Low-resolution Brain Electromagnetic Tomography Research Articles

P.050 Spike source localizations between the three non-REM sleep stages: resemblances to wakefulness and distinctions from REM sleep

Background: Sleep-wake states (SWS) affect the expression of interictal epileptiform discharges (“spikes”), which affects resultant source localization calculations used in epilepsy evaluation. We hypothesize that spike localizations from non-REM sleep 1-3 are most concordant with one another. Methods: We used Standardized low-resolution brain electromagnetic tomography (sLORETA) in Curry 8 software to calculate source localization voxels of spikes in N1-3, REM, or wakefulness (W). We assessed voxel concordance between N1-N2-N3/N1-N2-W/N1-N3-W/N2-N3-W/REM-N1-N2/REM-N1-N3/REM-N2-N3/REM-N1-W/REM-N2-W/REM-N3-W. We classified concordances into those containing and not containing a SWS (e.g. N1 vs. not-N1 = N1-N2-N3/N1-N2-W/N1-N3-W/REM-N1-N2/REM-N1-N3/REM-N1-W vs. REM-N2-W/REM-N3-W/REM-N2-N3/N2-N3-W) for comparison. Results: Concordances did not differ for N1-3 or W. However, concordances with REM were lower than those without REM as a fraction of source localization space (median 32.1% vs. 56.1%, p<0.001) and cortical grey matter (median 20.4% vs. 27.3%, p=0.003). Conclusions: As expected, source localizations from spikes in N1, N2, and N3 did not significantly differ from one another because these three states are constituent members of non-REM sleep. Surprisingly, however, source localizations derived from awake spikes – not a constituent of non-REM sleep – also did not differ. In contrast, REM was most different by reproducibly exhibiting the least three-way concordance. These findings reinforce the unique localizing ability of REM sleep.

Effects of transcranial direct current stimulation on brain activity and cortical functional connectivity in children with autism spectrum disorders

IntroductionTranscranial direct current stimulation (tDCS) has emerged as a therapeutic option to mitigate symptoms in individuals with autism spectrum disorder (ASD). Our study investigated the effects of a two-week regimen of tDCS targeting the left dorsolateral prefrontal cortex (DLPFC) in children with ASD, examining changes in rhythmic brain activity and alterations in functional connectivity within key neural networks: the default mode network (DMN), sensorimotor network (SMN), and dorsal attention network (DAN).MethodsWe enrolled twenty-six children with ASD and assigned them randomly to either an active stimulation group (n=13) or a sham stimulation group (n=13). The active group received tDCS at an intensity of 1mA to the left DLPFC for a combined duration of 10 days. Differences in electrical brain activity were pinpointed using standardized low-resolution brain electromagnetic tomography (sLORETA), while functional connectivity was assessed via lagged phase synchronization.ResultsCompared to the typically developing children, children with ASD exhibited lower current source density across all frequency bands. Post-treatment, the active stimulation group demonstrated a significant increase in both current source density and resting state network connectivity. Such changes were not observed in the sham stimulation group.ConclusiontDCS targeting the DLPFC may bolster brain functional connectivity in patients with ASD, offering a substantive groundwork for potential clinical applications.

The “Tipping Point” : When Electroencephalography (EEG), Quantitative EEG (QEEG) and Standardized Low Resolution Brain Electromagnetic Tomography (sLORETA) in COVID Went From “Ceasure” To “Non- Priority” To “First-Line” Tool in Triage, Diagnosis, Monitoring and Therapy

On the threshold of the COVID outbreak; electroencephalography (EEG) was used in diagnosis, crossborder disease differential diagnosis, disease-staging, monitoring of treatment, sedation and coma, in neuro-therapy and in declaration of brain death. EEG, quantitative EEG (QEEG), and standardized low resolution brain electromagnetic tomography (sLORETA) use entered the doldrums; reaching near “ceasure” due to COVID restrictions. Between 2020-2023, EEG use tipped, going from “Ceasure” to “First-Line” tool in triage, diagnosis, monitoring and therapy due to neurological, neurocognitive, neuropsychiatric, and neuromuscular sequelae of para- or acute- and post-COVID-19. The present paper will discuss this “Tipping point” in EEG, QEEG and sLORETA use.

Effectiveness of Low-Resolution Brain Electromagnetic Tomography Z Score Neurofeedback Comparison with Cognitive Rehabilitation in Depression and Anxiety in Opioid Use Disorder.

Background: Previous studies have shown that conventional neurofeedback (NFB) and cognitive rehabilitation can improve psychological outcomes in people with opioid use disorders (OUDs). However, the effectiveness of Low-Resolution Brain Electromagnetic Tomography (LORETA) Z-score neurofeedback (LZNFB) and attention bias modification training (ABMT) on depression and anxiety of these people has not been investigated yet. The present study aims to compare the effect of these two methods on depression and anxiety of men with OUD under methadone maintenance therapy (MMT). Methods: In this randomized controlled clinical trial with a pre-test, post-test, and follow-up design, 30 men with OUD under MMT were randomly assigned into three groups of LZNFB, ABMT, and control (MMT alone). The LZNFB group underwent LZNFB at 20 sessions. The ABMT using the dot-probe task was provided individually to the second group for 2 weeks at 15 sessions. The Beck Anxiety Inventory and the Beck Depression Inventory were completed by the participants before, immediately after, and 1-month after interventions. The collected data were analyzed in SPSS v.22 software. Results: Both intervention groups showed a significant reduction in anxiety and depression at the post-test phase (p < 0.05), where LZNFB group showed more decrease in anxiety and depression than the ABMT group. This decrease continued in the follow-up period. Conclusion: Both LZNFB and ABMT with the dot-robe task are effective in reducing depression and anxiety of men with OUD under MMT. However, LZNFB is more effective. These findings add to the growing body of literature supporting the effectiveness of NFB and cognitive rehabilitation therapy in treating addiction-related comorbidities.

Semiautomated electric source imaging determines epileptogenicity of encephaloceles in temporal lobe epilepsy.

We aimed to assess the ability of semiautomated electric source imaging (ESI) from long-term video-electroencephalographic (EEG) monitoring (LTM) to determine the epileptogenicity of temporopolar encephaloceles (TEs) in patients with temporal lobe epilepsy. We conducted a retrospective study involving 32 temporal lobe epilepsy patients with TEs as potentially epileptogenic lesions in structural magnetic resonance imaging scans. Findings were validated through invasive intracerebral stereo-EEG in six of 32 patients and postsurgical outcome after tailored resection of the TE in 17 of 32 patients. LTM (mean duration = 6 days) was performed using the 10/20 system with additional T1/T2 for all patients and sphenoidal electrodes in 23 of 32 patients. Semiautomated detection and clustering of interictal epileptiform discharges (IEDs) were carried out to create IED types. ESI was performed on the averages of the two most frequent IED types per patient, utilizing individual head models, and two independent inverse methods (sLORETA [standardized low-resolution brain electromagnetic tomography], MUSIC [multiple signal classification]). ESI maxima concordance and propagation in spatial relation to TEs were quantified for sources with good signal quality (signal-to-noise ratio > 2, explained signal > 60%). ESI maxima correctly colocalized with a TE in 20 of 32 patients (62.5%) either at the onset or half-rising flank of at least one IED type per patient. ESI maxima showed propagation from the temporal pole to other temporal or extratemporal regions in 14 of 32 patients (44%), confirming propagation originating in the area of the TE. The findings from both inverse methods validated each other in 14 of 20 patients (70%), and sphenoidal electrodes exhibited the highest signal amplitudes in 17 of 23 patients (74%). The concordance of ESI with the TE predicted a seizure-free postsurgical outcome (Engel I vs. >I) with a diagnostic odds ratio of 2.1. Semiautomated ESI from LTM often successfully identifies the epileptogenicity of TEs and the IED onset zone within the area of the TEs. Additionally, it shows potential predictive power for postsurgical outcomes in these patients.

Sensory Gating Networks in Normal-Hearing Adults With Minimal Tinnitus.

The goal of this study was to observe sensory gating-related networks underlying cortical auditory evoked potential (CAEP) peak components in individuals with and without minimal tinnitus, as measured using the Tinnitus Handicap Inventory (THI). This analysis was performed on previously published sensory gating responses in normal-hearing adults with and without minimal tinnitus. Independent component analysis was performed for each individual CAEP gating component (Pa, P50, N1, and P2). Significant components were retained for source localization analyses within the following groups: no tinnitus, tinnitus with a THI score ≤ 6, and tinnitus with a THI score > 6. Brain source localization was performed on the gating difference wave for each component using standardized low-resolution brain electromagnetic tomography. Gating-related networks were identified within each group. Different regional sources were observed between groups, with parietal sources underlying the Pa and P50 components as tinnitus severity increased. A larger prefrontal regional activation was also shown for the N1 gating component as tinnitus severity increased. These results expand upon the functional gating responses via CAEP waveforms in a previously published study. The auditory gating response, as measured via CAEPs, has previously been shown to significantly correlate with an increase in tinnitus severity in adults with normal hearing. The corresponding changes in the gating response appear to be supported by different cortical regions in those without tinnitus, those with a THI score ≤ 6, and those with a THI score > 6. Next, functional differences between localized cortical regions should be tested.

Differential effects of specific emotions on spatial decision-making: evidence from cross-frequency functionally independent brain networks.

Emotions significantly shape the way humans make decisions. However, the underlying neural mechanisms of this influence remain elusive. In this study, we designed an experiment to investigate how emotions (specifically happiness, fear, and sadness) impact spatial decision-making, utilizing EEG data. To address the inherent limitations of sensor-level investigations previously conducted, we employed standard low-resolution brain electromagnetic tomography and functional independent component analysis to analyze the EEG data at the cortical source level. Our findings showed that across various spectral-spatial networks, positive emotion activated the decision-making network in the left middle temporal gyrus and inferior temporal gyrus, in contrast to negative emotions. We also identified the common spectral-spatial networks and observed significant differences in network strength across emotions. These insights further revealed the important role of the gamma-band prefrontal network. Our research provides a basis for deciphering the roles of brain networks in the impact of emotions on decision-making.

Spectral decomposition of resting state electroencephalogram reveals unique theta/alpha activity in schizophrenia.

Resting state electroencephalographic (EEG) activity in schizophrenia (SZ) is frequently characterised by increased power at slow frequencies and/or a reduction of peak alpha frequency. Here we investigated the nature of these effects. As most studies to date have been limited by reliance on a priori frequency bands which impose an assumed structure on the data, we performed a data-driven analysis of resting EEG recorded in SZ patients and healthy controls (HC). The sample consisted of 39 chronic SZ and 36 matched HC. The EEG was recorded with a dense electrode array. Power spectral densities were decomposed via Varimax-rotated principal component analysis (PCA) over all participants and for each group separately. Spectral PCA was repeated at the cortical level on cortical current source density computed from standardised low resolution brain electromagnetic tomography. There was a trend for power in the theta/alpha range to be increased in SZ compared to HC, and peak alpha frequency was significantly reduced in SZ. PCA revealed that this frequency shift was because of the presence of a spectral component in the theta/alpha range (6-9 Hz) that was unique to SZ. The source distribution of the SZ > HC theta/alpha effect involved mainly prefrontal and parahippocampal areas. Abnormal low frequency resting EEG activity in SZ was accounted for by a unique theta/alpha oscillation. Other reports have described a similar phenomenon suggesting that the neural circuits oscillating in this range are relevant to SZ pathophysiology.

Quantifying active brain areas at spatial hearing process using Electroencephalography (EEG) source localization approach

The human ability for spatial hearing encourages the development of spatial audio technology to support many human activities. Spatial audio mimics the real-life sound and provides a more immersive hearing experience as if the listener were present in the environment where the sound source was recorded. Recently, research on how spatial hearing processes are encoded in the brain has begun to be developed. As a modality for brain activity measurement that is non-invasive and has a high temporal resolution, Electroencephalography (EEG) is suitable for studying brain responses to moving sound stimuli. This study compared brain activity in hearing stereo and spatial audio stimulus. Through measurements using 21 EEG electrodes on the scalp, the results showed an increase in the mean PSD for all Theta, Alpha, and Beta waves in the spatial audio stimulus compared to stereo audio. The most significant increase in the mean PSD occurred in the Beta wave of 93.8008 µV2/Hz. Furthermore, the source localization approach with Independent Component Analysis (ICA) and Low-resolution Brain Electromagnetic Tomography (LORETA) method was used to estimate and quantify the active brain area of this process. The results showed an activation of the non-auditory cortex when the subject was hearing spatial audio stimulus. By using Paired T-Test of current density for both hearing processes, the results showed there was no significant difference (p &gt; 0.05) in Brodmann area (BA) 41 (Primary Auditory Cortex) and BA 42 (Secondary Auditory Cortex). Meanwhile, for the spatial audio hearing process, there was a significant difference (p &lt; 0.05) in BA 6 (Premotor Cortex), which is related to spatial orientation, and BA 9 (Dorsolateral Prefrontal Cortex), which is associated with executive functions, including working memory and selective attention. This study offers potential insights into spatial hearing research and immersive audio production.

THETA CURRENT SOURCES DENSITY CHANGES IN CARDIAC SURGERY PATIENTS COGNITIVE AFTER MULTITASKING TRAINING

HighlightsWe estimated theta current sources density changes in patients who received cognitive multitasking training in the early postoperative period of cardiac surgery.Patients with cognitive multitasking training had a significantly lower theta current source density (t&lt;–3.89; p&lt;0.002) before surgery compared to baseline. The strongest differences were localized in Brodmann areas 7, 19 and 31 which related to the structures of the parieto-occipital lobes of the brain, cuneus and precuneus. AbstractAim. The effect of cognitive recovery techniques on brain activity remains a significant issue in modern health care. The aim of this study was to estimate theta current sources density changes using standardized low resolution electromagnetic tomography – sLORETA – (standardized low resolution brain electromagnetic tomography) in patients who have received cognitive multitasking training (CMT) in the postoperative period of coronary artery bypass grafting (CABG).Methods. The study included 30 patients aged between 45 and 75 years who underwent routine CABG. Clinical and neurophysiological examinations were carried out 2–3 days before surgery and 11–12 days after CABG. The cognitive training started 3–4 days after CABG and lasted until discharge and consisted of a motor task - a keypress in response to an object appearing on the screen and three consecutive cognitive tasks (counting backwards, verbal fluency and unusual use of an ordinary object).Results. Patients with CMT had lower theta current source density (t&lt;–3.89; p&lt;0.002) before CABG as compared to postoperative data, the most significant differences were noted in parieto-occipital lobes of the brain, cuneus and precuneus (Brodmann areas 7, 19 and 31).Conclusion. The results of our research contribute to a better understanding of the pathogenetic mechanisms underlying postoperative cognitive impairments in cardiac surgery patients.

Neural Process of Emotional Valence Quality in Extraversion: Localisation, Components and Specific Electrodes Analyses

The process of emotion is greatly influenced by people’s personalities. This study aimed to determine the interaction between extraversion continuum (extravert, ambivert, and introvert) and the process of emotion from different valence quality of visual stimuli (high, moderate, low). Potential participants had their extraversion trait screened using the Extraversion-Five-Factor Nonverbal Personality Questionnaire (E-FF-NPQ). Ninety participants (30 for each group of personality) enrolled in this study. In the Event-Related Potential (ERP) session, participants’ emotion was evoked by a series of pictures (visual stimuli) from the International Affective Picture System (IAPS) with different valence qualities (high, moderate, low). No significant interaction between extraversion continuum and valence quality was observed at component level of analysis. However, several specific electrodes revealed significant interaction – P3 latency (p=0.03) and F8 amplitude (p=0,02) of N200 and Cz latency of P300 (p=0.02). Low-resolution brain electromagnetic tomography (sLORETA) revealed almost similar pattern of N200 and P300 localisation in the high valence state, particularly among extraverts. At the very least, a minor difference between extravert and non-extravert could be expected with careful consideration of cultural and methodological bias.

Solution of the EEG inverse problem by random dipole sampling

Electroencephalography (EEG) source imaging aims to reconstruct brain activity maps from the neuroelectric potential difference measured on the skull. To obtain the brain activity map, we need to solve an ill-posed and ill-conditioned inverse problem that requires regularization techniques to make the solution viable. When dealing with real-time applications, dimensionality reduction techniques can be used to reduce the computational load required to evaluate the numerical solution of the EEG inverse problem. To this end, in this paper we use the random dipole sampling method, in which a Monte Carlo technique is used to reduce the number of neural sources. This is equivalent to reducing the number of the unknowns in the inverse problem and can be seen as a first regularization step. Then, we solve the reduced EEG inverse problem with two popular inversion methods, the weighted Minimum Norm Estimate (wMNE) and the standardized LOw Resolution brain Electromagnetic TomogrAphy (sLORETA). The main result of this paper is the error estimates of the reconstructed activity map obtained with the randomized version of wMNE and sLORETA. Numerical experiments on synthetic EEG data demonstrate the effectiveness of the random dipole sampling method.

Standardized hierarchical adaptive Lp regression for noise robust focal epilepsy source reconstructions

ObjectiveTo investigate the ability of standardization to reduce source localization errors and measurement noise uncertainties for hierarchical Bayesian algorithms with L1- and L2-norms as priors in electroencephalography and magnetoencephalography of focal epilepsy. MethodsDescription of the standardization methodology relying on the Hierarchical Bayesian framework, referred to as the Standardized Hierarchical Adaptive Lp-norm Regularization (SHALpR).The performance was tested using real data from two focal epilepsy patients. Simulated data that resembled the available real data was constructed for further localization and noise robustness investigation. ResultsThe proposed algorithms were compared to their non-standardized counterparts, Standardized low-resolution brain electromagnetic tomography, Standardized Shrinking LORETA-FOCUSS, and Dynamic statistical parametric maps. Based on the simulations, the standardized Hierarchical adaptive algorithm using L2-norm was noise robust for 10 dB signal-to-noise ratio (SNR), whereas the L1-norm prior worked robustly also with 5 dB SNR. The accuracy of the standardized L1-normed methodology to localize focal activity was under 1 cm for both patients. ConclusionsNumerical results of the proposed methodology display improved localization and noise robustness. The proposed methodology also outperformed the compared methods when dealing with real data. SignificanceThe proposed standardized methodology, especially when employing the L1-norm, could serve as a valuable assessment tool in surgical decision-making.

Artificial Intelligence Model‐based Alzheimer’s Disease Dementia Classification Using Electroencephalogram‐acquired Brain Functional Network

AbstractBackgroundAlzheimer’s disease dementia (ADD) is a predominant neurodegenerative disorder that results severe abnormalities in cognitive functions. However, most diagnoses rely on assessments made by individual clinical experts.Hence, we aim to establish a novel biomarker which can provide both accuracy and objectivity. Electroencephalogram (EEG) data were utilized for the training of artificial intelligence (AI) models, making use of its cost‐advantage and simple utility.Method19‐channel EEG data were recorded at Chung‐Ang University hospital in accordance with international 10‐20 system at resting state with eyes being closed. We first converted 19 channels into 68 regions of interest (ROIs) using standardized low resolution brain electromagnetic tomography (sLORETA). Henceforth, imaginary part of coherence (iCoh) between each ROI were calculated, reflecting functional connectivity between the ROIs. The iCoh values were then utilized in the calculation of the brain’s functional brain network.The subjects were divided into non‐ADD and ADD group. The resulting training dataset consisted of 118 non‐ADD and 110 ADD subjects, where the number of non‐ADD training data were adjusted in order to account for data imbalance. The test dataset included 476 non‐ADD and 27 ADD subjects.The classification model was trained using 68 ROI source powers and functional brain network features. Only statistically significant features were selected for feature reduction purposes. Through primary classification via light gradient boosting method (LGBM), top 30 features with high importance values were selected and were employed in the training of our final LGBM classification model.ResultThrough appropriate hyperparameter tuning, the LGBM classification model resulted in 87.67% accuracy, 88.89% sensitivity and 87.61% specificity. From the receiver operating characteristic (ROC) curve, 0.9506 area under ROC curve (AUC) was achieved.ConclusionThe LGBM model resulted in promising classification performance, solely through EEG features. In addition, functional brain network features had higher importance in comparison with source power features, indicating high potential of EEG brain network features in distinguishment of neurodegenerative diseases.

EEG-Based Cortical Alterations in Individuals With Chronic Knee Pain Secondary to Osteoarthritis: A Cross-sectional Investigation

Chronic painful knee osteoarthritis (OA) is a disabling physical health condition. Alterations in brain responses to arthritic changes in the knee may explain persistent pain. This study investigated source localized, resting-state electroencephalography activity and functional connectivity in people with knee OA, compared to healthy controls. Adults aged 44 to 85 years with knee OA (n = 37) and healthy control (n = 39) were recruited. Resting-state electroencephalography was collected for 10 minutes and decomposed into infraslow frequency (ISF) to gamma frequency bands. Standard low-resolution electromagnetic brain tomography statistical nonparametric maps were conducted, current densities of regions of interest were compared between groups and correlation analyses were performed between electroencephalography (EEG) measures and clinical pain and functional outcomes in the knee OA group. Standard low-resolution electromagnetic brain tomography nonparametric maps revealed higher (P = .006) gamma band activity over the right insula (RIns) in the knee OA group. A significant (P < .0001) reduction in ISF band activity at the pregenual anterior cingulate cortex, whereas higher theta, alpha, beta, and gamma band activity at the dorsal anterior cingulate cortex, pregenual anterior cingulate cortex, the somatosensory cortex, and RIns in the knee OA group were identified. ISF activity of the dorsal anterior cingulate cortex was positively correlated with pain measures and psychological distress scores. Theta and alpha activity of RIns were negatively correlated with pain interference. In conclusion, aberrations in infraslow and faster frequency EEG oscillations at sensory discriminative, motivational-affective, and descending inhibitory cortical regions were demonstrated in people with chronic painful knee OA. Moreover, EEG oscillations were correlated with pain and functional outcome measures. PerspectiveThis study confirms alterations in the rsEEG oscillations and its relationship with pain experience in people with knee OA. The study provides potential cortical targets and the EEG frequency bands for neuromodulatory interventions for managing chronic pain experience in knee OA.

Modification of cortical electrical activity in stroke survivors with abnormal subjective visual vertical: An eLORETA study

ObjectivesBalance impairment is among the main complications of stroke. The gravity-based subjective vertical (SV) is considered an important reference for upright posture and navigation affected by stroke. The correlation between injury location and pathological perception of verticality remains controversial. This study aimed to evaluate the cortico-cortical network of vertical perception among patients with the right hemisphere stroke and abnormal visual-vertical perception compared with healthy individuals. Materials and methodsThis observational cross-sectional study included 40 patients with the right hemisphere stroke and 35 healthy participants. All patients had abnormal visual-vertical perception. The EEG connectivity analysis was conducted through the exact low-resolution brain electromagnetic tomography analysis (eLORETA). ResultsStroke survivors manifested a power spectral density that reduced within the beta-2 frequency band in the left hemisphere and increased within the beta-3 frequency band in the right hemisphere compared with controls (p < 0.01). The lagged-phase synchronization was increased within alpha-1, beta-2, and beta-3 bands and decreased in stroke survivors compared with controls in the vestibular network involved in visual-vertical perception (p < 0.01). ConclusionThe results of this study demonstrated variations in the function and functional connectivity of cortical areas involved in the visual-vertical perception that are mainly located in the vestibular cortex.

Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features.

The electroencephalographic (EEG) based on the motor imagery task is derived from the physiological electrical signal caused by the autonomous activity of the brain. Its weak potential difference changes make it easy to be overwhelmed by noise, and the EEG acquisition method has a natural limitation of low spatial resolution. These have brought significant obstacles to high-precision recognition, especially the recognition of the motion intention of the same upper limb. This research proposes a method that combines signal traceability and Riemannian geometric features to identify six motor intentions of the same upper limb, including grasping/holding of the palm, flexion/extension of the elbow, and abduction/adduction of the shoulder. First, the EEG data of electrodes irrelevant to the task were screened out by low-resolution brain electromagnetic tomography. Subsequently, tangential spatial features are extracted by the Riemannian geometry framework in the covariance matrix estimated from the reconstructed EEG signals. The learned Riemannian geometric features are used for pattern recognition by a support vector machine with a linear kernel function. The average accuracy of the six classifications on the data set of 15 participants is 22.47%, the accuracy is 19.34% without signal traceability, the accuracy is 18.07% when the features are the filter bank common spatial pattern (FBCSP), and the accuracy is 16.7% without signal traceability and characterized by FBCSP. The results show that the proposed method can significantly improve the accuracy of intent recognition. In addressing the issue of temporal variability in EEG data for active Brain-Machine Interfaces, our method achieved an average standard deviation of 2.98 through model transfer on different days' data.

Effect of total sleep deprivation on effective EEG connectivity for young male in resting-state networks in different eye states

BackgroundMany studies have investigated the effect of total sleep deprivation (TSD) on resting-state functional networks, especially the default mode network (DMN) and sensorimotor network (SMN), using functional connectivity. While it is known that the activities of these networks differ based on eye state, it remains unclear how TSD affects them in different eye states. Therefore, we aimed to examine the effect of TSD on DMN and SMN in different eye states using effective functional connectivity via isolated effective coherence (iCoh) in exact low-resolution brain electromagnetic tomography (eLORETA).MethodsResting-state electroencephalogram (EEG) signals were collected from 24 male college students, and each participant completed a psychomotor vigilance task (PVT) while behavioral data were acquired. Each participant underwent 36-h TSD, and the data were acquired in two sleep-deprivation times (rested wakefulness, RW: 0 h; and TSD: 36 h) and two eye states (eyes closed, EC; and eyes open, EO). Changes in neural oscillations and effective connectivity were compared based on paired t-test.ResultsThe behavioral results showed that PVT reaction time was significantly longer in TSD compared with that of RW. The EEG results showed that in the EO state, the activity of high-frequency bands in the DMN and SMN were enhanced compared to those of the EC state. Furthermore, when compared with the DMN and SMN of RW, in TSD, the activity of DMN was decreased, and SMN was increased. Moreover, the changed effective connectivity in the DMN and SMN after TSD was positively correlated with an increased PVT reaction time. In addition, the effective connectivity in the different network (EO-EC) of the SMN was reduced in the β band after TSD compared with that of RW.ConclusionThese findings indicate that TSD impairs alertness and sensory information input in the SMN to a greater extent in an EO than in an EC state.

EEG Source Localization in Temporal Encephaloceles: Concordance With Surgical Resection and Clinical Outcomes.

Temporal encephaloceles are a cause of drug-resistant temporal lobe epilepsy; however, their relationship with epileptogenesis is unclear, and optimal surgical resection is uncertain. EEG source localization (ESL) may guide surgical decision-making. We reviewed patients at Mayo Clinic Rochester with drug-resistant temporal lobe epilepsy and temporal encephaloceles, who underwent limited resection and had 1-year outcomes. EEG source localization was performed using standard density scalp EEG of ictal and interictal activity. Distance from dipole and standardized low-resolution brain electromagnetic tomography (sLORETA) solutions to the encephalocele were measured. Concordance of ESL with encephalocele and surgical resection was compared with 1-year surgical outcomes. Seventeen patients met criteria. The mean distances from ESL results to encephalocele center for dipole and sLORETA analyses were 23 mm (SD 9) and 22 mm (SD 11), respectively. Ten patients (55.6%) had Engel I outcomes at 1 year. Dipole-encephalocele distance and sLORETA-encephalocele distance were significantly longer in patients with Engel I outcome and patients whose encephalocele was contained by sLORETA had worse outcome as well; however, multiple logistic regression analysis found that only containment of encephalocele by the sLORETA current density was significant (P < 0.05), odds ratio 0.12 (95% confidence interval [0.021, 0.71]). EEG source localization of scalp EEG localizes near encephaloceles, however, typically not in the encephalocele itself; this may be due to scalp EEG sampling propagated activity or alternatively that the seizure onset zone extends beyond the herniated cortex. Surprisingly, we observed increased ESL to encephalocele distances in patients with excellent surgical outcomes. Larger cohort studies including intracranial EEG data are needed to further explore this finding.

Electrical Source Imaging of Somatosensory Evoked Potentials from Intracranial EEG Signals.

Stereoelectroencephalography (SEEG) records electrical brain activity with intracerebral electrodes. However, it has an inherently limited spatial coverage. Electrical source imaging (ESI) infers the position of the neural generators from the recorded electric potentials, and thus, could overcome this spatial undersampling problem. Here, we aimed to quantify the accuracy of SEEG ESI under clinical conditions.We measured the somatosensory evoked potential (SEP) in SEEG and in high-density EEG (HD-EEG) in 20 epilepsy surgery patients. To localize the source of the SEP, we employed standardized low resolution brain electromagnetic tomography (sLORETA) and equivalent current dipole (ECD) algorithms.Both sLORETA and ECD converged to similar solutions. Reflecting the large differences in the SEEG implantations, the localization error also varied in a wide range from 0.4 to 10cm. The SEEG ESI localization error was linearly correlated with the distance from the putative neural source to the most activated contact.We show that it is possible to obtain reliable source reconstructions from SEEG under realistic clinical conditions, provided that the high signal fidelity recording contacts are sufficiently close to the source of the brain activity.