structure-function coupling provides a new class of signatures of cognition and individual brain organization at rest and during tasks. Further, they provide insights on clarifying the role of low and high spatial frequencies of the structural connectome, leading to new understanding of where key structure-function information for characterizing individuals can be found across the structural connectome graph spectrum.
功能活动的脑特征在解码脑状态（即区分不同任务）和指纹识别（即在大群体中识别个体）方面都显示出了有前景的结果。重要的是，这些脑特征并未考虑脑功能所发生的潜在脑解剖结构。基于图信号处理（GSP）的结构 - 功能耦合最近揭示了从单峰区域到跨峰区域的有意义的空间梯度，平均而言，这是在静息状态下健康受试者中观察到的。在这里，我们探讨结构 - 功能耦合对不同脑状态（任务）和个体受试者的特异性。我们使用了来自人类连接组计划的100名无亲属关系的健康受试者在静息和七种不同任务期间的多模态磁共振成像，并采用支持向量机分类方法进行解码和指纹识别，采用了各种交叉验证设置。我们发现结构 - 功能耦合测量允许对任务解码和指纹识别进行准确分类。特别是，指纹识别的关键信息存在于功能信号的更宽泛部分，其贡献显著地局限于额顶网络。此外，功能信号的宽泛部分与通过偏最小二乘分析评估的认知特征显示出强烈的相关性，证实了其对指纹识别的相关性。通过引入基于GSP的信号滤波和功能连接性（FC）分解的新视角，这些结果表明脑结构 - 功能耦合在静息和任务期间提供了一类新的认知和个体脑组织结构特征。此外，它们为阐明结构连接组的低空间频率和高空间频率的作用提供了见解，从而对在整个结构连接组图谱中何处可以找到用于表征个体的关键结构 - 功能信息有了新的理解。
