Resting-state EEG Connectivity in Young Children with ADHD

Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value

Attention Deficit Hyperactivity Disorder (ADHD) is a widespread neurobehavioral disorder affecting children and adolescents, requiring early detection for effective treatment. EEG connectivity measures can reveal the interdependencies between EEG recordings, highlighting brain network patterns and functional behavior that improve diagnostic accuracy. This study introduces a novel ADHD diagnostic method by combining linear and nonlinear brain connectivity maps with an attention-based convolutional neural network (Att-CNN). Pearson Correlation Coefficient (PCC) and Phase-Locking Value (PLV) are used to create fused connectivity maps (FCMs) from various EEG frequency subbands, which are then inputted into the Att-CNN. The attention module is strategically placed after the latest convolutional layer in the CNN. The performance of different optimizers (Adam and SGD) and learning rates are assessed. The suggested model obtained 98.88%, 98.41%, 98.19%, and 98.30% for accuracy, precision, recall, and F1 Score, respectively, using the SGD optimizer in the FCM of the theta band with a learning rate of 1e-1. With the use of FCM, Att-CNN, and advanced optimizers, the proposed technique has the potential to produce trustworthy instruments for the early diagnosis of ADHD, greatly enhancing both patient outcomes and diagnostic accuracy.

Altered corticalfunctional networks in Wilson's disease: A resting-state electroencephalogram study

The neuropsychiatric symptoms are common in Wilson's disease (WD) patients. However, it remains unclear about the associated functional brain networks. In this study, source localization-based functional connectivity analysis of close-eye resting-state electroencephalography (EEG) were implemented to assess the characteristics of functional networks in 17 WD patients with neurological involvements and 17 healthy controls (HCs). The weighted phase-lag index (wPLI) was subsequently calculated in source space across five different frequency bands and the resulting connectivity matrix was transformed into a weighted graph whose structure was measured by five graphical analysis indicators, which were finally correlated with clinical scores. Compared to HCs, WD patients revealed disconnected sub-networks in delta, theta and alpha bands. Moreover, WD patients exhibited significantly reduced global clustering coefficients and small-worldness in all five frequency bands. In WD group, the severity of neurological symptoms and structural brain abnormalities were significantly correlated with disrupted functional networks. In conclusion, our study demonstrated that functional network deficits in WD can reflect the severity of their neurological symptoms and structural brain abnormalities. Resting-state EEG may be used as a marker of brain injury in WD.

Abnormalities in Electroencephalographic Microstates in Patients with Late-Life Depression

Background

Late-life depression (LLD) is characterized by disrupted brain networks. Resting-state networks in the brain are composed of both stable and transient topological structures known as microstates, which reflect the dynamics of the neural activities. However, the specific pattern of EEG microstate in LLD remains unclear.

Methods

Resting-state EEG were recorded for 31 patients with episodic LLD (eLLD), 20 patients with remitted LLD (rLLD) and 32 healthy controls (HCs) using a 64-channel cap. The clinical data of the patients were collected and the 17-Item Hamilton Rating Scale for Depression (HAMD) was used for symptom assessment. Duration, occurrence, time coverage and syntax of the four microstate classes (A-D) were calculated. Group differences in EEG microstates and the relationship between microstates parameters and clinical features were analyzed.

Results

Compared with NC and patients with rLLD, patients with eLLD showed increased duration and time coverage of microstate class D. Besides, a decrease in occurrence of microstate C and transition probability between microstate B and C was observed. In addition, the time coverage of microstate D was positively correlated with the total score of HAMD, core symptoms, and miscellaneous items.

Conclusion

These findings suggest that disrupted EEG microstates may be associated with the pathophysiology of LLD and may serve as potential state markers for the monitoring of the disease.

Potential biomarker for early detection of ADHD using phase-based brain connectivity and graph theory

This research investigates an efficient strategy for early detection and intervention of attention-deficit hyperactivity disorder (ADHD) in children. ADHD is a neurodevelopmental condition characterized by inattention and hyperactivity/impulsivity symptoms, which can significantly impact a child's daily life. This study employed two distinct brain functional connectivity measurements to assess our approach across various local graph features. Six common classifiers are employed to distinguish between children with ADHD and healthy control. Based on the phase-based analysis, the study proposes two biomarkers that differentiate children with ADHD from healthy control, with a remarkable accuracy of 99.174%. Our findings suggest that subgraph centrality of phase-lag index brain connectivity within the beta and delta frequency bands could be a promising biomarker for ADHD diagnosis. Additionally, we identify node betweenness centrality of inter-site phase clustering connectivity within the delta and theta bands as another potential biomarker that warrants further exploration. These biomarkers were validated using a t-statistical test and yielded a p-value of under 0.05, which approved their significant difference in these two groups. Suggested biomarkers have the potential to improve the accuracy of ADHD diagnosis and could help identify effective intervention strategies for children with the condition.

Sleep Spindle-Related EEG Connectivity in Children with Attention-Deficit/Hyperactivity Disorder: An Exploratory Study

Attention-deficit/hyperactivity disorder (ADHD) is a neurobehavioral disorder with known brain abnormalities but no biomarkers to support clinical diagnosis. Recently, EEG analysis methods such as functional connectivity have rekindled interest in using EEG for ADHD diagnosis. Most studies have focused on resting-state EEG, while connectivity during sleep and spindle activity has been underexplored. Here we present the results of a preliminary study exploring spindle-related connectivity as a possible biomarker for ADHD. We compared sensor-space connectivity parameters in eight children with ADHD and nine age/sex-matched healthy controls during sleep, before, during, and after spindle activity in various frequency bands. All connectivity parameters were significantly different between the two groups in the delta and gamma bands, and Principal Component Analysis (PCA) in the gamma band distinguished ADHD from healthy subjects. Cluster coefficient and path length values in the sigma band were also significantly different between epochs, indicating different spindle-related brain activity in ADHD.

High Dimensional Convolutional Neural Network for EEG Connectivity-Based Diagnosis of ADHD

Background

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and adults and its early detection is effective in the successful treatment of children. Electroencephalography (EEG) has been widely used for classifying ADHD and normal children. In recent years, deep learning leads to more accurate classification.

Objective

This study aims to adapt convolutional neural networks (CNNs) for classifying ADHD and normal children based on the connectivity measure of their EEG signals.

Material and Methods

In this experimental study, the dataset consisted of 61 ADHD and 60 normal children from which 13021 epochs were extracted as input for model training and evaluation. Synchronization likelihood (SL) and wavelet coherence (WC) were considered connectivity measures. The neighborhood between EEG channels was arranged in a two-dimensional matrix for better representation. Four-dimensional (4D) and six-dimensional (6D) connectivity tensors were composed as model inputs. Two architectures were developed, one 4D and 6D CNN for SL and WC-based diagnosis of ADHD, respectively.

Results

A 5-fold cross-validation was utilized to assess developed models. The average accuracy of 98.56% for 4D CNN and 98.85% for 6D CNN in epoch-based classification were obtained. In the case of subject-based classification, the accuracy was 99.17% for both models.

Conclusion

Based on the evaluation metrics of the proposed models, ADHD children can be diagnosed and ADHD and normal children can be successfully distinguished.

Analysis of EEG brain connectivity of children with ADHD using graph theory and directional information transfer

Research shows that Attention Deficit Hyperactivity Disorder (ADHD) is related to a disorder in brain networks. The purpose of this study is to use an effective connectivity measure and graph theory to examine the impairments of brain connectivity in ADHD. Weighted directed graphs based on electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children were constructed. The edges between two nodes (electrodes) were calculated by Phase Transfer Entropy (PTE). PTE is calculated for five frequency bands: delta, theta, alpha, beta, and gamma. The graph theory measures were divided into two categories: global and local. Statistical analysis with global measures indicates that in children with ADHD, the segregation of brain connectivity increases while the integration of the brain connectivity decreases compared to healthy children. These brain network differences were identified in the delta and theta frequency bands. The classification accuracy of 89.4% is obtained for both in-degree and strength measures in the theta band. Our result indicated local graph measures classified ADHD and healthy subjects with accuracy of 91.2 and 90% in theta and delta bands, respectively. Our analysis may provide a new understanding of the differences in the EEG brain network of children with ADHD and healthy children.

Brain mechanisms underlying the influence of emotions on spatial decision-making: An EEG study

It is common for people to make bad decisions because of their emotions in life. When these decisions are important, such as aeronautical decisions and driving decisions, the mistakes of decisions can cause irreversible damage. Therefore, it is important to explore how emotions influence decision-making, so as to avoid the negative influence of emotions on decision-making as much as possible. Although existing researchers have found some mechanisms of emotion's influence on decision-making, only a few studies focused on the influence of emotions on decision-making based on electroencephalography (EEG). In addition, most of them were focused on risky and uncertain decision-making. We designed a novel experimental task to explore the influence of emotion on spatial decision-making and recorded subjective data, decision-making behavioral data, and EEG data. By analyzing these data, we came to three conclusions. Firstly, we observed three similar event-related potentials (ERP) microstates in the decision-making process under different emotions by microstate analysis. Additionally, the prefrontal, parietal and occipital lobes played key roles in decision-making. Secondly, we found that the P2 component of the prefrontal lobe presented the influence of different emotions on decision-making by ERP analysis. Among them, positive emotion evoked the largest P2 amplitude compared to negative emotions and no stimuli. Thirdly, we found some graph metrics that were significantly associated with decision accuracy by effective connectivity analysis combined with graph theoretic analysis. In consequence, the finding of our study may shed more light on the brain mechanisms underlying the influence of emotions on spatial decision-making, thereby providing a basis for avoiding decision-making accidents caused by emotions and realizing better decision-making.

EEG Global Coherence in Scholar ADHD Children during Visual Object Processing

Among neurodevelopmental disorders, attention deficit hyperactivity disorder (ADHD) is the main cause of school failure in children. Notably, visuospatial dysfunction has also been emphasized as a leading cause of low cognitive performance in children with ADHD. Consequently, the present study aimed to identify ADHD-related changes in electroencephalography (EEG) characteristics, associated with visual object processing in school-aged children. We performed Multichannel EEG recordings in 16-year-old children undergoing Navon's visual object processing paradigm. We mapped global coherence during the processing of local and global visual stimuli that were consistent, inconsistent, or neutral. We found that Children with ADHD showed significant differences in global weighted coherence during the processing of local and global inconsistent visual stimuli and longer response times in comparison to the control group. Delta and theta EEG bands highlighted important features for classification in both groups. Thus, we advocate EEG coherence and low-frequency EEG spectral power as prospective markers of visual processing deficit in ADHD. Our results have implications for the development of diagnostic interventions in ADHD and provide a deeper understanding of the factors leading to low performance in school-aged children.

Functional and structural brain network correlates of visual hallucinations in Lewy body dementia

Visual hallucinations are a common feature of Lewy body dementia. Previous studies have shown that visual hallucinations are highly specific in differentiating Lewy body dementia from Alzheimer’s disease dementia and Alzheimer–Lewy body mixed pathology cases. Computational models propose that impairment of visual and attentional networks is aetiologically key to the manifestation of visual hallucinations symptomatology. However, there is still a lack of experimental evidence on functional and structural brain network abnormalities associated with visual hallucinations in Lewy body dementia.

We used EEG source localization and network based statistics to assess differential topographical patterns in Lewy body dementia between 25 participants with visual hallucinations and 17 participants without hallucinations. Diffusion tensor imaging was used to assess structural connectivity between thalamus, basal forebrain and cortical regions belonging to the functionally affected network component in the hallucinating group, as assessed with network based statistics. The number of white matter streamlines within the cortex and between subcortical and cortical regions was compared between hallucinating and not hallucinating groups and correlated with average EEG source connectivity of the affected subnetwork. Moreover, modular organization of the EEG source network was obtained, compared between groups and tested for correlation with structural connectivity.

Network analysis showed that compared to non-hallucinating patients, those with hallucinations feature consistent weakened connectivity within the visual ventral network, and between this network and default mode and ventral attentional networks, but not between or within attentional networks. The occipital lobe was the most functionally disconnected region. Structural analysis yielded significantly affected white matter streamlines connecting the cortical regions to the nucleus basalis of Meynert and the thalamus in hallucinating compared to not hallucinating patients. The number of streamlines in the tract between the basal forebrain and the cortex correlated with cortical functional connectivity in non-hallucinating patients, while a correlation emerged for the white matter streamlines connecting the functionally affected cortical regions in the hallucinating group.

This study proposes, for the first time, differential functional networks between hallucinating and not hallucinating Lewy body dementia patients, and provides empirical evidence for existing models of visual hallucinations. Specifically, the outcome of the present study shows that the hallucinating condition is associated with functional network segregation in Lewy body dementia and supports the involvement of the cholinergic system as proposed in the current literature.

Analysis of Effective Connectivity Strength in Children with Attention Deficit Hyperactivity Disorder Using Phase Transfer Entropy

Objective: This study aimed to investigate differences in brain networks between healthy children and children with attention deficit hyperactivity disorder (ADHD) during an attention test. Method : To fulfill this, we constructed weighted directed graphs based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children with the same age. Nodes of graphs were 19 EEG electrodes, and the edges were phase transfer entropy (PTE) between each pair of electrodes. PTE is a measure for directed connectivity that determines the effective relationship between signals in linear and nonlinear coupling. Connectivity graphs of each sample were constructed using PTE in the five frequency bands as follows: delta, theta, alpha, beta, and gamma. To investigate the differences in connectivity strength of each node after the sparsification process with two values (0.5 and 0.25), the permutation statistical test was used with the statistical significance level of p<0.01. Results: The results indicate stronger inter-regional connectivity in the prefrontal brain regions of the control group compared to the ADHD group. However, the strength of inter-regional connectivity in the central regions of the ADHD group was higher. A comparison of the prefrontal regions between the two groups revealed that the areas of the Fp1 electrode (left prefrontal) in healthy individuals play stronger transmission roles. Conclusion: Our research can provide new insights into the strength and direction of connectivity in ADHD and healthy individuals during an attention task.

Altered Functional Connectivity in Children with ADHD Revealed by Scalp EEG: An ERP Study

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental brain disorders in childhood. Despite extensive researches, the neurobiological mechanism underlying ADHD is still left unveiled. Since the deficit functions, such as attention, have been demonstrated in ADHD, in our present study, based on the oddball P3 task, the corresponding electroencephalogram (EEG) of both healthy controls (HCs) and ADHD children was first collected. And we then not only focused on the event-related potential (ERP) evoked during tasks but also investigated related brain networks. Although an insignificant difference in behavior was found between the HCs and ADHD children, significant electrophysiological differences were found in both ERPs and brain networks. In detail, the dysfunctional attention occurred during the early stage of the designed task; as compared to HCs, the reduced P2 and N2 amplitudes in ADHD children were found, and the atypical information interaction might further underpin such a deficit. On the one hand, when investigating the cortical activity, HCs recruited much stronger brain activity mainly in the temporal and frontal regions, compared to ADHD children; on the other hand, the brain network showed atypical enhanced long-range connectivity between the frontal and occipital lobes but attenuated connectivity among frontal, parietal, and temporal lobes in ADHD children. We hope that the findings in this study may be instructive for the understanding of cognitive processing in children with ADHD.

Neural Mechanisms of Hearing Recovery for Cochlear-Implanted Patients: An Electroencephalogram Follow-Up Study

Background

Patients with severe profound hearing loss could benefit from cochlear implantation (CI). However, the neural mechanism of such benefit is still unclear. Therefore, we analyzed the electroencephalogram (EEG) and behavioral indicators of auditory function remodeling in patients with CI. Both indicators were sampled at multiple time points after implantation (1, 90, and 180 days).

Methods

First, the speech perception ability was evaluated with the recording of a list of Chinese words and sentences in 15 healthy controls (HC group) and 10 patients with CI (CI group). EEG data were collected using an oddball paradigm. Then, the characteristics of event-related potentials (ERPs) and mismatch negative (MMN) were compared between the CI group and the HC group. In addition, we analyzed the phase lag indices (PLI) in the CI group and the HC group and calculated the difference in functional connectivity between the two groups at different stages after implantation.

Results

The behavioral indicator, speech recognition ability, in CI patients improved as the implantation time increased. The MMN analysis showed that CI patients could recognize the difference between standard and deviation stimuli just like the HCs 90 days after cochlear implantation. Comparing the latencies of N1/P2/MMN between the CI group and the HC group, we found that the latency of N1/P2 in CI patients was longer, while the latency of MMN in CI users was shorter. In addition, PLI-based whole-brain functional connectivity (PLI-FC) showed that the difference between the CI group and the HC group mainly exists in electrode pairs between the bilateral auditory area and the frontal area. Furthermore, all those differences gradually decreased with the increase in implantation time.

Conclusion

The N1 amplitude, N1/P2/MMN latency, and PLI-FC in the alpha band may reflect the process of auditory function remodeling and could be an objective index for the assessment of speech perception ability and the effect of cochlear implantation.

Impaired visual working memory and reduced connectivity in undergraduates with a history of mild traumatic brain injury

Mild traumatic brain injury (mTBI), or concussion, accounts for 85% of all TBIs. Yet survivors anticipate full cognitive recovery within several months of injury, if not sooner, dependent upon the specific outcome/measure. Recovery is variable and deficits in executive function, e.g., working memory (WM) can persist years post-mTBI. We tested whether cognitive deficits persist in otherwise healthy undergraduates, as a conservative indicator for mTBI survivors at large. We collected WM performance (change detection, n-back tasks) using various stimuli (shapes, locations, letters; aurally presented numbers and letters), and wide-ranging cognitive assessments (e.g., RBANS). We replicated the observation of a general visual WM deficit, with preserved auditory WM. Surprisingly, visual WM deficits were equivalent in participants with a history of mTBI (mean 4.3 years post-injury) and in undergraduates with recent sports-related mTBI (mean 17 days post-injury). In seeking the underlying mechanism of these behavioral deficits, we collected resting state fMRI (rsfMRI) and EEG (rsEEG). RsfMRI revealed significantly reduced connectivity within WM-relevant networks (default mode, central executive, dorsal attention, salience), whereas rsEEG identified no differences (modularity, global efficiency, local efficiency). In summary, otherwise healthy current undergraduates with a history of mTBI present behavioral deficits with evidence of persistent disconnection long after full recovery is expected.

Precision Medicine Care in ADHD: The Case for Neural Excitation and Inhibition

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that has become increasingly prevalent worldwide. Its core symptoms, including difficulties regulating attention, activity level, and impulses, appear in early childhood and can persist throughout the lifespan. Current pharmacological options targeting catecholamine neurotransmissions have effectively alleviated symptoms in some, but not all affected individuals, leaving clinicians to implement trial-and-error approach to treatment. In this review, we discuss recent experimental evidence from both preclinical and human studies that suggest imbalance of excitation/inhibition (E/I) in the fronto-striatal circuitry during early development may lead to enduring neuroanatomical abnormality of the circuitry, causing persistence of ADHD symptoms in adulthood. We propose a model of precision medicine care that includes E/I balance as a candidate biomarker for ADHD, development of GABA-modulating medications, and use of magnetic resonance spectroscopy and scalp electrophysiology methods to monitor the effects of treatments on shifting E/I balance throughout the lifespan.