Cortical electrical activity changes in healthy aging using EEG-eLORETA analysis

Brain aging causes loss of synaptic spines, neuronal apoptosis, and a reduction in neurotransmitter levels. These aging phenomena disturb cortical electrical activity and its synchronization with connected regions. Previous electroencephalography (EEG) studies reported an age-related decrease in electrical activity in the alpha frequency band at occipital, parietal, and temporal areas as well as a decrease in occipital delta activity. However, there is an ongoing debate about whether there is an increase or decrease of the activity in other frequency bands with aging due to inconsistent study findings. In this study, we aimed to detect age-related changes of cortical electrical activities in all five frequency bands (delta, theta, alpha, beta, and gamma) in a large sample of healthy subjects for the first time. Using eLORETA (exact low-resolution brain electromagnetic tomography) analysis, we applied an eLORETA source estimation method to resting-state EEG data in 147 healthy subjects (median age 55, IQR 26.5–67.0) to obtain cortical electrical activity and assessed age-related changes in this activity using correlation analysis with multiple comparison correction. The combination of the eLORETA source estimation method and correlation analysis implemented in eLORETA software detected age-related changes in specific cortical regions for each frequency band: (1) delta and theta cortical electrical activities decreased at the occipital area with age, (2) alpha cortical electrical activity decreased at the occipitoparietotemporal areas with age, (3) beta cortical electrical activity increased at the insula, sensorimotor area, supplementary motor area, premotor area, and right temporal areas with age (most significant correlation at the right insula), (4) gamma cortical electrical activity increased at the frontoparietal and left temporal areas with age. These findings extend previous EEG study findings and provide valuable information related to mechanisms of healthy aging. Overall, our findings revealed that even healthy aging greatly affects cortical electrical activities in a region-specific way

Noninvasive prediction of shunt operation outcome in idiopathic normal pressure hydrocephalus

Idiopathic normal pressure hydrocephalus (iNPH) is a syndrome characterized by gait disturbance, cognitive deterioration and urinary incontinence in elderly individuals. These symptoms can be improved by shunt operation in some but not all patients. Therefore, discovering predictive factors for the surgical outcome is of great clinical importance. We used normalized power variance (NPV) of electroencephalography (EEG) waves, a sensitive measure of the instability of cortical electrical activity, and found significantly higher NPV in beta frequency band at the right fronto-temporo-occipital electrodes (Fp2, T4 and O2) in shunt responders compared to non-responders. By utilizing these differences, we were able to correctly identify responders and non-responders to shunt operation with a positive predictive value of 80% and a negative predictive value of 88%. Our findings indicate that NPV can be useful in noninvasively predicting the clinical outcome of shunt operation in patients with iNPH

Functional localization and effective connectivity of cortical theta and alpha oscillatory activity during an attention task

Objectives: The aim of this paper is to investigate cortical electric neuronal activity as an indicator of brain function, in a mental arithmetic task that requires sustained attention, as compared to the resting state condition. The two questions of interest are the cortical localization of different oscillatory activities, and the directional effective flow of oscillatory activity between regions of interest, in the task condition compared to resting state. In particular, theta and alpha activity are of interest here, due to their important role in attention processing. Methods: We adapted mental arithmetic as an attention ask in this study. Eyes closed 61-channel EEG was recorded in 14 participants during resting and in a mental arithmetic task (“serial sevens subtraction”). Functional localization and connectivity analyses were based on cortical signals of electric neuronal activity estimated with sLORETA (standardized low resolution electromagnetic tomography). Functional localization was based on the comparison of the cortical distributions of the generators of oscillatory activity between task and resting conditions. Assessment of effective connectivity was based on the iCoh (isolated effective coherence) method, which provides an appropriate frequency decomposition of the directional flow of oscillatory activity between brain regions. Nine regions of interest comprising nodes from the dorsal and ventral attention networks were selected for the connectivity analysis. Results: Cortical spectral density distribution comparing task minus rest showed significant activity increase in medial prefrontal areas and decreased activity in left parietal lobe for the theta band, and decreased activity in parietal-occipital regions for the alpha1 band. At a global level, connections among right hemispheric nodes were predominantly decreased during the task condition, while connections among left hemispheric nodes were predominantly increased. At more detailed level, decreased flow from right inferior frontal gyrus to anterior cingulate cortex for theta, and low and high alpha oscillations, and increased feedback (bidirectional flow) between left superior temporal gyrus and left inferior frontal gyrus, were observed during the arithmetic task. Conclusions: Task related medial prefrontal increase in theta oscillations possibly corresponds to frontal midline theta, while parietal decreased alpha1 activity indicates the active role of this region in the numerical task. Task related decrease of intracortical right hemispheric connectivity support the notion that these nodes need to disengage from one another in order to not interfere with the ongoing numerical processing. The bidirectional feedback between left frontal-temporal-parietal regions in the arithmetic task is very likely to be related to attention network working memory function. Significance: The methods of analysis and the results presented here will hopefully contribute to clarify the roles of the different EEG oscillations during sustained attention, both in terms of their functional localization and in terms of how they integrate brain function by supporting information flow between different cortical regions. The methodology presented here might be clinically relevant in evaluating abnormal attention function

Early visual processing alterations in obsessive–compulsive disorder: A marker of visual hypervigilance?

Ishii R.. Early visual processing alterations in obsessive–compulsive disorder: A marker of visual hypervigilance?. Clinical Neurophysiology 151, 128 (2023); https://doi.org/10.1016/j.clinph.2023.04.004

Automated Source Estimation of Scalp EEG Epileptic Activity Using eLORETA Kurtosis Analysis

Objectives: eLORETA (exact low-resolution brain electromagnetic tomography) is a technique created by Pascual-Marqui et al. [Int J Psychophysiol. 1994 Oct; 18(1): 49–65] for the 3-dimensional representation of current source density in the brain by electroencephalography (EEG) data. Kurtosis analysis allows for the identification of spiky activity in the brain. In this study, we focused on the evaluation of the reliability of eLORETA kurtosis analysis. For this purpose, the results of eLORETA kurtosis source localization of paroxysmal activity in EEG were compared with those of eLORETA current source density (CSD) analysis of EEG data in 3 epilepsy patients with partial seizures. Methods: EEG was measured using a digital EEG system with 19 channels. We set the bandpass filter at traditional frequency band settings (1–4, 4–8, 8–15, 15–30, and 30–60 Hz) and 5–10 and 20–70 Hz and performed eLORETA kurtosis to compare the source localization of paroxysmal activity with that of visual interpretation of EEG data and CSD analysis of eLORETA in focal epilepsy patients. Results: The eLORETA kurtosis analysis of EEG data preprocessed by bandpass filtering from 20 to 70 Hz and traditional frequency band settings did not show any discrete paroxysmal source activity compatible with the results of CSD analysis of eLORETA. In all 3 cases, eLORETA kurtosis analysis filtered at 5–10 Hz showed paroxysmal activities in the theta band, which were all consistent with the visual inspection results and the CSD analysis results. Discussion: Our findings suggested that eLORETA kurtosis analysis of EEG data might be useful for the identification of spiky paroxysmal activity sources in epilepsy patients. Since EEG is widely used in the clinical practice of epilepsy, eLORETA kurtosis analysis is a promising method that can be applied to epileptic activity mapping