A unique pattern of cortical connectivity characterizes patients with attention deficit disorders: a large electroencephalographic coherence study

A review on evaluating mental stress by deep learning using EEG signals

Mental stress is a common problem that affects individuals all over the world. Stress reduces human functionality during routine work and may lead to severe health defects. Early detection of stress is important for preventing diseases and other negative health-related consequences of stress. Several neuroimaging techniques have been utilized to assess mental stress, however, due to its ease of use, robustness, and non-invasiveness, electroencephalography (EEG) is commonly used. This paper aims to fill a knowledge gap by reviewing the different EEG-related deep learning algorithms with a focus on Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs) for the evaluation of mental stress. The review focuses on data representation, individual deep neural network model architectures, hybrid models, and results amongst others. The contributions of the paper address important issues such as data representation and model architectures. Out of all reviewed papers, 67% used CNN, 9% LSTM, and 24% hybrid models. Based on the reviewed literature, we found that dataset size and different representations contributed to the performance of the proposed networks. Raw EEG data produced classification accuracy around 62% while using spectral and topographical representation produced up to 88%. Nevertheless, the roles of generalizability across different deep learning models and individual differences remain key areas of inquiry. The review encourages the exploration of innovative avenues, such as EEG data image representations concurrently with graph convolutional neural networks (GCN), to mitigate the impact of inter-subject variability. This novel approach not only allows us to harmonize structural nuances within the data but also facilitates the integration of temporal dynamics, thereby enabling a more comprehensive assessment of mental stress levels.

Biomarkers Based on Comprehensive Hierarchical EEG Coherence Analysis: Example Application to Social Competence in Autism (Preliminary Results)

Electroencephalography (EEG) coherence analysis, based on measurement of synchronous oscillations of neuronal clusters, has been used extensively to evaluate functional connectivity in brain networks. EEG coherence studies have used a variety of analysis variables (e.g., time and frequency resolutions corresponding to the analysis time period and frequency bandwidth), regions of the brain (e.g., connectivity within and between various cortical lobes and hemispheres) and experimental paradigms (e.g., resting state with eyes open or closed; performance of cognitive tasks). This variability in study designs has resulted in difficulties in comparing the findings from different studies and assimilating a comprehensive understanding of the underlying brain activity and regions with abnormal functional connectivity in a particular disorder. In order to address the variability in methods across studies and to facilitate the comparison of research findings between studies, this paper presents the structure and utilization of a comprehensive hierarchical electroencephalography (EEG) coherence analysis that allows for formal inclusion of analysis duration, EEG frequency band, cortical region, and experimental test condition in the computation of the EEG coherences. It further describes the method by which this EEG coherence analysis can be utilized to derive biomarkers related to brain (dys)function and abnormalities. In order to document the utility of this approach, the paper describes the results of the application of this method to EEG and behavioral data from a social synchrony paradigm in a small cohort of adolescents with and without Autism Spectral Disorder.

Localization and Diagnosis of Attention-Deficit/Hyperactivity Disorder

In this paper, a random-forest-based method was proposed for the classification and localization of Attention-Deficit/Hyperactivity Disorder (ADHD), a common neurodevelopmental disorder among children. Experimental data were magnetic resonance imaging (MRI) from the public case-control dataset of 3D images for ADHD-200. Each MRI image was a 3D-tensor of 121×145×121 size. All 3D matrices (MRI) were segmented into the slices from each of three orthogonal directions. Each slice from the same position of the same direction in the training set was converted into a vector, and all these vectors were composed into a designed matrix to train the random forest classification algorithm; then, the well-trained RF classifier was exploited to give a prediction label in correspondence direction and position. Diagnosis and location results can be obtained upon the intersection of these three prediction matrices. The performance of our proposed method was illustrated on the dataset from New York University (NYU), Kennedy Krieger Institute (KKI) and full datasets; the results show that the proposed methods can archive more accuracy identification in discrimination of ADHD, and can be extended to the other practices of diagnosis. Moreover, another suspected region was found at the first time.

Early Attentional Modulation by Working Memory Training in Young Adult ADHD Patients during a Risky Decision-Making Task

Background: Working memory (WM) deficits and impaired decision making are among the characteristic symptoms of patients affected by attention deficit/hyperactivity disorder (ADHD). The inattention associated with the disorder is likely to be due to functional deficits of the neural networks inhibiting irrelevant sensory input. In the presence of unnecessary information, a good decisional process is impaired and ADHD patients tend to take risky decisions. This study is aimed to test the hypothesis that the level of difficulty of a WM training (WMT) is affecting the top-down modulation of the attentional processes in a probabilistic gambling task. Methods: Event-related potentials (ERP) triggered by the choice of the amount wagered in the gambling task were recorded, before and after WMT with a the dual n-back task, in young ADHD adults and matched controls. For each group of participants, randomly assigned individuals were requested to perform WMT with a fixed baseline level of difficulty. The remaining participants were trained with a performance-dependent adaptive n-level of difficulty. Results: We compared the ERP recordings before and after 20 days of WMT in each subgroup. The analysis was focused on the time windows with at least three recording sites showing differences before and after training, after Bonferroni correction ( p < 0.05 ). In ADHD, the P1 wave component was selectively affected at frontal sites and its shape was recovered close to controls' only after adaptive training. In controls, the strongest contrast was observed at parietal level with a left hemispheric dominance at latencies near 900 ms, more after baseline than after adaptive training. Conclusion: Partial restoration of early selective attentional processes in ADHD patients might occur after WMT with a high cognitive load. Modified frontal sites' activities might constitute a neural marker of this effect in a gambling task. In controls, conversely, an increase in late parietal negativity might rather be a marker of an increase in transfer effects to fluid intelligence.

Effects of brain activity, morning salivary cortisol, and emotion regulation on cognitive impairment in elderly people

OBJECTIVE

Cognitive impairment subjects exhibit high cortisol levels that are associated with low brain activity, but negative emotions with high cortisol are associated with high brain activity and reduced cognition. Emotion regulation, glucocorticoid hormones, and brain activity all interact with cognitive impairment. Therefore, we aimed to investigate cognitive impairment differences related to sex, morning salivary cortisol, emotion regulation, and brain activity in elderly people.

METHODS

A total of 64 participants (19 males and 45 females) were tested by the Montreal cognitive assessment. Next, morning saliva was collected from each participant and analyzed by enzyme-linked immunosorbent assay, and the brain activity of the participants was subsequently recorded. Finally, emotion regulation was assessed via the Brunel mood scale questionnaire.

RESULTS

The results revealed that attention was significantly lower in elderly females than in elderly males. Depression and vigor were significantly higher in elderly females than in elderly males. Brain activity of the slow (delta and theta) and fast (beta and high beta) waves was significantly higher in elderly females than in elderly males. Moreover, attention was negatively correlated with the theta wave, whereas delayed recall was positively correlated with the theta wave and salivary cortisol. Depression was positively correlated with the high beta wave and language skill, whereas the high beta wave was negatively correlated with visuoconstructional skill.

CONCLUSION

The brain activity, emotion, and cortisol were influenced by cognitive impairments, although the relation of brain activity with glucocorticoid hormones remains inconclusive. This finding may be useful to the brain aging process, promote healthy brain aging, and prevent neurodegenerative conditions.

Autism, spectrum or clusters? An EEG coherence study

BACKGROUND

Autism prevalence continues to grow, yet a universally agreed upon etiology is lacking despite manifold evidence of abnormalities especially in terms of genetics and epigenetics. The authors postulate that the broad definition of an omnibus 'spectrum disorder' may inhibit delineation of meaningful clinical correlations. This paper presents evidence that an objectively defined, EEG based brain measure may be helpful in illuminating the autism spectrum versus subgroups (clusters) question.

METHODS

Forty objectively defined EEG coherence factors created in prior studies demonstrated reliable separation of neuro-typical controls from subjects with autism, and reliable separation of subjects with Asperger's syndrome from all other subjects within the autism spectrum and from neurotypical controls. In the current study, these forty previously defined EEG coherence factors were used prospectively within a large (N = 430) population of subjects with autism in order to determine quantitatively the potential existence of separate clusters within this population.

RESULTS

By use of a recently published software package, NbClust, the current investigation determined that the 40 EEG coherence factors reliably identified two distinct clusters within the larger population of subjects with autism. These two clusters demonstrated highly significant differences. Of interest, many more subjects with Asperger's syndrome fell into one rather than the other cluster.

CONCLUSIONS

EEG coherence factors provide evidence of two highly significant separate clusters within the subject population with autism. The establishment of a unitary "Autism Spectrum Disorder" does a disservice to patients and clinicians, hinders much needed scientific exploration, and likely leads to less than optimal educational and/or interventional efforts.

Effect of combined neurofeedback and game-based cognitive training on the treatment of ADHD: A randomized controlled study

Neurofeedback (NF) is referred to as a "possibly efficacious" treatment in the current evidence-based reviews; therefore, more research is needed to determine its effects especially in combination with other treatments. The present study examines the effect of NF and game-based cognitive training on children with attention deficit hyperactivity disorder (ADHD). Thirty-two male students with ADHD were assigned to NF (N = 16; M_age=10.20; SD = 1.03) and waiting list control (N = 16; M_age = 10.05; SD = 0.83) in a randomized double-blind trial. The children in the NF group based on quantitative electroencephalography (QEEG) attended 30 three times-weekly sessions. The children were examined in pretest and post-test with EEG, Integrated Visual and Auditory Continuous Performance (IVA), and Conners Parent, and Teacher Rating Scales-Revised. The treatment was found significant all the symptom variables except for attention deficit (AD) and auditory response control (ARC). Normalization of the atypical EEG features with reduced [Formula: see text] wave and increased sensory motor (SMR) activity in central zero (Cz) was recorded in the NF condition participants. However, except for SMR activity there were no significant changes in the waves of frontocentral zero (FCz). It is concluded that technology developments provide an interesting vehicle for interposing interventions and that combined NF and game-based cognitive training can produce positive therapeutic effects on brainwaves and ADHD symptomatology.

Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular Analysis

BACKGROUND

Motivated by an inconsistency between reports of high diagnosis-classification accuracies and known heterogeneity in attention-deficit/hyperactivity disorder (ADHD), this study assessed classification accuracy in studies of ADHD as a function of methodological factors that can bias results. We hypothesized that high classification results in ADHD diagnosis are inflated by methodological factors.

METHODS

We reviewed 69 studies (of 95 studies identified) that used neuroimaging features to predict ADHD diagnosis. Based on reported methods, we assessed the prevalence of circular analysis, which inflates classification accuracy, and evaluated the relationship between sample size and accuracy to test if small-sample models tend to report higher classification accuracy, also an indicator of bias.

RESULTS

Circular analysis was detected in 15.9% of ADHD classification studies, lack of independent test set was noted in 13%, and insufficient methodological detail to establish its presence was noted in another 11.6%. Accuracy of classification ranged from 60% to 80% in the 59.4% of reviewed studies that met criteria for independence of feature selection, model construction, and test datasets. Moreover, there was a negative relationship between accuracy and sample size, implying additional bias contributing to reported accuracies at lower sample sizes.

CONCLUSIONS

High classification accuracies in neuroimaging studies of ADHD appear to be inflated by circular analysis and small sample size. Accuracies on independent datasets were consistent with known heterogeneity of the disorder. Steps to resolve these issues, and a shift toward accounting for sample heterogeneity and prediction of future outcomes, will be crucial in future classification studies in ADHD.

What does scalp electroencephalogram coherence tell us about long-range cortical networks?

Long-range interactions between cortical areas are undoubtedly a key to the computational power of the brain. For healthy human subjects, the premier method for measuring brain activity on fast timescales is electroencephalography (EEG), and coherence between EEG signals is often used to assay functional connectivity between different brain regions. However, the nature of the underlying brain activity that is reflected in EEG coherence is currently the realm of speculation, because seldom have EEG signals been recorded simultaneously with intracranial recordings near cell bodies in multiple brain areas. Here, we take the early steps towards narrowing this gap in our understanding of EEG coherence by measuring local field potentials with microelectrode arrays in two brain areas (extrastriate visual area V4 and dorsolateral prefrontal cortex) simultaneously with EEG at the nearby scalp in rhesus macaque monkeys. Although we found inter-area coherence at both scales of measurement, we did not find that scalp-level coherence was reliably related to coherence between brain areas measured intracranially on a trial-to-trial basis, despite that scalp-level EEG was related to other important features of neural oscillations, such as trial-to-trial variability in overall amplitudes. This suggests that caution must be exercised when interpreting EEG coherence effects, and new theories devised about what aspects of neural activity long-range coherence in the EEG reflects.

Feature Extraction with GMDH-Type Neural Networks for EEG-Based Person Identification

The brain activity observed on EEG electrodes is influenced by volume conduction and functional connectivity of a person performing a task. When the task is a biometric test the EEG signals represent the unique “brain print”, which is defined by the functional connectivity that is represented by the interactions between electrodes, whilst the conduction components cause trivial correlations. Orthogonalization using autoregressive modeling minimizes the conduction components, and then the residuals are related to features correlated with the functional connectivity. However, the orthogonalization can be unreliable for high-dimensional EEG data. We have found that the dimensionality can be significantly reduced if the baselines required for estimating the residuals can be modeled by using relevant electrodes. In our approach, the required models are learnt by a Group Method of Data Handling (GMDH) algorithm which we have made capable of discovering reliable models from multidimensional EEG data. In our experiments on the EEG-MMI benchmark data which include 109 participants, the proposed method has correctly identified all the subjects and provided a statistically significant ([Formula: see text]) improvement of the identification accuracy. The experiments have shown that the proposed GMDH method can learn new features from multi-electrode EEG data, which are capable to improve the accuracy of biometric identification.

Early steps toward understanding neuronal communication

PURPOSE OF REVIEW

The computational power of the brain arises from the complex interactions between neurons. One straightforward method to quantify the strength of neuronal interactions is by measuring correlation and coherence. Efforts to measure correlation have been advancing rapidly of late, spurred by the development of advanced recording technologies enabling recording from many neurons and brain areas simultaneously. This review highlights recent results that provide clues into the principles of neural coordination, connections to cognitive and neurological phenomena, and key directions for future research.

RECENT FINDINGS

The correlation structure of neural activity in the brain has important consequences for the encoding properties of neural populations. Recent studies have shown that this correlation structure is not fixed, but adapts in a variety of contexts in ways that appear beneficial to task performance. By studying these changes in biological neural networks and computational models, researchers have improved our understanding of the principles guiding neural communication.

SUMMARY

Correlation and coherence are highly informative metrics for studying coding and communication in the brain. Recent findings have emphasized how the brain modifies correlation structure dynamically in order to improve information-processing in a goal-directed fashion. One key direction for future research concerns how to leverage these dynamic changes for therapeutic purposes.

Suprathreshold fiber cluster statistics: Leveraging white matter geometry to enhance tractography statistical analysis

This work presents a suprathreshold fiber cluster (STFC) method that leverages the whole brain fiber geometry to enhance statistical group difference analyses. The proposed method consists of 1) a well-established study-specific data-driven tractography parcellation to obtain white matter tract parcels and 2) a newly proposed nonparametric, permutation-test-based STFC method to identify significant differences between study populations. The basic idea of our method is that a white matter parcel's neighborhood (nearby parcels with similar white matter anatomy) can support the parcel's statistical significance when correcting for multiple comparisons. We propose an adaptive parcel neighborhood strategy to allow suprathreshold fiber cluster formation that is robust to anatomically varying inter-parcel distances. The method is demonstrated by application to a multi-shell diffusion MRI dataset from 59 individuals, including 30 attention deficit hyperactivity disorder patients and 29 healthy controls. Evaluations are conducted using both synthetic and in-vivo data. The results indicate that the STFC method gives greater sensitivity in finding group differences in white matter tract parcels compared to several traditional multiple comparison correction methods.