Low-resolution Brain Electromagnetic Tomography Research Articles

Resting-State EEG Connectivity at High-Frequency Bands and Attentional Performance Dysfunction in Stabilized Schizophrenia Patients.

Background and Objectives: Attentional dysfunction has long been viewed as one of the fundamental underlying cognitive deficits in schizophrenia. There is an urgent need to understand its neural underpinning and develop effective treatments. In the process of attention, neural oscillation has a central role in filtering information and allocating resources to either stimulus-driven or goal-relevant objects. Here, we asked if resting-state EEG connectivity correlated with attentional performance in schizophrenia patients. Materials and Methods: Resting-state EEG recordings were obtained from 72 stabilized patients with schizophrenia. Lagged phase synchronization (LPS) was used to measure whole-brain source-based functional connectivity between 84 intra-cortical current sources determined by eLORETA (exact low-resolution brain electromagnetic tomography) for five frequencies. The Conners' Continuous Performance Test-II (CPT-II) was administered for evaluating attentional performance. Linear regression with a non-parametric permutation randomization procedure was used to examine the correlations between the whole-brain functional connectivity and the CPT-II measures. Results: Greater beta-band right hemispheric fusiform gyrus (FG)-lingual gyrus (LG) functional connectivity predicted higher CPT-II variability scores (r = 0.44, p < 0.05, corrected), accounting for 19.5% of variance in the CPT-II VAR score. Greater gamma-band right hemispheric functional connectivity between the cuneus (Cu) and transverse temporal gyrus (TTG) and between Cu and the superior temporal gyrus (STG) predicted higher CPT-II hit reaction time (HRT) scores (both r = 0.50, p < 0.05, corrected), accounting for 24.6% and 25.1% of variance in the CPT-II HRT score, respectively. Greater gamma-band right hemispheric Cu-TTG functional connectivity predicted higher CPT-II HRT standard error (HRTSE) scores (r = 0.54, p < 0.05, corrected), accounting for 28.7% of variance in the CPT-II HRTSE score. Conclusions: Our study indicated that increased right hemispheric resting-state EEG functional connectivity at high frequencies was correlated with poorer focused attention in schizophrenia patients. If replicated, novel approaches to modulate these networks may yield selective, potent interventions for improving attention deficits in schizophrenia.

Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks reconstruction: A simulation study

Along with the study of brain activity evoked by external stimuli, the past two decades witnessed an increased interest in characterizing the spontaneous brain activity occurring during resting conditions. The identification of connectivity patterns in this so-called "resting-state" has been the subject of a great number of electrophysiology-based studies, using the Electro/Magneto-Encephalography (EEG/MEG) source connectivity method. However, no consensus has been reached yet regarding a unified (if possible) analysis pipeline, and several involved parameters and methods require cautious tuning. This is particularly challenging when different analytical choices induce significant discrepancies in results and drawn conclusions, thereby hindering the reproducibility of neuroimaging research. Hence, our objective in this study was to shed light on the effect of analytical variability on outcome consistency by evaluating the implications of parameters involved in the EEG source connectivity analysis on the accuracy of resting-state networks (RSNs) reconstruction. We simulated, using neural mass models, EEG data corresponding to two RSNs, namely the default mode network (DMN) and dorsal attentional network (DAN). We investigated the impact of five channel densities (19, 32, 64, 128, 256), three inverse solutions (weighted minimum norm estimate (wMNE), exact low-resolution brain electromagnetic tomography (eLORETA), and linearly constrained minimum variance (LCMV) beamforming) and four functional connectivity measures (phase-locking value (PLV), phase-lag index (PLI), and amplitude envelope correlation (AEC) with and without source leakage correction), on the correspondence between reconstructed and reference networks. We showed that, with different analytical choices related to the number of electrodes, source reconstruction algorithm, and functional connectivity measure, high variability is present in the results. More specifically, our results show that a higher number of EEG channels significantly increased the accuracy of the reconstructed networks. Additionally, our results showed significant variability in the performance of the tested inverse solutions and connectivity measures. Such methodological variability and absence of analysis standardization represent a critical issue for neuroimaging studies that should be prioritized. We believe that this work could be useful for the field of electrophysiology connectomics, by increasing awareness regarding the challenge of variability in methodological approaches and its implications on reported results.

In silico study of the effects of cerebral circulation on source localization using a dynamical anatomical atlas of the human head

Objective. This study focuses on the effects of dynamical vascular modeling on source localization errors in electroencephalography (EEG). Our aim of this in silico study is to (a) find out the effects of cerebral circulation on the accuracy of EEG source localization estimates, and (b) evaluate its relevance with respect to measurement noise and interpatient variation. Approach. We employ a four-dimensional (3D + T) statistical atlas of the electrical properties of the human head with a cerebral circulation model to generate virtual patients with different cerebral circulatory conditions for EEG source localization analysis. As source reconstruction techniques, we use the linearly constraint minimum variance (LCMV) beamformer, standardized low-resolution brain electromagnetic tomography (sLORETA), and the dipole scan (DS). Main results. Results indicate that arterial blood flow affects source localization at different depths and with varying significance. The average flow rate plays an important role in source localization performance, while the pulsatility effects are very small. In cases where a personalized model of the head is available, blood circulation mismodeling causes localization errors, especially in the deep structures of the brain where the main cerebral arteries are located. When interpatient variations are considered, the results show differences up to 15 mm for sLORETA and LCMV beamformer and 10 mm for DS in the brainstem and entorhinal cortices regions. In regions far from the main arteries vessels, the discrepancies are smaller than 3 mm. When measurement noise is added and interpatient differences are considered in a deep dipolar source, the results indicate that the effects of conductivity mismatch are detectable even for moderate measurement noise. The signal-to-noise ratio limit for sLORETA and LCMV beamformer is 15 dB, while the limit is under 30 dB for DS. Significance. Localization of the brain activity via EEG constitutes an ill-posed inverse problem, where any modeling uncertainty, e.g. a slight amount of noise in the data or material parameter discrepancies, can lead to a significant deviation of the estimated activity, especially in the deep structures of the brain. Proper modeling of the conductivity distribution is necessary in order to obtain an appropriate source localization. In this study, we show that the conductivity of the deep brain structures is particularly impacted by blood flow-induced changes in conductivity because large arteries and veins access the brain through that region.

Cortical sources of electroencephalographic alpha rhythms related to the anticipation and experience of mirror visual feedback-induced illusion of finger movements.

Mirror visual feedback (MVF) technique consists in placing a mirror in a person's body midline to induce the illusion of bilateral synchronous movements of the limbs during actual unilateral movements. A recent electroencephalographical (EEG) study demonstrated that MVF-induced illusion was related to the event-related desynchronization (ERD) of alpha (8-12 Hz) rhythms (cortical activation) at the central and parietal scalp electrodes ipsilateral to the unilateral right finger movements. In the present study, we re-analyzed those data to localize the cortical sources of alpha ERD during the anticipation and experience of the MVF-induced illusion of index finger movements. To this aim, the exact Low-Resolution Brain Electromagnetic Tomography freeware was used for the estimation of the cortical sources of the alpha ERD. Results showed that as compared to the condition without MVF, the MVF condition was characterized by greater (p< .01, uncorrected) alpha ERD sources in right frontopolar areas during the anticipation of the MVF-induced illusion of left movements. The MVF condition was also characterized by greater (p< .05, corrected) alpha ERD sources in right premotor, primary somatomotor, and posterior inferior parietal areas during both the anticipation and experience of that MVF-induced illusion. These findings suggest that the MVF-induced illusory experience of left finger movements may be due to dynamic changes in alpha ERD in associative, premotor, somatomotor, and visuomotor frontal-parietal areas located in the hemisphere contralateral to the mirrored motor acts.

The Electropsychology of Mind, a Newfangled Route in Psychology by Utilizing Electrophysiological Means to Detect Mental Health Problems

Purpose: Modern electroencephalography or E.E.G analysis methods like quantitative-electroencephalography or Q.E.E.G entail capturing computerized E.E.G data and thereafter processing, transforming, and analyzing those outputs employing sophisticated computational methods. Q.E.E.G has introduced unique methods for extracting E.E.G signal features; including interconnection, circuit, as well as regularity range evaluation, and also signal intricacy assessment. Numerous therapeutic conditions, such as neuropsychological diseases, seizures, ischemia, Alzheimer, brain trauma, psychological issues, as well as more are treated with Q-E.E.G. In this paper, will be going over the available data on the real-world uses of this therapeutic technique in psychopathological cases. Objective: The primary objective of this article is to describe electrophysiological alterations in numerous well-known and widespread mental disorders. Another goal of this research is to spot electrophysiological alterations in attention disorder, a prevalent and nowadays more prevalent social disorder. In this instance, it will be examined using both electrophysiological research and low-resolution brain electromagnetic tomography analysis. The use of Q-EEG over conventional EEG is growing in popularity right now, and that trend will continue in the future. In this regard, another goal of this paper is to provide some insight into some of the areas of research or application where Q-E.E.G. can be used to its fullest potential. Design/Methodology/Approach: Scientific secondary clinical data from a variety of reputable and credible sources and publications, including Google Scholar, Academia, Researchgate, etc., were used to construct this research. A thorough, methodical, and scientific analysis has been performed to obtain the substance of all the scientific journal research results in order to make this article more accurate, dependable, and scientific. To make this article more engaging and trustworthy, opinions from a range of experienced specialists were gathered. In order to cover nearly all of the common and specific areas of knowledge regarding this issue, more than a hundred journal papers and conference proceedings have been methodically studied. Finding/Results: There is no one framework or integrated technique that can handle the tremendous amount of data that the E.E.G. capture generates. Comparing laboratory data is challenging because each investigator employs their own analytical frameworks. Similar to Q-E.E.G, this massive disparity prevents the creation of a novel, cohesive and replaceable information database. Understanding all the graphs and figures generated by the newest devices may be difficult for experts other than neurophysiologists. In conclusion, determining a clinical diagnosis of intellectual disability is a challenging process that depends on a variety of data. Given this, software-assisted assessment using Q-E.E.G. offers helpful assistance for identifying, evaluating, monitoring and determining responsiveness to intervention. It is accurate, reasonably priced, as well as manageable to use. Originality and Value: A novel effort has been made to depart some information regarding electrophysiological changes in various mental disorders. In order to make the paper clear and vivid, images of different EEG reports have been attached. The paper was built in such a way that the readers could understand this clinical topic regardless of their academic qualifications. A novel terminology, "Electropsychology,” has been used to refer to the electrophysiological alterations of mental disorders on an EEG paper, which is exclusively intended to rule out the mental disorder. Paper Type: Clinical analysis paper

Directed brain network analysis in anxious and non-anxious depression based on EEG source reconstruction and graph theory

Patients with anxious depression have more severe symptoms, more side effects, and higher resistance to treatment than patients with non-anxious depression; therefore, it is crucial to clarify the differences between these two types of patients. In this study, a 5-minute resting EEG was recorded in 15 patients with anxious depression and 9 patients with non-anxious depression under eyes open and closed conditions. Sixty-eight subcortical regions were extracted using exact low resolution brain electromagnetic tomography (eLORETA). The directed transfer function was then used to construct brain networks. Specific features based on graph theory including the strength of connectivity and betweenness centrality (BC) were calculated from the networks. Finally, significant features were selected using the Mann-Whitney U test, and patients were classified into anxious and non-anxious depressive groups using the Support Vector Machine (SVM). Results showed that features of outward connectivity strength led to the highest accuracy, F-score, and specificity with 91.66%, 87. 5%, and 100% in the eyes-closed state, respectively. Moreover, we found that the strength of connectivity in both directions increased for the anxious depressive group during the eyes-open state. In particular, higher outward connectivity was observed in the right hemisphere for the anxious depressive group. Further findings also revealed that features with the most significant difference were mainly associated with the beta band. In addition, significant increased inward and outward connectivity and decreased nodal centrality were observed in the posterior regions of the default mode network. These preliminary findings might provide new insights into the recognition of anxious depressed patients.

Current source density (sLORETA) in patients undergoing cognitive rehabilitation using dual task in the early postoperative period of coronary artery bypass grafting

Highlights. For the first time, the method of standardized low resolution electromagnetic tomography – sLORETA was used to assess the neurophysiological correlates of the success of cognitive rehabilitation using dual task to recover the brain functions affected by ischemia during cardiac surgery.The patients who were successfully rehabilitated have demonstrated lower postoperative values of resting state theta activity within the right hemisphere, indicating the transfer effect, which is an important component of successful cognitive rehabilitation.Aim. Visualization and monitoring of brain ischemia is important for the diagnosis of cerebrovascular diseases. The aim of the study was to evaluate the possibilities and applicability of the method of standardized low resolution electromagnetic brain tomography – sLORETA for analyzing brain electrical activity in patients undergoing cognitive rehabilitation using dual tasks to recover impaired brain functions during ischemia associated with cardiac surgery.Methods. The study included 16 male patients (45–75 years old) who were admitted for planned coronary artery bypass grafting (CABG) to the Clinic at the Research Institute for Complex Issues of Cardiovascular Diseases. Clinical and neurophysiological examinations were performed 2–3 days before CABG and 1 week after surgery. All patients underwent cognitive rehabilitation that started at postoperative day 3 or 4 and lasted until discharge, the outcome was assessed as well. Monopolar EEG (62 channels) was recorded at rest in a sitting position with eyes closed in a light- and noise-insulated room using a Neuvo SynAmps2 Amplifier. Data processing of EEG background activity for sLORETA analysis was performed using the EEGLAB in MATLAB software (The MathWorks, Natick, MA, USA). The sLORETA algorithms were used to calculate dynamic cross spectrum and current source density within the analyzed frequency range (4–6 Hz). Statistical analysis of current source density indicators was carried out by using the method of statistical non-parametric mapping in the sLORETA software package, and the difference between the current source density in the pre- and postoperative period (after cognitive rehabilitation) was calculated as well.Results. Cognitive rehabilitation was successful in 44% (7 patients) of cases. The differences have been revealed between the groups with successful and unsuccessful cognitive rehabilitation by using sLORETA. The group with unsuccessful cognitive rehabilitation demonstrated higher current source density estimates in theta rhythm compared with the successful rehabilitation group, it was most pronounced (t&gt;- 8.42; p&lt;0.004) in the right hemisphere – Brodmann area 22, temporal lobe and superior temporal gyrus.Conclusion. The sLORETA method demonstrated the positive effect of cognitive training on changes in the spatial patterns of brain activity in patients undergoing onpump CABG. The successful cognitive rehabilitation was associated with lower postoperative resting state theta activity within the right hemisphere, indicating the transfer effect, which is an important component of successful cognitive rehabilitation.

Is Functional Connectivity after a First Unprovoked Seizure Different Based on Subsequent Seizures and Future Diagnosis of Epilepsy?

There are no highly sensitive biomarkers for epilepsy to date. Recently, promising results regarding functional connectivity analysis have been obtained, which may improve epilepsy diagnosis even in the absence of visible abnormality in electroencephalography. We aimed to investigate the differences in functional connectivity after a first unprovoked seizure between patients diagnosed with epilepsy within 1 year due to subsequent seizures and those who were not. We compared quantitative electroencephalography power spectra and functional connectivity between 12 patients who were diagnosed with epilepsy (two or more unprovoked seizures) within 1 year and 17 controls (those not diagnosed within 1 year) using iSyncBrain® (iMediSync Inc., Suwon, Korea; https://isyncbrain.com/). In the source-level analysis, the current distribution across the brain was assessed using the standardized low-resolution brain electromagnetic tomography technique, to compare relative power values in 68 regions of interest and connectivity (the imaginary part of coherency) between regions of interest. In the epilepsy group, quantitative electroencephalography showed lower alpha2 band power in left frontal, central, superior temporal, and parietal regions and higher beta2 power in both frontal, central, temporal, occipital, and left parietal regions compared with the control group. Additionally, epilepsy patients had significantly lower connectivity in alpha2 and beta2 bands than the controls. Patients experiencing their first unprovoked seizure presented different brain function according to whether they have subsequent seizures and future epilepsy. Our results propose the potential clinical ability to diagnose epilepsy after the first unprovoked seizure in the absence of interictal epileptiform discharges.

Is Functional Connectivity after a First Unprovoked Seizure Different Based on Subsequent Seizures and Future Diagnosis of Epilepsy?

Background and Purpose: There are no highly sensitive biomarkers for epilepsy to date. Recently, promising results regarding functional connectivity analysis have been obtained, which may improve epilepsy diagnosis even in the absence of visible abnormality in electroencephalography. We aimed to investigate the differences in functional connectivity after a first unprovoked seizure between patients diagnosed with epilepsy within 1 year due to subsequent seizures and those who were not.Methods: We compared quantitative electroencephalography power spectra and functional connectivity between 12 patients who were diagnosed with epilepsy (two or more unprovoked seizures) within 1 year and 17 controls (those not diagnosed within 1 year) using iSyncBrain&lt;sup&gt;®&lt;/sup&gt; (iMediSync Inc., Suwon, Korea; https://isyncbrain.com/). In the source-level analysis, the current distribution across the brain was assessed using the standardized low-resolution brain electromagnetic tomography technique, to compare relative power values in 68 regions of interest and connectivity (the imaginary part of coherency) between regions of interest.Results: In the epilepsy group, quantitative electroencephalography showed lower alpha2 band power in left frontal, central, superior temporal, and parietal regions and higher beta2 power in both frontal, central, temporal, occipital, and left parietal regions compared with the control group. Additionally, epilepsy patients had significantly lower connectivity in alpha2 and beta2 bands than the controls.Conclusions: Patients experiencing their first unprovoked seizure presented different brain function according to whether they have subsequent seizures and future epilepsy. Our results propose the potential clinical ability to diagnose epilepsy after the first unprovoked seizure in the absence of interictal epileptiform discharges.

Sensory Inhibition and Speech Perception-in-Noise Performance in Children With Normal Hearing.

This study investigated whether sensory inhibition in children may be associated with speech perception-in-noise performance. Additionally, gating networks associated with sensory inhibition were identified via standardized low-resolution brain electromagnetic tomography (sLORETA), and the detectability of the cortical auditory evoked potential (CAEP) N1 response was enhanced using a 4- to 30-Hz bandpass filter. CAEP gating responses, reflective of inhibition, were evoked via click pairs and recorded using high-density electroencephalography in neurotypical 5- to 8-year-olds and 22- to 24-year-olds. Amplitude gating indices were calculated and correlated with speech perception in noise. Gating generators were estimated using sLORETA. A 4- to 30-Hz filter was applied to detect the N1 gating component. Preliminary findings indicate children showed reduced gating, but there was a correlational trend between better speech perception and decreased N2 gating. Commensurate with decreased gating, children presented with incomplete compensatory gating networks. The 4- to 30-Hz filter identified the N1 response in a subset of children. There was a tenuous relationship between children's speech perception and sensory inhibition. This may suggest that sensory inhibition is only implicated in atypically poor speech perception. Finally, the 4- to 30-Hz filter settings are critical in N1 detectability. Gating may help evaluate reduced sensory inhibition in children with clinically poor speech perception using the appropriate methodology. Cortical gating generators in typically developing children are also newly identified.

QEEG‐based discernment of dementia pathologies through machine learning: Lewy body and Alzheimer’s disease

AbstractBackgroundAlzheimer’s disease is the most common cause of dementia, which destroys nerve cells in the brain through the formation and accumulation of amyloid plaques. However, various other pathologies of dementia also exist, such as Lewy body pathology where the destruction is caused by the accumulation of Lewy body peptides. Differing dementia pathologies carry distinguishing symptoms and treatment methods; hence we cannot disregard the importance of correct identification of the pathology.Currently, the correct pathology can be identified through positron emission tomography (PET), which is expensive and results in exposure to harmful ionizing radiation. Therefore, the present study aims to overcome the disadvantages of current screening methods and distinguish the quantitative electroencephalography (QEEG) of two dementia pathologies through machine learning.MethodEEG data employed in the present study were recorded at the 19 electrode locations defined by the international 10‐20 system, in eyes‐closed resting‐state condition. The acquired data were then re‐referenced to a common average reference. Hereafter, bandpass filtering at 0.1‐45.5Hz, bad epoch rejection and independent component analysis (ICA) was performed to remove noise components from the data. Standardized low resolution brain electromagnetic tomography (sLORETA) was further used for mathematical estimation of source cortical activity, yielding data for 68 cortical regions based on the Desikan‐Killiany atlas.Gamma wave (30‐45Hz) features were excluded from the data due to is vulnerability to electromyography. Further feature reduction was also performed, through the designated criteria on p‐value threshold (0.05) and feature importance threshold determined by shapely values. The final dataset (N = 104; 30 ADD, 74 DLB) was established, where 20% of the data (N =21; 6 ADD, 15 DLB) were randomly selected and excluded as test data for model verification.ResultThe final machine learning model trained and tested through the dataset yielded test accuracy at 85.7% with ADD sensitivity at 83.3%, DLB sensitivity at 86.7%. The 5 fold cross validation accuracy was at 82.1%.ConclusionThe model developed in the present study yielded a promising classification performance, through EEG data which is cheap and harmless to record. Such QEEG‐based classification models may sufficiently replace the PET in future, resolving current disadvantages.

Feasibility and Safety of High-Definition Infraslow Pink Noise Stimulation for Treating Chronic Tinnitus—A Randomized Placebo-Controlled Trial

IntroductionTinnitus has been linked to activity and connectivity changes in the auditory cortex (AC), parahippocampus (PHC), and posterior cingulate cortex (PCC). Although previous studies have targeted these areas individually, no study has yet modulated them simultaneously. Furthermore, novel stimulation designs may be superior to traditional alternating or direct current stimulation. This pilot study investigated the feasibility and safety of a novel brain stimulation technique (high-definition transcranial infraslow pink noise stimulation [HD-tIPNS]) for treating chronic tinnitus targeting the AC, PHC, and PCC. Materials and MethodsA pilot, double-blind, randomized two-arm placebo-controlled parallel trial was conducted, with clinical outcomes collected at baseline, three days, and ten days after treatment. Participants with chronic tinnitus (n = 20) received 12 sessions (three per week for four weeks) of either HD-tIPNS or acti-sham stimulation. Primary outcomes included feasibility, safety, and resting-state electroencephalography (rsEEG) measures, and secondary outcomes included tinnitus and quality of life questionnaires. Feasibility, safety, and secondary measures were assessed using descriptive statistics. rsEEG was analyzed using standard low-resolution electromagnetic brain tomography. ResultsNo long-term adverse events were reported. Mild side effects included headache and dizziness, indicating the safety of this stimulation. Participants were rapidly recruited and adhered to the study protocol with minimal difficulty. Drop-out rate was 13%, and the treatment approach was acceptable to participants, supporting the feasibility of the protocol. Tinnitus measures were similar between HD-tIPNS and acti-sham stimulation. rsEEG analyses revealed decreased beta-1 activity in PCC ten days after treatment for acti-sham. The HD-tIPNS group showed decreased beta-1 activity in inferior parietal lobule three days after treatment. Increased functional connectivity in the beta-1 frequency between left and right PHC was found in the HD-tIPNS group compared with the acti-sham group, three days after treatment. ConclusionsIn conclusion, the novel approach is safe and feasible and revealed EEG changes unsupported by clinical benefit. Further research is essential to evaluate the potential of this multifocal network stimulation approach. Clinical Trial RegistrationThis study was registered with the Australia New Zealand Clinical Trial Registry (Registry number: ACTRN12621000151831; Universal Trial Number: U1111-1261-6945).

The effect of low-frequency rTMS on auditory hallucinations, EEG source localization and functional connectivity in schizophrenia

BackgroundLow-frequency repetitive transcranial magnetic stimulation (LF-rTMS) diminishes auditory hallucinations (AHs). The aims of our study were a) to assess the efficacy of LF-rTMS in a randomized, sham-controlled double-blind alignment, b) to identify the electrophysiological changes accompanying the LF-rTMS, and c) to identify the influence of LF-rTMS on brain functional connectivity (FC). MethodsNineteen schizophrenia patients with antipsychotic-resistant AHs were randomized to either active (n = 10) or sham (n = 9) LF-rTMS administered over the left temporo-parietal region for ten days. The clinical effect was assessed by the Auditory Hallucination Rating Scale (AHRS). The localization of the differences in electrical activity was identified by standardized low resolution brain electromagnetic tomography (sLORETA) and FC was measured by lagged phase synchronization. ResultsAHRS scores were significantly improved for patients receiving active rTMS compared to the sham (median reduction: 40 % vs 12 %; p = 0.01). sLORETA revealed a decrease of alpha-2, beta-1,-2 bands in the left hemisphere in the active group. Active rTMS led to a decrease of the lagged phase connectivity in beta bands originating in areas close to the site of stimulation, and to a prevailing increase of alpha-2 FC. No significant differences in current density or FC were observed in the sham group. LimitationsLimitations to our study included the small group sizes, and the disability of LORETA to assess subcortical neuronal activity. ConclusionsLF-rTMS attenuated AHs and induced a decrease of higher frequency bands on the left hemisphere. The FC changes support the assumption that LF-rTMS is linked to the modulation of cortico-cortical coupling.

EEG Network Analysis in Epilepsy with Generalized Tonic-Clonic Seizures Alone.

Many contradictory theories regarding epileptogenesis in idiopathic generalized epilepsy have been proposed. This study aims to define the network that takes part in the formation of the spike-wave discharges in patients with generalized tonic-clonic seizures alone (GTCSa) and elucidate the network characteristics. Furthermore, we intend to define the most influential brain areas and clarify the connectivity pattern among them. The data were collected from 23 patients with GTCSa utilizing low-density electroencephalogram (EEG). The source localization of generalized spike-wave discharges (GSWDs) was conducted using the Standardized low-resolution brain electromagnetic tomography (sLORETA) methodology. Cortical connectivity was calculated utilizing the imaginary part of coherence. The network characteristics were investigated through small-world propensity and the integrated value of influence (IVI). Source localization analysis estimated that most sources of GSWDs were in the superior frontal gyrus and anterior cingulate. Graph theory analysis revealed that epileptic sources created a network that tended to be regularized during generalized spike-wave activity. The IVI analysis concluded that the most influential nodes were the left insular gyrus and the left inferior parietal gyrus at 3 and 4 Hz, respectively. In conclusion, some nodes acted mainly as generators of GSWDs and others as influential ones across the whole network.

Infraslow closed-loop brain training for anxiety and depression (ISAD): a protocol for a randomized, double-blind, sham-controlled pilot trial in adult females with internalizing disorders

BackgroundThe core intrinsic connectivity networks (core-ICNs), encompassing the default-mode network (DMN), salience network (SN) and central executive network (CEN), have been shown to be dysfunctional in individuals with internalizing disorders (IDs, e.g. major depressive disorder, MDD; generalized anxiety disorder, GAD; social anxiety disorder, SOC). As such, source-localized, closed-loop brain training of electrophysiological signals, also known as standardized low-resolution electromagnetic tomography (sLORETA) neurofeedback (NFB), targeting key cortical nodes within these networks has the potential to reduce symptoms associated with IDs and restore normal core ICN function. We intend to conduct a randomized, double-blind (participant and assessor), sham-controlled, parallel-group (3-arm) trial of sLORETA infraslow (<0.1 Hz) fluctuation neurofeedback (sLORETA ISF-NFB) 3 times per week over 4 weeks in participants (n=60) with IDs. Our primary objectives will be to examine patient-reported outcomes (PROs) and neurophysiological measures to (1) compare the potential effects of sham ISF-NFB to either genuine 1-region ISF-NFB or genuine 2-region ISF-NFB, and (2) assess for potential associations between changes in PRO scores and modifications of electroencephalographic (EEG) activity/connectivity within/between the trained regions of interest (ROIs). As part of an exploratory analysis, we will investigate the effects of additional training sessions and the potential for the potentiation of the effects over time.MethodsWe will randomly assign participants who meet the criteria for MDD, GAD, and/or SOC per the MINI (Mini International Neuropsychiatric Interview for DSM-5) to one of three groups: (1) 12 sessions of posterior cingulate cortex (PCC) ISF-NFB up-training (n=15), (2) 12 sessions of concurrent PCC ISF up-training and dorsal anterior cingulate cortex (dACC) ISF-NFB down-training (n=15), or (3) 6 sessions of yoked-sham training followed by 6 sessions genuine ISF-NFB (n=30). Transdiagnostic PROs (Hospital Anxiety and Depression Scale, HADS; Inventory of Depression and Anxiety Symptoms – Second Version, IDAS-II; Multidimensional Emotional Disorder Inventory, MEDI; Intolerance of Uncertainty Scale – Short Form, IUS-12; Repetitive Thinking Questionnaire, RTQ-10) as well as resting-state neurophysiological measures (full-band EEG and ECG) will be collected from all subjects during two baseline sessions (approximately 1 week apart) then at post 6 sessions, post 12 sessions, and follow-up (1 month later). We will employ Bayesian methods in R and advanced source-localisation software (i.e. exact low-resolution brain electromagnetic tomography; eLORETA) in our analysis.DiscussionThis protocol will outline the rationale and research methodology for a clinical pilot trial of sLORETA ISF-NFB targeting key nodes within the core-ICNs in a female ID population with the primary aims being to assess its potential efficacy via transdiagnostic PROs and relevant neurophysiological measures.Trial registrationOur study was prospectively registered with the Australia New Zealand Clinical Trials Registry (ANZCTR; Trial ID: ACTRN12619001428156). Registered on October 15, 2019.

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.

Functional brain changes using electroencephalography after a 24-week multidomain intervention program to prevent dementia.

Quantitative electroencephalography (QEEG) has proven useful in predicting the response to various treatments, but, until now, no study has investigated changes in functional connectivity using QEEG following a lifestyle intervention program. We aimed to investigate neurophysiological changes in QEEG after a 24-week multidomain lifestyle intervention program in the SoUth Korean study to PrEvent cognitive impaiRment and protect BRAIN health through lifestyle intervention in at-risk elderly people (SUPERBRAIN). Participants without dementia and with at least one modifiable dementia risk factor, aged 60–79 years, were randomly assigned to the facility-based multidomain intervention (FMI) (n = 51), the home-based multidomain intervention (HMI) (n = 51), and the control group (n = 50). The analysis of this study included data from 44, 49, and 34 participants who underwent EEG at baseline and at the end of the study in the FMI, HMI, and control groups, respectively. The spectrum power and power ratio of EEG were calculated. Source cortical current density and functional connectivity were estimated by standardized low-resolution brain electromagnetic tomography. Participants who received the intervention showed increases in the power of the beta1 and beta3 bands and in the imaginary part of coherence of the alpha1 band compared to the control group. Decreases in the characteristic path lengths of the alpha1 band in the right supramarginal gyrus and right rostral middle frontal cortex were observed in those who received the intervention. This study showed positive biological changes, including increased functional connectivity and higher global efficiency in QEEG after a multidomain lifestyle intervention.Clinical trial registration[https://clinicaltrials.gov/ct2/show/NCT03980392] identifier [NCT03980392].

EEG -guided Characterization, Monitoring, and Therapy for Neurological and Neurocognitive Sequelae of COVID‐19 and Long COVID

The long-term effect of the newly emerged COVID-19 (SARS-CoV-2) virus has not been fully understood. It has been reported that several patients experienced neurological and neurocognitive problems after getting infected by the COVID-19 virus. This paper will review how the COVID-19 virus has impacted the brain, will aim to detect the location of COVID-19 in the brain, and will determine if the neurological complications are a result of “direct” or “indirect” effects of the virus on brain cells. Additionally, we will focus on the neurocognitive impact of COVID‐19 and the potential of digital electroencephalography (EEG), quantitative EEG (QEEG) and standardized low resolution brain electromagnetic tomography (sLORETA) to be able to capture, assess, monitor, characterize and facilitate the treatment of both neurological and neurocognitive sequelae seen in COVID‐19 and long COVID.

Enhanced effective connectivity from the middle frontal gyrus to the parietal lobe is associated with impaired mental rotation after total sleep deprivation: An electroencephalogram study.

Sleep deprivation impairs cognitive functions, including attention, memory, and decision-making. Studies on the neuro-electro-physiological mechanisms underlying total sleep deprivation (TSD) that impairs spatial cognition are limited. Based on electroencephalogram (EEG) and Exact Low Resolution Brain Electromagnetic Tomography (eLORETA), this study focused on the effects of TSD on mental rotation and the cognitive neural mechanisms underlying its damage. Twenty-four healthy college students completed mental rotation tasks while resting and after 36 h of TSD; their EEG data were simultaneously recorded. The amplitude of P300 component associated with mental rotation was observed and localized through source reconstruction, while changes in effective connectivity between multiple brain regions associated with mental rotation cognitive processing were calculated using isolated effective coherence (iCoh) of eLORETA. Compared with the baseline before TSD, the amplitude of the P300 component related to mental rotation decreased. The task-state data of P300 were localized to the source of the difference in ERP current density, and it was found that the brain regions related to the difference in the decrease in P300 amplitude included the superior parietal lobule, precuneus, prefrontal lobe, and other related regions. Effective connectivity analysis found that TSD enhanced the effective connectivity from the left middle frontal gyrus to the left superior parietal lobule, left inferior parietal lobule, and left precuneus under the identical condition. Pearson correlation analysis showed a positive correlation between the decrease in accuracy of mental rotation and increase in effective connectivity. Thus, our study suggests that TSD impairs the ability of the mental rotation, showing a decrease in P300 amplitude and an enhanced effective connectivity between the middle frontal gyrus and the parietal lobe in the task state.

Improvement in magnetic nanoparticle tomography estimation accuracy by combining sLORETA and non-negative least squares methods

Imaging methods that can detect biofunctionalized magnetic nanoparticles (MNPs) accumulated at cancerous tumor sites are expected to be part of the in vivo cancer diagnostic techniques. An imaging technique called magnetic nanoparticle tomography (MNT), which uses a magnetic sensor array, has been proposed. High sensitivity and spatial resolution were achieved using the non-negative least-squares (NNLS) inverse solution in MNT. However, owing to the presence of measurement noise, the concentration and position of certain MNPs were estimated inaccurately, i.e., artifacts were generated. To overcome this issue, this study first applied standardized low-resolution brain electromagnetic tomography (sLORETA), a spatial filter method with no location bias, to approximate the position of MNPs. The region of analysis was restricted to where the estimated value exceeded the threshold. Subsequently, the NNLS method was applied to estimate the concentration and position of MNPs in the restricted region. In the experiment, two Resovist MNP samples (300 or 500 μgFe) were arranged at a distance of 25–502 mm, and the concentration and position estimation were performed. The estimation results demonstrated that the proposed method successfully suppresses artifacts and adequately estimates the concentration and position of MNPs within a position error of 10 mm and a concentration error of 20 %.