Comparison of Quantitative Electroencephalography between Tic Disorder and Attention-Deficit/Hyperactivity Disorder in Children

Subtypes of tic disorders in children and adolescents: based on clinical characteristics

BACKGROUND

Tic disorder (TD) is a diverse neurodevelopmental disorder with various symptoms and comorbidities. Traditional classifications based on age onset and duration fail to adequately characterize the full clinical features of TD. This study aims to redefine TD subtypes by a comprehensive analysis of clinical features and comorbidities.

METHODS

We assessed 139 children and adolescents aged 6-18 years using 14 scales covering 43 dimensions. The k-means clustering algorithm was used to identify distinct TD subtypes. Differences between these subtypes were analyzed using t-tests and network analysis, with high expected influence (EI) metric representing key symptoms within each subtype.

RESULTS

We identified two distinct subtypes of TD, with 21.6% of participants classified as subtype1 and 78.4% as subtype2. Subtype1 exhibited more severe symptoms across TD, obsessive-compulsive spectrum disorders, and attention deficit hyperactivity disorder assessments compared to subtype2, with significant differences observed in 81.4% of the scale features. Network analysis revealed differences in core symptoms between the two subtypes; subtype1 primarily involved hyperactivity and vital activities, whereas subtype2 primarily involved attention deficit, hyperactivity and conduct. Furthermore, comparisons with DSM-5 classifications revealed distinct patterns, indicating the novel nature of the identified subtypes.

CONCLUSION

Our study identified two novel TD subtypes, highlighting its heterogeneity. Subtype 1 had more severe attention deficits and impulsivity, requiring comprehensive treatment, while subtype 2 had milder symptoms, focusing on support and monitoring. These findings provide insights into TD classification and may help refine treatment strategies. However, the cross-sectional design limits causal interpretations, and reliance on parent-reported data may introduce bias.

Vulnerable brain regions in adolescent attention deficit hyperactivity disorder: An activation likelihood estimation meta-analysis

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in adolescents characterized by inattention, hyperactivity, and impulsivity, which impact cognitive, behavioral, and emotional functioning. Resting-state functional magnetic resonance imaging (rs-fMRI) provides critical insights into the functional architecture of the brain in ADHD. Despite extensive research, specific brain regions consistently affected in ADHD patients during these formative years have not been comprehensively delineated.

AIM

To identify consistent vulnerable brain regions in adolescent ADHD patients using rs-fMRI and activation likelihood estimation (ALE) meta-analysis.

METHODS

We conducted a comprehensive literature search up to August 31, 2024, to identify studies investigating functional brain alterations in adolescents with ADHD. We utilized regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), dynamic ALFF (dALFF) and fractional ALFF (fALFF) analyses. We compared the regions of aberrant spontaneous neural activity in adolescents with ADHD with those in healthy controls (HCs) using ALE.

RESULTS

Fifteen studies (468 adolescent ADHD patients and 466 HCs) were included. Combining the ReHo and ALFF/fALFF/dALFF data, the results revealed increased activity in the right lingual gyrus [LING, Brodmann Area (BA) 18], left LING (BA 18), and right cuneus (CUN, BA 23) in adolescent ADHD patients compared with HCs (voxel size: 592-32 mm³, P < 0.05). Decreased activity was observed in the left medial frontal gyrus (MFG, BA 9) and left precuneus (PCUN, BA 31) in adolescent ADHD patients compared with HCs (voxel size: 960-456 mm³, P < 0.05). Jackknife sensitivity analyses demonstrated robust reproducibility in 11 of the 13 tests for the right LING, left LING, and right CUN and in 11 of the 14 tests for the left MFG and left PCUN.

CONCLUSION

We identified specific brain regions with both increased and decreased activity in adolescent ADHD patients, enhancing our understanding of the neural alterations that occur during this pivotal stage of development.

A Comparative Investigation of Functional Connectivity Utilizing Electroencephalography in Insomnia Patients with and without Restless Leg Syndrome

Objective

The current study aimed to identify distinctive functional brain connectivity characteristics that differentiate patients with restless legs syndrome (RLS) from those with primary insomnia.

Methods

Quantitative electroencephalography (QEEG) was employed to analyze connectivity matrices using the phaselocking value technique. A total of 107 patients with RLS (RLS group) and 17 patients with insomnia without RLS (primary insomnia group) were included in the study. Demographic variables were compared using t tests and chi-square tests, while differences in connectivity were examined through multiple analyses of covariance. Correlation analysis was conducted to explore the relationship between connectivity and the severity of RLS.

Results

The results indicated significant differences in the primary somatosensory cortex (F = 4.377, r = 0.039), primary visual cortex (F = 4.215, r = 0.042), and anterior prefrontal cortex (F = 5.439, r = 0.021) between the RLS and primary insomnia groups. Furthermore, the connectivity of the sensory cortex, including the primary somatosensory cortex (r = -0.247, p = 0.014), sensory association cortex (r = -0.238, p = 0.028), retrosplenial region (r = -0.302, p = 0.002), angular gyrus (r = -0.258, p = 0.008), supramarginal gyrus (r = -0.230, p = 0.020), primary visual cortex (r = -0.275, p = 0.005) and secondary visual cortex (r = -0.226, p = 0.025) exhibited an inverse association with RLS symptom severity.

Conclusion

The prefrontal cortex, primary somatosensory cortex, and visual cortex showed potential as diagnostic biomarkers for distinguishing RLS from primary insomnia. These findings indicate that QEEG-based functional connectivity analysis shows promise as a valuable diagnostic tool for RLS and provides insights into its underlying mechanisms. Further research is needed to explore this aspect further.

Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children

Attention deficit hyperactivity disorder (ADHD) is a neuro-developmental disorder that affects approximately 5-10% of school-aged children worldwide. Early diagnosis and intervention are essential to improve the quality of life of patients and their families. In this study, we propose ConvMixer-ECA, a novel deep learning architecture that combines ConvMixer with efficient channel attention (ECA) blocks for the accurate diagnosis of ADHD using electroencephalogram (EEG) signals. The model was trained and evaluated using EEG recordings from 60 healthy children and 61 children with ADHD. A series of experiments were conducted to evaluate the performance of the ConvMixer-ECA. The results showed that the ConvMixer-ECA performed well in ADHD recognition with 94.52% accuracy. The incorporation of attentional mechanisms, in particular ECA, improved the performance of ConvMixer; it outperformed other attention-based variants. In addition, ConvMixer-ECA outperformed state-of-the-art deep learning models including EEGNet, CNN, RNN, LSTM, and GRU. t-SNE visualization of the output of this model layer validated the effectiveness of ConvMixer-ECA in capturing the underlying patterns and features that separate ADHD from typically developing individuals through hierarchical feature learning. These outcomes demonstrate the potential of ConvMixer-ECA as a valuable tool to assist clinicians in the early diagnosis and intervention of ADHD in children.

Comparing executive functions in children with attention deficit hyperactivity disorder with or without reading disability: A resting-state EEG study

INTRODUCTION

As numerous studies have shown, executive dysfunction is the main impairment in attention-deficit/hyperactivity disorder. According to recent neuroimaging studies, the frontoparietal coherence plays a key role in overall cognitive functions. Therefore, the aim of this study was to compare executive functions during resting-state EEG by monitoring brain connectivity (coherence) patterns in children with attention deficit hyperactivity disorder (ADHD) with or without reading disability (RD).

METHODS

The statistical sample of the study consisted of 32 children with ADHD aged between 8 and 12 years old with or without specific RD. Each group consisted of 11 boys and 5 girls that were matched on chronological age and gender. EEG was recorded during eyes-opened condition and brain connectivity within and between frontal and parietal regions was analyzed within theta, alpha, and beta bands.

RESULTS

The results revealed that across the frontal regions, the comorbid group showed a significant reduction in the left intrahemispheric coherence in the alpha and beta bands. The ADHD-alone group exhibited increased theta and decreased alpha and beta coherence in frontal regions. In the frontoparietal regions, children in the comorbid group showed lower coherence between frontal and parietal networks compared to children without comorbid RD.

CONCLUSION

The findings indicate that brain connectivity (coherence) patterns of children with ADHD with comorbid RD were more abnormal and lend support to more disrupted cortical connectivity in the comorbid group. Thus, these findings can be a useful marker for better recognizing ADHD and comorbid disabilities.

The Loudness Dependence of Auditory Evoked Potentials is associated with the Symptom Severity and Treatment in Boys with Attention Deficit Hyperactivity Disorder

Objective

Methods

Results

Conclusion