Decreased resting gamma activity in adult attention deficit/hyperactivity disorder

Variations in Theta/Beta Ratio and Cognitive Performance in Subpopulations of Subjects with ADHD Symptoms: Towards Neuropsychological Profiling for Patient Subgrouping

ADHD is a neurodevelopmental disorder appearing in childhood but remaining in many cases in adults. There are both pharmacological and non-pharmacological approaches to treating ADHD, but they do not have the same efficacy in all subjects. Better knowledge of the neurophysiological basis of this disorder will allow for the design of more effective treatments. Studies performing qEEG analysis in children suggest the existence of subgroups of ADHD patients with different neurophysiological traits. There are fewer studies in adults, who might have undergone plastic changes allowing them to cope with ADHD symptoms along with brain maturation. Herein, we study cognitive performance and the theta/beta ratio in young adults with ADHD symptoms. We found that subjects with ADHD symptoms and low working memory performance (n = 30) present higher theta/beta ratios than controls (n = 40) at O2 and T6 in the eyes-closed condition, as well as a tendency toward a higher theta/beta ratio at O1 and Cz. Subjects with ADHD and high working memory performance (n = 50) do not differ from the controls in their theta/beta ratios at any derivation. Our results suggest that neuropsychological profiling could be useful for patient subgrouping. Further research will allow for the distinction of neuropsychological profiles and their neurophysiological correlates, leading to a better classification of ADHD subtypes, thus improving treatment selection.

Changes in the cortical network during sleep stage transitions

Sleep state transitions are closely related to insomnia, drowsiness, and sleep maintenance. However, how the cortical network varies during such a transition process remains unclear. Changes in the cortical interaction during the short-term process of sleep stage transitions were investigated. In all, 40 healthy young participants underwent overnight polysomnography. The phase transfer entropy of six frequency bands was obtained from 16 electroencephalography channels to assess the strength and direction of information flow between the cortical regions. Differences in the cortical network between the first and the last 10 s in a 40-s transition period across wakefulness, N1, N2, N3, and rapid eye movement were, respectively, studied. Various frequency bands exhibited different patterns during the sleep stage transitions. It was found that the mutual transitions between the sleep stages were not necessarily the opposite. More significant changes were observed in the sleep deepening process than in the process of sleep awakening. During sleep stage transitions, changes in the inflow and outflow strength of various cortical regions led to regional differences, but for the entire sleep progress, such an imbalance did not intensify, and a dynamic balance was instead observed. The detailed findings of variations in cortical interactions during sleep stage transition promote understanding of sleep mechanism, sleep process, and sleep function. Additionally, it is expected to provide helpful clues for sleep improvement, like reducing the time required to fall asleep and maintaining sleep depth.

Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021

BACKGROUND

With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis.

METHODS

QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis.

RESULTS

A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends.

CONCLUSION

The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.

Longitudinal qEEG changes correlate with clinical outcomes in patients with somatic symptom disorder

OBJECTIVE

The quantitative electroencephalography (qEEG) of patients with somatic symptom disorder (SSD) was not yet thoroughly studied. This study aimed to investigate qEEG of SSD patients compared with those of normal controls (NCs), and changes therein after treatment.

METHODS

SSD patients currently without treatment and age- and sex-matched NCs were recruited. Spectral analysis of 64-channel EEG recording was performed and somatization, anxiety, and depression were evaluated via self-rating scales at baseline. After six months of treatment as usual, SSD patients were longitudinally followed up for assessments.

RESULTS

At baseline, the SSD group (n = 44) had higher alpha (p = 0.047) and lower beta 2 (p = 0.027) and gamma power (p = 0.001) compared with NCs (n = 29). After 6-month treatment, SSD patients showed improvement in symptoms, as well as increased beta 1 (p = 0.032), beta 2 (p = 0.012), and gamma power (p = 0.009) compared with baseline. A significant correlation was observed between the change in somatization score and temporal gamma power (r = -0.424, p = 0.031), and between the change in anxiety score and beta 2 power in the frontal (r = -0.420, p = 0.033) and central (r = -0.484, p = 0.012) regions.

CONCLUSIONS

EEG findings in this study may provide neurophysiological features of SSD. The alpha enhancement and reduced fast wave activity may reflect attentional dysfunction in patients with SSD. Decreased fast wave activity is reversible and may serve as a state marker of SSD.

Atypical resting-state gamma band trajectory in adult attention deficit/hyperactivity disorder

Decreased gamma activity has been reported both in children and adults with attention deficit/hyperactivity disorder (ADHD). However, while ADHD is a lifelong neurodevelopmental disorder, our insight into the associations of spontaneous gamma band activity with age is limited, especially in adults. Therefore, we conducted an explorative study to investigate trajectories of resting gamma activity in adult ADHD patients (N = 42) versus matched healthy controls (N = 59). We investigated the relationship of resting gamma activity (30–48 Hz) with age in four right hemispheric electrode clusters where diminished gamma power in ADHD had previously been demonstrated by our group. We found significant non-linear association between resting gamma power and age in the lower frequency gamma₁ range (30–39 Hz) in ADHD as compared to controls in all investigated locations. Resting gamma₁ increased with age and was significantly lower in ADHD than in control subjects from early adulthood. We found no significant association between gamma activity and age in the gamma₂ range (39–48 Hz). Alterations of gamma band activity might reflect altered cortical network functioning in adult ADHD relative to controls. Our results reveal that abnormal gamma power is present at all ages, highlighting the lifelong nature of ADHD. Nonetheless, longitudinal studies are needed to confirm our results.

Characterization of the abnormal cortical effective connectivity in patients with sleep apnea hypopnea syndrome during sleep

BACKGROUND AND OBJECTIVES

Sleep apnea hypopnea syndrome (SAHS) is a prevalent sleep breathing disorder that can lead to brain damage and is also a risk factor for cognitive impairment and some common diseases. Studies on cortical effective connectivity (EC) during sleep may provide more direct and pathological information and shed new light on brain dysfunction due to SAHS. However, the EC is rarely explored in SAHS patients, especially during different sleep stages.

METHODS

To this end, six-channel EEG signals of 43 SAHS patients and 41 healthy participants were recorded by whole-night polysomnography (PSG). The symbolic transfer entropy (STE) was applied to measure the EC between cortical regions in different frequency bands. Posterior-anterior ratio (PA) was employed to evaluate the posterior-to-anterior pattern of information flow based on overall cortical EC. The statistical characteristics of the STE and PA and of the intra-individual normalized parameters (STE* and PA*) were served as different feature sets for classifying the severity of SAHS.

RESULTS

Although the patterns of STE across electrodes were similar, significant differences were found between the patient and the control groups. The variation trends across stages in the PA were also different in multiple frequency bands between groups. Important features extracted from the STE* and PA* were distributed in multiple rhythms, mainly in δ, α, and γ. The PA* feature set gave the best results, with accuracies of 98.8% and 83.3% for SAHS diagnosis (binary) and severity classification (four-way).

CONCLUSIONS

These results suggest that modifications in cortical EC were existed in SAHS patients during sleep, which may help characterize cortical abnormality in patients.

ADHD: Reviewing the Causes and Evaluating Solutions

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder in which patients present inattention, hyperactivity, and impulsivity. The etiology of this condition is diverse, including environmental factors and the presence of variants of some genes. However, a great diversity exists among patients regarding the presence of these ADHD-associated factors. Moreover, there are variations in the reported neurophysiological correlates of ADHD. ADHD is often treated pharmacologically, producing an improvement in symptomatology, albeit there are patients who are refractory to the main pharmacological treatments or present side effects to these drugs, highlighting the importance of developing other therapeutic options. Different non-pharmacological treatments are in this review addressed, finding diverse results regarding efficacy. Altogether, ADHD is associated with different etiologies, all of them producing changes in brain development, leading to the characteristic symptomatology of this condition. Given the heterogeneous etiology of ADHD, discussion is presented about the convenience of personalizing ADHD treatment, whether pharmacological or non-pharmacological, to reach an optimum effect in the majority of patients. Approaches to personalizing both pharmacological therapy and neurofeedback are presented.

Sleep Electroencephalographic Response to Respiratory Events in Patients With Moderate Sleep Apnea-Hypopnea Syndrome

Sleep apnea–hypopnea syndrome is a common breathing disorder that can lead to organic brain injury, prevent memory consolidation, and cause other adverse mental-related complications. Brain activity while sleeping during respiratory events is related to these dysfunctions. In this study, we analyzed variations in electroencephalography (EEG) signals before, during, and after such events. Absolute and relative powers, as well as symbolic transfer entropy (STE) of scalp EEG signals, were calculated to unveil the activity of brain regions and information interactions between them, respectively. During the respiratory events, only low-frequency power increased during rapid eye movement (REM) stage (δ-band absolute and relative power) and N1 (δ- and θ-band absolute power, δ-band relative power) sleep. But absolute power increased in low- and medium-frequency bands (δ, θ, α, and σ bands), and relative power increased mainly in the medium-frequency band (α and σ bands) during stage N2 sleep. After the respiratory events, absolute power increased in all frequency bands and sleep stages, but relative power increased in medium and high frequencies. Regarding information interactions, the β-band STE decreased during and after events. In the γ band, the intrahemispheric STE increased during events and decreased afterward. Moreover, the interhemisphere STE increased after events during REM and stage N1 sleep. The EEG changes throughout respiratory events are supporting evidence for previous EEG knowledge of the impact of sleep apnea on the brain. These findings may provide insights into the influence of the sleep apnea–hypopnea syndrome on cognitive function and neuropsychiatric defects.

EEG for Diagnosis of Adult ADHD: A Systematic Review With Narrative Analysis

BACKGROUND

Attention deficit hyperactivity disorder is a common neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity and or impulsivity. Since the development of the concept, a reliable biomarker to aid diagnosis has been sought. One potential method is the use of electroencephalogram to measure neuronal activity. The aim of this review is to provide an up to date synthesis of the literature surrounding the potential use of electroencephalogram for diagnosis of attention deficit hyperactivity disorder in adulthood.

METHODS

A search of PsycINFO, PubMed, and EMBASE was undertaken in February 2019 for peer-reviewed articles exploring electroencephalogram patterns in adults (18 years with no upper limit) diagnosed with attention deficit hyperactivity disorder.

RESULTS

Differences in electroencephalogram activity are potentially unique to adult attention deficit hyperactivity disorder populations. Strongest support was derived for elevated levels of both absolute and relative theta power, alongside the observation that alpha activity is able to typically differentiate between adult attention deficit hyperactivity disorder and normative populations.

CONCLUSIONS

Electroencephalogram can have a use in clinical settings to aid adult attention deficit hyperactivity disorder diagnosis, but areas of inconsistency are apparent.

EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies

A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.