- Abstracts of Presentations at the Joint Meeting of the EEG & Clinical Neuroscience Society (ECNS), the International Society for Brain Electromagnetic Topography (ISBET), and the International Society for Neuroimaging in Psychiatry (ISNIP) in Halifax, Nova Scotia, Canada, September 3-7, 2014. [Journal Article]
- CEClin EEG Neurosci 2016; 47(4):NP1-NP15
- Local and Widely Distributed EEG Activity in Schizophrenia With Prevalence of Negative Symptoms. [Journal Article]
- CEClin EEG Neurosci 2017 Jan 01; :1550059416683283
- We evaluated EEG frequency abnormalities in resting state (eyes closed and eyes open) EEG in a group of chronic schizophrenia patients as compared with healthy subjects. The study included 3 methods ...
We evaluated EEG frequency abnormalities in resting state (eyes closed and eyes open) EEG in a group of chronic schizophrenia patients as compared with healthy subjects. The study included 3 methods of analysis of deviation of EEG characteristics: genuine EEG, current source density (CSD), and group independent component (gIC). All 3 methods have shown that the EEG in schizophrenia patients is characterized by enhanced low-frequency (delta and theta) and high-frequency (beta) activity in comparison with the control group. However, the spatial pattern of differences was dependent on the type of method used. Comparative analysis has shown that increased EEG power in schizophrenia patients apparently concerns both widely spatially distributed components and local components of signal. Furthermore, the observed differences in the delta and theta range can be described mainly by the local components, and those in the beta range mostly by spatially widely distributed ones. The possible nature of the widely distributed activity is discussed.
- Application and Evaluation of Independent Component Analysis Methods to Generalized Seizure Disorder Activities Exhibited in the Brain. [Journal Article]
- CEClin EEG Neurosci 2016 Nov 11
- EEG records the spontaneous electrical activity of the brain using multiple electrodes placed on the scalp, and it provides a wealth of information related to the functions of brain. Nevertheless, th...
EEG records the spontaneous electrical activity of the brain using multiple electrodes placed on the scalp, and it provides a wealth of information related to the functions of brain. Nevertheless, the signals from the electrodes cannot be directly applied to a diagnostic tool like brain mapping as they undergo a "mixing" process because of the volume conduction effect in the scalp. A pervasive problem in neuroscience is determining which regions of the brain are active, given voltage measurements at the scalp. Because of which, there has been a surge of interest among the biosignal processing community to investigate the process of mixing and unmixing to identify the underlying active sources. According to the assumptions of independent component analysis (ICA) algorithms, the resultant mixture obtained from the scalp can be closely approximated by a linear combination of the "actual" EEG signals emanating from the underlying sources of electrical activity in the brain. As a consequence, using these well-known ICA techniques in preprocessing of the EEG signals prior to clinical applications could result in development of diagnostic tool like quantitative EEG which in turn can assist the neurologists to gain noninvasive access to patient-specific cortical activity, which helps in treating neuropathologies like seizure disorders. The popular and proven ICA schemes mentioned in various literature and applications were selected (which includes Infomax, JADE, and SOBI) and applied on generalized seizure disorder samples using EEGLAB toolbox in MATLAB environment to see their usefulness in source separations; and they were validated by the expert neurologist for clinical relevance in terms of pathologies on brain functionalities. The performance of Infomax method was found to be superior when compared with other ICA schemes applied on EEG and it has been established based on the validations carried by expert neurologist for generalized seizure and its clinical correlation. The results are encouraging for furthering the studies in the direction of developing useful brain mapping tools using ICA methods.
- Toward an Attention-Based Diagnostic Tool for Patients With Locked-in Syndrome. [Journal Article]
- CEClin EEG Neurosci 2016 Nov 07
- Electroencephalography (EEG) has been proposed as a supplemental tool for reducing clinical misdiagnosis in severely brain-injured populations helping to distinguish conscious from unconscious patien...
Electroencephalography (EEG) has been proposed as a supplemental tool for reducing clinical misdiagnosis in severely brain-injured populations helping to distinguish conscious from unconscious patients. We studied the use of spectral entropy as a measure of focal attention in order to develop a motor-independent, portable, and objective diagnostic tool for patients with locked-in syndrome (LIS), answering the issues of accuracy and training requirement. Data from 20 healthy volunteers, 6 LIS patients, and 10 patients with a vegetative state/unresponsive wakefulness syndrome (VS/UWS) were included. Spectral entropy was computed during a gaze-independent 2-class (attention vs rest) paradigm, and compared with EEG rhythms (delta, theta, alpha, and beta) classification. Spectral entropy classification during the attention-rest paradigm showed 93% and 91% accuracy in healthy volunteers and LIS patients respectively. VS/UWS patients were at chance level. EEG rhythms classification reached a lower accuracy than spectral entropy. Resting-state EEG spectral entropy could not distinguish individual VS/UWS patients from LIS patients. The present study provides evidence that an EEG-based measure of attention could detect command-following in patients with severe motor disabilities. The entropy system could detect a response to command in all healthy subjects and LIS patients, while none of the VS/UWS patients showed a response to command using this system.
- Serial EEG Monitoring in a Patient With Anti-NMDA Receptor Encephalitis. [Journal Article]
- CEClin EEG Neurosci 2016 Nov 07
- Electroencephalogram (EEG) abnormalities are very common in anti-N-methyl-d-aspartate receptor (anti-NMDAR) encephalitis. Extreme delta brush (EDB) is a distinctive EEG pattern that is can be suggest...
Electroencephalogram (EEG) abnormalities are very common in anti-N-methyl-d-aspartate receptor (anti-NMDAR) encephalitis. Extreme delta brush (EDB) is a distinctive EEG pattern that is can be suggestive of the diagnosis; however, the etiology of the EDB remains unclear. Furthermore, there is question with regard to its ictal or interictal nature. We report a 20-year-old woman with anti-NMDAR encephalitis whose serial video-EEG monitoring was obtained at 2, 2.5, 4, and 6 months after admission. There was a long-standing EDB lasting up to several hours, with no evolution in frequency, amplitude, or morphology, and without clear association her frequent orofacial dyskinesia. Intravenous benzodiazepine administrations did not change the EDB pattern. As her clinical symptoms improved, the EDB gradually became less prominent and less frequent, with complete resolution at 6 months after admission. These findings suggest that EDB is more likely a marker of the severity of the disease in contrast to an epileptic seizure and is useful for diagnosis and monitoring of treatment response in conjunction with clinical improvement.
- Frederic Andrews Gibbs, M.D., EEG Pioneer. [Journal Article]
- CEClin EEG Neurosci 2016; 47(4):255-259
- Beta Responses in Healthy Elderly and in Patients With Amnestic Mild Cognitive Impairment During a Task of Temporal Orientation of Attention. [Journal Article]
- CEClin EEG Neurosci 2016 Nov 02
- Recent studies demonstrated that beta oscillations are elicited during cognitive processes. To investigate their potential as electrophysiological markers of amnestic mild cognitive impairment (aMCI)...
Recent studies demonstrated that beta oscillations are elicited during cognitive processes. To investigate their potential as electrophysiological markers of amnestic mild cognitive impairment (aMCI), we recorded beta EEG activity during resting and during an omitted tone task in patients and healthy elderly. Thirty participants were enrolled (15 patients, 15 healthy controls). In particular, we investigated event-related spectral perturbation and intertrial coherence indices. Analyses showed that (a) healthy elderly presented greater beta power at rest than patients with aMCI patients; (b) during the task, healthy elderly were more accurate than aMCI patients and presented greater beta power than aMCI patients; (c) both groups showed qualitatively similar spectral perturbation responses during the task, but different spatiotemporal response patterns; and (d) aMCI patients presented greater beta phase locking than healthy elderly during the task. Results indicate that beta activity in healthy elderly differs from that of patients with aMCI. Furthermore, the analysis of task-related EEG activity extends evidences obtained during resting and suggests that during the prodromal phase of Alzheimer's disease there is a reduced efficiency in information exchange by large-scale neural networks. The study for the first time shows the potential of task-related beta responses as early markers of aMCI impairments.
- Post-Encephalitic Parkinsonism and Sleep Disorder Responsive to Immunological Treatment: A Case Report. [Journal Article]
- CEClin EEG Neurosci 2016; 47(4):324-329
- We describe a 70-year-old man who, after a viral encephalitis associated with pneumonia, progressively developed a parkinsonism associated with lethargy. Encephalitis manifested with persistent hiccu...
We describe a 70-year-old man who, after a viral encephalitis associated with pneumonia, progressively developed a parkinsonism associated with lethargy. Encephalitis manifested with persistent hiccups, seizures and impairment of consciousness. After 2 weeks, the initial neurologic symptoms subsided and the patient progressively developed movement disorders (rigidity and bradykinesia, resistant to L-DOPA), lethargy and behavioral hypersomnia. Magnetic resonance imaging showed thalamic and hippocampal signal abnormalities, immunohistochemistry on a mouse brain substrate revealed serum autoantibodies binding to the brainstem neuropil. Polysomnographic monitoring was consistent with a very severe disruption of sleep: the sleep-wake cycle was fragmented, and the NREM-REM ultradian cycle was irregular. Intravenous immune globulin therapy resulted in the complete reversal of the movement and the sleep disorders. Our observation confirms that parkinsonism and sleep disorders may be consequences of encephalitis, that an immune-mediated pathogenesis is likely, and, consequently, that immunotherapy can be beneficial in these patients. The polysomnographic monitoring suggests that lethargia, rather than a mere hypersomnia, is the result of a combination between sleep disruption and altered motor control.
- Low-Complexity Discriminative Feature Selection From EEG Before and After Short-Term Memory Task. [Journal Article]
- CEClin EEG Neurosci 2016; 47(4):291-297
- A reliable and unobtrusive quantification of changes in cortical activity during short-term memory task can be used to evaluate the efficacy of interfaces and to provide real-time user-state informat...
A reliable and unobtrusive quantification of changes in cortical activity during short-term memory task can be used to evaluate the efficacy of interfaces and to provide real-time user-state information. In this article, we investigate changes in electroencephalogram signals in short-term memory with respect to the baseline activity. The electroencephalogram signals have been analyzed using 9 linear and nonlinear/dynamic measures. We applied statistical Wilcoxon examination and Davis-Bouldian criterion to select optimal discriminative features. The results show that among the features, the permutation entropy significantly increased in frontal lobe and the occipital second lower alpha band activity decreased during memory task. These 2 features reflect the same mental task; however, their correlation with memory task varies in different intervals. In conclusion, it is suggested that the combination of the 2 features would improve the performance of memory based neurofeedback systems.
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- Brain Network Connectivity and Topological Analysis During Voluntary Arm Movements. [Journal Article]
- CEClin EEG Neurosci 2016; 47(4):276-290
- Functional connectivity estimates the temporal synchrony among functionally homogeneous brain regions based on the assessment of the dynamics of topologically localized neurophysiological responses. ...
Functional connectivity estimates the temporal synchrony among functionally homogeneous brain regions based on the assessment of the dynamics of topologically localized neurophysiological responses. The aim of this study was to investigate task-related changes in brain activity and functional connectivity by applying different methods namely event-related desynchronization (ERD), coherence, and graph-theoretical analysis to electroencephalographic (EEG) recordings, for comparing their respective descriptive power and complementarity. As it is well known, ERD provides an estimate of differences in power spectral densities between active (or task) and rest conditions, functional connectivity allows assessing the level of synchronization between the signals recorded at different scalp locations and graph analysis enables the estimation of the functional network features and topology. EEG activity was recorded on 10 subjects during left/right arm movements. The theta, alpha, and beta bands were considered. Conventional analysis showed a significant ERD in both alpha and beta bands over the sensorimotor cortex during the left arm movement and in beta band during the right arm movement, besides identifying the regions involved in the task, as it was expected. On the other hand, connectivity assessment highlighted that stronger connections are those that involved the motor regions for which graph analysis revealed reduced accessibility and an increased centrality during the movement. Jointly, the last two methods allow identifying the cortical areas that are functionally related in the active condition as well as the topological organization of the functional network. Results support the hypothesis that network analysis brings complementary knowledge with respect to established approaches for modeling motor-induced functional connectivity and could be profitably exploited in clinical contexts.