How mental fatigue affects the neural sources of P300 component

Existence of allocentric and egocentric systems for human navigation, mediating spatial, and response learning, respectively, has so far been discussed. It is controversial whether navigational strategies and their underlying learning systems and, accordingly, the activation of their associated brain areas are independent/parallel or whether they functionally/causally interact in a competitive or in a cooperative manner to solve navigational tasks. The insights provided by neural networks involved in reward-based navigation attributed to individual involvement or interactions of learning systems have been surveyed. This paper characterizes the interactions of neural networks by constructing generative neural models and investigating their functional and effective connectivity patterns. A single-subject computer-based virtual reality environment was constructed to simulate a navigation task within a naturalistic large-scale space wherein participants were rewarded for using either a place, response, or mixed strategy at different navigational stages. First, functional analyses were undertaken to evaluate neural activities via mapping brain activation and making statistical inference. Effects of interest, spatial and response learning/retrieval, and their competition and cooperation were investigated. The optimal generative model was then estimated using dynamic casual modeling to quantify effective connectivities within the network. This analysis revealed how experimental conditions supported competition and cooperation strategies and how they modulated the underlying network. Results suggest that when navigational strategies cooperated, there were statistically significant, functional, and effective connectivities between hippocampus and striatum. However, when the strategies competed, effective connections were not established among these regions. Instead, connections between hippocampus/striatum and prefrontal cortex were strengthened. It can be inferred that a type of dynamical reconfiguration occurs within a network responsible for navigation when strategies interact either cooperatively or competitively. This supports adaptive causal organization of the brain when it is engaged with goal directed behavior.

Source Localization and Spectrum Analyzing of EEG in Stuttering State upon Dysfluent Utterances.

Purpose: The present study which addressed adults who stutter (AWS) attempted to investigate power spectral dynamics in the stuttering state by answering the questions using quantitative electroencephalography (qEEG). Method: A 64-channel electroencephalography (EEG) setup was used for data acquisition at 20 AWS. Since the speech, especially stuttering, causes significant noise in the EEG, 2 conditions of speech preparation (SP) and imagined speech (IS) were considered. EEG signals were decomposed into 6 bands. The corresponding sources were localized using the standard low-resolution electromagnetic tomography (sLORETA) tool in both fluent and dysfluent states. Results: Significant differences were noted after analyzing the time-locked EEG signals in fluent and dysfluent utterances. Consistent with previous studies, poor alpha and beta suppression in SP and IS conditions were localized in the left frontotemporal areas in a dysfluent state. This was partly true for the right frontal regions. In the theta range, disfluency was concurrence with increased activation in the left and right motor areas. Increased delta power in the left and right motor areas as well as increased beta2 power over left parietal regions was notable EEG features upon fluent speech. Conclusion: Based on the present findings and those of earlier studies, explaining the neural circuitries involved in stuttering probably requires an examination of the entire frequency spectrum involved in speech.

Analysis of neural sources of p300 event-related potential in normal and schizophrenic participants.

The P300 event-related potential (ERP) is associated with attention and memory operations of the brain. P300 is changed in many cognitive disorders such as dementia, Alzheimer, schizophrenia, and major depression disorder. Therefore, investigation on basis of this component can help to improve our understanding of pathophysiology of such disorders and fundamentals of memory and attention mechanism. In this study, electroencephalography (EEG) signals of 20 schizophrenic patients and 20 age-matched normal subjects are analyzed. The oddball paradigm has been used to record the P300, where two stimuli including target and standard are presented with different probabilities in a random order. Data analysis is carried out using conventional averaging techniques as well as P300 source localization with low-resolution brain electromagnetic tomography (LORETA). The results show that the P300 components stem from a wide cerebral cortex network and defining a small definite cortical zone as its generator is impossible. In normal group, cingulate gyrus, one of the essential components of working memory circuit that was reported by Papez, is found to be the most activated area and it can be in line with the hypothesis that at least a part of the P300 is elicited by working-memory circuit. In schizophrenic group, frontal lobe is the most activated area that was responsible for P300 sources. Our results show that the cingulate gyrus is not activated in comparison with normal group, which is in line with previous results that dysfunction of the anterior cingulate cortex plays a prominent role in the schizophrenia disorder.

Separation of P300 event-related potential using time varying time-lag blind source separation algorithm.

Synchronous averaging over time locked single-trial of event-related potential (ERP) is known as the simplest scheme to extract P300 component. This method assumes the P300 features are invariant through the time while they are affected by factors like brain fatigue and habitation. In this study, a new scheme is proposed termed as time-varying time-lag blind source separation (TT-BSS) which is upon the second order statistics of signal to separate P300 waveform from the background electroencephalogram (EEG) while it captures the time variation of P300 component. The time-lag parameter for all channels is determined by maximizing the correlation (similarity) between two successive trials. As the time-lag parameter is varying by time (trial to trial), an average is taken over the time-lag covariance matrices of all two consecutive trials. TT-BSS finally estimates a transform (separating matrix) by joint diagnolization of the covariance matrix of trials and the averaged covariance matrix of the time varying time-lag. To assess the proposed scheme, synthetic and real EEGs containing P300 are used. The EEG signals were collected from twenty schizophrenic and twenty age-matched normal subjects via 20 channels through the resting state and in presence of the oddball audio stimulus. Empirical achievements over the simulated and real EEGs imply on the superiority of TT-BSS in dynamic estimation of P300 characteristics compared to state-of-the-art counterparts such as constant time-lag BSS, constrained BSS and synchronous averaging.

Usefulness of approximate entropy in the diagnosis of schizophrenia.

OBJECTIVES: Diagnosis of the psychiatric diseases is a bit challenging at the first interview due to this fact that qualitative criteria are not as accurate as quantitative ones. Here, the objective is to classify schizophrenic patients from the healthy subject using a quantitative index elicited from their electroencephalogram (EEG) signals. METHODS: Ten right handed male patients with schizophrenia who had just auditory hallucination and did not have any other psychotic features and ten age-matched right handed normal male control participants participated in this study. The patients used haloperidol to minimize the drug-related affection on their EEG signals. Electrophysiological data were recorded using a Neuroscan 24 Channel Synamps system, with a signal gain equal to 75K (150 xs at the headbox). According to the observable anatomical differences in the brain of schizophrenic patients from controls, several discriminative features including AR coefficients, band power, fractal dimension, and approximation entropy (ApEn) were chosen to extract quantitative values from the EEG signals. RESULTS: The extracted features were applied to support vector machine (SVM) classifier that produced 88.40% accuracy for distinguishing the two groups. Incidentally, ApEn produces more discriminative information compare to the other features. CONCLUSION: This research presents a reliable quantitative approach to distinguish the control subjects from the schizophrenic patients. Moreover, other representative features are implemented but ApEn produces higher performance due to complex and irregular nature of EEG signals.

Entropy and complexity measures for EEG signal classification of schizophrenic and control participants.

OBJECTIVE: In this paper, electroencephalogram (EEG) signals of 20 schizophrenic patients and 20 age-matched control participants are analyzed with the objective of classifying the two groups. MATERIALS AND METHODS: For each case, 20 channels of EEG are recorded. Several features including Shannon entropy, spectral entropy, approximate entropy, Lempel-Ziv complexity and Higuchi fractal dimension are extracted from EEG signals. Leave-one (participant)-out cross-validation is used for reliable estimate of the separability of the two groups. The training set is used for training the two classifiers, namely, linear discriminant analysis (LDA) and adaptive boosting (Adaboost). Each classifier is assessed using the test dataset. RESULTS: A classification accuracy of 86% and 90% is obtained by LDA and Adaboost respectively. For further improvement, genetic programming is employed to select the best features and remove the redundant ones. Applying the two classifiers to the reduced feature set, a classification accuracy of 89% and 91% is obtained by LDA and Adaboost respectively. The proposed technique is compared and contrasted with a recently reported method and it is demonstrated that a considerably enhanced performance is achieved. CONCLUSION: This study shows that EEG signals can be a useful tool for discrimination of the schizophrenic and control participants. It is suggested that this analysis can be a complementary tool to help psychiatrists diagnosing schizophrenic patients.