Cognitive Networks Research Articles

Comprehensive survey on security challenges and solutions in cognitive radio networks

Comprehensive survey on security challenges and solutions in cognitive radio networks

Federated Learning-Based Traffic Flow Prediction Model in Intelligent Transportation Systems

The existing Intelligent Transportation System (ITS) achieves high success. As an essential component of ITS, Traffic Flow Prediction (TFP) has attracted tremendous attention. It is a critical but challenging task to improve the robust convergence and high accuracy of TFP in real-world scenarios. This study presents an optimized Federated Learning (FL)-based ChebNet model, FedproxChebNet, to realize the highly effective and accurate TFP. By selecting the best penalty constant in the proximal term to optimize the objective function, this model can achieve fast and stable convergence. Using the ChebNet model to aggregate neighbor nodes’ characteristics, more hidden information underlying the spatio-temporal traffic data can be taken into consideration for training the global and local models. All the superiority of the FedproxChebNet model makes it outperform other FL models with the Graph Convolutional Network (GCN), Graph Attention Network (GAT) and Spatio-Temporal Graph Convolutional Network (STGCN). We designed a series of experiments on various FL-based GNN model comparisons, parameter sensitivity tests, and on verifying the performance of FedproxChebNet with different heterogeneous systems with [Formula: see text], [Formula: see text] and [Formula: see text]. Based on four real-world data sets from the cognitive network, the experimental results demonstrate that the presented FedproxChebNet provides the best convergence and the highest accuracy in TFP, and achieves the best performance in a highly heterogeneous system ([Formula: see text]). Specifically, the accuracy of FedproxChebNet is at least improved by [Formula: see text] on PeMS07 than other FL-based GNN models. This proposed FedproxChebNet model may be preferable for different scenarios in ITS such as route planning, traffic congestion control and reversible lanes.

Long-term cognitive training enhances fluid cognition and brain connectivity in individuals with MCI

Amnestic mild cognitive impairment (aMCI) is a risk factor for Alzheimer’s disease (AD). Multi-domain cognitive training (CT) may slow cognitive decline and delay AD onset. However, most work involves short interventions, targeting single cognitive domains or lacking active controls. We conducted a single-blind randomized controlled trial to investigate the effect of a 6-month, multi-domain CT on Fluid Cognition, functional connectivity in memory and executive functioning networks (primary outcomes), and white matter microstructural properties (secondary outcome) in aMCI. Sixty participants were randomly assigned to either a multi-domain CT or crossword training (CW) group, and thirty-four participants completed the intervention. We found a significant group-by-time interaction in Fluid Cognition (p = 0.007, F (1,28) = 8.26, Cohen’s d = 0.38, 95% confidence interval [CI]: 2.45–14.4), with 90% of CT patients showing post-intervention improvements (p < 0.01, Cohen’sd = 0.7). The CT group also showed better post-intervention Fluid Cognition than healthy controls (HCs, N = 45, p = 0.045). Functional connectivity analyses showed a significant group-by-time interaction (Cohen’s d ≥ 0.8) in the dorsolateral prefrontal cortex (DLPFC) and inferior parietal cortex (IPC) networks. Specifically, CT displayed post-intervention increases whereas CW displayed decreases in functional connectivity. Moreover, increased connectivity strength between the left DLPFC and medial PFC was associated with improved Fluid Cognition. At a microstructural level, we observed a decline in fiber density (FD) for both groups, but the CT group declined less steeply (1.3 vs. 2%). The slower decline in FD for the CT group in several tracts, including the cingulum-hippocampus tract, was associated with better working memory. Finally, we identified regions in cognitive control and memory networks for which baseline functional connectivity and microstructural properties were associated with changes in Fluid Cognition. Long-term, multi-domain CT improves cognitive functioning and functional connectivity and delays structural brain decline in aMCI (ClinicalTrials.gov number: NCT03883308).

Secrecy performance intelligent prediction for CRNs: An Self-CondenseNet approach

This paper studies the physical layer security (PLS) of cognitive radio networks (CRNs) with Fisher–Snedecor F distribution. To resolve the security issues within CRNs, we derived exact expressions of the security outage probability (SOP) and the probability of strict positive secrecy capacity (SPSC) for the first time, where the SOP and SPSC are uniformly given by Meijer’s G-function. The correctness of the theoretical derivations is proved by Monte Carlo simulations. The results indicate that reducing m and increasing the ratio of (ms,μE) will reduce SOP and increase SPSC. Moreover, we proposed the Self-CondenseNet model to predict the security performance of the system. By comparing with three deep learning algorithms of Transformer, MLP-Mixer and CondenseNet, the results show that the proposed Self-CondenseNet has the best prediction performance. Compared with the CondenseNet, the proposed Self-CondenseNet has a 78.26% higher accuracy and a 12.86% lower time complexity. Compared with the MLP-Mixer, the proposed Self-CondenseNet has a 85.29% higher accuracy. The comparison results show that the proposed algorithm has high prediction accuracy and low time complexity, and can be widely used in complex and changeable scenarios such as 5G, Internet of Vehicles (IoV), and mobile vehicle networking .etc.

Enhanced Cooperative Compressive Spectrum Sensing in Cognitive Radio Networks

ABSTRACTThe demand for bandwidth in cognitive radio networks (CRNs) is growing as applications become increasingly data intensive. Techniques such as compressed spectrum sensing (CSS) and cooperative spectrum sensing (C‐SS) are employed to address this challenge. C‐SS enhances overall detection accuracy and reliability by enabling multiple nodes to share and combine their local sensing data. Conversely, CSS effectively reduces the required information for spectrum usage decision making, thereby improving bandwidth utilization. Integrating these two methods allows CRNs to utilize the spectrum reliably and efficiently, leading to increased spectral efficiency. To further improve reconstruction performance, we leverage the sparsity concept to transcend hardware constraints and merge restrictions from both real and synthesized channels. This approach involves the virtual synthesis of channels, which linearly enhances the signal‐to‐noise ratio (SNR) within the network's size range. Simulation results demonstrate that our proposed method offers significant advantages over single‐node recovery, as validated by simulations and software defined radio (SDR) Implementation. The integration of spectral estimations from various local CR detectors enhances spatial diversity gain and sensing quality, particularly in fading channels. Compared to traditional approaches, our method achieves superior performance, evidenced by an increase in (from 93.97% to 96.52%) with almost the same .

Elucidating a statistical learning brain network: Coordinate-based meta-analyses and functional connectivity profiles of artificial grammar learning in healthy adults

Abstract Language rehabilitation centers on modifying its use through experience-based neuroplasticity. Statistical learning of language is essential to its acquisition and likely its rehabilitation following brain injury, but its corresponding brain networks remain elusive. Coordinate-based meta-analyses were conducted to identify common and distinct brain activity across 25 studies coded for meta-data and experimental contrasts (Grammatical and Ungrammatical). The resultant brain regions served as seeds for profiling functional connectivity in large task-independent and task-dependent data sets. Hierarchical clustering of these profiles grouped brain regions into three subnetworks associated with statistical learning processes. Functional decoding clarified the mental operations associated with those subnetworks. Results support a left-dominant language sub-network and two cognitive control networks as scaffolds for language rule identification, maintenance, and application in healthy adults. These data suggest that cognitive control is necessary to track regularities across stimuli and imperative for rule identification and application of grammar. Future empirical investigation of these brain networks in language learning in individuals with brain injury will clarify their prognostic role in language recovery.

Joint channel allocation and transmit power control for underlay EH‐CRNs: A clustering‐based multi‐agent DDPG approach

AbstractTo address the concerns of energy supply and spectrum scarcity for wireless devices, energy harvesting cognitive radio networks have been proposed. To improve spectrum utilization, secondary users (SUs) access the licensed spectrum in underlay mode, which may cause severe interference to primary users and SUs. The focus is on the underlay energy harvesting cognitive radio networks with multiple pairs of SUs, and formulate the long‐term secondary throughput maximization problem as a mixed‐integer non‐linear programming problem. As traditional approaches could hardly solve the mixed‐integer non‐linear programming problem well, a centralized deep deterministic policy gradient (C‐DDPG) approach is proposed that achieves satisfactory throughput performance. To reduce the computational complexity of C‐DDPG, we further propose a clustering‐based multi‐agent DDPG (CMA‐DDPG) approach that combines the advantages of the centralized deep reinforcement learning approach and the distributed deep reinforcement learning approach. In the CMA‐DDPG, a novel interference‐based clustering algorithm is proposed, which partitions the SUs that cause severe mutual interference into one cluster, and the sizes of state space and action space are smaller than those in C‐DDPG. Numerical results validate the superiority of the proposed approaches in terms of the throughput and outage probability, and validate the clustering performance of the interference‐based clustering algorithm in terms of the outage probability of the secondary network.

Arithmetic Optimization Algorithm Quantization Optimized With Energy Detection Using Nonparametric Amplitude

ABSTRACTThe spectrum sensing is a major significant task in cognitive radio networks (CRNs) to avoid the unacceptable interference to primary users (PUs). Here, the threshold value determines the effectiveness of spectrum sensing and regarded as a sensing system. The fixed threshold used by the current energy detection‐based spectrum sensing (SS) techniques does not provide sufficient safety for the main users. The threshold is determined by lowering the complete probability of decision error in addition to these guidelines. Therefore, an energy detection using nonparametric amplitude quantization optimized with arithmetic optimization algorithm for enhanced spectrum sensing in CRNs (ED‐NAQ‐AOA‐SS CRN) is proposed in this paper to acquire the ideal threshold for decreasing the total error probability. The proposed method achieves greater probability of detection of 99.67%, 98.38%, 92.34%, and 97.45%, lower settling time of 98.33%, 89.34%, 83.12%, and 88.96%, and lower error rate of 93.15%, 91.25%, 79.90%, and 92.88% compared with existing techniques, like intelligent spectrum sharing and sensing in CRN with adaptive rider optimization algorithm (AROA), a novel technique for spectrum sensing in CRN utilizing fractional gray wolf optimization with the cuckoo search optimization (GWOCS), and adaptive neuro‐fuzzy inference scheme depending on cooperative spectrum sensing optimization in CRNs (ANFIS).

Three-State Hidden Markov Model for Spectrum Prediction in Cognitive Radio Networks

The exponential growth and proliferation of wireless devices for different wireless applications have led to the emergence of cognitive radio network (CRN) for optimal utilization of scarce spectrum resources. However, these resources have grossly been under-utilized due to the inaccurate spectrum predictions. Existing spectrum occupancy and prediction techniques which rely on 2-state hidden Markov model (HMM) results in false alarm or missed detection caused by noisy or incomplete observable effects. In this paper, a 3-state HMM spectrum occupancy and prediction technique in CRNs is proposed. The transmission, emission and initial state probabilities of the proposed 3-state HMM parameters were derived based on the three canonical problems associated with HMM. The evaluation, decoding and learning problems were solved using Forward algorithm, Viterbi algorithm and the Baum-Welch algorithm, respectively. The performance of the proposed 3-state HMM spectrum prediction technique was evaluated using prediction accuracy, probability of detection and spectrum utilization efficiency. The simulation results obtained revealed that the 3-state HMM outperformed the 2-state HMM spectrum prediction technique by 24.1% in prediction accuracy.

Cognition and Ventral Attention Network Connectivity: Associations With Treatment Response in Posttraumatic Stress Disorder.

Posttraumatic stress disorder (PTSD) is a highly heterogeneous disorder, which makes it difficult to link clinical phenotypes with biomarkers to improve treatment outcomes. Findings from previous studies suggest that cognitive measures such as verbal memory or attention paired with within-ventral attention network (VAN) or salience network resting-state functional connectivity may predict treatment response among individuals with PTSD. In a sample comprising 20 individuals with PTSD and 10 healthy control group individuals, the investigators subtyped individuals by using both discriminant function analysis and standardized norms for a single measure of memory and neuropsychological batteries of memory, attention, and executive functioning; attempted to replicate previous findings of lower within-VAN connectivity among individuals with cognitive impairment; and explored whether within-VAN connectivity paired with cognitive impairment predicted treatment outcomes. PTSD patients with cognitive impairment (defined by using a discriminant function analysis with verbal memory performance) had greater within-VAN resting-state functional connectivity compared with control group individuals and cognitively intact PTSD patients at a level that fell short of statistical significance (F=3.41; df=2, 21; ηp2=0.237). The interaction between verbal memory performance and within-VAN connectivity also predicted treatment-related change in PTSD symptoms at a level that also fell short of statistical significance (β=-0.442). These findings somewhat support the clinical utility of identifying cognitive phenotypes within PTSD (by using discriminant function analysis and verbal memory performance) to predict treatment outcomes.

The distinct functional brain network and its association with psychotic symptom severity in men with methamphetamine-associated psychosis

BackgroundIndividuals using methamphetamine (METH) may experience psychosis, which usually requires aggressive treatment. Studies of the neural correlates of METH-associated psychosis (MAP) have focused predominantly on the default mode network (DMN) and cognitive control networks. We hypothesize that METH use alters global functional connections in resting-state brain networks and that certain cross-network connections could be associated with psychosis.MethodsWe recruited 24 healthy controls (CRL) and 54 men with METH use disorder (MUD) who were then divided into 25 without psychosis (MNP) and 29 with MAP. Psychotic symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS), evaluating (1) large-scale alterations in regional-wise resting-state functional connectivity (rsFC) across 11 brain networks and (2) associations between rsFC and psychotic symptom severity.ResultsThe MUD group exhibited greater rsFC between the salience network (SN)-DMN, and subcortical network (SCN)-DMN compared to the CRL group. The MAP group exhibited decreased rsFC in the sensory/somatomotor network (SMN)-dorsal attention network (DAN), SMN-ventral attention network (VAN), SMN-SN, and SMN-auditory network (AN), whereas the MNP group exhibited increased rsFC in the SMN-DMN and the frontoparietal network (FPN)-DMN compared to CRL. Additionally, the MAP group exhibited decreased rsFC strength between the SMN-DMN, SMN-AN, SMN-FPN, and DMN-VAN compared to the MNP group. Furthermore, across the entire MUD group, the PANSS-Positive subscale was negatively correlated with the DMN-FPN and FPN-SMN, while the PANSS-Negative subscale was negatively correlated with the DMN-AN and SMN-SMN.ConclusionMUD is associated with altered global functional connectivity. In addition, the MAP group exhibits a different brain functional network compared to the MNP group.

Quantum cryptography in the security of cognitive radio networks

Cognitive radio networks face challenges from malicious users who aim to disrupt decision-making during spectrum sensing and decrease spectrum efficiency. The proposed model seeks to distinguish between the presence and absence of primary user signals using energy detection and also it introduces a quantum-inspired outlier detection mechanism for cognitive radio networks to enhance security and reliability during spectrum sensing by ignoring the identified outliers. It employs BB84 quantum key distribution (QKD) protocol and one time pad for secure transmission of secondary user (SU) local decisions to the fusion center (FC), ensuring data integrity and confidentiality. These decisions are transmitted securely establishing a shared secret key between SU and FC and arrives at a global decision. To make the model more robust, we proposed another novel model with a modified BB84 protocol and compared the results of both the models, offering a robust solution for enhancing the reliability and performance of cognitive radio networks with proven results from simulation.

Energy-Efficient Adaptive Bidirectional Transmission Strategy in Simultaneous Wireless Information and Power Transfer (SWIPT)-Enabled Cognitive Relay Network.

Introducing collaborative relay and simultaneous wireless information and power transfer (SWIPT) techniques into a cognitive wireless network, named the SWIPT-enabled cognitive relay network (CRN), is considered a promising approach to deal with insufficiency and the low utilization of spectrum resources, as well as the node's energy-constrained issues in wireless networks. In this paper, to improve the network spectrum efficiency (SE) and energy efficiency (EE) of the SWIPT-enabled CRN, we design an energy-efficient adaptive bidirectional transmission strategy. To be specific, we first select an energy-constrained best relay node with the consideration of signal-to-noise ratio and global channel gain to achieve a better bidirectional relay transmission (BRT). At the same time, we let the energy-constrained best relay node transmit a signal with the SWIPT technique, which can solve the node's energy-constrained issue and improve the network EE. Then, with the selected energy-constrained best relay node, we design a total transmit power threshold (TTPT) determining algorithm to find the TTPT, which lets the total transmission rate of the BRT be equal to the bidirectional direct transmission (BDT). Based on this TTPT, we further design an adaptive bidirectional transmission strategy and let the network achieve adaptive transmission between the BRT and BDT to obtain a higher network SE. Furthermore, to further achieve the energy-efficient transmission of the adaptive bidirectional transmission strategy, we optimize the nodes' power under the requirement of primary users' interference threshold and obtain the analytical expressions of the optimal power. Simulation results show that the transmission rate, the outage probability, and the EE of the designed energy-efficient adaptive bidirectional transmission strategy in the SWIPT-enabled CRN are, respectively, 3.01, 0.07, and 3.10 times that of the non-collaborative transmission, which show the effectiveness of our designed transmission strategy.

Enhancing flow states in neurodivergent individuals through cognitive network integration

This article explores the concept of &amp;ldquo;flow&amp;rdquo; in the context of neurodivergence, focusing on the interaction between the default mode network (DMN) and task-positive networks (TPNs), particularly in conditions such as autism spectrum condition (ASC) and attention-deficit/hyperactivity disorder (ADHD). Flow, a state of deep immersion and focused engagement, is associated with enhanced performance and satisfaction across activities. The DMN, typically active during rest and self-referential thinking, interacts uniquely with TPNs during flow states, creating a dynamic balance crucial for sustained focus and reduced self-referential thinking. Neurodivergent individuals often exhibit distinct DMN activity patterns, which affect their capacity for achieving flow. For instance, individuals with ADHD may experience flow in stimulating tasks, whereas those with ASC may achieve it in areas of deep interest due to their intense focus. This paper examines the expertise-and-release model in creative flow and transient hypofrontality as a potential mechanism facilitating flow in neurodivergent individuals. It also proposes strategies to enhance flow through personalized cognitive training, specialized task environments, and technology-assisted interventions, aiming to harness neurodivergent strengths and accommodate unique cognitive profiles. This comprehensive analysis deepens understanding of neurodiversity and offers practical applications for improving quality of life and performance in neurodivergent populations.

Cross-attention mechanism-based spectrum sensing in generalized Gaussian noise

Spectrum sensing (SS) technology is essential for cognitive radio (CR) networks to effectively identify and utilize idle spectrum resources. Due to the influence of noise characteristics in the channel, providing accurate sensing results is challenging. In order to improve the performance of SS under non-Gaussian noise and overcome the limitations of existing methods that are mostly based on a single feature, we propose a novel time-frequency cross fusion network (TFCFN). Specifically, we utilize gated recurrent units (GRU) to capture long-term dependencies in the time domain on the original signals, meanwhile, we perform a fast Fourier transform (FFT) on the original signals to obtain the frequency domain information, and subsequently use convolutional neural networks (CNN) to extract the local spatial features in the frequency domain. Ultimately, these time-domain and frequency-domain features are dynamically fused through a cross-attention mechanism to construct more comprehensive and robust features for signal classification. We use generalized Gaussian distribution (GGD) as the noise model and reconstruct the RadioML2016.10a dataset to explore the performance under various noise conditions. The experimental results show that compared with the baseline methods, TFCFN exhibits better detection ability and maintains lower complexity in both Gaussian and non-Gaussian noise environments. Notably, when the shape parameter of GGD is set to 0.5 and the signal-to-noise ratio (SNR) of the received signal is -16dB, it can maintain the probability of false alarm (Pf\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_f$$\\end{document}) of 10% while still ensuring the probability of detection (Pd\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P_d$$\\end{document}) of over 90%.

Improving the security of NOMA cognitive networks

Aiming at the scenario where the eavesdropper try to eavesdrop on the confidential messages of secondary trusted users, two physical-layer network coding (PNC) strategies, namely dual power superposition (PS-PS) encoding strategy and bit-level exclusive-or power superposition (XOR-PS) encoding strategy, are proposed to improve the security performance of cognitive non-orthogonal multiple access (NOMA) networks with the help of the data transmitted by the primary user. Considering the overlay spectrum sharing mechanism and the imperfect successive interference cancellation (SIC) technology, we first derive the closed-form accurate expressions of the outage probability and intercept probability of the secondary network with these two schemes over Rayleigh fading, and also give the asymptotic outage probability in the high signal-to-noise ratio (SNR) regime. Then, the correctness of the theoretical results is verified by simulation. For comparison, we also use the scheme without the assistance of the primary user as the benchmark scheme, which is represented by NPS. Finally, the simulation results show that the security of the proposed PS-PS and XOR-PS schemes is much better than that of the NPS scheme in the case of without reducing the outage performance.

ADAPTIVE METHOD OF FORMING COMPLEX SIGNALS ENSEMBLES BASED ON MULTI-LEVEL RECURRENT TIME-FREQUENCY SEGMENT MODELING

The article investigates the implementation of an adaptive method for forming ensembles of complex signals based on multilevel recurrent time-frequency segmentation. It addresses the key challenges faced by cognitive wireless networks operating in dynamic radio frequency environments with high levels of interference, necessitating rapid adaptation to changes in the spectral characteristics of signals. The study substantiates the need for adaptive filters and specific transformations to enhance signal processing quality, particularly in environments with high variability in frequency characteristics and significant noise interference. The proposed method of multilevel recurrent time-frequency segmentation allows for the modification of time segment durations and the use of segments of varying lengths, providing flexibility in signal processing and adaptation to current conditions. This adaptability enables optimal signal processing for each individual case, taking into account short-term impulses, long-term fluctuations, and various types of noise and distortions. This approach effectively separates frequency components and reduces interference between them, which is crucial for maintaining high signal quality and communication stability in cognitive networks. It has been proven that the use of adaptive filters such as LMS (Least Mean Squares) and RLS (Recursive Least Squares), as well as fast Fourier Transform (STFT), wavelet, and Hilbert transforms at different stages of multilevel time-frequency segmentation, significantly enhances signal interference resistance and energy efficiency. Comparative analysis of signal metrics before and after filtering and transformation shows an increase in signal quality by 14,3–24,5% and a reduction in noise levels by 21,7–29,6%. The wavelet transform, in particular, proved to be highly effective, allowing for precise extraction of useful frequency components from the noise background and improving signal parameters through dynamic adjustment to specific radio environment conditions. Experimental results confirm the effectiveness of the proposed method, demonstrating its ability to ensure consistently high-quality processing of complex signal ensembles even in dynamic cognitive radio environments.

EVALUATING THE CHARACTERISTICS OF THE VTSM SPECTRUM SENSING METHOD IN COGNITIVE RADIO NETWORKS

The article investigates the complexities associated with spectrum analysis in cognitive radio networks (CRNs). It begins by acknowledging the evolving challenges of spectrum sensing due to the dynamic nature of wireless environments and the increasing demand for efficient spectrum utilization. The study thoroughly examines various traditional spectrum sensing methods, including Energy Detector Based Sensing, Waveform-Based Sensing, Cyclostationarity-Based Sensing, and Matched Filtering. The focus of the research is on the Variable Time Segment Monitoring (VTSM) method, a novel approach that optimizes spectrum sensing by dynamically adjusting the time segments used for analyzing spectral characteristics. The paper highlights the VTSM method's ability to enhance detection accuracy and reduce false alarms by adapting to different signal environments, making it particularly suited for complex and dynamic CRN scenarios. Furthermore, the article compares VTSM with traditional methods across key performance metrics such as detection accuracy, false alarm rate, latency, and computational complexity. The analysis reveals that while traditional methods have their strengths, VTSM offers a balanced approach, combining flexibility, accuracy, and moderate computational demands, thereby providing a versatile solution for modern spectrum sensing challenges. The findings contribute to the broader understanding and advancement of cognitive radio technologies, supporting the development of more robust and efficient spectrum sensing solutions, which are crucial for optimizing network performance and ensuring reliable communication in increasingly congested and complex wireless environments. The article concludes by emphasizing the importance of selecting the appropriate spectrum sensing method based on the specific requirements of the CRN application, considering factors such as accuracy, computational resources, and environmental dynamics. The findings contribute to the broader understanding and advancement of cognitive radio technologies, supporting the development of more robust and efficient spectrum sensing solutions.

Distinct Longitudinal Changes in EEG Measures Reflecting Functional Network Disruption in ALS Cognitive Phenotypes

Amyotrophic lateral sclerosis (ALS) is characterised primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. We have shown previously that resting-state EEG captures dysfunction in motor and cognitive networks in ALS. However, the longitudinal development of these dysfunctional patterns, especially in networks linked with cognitive-behavioural functions, remains unclear. Longitudinal studies on non-motor changes in ALS are essential to further develop our understanding of disease progression, improve care and enhance the evaluation of new treatments. To address this gap, we examined 124 ALS individuals with 128-channel resting-state EEG recordings, categorised by cognitive impairment (ALSci, n = 25), behavioural impairment (ALSbi, n = 58), or non-impaired (ALSncbi, n = 53), with 12 participants meeting the criteria for both ALSci and ALSbi. Using linear mixed-effects models, we characterised the general and phenotype-specific longitudinal changes in brain network, and their association with cognitive performance, behaviour changes, fine motor symptoms, and survival. Our findings revealed a significant decline in \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\ heta\\:$$\\end{document}-band spectral power over time in the temporal region along with increased \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\gamma\\:}_{l}$$\\end{document}-band power in the fronto-temporal region in the ALS group. ALSncbi participants showed widespread β-band synchrony decrease, while ALSci participants exhibited increased co-modulation correlated with verbal fluency decline. Longitudinal network-level changes were specific of ALS subgroups and correlated with motor, cognitive, and behavioural decline, as well as with survival. Spectral EEG measures can longitudinally track abnormal network patterns, serving as a candidate stratification tool for clinical trials and personalised treatments in ALS.

Functional connectivity across the lifespan: a cross-sectional analysis of changes.

In the era of functional brain networks, our understanding of how they evolve across life in a healthy population remains limited. Here, we investigate functional connectivity across the human lifespan using magnetoencephalography in a cohort of 792 healthy individuals, categorized into young (13 to 30 yr), middle (31 to 54 yr), and late adulthood (55 to 80 yr). Employing corrected imaginary phase-locking value, we map the evolving landscapes of connectivity within delta, theta, alpha, beta, and gamma classical frequency bands among brain areas. Our findings reveal significant shifts in functional connectivity patterns across all frequency bands, with certain networks exhibiting increased connectivity and others decreased, dependent on the frequency band and specific age groups, showcasing the dynamic reorganization of neural networks as age increases. This detailed exploration provides, to our knowledge, the first all-encompassing view of how electrophysiological functional connectivity evolves at different life stages, offering new insights into the brain's adaptability and the intricate interplay of cognitive aging and network connectivity. This work not only contributes to the body of knowledge on cognitive aging and neurological health but also emphasizes the need for further research to develop targeted interventions for maintaining cognitive function in the aging population.