An Intelligent and Soluble Microneedle Composed of Bi/BiVO4 Schottky Heterojunction for Tumor Ct Imaging and Starvation/Gas Therapy-Promoted Synergistic Cancer Treatment.

Phototherapy and sonodynamic therapy (SDT) are widely used for the synergistic treatment of tumors and have received considerable attention. However, an inappropriate tumor microenvironment, including pH, H2O2, oxygen, and glutathione levels, can reduce the therapeutic effects of synergistic phototherapy and SDT. Here, a novel Bi-based soluble microneedle (MN) is designed for the CT imaging of breast tumors and starvation therapy/gas therapy-enhanced phototherapy/SDT. The optimized Bi/BiVO4 Schottky heterojunction serves as the tip of the MN, which not only has excellent photothermal conversion ability and CT contrast properties, but its heterojunction can also avoid the rapid combination of electrons and hole pairs, thereby enhancing the photodynamic/sonodynamic effects. A degradable MN with excellent mechanical properties is fabricated by optimizing the ratios of poly(vinyl alcohol), poly(vinyl pyrrolidone), and sodium hyaluronate. Glucose oxidase (GOx) and diallyl trisulfide are loaded into the MN to achieve tumor starvation and gas therapy, respectively; And the controlled release of GOx and H2S can be achieved under ultrasound or near-infrared laser irradiation. The in vitro and in vivo results demonstrate that this multifunctional MN can achieve high therapeutic efficacy through starvation therapy/gas therapy-enhanced phototherapy/SDT. The designed multifunctional MN provides a prospective approach for synergistic phototherapy and SDT.

Strength of predicted information content in the brain biases decision behavior.

Prediction-for-perception theories suggest that the brain predicts incoming stimuli to facilitate their categorization.1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17 However, it remains unknown what the information contents of these predictions are, which hinders mechanistic explanations. This is because typical approaches cast predictions as an underconstrained contrast between two categories18,19,20,21,22,23,24-e.g., faces versus cars, which could lead to predictions of features specific to faces or cars, or features from both categories. Here, to pinpoint the information contents of predictions and thus their mechanistic processing in the brain, we identified the features that enable two different categorical perceptions of the same stimuli. We then trained multivariate classifiers to discern, from dynamic MEG brain responses, the features tied to each perception. With an auditory cueing design, we reveal where, when, and how the brain reactivates visual category features (versus the typical category contrast) before the stimulus is shown. We demonstrate that the predictions of category features have a more direct influence (bias) on subsequent decision behavior in participants than the typical category contrast. Specifically, these predictions are more precisely localized in the brain (lateralized), are more specifically driven by the auditory cues, and their reactivation strength before a stimulus presentation exerts a greater bias on how the individual participant later categorizes this stimulus. By characterizing the specific information contents that the brain predicts and then processes, our findings provide new insights into the brain's mechanisms of prediction for perception.

Network Communications Flexibly Predict Visual Contents That Enhance Representations for Faster Visual Categorization.

Models of visual cognition generally assume that brain networks predict the contents of a stimulus to facilitate its subsequent categorization. However, understanding prediction and categorization at a network level has remained challenging, partly because we need to reverse engineer their information processing mechanisms from the dynamic neural signals. Here, we used connectivity measures that can isolate the communications of a specific content to reconstruct these network mechanisms in each individual participant (N = 11, both sexes). Each was cued to the spatial location (left vs right) and contents [low spatial frequency (LSF) vs high spatial frequency (HSF)] of a predicted Gabor stimulus that they then categorized. Using each participant's concurrently measured MEG, we reconstructed networks that predict and categorize LSF versus HSF contents for behavior. We found that predicted contents flexibly propagate top down from temporal to lateralized occipital cortex, depending on task demands, under supervisory control of prefrontal cortex. When they reach lateralized occipital cortex, predictions enhance the bottom-up LSF versus HSF representations of the stimulus, all the way from occipital-ventral-parietal to premotor cortex, in turn producing faster categorization behavior. Importantly, content communications are subsets (i.e., 55-75%) of the signal-to-signal communications typically measured between brain regions. Hence, our study isolates functional networks that process the information of cognitive functions.SIGNIFICANCE STATEMENT An enduring cognitive hypothesis states that our perception is partly influenced by the bottom-up sensory input but also by top-down expectations. However, cognitive explanations of the dynamic brain networks mechanisms that flexibly predict and categorize the visual input according to task-demands remain elusive. We addressed them in a predictive experimental design by isolating the network communications of cognitive contents from all other communications. Our methods revealed a Prediction Network that flexibly communicates contents from temporal to lateralized occipital cortex, with explicit frontal control, and an occipital-ventral-parietal-frontal Categorization Network that represents more sharply the predicted contents from the shown stimulus, leading to faster behavior. Our framework and results therefore shed a new light of cognitive information processing on dynamic brain activity.

Different computations over the same inputs produce selective behavior in algorithmic brain networks.

A key challenge in neuroimaging remains to understand where, when, and now particularly how human brain networks compute over sensory inputs to achieve behavior. To study such dynamic algorithms from mass neural signals, we recorded the magnetoencephalographic (MEG) activity of participants who resolved the classic XOR, OR, and AND functions as overt behavioral tasks (N = 10 participants/task, N-of-1 replications). Each function requires a different computation over the same inputs to produce the task-specific behavioral outputs. In each task, we found that source-localized MEG activity progresses through four computational stages identified within individual participants: (1) initial contralateral representation of each visual input in occipital cortex, (2) a joint linearly combined representation of both inputs in midline occipital cortex and right fusiform gyrus, followed by (3) nonlinear task-dependent input integration in temporal-parietal cortex, and finally (4) behavioral response representation in postcentral gyrus. We demonstrate the specific dynamics of each computation at the level of individual sources. The spatiotemporal patterns of the first two computations are similar across the three tasks; the last two computations are task specific. Our results therefore reveal where, when, and how dynamic network algorithms perform different computations over the same inputs to produce different behaviors.

The Influence of Action Video Gaming Experience on the Perception of Emotional Faces and Emotional Word Meaning.

Action video gaming (AVG) experience has been found related to sensorimotor and attentional development. However, the influence of AVG experience on the development of emotional perception skills is still unclear. Using behavioral and ERP measures, this study examined the relationship between AVG experience and the ability to decode emotional faces and emotional word meanings. AVG experts and amateurs completed an emotional word-face Stroop task prior to (the pregaming phase) and after (the postgaming phase) a 1 h AVG session. Within-group comparisons showed that after the 1 h AVG session, a more negative N400 was observed in both groups of participants, and a more negative N170 was observed in the experts. Between-group comparisons showed that the experts had a greater change of N170 and N400 amplitudes across phases than the amateurs. The results suggest that both the 1 h and long-term AVG experiences may be related to an increased difficulty of emotional perception. Furthermore, certain behavioral and ERP measures showed neither within- nor between-group differences, suggesting that the relationship between AVG experience and emotional perception skills still needs further research.

The high-working load states induced by action real-time strategy gaming: An EEG power spectrum and network study.

Action Real-time Strategy Gaming (ARSG) is a cognitively demanding task that requires attention, sensorimotor skills, high-level team coordination, and strategy-making abilities. Thus, ARSG can offer important, new insights into learning-related neural plasticity. However, little research has examined how the brain allocates cognitive resources in ARSG. By analyzing power spectrums and electroencephalograph (EEG) functional connectivity (FC) networks, this study compared multiple conditions (resting, movie watching, ARSG, and Life simulation gaming - LSG) in two experiments. Consistent with previous research, we found that brain waves appeared to be de-assimilated after activation. Furthermore, results showed that ARSG was associated with higher activation and workload as indicated by θ-waves, and required higher attention as reflected by ß-waves. Furthermore, as participants began ARSG, the allocation of cognitive resource gradually prioritized the frontal area, which controls attention, decision-making, monitoring, and mnemonic processing, while participants also showed an enhanced ability to process information under the ARSG condition as indicated by network characteristics. These electrophysiological changes observed in ARSG were not found under LSG. Thus, this study applied both power spectrum and EEG FC networks analyses to ARSG research, revealing characteristics of brain waves in typical areas and how the brain gradually changes from low-working load states to high-working load states based on real-time EEG recordings.

Electronic-Sports Experience Related to Functional Enhancement in Central Executive and Default Mode Areas.

Electronic-sports (e-sports) is a form of organized, online, multiplayer video game competition, which requires both action skills and the ability and process of forming and adapting a strategy (referred to as strategization hereafter) to achieve goals. Over the past few decades, research has shown that video gaming experience has an important impact on the plasticity of the sensorimotor, attentional, and executive brain areas. However, little research has examined the relationship between e-sports experience and the plasticity of brain networks that are related to strategization. Using resting-state fMRI data and the local functional connectivity density (lFCD) analysis, this study investigated the relationship between e-sports experience (League of Legends [LOL] in this study) and brain plasticity by comparing between top-ranking LOL players and lower-ranking (yet experienced) LOL players. Results showed that the top-ranking LOL players had superior local functional integration in the executive areas compared to lower-ranking players. Furthermore, the top-ranking players had higher lFCD in the default mode areas, which have been found related to various subprocesses (e.g., memory and planning) essential for strategization. Finally, the top-ranking players' lFCD was related to their LOL expertise rank level, as indicated by a comprehensive score assigned by the gaming software based on players' gaming experience and expertise. Thus, the result showed that the local functional connectivity in central executive and default mode brain areas was enhanced in the top-ranking e-sports players, suggesting that e-sports experience is related to the plasticity of the central executive and default mode areas.

Rapid Improvement in Visual Selective Attention Related to Action Video Gaming Experience.

A central issue in cognitive science is understanding how learning induces cognitive and neural plasticity, which helps illuminate the biological basis of learning. Research in the past few decades showed that action video gaming (AVG) offered new, important perspectives on learning-related cognitive and neural plasticity. However, it is still unclear whether cognitive and neural plasticity is observable after a brief AVG session. Using behavioral and electrophysiological measures, this study examined the plasticity of visual selective attention (VSA) associated with a 1 h AVG session. Both AVG experts and non-experts participated in this study. Their VSA was assessed prior to and after the AVG session. Within-group comparisons on the participants' performance before and after the AVG session showed improvements in response time in both groups and modulations of electrophysiological measures in the non-experts. Furthermore, between-group comparisons showed that the experts had superior VSA, relative to the non-experts, prior to the AVG session. These findings suggested an association between the plasticity of VSA and AVG. Most importantly, this study showed that the plasticity of VSA was observable after even a 1 h AVG session.