scholarly journals Increased Beta Activity Links to Impaired Emotional Control in ADHD Adults With High IQ

2017 ◽  
Vol 23 (7) ◽  
pp. 754-764 ◽  
Author(s):  
Hui Li ◽  
Qihua Zhao ◽  
Fang Huang ◽  
Qingjiu Cao ◽  
Qiujin Qian ◽  
...  
Keyword(s):  
Resting State ◽  
Emotional Control ◽  
Control Group ◽  
Beta Activity ◽  
Adhd Group ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Beta Power ◽  
Eeg Power ◽  
Iq Method

Objective: The present study investigated the neuropathology of everyday-life executive function (EF) deficits in adults with ADHD with high IQ. Method: Forty adults with ADHD with an IQ ≥ 120 and 40 controls were recruited. Ecological EFs were measured, and eyes-closed Electroencephalograph (EEG) signals were recorded during a resting-state condition; EEG power and correlations with impaired EFs were analyzed. Results: Compared with controls, the ADHD group showed higher scores on all clusters of EF. The ADHD group showed globally increased theta, globally decreased alpha, and increased central beta activity. In the ADHD group, central beta power was significantly related to emotional control ratings, while no such correlation was evident in the control group. Conclusion: The results suggest that resting-state beta activity might be involved in the neuropathology of emotional control in adults with ADHD with high IQ.

scholarly journals Head motion in children with ADHD during resting-state brain imaging

2014 ◽  
Author(s):  
Xiang-zhen Kong
Keyword(s):  
Resting State ◽  
Head Motion ◽  
Control Group ◽  
Adhd Group ◽  
Typically Developing ◽  
Children With Adhd ◽  
Neural Imaging ◽  
Hyperactivity Disorder ◽  
Healthy Control ◽  
Motion Parameters

Resting-state functional MRI (rs-fMRI) has become an important method for analyzing the neural mechanisms underlying mental disorders. But studies targeting head motion during an rs-fMRI examination are rare. Since head motion may pollute the data in the neural imaging studies and further mislead the understanding of the causes of some disorders, systematic investigations on this topic were badly needed. To this end, in this study, children with attention-deficit/hyperactivity disorder (ADHD) and demographically-matched typically developing control (TDC) participants underwent an rs-fMRI examination. We obtained a summary motion index and six mean single head motion parameters (three translational and three rotational) for each participant. With the summary index, we found that motion was significantly increased in the ADHD group and the results showed that the increase was mainly contributed by the motion around and along the superior-to-inferior direction. Moreover, the classification analysis showed that these head motion parameters during scanning could accurately distinguish children with ADHD from the healthy control group. These results suggest that accounting for head motion during scanning may be helpful for ADHD diagnosis and treatment with neuroimaging.

Detection of ADHD From EEG Signals Using Different Entropy Measures and ANN

2021 ◽  
pp. 155005942110367
Author(s):  
R. Catherine Joy ◽  
S. Thomas George ◽  
A. Albert Rajan ◽  
M.S.P. Subathra
Keyword(s):  
Shannon Entropy ◽  
Normal Subjects ◽  
Permutation Entropy ◽  
Control Group ◽  
P Value ◽  
Eeg Signals ◽  
Hyperactivity Disorder ◽  
Eyes Closed ◽  
Artificial Neural Network Classifier ◽  
Entropy Measures

Attention deficit hyperactivity disorder (ADHD) is a prevalent behavioral, cognitive, neurodevelopmental pediatric disorder. Clinical evaluations, symptom surveys, and neuropsychological assessments are some of the ADHD assessment methods, which are time-consuming processes and have a certain degree of uncertainty. This research investigates an efficient computer-aided technological solution for detecting ADHD from the acquired electroencephalography (EEG) signals based on different nonlinear entropy estimators and an artificial neural network classifier. Features extracted through fuzzy entropy, log energy entropy, permutation entropy, SURE entropy, and Shannon entropy are analyzed for effective discrimination of ADHD subjects from the control group. The experimented results confirm that the proposed techniques can effectively detect and classify ADHD subjects. The permutation entropy gives the highest classification accuracy of 99.82%, sensitivity of 98.21%, and specificity of 98.82%. Also, the potency of different entropy estimators derived from the t-test reflects that the Shannon entropy has a higher P-value (>.001); therefore, it has a limited scope than other entropy estimators for ADHD diagnosis. Furthermore, the considerable variance found from potential features obtained in the frontal polar (FP) and frontal (F) lobes using different entropy estimators under the eyes-closed condition shows that the signals received in these lobes will have more significance in distinguishing ADHD from normal subjects.

scholarly journals Reduced Functional Connectivity in Children With Congenital Cataracts Using Resting-State Electroencephalography Measurement

2021 ◽  
Vol 15 ◽  
Author(s):  
Wan Chen ◽  
Liping Lan ◽  
Wei Xiao ◽  
Jiahong Li ◽  
Jiahao Liu ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Functional Connectivity ◽  
Resting State ◽  
Functional Improvement ◽  
Control Group ◽  
Salience Network ◽  
Alpha Band ◽  
Congenital Cataracts ◽  
Eyes Closed ◽  
Parietal Cortices

ObjectivesNumerous task-based functional magnetic resonance imaging studies indicate the presence of compensatory functional improvement in patients with congenital cataracts. However, there is neuroimaging evidence that shows decreased sensory perception or cognition information processing related to visual dysfunction, which favors a general loss hypothesis. This study explored the functional connectivity between visual and other networks in children with congenital cataracts using resting state electroencephalography.MethodsTwenty-one children with congenital cataracts (age: 8.02 ± 2.03 years) and thirty-five sex- and age-matched normal sighted controls were enrolled to investigate functional connectivity between the visual cortex and the default mode network, the salience network, and the cerebellum network during resting state electroencephalography (eyes closed) recordings.ResultThe congenital cataract group was less active, than the control group, in the occipital, temporal, frontal and limbic lobes in the theta, alpha, beta1 and beta2 frequency bands. Additionally, there was reduced alpha-band connectivity between the visual and somatosensory cortices and between regions of the frontal and parietal cortices associated with cognitive and attentive control.ConclusionThe results indicate abnormalities in sensory, cognition, motion and execution functional connectivity across the developing brains of children with congenital cataracts when compared with normal controls. Reduced frontal alpha activity and alpha-band connectivity between the visual cortex and salience network might reflect attenuated inhibitory information flow, leading to higher attentional states, which could contribute to adaptation of environmental change in this group of patients.

scholarly journals Towards a minimal EEG channel array for a biometric system using resting-state and a genetic algorithm for channel selection

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Luis Alfredo Moctezuma ◽  
Marta Molinas
Keyword(s):  
Genetic Algorithm ◽  
Resting State ◽  
Channel Selection ◽  
Discrete Wavelet ◽  
Biometric System ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Minimal Subset ◽  
Eyes Open ◽  
The Subject

Abstract We present a new approach for a biometric system based on electroencephalographic (EEG) signals of resting-state, that can identify a subject and reject intruders with a minimal subset of EEG channels. To select features, we first use the discrete wavelet transform (DWT) or empirical mode decomposition (EMD) to decompose the EEG signals into a set of sub-bands, for which we compute the instantaneous and Teager energy and the Higuchi and Petrosian fractal dimensions for each sub-band. The obtained features are used as input for the local outlier factor (LOF) algorithm to create a model for each subject, with the aim of learning from it and rejecting instances not related to the subject in the model. In search of a minimal subset of EEG channels, we used a channel-selection method based on the non-dominated sorting genetic algorithm (NSGA)-III, designed with the objectives of minimizing the required number EEG channels and increasing the true acceptance rate (TAR) and true rejection rate (TRR). This method was tested on EEG signals from 109 subjects of the public motor movement/imagery dataset (EEGMMIDB) using the resting-state with the eyes-open and the resting-state with the eyes-closed. We were able to obtain a TAR of $$1.000 \pm 0.000$$ 1.000 ± 0.000 and TRR of $$0.998 \pm 0.001$$ 0.998 ± 0.001 using 64 EEG channels. More importantly, with only three channels, we were able to obtain a TAR of up to $$0.993 \pm 0.01$$ 0.993 ± 0.01 and a TRR of up to $$0.941 \pm 0.002$$ 0.941 ± 0.002 for the Pareto-front, using NSGA-III and DWT-based features in the resting-state with the eyes-open. In the resting-state with the eyes-closed, the TAR was $$0.997 \pm 0.02$$ 0.997 ± 0.02 and the TRR $$0.950 \pm 0.05,$$ 0.950 ± 0.05 , also using DWT-based features from three channels. These results show that our approach makes it possible to create a model for each subject using EEG signals from a reduced number of channels and reject most instances of the other 108 subjects, who are intruders in the model of the subject under evaluation. Furthermore, the candidates obtained throughout the optimization process of NSGA-III showed that it is possible to obtain TARs and TRRs above 0.900 using LOF and DWT- or EMD-based features with only one to three EEG channels, opening the way to testing this approach on bigger datasets to develop a more realistic and usable EEG-based biometric system.

scholarly journals Peripheral Electrical Stimulation Modulates Cortical Beta-Band Activity

2021 ◽  
Vol 15 ◽  
Author(s):  
Laura J. Arendsen ◽  
Robert Guggenberger ◽  
Manuela Zimmer ◽  
Tobias Weigl ◽  
Alireza Gharabaghi
Keyword(s):  
Chronic Pain ◽  
Electrical Stimulation ◽  
Resting State ◽  
Low Frequency ◽  
Oscillatory Activity ◽  
Brain State ◽  
Beta Activity ◽  
Beta Power ◽  
Significant Difference ◽  
Peripheral Electrical Stimulation

Low-frequency peripheral electrical stimulation using a matrix electrode (PEMS) modulates spinal nociceptive pathways. However, the effects of this intervention on cortical oscillatory activity have not been assessed yet. The aim of this study was to investigate the effects of low-frequency PEMS (4 Hz) on cortical oscillatory activity in different brain states in healthy pain-free participants. In experiment 1, PEMS was compared to sham stimulation. In experiment 2, motor imagery (MI) was used to modulate the sensorimotor brain state. PEMS was applied either during MI-induced oscillatory desynchronization (concurrent PEMS) or after MI (delayed PEMS) in a cross-over design. For both experiments, PEMS was applied on the left forearm and resting-state electroencephalography (EEG) was recording before and after each stimulation condition. Experiment 1 showed a significant decrease of global resting-state beta power after PEMS compared to sham (p = 0.016), with a median change from baseline of −16% for PEMS and −0.54% for sham. A cluster-based permutation test showed a significant difference in resting-state beta power comparing pre- and post-PEMS (p = 0.018) that was most pronounced over bilateral central and left frontal sensors. Experiment 2 did not identify a significant difference in the change from baseline of global EEG power for concurrent PEMS compared to delayed PEMS. Two cluster-based permutation tests suggested that frontal beta power may be increased following both concurrent and delayed PEMS. This study provides novel evidence for supraspinal effects of low-frequency PEMS and an initial indication that the presence of a cognitive task such as MI may influence the effects of PEMS on beta activity. Chronic pain has been associated with changes in beta activity, in particular an increase of beta power in frontal regions. Thus, brain state-dependent PEMS may offer a novel approach to the treatment of chronic pain. However, further studies are warranted to investigate optimal stimulation conditions to achieve a reduction of pain.

scholarly journals Comparison of resting-state EEG between adults with Down syndrome and typically developing controls

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sarah Hamburg ◽  
Daniel Bush ◽  
Andre Strydom ◽  
Carla M. Startin
Keyword(s):  
Down Syndrome ◽  
Cognitive Decline ◽  
Resting State ◽  
Peak Frequency ◽  
Peak Amplitude ◽  
Typically Developing ◽  
Eyes Closed ◽  
Eeg Spectrum ◽  
Beta Power ◽  
Diagnosis Of Dementia

Abstract Background Down syndrome (DS) is the most common genetic cause of intellectual disability (ID) worldwide. Understanding electrophysiological characteristics associated with DS provides potential mechanistic insights into ID, helping inform biomarkers and targets for intervention. Currently, electrophysiological characteristics associated with DS remain unclear due to methodological differences between studies and inadequate controls for cognitive decline as a potential cofounder. Methods Eyes-closed resting-state EEG measures (specifically delta, theta, alpha, and beta absolute and relative powers, and alpha peak amplitude, frequency and frequency variance) in occipital and frontal regions were compared between adults with DS (with no diagnosis of dementia or evidence of cognitive decline) and typically developing (TD) matched controls (n = 25 per group). Results We report an overall ‘slower’ EEG spectrum, characterised by higher delta and theta power, and lower alpha and beta power, for both regions in people with DS. Alpha activity in particular showed strong group differences, including lower power, lower peak amplitude and greater peak frequency variance in people with DS. Conclusions Such EEG ‘slowing’ has previously been associated with cognitive decline in both DS and TD populations. These findings indicate the potential existence of a universal EEG signature of cognitive impairment, regardless of origin (neurodevelopmental or neurodegenerative), warranting further exploration.

scholarly journals EEG Power Spectrum Correlates of Working Memory in Children with Learning Disorders

2020 ◽  
Author(s):  
Benito Javier Martínez Briones ◽  
Thalia Fernandez Harmony ◽  
Nicolás Garófalo Gómez ◽  
Rolando Jose Biscay Lirio ◽  
Jorge Bosch-Bayard
Keyword(s):  
Working Memory ◽  
Neural Development ◽  
Academic Skills ◽  
Learning Disorders ◽  
Mapping Technique ◽  
Control Group ◽  
Beta Activity ◽  
Alpha Power ◽  
Eigenvector Centrality ◽  
Eeg Power

Learning disorders (LD) are diagnosed in children whose academic skills of reading, writing or mathematics are impaired and lagged according to their age, schooling and intelligence. Children with LD experience substantial working memory (WM) deficits, even more pronounced if more than one of the academic skills is affected. We compared the task-related EEG power spectral density of children with LD (n= 23), with a control group of children with good academic achievement (n= 22), during the performance of a WM task. sLoreta was used to estimate the current distribution at the sources, and 18 brain regions of interests (ROIs) were chosen with an extended version of the eigenvector centrality mapping technique. In this way, we lessen some drawbacks of the traditional EEG at the sensor space by an analysis at the brain sources level over data-driven selected ROIs. Results: The LD group showed fewer correct responses at the WM task, an overall slower EEG with more theta activity in all ROIs, less upper-alpha power at posterior areas, and less high-frequency beta activity in frontal areas. We explain these EEG patterns in LD children as indices of an inefficient neural resource management related with a delay in the neural development.

scholarly journals Effects of Neurofeedback in the Working Memory of Children with Learning Disorders: An EEG Power-Spectrum Analysis

2021 ◽  
Author(s):  
Benito Javier Martínez-Briones ◽  
Jorge Bosch-Bayard ◽  
Rolando Jose Biscay-Lirio ◽  
Lucero Albarrán-Cárdenas ◽  
Juan Silva-Pereyra ◽  
...  
Keyword(s):  
Working Memory ◽  
Power Spectrum ◽  
Resting State ◽  
Learning Disorders ◽  
Post Treatment ◽  
Gamma Activity ◽  
Eeg Power Spectrum ◽  
Eeg Abnormalities ◽  
Beta Power ◽  
Eeg Power

Learning disorders (LD) are diagnosed in children impaired in the academic skills of reading, writing and/or mathematics. Children with LD usually show a slower resting-state electroencephalogram (EEG), with EEG patterns corresponding to a neurodevelopmental lag. LD-children also show a consistent cognitive impairment in working memory (WM), including an abnormal task-related EEG with an overall slower EEG activity of more delta and theta power, and less gamma activity in posterior sites; task-related EEG patterns considered indices of an inefficient neural resource management. Neurofeedback (NFB) treatments aimed at normalizing the resting-state EEG of LD-children have shown improvements in cognitive-behavioral indices and diminished EEG abnormalities. Given the typical findings of a WM impairment in LD-children; we aimed to explore the effects of a NFB treatment in the WM of children with LD, by analyzing the WM-related EEG power-spectrum. We recruited 18 children with LD (8-10 years old). They performed a Sternberg-type WM-task synchronized with an EEG of 19 leads (10-20 system) twice in pre-post treatment conditions. They went through either 30 sessions of a NFB treatment (NFB-group, n= 10); or through 30 sessions of a placebo-sham treatment (Sham-group, n= 8). We analyzed the before-after treatment group differences for the behavioral performance and the WM-related power-spectrum. The NFB group showed faster response times in the WM-task post-treatment. They also showed an increased gamma power at posterior sites and a decreased beta power. We explain these findings in terms of NFB improving the maintenance of memory representations coupled with a reduction of anxiety.

scholarly journals Spectral Power Density analysis of the resting-state as a marker of the central effects of opioid use in fibromyalgia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maxciel Zortea ◽  
Gerardo Beltran ◽  
Rael Lopes Alves ◽  
Paul Vicuña ◽  
Iraci L. S. Torres ◽  
...  
Keyword(s):  
Power Density ◽  
Resting State ◽  
Spectral Power ◽  
Surrogate Marker ◽  
Spectral Power Density ◽  
Beta Activity ◽  
Opioid Use ◽  
Eyes Closed ◽  
Promising Tool ◽  
Opioid Users

AbstractSpectral power density (SPD) indexed by electroencephalogram (EEG) recordings has recently gained attention in elucidating neural mechanisms of chronic pain syndromes and medication use. We compared SPD variations between 15 fibromyalgia (FM) women in use of opioid in the last three months (73.33% used tramadol) with 32 non-users. EEG data were obtained with Eyes Open (EO) and Eyes Closed (EC) resting state. SPD peak amplitudes between EO-EC were smaller in opioid users in central theta, central beta, and parietal beta, and at parietal delta. However, these variations were positive for opioid users. Multivariate analyses of variance (ANOVAs) revealed that EO-EC variations in parietal delta were negatively correlated with the disability due to pain, and central and parietal beta activity variations were positively correlated with worse sleep quality. These clinical variables explained from 12.5 to 17.2% of SPD variance. In addition, central beta showed 67% sensitivity / 72% specificity and parietal beta showed 73% sensitivity/62% specificity in discriminating opioid users from non-users. These findings suggest oscillations in EEG might be a sensitive surrogate marker to screen FM opioid users and a promising tool to understand the effects of opioid use and how these effects relate to functional and sleep-related symptoms.

scholarly journals Epileptiform abnormalities and quantitative EEG in children with attention-deficit / hyperactivity disorder

2008 ◽  
Vol 66 (3a) ◽  
pp. 462-467 ◽  
Author(s):  
Lineu Corrêa Fonseca ◽  
Glória Maria A.S. Tedrus ◽  
César de Moraes ◽  
Amanda de Vicente Machado ◽  
Marcela Pupin de Almeida ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Epileptiform Activity ◽  
Quantitative Eeg ◽  
Multiple Regression Model ◽  
Adhd Group ◽  
Hyperactivity Disorder ◽  
Eyes Closed ◽  
Beta Power ◽  
Adhd Children

There is much controversy about the importance of the electroencephalogram (EEG) in assessing the attention-deficit/hyperactivity disorder (ADHD). The objective of this study was to assess the use of EEG and quantitative EEG (qEEG) in ADHD children. Thirty ADHD children and 30 sex- and age-matched controls with no neurological or psychiatric problems were studied. The EEG was recorded from 15 electrode sites during an eyes-closed resting condition. Epileptiform activity was assessed, as were the absolute and relative powers in the classical bands after application of the Fast Fourier transform. Epileptiform activity was found in 3 (10%) ADHD children. As compared to the controls, the ADHD group showed significantly greater absolute delta and theta powers in a diffuse way, and also greater absolute beta power and smaller relative alpha 1 and beta powers at some electrodes. A logistic multiple regression model, allowed for 83.3% sensibility and specificity in diagnosing ADHD.

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