Cognitive Networks Research Articles

Multi-timescale neural dynamics for multisensory integration.

Carrying out any everyday task, be it driving in traffic, conversing with friends or playing basketball, requires rapid selection, integration and segregation of stimuli from different sensory modalities. At present, even the most advanced artificial intelligence-based systems are unable to replicate the multisensory processes that the human brain routinely performs, but how neural circuits in the brain carry out these processes is still not well understood. In this Perspective, we discuss recent findings that shed fresh light on the oscillatory neural mechanisms thatmediate multisensory integration (MI), including power modulations, phase resetting, phase-amplitude coupling and dynamic functional connectivity. We then consider studies that also suggest multi-timescale dynamics in intrinsic ongoing neural activity and during stimulus-driven bottom-up and cognitive top-down neural network processing in the context of MI. We propose a new concept of MI that emphasizes the critical role of neural dynamics at multiple timescales within and across brain networks, enabling the simultaneous integration, segregation, hierarchical structuring and selection of information in different time windows. To highlight predictions from our multi-timescale concept of MI, real-world scenarios in which multi-timescale processes may coordinate MI in a flexible and adaptive manner are considered.

Brain Network Localization of Gray Matter Atrophy and Neurocognitive and Social Cognitive Dysfunction in Schizophrenia

BackgroundNumerous studies have established the presence of gray matter atrophy and brain activation abnormalities during neurocognitive and social cognitive tasks in schizophrenia. Despite a growing consensus that diseases localize better to distributed brain networks than individual anatomical regions, relatively few studies have examined brain network localization of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia. MethodsTo address this gap, we initially identified brain locations of structural and functional abnormalities in schizophrenia from 301 published neuroimaging studies with 8712 individuals with schizophrenia and 9275 healthy control participants. By applying novel functional connectivity network mapping to large-scale resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 brain abnormality networks of schizophrenia. ResultsThe gray matter atrophy network of schizophrenia comprised a broadly distributed set of brain areas predominantly implicating the ventral attention, somatomotor, and default networks. The neurocognitive dysfunction network was also composed of widespread brain areas primarily involving the frontoparietal and default networks. By contrast, the social cognitive dysfunction network consisted of circumscribed brain regions mainly implicating the default, subcortical, and visual networks. ConclusionsOur findings suggest shared and unique brain network substrates of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia, which may not only refine the understanding of disease neuropathology from a network perspective but may also contribute to more targeted and effective treatments for impairments in different cognitive domains in schizophrenia.

Macroscale brain states support the control of semantic cognition

A crucial aim in neuroscience is to understand how the human brain adapts to varying cognitive demands. This study investigates network reconfiguration during controlled semantic retrieval in differing contexts. We analyze brain responses to two semantic tasks of varying difficulty – global association and feature matching judgments – which are contrasted with non-semantic tasks on the cortical surface and within a whole-brain state space. Demanding semantic association tasks elicit activation in anterior prefrontal and temporal regions, while challenging semantic feature matching and non-semantic tasks predominantly activate posterior regions. Task difficulty also modulates activation along different dimensions of functional organization, suggesting different mechanisms of cognitive control. More demanding semantic association judgments engage cognitive control and default mode networks together, while feature matching and non-semantic tasks are skewed towards cognitive control networks. These findings highlight the brain’s dynamic ability to tailor its networks to support diverse neurocognitive states, enriching our understanding of controlled cognition.

A Channel Usage Probability Awareness Anti-Jamming Channel Hopping Protocol for Cognitive Radio Networks

A Channel Usage Probability Awareness Anti-Jamming Channel Hopping Protocol for Cognitive Radio Networks

Cross-layer Design Based Common Data Channel Selection Scheme in Cognitive Radio Ad-hoc Network

Cross-layer Design Based Common Data Channel Selection Scheme in Cognitive Radio Ad-hoc Network

Enhancing spectral efficiency of green metric cognitive radio network using an adaptive optimization and communication protocol

SummaryInformation technology enables the process of spectral sensing and spectral efficiency (SE) with the help of different strategies attracted by researchers in cooperative cognitive radio networks (CCRN). Compared with other wireless technologies, spectral sharing in green metric CCRN (GMCCRN) is an effective strategy. Due to the collaboration between the unlicensed and licensed customers, the spectral sharing between the cooperative customers possesses various challenges. Here, the effectiveness of green CCRN is demonstrated through a variety of useful techniques. The proposed work designed a channel using Markov Gaussian wideband distribution (MGWD), and for communication, dynamic optimal relay‐based protocol (DORP) is used. Also, an effective optimization known as adaptive dynamic group‐based optimization algorithm (ADGCO) is used to examine the false alarm detection and finest spectral sensing. Finally, the effectiveness of GMCCRN is validated in terms of outage probability, spectral efficiency, energy efficiency, and throughput. Furthermore, the results revealed that the proposed method in CCRN reduces power consumption at both the secondary user (SU) and primary user (PU) sides. Also, the method maximized the throughput compared with existing schemes and achieved 0.3 as error prospect and 92.6% as accuracy.

Some challenges in using multilayer networks to bridge brain and mind.

The application of techniques from network science to create single-layer networks of the brain and mind has resulted in significant advances in the neuro- (i.e., structural and functional brain networks) and cognitive sciences (i.e., cognitive network science). Recent advances in network science on multilayer networks increase the possibility that a "network of networks" might finally connect the physical brain to the intangible mind, much like physical fibre optic cables and wires connect to other layers of the internet to allow intangible social networks to form in various social media platforms. Several advances in structural brain networks, functional brain networks, cognitive networks, and multilayer networks are briefly reviewed. The possibility that these single-layer networks can be connected in a multilayer network to connect the brain to the mind is discussed, as well as some of the challenges that face such an ambitious endeavour. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Deficit of satellite oligodendrocytes of neurons in the rostral part of the head of the caudate nucleus in schizophrenia.

Increasing evidence implicates compromised myelin integrity and oligodendrocyte abnormalities in the dysfunction of neuronal networks in schizophrenia. We previously reported a deficiency of myelinating oligodendrocytes (OL), oligodendrocyte progenitors (OP) and satellite oligodendrocytes of neurons (Sat-OL) in the prefrontal cortex and the inferior parietal cortex - cortical hubs of the frontoparietal cognitive network and default mode network (DMN) altered in schizophrenia. Deficiency of OL and OP was also detected in the head of the caudate nucleus (HCN), which accumulates cortical projections from the associative cortex and is the central node of these networks. However, the number of Sat-Ol per neuron in schizophrenia has not been studied in the HCN. In the current study we estimated the number of Sat-Ol per neuron in the rostral part of the HCN in schizophrenia (n = 18) compared to healthy controls (n = 18) in the same section collection that was previously used to study the number Ol and OP. We found a significant decrease of the number of Sat-Ol per neuron (- 50%, p < 0.001) in schizophrenia as compared to normal controls. Considering that the rostral part of the HCN is an individual network-specific projection zone of the DMN, the deficit of Sat-Ol found in schizophrenia may be related to the dysfunctional DMN-HCN connections, which has been repeatedly described in schizophrenia. The dramatic decrease of the number of Sat-Ol per neuron may be partially related to a pronounced excess of dopamine concentration in the rostral part of the HCN in schizophrenia.

Hybrid game theoretic strategy for optimal relay selection in energy harvesting cognitive radio network

SummaryRelay selection plays a crucial role in enhancing the performance of wireless networks particularly in the context of cognitive radio (CR) systems with energy harvesters. In this paper, we propose a novel approach, namely, CGAPSO Shapley, for the best relay selection while simultaneously optimizing the parameters of signal‐to‐interference‐plus‐noise ratio (SINR), throughput, and outage probability. The CGAPSO Shapley algorithm combines the Shapley value, a cooperative game theory concept, with cellular genetic algorithm particle swarm optimization (CGAPSO) to achieve effective and efficient optimization of relay selection. The CGAPSO framework provides a hybrid structure that integrates cellular genetic algorithm (CGA) and particle swarm optimization (PSO), enabling simultaneous evolution of the population and particles within cells. The incorporation of the Shapley value and the hybrid CGAPSO framework enables effective exploration of the solution space and provides decision‐makers with comprehensive insights for relay selection. By utilizing the Shapley value, we assign weights to the relay nodes based on their contributions to the overall optimization objectives, considering their CR capabilities and energy harvesting capabilities. Some benchmark test functions are used to compare the hybrid algorithm with both the standard CGAPSO, Particle swarm optimization gravitational search algorithm (PSOGSA) and PSO algorithms in evolving best solution. The results show the hybrid algorithm possesses a better capability to escape from local optimums with faster convergence than the standard algorithms. The novel CGAPSO Shapley approach achieves an outage probability of 0.323324, marking a significant improvement of 60% over the outage probability achieved with conventional approach.

Enhancing Reconfigurable Intelligent Surface-Enabled Cognitive Radio Networks for Sixth Generation and Beyond: Performance Analysis and Parameter Optimization.

In this paper, we propose a novel system integrating reconfigurable intelligent surfaces (RISs) with cognitive radio (CR) technology, presenting a forward-looking solution aligned with the evolving standards of 6G and beyond networks. The proposed RIS-assisted CR networks operate with a base station (BS) transmitting signals to two users, the primary user (PU) and secondary user (SU), through direct and reflected signal paths, respectively. Our mathematical analysis focuses on deriving expressions for SU in the RIS-assisted CR system, validated through Monte Carlo simulations. The investigation covers diverse aspects, including the impact of the signal-to-noise ratio (SNR), power allocations, the number of reflected surfaces, and blocklength variations. The results provide nuanced insights into RIS-assisted CR system performance, highlighting its sensitivity to factors like the number of reflectors, fading severity, and correlation coefficient. Careful parameter selection, such as optimizing the configuration of reflectors, is shown to prevent a complete outage, showcasing the system's robustness. Additionally, the work suggests that the optimization of reflectors configuration can significantly enhance overall system performance, and RIS-assisted CR systems outperform reference schemes. This work contributes a thorough analysis of the proposed system, offering valuable insights for efficient performance evaluation and parameter optimization in RIS-assisted CR networks.

Spectrum and Energy Efficiency in CRNs Using Dynamic Channel Reservation and Komodo Mlipir Optimization

Cognitive Radio Networks (CRNs) block new arriving services and remove existing services to increase the spectrum efficiency. Particularly, Secondary Users (SUs) experience a decline in service deprivation in the arrival of Primary Users (PUs). To overcome this, a Spectrum and energy Efficiency in Cognitive Radio Networks using Dynamic Channel Reservation and Komodo Mlipir Optimization (SE-KMOA-CRN) is proposed. The proposed method splits reserved and non-reserved bands from the accessible spectrum. Numerous channels from the non-reserved band are dynamically assigned to the reserved band to support services that experience disruptions when functioning on non-reserved band. From the non-reserved band, a count of channels dynamically assigned to the reserved band to support services that experience disruptions when functioning on the non-reserved band. It enables priority-based channel assignment and termination while providing dynamic access to the obtainable spectrum. Here, Allocation of channels to Reserved Band CRN using Dynamic Channel Reservation Algorithm (DCRA). Komodo Mlipir Optimization Algorithm (KMOA) is employed for Service selection and Channel restoration. The proposed SE-KMOA-CRN method is modelled utilizing Continuous Time Markov Chain (CTMC) with mathematical equations for some quality of service (QoS) factors is obtained. The SE-KMOA-CRN approach attains higher Throughput, lower End-to-End Delay, higher Energy Efficiency, greater spectral efficiency and higher Packet Delivery Ratio compared with existing methods, like Multiple objective optimization for spectrum along energy efficacy trade-off in IRS-assisted CRNs using NOMA (SE-MOO-CRN), Energy efficacy in CRN utilizing cooperative spectrum sensing under hybrid spectrum handoff (SE-TOA-CRN) and Energy-effectual cross-layer spectrum sharing on CR green IoT (SE-CRG-CRN) respectively.

Physical-layer security for primary users in 5G underlay cognitive radio system via artificial-noise-aided by secondary users

The advent of the Internet of Things (IoT) has revolutionized connectivity by interconnecting a vast array of devices, underscoring the critical need for robust data security, particularly at the Physical Layer Security (PLS). Ensuring data confidentiality and integrity during wireless communications poses a primary challenge in IoT environments. Additionally, within the constrained frequency bands available, Cognitive Radio Networks (CRNs) has emerged as an urgent necessity to optimize spectrum utilization. This technology enables intelligent management of radio frequencies, enhancing network efficiency and adaptability to dynamic environmental changes. In this research, we focus on examining the PLS for the primary channel within the underlying CRNs. Our proposed model involves a primary source-destination pair and a secondary transmitter-receiver pair sharing the same frequency band simultaneously. In the presence of a common eavesdropper, the primary concern lies in securing the primary link communication. The secondary user (SU) acts as cooperative jamming, strategically allocating a portion of its transmission power to transmit artificial interference, thus confusing the eavesdropper and protecting the primary user's (PU) communication. The transmit power of the SU is regulated by the maximum interference power tolerated by the primary network's receiver. To evaluate the effectiveness of our proposed protocol, we develop closed-form mathematical expressions for intercept probability ( P int ) and outage probability (OP) along the primary communication link. Additionally, we derive mathematical expressions for OP along the secondary communications network. Furthermore, we investigate the impact of transmit power allocation on intercept and outage probabilities across various links. Through both simulation and theoretical analysis, our protocol aims to enhance protection and outage efficiency for the primary link while ensuring appropriate secondary OP, thereby contributing to advancements in IoT security and spectrum management.

Whom Would You Help? The Impact of Perpetrator and Victim Gender on Bystander Behavior During a Sexual Assault.

We examined the impact of perpetrator and victim gender on bystander helping choices and assault perceptions. Participants (32 females, 37 males) read about two simultaneously occurring sexual assaults, indicated which victim they would help, and gave their perceptions of the assaults. We used a within-participants design that fully manipulated the perpetrator and victim gender for both assaults. Results showed female victims of male perpetrators and male victims of female perpetrators were most and least likely to be chosen for help, respectively. Cognitive networks derived from open-ended responses provided insight into the rationale used by participants to make helping decisions in ways that differed by perpetrator and victim gender.

Hybrid Boosted Chameleon and modified Honey Badger optimization algorithm-based energy efficient cluster routing protocol for cognitive radio sensor network

Single clustering protocols cannot meet the event-driven and time-triggered traffic requirements of Cognitive Radio Sensor Networks (CRSNs). The long wait between the completion of events and the process of clustering and searching for accessible routes results in increased time for information transmission. This paper proposed a Hybrid Boosted Chameleon and Modified Honey Badge optimization Algorithm-based Energy Efficient cluster routing protocol (HBCMHBOA) for handling the issues of traffic driven information transfer with energy efficiency in the CRSNs. This HBCMHBOA is proposed as one among few event-driven and time-triggered clustering protocol for the requirements of CRSNs. The integration of Boosted Chameleon and Modified Honey Badge optimization Algorithm is adopted for determining optimal number of clusters and constructs the structure of primitive clusters in an automated way to serve the time-triggered traffic in a periodic manner. It adopted priority-based schedule and its associated frame structure for guaranteeing reliable event-driven information delivery. It leveraged the merits of time-triggering for the construction of clustering architecture and confirmed than none of the cluster construction and selection of routes are facilitated after the emergent events. This characteristic helps in permitting only the nodes and their associated Cluster Heads (CHs) of CRSNs to discover emergent events. It facilitates the coverage of a fewer nodes, especially when sink is positioned in a corner to minimize the delay and node energy consumption. The simulation results of the proposed HBCMHBOA confirmed a reduction in total energy consumption and number of covered nodes on an average of 34.12 %, and 26.89 % than the prevailing studies.

Changes on Cognition and Brain Network Temporal Variability After Pediatric Neurosurgery.

Pediatric intracranial space-occupying lesions are common, with prognoses improving markedly in recent years, significantly extending survival. As such, there is an imperative to pay increased attention to the postoperative cognitive functions and brain network alterations in these children because these factors significantly influence their quality of life. Temporal variability (TV) analysis of brain networks captures the full extent of resting-state activities, reflecting cognitive functions and rehabilitation potential. However, previous research rarely uses TV analyses and most focus on adults or children after multidisciplinary treatments, not reflecting the combined effect caused by neurosurgery only and self-repair. This study gives our insights into this field from a holistic perspective. We studied 35 children with intracranial space-occupying lesions, analyzing pre- and postsurgery MRI and cognitive tests. We used TV analysis to assess changes and correlated imaging indicators with cognitive performance. We observed a tendency for cognitive recovery after about 3 months postsurgery, primarily in the domains of social cognition and nonverbal reasoning. TV analysis of brain networks indicated increased nodal variability within systems such as the visual and sensorimotor networks, which are integral to external interactions. Correlative analysis showed that alterations in certain occipital regions were associated with changes in social cognition and nonverbal reasoning. These findings suggest significant intrinsic repair in cognitive functions and brain networks at around 3 months postneurosurgery in children. This study not only enriches our comprehension of postoperative cognitive and brain network self-repair processes in children but also furnishes potential therapeutic targets for rehabilitation interventions and establishes a theoretical foundation for proactive surgical interventions.

A Cognitive Network Perspective on Creativity: Theorizing Network Mobilization Scripts

A Cognitive Network Perspective on Creativity: Theorizing Network Mobilization Scripts

Redundancy protects processing speed in healthy individuals with accelerated brain aging.

Recent advancements in computational learning techniques have enabled the estimation of brain age (BA) from neuroimaging data. The difference between chronological age (CA) and BA, known as the BA gap, can potentially serve as a biomarker of brain health. Studies, however, have documented low correlations between BA gap and cognition in healthy aging. This suggests that protective mechanisms in the brain may help counter the effect of accelerated brain aging. Here, we investigated whether redundancy in brain networks may protect cognitive function in individuals with accelerated brain aging. First, we employed deep learning to estimate individual brain ages from structural magnetic resonance imaging (MRI). Next, we associated CA, BA, and BA gap, with cognitive measures and network topology derived from diffusion MRI and tractography. We found that CA and BA were both similarly related to cognitive measures and network topology, while BA gap did not show strong relationships in either domain. Despite observing no strong relationships between brain-age gap (BA gap) and demographic variables, cognitive measures, or topological features in healthy aging, individuals with accelerated aging (BA gap+) exhibited lower average degree and redundancy within the dorsal attention network compared to those with delayed aging (BA gap-). Furthermore, redundancy in the dorsal attention network was positively associated with processing speed in BA gap+ individuals. These results indicate a potential neuroprotective role of redundancy in structural brain networks for mitigating the impact of accelerated brain atrophy on cognitive performance in healthy aging.

Interventional neurorehabilitation for glioma patients: A systematic review

Abstract Harnessing the neuroplastic potential of the human brain is being increasingly recognized as an important neuro-oncological paradigm to facilitate safe resection of brain tumors while preserving neurological function and quality of life. Interventional neurorehabilitation, employing both invasive and noninvasive neuromodulation techniques, represents an important emerging therapeutic strategy to induce or enhance neural plasticity to promote functional recovery in brain tumor patients. This study aimed to conduct a comprehensive review of interventional neurorehabilitation techniques for glioma patients. Methods: In accordance with PRISMA guidelines, searches of Medline, Embase, Web of Science, APA PsycINFO, and Cochrane were undertaken from database inception to November 28, 2023. Studies reporting on neuromodulation applied to glioma patients were included. Results: Seven studies reporting findings from 118 patients met the inclusion criteria. Three neuromodulation techniques were identified and included transcranial magnetic stimulation (TMS) reported in 5 out of 7 (71.4%) studies; transcranial direct current stimulation (tDCS); and continuous cortical electrical stimulation (cCES) using grid electrodes, reported in one study each. All studies applying noninvasive stimulation to ameliorate postoperative deficits demonstrated an improvement on at least one outcome measure. The 2 studies applying tDCS and cCES to induce plasticity reported evidence of functional reorganization. Conclusions: There is emerging evidence of benefits of neuromodulation to improve postoperative outcome in glioma patients. In the current literature, noninvasive stimulation has shown to have a favorable safety profile. Large-scale, double-blind, sham-controlled trials are warranted to further investigate the effectiveness of these interventions for modulating different cognitive networks in patients undergoing glioma surgery.

Sparking creativity: Encouraging creative idea generation through automatically generated word recommendations

Creative block is a familiar foe to any who attempt to create and is especially related to “writers block”. While significant effort has been focused on developing methods to break such blocks, it remains an active challenge. Here, we focus on the role of semantic memory structure in driving creative block, by having people get “stuck” in a certain part of their semantic memory network. We directly examine whether we can “pull out” a participant from where they got “stuck” in their semantic memory, breaking their creative impasse. Our Associative Creativity Sparker (ACS) is a cognitive network science-based online tool that aims to spark creative ideas and break creative impasse: Once a participant runs out of ideas in a creative idea generation task, word recommendations are suggested to prime new ideas. These word recommendations are either towards or away from previous ideas, as well as close or far from the target object, based on a conceptual space extracted from the participants responses using online text analysis. In Study 1, 121 participants use the ACS to generate creative alternative uses for five different objects and completed creativity and Gf tasks. In Study 2, we repeat the design of Study 1, but further examine the impact of writing experience on the ACS, by examining 120 novice and 120 experienced writers. Across both studies, our results indicate that the location of word recommendations affects the fluency and originality of one’s ideas, and that novice and experienced writers differently benefit from these word recommendations.

Energy-Efficient Quantized Data Fusion Based on Differential Mechanism in Cognitive Radio Networks

Energy-Efficient Quantized Data Fusion Based on Differential Mechanism in Cognitive Radio Networks