The time course of creativity: Multivariate classification of default and executive network contributions to creative cognition over time

Research indicates that creative cognition depends on both associative and controlled processes, corresponding to the brain's default mode network (DMN) and executive control network (ECN) networks. However, outstanding questions include how the DMN and ECN operate over time during creative task performance, and whether creative cognition involves distinct generative and evaluative stages. To address these questions, we used multivariate pattern analysis (MVPA) to assess how the DMN and ECN contribute to creative cognition over three successive time phases during the production of a single creative idea. Training classifiers to predict trial condition (creative vs non-creative), we used classification accuracy as a measure of the extent of creative activity in each brain network and time phase. Across both networks, classification accuracy was highest in early phases, decreased in mid phases, and increased again in later phases, following a U-shaped curve. Notably, classification accuracy was significantly greater in the ECN than the DMN during early phases, while differences between networks at later time phases were non-significant. We also computed correlations between classification accuracy and human-rated creative performance, to assess how relevant the creative activity in each network was to the creative quality of ideas. In line with expectations, classification accuracy in the DMN was most related to creative quality in early phases, decreasing in later phases, while classification accuracy in the ECN was least related to creative quality in early phases, increasing in later phases. Given the theorized roles of the DMN in generation and the ECN in evaluation, we interpret these results as tentative evidence for the existence of separate generative and evaluative stages in creative cognition that depend on distinct neural substrates

Thought Control Ability Moderates the Effect of Mind Wandering on Positive Affect via the Frontoparietal Control Network

Mind wandering is a phenomenon that involves thoughts shifting away from a primary task to the process of dealing with other personal goals. A large number of studies have found that mind wandering can predict negative emotions, but researchers have seldom focused on the positive role of mind wandering. The current study aimed to explore the relationships among mind wandering, emotions and thought control ability, which is the ability to inhibit one’s own unpleasant or unwanted intrusive thoughts. Here, we collected resting-state functional magnetic resonance imaging (rsfMRI) data from 368 participants who completed a set of questionnaires involving mind wandering, thought control ability and positive or negative emotions. The results revealed that (1) rsfMRI connectivity features related to thought control ability and mind wandering could divide individuals into two groups: HMW (high mind-wandering) group and LMW (low mind-wandering) group. The HMW group scored lower in thought control ability (TCA), higher in negative emotion (NE) and lower in positive emotion (PE) than the LMW group. (2) TCA moderated the association between MW and positive affect (PA). (3) Two groups exhibited different segregation within key nodes (SWKN) of the frontoparietal control network (FPCN), and the subsequent analysis showed that the SWKN of the FPCN was negatively correlated with PA. These findings indicate that TCA moderates the effect of mind wandering on affect via the FPCN, which may have important implications for our understanding of the positive role of mind wandering

Case report: Beneficial effects of visual cortex tDCS stimulation combined with visual training in patients with visual field defects

BackgroundVisual field defect (VFD) refers to the phenomenon that the eye is unable to see a certain area within the normal range of vision, which may be caused by eye diseases, neurological diseases and other reasons. Transcranial direct current stimulation (tDCS) is expected to be an effective treatment for the recovery or partial recovery of VFD. This paper describes the potential for tDCS in combination with visual retraining strategies to have a positive impact on vision recovery, and the potential for neuroplasticity to play a key role in vision recovery.MethodsThis case report includes two patients. Patient 1 was diagnosed with a right occipital hemorrhage and homonymous hemianopia. Patient 2 had multiple facial fractures, a contusion of the right eye, and damage to the optic nerve of the right eye, which was diagnosed as a peripheral nerve injury (optic nerve injury). We administered a series of treatments to two patients, including transcranial direct current stimulation; visual field restoration rehabilitation: paracentric gaze training, upper and lower visual field training, VR rehabilitation, and perceptual training. One time per day, 5 days per week, total 6 weeks.ResultsAfter 6 weeks of visual rehabilitation and tDCS treatment, Patient 1 Humphrey visual field examination showed a significant improvement compared to the initial visit, with a reduction in the extent of visual field defects, increased visual acuity, and improvement in most visual functions. Patient 2 had an expanded visual field, improved visual sensitivity, and substantial improvement in visual function.ConclusionOur case reports support the feasibility and effectiveness of tDCS combined with visual rehabilitation training in the treatment of occipital stroke and optic nerve injury settings

PARP9 affects myocardial function through TGF-β/Smad axis and pirfenidone

Cardiac arrhythmias are often linked to the overactivity of cardiac fibroblasts (CFs). Investigating the impact of poly (ADP-ribose) polymerase 9 (PARP9) on Angiotensin II (Ang II)-induced fibroblast activation and the therapeutic effects of pirfenidone (PFD) offers valuable insights into cardiac arrhythmias. This study utilized weighted gene co-expression network analysis (WGCNA), differential gene expression (DEG) analysis, protein-protein interaction (PPI), and receiver operating characteristic (ROC) analysis on the GSE42955 dataset to identify the hub gene with significant diagnostic value. The ImmuCellAI tool revealed an association between PARP9 and immune cell infiltration. Our in vitro assessments focused on the influence of PFD on myofibroblast differentiation, TGF-β expression, and Ang II-induced proliferation and migration in CFs. Additionally, we explored the impact on fibrosis markers and the TGF-β/Smad signaling pathway in the context of PARP9 overexpression. Analysis of the GSE42955 dataset revealed PARP9 as a central gene with high clinical diagnostic value, linked to seven types of immune cells. The in vitro studies demonstrated that PFD significantly mitigates Ang II-induced CF proliferation, migration, and fibrosis. It also reduces Ang II-induced PARP9 expression and decreases fibrosis markers, including TGF-β, collagen I, collagen III, and α-SMA. Notably, PARP9 overexpression can partially counteract PFD's inhibitory effects on CFs and modify the expression of fibronectin, CTGF, α-SMA, collagen I, collagen III, MMP2, MMP9, TGF-β, and p-Smad2/3 in the TGF-β/Smad signaling pathway. In summary, our findings suggestes that PFD effectively counteracts the adverse effects of Ang II-induced CF proliferation and fibrosis, and modulates the TGF-β/Smad signaling pathway and PARP9 expression. This identifies a potential therapeutic approach for managing myocardial fibrosis

Low-Complexity Adaptive Sampling of Block Compressed Sensing Based on Distortion Minimization

Block compressed sensing (BCS) is suitable for image sampling and compression in resource-constrained applications. Adaptive sampling methods can effectively improve the rate-distortion performance of BCS. However, adaptive sampling methods bring high computational complexity to the encoder, which loses the superiority of BCS. In this paper, we focus on improving the adaptive sampling performance at the cost of low computational complexity. Firstly, we analyze the additional computational complexity of the existing adaptive sampling methods for BCS. Secondly, the adaptive sampling problem of BCS is modeled as a distortion minimization problem. We present three distortion models to reveal the relationship between block sampling rate and block distortion and use a simple neural network to predict the model parameters from several measurements. Finally, a fast estimation method is proposed to allocate block sampling rates based on distortion minimization. The results demonstrate that the proposed estimation method of block sampling rates is effective. Two of the three proposed distortion models can make the proposed estimation method have better performance than the existing adaptive sampling methods of BCS. Compared with the calculation of BCS at the sampling rate of 0.1, the additional calculation of the proposed adaptive sampling method is less than 1.9%

Research on the Implementation Method of Database Security in Management Information System Based on Big Data Analysis

With the rapid development of China’s society and economy, and the support of modern information technology, we have entered the era of big data. The amount of data in management information systems continues to increase. Big data has become an inevitable trend in the development of modern information technology. At the same time, the security of the database in the management information system has received increasing attention. This article studies the implementation method of database security in management information system based on big data analysis, hoping to provide reference for relevant people's research

Low-Complexity Rate-Distortion Optimization of Sampling Rate and Bit-Depth for Compressed Sensing of Images

Compressed sensing (CS) offers a framework for image acquisition, which has excellent potential in image sampling and compression applications due to the sub-Nyquist sampling rate and low complexity. In engineering practices, the resulting CS samples are quantized by finite bits for transmission. In circumstances where the bit budget for image transmission is constrained, knowing how to choose the sampling rate and the number of bits per measurement (bit-depth) is essential for the quality of CS reconstruction. In this paper, we first present a bit-rate model that considers the compression performance of CS, quantification, and entropy coder. The bit-rate model reveals the relationship between bit rate, sampling rate, and bit-depth. Then, we propose a relative peak signal-to-noise ratio (PSNR) model for evaluating distortion, which reveals the relationship between relative PSNR, sampling rate, and bit-depth. Finally, the optimal sampling rate and bit-depth are determined based on the rate-distortion (RD) criteria with the bit-rate model and the relative PSNR model. The experimental results show that the actual bit rate obtained by the optimized sampling rate and bit-depth is very close to the target bit rate. Compared with the traditional CS coding method with a fixed sampling rate, the proposed method provides better rate-distortion performance, and the additional calculation amount amounts to less than 1%