Functional EEG connectivity is a neuromarker for adult attention deficit hyperactivity disorder symptoms

Kiiski, Hanni; Rueda‐Delgado, Laura M.; Bennett, Marc; Knight, Rachel; Rai, Laura; Roddy, Darren; Grogan, Katie; Bramham, Jessica; Kelly, Clare; Whelan, Robert

doi:10.1016/j.clinph.2019.08.010

Cited by 40 publications

(26 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies using EEG on ADHD patients to investigate functional connectivity and brain networks in working memory are limited. Some studies on adults with ADHD have been reported using EEG/MEG to investigate brain networks while resting or performing cognitive control tasks ( Franzen et al, 2013 ; Sudre et al, 2017 ; Michelini et al, 2019 ; Kiiski et al, 2020 ). However, explorative research is considered to be necessary, as no studies have been reported on functional connectivity and brain network related to working memory.…”

Section: Introductionmentioning

confidence: 99%

The Dynamic Properties of a Brain Network During Spatial Working Memory Tasks in College Students With ADHD Traits

Jang

Kim

2020

Front. Hum. Neurosci.

View full text Add to dashboard Cite

This study investigated deficits of spatial working memory in college students with attention-deficit/hyperactivity disorder (ADHD) traits using event-related potentials (ERPs) and the spatial 2-back task. We also computed sensory-level activity using EEG data and investigated theta and alpha neural oscillations, phase-locking values (PLV), and brain networks. Based on the scores from the Adult ADHD Self-Report Scale (ASRS) and Conners’ Adult ADHD Rating Scales (CAARS), an ADHD-trait group ( n = 40) and a normal control group ( n = 41) were selected. Participants were required to respond to whether the presented stimulus was at the same location as that presented two trials earlier. The ADHD-trait group showed significantly slower response times than the control group in the spatial 2-back task. In terms of spectrum, the ADHD-trait group showed significantly reduced theta power than the control group. In contrast, the ADHD-trait group exhibited an increased alpha power compared to the control group with the 250–1000 ms interval after stimulus onset. In terms of the PLV, the ADHD-trait group showed significantly weaker theta phase synchrony and fewer connection numbers in frontal-occipital areas than the control group. In terms of the theta brain network, the ADHD-trait group showed a significantly lower clustering coefficient and longer characteristic path length than the control group for the theta band. The present results indicate that college students with ADHD traits have deficits in spatial working memory and that these abnormal activities in neural oscillation, functional connectivity, and the network may contribute to spatial working memory deficits.

show abstract

Section: Introductionmentioning

confidence: 99%

The Dynamic Properties of a Brain Network During Spatial Working Memory Tasks in College Students With ADHD Traits

Jang

Kim

2020

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…In both scales, values near 0 in LCβ (healthy and paretic) are correlated with better grasp ability and less tremor. Other studies showed correlations between the EEG activity in beta band and concentration (Janssen et al, 2017;Kiiski et al, 2020).…”

Section: Lc In Beta Bandmentioning

confidence: 91%

EEG Biomarkers Related With the Functional State of Stroke Patients

et al. 2020

View full text Add to dashboard Cite

Introduction: Recent studies explored promising new quantitative methods to analyze electroencephalography (EEG) signals. This paper analyzes the correlation of two EEG parameters, Brain Symmetry Index (BSI) and Laterality Coefficient (LC), with established functional scales for the stroke assessment. Methods: Thirty-two healthy subjects and thirty-six stroke patients with upper extremity hemiparesis were recruited for this study. The stroke patients where subdivided in three groups according to the stroke location: Cortical, Subcortical, and Cortical + Subcortical. The participants performed assessment visits to record the EEG in the resting state and perform functional tests using rehabilitation scales. Then, stroke patients performed 25 sessions using a motor-imagery based Brain Computer Interface system (BCI). BSI was calculated with the EEG data in resting state and LC was calculated with the Event-Related Synchronization maps. Results: The results of this study demonstrated significant differences in the BSI between the healthy group and Subcortical group (P = 0.001), and also between the healthy and Cortical+Subcortical group (P = 0.019). No significant differences were found between the healthy group and the Cortical group (P = 0.505). Furthermore, the BSI analysis in the healthy group based on gender showed statistical differences (P = 0.027). In the stroke group, the correlation between the BSI and the functional state of the upper extremity assessed by Fugl-Meyer Assessment (FMA) was also significant, ρ = −0.430 and P = 0.046. The correlation between the BSI and the FMA-Lower extremity was not significant (ρ = −0.063, P = 0.852). Similarly, the LC calculated in the alpha band has significative correlation with FMA of upper extremity (ρ = −0.623 and P < 0.001) and FMA of lower extremity (ρ = −0.509 and P = 0.026). Other important significant correlations between LC and functional scales were observed. In addition, the patients showed an improvement in the FMA-upper extremity after the BCI therapy (FMA = 1 median [IQR: 0-8], P = 0.002). Conclusion: The quantitative EEG tools used here may help support our understanding of stroke and how the brain changes during rehabilitation therapy. These tools can help identify changes in EEG biomarkers and parameters during therapy that might lead to improved therapy methods and functional prognoses.

show abstract

“…Pereda (2018) found approximately 95% of classification accuracy for a functional connectivity pattern with 280 features [13]. Recently, Kiiski (2020) used the Weighted Phase Lag Index (WPLI) measure as a neuromarker for adults with ADHD [14]. WPLI measures the phase difference between two signals; and attenuates the effect of volume conduction by weighting phase differences close to zero-phase lag.…”

Section: Introductionmentioning

confidence: 99%

“…Because WPLI is not based on signal amplitude, it is insensitive to ADHD-specific spectral changes [15,16]. Kiiski found that the elevated functional connectivity in the delta and theta bands was the best predictor for ADHD status [14]. Accordingly, in this paper, the directed Phase Transfer Entropy (dPTE) was used, which was recently introduced to determine the strength and direction of connectivity [17].…”

Section: Introductionmentioning

confidence: 99%

Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals

Ekhlasi

Nasrabadi

Mohammadi

2021

fbt

View full text Add to dashboard Cite

Purpose: The present study was conducted to investigate and classify two groups of healthy children and children with Attention Deficit Hyperactivity Disorder (ADHD) by Effective Connectivity (EC) measure. Since early detection of ADHD can make the treatment process more effective, it is important to diagnose it using new methods. Materials and Methods: For this purpose, Effective Connectivity Matrices (ECMs) were constructed based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children of the same age. ECMs of each individual were obtained by the directed Phase Transfer Entropy (dPTE) between each pair of electrodes. ECMs were calculated in five frequency bands including, delta, theta, alpha, beta, and gamma. Based on ECM, an Effective Connectivity Vector (ECV) was constructed as a feature vector for the classification process. Furthermore, ECV of different frequency bands was pooled in one global ECV (gECV). Multilayer Artificial Neural Network (ANN) was used in the steps of classification and feature selection by the Genetic Algorithm (GA). Results: The highest classification accuracy with the selected features of ECV was related to theta frequency band with 89.7%. After that, the delta frequency band had the highest accuracy with 89.2%. The results of ANN classification and GA on the gECV reported 89.1% of accuracy. Conclusion: Our findings show that the dPTE measure, which determines effective connectivity between the brain regions, can be used to classify between ADHD and healthy groups. The results of the classification have improved compared to some studies that used the functional connectivity measures.

show abstract

Functional EEG connectivity is a neuromarker for adult attention deficit hyperactivity disorder symptoms

Cited by 40 publications

References 57 publications

The Dynamic Properties of a Brain Network During Spatial Working Memory Tasks in College Students With ADHD Traits

The Dynamic Properties of a Brain Network During Spatial Working Memory Tasks in College Students With ADHD Traits

EEG Biomarkers Related With the Functional State of Stroke Patients

Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals

Contact Info

Product

Resources

About