Sparacino Laura, Faes Luca, Mijatović Gorana, Parla Giuseppe, Lo Re Vincenzina, Miraglia Roberto, de Ville de Goyet Jean, Sparacia Gianvincenzo
Department of Engineering, University of Palermo, 90128 Palermo, Italy.
Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia.
Life (Basel). 2023 Oct 18;13(10):2075. doi: 10.3390/life13102075.
Keeping up with the shift towards personalized neuroscience essentially requires the derivation of meaningful insights from individual brain signal recordings by analyzing the descriptive indexes of physio-pathological states through statistical methods that prioritize subject-specific differences under varying experimental conditions. Within this framework, the current study presents a methodology for assessing the value of the single-subject fingerprints of brain functional connectivity, assessed both by standard pairwise and novel high-order measures. Functional connectivity networks, which investigate the inter-relationships between pairs of brain regions, have long been a valuable tool for modeling the brain as a complex system. However, their usefulness is limited by their inability to detect high-order dependencies beyond pairwise correlations. In this study, by leveraging multivariate information theory, we confirm recent evidence suggesting that the brain contains a plethora of high-order, synergistic subsystems that would go unnoticed using a pairwise graph structure. The significance and variations across different conditions of functional pairwise and high-order interactions (HOIs) between groups of brain signals are statistically verified on an individual level through the utilization of surrogate and bootstrap data analyses. The approach is illustrated on the single-subject recordings of resting-state functional magnetic resonance imaging (rest-fMRI) signals acquired using a pediatric patient with hepatic encephalopathy associated with a portosystemic shunt and undergoing liver vascular shunt correction. Our results show that (i) the proposed single-subject analysis may have remarkable clinical relevance for subject-specific investigations and treatment planning, and (ii) the possibility of investigating brain connectivity and its post-treatment functional developments at a high-order level may be essential to fully capture the complexity and modalities of the recovery.
紧跟向个性化神经科学的转变，本质上需要通过统计方法分析生理病理状态的描述性指标，从个体脑信号记录中得出有意义的见解，这些统计方法在不同实验条件下优先考虑个体特异性差异。在此框架内，本研究提出了一种方法，用于评估脑功能连接的单受试者指纹的价值，该价值通过标准的成对测量和新颖的高阶测量进行评估。功能连接网络研究脑区对之间的相互关系，长期以来一直是将大脑建模为复杂系统的宝贵工具。然而，它们的有用性受到其无法检测超出成对相关性的高阶依赖性的限制。在本研究中，通过利用多元信息理论，我们证实了最近的证据，表明大脑包含大量高阶协同子系统，使用成对图结构会忽略这些子系统。通过使用替代和自助数据分析，在个体水平上对脑信号组之间功能成对和高阶相互作用（HOIs）在不同条件下的显著性和变化进行了统计验证。该方法在一名患有与门体分流相关的肝性脑病并接受肝血管分流矫正的儿科患者的静息态功能磁共振成像（rest-fMRI）信号的单受试者记录上进行了说明。我们的结果表明：（i）所提出的单受试者分析对于个体特异性研究和治疗计划可能具有显著的临床相关性；（ii）在高阶水平上研究脑连接及其治疗后功能发展的可能性对于充分捕捉恢复的复杂性和模式可能至关重要。
