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Modern approaches to investigate complex brain dynamics suggest to represent the brain as a functional network of brain regions defined by a brain atlas, while edges represent the structural or functional connectivity among them. This approach is also utilized for mathematical modeling of the resting-state brain dynamics, where the applied brain parcellation plays an essential role in deriving the model network and governing the modeling results. There is however no consensus and empirical evidence on how a given brain atlas affects the model outcome, and the choice of parcellation is still rather arbitrary. Accordingly, we explore the impact of brain parcellation on inter-subject and inter-parcellation variability of model fitting to empirical data. Our objective is to provide a comprehensive empirical evidence of potential influences of parcellation choice on resting-state whole-brain dynamical modeling. We show that brain atlases strongly influence the quality of model validation and propose several variables calculated from empirical data to account for the observed variability. A few classes of such data variables can be distinguished depending on their inter-subject and inter-parcellation explanatory power.

Rockburst represents a very dangerous phenomenon in deep underground mining in unfavourable conditions such as great depth, high horizontal stress, proximity of important tectonic structures, and unmined pillars. The case study describes a recorded heavy rockburst in the Czech part of the Upper Silesian Coal Basin, which occurred during longwall mining near the protective pillar. The artificial dividing of geological blocks and creation of mining protective pillars (shaft pillars, crosscut pillars etc.) is a dangerous task in light of rockbursts occurring mainly due to overstressing of remaining pillars. A simple model of this situation is presented. Natural and mining conditions are analysed and presented in detail as well as registered seismicity during longwall mining in the area. Recorded rockbursts in the area of interest are described and their causes discussed. Many rockbursts near protective pillars were recorded in this mining region. Methodical instructions for rockburst prevention in proximity of protective pillars as well as for gates driving were devised based on the evaluation of rockburst causes. The paper presents these principles for prevention. Keywords: Rockburst, Rockburst prevention, Hardcoal longwall mining, Induced seismicity, Protective pillar

Background: Progressive cognitive decline following multimodal neurooncological treatment is a common observation in patients suffering from malignant glioma. Alterations of the default-mode network (DMN) represent a possible source of impaired neurocognitive functioning and were analyzed in these patients. Methods: Eighty patients (median age, 51 years) with glioma (WHO grade IV glioblastoma, n = 57; WHO grade III anaplastic astrocytoma, n = 13; WHO grade III anaplastic oligodendroglioma, n = 10) and ECOG performance score 0–1 underwent resting-state functional MRI (rs-fMRI) and neuropsychological testing at a median interval of 13 months (range, 1–114 months) after initiation of therapy. For evaluation of structural and metabolic changes after treatment, anatomical MRI and amino acid PET using O-(2-[18F]fluoroethyl)-L-tyrosine (FET) were simultaneously acquired to rs-fMRI on a hybrid MR/PET scanner. A cohort of 80 healthy subjects matched for gender, age, and educational status served as controls. Results: The connectivity pattern within the DMN (12 nodes) of the glioma patients differed significantly from that of the healthy subjects but did not depend on age, tumor grade, time since treatment initiation, presence of residual/recurrent tumor, number of chemotherapy cycles received, or anticonvulsive medication. Small changes in the connectivity pattern were observed in patients who had more than one series of radiotherapy. In contrast, structural tissue changes located at or near the tumor site (including resection cavities, white matter lesions, edema, and tumor tissue) had a strong negative impact on the functional connectivity of the adjacent DMN nodes, resulting in a marked dependence of the connectivity pattern on tumor location. In the majority of neurocognitive domains, glioma patients performed significantly worse than healthy subjects. Correlation analysis revealed that reduced connectivity in the left temporal and parietal DMN nodes was associated with low performance in language processing and verbal working memory. Furthermore, connectivity of the left parietal DMN node also correlated with processing speed, executive function, and verbal as well as visual working memory. Overall DMN connectivity loss and cognitive decline were less pronounced in patients with higher education. Conclusion: Personalized treatment strategies for malignant glioma patients should consider the left parietal and temporal DMN nodes as vulnerable regions concerning neurocognitive outcome.