Power In Frequency Bands Research Articles

Processed EEG for personalized dosing of anesthetics during general anesthesia

Electroencephalogram (EEG)-guided anesthesia is indispensable in modern operating rooms and has become established as the standard form of monitoring. Many anesthesiologists rely on processed EEG indices in the hope of averting anesthesia-related complications, such as intraoperative awareness, postoperative delirium and other cognitive complications in their patients. This educational review aims to provide information on the five most prevalent monitors used to guide depth of sedation during general anesthesia. This article elucidates the principles underpinning the application of these monitors where known, which are generally based on power in various EEG frequency bands and on the burst suppression pattern. Convinced that EEG-guided anesthesia has the potential of benefitting many surgical patients, it is felt that many basic principles and shortcomings of processed EEG indices need to be better understood in the clinical practice. After discussing the different monitors and clinically relevant data from the literature, the article gives a short practical guidance on how to critically interpret processed EEG information and troubleshooting of confounded indices in the context of clinical situations.

Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture.

Functional and motility-related gastrointestinal (GI) disorders affect nearly 40% percent of the population. Disturbances of GI myoelectric activity have been proposed to play a significant role in these disorders. A significant barrier to usage of these signals in diagnosis and treatment is the lack of consistent relationships between GI myoelectric features and function. A potential cause of this issue is the use of arbitrary classification criteria, such as percentage of power in tachygastric and bradygastric frequency bands. Here we applied automatic feature extraction using a deep neural network architecture on GI myoelectric signals from free-moving ferrets. For each animal, we recorded during baseline control and feeding conditions lasting for 1 h. Data were trained on a 1-dimensional residual convolutional network, followed by a fully connected layer, with a decision based on a sigmoidal output. For this 2-class problem, accuracy was 90%, sensitivity (feeding detection) was 90%, and specificity (baseline detection) was 89%. By comparison, approaches using hand-crafted features (e.g., SVM, random forest, and logistic regression) produced an accuracy from 54% to 82%, sensitivity from 46% to 84% and specificity from 66% to 80%. These results suggest that automatic feature extraction and deep neural networks could be useful to assess GI function for comparing baseline to an active functional GI state, such as feeding. In future testing, the current approach could be applied to determine normal and disease-related GI myoelectric patterns to diagnosis and assess patients with GI disease.

Vagal cardiac control in rats with LPS-induced lung injury

Heart rate variability (HRV) as an index of cardiac autonomic control in acute lung injury (ALI) has been evaluated in anaesthetized rats intratracheally instilled with bacterial lipopolysaccharide (LPS) and ventilated with breathing frequency of 60/min, 40% oxygen, inspiratory time 40%, tidal volume of 6 mL/kg. ECG was recorded before and 30, 60, 120, 180 and 240 min after LPS or saline (control) administration. HRV was quantified by time and frequency-domain analysis (mean RR interval, SDRR, RMSSD and spectral power in high frequency (HF) band. Lactate in plasma, and oxidative stress, IL-1β, IL-5, IL-12p70 and IL-13 and galectin-3 in heart tissue raised in LPS-injured rats. Overall HRV magnitude (SDRR) and marker of vagal heart rate control (RMSSD), as well as frequency domain parameter, spectral power HF was increased 120 and 180 min since ALI onset. In conclusion, LPS-induced ALI is accompanied by altered vagal cardiac control mediated by autonomic nervous system, likely based on the close relationship between immune response and vagally mediated autonomic nervous activity.

Micro-expression recognition based on EEG signals

Research into micro-expression recognition is increasingly motivated by its potential applications to mental-health diagnosis and policing. Thus far, micro-expression recognition research and applications have been based on computer vision processing technology, with notable limitations. For instance, changes in ambient lighting or in the head posture and facial occlusion can all affect micro-expression recognition. The high temporal resolution of EEG technology captures brain activity associated with micro-expressions, and hence objectively identifies micro-expressions from a neurophysiological perspective. Herein, we report a real-time supervision and emotional expression suppression (SEES) experimental paradigm. We collected micro-expression video and EEG data from 68 subjects under positive emotion induction. Thereafter, the EEG frequency-band power, statistical features, autoregressive (AR) model parameters, and wavelet entropy were extracted as features. We ranked all 128 channels based on the micro-expression recognition task classification accuracy using all the features extracted for each channel. Based on the channel ranking, different sizes of channel combinations were chosen. Subsequently, the optimal subset of these features from different combinations was selected using a sequential backward selection (SBS) algorithm and fed into different classifiers, including a support vector machine (SVM), K-nearest neighbor (KNN), gradient-boosted decision tree (GBDT), and back-propagation (BP) neuron network. The different classifiers achieve promising accuracy for micro-expression recognition, with GBDT exhibiting the highest accuracy of 85.96 % when 11 channels were selected. This demonstrates the feasibility of recognizing micro-expressions based on EEG signals.

Nociception Effect on Frontal Electroencephalogram Waveform and Phase-Amplitude Coupling in Laparoscopic Surgery.

Electroencephalographic pattern changes during anesthesia reflect the nociception-analgesia balance. Alpha dropout, delta arousal, and beta arousal with noxious stimulation have been described during anesthesia; however, data on the reaction of other electroencephalogram signatures toward nociception are scarce. Analyzing the effects of nociception on different electroencephalogram signatures may help us find new nociception markers in anesthesia and understand the neurophysiology of pain in the brain. This study aimed to analyze the electroencephalographic frequency pattern and phase-amplitude coupling change during laparoscopic surgeries. This study evaluated 34 patients who underwent laparoscopic surgery. The electroencephalogram frequency band power and phase-amplitude coupling of different frequencies were analyzed across 3 stages of laparoscopy: incision, insufflation, and opioid stages. Repeated-measures analysis of variance with a mixed model and the Bonferroni method for multiple comparisons were used to analyze the changes in the electroencephalogram signatures between the preincision and postincision/postinsufflation/postopioid phases. During noxious stimulation, the frequency spectrum showed obvious decreases in the alpha power percentage after the incision (mean ± standard error of the mean [SEM], 26.27 ± 0.44 and 24.37 ± 0.66; P < .001) and insufflation stages (26.27 ± 0.44 and 24.40 ± 0.68; P = .002), which recovered after opioid administration. Further phase-amplitude analyses showed that the modulation index (MI) of the delta-alpha coupling decreased after the incision stage (1.83 ± 0.22 and 0.98 ± 0.14 [MI × 10 3 ]; P < .001), continued to be suppressed during the insufflation stage (1.83 ± 0.22 and 1.17 ± 0.15 [MI × 10 3 ]; P = .044), and recovered after opioid administration. Alpha dropout during noxious stimulation is observed in laparoscopic surgeries under sevoflurane. In addition, the modulation index of delta-alpha coupling decreases during noxious stimulation and recovers after the administration of rescue opioids. Phase-amplitude coupling of the electroencephalogram may be a new approach for evaluating the nociception-analgesia balance during anesthesia.

The effect of HD-tDCS on brain oscillations and frontal synchronicity during resting-state EEG in violent offenders with a substance dependence.

Violence is a major problem in our society and therefore research into the neural underpinnings of aggression has grown exponentially. Although in the past decade the biological underpinnings of aggressive behavior have been examined, research on neural oscillations in violent offenders during resting-state electroencephalography (rsEEG) remains scarce. In this study we aimed to investigate the effect of high-definition transcranial direct current stimulation (HD-tDCS) on frontal theta, alpha and beta frequency power, asymmetrical frontal activity, and frontal synchronicity in violent offenders. Fifty male violent forensic patients diagnosed with a substance dependence were included in a double-blind sham-controlled randomized study. The patients received 20 minutes of HD-tDCS two times a day on five consecutive days. Before and after the intervention, the patients underwent a rsEEG task. Results showed no effect of HD-tDCS on the power in the different frequency bands. Also, no increase in asymmetrical activity was found. However, we found increased synchronicity in frontal regions in the alpha and beta frequency bands indicating enhanced connectivity in frontal brain regions as a result of the HD-tDCS-intervention. This study has enhanced our understanding of the neural underpinnings of aggression and violence, pointing to the importance of alpha and beta frequency bands and their connectivity in frontal brain regions. Although future studies should further investigate the complex neural underpinnings of aggression in different populations and using whole-brain connectivity, it can be suggested with caution, that HD-tDCS could be an innovative method to regain frontal synchronicity in neurorehabilitation.

Quantitative Electroencephalographic Analysis in Women with Migraine during the Luteal Phase

Migraine is a common, headache disorder characterized by recurrent episodes of headache often associated with nausea, vomiting, photophobia, and phonophobia. Prior to puberty, boys and girls are equally affected. Female preponderance emerges after puberty. Migraine pathophysiology is not fully understood, and although the hormonal effect of estrogen is significant, it is not clear how hormonal phases affect brain excitability and EEG patterns in women with migraine. The objective of this research was to study the effect of migraine on the resting-state EEG activity of women during the luteal phase. This work compares electroencephalographic (EEG) absolute power in different frequency bands and scalp areas between young women who suffer from migraine and had a migraine attack within 24 h prior to EEG recording (experimental) and ten age-matched young healthy women (controls), all with normal menstrual cycles. For women with migraine, we found a significant decrease/increase in alpha power in the occipitoparietal/frontocentral area, significant decrease in beta power for all areas, significant decrease in delta power in the temporal area, and significant decrease in theta power in the frontocentral and occipitoparietal area. We concluded that women with migraine have a distinct electroencephalographic pattern during the luteal phase in comparison with control women. A possible explanation might be an intermittent rhythmic activity linked to pain.

Corticostriatal beta oscillation changes associated with cognitive function in Parkinson's disease.

Cognitive impairment is the most frequent non-motor symptom in Parkinson's disease and is associated with deficits in a number of cognitive functions including working memory. However, the pathophysiology of Parkinson's disease cognitive impairment is poorly understood. Beta oscillations have previously been shown to play an important role in cognitive functions including working memory encoding. Decreased dopamine in motor cortico-striato-thalamo-cortical (CSTC) circuits increases the spectral power of beta oscillations and results in Parkinson's disease motor symptoms. Analogous changes in parallel cognitive CSTC circuits involving the caudate and dorsolateral prefrontal cortex (DLPFC) may contribute to Parkinson's disease cognitive impairment. The objective of our study is to evaluate whether changes in beta oscillations in the caudate and DLPFC contribute to cognitive impairment in Parkinson's disease patients. To investigate this, we used local field potential recordings during deep brain stimulation surgery in 15 patients with Parkinson's disease. Local field potentials were recorded from DLPFC and caudate at rest and during a working memory task. We examined changes in beta oscillatory power during the working memory task as well as the relationship of beta oscillatory activity to preoperative cognitive status, as determined from neuropsychological testing results. We additionally conducted exploratory analyses on the relationship between cognitive impairment and task-based changes in spectral power in additional frequency bands. Spectral power of beta oscillations decreased in both DLPFC and caudate during working memory encoding and increased in these structures during feedback. Subjects with cognitive impairment had smaller decreases in caudate and DLPFC beta oscillatory power during encoding. In our exploratory analysis, we found that similar differences occurred in alpha frequencies in caudate and theta and alpha in DLPFC. Our findings suggest that oscillatory power changes in cognitive CSTC circuits may contribute to cognitive symptoms in patients with Parkinson's disease. These findings may inform the future development of novel neuromodulatory treatments for cognitive impairment in Parkinson's disease.

Early development of electrophysiological activity: Contribution of periodic and aperiodic components of the EEG signal.

Brain function rapidly changes in the first 2 years of life. In the last decades, resting-state EEG has been widely used to explore those changes. Previous studies have focused on the relative power of the signal in established frequency bands (i.e., theta, alpha, and beta). However, EEG power is a mixture of a 1/f-like background power (aperiodic) in combination with narrow peaks that appear over that curve (periodic activity, e.g., alpha peak). Therefore, it is possible that relative power captures both, aperiodic and periodic brain activity, contributing to changes in electrophysiological activity observed in infancy. For this reason, we explored the early developmental trajectory of the relative power in theta, alpha, and beta frequency bands from infancy to toddlerhood and compared it with changes in periodic activity in a longitudinal study with three waves at age 6, 9, and 16 to 18 months. Finally, we tested the contribution of periodic activity and aperiodic components of the EEG to age changes in relative power. We found that relative power and periodic activity trajectories differed in this period in all the frequency bands but alpha. Furthermore, aperiodic EEG activity flattened between 6 and 18 months. More importantly, only alpha relative power was exclusively related to periodic activity, whereas aperiodic components of the signal significantly contributed to the relative power of activity in theta and beta bands. Thus, relative power in these frequencies is influenced by developmental changes of the aperiodic activity, which should be considered for future studies.

Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals.

In the field of upper limb brain computer interfaces (BCIs), the research focusing on bilateral decoding mostly based on the neural signals from two cerebral hemispheres. In addition, most studies used spikes for decoding. Here we examined the representation and decoding of different laterality and regions arm motor imagery in unilateral motor cortex based on local field potentials (LFPs). The LFP signals were recorded from a 96-channel Utah microelectrode array implanted in the left primary motor cortex of a paralyzed participant. There were 7 kinds of tasks: rest, left, right and bilateral elbow and wrist flexion. We performed time-frequency analysis on the LFP signals and analyzed the representation and decoding of different tasks using the power and energy of different frequency bands. The frequency range of <8 Hz and >38 Hz showed power enhancement, whereas 8-38 Hz showed power suppression in spectrograms while performing motor imagery. There were significant differences in average energy between tasks. What's more, the movement region and laterality were represented in two dimensions by demixed principal component analysis. The 135-300 Hz band signal had the highest decoding accuracy among all frequency bands and the contralateral and bilateral signals had more similar single-channel power activation patterns and larger signal correlation than contralateral and ipsilateral signals, bilateral and ipsilateral signals. The results showed that unilateral LFP signals had different representations for bilateral motor imagery on the average energy of the full array and single-channel power levels, and different tasks could be decoded. These proved the feasibility of multilateral BCI based on the unilateral LFP signal to broaden the application of BCI technology. https://www.chictr.org.cn/showproj.aspx?proj=130829, identifier ChiCTR2100050705.

Effects of spaceflight on the EEG alpha power and functional connectivity

Electroencephalography (EEG) can detect changes in cerebral activity during spaceflight. This study evaluates the effect of spaceflight on brain networks through analysis of the Default Mode Network (DMN)'s alpha frequency band power and functional connectivity (FC), and the persistence of these changes. Five astronauts' resting state EEGs under three conditions were analyzed (pre-flight, in-flight, and post-flight). DMN’s alpha band power and FC were computed using eLORETA and phase-locking value. Eyes-opened (EO) and eyes-closed (EC) conditions were differentiated. We found a DMN alpha band power reduction during in-flight (EC: p < 0.001; EO: p < 0.05) and post-flight (EC: p < 0.001; EO: p < 0.01) when compared to pre-flight condition. FC strength decreased during in-flight (EC: p < 0.01; EO: p < 0.01) and post-flight (EC: ns; EO: p < 0.01) compared to pre-flight condition. The DMN alpha band power and FC strength reduction persisted until 20 days after landing. Spaceflight caused electrocerebral alterations that persisted after return to earth. Periodic assessment by EEG-derived DMN analysis has the potential to become a neurophysiologic marker of cerebral functional integrity during exploration missions to space.

Refugee visa insecurity disrupts the brain’s default mode network

ABSTRACT Background: Research has largely focused on the psychological consequences of refugee trauma exposure, but refugees living with visa insecurity face an uncertain future that also adversely affects psychological functioning and self-determination. Objective: This study aimed to examine how refugee visa insecurity affects the functional brain. Method: We measured resting state brain activity via fMRI in 47 refugees with insecure visas (i.e. temporary visa status) and 52 refugees with secure visas (i.e. permanent visa status) residing in Australia, matched on key demographic, trauma exposure and psychopathology. Data analysis comprised independent components analysis to identify active networks and dynamic functional causal modelling tested visa security group differences in network connectivity. Results: We found that visa insecurity specifically affected sub-systems within the default mode network (DMN) – an intrinsic network subserving self-referential processes and mental simulations about the future. The insecure visa group showed less spectral power in the low frequency band in the anterior ventromedial DMN, and reduced activity in the posterior frontal DMN, compared to the secure visa group. Using functional dynamic causal modelling, we observed positive coupling between the anterior and posterior midline DMN hubs in the secure visa group, while the insecure visa group displayed negative coupling that correlated with self-reported fear of future deportation. Conclusions: Living with visa-related uncertainty appears to undermine synchrony between anterior-posterior midline components of the DMN responsible for governing the construction of the self and making mental representations of the future. This could represent a neural signature of refugee visa insecurity, which is marked by a perception of living in limbo and a truncated sense of the future.

The potential of convolutional neural networks for identifying neural states based on electrophysiological signals: experiments on synthetic and real patient data.

Processing incoming neural oscillatory signals in real-time and decoding from them relevant behavioral or pathological states is often required for adaptive Deep Brain Stimulation (aDBS) and other brain-computer interface (BCI) applications. Most current approaches rely on first extracting a set of predefined features, such as the power in canonical frequency bands or various time-domain features, and then training machine learning systems that use those predefined features as inputs and infer what the underlying brain state is at each given time point. However, whether this algorithmic approach is best suited to extract all available information contained within the neural waveforms remains an open question. Here, we aim to explore different algorithmic approaches in terms of their potential to yield improvements in decoding performance based on neural activity such as measured through local field potentials (LFPs) recordings or electroencephalography (EEG). In particular, we aim to explore the potential of end-to-end convolutional neural networks, and compare this approach with other machine learning methods that are based on extracting predefined feature sets. To this end, we implement and train a number of machine learning models, based either on manually constructed features or, in the case of deep learning-based models, on features directly learnt from the data. We benchmark these models on the task of identifying neural states using simulated data, which incorporates waveform features previously linked to physiological and pathological functions. We then assess the performance of these models in decoding movements based on local field potentials recorded from the motor thalamus of patients with essential tremor. Our findings, derived from both simulated and real patient data, suggest that end-to-end deep learning-based methods may surpass feature-based approaches, particularly when the relevant patterns within the waveform data are either unknown, difficult to quantify, or when there may be, from the point of view of the predefined feature extraction pipeline, unidentified features that could contribute to decoding performance. The methodologies proposed in this study might hold potential for application in adaptive deep brain stimulation (aDBS) and other brain-computer interface systems.

P.078 Desynchronization in high-definition transcranial direct current stimulation of refractory status epilepticus

Background: Recently, we showed cathodal high-definition transcranial direct current stimulation (HD-tDCS) inhibits spikes in refractory status epilepticus (RSE) patients. We sought to characterize relative power changes within regional frequency bands to explore connectivity throughout stimulation. Methods: 28 ICU-EEGs from 10 patients were filtered with 1Hz-lower/127Hz-upper/60Hz-notch. Power spectral density was computed for each individual bipolar-longitudinal (excluding midline) electrode chain in delta/theta/alpha/beta/gamma frequency bands. Relative power was extracted throughout recordings by Welch’s method with 2.5-second Hanning window size with 50% overlap every 10 seconds. The average of relative powers from ≤20-minute pre/during/post stimulation windows were calculated per session/chain/band. Absolute maximal relative power change between pre-during and during-post were computed. ANOVA statistically tested for differences. Results: Change in pre-during for delta/theta/alpha/beta/gamma were respectively -0.7%/-3.1%/-2.8%/-2.1%/8.2%, and during-post were 1.3%/2.8%/2.3%/1.7%/-7%. Positive average changes are increases in average relative power where negative changes show decreases. Changes in absolute maximal average relative power between pre-during and during-post stimulation between frequency bands were significant respectively (p=0.0011, p=0.0139). Conclusions: Relative power in all bands drop pre-during and increase during-post except gamma which correlates with desynchronization being a mechanism for HD-tDCS efficacy in RSE. These frequency band power changes may imply brain connectivity changes that need to be further explored.

Theta‐Gamma Phase‐Amplitude Coupling Deficit in Streptozotocin AD Rat Model

AbstractBackgroundTheta rhythms are known to encode sensory information and reflect their effect into gamma oscillations through a modulation process known as theta‐gamma phase‐amplitude coupling (PAC) (Amemiya et al., Cell Reports 2018). The role of the PAC as a network‐level indicator of cognitive impairment in disorders such as dementia and Alzheimer’s disease has received much attention recently (Zheng et al. Nature communication 2020).Injection of Streptozotocin (STZ) in animals is reported to cause resistance to insulin uptake in the brain, leading to memory and cognitive impairment (Kamat, Neural Regeneration Research 2015). The current study examines the induced deficit in the PAC as an early indicator of neurodegenerative effects of STZ.Method3 adult male Wistar rats were included in this study (270 – 300 g). Intraventricular injection of STZ was performed at a dose of 3 mg / Kg with a volume of 10 μl. Two stainless steel electrodes were implanted in the hippocampus (CA1) and medial prefrontal cortex (mPFC). The rats were free to move during recording. LFP data were recorded and segmented into windows of 20 s. Preprocessing and noisy trial rejection were employed for artifact removal, and the theta‐gamma PAC was measured using the mean vector length (tf‐MVL) method (Munia et al., Scientific Reports 2019).ResultOne week after STZ injection, the theta‐gamma PAC values did not show a significant change but after two weeks variation was observed. During this period, the power of different frequency bands was also measured and no significant change was observed even after two weeks. Theta‐gamma PAC deficiency was observed in both regions (CA1 and mPFC).ConclusionSTZ’s intracerebro‐ventricular injection is known to produce cognitive deficits along with an increase in Aβ deposition. This study reports that cross‐frequency coupling deficiency is also observed in this AD mouse model. Moreover, the PAC deficit was detected earlier than any change was observed in the spectral analysis of the data, suggesting that it can serve as a diagnosis tool for memory impairment due to neurodegeneration in the STZ mouse model of AD.

Effects of emotional decorative pictures on cognitive load as assessed by pupil dilation and EEG frequency band power

AbstractThe present study examined the effects of decorative pictures (DP) on cognitive load during learning factual knowledge from texts assessed by physiological proxies, namely pupil dilation and the electroencephalogram (EEG) theta (4–6 Hz) and alpha (8–13 Hz) frequency band power at frontal and parietal electrodes, respectively. In a complete within‐subjects design the presence of DP positioned beside learning texts and the emotional content of the DP (i.e., negative, neutral, and positive DP) were manipulated. For DP present, pupil dilation and the EEG alpha frequency band power indicated increased cognitive load, specifically for negative DP. Behavioral performance measures (i.e., learning times and learning outcomes) were not affected by the presence of DP. Subjective effort ratings were also not affected by DP, although specifically the negative DP were subjectively perceived as distracting. In sum, DP only moderately affected cognitive load allowing subjects to compensate.

Electrocortical Dynamics of Usual Walking and the Planning to Step over Obstacles in Parkinson's Disease.

The neural correlates of locomotion impairments observed in people with Parkinson's disease (PD) are not fully understood. We investigated whether people with PD present distinct brain electrocortical activity during usual walking and the approach phase of obstacle avoidance when compared to healthy individuals. Fifteen people with PD and fourteen older adults walked overground in two conditions: usual walking and obstacle crossing. Scalp electroencephalography (EEG) was recorded using a mobile 64-channel EEG system. Independent components were clustered using a k-means clustering algorithm. Outcome measures included absolute power in several frequency bands and alpha/beta ratio. During the usual walk, people with PD presented a greater alpha/beta ratio in the left sensorimotor cortex than healthy individuals. While approaching obstacles, both groups reduced alpha and beta power in the premotor and right sensorimotor cortices (balance demand) and increased gamma power in the primary visual cortex (visual demand). Only people with PD reduced alpha power and alpha/beta ratio in the left sensorimotor cortex when approaching obstacles. These findings suggest that PD affects the cortical control of usual walking, leading to a greater proportion of low-frequency (alpha) neuronal firing in the sensorimotor cortex. Moreover, the planning for obstacle avoidance changes the electrocortical dynamics associated with increased balance and visual demands. People with PD rely on increased sensorimotor integration to modulate locomotion.

Higher theta-beta ratio during screen-based vs. printed paper is related to lower attention in children: An EEG study.

Reading is considered a non-intuitive, cognitively demanding ability requiring synchronization between several neural networks supporting visual, language processing and higher-order abilities. With the involvement of technology in our everyday life, reading from a screen has become widely used. Several studies point to challenges in processing written materials from the screen due to changes in attention allocation when reading from a screen compared to reading from a printed paper. The current study examined the differences in brain activation when reading from a screen compared to reading from a printed paper focusing on spectral power related to attention in fifteen 6-8-year-old children. Using an electroencephalogram, children read two different age-appropriate texts, without illustrations, presented randomly on the screen and on a printed paper. Data were analyzed using spectral analyses in brain regions related to language, visual processing, and cognitive control, focusing on theta vs. beta waveforms. Results indicated that while reading from a printed paper was accompanied by higher energy in high-frequency bands (beta, gamma), reading from the screen was manifested by a higher power in the lower frequency bands (alpha, theta). Higher theta compared to the beta ratio, representing challenges in allocating attention to a given task, was found for the screen reading compared to the printed paper reading condition. Also, a significant negative correlation was found between differences in theta/beta ratio for screen vs paper reading and accuracy level in the age-normalized Sky-Search task measuring attention and a positive correlation with performance time. These results provide neurobiological support for the greater cognitive load and reduced focused attention during screen-based compared to print-based reading and suggest a different reliance on attention resources for the two conditions in children.

Obsessive-compulsive disorder (OCD) is associated with increased electroencephalographic (EEG) delta and theta oscillatory power but reduced delta connectivity

Obsessive-Compulsive Disorder (OCD) is a mental health condition causing significant decline in the quality of life of sufferers and the limited knowledge on the pathophysiology hinders successful treatment. The aim of the current study was to examine electroencephalographic (EEG) findings of OCD to broaden our understanding of the disease. Resting-state eyes-closed EEG data was recorded from 25 individuals with OCD and 27 healthy controls (HC). The 1/f arrhythmic activity was removed prior to computing oscillatory powers of all frequency bands (delta, theta, alpha, beta, gamma). Cluster-based permutation was used for between-group statistical analyses, and comparisons were performed for the 1/f slope and intercept parameters. Functional connectivity (FC) was measured using coherence and debiased weighted phase lag index (d-wPLI), and statistically analyzed using the Network Based Statistic method. Compared to HC, the OCD group showed increased oscillatory power in the delta and theta bands in the fronto-temporal and parietal brain regions. However, there were no significant between-group findings in other bands or 1/f parameters. The coherence measure showed significantly reduced FC in the delta band in OCD compared to HC but the d-wPLI analysis showed no significant differences. OCD is associated with raised oscillatory power in slow frequency bands in the fronto-temporal brain regions, which agrees with the previous literature and therefore is a potential biomarker. Although delta coherence was found to be lower in OCD, due to inconsistencies found between measures and the previous literature, further research is required to ascertain definitive conclusions.

Correlation between electroencephalographic markers in the healthy brain

Mental disorders have an increasing tendency and represent the main burden of disease to society today. A wide variety of electroencephalographic (EEG) markers have been successfully used to assess different symptoms of mental disorders. Different EEG markers have demonstrated similar classification accuracy, raising a question of their independence. The current study is aimed to investigate the hypotheses that different EEG markers reveal partly the same EEG features reflecting brain functioning and therefore provide overlapping information. The assessment of the correlations between EEG signal frequency band power, dynamics, and functional connectivity markers demonstrates that a statistically significant correlation is evident in 37 of 66 (56%) comparisons performed between 12 markers of different natures. A significant correlation between the majority of the markers supports the similarity of information in the markers. The results of the performed study confirm the hypotheses that different EEG markers reflect partly the same features in brain functioning. Higuchi’s fractal dimension has demonstrated a significant correlation with the 82% of other markers and is suggested to reveal a wide spectrum of various brain disorders. This marker is preferable in the early detection of symptoms of mental disorders.