Background

: Brain functional connectivity (FC) analyses based on magneto/electroencephalography (M/EEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, with the latter leading to conservative hypothesis testing.

New method

: We removed such constraint by estimating high-dimensional source-based M/EEG-FC using cluster-permutation statistic (CPS) and demonstrated the feasibility of this approach by identifying resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimer’s disease. Particularly, we proposed a unified framework for CPS analysis together with a novel neighbourhood measure to estimate more compact and neurophysiological plausible neural communication. As clusters could more confidently reveal interregional communication, we proposed and tested a cluster-strength index to demonstrate other advantages of CPS analysis.

Results

: We found clusters of increased communication or hypersynchronization in MCI compared to healthy controls in delta (1−4 Hz) and higher-theta (6−8 Hz) bands oscillations. These mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere, which may be critically affected in the early stages of Alzheimer’s disease.

Conclusions

: Our approach could be important to create high-resolution FC maps from neuroimaging studies in general, allowing the multimodal analysis of neural communication across multiple spatial scales. Particularly, FC clusters more robustly represent the interregional communication by identifying dense bundles of connections that are less sensitive to inter-individual anatomical and functional variability. Overall, this approach could help to better understand neural information processing in healthy and disease conditions as needed for developing biomarker research.

中文翻译：

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脑磁图中的高维全脑功能连接映射

背景

：基于磁/脑电图（M / EEG）信号的大脑功能连接（FC）分析尚未利用固有的高维信息。通常，这些分析仅限于感兴趣的区域，以避免维数的诅咒，而后者导致保守的假设检验。

新方法

：我们通过使用聚类排列统计量（CPS）估计基于高维源的M / EEG-FC来消除了此类限制，并通过识别轻度认知障碍（MCI）的静止状态变化（前驱阶段）证明了该方法的可行性阿尔茨海默氏病。特别是，我们提出了一个用于CPS分析的统一框架，以及一种新颖的邻域测度，以评估更紧凑和神经生理学上合理的神经沟通。由于聚类可以更自信地揭示区域间的交流，因此我们提出并测试了聚类强度指数，以证明CPS分析的其他优势。

结果

：我们发现，与健康对照组相比，MCI中的交流或超同步性增强的集群在δ（1-4 Hz）和较高θ（6-8 Hz）的波段振荡中。这些主要由左半球的枕额和枕颞区之间的相互作用组成，这可能在阿尔茨海默氏病的早期阶段受到严重影响。

结论

：我们的方法对于从神经影像研究总体上创建高分辨率FC映射可能很重要，从而可以跨多个空间尺度对神经通讯进行多模式分析。特别是，FC群集通过识别对个体间的解剖结构和功能变异性较不敏感的密集连接束，更稳健地表示区域间通信。总体而言，这种方法可以帮助更好地了解健康和疾病条件下神经信息的处理，这是开展生物标志物研究所需的。