Overall Survival Time Prediction for High-grade Glioma Patients based on Large-scale Brain Functional Networks

High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI). However, the highly complex lesion MRI appearance may have large individual variability, which could impede accurate individualized OS prediction. In this paper, we propose a novel concept, namely brain connectomics-based OS prediction. It is based on presurgical resting-state functional MRI (rs-fMRI) and the non-local, large-scale brain functional networks where the global and systemic prognostic features rather than the local lesion appearance are used to predict OS. We propose that the connectomics features could capture tumor-induced network-level alterations that are associated with prognosis. We construct both low-order (by means of sparse representation with regional rs-fMRI signals) and high-order functional connectivity (FC) networks (characterizing more complex multi-regional relationship by synchronized dynamics FC time courses). Then, we conduct a graph-theoretic analysis on both networks for a jointly, machine-learning-based individualized OS prediction. Based on a preliminary dataset (N = 34 with bad OS, mean OS, ~400 days; N = 34 with good OS, mean OS, ~1030 days), we achieve a promising OS prediction accuracy (86.8%) on separating the individuals with bad OS from those with good OS. However, if using only conventionally derived descriptive features (e.g., age and tumor characteristics), the accuracy is low (63.2%). Our study highlights the importance of the rs-fMRI and brain functional connectomics for treatment planning

Circadian rhythms in temporal-network connectivity

Human activity follows a circadian rhythm. In online activity, this rhythm is visible both at the level of individuals as well as at the population level from Wikipedia edits to mobile telephone calls. However, much less is known about circadian patterns at the level of network structure, that is, beyond the day-night cycle of the frequency of activity. Here, we study how the temporal connectivity of communication networks changes through the day, focusing on sequences of communication events that follow one another within a limited time. Such sequences can be thought to be characteristic of information transfer in the network. We find that temporal connectivity also follows a circadian rhythm, where at night a larger fraction of contacts is associated with such sequences and where contacts appear more independent at daytime. This result points out that temporal networks show richer variation in time than what has been known thus far.Peer reviewe