SEE MATTAR ET AL DOI101093/AWW151 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demonstrate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architecture is modulated by local blood oxygen level-dependent activity and α-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dyna...

Generalized anxiety disorder (GAD) and panic disorder (PD) are most common anxiety disorders with high lifetime prevalence while the pathophysiology and disease-specific alterations still remain largely unclear. Few studies have taken a whole-brain perspective in the functional connectivity (FC) analysis of these two disorders in resting state. It limits the ability to identify regionally and psychopathologically specific network abnormalities with their subsequent use as diagnostic marker and novel treatment strategy. The whole brain FC using a novel FC metric was compared, that is, scaled correlation, which they demonstrated to be a reliable FC statistics, but have higher statistical power in two-sample t-test of whole brain FC analysis. About 21 GAD and 18 PD patients were compared with 22 matched control subjects during resting-state, respectively. It was found that GAD patients demonstrated increased FC between hippocampus/parahippocampus and fusiform gyrus among the most significantly changed FC, while PD was mainly associated with greater FC between somatosensory cortex and thalamus. Besides such regional specificity, it was observed that psychopathological specificity in that the disrupted FC pattern in PD and GAD correlated with their respective symptom severity. The findings suggested that the increased FC between hippocampus/parahippocampus and fusiform gyrus in GAD were mainly associated with a fear generalization related neural circuit, while the greater FC between somatosensory cortex and thalamus in PD were more likely linked to interoceptive processing. Due to the observed regional and psychopathological specificity, their findings bear important clinical implications for the potential treatment strategy.

The artificial intelligence (AI) techniques have been widely used in the transient stability analysis of a power system. They are recognized as the most promising approaches for predicting the post-fault transient stability status with the use of phasor measurement units data. However, the popular AI methods used for power systems are often ''black boxes,'' which result in the poor interpretation of the model. In this paper, a transient stability prediction method based on extreme gradient boosting is proposed. In this model, a decision graph and feature importance scores are provided to discover the relationship between the features of the power system and transient stability. Meanwhile, the key features are selected according to the feature importance scores to remove redundant variables. The simulation results on the New England 39-bus system have demonstrated the superiority of the proposed model over the prior methods in the computation speed and prediction accuracy. Finally, an algorithm is proposed to interpret the prediction results for a specific fault of the power system, which further improves the interpretability of the model and makes it attractive for real-time transient stability prediction. INDEX TERMSFeature importance scores, model interpretation, XGBoost model, transient stability prediction.