Brain Sources Research Articles

Neuroimaging of EEG Rhythms at Resting State in Normal Elderly Adults: A Standard Low-Resolution Electromagnetic Tomography Study.

Brain source mechanisms of the cortical EEG brainwave at the resting state in the elderly during normal aging are rarely known. To solve the problem, we use a standard low-resolution electromagnetic tomography to explore the brain source mechanisms on the effects of healthy aging on brain function at the resting state. Eye-closed EEG signals at resting state were sampled in 13 normal elderly adults and 17 normal young adults. The EEG rhythms by frequency band, delta, theta, alpha 1, alpha 2, beta 1, and beta 2 were of interest for this analysis. Brain sources of these rhythms were estimated by standard low-resolution electromagnetic tomography. Statistical results demonstrated that central, parietal, occipital, and temporal alpha 1 and theta brain sources presented the pattern normal young adults > normal elderly adults (P < 0.05), whereas the global beta 1 and beta 2 brain sources presented the pattern normal elderly adults > normal young adults (P < 0.05). Moreover, the same is true that amplitude of central, parietal, occipital, and temporal alpha 2 brain sources were lower in normal elderly adults compared with those in normal young adults (P < 0.05). These results imply that normal aging is linked to cortical neural desynchronization of alpha and delta rhythms and synchronization of beta rhythm in central, parietal, and frontal cortices at resting state.

Quantifying time-varying sources in magnetoencephalography—A discrete approach

We study the distribution of brain source from the most advanced brain imaging technique, Magnetoencephalography (MEG) which measures the magnetic fields outside of the human head produced by the electrical activity inside the brain. Common time-varying source localization methods assume the source current with a time-varying structure and solve the MEG inverse problem by mainly estimating the source moment parameters. These methods use the fact that the magnetic fields linearly depend on the moment parameters of the source and work well under the linear dynamic system. However, magnetic fields are known to be nonlinearly related to the location parameters of the source. The existing work on estimating the time-varying unknown location parameters is limited. We are motivated to investigate the source distribution for the location parameters based on a dynamic framework, where the posterior distribution of the source is computed in a closed form discretely. The new framework allows us not only to directly approximate the posterior distribution of the source current, where sequential sampling methods may suffer from slow convergence due to the large volume of measurement, but also to quantify the source distribution at any time point from the entire set of measurements reflecting the distribution of the source, rather than using only the measurements up to the time point of interest. Both a dynamic procedure and a switch procedure are pro- posed for the new discrete approach, balancing estimation accuracy and computational efficiency when multiple sources are present. In both simulation and real data, we illustrate that the new method is able to provide comprehensive insight into the time evolution of the sources at different stages of the MEG and EEG experiment.

Spatiotemporal brain signal associated with high and low levels of proactive motor response inhibition

Proactive motor response inhibition is used to strategically restrain actions in preparation for stopping. In this study, we first examined the event related potential (ERP) elicited by low and high level of proactive response inhibition, as assessed by the stop-signal task. Corroborating previous studies, we found an increased amplitude of the contingent negative variation (CNV) in the high level of proactive inhibition. As the main goal of the present study, swLORETA was used to determine the neural generators characterising CNV differences between low and high levels of proactive inhibition. Results showed that the higher level of proactive inhibition involved numerous generators, including within the middle and medial frontal gyrus. Importantly, we observed that the lower level of proactive inhibition also involved a specific neural generator, within the frontopolar cortex. Altogether, present findings identified the specific brain sources of ERP signals involved in the later phase of motor preparation under low or high levels of proactive motor response inhibition.

Motor imagery classification by active source dynamics

Nowadays Brain Computer Interface (BCI) is one of the most important fields in neuroscience in which machine works are controlled with the human brain. Motor imagery recognition is one part of BCI study in which several papers are published in this field. In this paper, four individual motor imagery (left and right hand, foot, and tongue) are classified by using visibility graph features of brain source dynamic which are extracted from EEG signals. In the proposed method, firstly EEG signals are recorded and preprocessed. Source localization algorithm (sLORETA in this paper) is applied to EEG signals and active sources during time are extracted. Then the multivariate autoregressive model (MVAR) is fitted to active sources and dual Kalman filter is applied to MVAR model for source dynamics and their relations estimation. Then extracted source dynamic signals are mapped to 5 different visibility graphs and some features are extracted from each graph. Extracted features number are reduced by analysis of variance (ANOVA) method and support vector machine (SVM) classifiers are applied for motor imagery classification. In this research, the competition IV of BCI database was used and the results indicate that the proposed algorithm has a superior performance to the other methods. By classifying motor imaginary tasks with source dynamic signals better results with more accuracy and good performance measures are achieved in comparison with EEG signals classification by extracted from visibility graph gives better performance between graphs.

Dynamic cognitive workload assessment for fighter pilots in simulated fighter aircraft environment using EEG

The trend in aviation automation demands more mental workload of pilots, in addition to their routine manoeuvring task. For fighter aircraft pilots, the mental workload multifold due to read-back or hear-back with precise weapon handling in combat scenarios. Assessing the pilot cognitive workload is an important aspect, as it influences the pilot performance, and this is an error intolerance environment. This study aims to assess fighter aircraft Pilot’s Cognitive WorkLoad (PCWL) and attention when they exposed to a dynamic workload environment; by monitoring the neuronal activities. Here the spectral Electroencephalographic (EEG) features were extracted to assess the dynamic workload (normal, moderate, high, and very high workload), and attention monitored by National Aeronautics and Space Administration-Task Load Index (NASA-TLX) and it serves as a validation for cognitive findings. Also, brain source localization obtained from EEG inverse calculation technique, Standardized Low-Resolution brain Electromagnetic Tomography (sLORETA) performed to understand the coherence of workload and neuronal activity engagement concerning brain lobes. Statistical significance (p < 0.05) of EEG feature sets indicate that the approach and withdrawal behaviour of pilots are getting modulated during the different workload of the flying conditions. Further, NASA-TLX findings are well correlated and indicate a cognitive demand during high and very high workload. Furthermore, EEG findings correlated with the sLORETA study, which shows the engagement of prefrontal sensorimotor, fronto-temporal, and parietal regions during various flying conditions, under different cognitive loading. In the future, these findings could be adopted for pilot assessment and as a validation tool of the pilot's training program.

MAGNETOENCEPHALOGRAPHY–ELECTROENCEPHALOGRAPHY CO-REGISTRATION USING 3D GENERALIZED HOUGH TRANSFORM

This study proposes an advanced co-registration method for an integrated high temporal resolution electroencephalography (EEG) and magnetoencephalography (MEG) data. The MEG has a higher accuracy for source localization techniques and spatial resolution by sensing magnetic fields generated by the entire brain using multichannel superconducting quantum interference devices, whereas EEG can record electrical activities from larger cortical surface to detect epilepsy. However, by integrating the two modality tools, we can accurately localize the epileptic activity compared to other non-invasive modalities. Integrating the two modality tools is challenging and important. This study proposes a new algorithm using an extended three-dimensional generalized Hough transform (3D GHT) to co-register the two modality data. The pre-process steps require the locations of EEG electrodes, MEG sensors, head-shape points of subjects and fiducial landmarks. The conventional GHT algorithm is a well-known method used for identifying or locating two 2D images. This study proposes a new co-registration method that extends the 2D GHT algorithm to a 3D GHT algorithm that can automatically co-register 3D image data. It is important to study the prospective brain source activity in bio-signal analysis. Furthermore, the study examines the registration accuracy evaluation by calculating the root mean square of the Euclidean distance of MEG–EEG co-registration data. Several experimental results are used to show that the proposed method for co-registering the two modality data is accurate and efficient. The results demonstrate that the proposed method is feasible, sufficiently automatic, and fast for investigating brain source images.

PPG neurons in the nucleus of the solitary tract modulate heart rate but do not mediate GLP-1 receptor agonist-induced tachycardia in mice

ObjectiveGlucagon-like peptide-1 receptor agonists (GLP-1RAs) are used as anti-diabetic drugs and are approved for obesity treatment. However, GLP-1RAs also affect heart rate (HR) and arterial blood pressure (ABP) in rodents and humans. Although the activation of GLP-1 receptors (GLP-1R) is known to increase HR, the circuits recruited are unclear, and in particular, it is unknown whether GLP-1RAs activate preproglucagon (PPG) neurons, the brain source of GLP-1, to elicit these effects. MethodsWe investigated the effect of GLP-1RAs on heart rate in anaesthetized adult mice. In a separate study, we manipulated the activity of nucleus tractus solitarius (NTS) PPG neurons (PPGNTS) in awake, freely behaving transgenic Glu-Cre mice implanted with biotelemetry probes and injected with AAV-DIO-hM3Dq:mCherry or AAV-mCherry-FLEX-DTA. ResultsSystemic administration of the GLP-1RA Ex-4 increased resting HR in anaesthetized or conscious mice, but had no effect on ABP in conscious mice. This effect was abolished by β-adrenoceptor blockade with atenolol, but unaffected by the muscarinic antagonist atropine. Furthermore, Ex-4-induced tachycardia persisted when PPGNTS neurons were ablated, and Ex-4 did not induce expression of the neuronal activity marker cFos in PPGNTS neurons. PPGNTS ablation or acute chemogenetic inhibition of these neurons via hM4Di receptors had no effect on resting HR. In contrast, chemogenetic activation of PPGNTS neurons increased resting HR. Furthermore, the application of GLP-1 within the subarachnoid space of the middle thoracic spinal cord, a major projection target of PPG neurons, increased HR. ConclusionsThese results demonstrate that both systemic application of Ex-4 or GLP-1 and chemogenetic activation of PPGNTS neurons increases HR. Ex-4 increases the activity of cardiac sympathetic preganglionic neurons of the spinal cord without recruitment of PPGNTS neurons, and thus likely recapitulates the physiological effects of PPG neuron activation. These neurons therefore do not play a significant role in controlling resting HR and ABP but are capable of inducing tachycardia and so are likely involved in cardiovascular responses to acute stress.

Space-Time-Frequency Multi-Sensor Analysis for Motor Cortex Localization Using Magnetoencephalography.

Brain source imaging and time frequency mapping (TFM) are commonly used in magneto/electro encephalography (M/EEG) imaging. However, these methods suffer from important limitations. Source imaging is based on an ill-posed inverse problem leading to instability of source localization solutions, has a limited capacity to localize high frequency oscillations and loses its robustness for induced responses (ill-defined trigger). The drawback of TFM is that it involves independent analysis of signals from a number of frequency bands, and from co-localized sensors. In the present article, a regression-based multi-sensor space–time–frequency analysis (MSA) approach, which integrates co-localized sensors and/or multi-frequency information, is proposed. To estimate task-specific brain activations, MSA uses cross-validated, shifted, multiple Pearson correlation, calculated from the time–frequency transformed brain signal and the binary signal of stimuli. The results are projected from the sensor space onto the cortical surface. To assess MSA performance, the proposed method was compared to the weighted minimum norm estimate (wMNE) source imaging method, in terms of spatial selectivity and robustness against an ill-defined trigger. Magnetoencephalography (MEG) recordings were performed in fourteen subjects during two motor tasks: finger tapping and elbow flexion/extension. In particular, our results show that the MSA approach provides good localization performance when compared to wMNE and statistically significant improvement of robustness against ill-defined trigger.

REM-sleep related hypermotor seizures: Video documentation and ictal source imaging

IntroductionRapid eye movement (REM) sleep has an inhibitory effect on epileptiform EEG discharges, and seizures occur extremely rarely in REM sleep. Case studyWe present the case and video recordings of a 10-year-old boy, with sleep-related hypermotor seizures starting from REM sleep, identified from videoEEG recordings. The semiology comprised intense fear, tachycardia, tachypnea, followed by hypermotor manifestations. Further investigations included brain MRI and source localization of the EEG signals. Multiple antiepileptic drugs were tried, the patient obtaining a good control of the seizures in the last 2.5 years with eslicarbazepine. Discussion and conclusionThe ictal EEG source imaging showed seizure onset in the anterior part of the right insula, with propagation to the orbitofrontal area, confirmed by the semiological sequence. Although rare, focal seizures can be triggered by REM sleep and our findings suggest that deficient maturation of brain areas involved in sleep modulation might induce insufficient desynchronization during REM sleep, thus allowing seizure emergence.

Noninvasive electromagnetic source imaging of spatiotemporally distributed epileptogenic brain sources

Brain networks are spatiotemporal phenomena that dynamically vary over time. Functional imaging approaches strive to noninvasively estimate these underlying processes. Here, we propose a novel source imaging approach that uses high-density EEG recordings to map brain networks. This approach objectively addresses the long-standing limitations of conventional source imaging techniques, namely, difficulty in objectively estimating the spatial extent, as well as the temporal evolution of underlying brain sources. We validate our approach by directly comparing source imaging results with the intracranial EEG (iEEG) findings and surgical resection outcomes in a cohort of 36 patients with focal epilepsy. To this end, we analyzed a total of 1,027 spikes and 86 seizures. We demonstrate the capability of our approach in imaging both the location and spatial extent of brain networks from noninvasive electrophysiological measurements, specifically for ictal and interictal brain networks. Our approach is a powerful tool for noninvasively investigating large-scale dynamic brain networks.

Synchronisation of Neural Oscillations and Cross-modal Influences.

At any given moment, we receive multiple signals from our different senses. Prior research has shown that signals in one sensory modality can influence neural activity and behavioural performance associated with another sensory modality. Recent human and nonhuman primate studies suggest that such cross-modal influences in sensory cortices are mediated by the synchronisation of ongoing neural oscillations. In this review, we consider two mechanisms proposed to facilitate cross-modal influences on sensory processing, namely cross-modal phase resetting and neural entrainment. We consider how top-down processes may further influence cross-modal processing in a flexible manner, and we highlight fruitful directions for further research.

Expanding the search for genetic biomarkers of Parkinson's disease into the living brain

Altered gene expression related to Parkinson's Disease (PD) has not been described in the living brain, yet this information may support novel discovery pertinent to disease pathophysiology and treatment. This study compared the transcriptome in brain biopsies obtained from living PD and Control patients. To evaluate the novelty of this data, a comprehensive literature review also compared differentially expressed gene (DEGs) identified in the current study with those reported in PD cadaveric brain and peripheral tissues. RNA was extracted from rapidly cryopreserved frontal lobe specimens collected from PD and Control patients undergoing neurosurgical procedures. RNA sequencing (RNA-Seq) was performed and validated using quantitative polymerase chain reaction. DEG data was assessed using bioinformatics and subsequently included within a comparative analysis of PD RNA-Seq studies. 370 DEGs identified in living brain specimens reflected diverse gene groups and included key members of trophic signaling, apoptosis, inflammation and cell metabolism pathways. The comprehensive literature review yielded 7 RNA-Seq datasets generated from blood, skin and cadaveric brain but none from a living brain source. From the current dataset, 123 DEGs were identified only within the living brain and 267 DEGs were either newly found or had distinct directional change in living brain relative to other tissues. This is the first known study to analyze the transcriptome in brain tissue from living PD and Control patients. The data produced using these methods offer a unique, unexplored resource with potential to advance insight into the genetic associations of PD.

Glymphatic clearance of simulated silicon dispersion in mouse brain analyzed by laser induced breakdown spectroscopy

Silicon-based devices, such as neural probes, are increasingly used as electrodes for receiving electrical signals from neural tissue. Neural probes used chronically have been known to induce inflammation and elicit an immune response. The current study detects and evaluates silicon dispersion from a concentrated source in the mouse brain using laser induced breakdown spectroscopy. Element lines for Si (I) were found at the injection site at approximately 288 nm at 3hr post-implantation, even with tissue perfusion, indicating possible infusion into neural tissue. At 24hr and 1-week post-implantation, no silicon lines were found, indicating clearance. An isolated immune response was found by CD68 macrophage response at 24hr post injection. Future studies should measure chronic silicon exposure to determine if the inflammatory response is proportional to silicon administration. The present type of protocol, coupling laser induced breakdown spectroscopy, neuroimaging, histology, immunohistochemistry, and determination of clearance could be used to investigate the glymphatic system and different tissue states such as in disease (e.g. Alzheimer's).

Emotion Recognition Based on High-Resolution EEG Recordings and Reconstructed Brain Sources

Electroencephalography (EEG)-based emotion recognition is currently a hot issue in the affective computing community. Numerous studies have been published on this topic, following generally the same schema: 1) presentation of emotional stimuli to a number of subjects during the recording of their EEG, 2) application of machine learning techniques to classify the subjects’ emotions. The proposed approaches vary mainly in the type of features extracted from the EEG and in the employed classifiers, but it is difficult to compare the reported results due to the use of different datasets. In this paper, we present a new database for the analysis of valence (positive or negative emotions), which is made publicly available. The database comprises physiological recordings and 257-channel EEG data, contrary to all previously published datasets, which include at most 62 EEG channels. Furthermore, we reconstruct the brain activity on the cortical surface by applying source localization techniques. We then compare the performances of valence classification that can be achieved with various features extracted from all source regions (source space features) and from all EEG channels (sensor space features), showing that the source reconstruction improves the classification results. Finally, we discuss the influence of several parameters on the classification scores.

Brain source imaging based on movement-related cortical potentials induced by fatigue during self-paced handgrip contractions.

By using standard low resolution electromagnetic tomography (sLORETA), we sought to explore the changes in brain source localization when performing right handgrip contractions in the condition of muscular fatigue. Ten healthy adults volunteered for this study, and were asked to perform repeated and intermittent self-paced right handgrip contractions at 30% maximal voluntary contraction based on visual feedback leading to fatigue of right flexor digitorum profundus. Motor potentials from the movement-related cortical potentials were extracted from the electroencephalogram and were further analyzed by sLORETA. The activated cortical regions were mainly the Brodmann area 6 on the superior frontal and medial frontal gyri, and the BA 10 on the frontal and medial frontal gyri. With the development of muscular fatigue, current density of the motor potential significantly increased and the activated cortical areas markedly enlarged. In an attempt to maintain a target level of force during upper limb muscle fatigue induced by low intensity repetitive activation, the brain enhances the activation of sensorimotor cortex and enlarges the sensorimotor cortex area, especially in the ipsilateral hemisphere.

Comparison of Machine Learning Techniques Based Brain Source Localization Using EEG Signals

The brain is the most complex organ of the human body with millions of connections and activations. The electromagnetic signals are generated inside the brain due to a mental or physical task performed. These signals excite a bunch of neurons within a particular lobe depending upon the nature of the task performed. To localize this activity, certain machine learning (ML) techniques in conjunction with a neuroimaging technique (M/EEG, fMRI, PET) are developed. Different ML techniques are provided in the literature for brain source localization. Among them, the most common are: minimum norm estimation (MNE), low resolution brain electromagnetic tomography (LORETA) and Bayesian framework based multiple sparse priors (MSP). In this research work, EEG is used as a neuroimaging technique. EEG data is synthetically generated at SNR=5dB. Afterwards, ML techniques are applied to estimate the active sources. Each dataset is run for multiple trials (>40). The performance is analyzed using free energy and localization error as performance indicators. Furthermore, MSP is applied with a variant number of patches to observe the impact of patches on source localization. It is observed that with an increased number of patches, the sources are localized with more precision and accuracy as expressed in terms of free energy and localization error, respectively. The patches optimization within the Bayesian Framework produces improved results in terms of free energy and localization error.

Nonlinear interaction decomposition (NID): A method for separation of cross-frequency coupled sources in human brain

Cross-frequency coupling (CFC) between neuronal oscillations reflects an integration of spatially and spectrally distributed information in the brain. Here, we propose a novel framework for detecting such interactions in Magneto- and Electroencephalography (MEG/EEG), which we refer to as Nonlinear Interaction Decomposition (NID). In contrast to all previous methods for separation of cross-frequency (CF) sources in the brain, we propose that the extraction of nonlinearly interacting oscillations can be based on the statistical properties of their linear mixtures. The main idea of NID is that nonlinearly coupled brain oscillations can be mixed in such a way that the resulting linear mixture has a non-Gaussian distribution. We evaluate this argument analytically for amplitude-modulated narrow-band oscillations which are either phase-phase or amplitude-amplitude CF coupled. We validated NID extensively with simulated EEG obtained with realistic head modelling. The method extracted nonlinearly interacting components reliably even at SNRs as small as −15 dB. Additionally, we applied NID to the resting-state EEG of 81 subjects to characterize CF phase-phase coupling between alpha and beta oscillations. The extracted sources were located in temporal, parietal and frontal areas, demonstrating the existence of diverse local and distant nonlinear interactions in resting-state EEG data. All codes are available publicly via GitHub.

External noise removed from magnetoencephalographic signal using independent component analyses of reference channels

BackgroundMany magnetoencephalographs (MEG) contain, in addition to data channels, a set of reference channels positioned relatively far from the head that provide information on magnetic fields not originating from the brain. This information is used to subtract sources of non-neural origin, with either geometrical or least mean squares (LMS) methods. LMS methods in particular tend to be biased toward more constant noise sources and are often unable to remove intermittent noise. New methodTo better identify and eliminate external magnetic noise, we propose performing ICA directly on the MEG reference channels. This in most cases produces several components which are clear summaries of external noise sources with distinct spatio-temporal patterns. We present two algorithms for identifying and removing such noise components from the data which can in many cases significantly improve data quality. ResultsWe performed simulations using forward models that contained both brain sources and external noise sources. First, traditional LMS-based methods were applied. While this removed a large amount of noise, a significant portion still remained. In many cases, this portion could be removed using the proposed technique, with little to no false positives. Comparison with existing method(s)The proposed method removes significant amounts of noise to which existing LMS-based methods tend to be insensitive. ConclusionsThe proposed method complements and extends traditional reference based noise correction with little extra computational cost and low chances of false positives. Any MEG system with reference channels could profit from its use, particularly in labs with intermittent noise sources.

EEG anticipation of random high and low arousal faces and sounds

Background: In this study, we investigated the neural correlates of the anticipatory activity of randomly presented faces and sounds of both high and low arousal level by recording EEG activity with a high spatial resolution EEG system. Methods: We preregistered the following three hypotheses: 1) a contingent Negative Variation (CNV) difference in the amplitude voltage between auditory vs faces stimuli; 2) a greater amplitude voltage in the CNV, in high arousal stimuli vs low arousal stimuli, both in auditory and faces stimuli, in the temporal window from 0 to 1000 ms before the stimulus presentation; 3) in the time window from 0 to 1000 ms a sensory specific activation at the brain source level in the temporal lobe and auditory cortex before the presentation of an auditory stimulus and an activation of occipital area, dedicated to the elaboration of visual stimuli, before the presentation of faces . Results: Using a preregistered, hypothesis-driven approach, we found no statistically significant differences in the CNV due to an overly conservative correction for multiple comparisons for the control of Type I error. By contrast, using a data-driven approach based on a machine learning algorithm (Support Vector Machine), we found a significantly larger amplitude in the occipital cluster of electrodes before the presentation of faces with respect to sounds, along with a larger amplitude in the right auditory cortex before the presentation of sounds with respect to faces. Furthermore, we found greater CNV activity in the late prestimulus interval for high vs. low-arousal sounds stimuli in the left centro-posterior scalp regions. Conclusions: These findings, although preliminary, seem to support the hypothesis that the neurophysiological anticipatory activity of random events is specifically driven by either the sensory characteristics or the arousal level of future stimuli.

Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG.

This study aimed to examine the effects of chronic methamphetamine use on the topological organization of whole-brain functional connectivity network (FCN) by reconstruction of neural-activity time series at resting-state. The EEG of 36 individuals with methamphetamine use disorder (IWMUD) and 24 normal controls (NCs) were recorded, pre-processed and source-reconstructed using standardized low-resolution tomography (sLORETA). The brain FCNs of participants were constructed and between-group differences in network topological properties were investigated using graph theoretical analysis. IWMUD showed decreased characteristic path length, increased clustering coefficient and small-world index at delta and gamma frequency bands compared to NCs. Moreover, abnormal changes in inter-regional connectivity and network hubs were observed in all the frequency bands. The results suggest that the IWMUD and NCs have distinct FCNs at all the frequency bands, particularly at the delta and gamma bands, in which deviated small-world brain topology was found in IWMUD.