Inter-individual variability during neurodevelopment: an investigation of linear and nonlinear resting-state EEG features in an age-homogenous group of infants.

Electroencephalography measures are of interest in developmental neuroscience as potentially reliable clinical markers of brain function. Features extracted from electroencephalography are most often averaged across individuals in a population with a particular condition and compared statistically to the mean of a typically developing group, or a group with a different condition, to define whether a feature is representative of the populations as a whole. However, there can be large variability within a population, and electroencephalography features often change dramatically with age, making comparisons difficult. Combined with often low numbers of trials and low signal-to-noise ratios in pediatric populations, establishing biomarkers can be difficult in practice. One approach is to identify electroencephalography features that are less variable between individuals and are relatively stable in a healthy population during development. To identify such features in resting-state electroencephalography, which can be readily measured in many populations, we introduce an innovative application of statistical measures of variance for the analysis of resting-state electroencephalography data. Using these statistical measures, we quantified electroencephalography features commonly used to measure brain development-including power, connectivity, phase-amplitude coupling, entropy, and fractal dimension-according to their intersubject variability. Results from 51 6-month-old infants revealed that the complexity measures, including fractal dimension and entropy, followed by connectivity were the least variable features across participants. This stability was found to be greatest in the right parietotemporal region for both complexity feature, but no significant region of interest was found for connectivity feature. This study deepens our understanding of physiological patterns of electroencephalography data in developing brains, provides an example of how statistical measures can be used to analyze variability in resting-state electroencephalography in a homogeneous group of healthy infants, contributes to the establishment of robust electroencephalography biomarkers of neurodevelopment through the application of variance analyses, and reveals that nonlinear measures may be most relevant biomarkers of neurodevelopment.

Prefrontal Lesions Disrupt Posterior Alpha-Gamma Coordination of Visual Working Memory Representations.

How does the human brain prioritize different visual representations in working memory (WM)? Here, we define the oscillatory mechanisms supporting selection of "where" and "when" features from visual WM storage and investigate the role of pFC in feature selection. Fourteen individuals with lateral pFC damage and 20 healthy controls performed a visuospatial WM task while EEG was recorded. On each trial, two shapes were presented sequentially in a top/bottom spatial orientation. A retro-cue presented mid-delay prompted which of the two shapes had been in either the top/bottom spatial position or first/second temporal position. We found that cross-frequency coupling between parieto-occipital alpha (α; 8-12 Hz) oscillations and topographically distributed gamma (γ; 30-50 Hz) activity tracked selection of the distinct cued feature in controls. This signature of feature selection was disrupted in patients with pFC lesions, despite intact α-γ coupling independent of feature selection. These findings reveal a pFC-dependent parieto-occipital α-γ mechanism for the rapid selection of visual WM representations.

Prefrontal lesions disrupt oscillatory signatures of spatiotemporal integration in working memory.

How does the human brain integrate spatial and temporal information into unified mnemonic representations? Building on classic theories of feature binding, we first define the oscillatory signatures of integrating 'where' and 'when' information in working memory (WM) and then investigate the role of prefrontal cortex (PFC) in spatiotemporal integration. Fourteen individuals with lateral PFC damage and 20 healthy controls completed a visuospatial WM task while electroencephalography (EEG) was recorded. On each trial, two shapes were presented sequentially in a top/bottom spatial orientation. We defined EEG signatures of spatiotemporal integration by comparing the maintenance of two possible where-when configurations: the first shape presented on top and the reverse. Frontal delta-theta (δθ; 2-7 Hz) activity, frontal-posterior δθ functional connectivity, lateral posterior event-related potentials, and mesial posterior alpha phase-to-gamma amplitude coupling dissociated the two configurations in controls. WM performance and frontal and mesial posterior signatures of spatiotemporal integration were diminished in PFC lesion patients, whereas lateral posterior signatures were intact. These findings reveal both PFC-dependent and independent substrates of spatiotemporal integration and link optimal performance to PFC.

Decoding covert visual attention based on phase transfer entropy.

Covert attention to spatial and color features in the visual field is a relatively new control signal for brain-computer interfaces (BCI). To guide the processing resources to the related visual scene aspects, covert attention should be decoded from human brain. Here, a novel expert system is designed to decode covert visual attention based on the EEG signal provided from 15 subjects during a new task based on a change in lumination to two blue and orange color on the right and the left side of the screen, which is evaluated in two cases of binary and multi-class systems. For the first time, Phase transfer entropy (PTE) has been used in these systems, and after selecting the optimal decoding feature, the frequency band (8-13 Hz) Alpha and Beta1 (13-20 Hz) have the best performance compared to other frequency bands. Two-class classification accuracies of the designed system in two frequency bands (Alpha and Beta1) are 91.87% and 89.53%, respectively. Also, the accuracies are 65.11% and 63.38% for multi-class classification in specified frequency bands. In these frequency bands, the parietal and frontal lobes showed the most significant difference in comparison to the other lobes. Also, the obtained results declared that the expert system's performance in the Alpha band by the extracted features from the Posterior region is better than all frequency bands in other different brain regions. The performance of the proposed expert system by PTE is significantly better than the previous phase synchronization based features. Results have shown that the PTE feature performs better than the common methods for decoding covert visual attention.

Computer Aided Diagnosis System for multiple sclerosis disease based on phase to amplitude coupling in covert visual attention.

BACKGROUND AND OBJECTIVE: Computer Aided Diagnosis (CAD) techniques have widely been used in research to detect the neurological abnormalities and improve the consistency of diagnosis and treatment in medicine. In this study, a new CAD system based on EEG signals was developed. The motivation for the development of the CAD system was to diagnose multiple sclerosis (MS) disease during covert visual attention tasks. It is worth noting that research of this kind on the efficacy of attention tasks is limited in scope for MS patients; therefore, it is vital to develop a feature of EEG to characterize the patient's state with high sensitivity and specificity. METHODS: We evaluated the use of phase-amplitude coupling (PAC) of EEG signals to diagnose MS. It is assumed that the role of PAC for information encoding during visual attention in MS is greatly unknown; therefore, we made an attempt to investigate it via CAD systems. The EEG signals were recorded from healthy and MS patients while performing new visual attention tasks. Machine learning algorithms were also used to identify the EEG signals as to whether the disease existed or not. The challenge regarding the dimensionality of the extracted features was addressed through selecting the relevant and efficient features using T-test and Bhattacharyya distance criteria, and the validity of the system was assessed through leave-one-subject-out cross-validation method. RESULTS: Our findings indicated that online sequential extreme learning machine (OS-ELM) classifier with T-test feature selection method yielded peak accuracy, sensitivity and specificity in both color and direction tasks. These values were 91%, 83% and 96% for color task, and 90%, 82% and 96% for the direction task. CONCLUSIONS: Based on the results, it can be concluded that this procedure can be used for the automatic diagnosis of early MS, and can also facilitate the treatment assessment in patients.