Exact Low-resolution Brain Electromagnetic Tomography Research Articles

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.

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.

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.

Quantitative EEG Based on Direct Recording and Source Localization Estimation in Repetitive Hand Motor Activity

Electroencephalography (EEG) has been widely used in clinical practice as a tool to guide an efficient post-stroke functional recovery program and adjust the rehabilitation training program. EEG has some advantages in temporal resolution, portability, and relatively low cost. Motor-related brain activities can be quantified by measuring the mu rhythm’s event-related desynchronization (ERD), acquired from a direct surface EEG recording. The common problem of direct surface EEG is the crosstalk between brain regions that are unrelated to the sensorimotor cortex. This problem can be solved through the EEG source localization technique. Essentially, the idea of source reconstruction is to estimate the primary cortical current densities during the given EEG recording across the head surface. The recent work aims to compare EEG power spectral density (PSD) analysis and source localization. Modification of exact-Low Resolution Brain Electromagnetic Tomography (eLORETA) was used as an EEG source localization method. EEG data recording was performed on a healthy, able-bodied male subject using 21 electrodes based on the case study’s 10-20 electrode placement system. The subject was asked to perform basic hand movements, i.e., grasping with and without hand load. Mu power was calculated from the electrodes that are located around the motor cortex (C3 and C4), while the average current density was calculated from the Brodmann Area (BA) 4 and 6, both during grasping and relaxation (baseline). The comparison showed that during grasping, there was a decrease in aver-age current density around BA-4 and BA-6 and a decrease in mu power at C3 and C4 when compared to baseline. These results indicate: (1) the activity of mu rhythm’s ERD around the sensorimotor cortex during hand movement; and (2) the possibility of using the EEG source localization method as an alternative biofeedback modality.

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.

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.

Performance Analysis of Low-Resolution Electroencephalogram Source Localization Techniques

Brain source localization has attained significant fascination over the last few decades. Source localization in the human brain is a prospective complication that is derived in the multifaceted real-world complications because of the brain’s practical and biological density, other than medical precincts of assembling Electroencephalogram (EEG) from enormously various subjects. It is validated that the electromagnetic signal recorded on the top of the scalp is owing to the collective actions of neurons inside the brain. Any impulsive action of the brain, sensory stimulus, cognitive action, or the generation of motor yield possibly will offer intensification to such neuronal actions. Source- localization in the human brain implicates the localization and detection of such primary neuronal originators into the brain. Although renowned and different research in the area, the complications remnants to be a mysterious inverse problem in the brain signal processing research. The performance of EEG source localization techniques based on standardized Low-Resolution Brain Electromagnetic Tomography (sLORETA) and exact Low-Resolution Brain Electromagnetic Tomography (eLORETA) is highlighted in this research. The Event-Related Potential (ERP) records with chromatic stimulus are considered for analysis at diverse time intervals for both techniques and final results are discussed in reports of scalp map, slice view, and, cortex map and proposed the optimum techniques for EEG source localization.

Combination of Group Singular Value Decomposition and eLORETA Identifies Human EEG Networks and Responses to Transcranial Photobiomodulation.

Transcranial Photobiomodulation (tPBM) has demonstrated its ability to alter electrophysiological activity in the human brain. However, it is unclear how tPBM modulates brain electroencephalogram (EEG) networks and is related to human cognition. In this study, we recorded 64-channel EEG from 44 healthy humans before, during, and after 8-min, right-forehead, 1,064-nm tPBM or sham stimulation with an irradiance of 257 mW/cm2. In data processing, a novel methodology by combining group singular value decomposition (gSVD) with the exact low-resolution brain electromagnetic tomography (eLORETA) was implemented and performed on the 64-channel noise-free EEG time series. The gSVD+eLORETA algorithm produced 11 gSVD-derived principal components (PCs) projected in the 2D sensor and 3D source domain/space. These 11 PCs took more than 70% weight of the entire EEG signals and were justified as 11 EEG brain networks. Finally, baseline-normalized power changes of each EEG brain network in each EEG frequency band (delta, theta, alpha, beta and gamma) were quantified during the first 4-min, second 4-min, and post tPBM/sham periods, followed by comparisons of frequency-specific power changes between tPBM and sham conditions. Our results showed that tPBM-induced increases in alpha powers occurred at default mode network, executive control network, frontal parietal network and lateral visual network. Moreover, the ability to decompose EEG signals into individual, independent brain networks facilitated to better visualize significant decreases in gamma power by tPBM. Many similarities were found between the cortical locations of SVD-revealed EEG networks and fMRI-identified resting-state networks. This consistency may shed light on mechanistic associations between tPBM-modulated brain networks and improved cognition outcomes.

Abnormal neural oscillations in clinical high risk for psychosis: a magnetoencephalography method study

BackgroundNeural oscillations directly reflect the rhythmic changes of brain activities during the resting state or while performing specific tasks. Abnormal neural oscillations have been discovered in patients with schizophrenia. However,...

Functional integration of mirror neuron system and sensorimotor cortex under virtual self-actions visual perception

Virtual reality (VR) technology, with the advantage of immersive visual experience, has been increasingly applied in the rehabilitation therapy of motor deficits. The functional integration of the mirror neuron system and the sensorimotor cortex under the visual perception of actions is one of the theoretical bases for the application of action observation in the neurorehabilitation of motor deficits. Whether the visual experience changes brought by VR technology can further promote this functional integration to be further confirmed. Using the exact low-resolution brain electromagnetic tomography (eLORETA) source localization method, we calculated and statistically tested the whole brain cortical voxel current density estimation under the Electroencephalogram (EEG) signals collected during action observation under the first-person and third-person perspectives in the VR scene for twenty healthy adults. Furthermore, the functional connectivity between the mirror neuron system and the sensorimotor cortex was analyzed using the lagged phase synchronization method. Under the first-person perspective in the VR scene, the current density changes of the core cortices of the mirror neuron system lead to a larger average event-related potential, more significant suppression in the α1 and α2 frequency bands of EEG signals, and a significant enhancement of functional connectivity between the core cortices of the mirror neuron system and the sensorimotor cortex. These findings indicate that compared with the traditional action observation, the visual reappearance of self-actions in the VR scene further stimulates the activity of the core cortices of the mirror neuron system, and promotes the functional integration of the core cortices of the mirror neuron system and the sensorimotor cortex.

Comparison of Quantitative Electroencephalography between Tic Disorder and Attention-Deficit/Hyperactivity Disorder in Children.

ObjectiveAttention-deficit/hyperactivity disorder (ADHD) and tic disorder (TD) are among the most common comorbid psychopathologies and have a shared genetic basis. The psychopathological and neurophysiological aspects of the mechanism underlying the comorbidity of both disorders have been investigated, but the pathophysiological aspects remain unclear. Therefore, this study aimed to compare the neurophysiological characteristics of ADHD with those of TD using resting-state electroencephalography and exact low-resolution brain electromagnetic tomography (eLORETA) analysis.MethodsWe performed eLORETA analysis based on the resting-state scalp-recorded electrical potential distribution in 34 children with ADHD and 21 age-matched children with TD. Between-group differences in electroencephalography (EEG) current source density in delta, theta, alpha, beta, and gamma bands were investigated in each cortical region.ResultsCompared with the TD group, the ADHD group showed significantly increased theta activity in the frontal region (superior frontal gyrus, t = 3.37, p < 0.05; medial frontal gyrus, t = 3.35, p < 0.05). In contrast, children with TD showed decreased posterior alpha activity than those with ADHD (precuneus, t = −3.40, p < 0.05; posterior cingulate gyrus, t = −3.38, p < 0.05). These findings were only significant when the eyes were closed.ConclusionIncreased theta activity in the frontal region is a neurophysiological marker that can distinguish ADHD from TD. Also, reduced posterior alpha activity might represent aberrant inhibitory control. Further research needs to confirm these characteristics by simultaneously measuring EEG-functional magnetic resonance imaging.

Evaluation of Sensor and Analysis Area in the Signal Source Estimation by Spatial Filter for Magnetocardiography

Magnetocardiography (MCG) has been extensively investigated and evaluated in various clinical studies for its potential in detecting the early stages of cardiac diseases. In particular, the establishment of a technology that can provide detailed information on the lesion parts of the heart is expected. The estimation result of the current spatial filter method, such as the exact low-resolution brain electromagnetic tomography (eLORETA), contains errors and has insufficient estimation accuracy for clinical applications. The information of sensors located at the ends cannot be utilized, and the amount of information that affects the estimation result is different for each sensor, which causes the derivation of a biased estimated solution. Therefore, in this study, we considered that the amount of information of each sensor can be used equally by setting an analysis area larger than the sensor area and proposed a method focusing on the relationship between the analysis area of signal source estimation and the sensor plane. We simulated the most efficient and optimized analysis area to be used and performed the source signal estimation by eLORETA using the QRS-complex peak data of the actual MCGs used in adjusting the 51ch sensor directly above the xiphoid process. We compared the following three sizes of the analysis area: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x =120$ </tex-math></inline-formula> mm, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y =120$ </tex-math></inline-formula> mm, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z =100$ </tex-math></inline-formula> mm (a); <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x =180$ </tex-math></inline-formula> mm, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y =180$ </tex-math></inline-formula> mm, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z =100$ </tex-math></inline-formula> mm (b); and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x =240$ </tex-math></inline-formula> mm, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y =240$ </tex-math></inline-formula> mm, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$z =100$ </tex-math></inline-formula> mm (c). Thus, because the signals from the heart are originally considered to be a single block, the estimated solution is also expected to be a single block. The solution was estimated as a single block, except case with analysis area (a), which corresponds to the conventional method. In addition, the position and size of the estimated solution are similar to those of the heart obtained from the computed tomography (CT) images. The goodness of fit of each analysis area was approximately 0.99 or more, which indicated that there was no significant difference. Thus, we demonstrated that it is possible to visualize the movement of the myocardium by estimating the time-series waveform of the MCG.

Electrical Source Imaging in Freely Moving Rats: Evaluation of a 12-Electrode Cortical Electroencephalography System.

This work presents and evaluates a 12-electrode intracranial electroencephalography system developed at the National Institute of Mental Health (Klecany, Czech Republic) in terms of an electrical source imaging (ESI) technique in rats. The electrode system was originally designed for translational research purposes. This study demonstrates that it is also possible to use this well-established system for ESI, and estimates its precision, accuracy, and limitations. Furthermore, this paper sets a methodological basis for future implants. Source localization quality is evaluated using three approaches based on surrogate data, physical phantom measurements, and in vivo experiments. The forward model for source localization is obtained from the FieldTrip-SimBio pipeline using the finite-element method. Rat brain tissue extracted from a magnetic resonance imaging template is approximated by a single-compartment homogeneous tetrahedral head model. Four inverse solvers were tested: standardized low-resolution brain electromagnetic tomography, exact low-resolution brain electromagnetic tomography (eLORETA), linear constrained minimum variance (LCMV), and dynamic imaging of coherent sources. Based on surrogate data, this paper evaluates the accuracy and precision of all solvers within the brain volume using error distance and reliability maps. The mean error distance over the whole brain was found to be the lowest in the eLORETA solution through signal to noise ratios (SNRs) (0.2 mm for 25 dB SNR). The LCMV outperformed eLORETA under higher SNR conditions, and exhibiting higher spatial precision. Both of these inverse solvers provided accurate results in a phantom experiment (1.6 mm mean error distance across shallow and 2.6 mm across subcortical testing dipoles). Utilizing the developed technique in freely moving rats, an auditory steady-state response experiment provided results in line with previously reported findings. The obtained results support the idea of utilizing a 12-electrode system for ESI and using it as a solid basis for the development of future ESI dedicated implants.

Resting-state electroencephalographic biomarkers of Alzheimer’s disease

ObjectiveWe evaluated the value of resting-state EEG source biomarkers to characterize mild cognitive impairment (MCI) subjects with an Alzheimer’s disease (AD)-like cerebrospinal fluid (CSF) profile and to track neurodegeneration throughout the AD continuum. We further applied a resting-state functional MRI (fMRI)-driven model of source reconstruction and tested its advantage in terms of AD diagnostic accuracy. MethodsThirty-nine consecutive patients with AD dementia (ADD), 86 amnestic MCI, and 33 healthy subjects enter the EEG study. All ADD subjects, 37 out of 86 MCI patients and a distinct group of 53 healthy controls further entered the fMRI study. MCI subjects were divided according to the CSF phosphorylated tau/β amyloid-42 ratio (MCIpos: ≥ 0.13, MCIneg: < 0.13). Using Exact low-resolution brain electromagnetic tomography (eLORETA), EEG lobar current densities were estimated at fixed frequencies and analyzed. To combine the two imaging techniques, networks mostly affected by AD pathology were identified using Independent Component Analysis applied to fMRI data of ADD subjects. Current density EEG analysis within ICA-based networks at selected frequency bands was performed. Afterwards, graph analysis was applied to EEG and fMRI data at ICA-based network level. ResultsADD patients showed a widespread slowing of spectral density. At a lobar level, MCIpos subjects showed a widespread higher theta density than MCIneg and healthy subjects; a lower beta2 density than healthy subjects was also found in parietal and occipital lobes. Evaluating EEG sources within the ICA-based networks, alpha2 band distinguished MCIpos from MCIneg, ADD and healthy subjects with good accuracy. Graph analysis on EEG data showed an alteration of connectome configuration at theta frequency in ADD and MCIpos patients and a progressive disruption of connectivity at alpha2 frequency throughout the AD continuum. ConclusionsTheta frequency is the earliest and most sensitive EEG marker of AD pathology. Furthermore, EEG/fMRI integration highlighted the role of alpha2 band as potential neurodegeneration biomarker.

Holographic Near-Field Microwave Imaging With Antenna Arrays in a Cylindrical Setup

Holographic microwave imaging is fast and robust and it has been adapted for near-field applications such as biomedical imaging and nondestructive testing. While the imaging technique is fast, synthesizing a 2-D aperture by mechanical scanning of the antennas takes time. Here, antenna arrays are used to expedite data acquisition along the azimuthal direction in a cylindrical holographic near-field microwave imaging setup. To deal with the limited and nonuniform samples along the azimuthal direction, three holographic imaging approaches are evaluated, in which, we use interpolation, uniform or nonuniform discrete Fourier transform (DFT), standardized low-resolution brain electromagnetic tomography (sLORETA), and exact low-resolution brain electromagnetic tomography (eLORETA). Besides, to make the system low-cost and portable, off-the-shelf components are used to construct a data acquisition system replacing the vector network analyzers. Simulation and experimental studies are conducted to validate the performance of the proposed imaging system. Structural similarity (SSIM) index is used to assess the quality of the reconstructed images.

Resting-state hyperconnectivity within the default mode network impedes the ability to initiate cognitive performance in first-episode schizophrenia patients

Among multiple cognitive impairments present in schizophrenia, a decline in fast information processing is one of the most severe neuropsychological deficit. Reduced ability to efficiently launch a coherent cognitive activity might be a significant factor contributing to poor results in time-limited tasks obtained by schizophrenia patients. The aim of this study was to identify neurophysiological predictors of expected cognitive initiation failures in a group of first-episode schizophrenia individuals (SZ).To evaluate the effectiveness of initiation, a dynamic analysis of design fluency test was applied, assessing to what extent the productivity was focused within the first interval of the performance, what is a typical way healthy subjects execute this task. Resting-state EEG recordings were obtained from SZ patients (n = 34) and controls (n = 30) to examine functional connectivity between 84 intra-cortical current sources determined by eLORETA (exact low-resolution brain electromagnetic tomography) for six conventionally analyzed frequencies. The nonparametric randomization approach was used to identify hypo- and hyper-connections, i.e. synchronizations significantly differentiating the studied samples in terms of connectivity strength.Generally, SZ patients obtained poor outcomes in fluency test and dynamic analysis of performance confirmed the presence of initiation deficit in clinical sample, which was a single factor explaining the intergroup difference regarding the entire task. In the majority of frequencies, the arrangement of synchronizations in SZ group was dominated by hypo-connections, except for the theta band, in which the strength of synchronizations between posterior cingulate cortex, cuneus and precuneus was significantly higher for SZ group. These theta-band hyper-connections turned out to be significant predictors of cognitive initiation failure in the clinical sample. Additionally, theta hyper-connections correlated negatively with the total number of unique designs generated by patients, however, the strength of this correlation was weaker than regarding initiation index.The results of this study suggest that baseline hyperconnectivity within the posterior hub of the Default Mode Network, containing posterior cingulate gyrus and precuneus, might disturb effective cognitive outcome, not only by interfering with task-positive functional networks but also by delaying the starting phase of performance, which might be specifically deleterious for the execution of time-limited tests.

Genetic Association Between Alzheimer's Disease Risk Variant of the PICALM Gene and EEG Functional Connectivity in Non-demented Adults.

Genome wide association studies (GWAS) have identified and validated the association of the PICALM genotype with Alzheimer’s disease (AD). The PICALM rs3851179 A allele is thought to have a protective effect, whereas the G allele appears to confer risk for AD. The influence of the PICALM genotype on brain functional connectivity in non-demented subjects remains largely unknown. We examined the association of the PICALM rs3851179 genotype with the characteristics of lagged linear connectivity (LLC) of resting EEG sources in 104 non-demented adults younger than 60 years of age. The EEG analysis was performed using exact low-resolution brain electromagnetic tomography (eLORETA) freeware (Pascual-Marqui et al., 2011). We found that the carriers of the A PICALM allele (PICALM AA and AG genotypes) had higher widespread interhemispheric LLC of alpha sources compared to the carriers of the GG PICALM allele. An exploratory correlation analysis showed a moderate positive association between the alpha LLC interhemispheric characteristics and the corpus callosum size and between the alpha interhemispheric LLC characteristics and the Luria word memory scores. These results suggest that the PICALM rs3851179 A allele provides protection against cognitive decline by facilitating neurophysiological reserve capacities in non-demented adults. In contrast, lower functional connectivity in carriers of the AD risk variant, PICALM GG, suggests early functional alterations in alpha rhythm networks.

Electrophysiological Correlates of Basic and Higher Order Cognitive and Affective Theory of Mind Processing in Emerging and Early Adulthood-An Explorative Event-Related Potentials Study to Investigate First-, Second-, and Third-Order Theory of Mind Processing Based on Visual Cues.

Attributing mental states to others in social interactions [Theory of Mind (ToM)] often depends on visual social cues like eye gaze or mimic. This study presents an event-related potentials task (Brainy-ERP) that was developed in order to investigate the electrophysiological correlates of first-, second-, and third-order cognitive and affective ToM processing. The task was based on social visual cues and involved electroencephalographic event-related potential (ERP) analyses and exact low-resolution brain electromagnetic tomography analyses (eLORETA) source localization analyses. Results showed that in cognitive and affective conditions, first-order trials elicited greater Anterior P2 (180–370 ms) amplitudes. In the cognitive condition, third-order trials elicited greatest amplitudes in the broadly distributed early negative slow wave (eNSW, 260–470 ms) and the late NSW (LNSW, 460–1,000 ms). In the affective condition, third-order and second-order trials elicited greatest amplitudes in a broadly distributed NSW (250–1,000 ms). Regarding affective trials in the NSW time span, statistical significant differences and trends were shown regarding activation of underlying brain regions. Third-order trials elicited greatest activation in a number of regions typically associated with the ToM network, especially the posterior cingulate cortex (PCC), cuneus, and temporoparietal junction (TPJ). Furthermore, ToM low performers (participants with high accuracy but longer reaction times) showed by trend smaller Posterior N1 and significantly smaller eNSW amplitudes compared to average and high performers. This study offers new insights into electrophysiological correlates of basic and higher order cognitive and affective ToM processing and its precise time course.