Brain cancer is one of the deadliest diseases, although many efforts have been made to treat it, there is no comprehensive and effective treatment approach yet. In recent years, the use of network-based analysis to identify important biological genes and pathways involved in various complex diseases, including brain cancer, has attracted the attention of researchers. The goal of this manuscript is to perform a comprehensive analysis of the various results presented related to brain cancer. For this purpose, firstly, based on the CORMINE medical database, collected all the genes related to brain cancer with a valid P-value. Then the structural and functional relationships between the above gene sets have been identified based on the STRING database. Next, in the PPI network, hub centrality analysis was performed to determine the proteins that have many connections with other proteins. After the modularization of the network, the module with the most hub vertices is considered as the most relevant module to the formation and progression of brain cancer. Since the driver vertices play an important role in biological systems, the edges of the selected module were oriented, and by analyzing the controllability of complex networks, a set of five proteins with the highest control power has been identified. Finally, based on the drug-gene interaction, a set of drugs effective on each of the driver genes has been obtained, which can potentially be used as new combination drugs. Validation of the hub and driver proteins shows that they are mainly essential proteins in the biological processes related to the various cancers and therefore the drugs that affect them can be considered as new combination therapy. The presented procedure can be used for any other complex disease.

The role of network metrics in exploring brain networks of mental illness is crucial. This study focuses on quantifying a node controllability index (CA-scores) and developing a novel framework for studying the dysfunction of attention deficit hyperactivity disorder (ADHD) brains. By analyzing fMRI data from 143 healthy controls and 102 ADHD patients, the controllability metric reveals distinct differences in nodes (brain regions) and subsystems (functional modules). There are significantly atypical CA-scores in the Rolandic operculum, superior medial orbitofrontal cortex, insula, posterior cingulate gyrus, supramarginal gyrus, angular gyrus, precuneus, heschl gyrus, and superior temporal gyrus of ADHD patients. A comparison with measures of connection strength, eigenvector centrality, and topology entropy suggests that the controllability index may be more effective in identifying abnormal regions in ADHD brains. Furthermore, our controllability index could be extended to investigate functional networks associated with other psychiatric disorders.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s11571-023-10063-z.

The controllability of complex networks is a core issue in network research. Assessing the controllability robustness of networks under destructive attacks holds significant practical importance. This paper studies the controllability of networks from the perspective of malicious attacks. A novel attack model is proposed to evaluate and challenge network controllability. This method disrupts network controllability with high precision by identifying and targeting critical candidate nodes. The model is compared with traditional attack methods, including degree-based, betweenness-based, closeness-based, pagerank-based, and hierarchical attacks. Results show that the model outperforms these methods in both disruption effectiveness and computational efficiency. Extensive experiments on both synthetic and real-world networks validate the superior performance of this approach. This study provides valuable insights for identifying key nodes crucial for maintaining network controllability. It also offers a solid framework for enhancing network resilience against malicious attacks.

BACKGROUND: Controllability analysis is an approach developed for evaluating the ability of a brain region to modulate function in other regions, which has been found to be altered in major depressive disorder (MDD). Both depressive symptoms and cognitive impairments are prominent features of MDD, but the case-control differences of controllability between MDD and controls can not fully interpret the contribution of both clinical symptoms and cognition to brain controllability and linked patterns among them in MDD.
METHODS: Sparse canonical correlation analysis was used to investigate the associations between resting-state functional brain controllability at the network level and clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD.
RESULTS: Average controllability was significantly correlated with clinical features. The average controllability of the dorsal attention network (DAN) and visual network had the highest correlations with clinical variables. Among clinical variables, depressed mood, suicidal ideation and behaviour, impaired work and activities, and gastrointestinal symptoms were significantly negatively associated with average controllability, and reduced cognitive flexibility was associated with reduced average controllability.
CONCLUSIONS: These findings highlight the importance of brain regions in modulating activity across brain networks in MDD, given their associations with symptoms and cognitive impairments observed in our study. Disrupted control of brain reconfiguration of DAN and visual network during their state transitions may represent a core brain mechanism for the behavioural impairments observed in MDD.
BACKGROUND: National Natural Science Foundation of China (82001795 and 82027808), National Key R&D Program (2022YFC2009900), and Sichuan Science and Technology Program (2024NSFSC0653).

Intertemporal decision-making is important for both economy and physical health. Nevertheless, in daily life, individuals tend to prefer immediate and smaller rewards to delayed and larger rewards, which is known as delay discounting (DD). Episodic future thinking (EFT) has been proven to influence DD. However, there is still no inconsistent conclusion on the effect of negative EFT on DD. Considering the perceived controllability of negative EFT may address the issue (Controllability refers to the extent to which progress and result of an event could be controlled by ourselves). In the current study, we manipulated EFT conditions (baseline, neutral EFT, negative-controllable EFT and negative-uncontrollable EFT), delayed time (i.e. 1 week, 1 month, 3 months, 6 months, 1 year and 3 years) and reward magnitude (small, large). We mainly found that when experiencing negative-uncontrollable EFT compared to negative-controllable EFT in the delayed time of 6 months with large rewards, individuals chose more delayed rewards, suggesting that negative-uncontrollable EFT effectively reduced DD under conditions of both large-magnitude reward and longer delayed time. The current study provides new insight for healthy groups on optimising EFT. In that case, individuals are able to gain long-term benefits in financial management and healthcare.

Although the effects of counterstereotypic individuating information (i.e., information specific to individual members of stereotyped groups that disconfirms the group stereotype) on biases in explicit person perception are well-established, research shows mixed effects of such information on implicit person perception. The present research tested the overarching hypothesis that, when social group membership is perceived to be under an individual\'s control, diagnostic individuating information would have lesser effects on implicit person perception than it would when social group membership is perceived not to be under an individual\'s control. This hypothesis was tested in the domain of implicit attitudinal and stereotype-relevant judgments of individuals who belonged to existing social groups and individuals who belonged to novel social groups. We found that individuating information consistently shifted scores on implicit measures among targets belonging to existing social groups, but not in a theoretically predicted direction among targets belonging to novel social groups. Controllability of group membership did not moderate such effects. Results of implicit and explicit measures were mostly consistent when targets belonged to existing social groups, but mostly inconsistent when targets belonged to novel social groups.

Seven preregistered experimental studies investigated a potential mediator (self-blame) and moderator (the perceived responsibility of the helper for the help recipient\'s behavior) of Weiner\'s attribution-emotion-action model. When participants considered a nonchild close other experiencing depression, higher perceived controllability was related to lower sympathy, which correlated with less willingness to provide support; however, among parents considering their child experiencing depression, perceived controllability was either positively associated with sympathy (study 1) or did not influence sympathy (study 2). Offering an explanation, studies 3a/3b indicated a significantly weaker relationship between controllability and responsibility attributions when the target of help was the participant\'s child. Study 4 investigated the underlying mechanism. Parents experienced self-blame when the cause was controllable, which lowered the association between controllability and responsibility attributions. Studies 5 and 6 revealed this pattern was not specific to the parent-child relationship but occurred whenever the potential helper felt responsible for the help recipient\'s behavior.

This paper investigates the controllability of impulsive systems with input delay and impulse delay and its applications in multi-agent networks. We adopt the geometric and algebraic analytical tools to establish some easily verified controllability conditions for the considered system model. First, the analytic solution of the considered system is established on every impulsive interval by using ordinary differential equation theory. Then, according to the solution derived, some sufficient complete controllability criteria are developed to reveal the role of the Gramian matrices in different subintervals. By introducing a row matrix of different kinds of Gramian matrices, a complete controllability condition that is proved to be necessary and sufficient is further obtained. By using the relevant geometric matrix theory, the derived algebraic controllability condition is then converted to a geometric one. On other hand, we introduce a multi-agent network with delayed input and impulse and investigate its controllability. By resorting to graph theory and matrix theory, several factors affecting the controllability of the considered multi-agent networks are investigated, such as the topology structure, the inner coupling matrix, and the dynamics of each agent. Finally, two numerical examples are worked out to verify the derived controllability criteria.

Objective. In recent studies, network control theory has been applied to clarify transitions between brain states, emphasizing the significance of assessing the controllability of brain networks in facilitating transitions from one state to another. Despite these advancements, the potential alterations in functional network controllability associated with Alzheimer\'s disease (AD), along with the underlying genetic mechanisms responsible for these alterations, remain unclear.Approach. We conducted a comparative analysis of functional network controllability measures between patients with AD (n= 64) and matched normal controls (NCs,n= 64). We investigated the association between altered controllability measures and cognitive function in AD. Additionally, we conducted correlation analyses in conjunction with the Allen Human Brain Atlas to identify genes whose expression was correlated with changes in functional network controllability in AD, followed by a set of analyses on the functional features of the identified genes.Main results. In comparison to NCs, patients with AD exhibited a reduction in average controllability, predominantly within the default mode network (DMN) (63% of parcellations), and an increase in average controllability within the limbic (LIM) network (33% of parcellations). Conversely, AD patients displayed a decrease in modal controllability within the LIM network (27% of parcellations) and an increase in modal controllability within the DMN (80% of parcellations). In AD patients, a significant positive correlation was found between the average controllability of the salience network and the mini-mental state examination scores. The changes in controllability measures exhibited spatial correlation with transcriptome profiles. The significant genes identified exhibited enrichment in neurobiologically relevant pathways and demonstrated preferential expression in various tissues, cell types, and developmental periods.Significance. Our findings have the potential to offer new insights into the genetic mechanisms underlying alterations in the controllability of functional networks in AD. Additionally, these results offered perspectives for a deeper understanding of the pathogenesis and the development of therapeutic strategies for AD.

Quantum dots (QDs) hold immense promise for bioimaging, yet technical challenges in surface engineering limit their wider scientific use. We introduce poly(pentafluorophenyl acrylate) (PPFPA) as a user-friendly prepolymer platform for creating precisely controlled multidentate polymeric ligands for QD surface engineering, accessible to researchers without extensive synthetic expertise. PPFPA combines the benefits of both bottom-up and prepolymer approaches, offering minimal susceptibility to hydrolysis and side reactions for controlled chemical composition, along with simple synthetic procedures using commercially available reagents. Live cell imaging experiments highlighted a significant reduction in nonspecific binding when employing PPFPA, owing to its minimal hydrolysis, in contrast to ligands synthesized by using a conventional prepolymer prone to uncontrolled hydrolysis. This observation underscores the distinct advantage of our prepolymer system. Leveraging PPFPA, we synthesized biomolecule-conjugated QDs and performed QD-based immunofluorescence to detect a cytosolic protein. To effectively label cytosolic targets in such a dense and complex environment, probes must exhibit minimal nonspecific binding and be compact. As a result, QD-immunofluorescence has focused primarily on cell surface targets. By creating compact QD-F(ab\')2, we sensitively detected alpha-tubulin with a ∼50-fold higher signal-to-noise ratio compared to organic dye-based labeling. PPFPA represents a versatile and accessible platform for tailoring QD surfaces, offering a pathway to realize the full potential of colloidal QDs in various scientific applications.