Spectral specific standardized low-resolution brain electromagnetic tomography default mode connectivity network and plasticity alterations in left vertebral artery stent patients

Objective: Vertebral stenting is a valid treatment for posterior stroke. However, the outcome and prognostic factors in the Asian population of vertebral stent are not clear. This study was performed to investigate the quick effect and plasticity alterations of left vertebral artery stenting treatment and to explore the underlying electroencephalogram biomarkers for the prognosis. Method: Electroencephalogram default mode network activity, serum brain-derived neurotrophic factor, and basic neuropsychology estimations were obtained from nine male left vertebral artery stenosis patients who underwent left vertebral artery stenting 24 h before and after left vertebral artery stenting therapy. Result: The beta-1 (13–24 Hz) electroencephalogram field power of the pre–left vertebral artery stenting group was significantly higher compared to that of the post–left vertebral artery stenting and control group ( p < 0.05). The significant different standardized low-resolution brain electromagnetic tomography brain areas of beta-1 band Brodmann areas are 17R, 17L, 18R, 18L, 22R, 22L, 37R, and 37L. The nonliner lag ratio–based functional connectivity analysis showed global increase of connectivity in beta-1 standardized low resolution brain electromagnetic tomography network. Serum brain-derived neurotrophic factor did not show statistically significant changes during groups. Conclusion: Electroencephalogram default mode network provided a functional aspect of left vertebral artery stenting patients, and the beta-1 band power and distribution alterations could be candidate measurements for the plasticity alterations and prognosis evaluation.

Spectrum-specific encephalography standardized low-resolution brain electromagnetic tomography network and gray matter correlations in vascular dementia patients

Vascular dementia, secondary to Alzheimer’s dementia, ranks as one of the most frequent dementia types. The process of vascular dementia is divergent with other neurodegenerative dementias and thus reversible at the early cognitive disorder or mild dementia stages. The encephalography and neuroimaging data mining at different stages would bring neuromodulation strategies in practice; 15 mild cognitive impairment patients and 16 mild vascular dementia patients as well as 17 cognitive healthy controls were screened in this study. Cognitive tests such as Mini-Mental State Examination, Montreal cognitive assessment, voxel-based morphometry, electroencephalography, and standardized low-resolution brain electromagnetic tomography connectivity network were conducted. Compared with healthy group, voxel-based morphometry analysis showed a decrease in gray/cerebrospinal fluid ratio ( p < .05) in mild dementia group; the energy power of gamma band decreased ( p < .05) in mild dementia group; and electroencephalography standardized low-resolution brain electromagnetic tomography analysis showed wider frontal and temporal lobe involvement in mild dementia patients ( p < .05). Network topological analysis screened top 10 key Brodmann areas (44R, 7R, 8L, 22L, 47L, 27L, 1L, 1R, 7R, 43L), which could be underlying neuromodulators for dementia patients. Electroencephalography as well as structural magnetic resonance imaging could be used for the evaluation of cognitive disorder patients. The spectrum-specific standardized low-resolution brain electromagnetic tomography analysis and connectivity network analysis could shed light on the neuromodulator targets in the early phase of dementia.

Electroencephalography-Based Source Localization for Depression Using Standardized Low Resolution Brain Electromagnetic Tomography – Variational Mode Decomposition Technique

Background: Electroencephalography (EEG) may be used as an objective diagnosis tool for diagnosing various disorders. Recently, source localization from EEG is being used in the analysis of real-time brain monitoring applications. However, inverse problem reduces the accuracy in EEG signal processing systems. Objectives: This paper presents a new method of EEG source localization using variational mode decomposition (VMD) and standardized the low resolution brain electromagnetic tomography (sLORETA) inverse model. The focus is to compare the effectiveness of the proposed approach for EEG signals of depression patients. Method: As the first stage, real EEG recordings corresponding to depression patients are decomposed into various mode functions by applying VMD. Then, closely related functions are analyzed using the inverse modelling-based source localization procedures such as sLORETA. Simulations have been carried out on real EEG databases for depression to demonstrate the effectiveness of the proposed techniques. Results: The performance of the algorithm has been assessed using localization error (LE), mean square error and signal to noise ratio output corresponding to simulated EEG dipole sources and real EEG signals for depression. In order to study the spatial resolution for cortical potential distribution, the main focus has been on studying the effects of noise sources and estimating LE of inverse solutions. More accurate and robust localization results show that this methodology is very promising for EEG source localization of depression signals. Conclusion: It can be said that proposed algorithm efficiently suppresses the influence of noise in the EEG inverse problem using simulated EEG activity and EEG database for depression. Such a system may offer an effective solution for clinicians as a crucial stage of EEG pre-processing in automated depression detection systems and may prevent delay in diagnosis.