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Cognitive impairment is a common symptom in patients with glioma (Caramanna et al., 2021; Klein, Duffau, & De Witt Hamer, 2012; van Kessel, Baumfalk, van Zandvoort, Robe, & Snijders, 2017), seen across multiple cognitive domains including executive functioning. The latter is a set of higher-order mental skills that include working memory, flexible thinking, and self-control. Integration of information across the brain is crucial to maintain executive functioning performance (Bullmore & Sporns, 2012). Moreover, further deterioration of executive functioning has been observed after surgical removal of a brain tumor (Ng et al., 2019; Noll et al., 2015; Satoer et al., 2012; Talacchi, Santini, Savazzi, & Gerosa, 2011; Wu et al., 2011). Neural correlates of this cognitive impairment and subsequent deficit are sparse. Magnetoencephalography (MEG) studies have revealed that post-operative cognitive changes are associated with both preoperative characteristics and temporal changes of the functional brain network using graph theory measures including functional connectivity and hub load (Carbo et al., 2017; Derks, Reijneveld, & Douw, 2014; van Dellen et al., 2012). That is, Van Dellen et al. (2012) showed that higher lower alpha band connectivity within the default mode resting state network after surgery was related to better verbal memory. Within the upper alpha band, higher connectivity of the right frontoparietal resting state network correlated with better attention, working memory and executive functioning. As also suggested from cross-sectional studies, increasing integration of the network related to improving cognitive functioning (Carbo et al., 2017; Derks, Reijneveld, & Douw, 2014): deteriorating verbal memory after tumor resection correlated with decreasing dynamic integrative hub functioning. More importantly, cognitive decline could be predicted by lower baseline hub integration (Carbo et al., 2017). Hub nodes are crucial for information flow throughout the system, serving as a connector to other nodes within or between brain networks. The extent to which hubs play an integrative or central role within the entire brain network seems generally related to cognitive functioning in glioma. However, all the mentioned studies calculated graph theory network measures in each MEG frequency band separately (alpha, beta, delta etc). Recent work shows that applying a multilayer network approach that calculates network measures across different layers (frequency bands in the case of MEG data) yields additional information compared to single layer results (Brookes et al., 2016; De Domenico, Sasai, & Arenas, 2016; Guillon et al., 2017; Tewarie et al., 2016; Yu et al., 2017). For example, (Breedt et al., 2021) showed that higher multilayer centrality in the frontoparietal network related to better executive functioning in healthy controls, whereas this association was not observed in single layer analyses. In this study, we therefore aim to explore multilayer MEG connectivity before and after resective surgery in relation to executive functioning in glioma patients.

Objective: To determine the degree of agreement between delirium experts on the diagnosis of delirium based on exactly the same information, and to assess the sensitivity of delirium screening methods used by clinical nurses. Design: Prospective observational longitudinal study. Method: Older patients (≥ 60 years) who underwent major surgery were included. During the first three days after surgery they had a standardised cognitive screening test which was recorded on video. Two delirium experts independently evaluated these videos and the information from the patient records. They classified the patients as having 'no delirium', 'possible delirium' or 'delirium'. If there was disagreement, a third expert was consulted. The final classification, based on consensus of two or three delirium experts, was compared with the result of the delirium screening carried out by the clinical nurses. Results: A total of 167 patients were included and 424 postoperative classifications were obtained. The agreement between the experts was 0.61(95% confidence interval (CI):0.53-0.68), based on Cohen's kappa. In 89 (21.0%) of the postoperative classifications there was no agreement between the experts and a third expert was consulted. The nurses using the delirium screening tools recognised 32% of the cases that had been classified as delirium by the experts. Conclusion: There was considerable disagreement between the classifications of individual delirium experts, based on exactly the same information, indicating the difficulty of the diagnosis. Furthermore, the sensitivity of the delirium screening tools used by the clinical nurses was poor. Further research should focus on the development of objective methods for recognising delirium.