Theta Frequency Bands Research Articles

P117 A pilot study of a novel electrode system for brainwave acquisition during sleep: Tripolar Concentric Ring Electrodes

Abstract Introduction Tri-concentric ring electrodes (TCRE) evaluate current density underlying each electrode rather than a voltage difference between two conventionally placed electroencephalography (EEG) electrodes. TCRE signals are more focal and show improved signal-to-noise with markedly reduced electromyographic interference compared to conventional EEG. This pilot study is the first to evaluate sleep using TCRE and power spectral analysis of TCRE versus conventional EEG emulated (eEEG) recordings obtained from TCRE and the outer rings of paired electrodes. Method Twenty healthy sleepers (8 men, mean±SD age 27.8±9.6 y) completed a single overnight sleep study with a 9-hr sleep opportunity. Participants were set up for polysomnography recording using TCRE for brain activity (18 sites) and conventional gold-cup electrodes for eye movements, muscle activity, and heart rate. A fast Fourier transform using multitaper-based estimation was applied in 5-s epochs to calculate relative powers in delta, alpha, theta, sigma and beta frequency bands for eEEG and TCRE signals throughout sleep. Results At the Cz position, TCRE showed greater relative delta power and reduced beta powers across sleep stages compared to eEEG. For example, TCRE had higher delta activity during NREM stage 2 sleep than eEEG (mean [SD]; 89% [5.7] vs 64% [6]) and lower beta activity during wake (3.4% [2] vs 17.3% [7.9]). Ongoing analyses will compare outcomes across electrode locations. Conclusions Brain activity acquisition via TCRE may reduce artefact interference and potentially identify new markers during sleep that are associated with sleep quality, health, and functioning.

O072 Episodes of drowsiness present during cognitively demanding tasks

Abstract Introduction Insufficient sleep is associated with impaired performance in cognitive tasks and executive functioning. Adolescence is a time of increasing autonomy and lifestyle changes, where social, educational, and work demands often impact on sleep opportunity. The impact of habitual short sleep opportunity on cognitive functioning is poorly understood. We examined the difference in EEG-based alertness during low and high load cognitive tasks in sleep restricted young adults. Methods Ninety-nine habitually sleep restricted young adults (M=20.65, SD=1.91, 73% female) participated in the study. EEG-based (F4-M1, O2-M1) alertness was examined under two conditions: (1) low load; a simulated slow drive and (2) high load; a weekly activity mapping task. A novel model predicted alertness as a continuous function of EEG power in beta, alpha, theta, and delta frequency bands. Model output 1 indicates alertness (i.e., wake), while values approaching zero indicate reductions in alertness. Statistical significance was assessed by two-tailed t-test (unequal variance), and effect size reported by Cohen’s D. Results Mean model output values were significantly (p<0.0001) lower during the driving task (0.16, 95%CI=0.12-0.19) compared to the mapping task (0.92, 0.87-0.97), with substantial effect size (D=3.9). Conclusions This study demonstrates that sleep restricted young adults are less alert during tasks with low cognitive demands, even though the task we tested (simulated drive) represents high real-world risk. In contrast, these individuals show elevated levels of alertness during cognitively demanding tasks despite absence of real-world risk. Further study investigating potential mitigating effects through sleep modulation may reveal simple health recommendations

The Effects of Different Theta and Beta Neurofeedback Training Protocols on Cognitive Control in ADHD

Neurofeedback (NF) is an important treatment for attention deficit/hyperactivity disorder (ADHD). In ADHD, cognitive control deficits pose considerable problems to patients. However, NF protocols are not yet optimized to enhance cognitive control alongside with clinical symptoms, partly because they are not driven by basic cognitive neuroscience. In this study, we evaluated different EEG theta and/or beta frequency band NF protocols designed to enhance cognitive control. Participants were n = 157 children and adolescents, n = 129 of them were patients with ADHD (n = 28 typically developing (TD) controls). Patients with ADHD were divided into five groups in the order of referral, with four of them taking part in different NF protocols systematically varying theta and beta power. The fifth ADHD group and the TD group did not undergo NF. All NF protocols resulted in reductions of ADHD symptoms. Importantly, only when beta frequencies were enhanced during NF (without any theta regulation or in combination with theta upregulation), consistent enhancing effects in both response inhibition and conflict control were achieved. The theta/beta NF protocol most widely used in clinical settings revealed comparatively limited effects. Enhancements in beta band activity are key when aiming to improve cognitive control functions in ADHD. This calls for a change in the use of theta/beta NF protocols and shows that protocols differing from the current clinical standard are effective in enhancing important facets of cognitive control in ADHD. Further studies need to examine regulation data within the neurofeedback sessions to provide more information about the mechanisms underlying the observed effects.

Electroencephalogram pattern association with drug-related cues in a long-duration virtual reality environment in patients with methamphetamine use disorder.

The cognitive processing of drug-related cues and the subsequent dysregulation of behaviour play a central role in the pathophysiology of substance use disorders. Prior studies are limited by small sample sizes and a lack of immersion in stimulus presentation. In the present study, we recruited patients with methamphetamine use disorder (MUD; N= 1099) from four compulsory isolated detoxification centres and healthy control participants (N= 305). With a 12-min-long virtual reality (VR) protocol stimulus, we discovered that patients showed a decrease in electroencephalogram(EEG) power across alpha to gamma bands in anterior scalp regions under methamphetamine-related VR stimuli (e.g. a glass pipe and medical tubing) compared with the control stimuli (e.g. balls and cubes). Analysis of variance (ANOVA) showed that the interaction effects of stimuli type and group were significant in five EEG bands. Using generalised linear models, we classified the stimuli type (i.e. drug-related vs. drug-unrelated cues) in MUD patients with an f1 score of 90% on an out-of-sample testing set. The decreases of EEG between drug-related cues and drug-unrelated cues in delta, theta and alpha frequency bands are more frequently seen in patients than in healthy controls, perhaps reflecting general arousal and attenuated impulsive control. Our results suggest that EEG responses elicited by long-duration methamphetamine-related VR cues showed a specific signature, which may have future clinical implications.

Hyperconnectivity matters in early-onset Alzheimer's disease: a resting-state EEG connectivity study

ObjectivesThe aim of this study was to differentiate individuals with early-onset Alzheimer's disease (EOAD) and identify differences of functional connectivity in resting-state EEG between individuals with EOAD and late-onset AD (LOAD) in comparison with both healthy young and elderly individuals. MethodsForty EOAD and 56 LOAD patients were included along with 51 demographically matched young, and 54 elderly healthy individuals as controls to the EOAD and LOAD groups. Four minutes of resting-state EEG were recorded during the eyes-closed condition. The absolute value of imaginary coherence (ICoh) was measured for connectivity. The maximum values of ICoh were measured at delta (0.5-3.5 Hz), theta (4-7.5Hz), alpha (8-13 Hz), alpha-1 (8-10 Hz), alpha-2 (10.5-13 Hz), beta (13-30 Hz), beta-1 (13-20 Hz), and beta-2 (20.5-30 Hz) frequency bands. ResultsIndividuals with EOAD showed higher coherence values in all frequency bands than LOAD patients. Compared to young healthy controls (YHC), EOAD had increased ICoh values in theta and beta-2 bands, whereas LOAD had lower ICoh values in the alpha-1 band than elderly healthy controls (EHC). Lastly, patients with EOAD demonstrated negative moderate correlations between language domains and beta-1 ICoh values. ConclusionTo the authors’ knowledge, this is the first study evaluating coherence alterations among early-and late-onset AD patients and the diagnostic value of coherence measures. It was suggested that EOAD patients had more severe pathological changes compared with LOAD.

Effects of decorative pictures on mental processing demands and learning: An EEG and eye-tracking study

Across two experiments, the effects of presenting decorative pictures (DP) alongside texts were assessed with respect to the mental processing demands during reading, learning outcomes, and subjective task experience. DP were thought to function as so-called seductive details. Eye-tracking and the electroencephalogram (EEG) were recorded during reading. Pupil dilation and the EEG theta (4–8 Hz) and alpha (8–13 Hz) frequency band power served as measures of the mental processing demands. Texts on features and habitats of animals served as learning materials. Working memory (WM) load was manipulated as an additional factor during reading. Neither the additional WM load nor DP had significant effects on learning outcomes, albeit in both experiments the increased WM load affected the physiological measures as expected. DP with a few different motifs generally were not very seductive (Experiment 1). DP with more diverse motifs resulted in increased mental processing demands as indicated by the EEG alpha frequency band power (Experiment 2). Subjective task experience was positively affected by the DP present in Experiment 2 as indicated by higher subjective ratings of interestingness, comprehensibility, emotionality, and aesthetical pleasantness when DP were present. In sum, the study indicates that DP with diverse motifs may function as seductive details, increasing mental processing demands without detrimental effects on learning outcomes.

What a single electroencephalographic (EEG) channel can tell us about patients with dementia due to Alzheimer's disease

Abnormalities in cortical sources of resting-state eyes-closed electroencephalographic (rsEEG) rhythms recorded by hospital settings (10–20 electrode montage) with 19 scalp electrodes provide useful markers of neurophysiological dysfunctions in the vigilance regulation in patients with Alzheimer's disease dementia (ADD). Here we tested whether these markers may be effective from a few scalp electrodes towards the use of low-cost recording devices.Clinical and rsEEG data acquired in hospital settings (10–20 electrode montage) from 88 ADD participants and 68 age-, education-, and sex-matched normal elderly controls (Nold) were available in an international Eurasian database. Standard spectral FFT analysis of rsEEG data for individual delta, theta, and alpha frequency bands was from C3-P3, C4-P4, P3-O1, and P4-O2 bipolar channels.As compared to the Nold group, the ADD group showed increased delta, theta, low-frequency alpha power density and decreased high-frequency alpha power density at all those bipolar channels. The highest classification accuracy between the ADD and Nold individuals reached 90 % (area under the receiver operating characteristic curve) using Alpha2/Theta power density computed at the C3-P3 bipolar channel.Standard rsEEG power density computed from a few posterior bipolar channels successfully classified Nold and ADD individuals, thus encouraging a massive prescreening of neurophysiological mechanisms underpinning the vigilance dysregulation in underserved old seniors.

Large-scale frontoparietal theta, alpha, and beta phase synchronization: A set of EEG differential characteristics for freezing of gait in Parkinson’s disease?

Freezing of gait (FOG) is a complex gait disturbance in Parkinson's disease (PD), during which the patient is not able to effectively initiate gait or continue walking. The mystery of the FOG phenomenon is still unsolved. Recent studies have revealed abnormalities in cortical activities associated with FOG, which highlights the importance of cortical and cortical-subcortical network dysfunction in PD patients with FOG. In this paper, phase-locking value (PLV) of eight frequency sub-bands between 0.05 Hz and 35 Hz over frontal, motor, and parietal areas [during an ankle dorsiflexion (ADF) task] is used to investigate EEG phase synchronization. PLV was investigated over both superficial and deeper networks by analyzing EEG signals preprocessed with and without Surface Laplacian (SL) spatial filter. Four groups of participants were included: PD patients with severe FOG (N = 5, 5 males), PD patients with mild FOG (N = 7, 6 males), PD patients without FOG (N = 14, 13 males), and healthy age-matched controls (N = 13, 10 males). Fifteen trials were recorded from each participant. At superficial layers, frontoparietal theta phase synchrony was a unique feature present in PD with FOG groups. At deeper networks, significant dominance of interhemispheric frontoparietal alpha phase synchrony in PD with FOG, in contrast to beta phase synchrony in PD without FOG, was identified. Alpha phase synchrony was more distributed in PD with severe FOG, with higher levels of frontoparietal alpha phase synchrony. In addition to FOG-related abnormalities in PLV analysis, phase-amplitude coupling (PAC) analysis was also performed on frequency bands with PLV abnormalities. PAC analysis revealed abnormal coupling between theta and low beta frequency bands in PD with severe FOG at the superficial layers over frontal areas. At deeper networks, theta and alpha frequency bands show high PAC over parietal areas in PD with severe FOG. Alpha and low beta also presented PAC over frontal areas in PD groups with FOG. The results introduced significant phase synchrony differences between PD with and without FOG and provided important insight into a possible unified underlying mechanism for FOG. These results thus suggest that PLV and PAC can potentially be used as EEG-based biomarkers for FOG.

Working memory-related alterations in neural oscillations reveal the influence of in-vehicle toluene on cognition at low concentration.

Although toluene is a typical in-vehicle pollutant, the impacts of in-vehicle toluene exposure on cognitive functions remain unestablished. Therefore, this study aimed to investigate the effects of short-term toluene exposure in vehicles on working memory based on neural oscillations. In total, 24 healthy adults were recruited. Each subject was exposed to four different concentrations of toluene and divided into 0ppb, 17.5ppb, 35ppb, and 70ppb groups for self-control studies. After 4h of exposure to each concentration of toluene, a behavioral test of visual working memory was performed while 19-channel electroencephalogram (EEG) signals were collected. Meanwhile, the power spectral density (PSD) and spatial distribution of working memory encoding, maintenance, and extraction periods were calculated by short-time Fourier transform to clarify the characteristic frequency bands, major brain regions, and characteristic channels of each period. To compare the changes in the characteristic patterns of neural oscillations under the effect of different concentrations of toluene. There was no significant difference in working memory reaction time and correct rate between the groups at different toluene concentrations (p > 0.05). The characteristic frequency band of the working memory neural oscillations in each group was the theta frequency band; the PSD of the theta frequency band was predominantly concentrated in the frontal area, and the characteristic channel was the Fz channel. The whole brain (F = 3.817, p < 0.05; F = 4.758, p < 0.01; F = 3.694, p < 0.05), the frontal area (F = 2.505, p < 0.05; F = 2.839, p < 0.05; F = 6.068, p < 0.05), the Fz channel (F = 3.522, p < 0.05; F = 3.745, p < 0.05; F = 6.526, p < 0.05), and the PSD of working memory in the theta frequency band was significantly increased in the 70ppb group compared with the other three groups during the coding, maintenance, and retrieval phases of working memory. When the in-vehicle toluene exposure concentration was 70ppb, the PSD of the characteristic frequency bands of working memory was significantly increased in the whole brain, major brain regions, and characteristic channels.

Graph Theory-Based Electroencephalographic Connectivity via Phase-Locking Value and Its Association with Ketogenic Diet Responsiveness in Patients with Focal Onset Seizures.

Ketogenic diets (KDs) are a promising alternative therapy for pediatric refractory epilepsy. Several predictors of KD responsiveness have been identified, including biochemical parameters, seizure types, and electroencephalography (EEG) examinations. We hypothesized that graph theory-based EEG functional connectivity could explain KD responses in patients presenting focal onset seizure (FOS). A total of 17 patients aged 0-30 years old with focal onset seizures (FOS) were recruited as a study group between January 2015 and July 2021. Twenty age-matched children presenting headache with no intracranial complications nor other medical issues were enrolled as a control group. Data were obtained at baseline and at 12 months after initiating KD therapy (KDT) using the child behavior checklist (CBCL) and brain functional connectivity parameters based on phase-locking value from 19 scalp EEG signals, including nodal strength, global efficiency, clustering coefficient, and betweenness centrality. Compared with age-matched controls, patients presenting FOS with right or bilateral EEG lateralization presented higher baseline functional connectivity, including parameters such as global efficiency, mean cluster coefficient and mean nodal strength in the delta and beta frequency bands. In patients presenting FOS with right or bilateral EEG lateralization, the global efficiency of functional connectivity parameters in the delta and theta frequency bands was significantly lower at 12 months after KDT treatment than before KDT. Those patients also presented a significantly lower mean clustering coefficient and mean nodal strength in the theta frequency band at 12 months after KDT treatment. Changes in brain functional connectivity were positively correlated with social problems, attention, and behavioral scores based on CBCL assessments completed by parents. This study provides evidence that KDT might be beneficial in the treatment of patients with FOS. Graph theoretic analysis revealed that the observed effects were related to decreased functional connectivity, particularly in terms of global efficiency. Our findings related to brain connectivity revealed lateralization to the right (non-dominant) hemisphere; however, we were unable to define the underlying mechanism. Our data revealed that in addition to altered brain connectivity, KDT improved the patient's behavior and emotional state.

Quantitative pretreatment EEG predicts efficacy of ACTH therapy in infantile epileptic spasms syndrome

ObjectiveThis study aimed to determine the correlation between outcomes following adrenocorticotrophic hormone (ACTH) therapy and measurements of relative power spectrum (rPS), weighted phase lag index (wPLI), and graph theoretical analysis on pretreatment electroencephalography (EEG) in infants with non-lesional infantile epileptic spasms syndrome (IESS). MethodsTwenty-eight patients with non-lesional IESS were enrolled. Outcomes were classified based on seizure recurrence following ACTH therapy: seizure-free (F, n = 21) and seizure-recurrence (R, n = 7) groups. The rPS, wPLI, clustering coefficient, and betweenness centrality were calculated on pretreatment EEG and were statistically analyzed to determine the correlation with outcomes following ACTH therapy. ResultsThe rPS value was significantly higher in the delta frequency band in group R than in group F (p < 0.001). The wPLI values were significantly higher in the delta, theta, and alpha frequency bands in group R than in group F (p = 0.007, <0.001, and <0.001, respectively). The clustering coefficient in the delta frequency band was significantly lower in group R than in group F (p < 0.001). ConclusionsOur findings demonstrate the significant differences in power and functional connectivity between outcome groups. SignificanceThis study may contribute to an early prediction of ACTH therapy outcomes and thus help in the development of appropriate treatment strategies.

Evoked Acute Stress Alters Frontal Midline Neural Oscillations Affecting Behavioral Inhibition in College Students.

PurposeThe current research of the effect of acute stress on individual behavioral inhibition remains divergent. The present study aims to explore the effects of acute stress on behavioral inhibition in college students and to understand the neural oscillatory characteristics of their behavioral inhibition process.Patients and MethodsWe invited 27 college students (12 males and 15 females) to participate in the study. The experiment was conducted using the Trier Social Stress paradigm to evoke an acute stress state and an out-of-speech reading to set a neutral state. Participants completed a two-choice Oddball task in the acute stress state and the neutral state, respectively. We used a 64-channel EEG cap to record EEG data from university students during the experimental task. In combination with the ERO technique, we compared the reaction time, the number of errors, and the power of the alpha (8–13 Hz) and theta (4–8 Hz) frequency bands at the midline of the frontal lobe for subjects in both states. The correlation between the area under the stress area line and the alpha as well as theta frequency bands was also analyzed.ResultsWe found that in the two-choice Oddball task, the response inhibition time was shorter, the number of response errors decreased, and the alpha-band power values decreased in the acute stress state compared to the neutral state. For the standard stimulus, the theta-band power increase in the acute stress state.ConclusionOur results suggest that evoked acute stress promotes behavioral inhibition in college students by affecting their frontal midline neural oscillations.

Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer.

Research shows that Attention Deficit Hyperactivity Disorder (ADHD) is related to a disorder in brain networks. The purpose of this study is to use an effective connectivity measure and graph theory to examine the impairments of brain connectivity in ADHD. Weighted directed graphs based on electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children were constructed. The edges between two nodes (electrodes) were calculated by Phase Transfer Entropy (PTE). PTE is calculated for five frequency bands: delta, theta, alpha, beta, and gamma. The graph theory measures were divided into two categories: global and local. Statistical analysis with global measures indicates that in children with ADHD, the segregation of brain connectivity increases while the integration of the brain connectivity decreases compared tohealthy children. These brain network differences were identified in the delta and theta frequency bands. The classification accuracy of 89.4% is obtained for both in-degree and strength measures in the theta band. Our result indicated local graph measures classified ADHD and healthy subjects with accuracy of 91.2 and 90% in theta anddelta bands, respectively. Our analysis may provide a new understanding of the differences in the EEG brain network of children with ADHD and healthy children.

Flexibility of Fast Brain Dynamics and Disease Severity in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a multisystem disorder, as supported by clinical, molecular, and neuroimaging evidence. As a consequence, predicting clinical features requires a description of large-scale neuronal dynamics. Normally, brain activity dynamically reconfigures over time, recruiting different brain areas. Brain pathologies induce stereotyped dynamics which, in turn, are linked to clinical impairment. Hence, based on recent evidence showing that brain functional networks become hyperconnected as ALS progresses, we hypothesized that the loss of flexible dynamics in ALS would predict the symptoms severity. To test this hypothesis, we quantified flexibility using the "functional repertoire" (i.e., the number of configurations of active brain areas) as measured from source-reconstructed magnetoencephalography (MEG) in patients with ALS and healthy controls. The activity of brain areas was reconstructed in the classic frequency bands, and the functional repertoire was estimated to quantify spatiotemporal fluctuations of brain activity. Finally, we built a k-fold cross-validated multilinear model to predict the individual clinical impairment from the size of the functional repertoire. Comparing 42 patients with ALS and 42 healthy controls, we found a more stereotyped brain dynamics in patients with ALS (p < 0.05), as conveyed by the smaller functional repertoire. The relationship between the size of the functional repertoire and the clinical scores in the ALS group showed significant correlations in both the delta and the theta frequency bands. Furthermore, through a k-fold cross-validated multilinear regression model, we found that the functional repertoire predicted both clinical staging (p < 0.001 and p < 0.01, in the delta and theta bands, respectively) and symptoms severity (p < 0.001, in both the delta and theta bands). Our work shows that (1) ALS pathology reduces the flexibility of large-scale brain dynamics, (2) subcortical regions play a key role in determining brain dynamics, and (3) reduced brain flexibility predicts disease stage and symptoms severity. Our approach provides a noninvasive tool to quantify alterations in brain dynamics in ALS (and, possibly, other neurodegenerative diseases), thus opening new opportunities in disease management and a framework to test, in the near future, the effects of disease-modifying interventions at the whole-brain level.

Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain.

ObjectiveWe assessed the potential of using EEG to detect cold thermal pain in adolescents with and without chronic musculoskeletal pain.MethodsThirty-nine healthy controls (15.2 ± 2.1 years, 18 females) and 121 chronic pain participants (15.0 ± 2.0 years, 100 females, 85 experiencing pain ≥12-months) had 19-channel EEG recorded at rest and throughout a cold-pressor task (CPT). Permutation entropy, directed phase lag index, peak frequency, and binary graph theory features were calculated across 10-second EEG epochs (Healthy: 292 baseline / 273 CPT epochs; Pain: 1039 baseline / 755 CPT epochs). Support vector machine (SVM) and logistic regression models were trained to classify between baseline and CPT conditions separately for control and pain participants.ResultsSVM models significantly distinguished between baseline and CPT conditions in chronic pain (75.2% accuracy, 95% CI: 71.4%–77.1%; p < 0.0001) and control (74.8% accuracy, 95% CI: 66.3%–77.6%; p < 0.0001) participants. Logistic regression models performed similar to the SVM (Pain: 75.8% accuracy, 95% CI: 69.5%–76.6%, p < 0.0001; Controls: 72.0% accuracy, 95% CI: 64.5%–78.5%, p < 0.0001). Permutation entropy features in the theta frequency band were the largest contributor to model accuracy for both groups.ConclusionsOur results demonstrate that subjective pain experiences can accurately be detected from electrophysiological data, and represent the first step towards the development of a point-of-care system to detect pain in the absence of self-report.

Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures.

Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM). In most cases, the broad band returned higher accuracy, gamma returned the lowest, and combining the six bands together improved classifier performance. The Renyi entropy was the best feature and returned high accuracy in every band. The highest balanced accuracy, 95.03%, was obtained by the kNN with Renyi entropy and combining all bands except broad. This analysis showed that entropy measures can differentiate between interictal PNES and epilepsy with high accuracy, and improved performances indicate that combining bands is an effective improvement for diagnosing PNES from EEGs and ECGs.

Brain mechanisms of automated conflict avoidance simulator supervision.

Supervision of automated systems is an ubiquitous aspect of most of our everyday life activities which is even more necessary in high risk industries (aeronautics, power plants, etc.). Performance monitoring related to our own error making has been widely studied. Here we propose to assess the neurofunctional correlates of system error detection. We used an aviation-based conflict avoidance simulator with a 40% error-rate and recorded the electroencephalographic activity of participants while they were supervising it. Neural dynamics related to the supervision of system's correct and erroneous responses were assessed in the time and time-frequency domains to address the dynamics of the error detection process in this environment. Two levels of perceptual difficulty were introduced to assess their effect on system's error detection-related evoked activity. Using a robust cluster-based permutation test, we observed a lower widespread evoked activity in the time domain for errors compared to correct responses detection, as well as a higher theta-band activity in the time-frequency domain dissociating the detection of erroneous from that of correct system responses. We also showed a significant effect of difficulty on time-domain evoked activity, and of the phase of the experiment on spectral activity: a decrease in early theta and alpha at the end of the experiment, as well as interaction effects in theta and alpha frequency bands. These results improve our understanding of the brain dynamics of performance monitoring activity in closer-to-real-life settings and are a promising avenue for the detection of error-related components in ecological and dynamic tasks.

Brain functional connectivity analysis in patients with relapsing-remitting multiple sclerosis: A graph theory approach of EEG resting state.

Multiple sclerosis (MS) is an autoimmune disease related to the central nervous system (CNS). This study aims to investigate the effects of MS on the brain's functional connectivity network using the electroencephalogram (EEG) resting-state signals and graph theory approach. Resting-state eyes-closed EEG signals were recorded from 20 patients with relapsing-remitting MS (RRMS) and 18 healthy cases. In this study, the prime objective is to calculate the connectivity between EEG channels to assess the differences in brain functional network global features. The results demonstrated lower cortical activity in the alpha frequency bands and higher activity for the gamma frequency bands in patients with RRMS compared to the healthy group. In this study, graph metric calculations revealed a significant difference in the diameter of the functional brain network based on the directed transfer function (DTF) measure between the two groups, indicating a higher diameter in RRMS cases for the alpha frequency band. A higher diameter for the functional brain network in MS cases can result from anatomical damage. In addition, considerable differences between the networks' global efficiency and transitivity based on the imaginary part of the coherence (iCoh) measure were observed, indicating higher global efficiency and transitivity in the delta, theta, and beta frequency bands for RRMS cases, which can be related to the compensatory functional reaction from the brain. This study indicated that in RRMS cases, some of the global characteristics of the brain's functional network, such as diameter and global efficiency, change and can be illustrated even in the resting-state condition when the brain is not under cognitive load.

Altered EEG power spectrum, but not sleep-wake architecture, in HCN1 knockout mice

Sleep is a complex biological state characterized by large populations of neurons firing in a rhythmic or synchronized manner. HCN channels play a critical role in generating and sustaining synchronized neuronal firing and are involved in the actions of anaesthetics. However, the role of these channels in sleep-wakefulness per se has yet to be studied. We conducted polysomnographic recordings of Hcn1 constitutive knockout (Hcn1 KO) and wild-type (WT) mice in order to investigate the potential role of HCN1 channels in sleep/wake regulation. EEG and EMG data were analysed using the Somnivore™ machine learning algorithm. Time spent in each vigilance state, bout number and duration, and EEG power spectral activity were compared between genotypes. There were no significant differences in the time spent in wake, rapid eye movement (REM) or non-REM (NREM) sleep between Hcn1 KO and WT mice. Wake bout duration during the inactive phase was significantly shorter in Hcn1 KO mice whilst no other bout parameters were affected by genotype. Hcn1 KO mice showed a reduction in overall EEG power which was particularly prominent in the theta (5–9 Hz) and alpha (9–15 Hz) frequency bands and most evident during NREM sleep. Together these data suggest that HCN1 channels do not play a major role in sleep architecture or modulation of vigilance states. However, loss of these channels significantly alters underlying neuronal activity within these states which may have functional consequences.

Effect of acetylcholine deficiency on neural oscillation in a brainstem-thalamus-cortex neurocomputational model related with Alzheimer’s disease

Previous works imply that involving brainstem in neuropathological studies of Alzheimer’s disease (AD) is of clinically significant. This work constructs a comprehensive neural mass model for cholinergic neuropathogenesis that involves brainstem, thalamus and cortex, wherein how acetylcholine deficiency in AD affects neural oscillation of the model output is systematically explored from the perspective of neurocomputation. By decreasing synapse connectivity parameters in direct cholinergic pathway from brainstem to thalamus or in indirect glutamatergic synapse pathway from cortex to brainstem to mimic the pathological condition of reduced acetylcholine release in patients with AD, the property of neural oscillation in this model is numerically investigated by means of power spectrum in frequency domain and amplitude distribution in time domain. Simulated results demonstrate that decreasing synapse connectivity whether in the direct cholinergic pathway or in the indirect glutamatergic synapse pathway can alter the neural oscillation significantly in three aspects: it induces an obvious decrease of dominant frequency; it leads to a degraded rhythmic activity in the alpha frequency band as well as an enhanced rhythmic activity in the theta frequency band; it results in reduced oscillation amplitude of the model output. These results are agreement with the characteristic of electrophysiological EEG measurement recorded in AD, especially support the hypothesis that cholinergic deficiency is a promising pathophysiological origin of EEG slowing in AD. Our analysis indicates that targeting the cholinergic system may have potential prospects in early diagnosis and treatment of AD.