The Effect of Noise Exposure on Cognitive Performance and Brain Activity Patterns

Background: It seems qualitative measurements of subjective reactions are not appropriate indicators to assess the effect of noise on cognitive performance. In this study, quantitative and combined indicators were applied to study the effect of noise on cognitive performance. Materials and Methods: A total of 54 young subjects were included in this experimental study. The participants’ mental work load and attention were evaluated under different levels of noise exposure including, background noise, 75, 85 and 95 dBA noise levels. The study subject’s EEG signals were recorded for 10 minutes while they were performing the IVA test. The EEG signals were used to estimate the relative power of their brain frequency bands. Results: Results revealed that mental work load and visual/auditory attention is significantly reduced when the participants are exposed to noise at 95 dBA level (P<0.05). Results also showed that with the rise in noise levels, the relative power of the Alpha band increases while the relative power of the Beta band decreases as compared to background noise. The most prominent change in the relative power of the Alpha and Beta bands occurs in the occipital and frontal regions of the brain respectively. Conclusions: The application of new indicators including brain signal analysis and power spectral density analysis is strongly recommended in the assessment of cognitive performance during noise exposure. Further studies are suggested regarding the effects of other psychoacoustic parameters such as tonality, noise pitch (treble or bass) at extended exposure levels

ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal

Emotions play an important role in human cognition, perception, decision making, and interaction. This paper presents a six-layer biologically inspired feedforward neural network to discriminate human emotions from EEG. The neural network comprises a shift register memory after spectral filtering for the input layer, and the estimation of coherence between each pair of input signals for the hidden layer. EEG data are collected from 57 healthy participants from eight locations while subjected to audio-visual stimuli. Discrimination of emotions from EEG is investigated based on valence and arousal levels. The accuracy of the proposed neural network is compared with various feature extraction methods and feedforward learning algorithms. The results showed that the highest accuracy is achieved when using the proposed neural network with a type of radial basis function

The effect of cognitive task difficulty and articulation on postural sway

Introduction: Many studies evaluate the interaction between cognition and posture but the effect of cognitive task difficulty and articulation on postural control remains inconclusive. The purpose of the present study was to examine the interaction effect of cognitive task difficulty and articulation on postural control.Methods: Twenty healthy young volunteers (Mean age 22 ± 2.3 years) performed four random conditions while standing on a force platform. Conditions involved combinations of two level of cognitive task difficulty (easy and difficult cognitivetask) and two level of verbal response (simultaneous and final).Results: Two-way ANOVA (significant level P<0.05) results demonstrated marked increased in the standard deviation and area of 95% confidence ellipse of the center of pressure in the difficult cognitive task condition with simultaneous verbalresponse. (P<0.05). Also, in the final response condition, difficult cognitive task reduced area of 95% confidence ellipse.Conclusion: According to the results of the present research, it seems that the difficult cognitive task requires a greater part of attention capacity; subsequently,attention is withdrawn from the postural task and automatic control regulates posture more efficiently through unconscious, fast, and reflexive processes. Also, postural control is simultaneously affected by the difficulty of cognitive task and articulation.Therefore, for designing appropriate interventions, it seems necessary to pay attention to the interactive effects of the difficulty and articulation of cognitive task

Differential pattern of brain functional connectome in obsessive-compulsive disorder versus healthy controls

Researchers believe that recognition of functional impairment in some of brain networks such as frontal-parietal, default mode network (DMN), anterior medial prefrontal cortex (MPFC) and striatal structures could be a beneficial biomarker for diagnosis of obsessive-compulsive disorder (OCD). Although it is well recognized brain functional connectome in OCD patients shows changes, debate still remains on characteristics of the changes. In this regard, little has been done so far to statistically assess the altered pattern using whole brain electroencephalography. In this study, resting state EEG data of 39 outpatients with OCD and 19 healthy controls (HC) were recorded. After, brain functional network was estimated from the cleaned EEG data using the weighted phase lag index algorithm. Output matrices of OCD group and HCs were then statistically compared to represent meaningful differences. Significant differences in functional connectivity pattern were demonstrated in several regions. As expected the most significant changes were observed in frontal cortex, more significant in frontal-temporal connections (between F3 and F7, and T5 regions). These results in OCD patients are consistent with previous studies and confirm the role of frontal and temporal brain regions in OCD

Alterations in Brain Network Topology and Structural-Functional Connectome Coupling Relate to Cognitive Impairment

According to the network-based neurodegeneration hypothesis, neurodegenerative diseases target specific large-scale neural networks, such as the default mode network, and may propagate along the structural and functional connections within and between these brain networks. Cognitive impairment no dementia (CIND) represents an early prodromal stage but few studies have examined brain topological changes within and between brain structural and functional networks. To this end, we studied the structural networks [diffusion magnetic resonance imaging (MRI)] and functional networks (task-free functional MRI) in CIND (61 mild, 56 moderate) and healthy older adults (97 controls). Structurally, compared with controls, moderate CIND had lower global efficiency, and lower nodal centrality and nodal efficiency in the thalamus, somatomotor network, and higher-order cognitive networks. Mild CIND only had higher nodal degree centrality in dorsal parietal regions. Functional differences were more subtle, with both CIND groups showing lower nodal centrality and efficiency in temporal and somatomotor regions. Importantly, CIND generally had higher structural-functional connectome correlation than controls. The higher structural-functional topological similarity was undesirable as higher correlation was associated with poorer verbal memory, executive function, and visuoconstruction. Our findings highlighted the distinct and progressive changes in brain structural-functional networks at the prodromal stage of neurodegenerative diseases

Stress and Perception of Emotional Stimuli: Long-term Stress Rewiring the Brain

Introduction: Long-term stressful situations can drastically influence one mental life. However, the effect of mental stress on recognition of emotional stimuli needs to be explored. In this study, recognition of emotional stimuli in a stressful situation was investigated. Four emotional conditions, including positive and negative states in both low and high levels of arousal were analyzed. Methods: Twenty-six healthy right-handed university students were recruited within or after examination period. Participants stress conditions were measured using the Perceived Stress Scale-14 (PSS-14). All participants were exposed to some audio-visual emotional stimuli while their brains responses were measured using the Electroencephalography (EEG) technique. During the experiment, the subject perception of emotional stimuli is evaluated using the Self-Assessment Manikin (SAM) questionnaire. After recording, EEG signatures of emotional states were estimated from connectivity patterns among 8 brain regions. Connectivity patterns were calculated using Phase Slope Index (PSI), Directed Transfer Function (DTF), and Generalized Partial Direct Coherence (GPDC) methods. The EEG-based connectivity features were then labeled with SAM responses. Subsequently, the labeled features were categorized using two different classifiers. Classification accuracy of the system was validated by leave-one-out method. Results: As expected, performance of the system is significantly improved by grouping the subjects to stressed and stress-free groups. EEG-based connectivity pattern was influenced by mental stress level. Conclusion: Changes in connectivity patterns related to long-term mental stress have overlapped with changes caused by emotional stimuli. Interestingly, these changes are detectable from EEG data in eyes-closed condition

Modelling of emotions based on EEG signal

Emotions are important not only in human creativity and intelligence but also in human rational thinking, decision making, curiosity and human interaction. These facts have opened new areas for multidisciplinary research in psychology, neuroscience and affective computing. The electroencephalogram (EEG)-based emotion recognition is an aspect of affective computing (AC) with challenging issues regarding the feature extraction from EEG and learning paradigm to achieve a better classification performance. In this thesis, the conscious processing of audio-visual emotional stimuli is investigated using EEG data. The changes in EEG data and patterns of interactions between eight brain regions correlated to emotions are estimated using various feature extraction methods. The subject-independent patterns are selected and then categorized using various machine learning techniques in a supervised manner. Subsequently, a novel biologically plausible emotion recognition neural network (ERNN) is proposed based on the connectivity features. The proposed EEG-based emotion recognition system comprises six layers; including spectral filtering, a shift register memory, two layers for estimation of coherence between each pair of input signals and a two-layer of radial basis function (RBF) type learning algorithm.Doctor of Philosophy (SCE

Behavioral, Cognitive and Neural Markers of Asperger Syndrome

Asperger syndrome (AS) is a subtype of Autism Spectrum Disorder (ASD) characterized by major problems in social and nonverbal communication, together with limited and repetitive forms of behavior and interests. The linguistic and cognitive development in AS is preserved which help us to differentiate it from other subtypes of ASD. However, significant effects of AS on cognitive abilities and brain functions still need to be researched. Although a clear cut pathology for Asperger has not been identified yet, recent studies have largely focused on brain imaging techniques to investigate AS. In this regard, we carried out a systematic review on behavioral, cognitive, and neural markers (specifically using MRI and fMRI) studies on AS. In this paper, behavior, motor skills and language capabilities of individuals with Asperger are compared to those in healthy controls. In addition, common findings across MRI and fMRI based studies associated with behavior and cognitive disabilities are highlighted.&nbsp