Dynamic network reorganization underlying neuroplasticity: the deficits-severity-related language network dynamics in patients with left hemispheric gliomas involving language network.

Brain network dynamics not only endow the brain with flexible coordination for various cognitive processes but also with a huge potential of neuroplasticity for development, skill learning, and after cerebral injury. Diffusive and progressive glioma infiltration triggers the neuroplasticity for functional compensation, which is an outstanding pathophysiological model for the investigation of network reorganization underlying neuroplasticity. In this study, we employed dynamic conditional correlation to construct framewise language networks and investigated dynamic reorganizations in 83 patients with left hemispheric gliomas involving language networks (40 patients without aphasia and 43 patients with aphasia). We found that, in healthy controls (HCs) and patients, the language network dynamics in resting state clustered into 4 temporal-reoccurring states. Language deficits-severity-dependent topological abnormalities of dFCs were observed. Compared with HCs, suboptimal language network dynamics were observed for those patients without aphasia, while more severe network disruptions were observed for those patients with aphasia. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the 4 states significantly predicted individual patients' language scores. These findings shed light on our understanding of metaplasticity in glioma. Glioma-induced language network reorganizations were investigated under a dynamic "meta-networking" (network of networks) framework. In healthy controls and patients with glioma, the framewise language network dynamics in resting-state robustly clustered into 4 temporal-reoccurring states. The spatial but not temporal language deficits-severity-dependent abnormalities of dFCs were observed in patients with left hemispheric gliomas involving language network. Language network dynamics significantly predicted individual patients' language scores.

The domain-separation language network dynamics in resting state support its flexible functional segregation and integration during language and speech processing.

Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic "meta-network" framework to support flexible functional segregation and integration during language and speech processing.

Rapid adsorption and reductive degradation of Naphthol Green B from aqueous solution by Polypyrrole/Attapulgite composites supported nanoscale zero-valent iron.

Polypyrrole/Attapulgite-supported nanoscale zero-valent iron (PPy/APT-nZVI) composites employed to extract Naphthol Green B (NGB) from aqueous solution, were successfully fabricated by chemical oxidative polymerization and liquid-phase reduction method. Comparison experiment of different materials showed that 99.59% of NGB was removed using PPy/APT-nZVI (1:0.5) after 25 min, much higher than APT, PPy, PPy/APT and nZVI. The morphology and structure of PPy/APT-nZVI (1:0.5) composites were characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), which confirmed the high disperse and activity of nZVI after supported by PPy/APT. Furthermore, dynamic studies revealed that removal process was highly consistent with not only the pseudo-second-order model for adsorption but also pseudo-first-order model for degradation process, which proved the removal was controlled by chemical surface-limiting step. A possible removal mechanism, containing prompt adsorption of NGB onto the PPy/APT-nZVI (1:0.5) surface and being degraded by nZVI, was put forward. Additionally, the stability study verified the activity of nZVI can retain longer time than that of single nZVI due to such powerfully protective layers of PPy/APT.