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Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals' brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios. [ABSTRACT FROM AUTHOR]

In 2019, the International Classification of Diseases 11th Revision International Classification of Diseases (ICD-11) put forward a new concept of “chronic primary pain” (CPP), a kind of chronic pain characterized by severe functional disability and emotional distress, which is a medical problem that deserves great attention. Although CPP is closely related to depressive disorder, its potential neural characteristics are still unclear. This paper collected EEG data from 67 subjects (23 healthy subjects, 22 patients with depression, and 22 patients with CPP) under the auditory oddball paradigm, systematically analyzed the brain network connection matrix and graph theory characteristic indicators, and classified the EEG and PLI matrices of three groups of people by frequency band based on deep learning. The results showed significant differences in brain network connectivity between CPP patients and depressive patients. Specifically, the connectivity within the frontoparietal network of the Theta band in CPP patients is significantly enhanced. The CNN classification model of EEG is better than that of PLI, with the highest accuracy of 85.01% in Gamma band in former and 79.64% in Theta band in later. We propose hyperexcitability in attentional control in CPP patients and provide a novel method for objective assessment of chronic primary pain. [ABSTRACT FROM AUTHOR]

• Cognitive workload modulates the dorsal attention network (DAN), responsible for attention-related tasks. • Adjusting the difficulty level of exergames can impact the engagement of brain resources, recruiting more areas of the brain. • The correlation between brain activity and performance data highlights that increased cognitive workload decreased performance and heightened frontal alpha activity. • The temporal lobe is crucial in processing complex visual stimuli, object recognition, and semantic memory, making it highly relevant to the exergame task. • Interaction between inferior temporal and parsopercularis in DAN strengthens the ability to optimise strategic decision-making under higher cognitive demands. This study investigates how adding a cognitive task on a balance board (exergame) affects connectivity in the dorsal attention network (DAN) during an exergame task. Healthy young adults performed a soccer ball-moving task by tilting a balance board with their feet while their brain activity was measured using electroencephalography (EEG). In this exergame, the speed of obstacles in front of the goal manipulated the cognitive workload. Higher speed means a higher cognitive workload. The study found significant changes in functional connectivity within DAN regions, specifically in the alpha band. During the shift from easy to medium cognitive task, we observed a significant increase in connectivity (p = 0.0436) between the right inferior temporal (ITG R) and the Left middle temporal (MTG L). During the transition from easy to hard cognitive tasks, strengthened interactions (p = 0.0324) between inferior temporal (ITG) and parsopercularis (pOPPER) were found. This suggests that the proposed balanceboard-based exergame enhances the functionality of specific brain regions, such as ITG and MTG regions, and improves connectivity in the frontal cortex. We also found a correlation between brain activity and performance data, highlighting that increased cognitive workload resulted in decreased performance and heightened frontal alpha activity. These findings align with research suggesting that adding cognitive games to physical activity-based tasks in rehabilitation programs can boost brain activity, resulting in improved decision-making and visual processing skills. This information can help clinicians tailor rehabilitation methods that target specific brain regions. [ABSTRACT FROM AUTHOR]

Background: This work is a part of a new concept. The aim is to study the measurements that can be used as input to an intelligent system for detecting cerebral malaria from an electroencephalogram (EEG). Materials and Methods: The study of brain connectivity through the calculation of the phase lag index, which is an adjacency matrix, allows evaluating the units such as degree, density and strength on each channel. These units were evaluated on 29 EEG recordings, consisting of twenty people suffering from coma and nine healthy individuals. Results: Considering analysis of variance with two factors that are frequency band and group (patient and healthy control), the degree and the density are always higher in healthy children compared to sick children. Nevertheless, the strength is always higher in healthy children compared to sick children with the exception of the delta band on which the values are equal and the alpha band on which the strength is higher in sick children by the report to healthy children. Conclusion: There is a significant difference (P = 0.002) between the strength of sick people compared to healthy people. Such technology could help reduce the death rate from malaria, in general, and cerebral malaria, in particular, especially in sub-Saharan Africa where this rate is very high.

Despite growing evidence that high creativity leads to mental well-being in older individuals, the neurophysiological bases of creativity remain elusive. Creativity reportedly involves multiple brain areas and their functional interconnections. In particular, functional magnetic resonance imaging (fMRI) is used to investigate the role of patterns of functional connectivity between the default network and other networks in creative activity. These interactions among networks play the role of integrating various neural processes to support creative activity and involve attention, cognitive control, and memory. The electroencephalogram (EEG) enables researchers to capture a pattern of band-specific functional connectivity, as well as moment-to-moment dynamics of brain activity; this can be accomplished even in the resting-state by exploiting the excellent temporal resolution of the EEG. Furthermore, the recent advent of functional connectivity analysis in EEG studies has focused on the phase-difference variable because of its fine spatio-temporal resolution. Therefore, we hypothesized that the combining method of EEG signals having high-temporal resolution and the phase synchronization analysis having high-spatio-temporal resolutions brings a new insight of functional connectivity regarding high creative activity of older participants. In this study, we examined the resting-state EEG signal in 20 healthy older participants and estimated functional connectivities using the phase lag index (PLI), which evaluates the phase synchronization of EEG signals. Individual creativity was assessed using the S-A creativity test in a separate session before the EEG recording. In the analysis of associations of EEG measures with the S-A test scores, the covariate effect of the intelligence quotient was evaluated. As a result, higher individual S-A scores were significantly associated with higher node degrees, defined as the average PLI of a node (electrode) across all links with the remaining nodes, across all nodes at the alpha band. A conventional power spectrum analysis revealed no significant association with S-A scores in any frequency band. Older participants with high creativity exhibited high functional connectivity even in the resting-state, irrespective of intelligence quotient, which supports the theory that creativity entails widespread brain connectivity. Thus, PLIs derived from EEG data may provide new insights into the relationship between functional connectivity and creativity in healthy older people.

Despite growing evidence that high creativity leads to mental well-being in older individuals, the neurophysiological bases of creativity remain elusive. Creativity reportedly involves multiple brain areas and their functional interconnections. In particular, functional magnetic resonance imaging (fMRI) is used to investigate the role of patterns of functional connectivity between the default network and other networks in creative activity. These interactions among networks play the role of integrating various neural processes to support creative activity and involve attention, cognitive control, and memory. The electroencephalogram (EEG) enables researchers to capture a pattern of band-specific functional connectivity, as well as moment-to-moment dynamics of brain activity; this can be accomplished even in the resting-state by exploiting the excellent temporal resolution of the EEG. Furthermore, the recent advent of functional connectivity analysis in EEG studies has focused on the phase-difference variable because of its fine spatio-temporal resolution. Therefore, we hypothesized that the combining method of EEG signals having high-temporal resolution and the phase synchronization analysis having high-spatio-temporal resolutions brings a new insight of functional connectivity regarding high creative activity of older participants. In this study, we examined the resting-state EEG signal in 20 healthy older participants and estimated functional connectivities using the phase lag index (PLI), which evaluates the phase synchronization of EEG signals. Individual creativity was assessed using the S-A creativity test in a separate session before the EEG recording. In the analysis of associations of EEG measures with the S-A test scores, the covariate effect of the intelligence quotient was evaluated. As a result, higher individual S-A scores were significantly associated with higher node degrees, defined as the average PLI of a node (electrode) across all links with the remaining nodes, across all nodes at the alpha band. A conventional power spectrum analysis revealed no significant association with S-A scores in any frequency band. Older participants with high creativity exhibited high functional connectivity even in the resting-state, irrespective of intelligence quotient, which supports the theory that creativity entails widespread brain connectivity. Thus, PLIs derived from EEG data may provide new insights into the relationship between functional connectivity and creativity in healthy older people. [ABSTRACT FROM AUTHOR]

We examined functional connectivity analyses in electroencephalograms (EEGs) of patients with attention deficit hyperactivity disorder (ADHD) and in those of typically developing children (TDC) to uncover neurobiological abnormalities. We enrolled 31 children with ADHD (mean age 11.1 years; 23 boys) and 17 sex-, age-, and intelligence-matched TDC to undergo 19-channel EEGs during light sleep. We estimated functional connectivity using the phase lag index (PLI) and coherence measurements that capture the synchronization of EEG signals and graphed metrics with GRETNA. We also performed continuous performance tests (CPTs) on the children and obtained answered questionnaires on ADHD and autism spectrum disorder. The central-to-posterior gamma PLI was lower in children with ADHD than that in TDC. The other PLI frequency bands and all coherence frequency bands were not statistically different between both groups. Individuals with high hyperactivity scores on questionnaires and low reaction times (SDs) on CPT had low motor and occipital pairs of gamma PLIs. Graph metrics showed no differences between the groups. The difference in averaged gamma PLI (especially with motor and occipital pairs) between groups was more suitable for diagnosis than the averaged coherence. Lower averaged gamma PLIs reflected more severe ADHD symptoms. A prospective study with more controlled conditions is warranted to determine if gamma-band PLI can be used as an auxiliary tool for ADHD diagnosis. [ABSTRACT FROM AUTHOR]

There is general agreement that the neuropathological processes leading to Alzheimer’s disease (AD) begin decades before the clinical onset. In order to detect early topological changes, we applied functional connectivity and network analysis to magnetoencephalographic (MEG) data obtained from 16 patients with amnestic Mild Cognitive Impairment (aMCI), a prodromal stage of AD, and 16 matched healthy control (HCs). Significant differences between the two groups were found in the theta band, which is associated with memory processes, in both temporal poles (TPs). In aMCI, the degree and betweenness centrality (BC) were lower in the left superior TP, whereas in the right middle TP the BC was higher. A statistically significant negative linear correlation was found between the BC of the left superior TP and a delayed recall score, a sensitive marker of the “hippocampal memory” deficit in early AD. Our results suggest that the TPs, which are involved early in AD pathology and belong to the memory circuitry, have an altered role in the functional network in aMCI.

To investigate gender differences in functional connectivity during the unattended processing of facial expressions, we recorded visual mismatch negativity (vMMN) in 34 adults using a deviant-standard reverse oddball paradigm. Using wavelet analysis, we calculated the time-frequency (TF) power at each electrode associated with happy-deviant, sad-deviant, happy-standard and sad-standard conditions. We also calculated the phase lag index (PLI) between electrode pairs and analyzed the dynamic network topologies of the functional connectivity for happy and sad vMMNs in the delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–45 Hz) bands. The results showed that females induced stronger TF power and PLI values than males in only the alpha band over the whole brain regarding the vMMN. Moreover, females had a higher ratio of the number of connections between long-distance electrode pairs than males. While theoretical analysis of dynamic network topologies indicated that high node degree values were found in local brain regions of males and in almost the entire female brain, our findings suggested that female brain activation and connections between brain regions are not only stronger but also more widely distributed during the unattended processing of facial expressions than those in males.

To investigate gender differences in functional connectivity during the unattended processing of facial expressions, we recorded visual mismatch negativity (vMMN) in 34 adults using a deviant-standard reverse oddball paradigm. Using wavelet analysis, we calculated the time-frequency (TF) power at each electrode associated with happy-deviant, sad-deviant, happy-standard and sad-standard conditions. We also calculated the phase lag index (PLI) between electrode pairs and analyzed the dynamic network topologies of the functional connectivity for happy and sad vMMNs in the delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–45 Hz) bands. The results showed that females induced stronger TF power and PLI values than males in only the alpha band over the whole brain regarding the vMMN. Moreover, females had a higher ratio of the number of connections between long-distance electrode pairs than males. While theoretical analysis of dynamic network topologies indicated that high node degree values were found in local brain regions of males and in almost the entire female brain, our findings suggested that female brain activation and connections between brain regions are not only stronger but also more widely distributed during the unattended processing of facial expressions than those in males. [ABSTRACT FROM AUTHOR]

Objective: Integrity of resting-state functional brain networks (RSNs) is important for proper cognitive functioning. In type 1 diabetes mellitus (T1DM) cognitive decrements are commonly observed, possibly due to alterations in RSNs, which may vary according to microvascular complication status. Thus, we tested the hypothesis that functional connectivity in RSNs differs according to clinical status and correlates with cognition in T1DM patients, using an unbiased approach with high spatio-temporal resolution functional network. Methods: Resting-state magnetoencephalographic (MEG) data for T1DM patients with (n = 42) and without (n = 41) microvascular complications and 33 healthy participants were recorded. MEG time-series at source level were reconstructed using a recently developed atlas-based beamformer. Functional connectivity within classical frequency bands, estimated by the phase lag index (PLI), was calculated within eight commonly found RSNs. Neuropsychological tests were used to assess cognitive performance, and the relation with RSNs was evaluated. Results: Significant differences in terms of RSN functional connectivity between the three groups were observed in the lower alpha band, in the default-mode (DMN), executive control (ECN) and sensorimotor (SMN) RSNs. T1DM patients with microvascular complications showed the weakest functional connectivity in these networks relative to the other groups. For DMN, functional connectivity was higher in patients without microangiopathy relative to controls (all p

Objective Altered brain connectivity has been theorized as a key neural underpinning of autism spectrum disorder (ASD), but recent investigations have revealed conflicting patterns of connectivity, particularly hyper-connectivity and hypo-connectivity across age groups. The application of graph theory to neuroimaging data has become an effective approach for characterizing topographical patterns of large-scale functional networks. We used a graph approach to investigate alteration of functional networks in childhood ASD. Method Magnetoencephalographic signals were quantified using graph-theoretic metrics with a phase lag index (PLI) for specific bands in 24 children with autism spectrum disorder and 24 typically developing controls. Results No significant group difference of PLI was found. Regarding topological organization, enhanced and reduced small-worldness, representing the efficiency of information processing, were observed respectively in ASD children, particularly in the gamma band and delta band. Conclusions Analyses revealed frequency-dependent atypical neural network topologies in ASD children. Significance Our findings underscore the recently proposed atypical neural network theory of ASD during childhood. Graph theory with PLI applied to magnetoencephalographic signals might be a useful approach for characterizing the frequency-specific neurophysiological bases of ASD. [ABSTRACT FROM AUTHOR]

Purpose: Diabetes is a great risk factor for dementia and mild cognitive impairment. This study inves-tigates whether complex network-derived features in resting state EEG (rsEEG) could be applied as a biomarker to distinguish amnestic mild cognitive impairment (aMCI) from normal cognitive function in subjects with type 2 diabetes. Method: In this study, EEG was recorded in 28 patients with type 2 diabetes (16 aMCI patients and 12 controls) during a no-task eyes-closed resting state. Pair-wise synchronization of rsEEG signals were assessed in six frequency bands (delta, theta, lower alpha, upper alpha, beta, and gamma) using phase lag index (PLI) and grouped into long distance (intra- and inter-hemispheric) and short distance inter-actions. PLI-weighted connectivity networks were also constructed, and characterized by mean clus-tering coefficient and path length. The correlation of these features and Montreal Cognitive Assess-ment (MoCA) scores was assessed.Results: Main findings of this study were as follows: (1) In comparison with controls, patients with aMCI had a significant decrease of global mean PLI in lower alpha, upper alpha, and beta bands. Lower functional connection at short and long intra-hemispheric distance mainly appeared on the left hemisphere. (2) In the lower alpha band, clustering coefficient was significantly lower in aMCI group, and the path length significantly increased. (3) Cognitive status measured by MoCA had a significant positive correlation with cluster coefficient and negative correlation with path length in lower alpha band. Conclusions: The brain network of aMCI patients displayed a disconnection syndrome and a loss of small-world architecture. The correlation between cognitive states and network characteristics sug-gested that the more in deterioration of the diabetes patients’ cognitive state, the less optimal the net-work organization become. Hence, the complex network-derived biomarkers based on EEG could be employed to track cognitive function of diabetic patients and provide a new diagnosis tool for aMCI.

Abstract: Seizure freedom after resective epilepsy surgery is not obtained in a substantial number of patients with medically intractable epilepsy. Functional neural network analysis is a promising technique for more accurate identification of the target areas for epilepsy surgery, but a better understanding of the correlations between changes in functional network organization due to surgery and postoperative seizure status is required. We explored these correlations in longitudinal magnetoencephalography (MEG) recordings of 20 lesional epilepsy patients. Resting-state MEG recordings were obtained at baseline (preoperatively; T0) and at 3–7 (T1) and 9–15months after resection (T2). We assessed frequency-specific functional connectivity and performed a minimum spanning tree (MST) network analysis. The MST captures the most important connections in the network. We found a significant positive correlation between functional connectivity in the lower alpha band and seizure frequency at T0, especially in regions where lesions were located. MST leaf fraction, a measure of integration of information in the network, was significantly increased between T0 and T2, only for the seizure-free patients. This is in line with previous work, which showed that lower functional network integration in lesional epilepsy patients is related to higher epilepsy burden. Finally, eccentricity and betweenness centrality, which are measures of hub-status, decreased between T0 and T2 in seizure free patients, also in regions that were anatomically close to resection cavities. Our results increase insight into functional network changes in successful epilepsy surgery and might eventually be utilized for optimization of neurosurgical approaches. [Copyright &y& Elsevier]

Highlights: [•] We explored the effects of music on EEG functional networks in six different bands. [•] We found a more efficient but less economical configuration under music perception. [•] Differences in network structure were not observed between musical stimuli. [•] The results further supported reorganization of brain networks for music listening. [ABSTRACT FROM AUTHOR]