Exact Low-resolution Electromagnetic Tomography Research Articles

Localized estimation of event-related neural source activity from simultaneous MEG-EEG with a recurrent neural network

Estimating intracranial current sources underlying the electromagnetic signals observed from extracranial sensors is a perennial challenge in non-invasive neuroimaging. Established solutions to this inverse problem treat time samples independently without considering the temporal dynamics of event-related brain processes.This paper describes current source estimation from simultaneously recorded magneto- and electro-encephalography (MEEG) using a recurrent neural network (RNN) that learns sequential relationships from neural data. The RNN was trained in two phases: (1) pre-training and (2) transfer learning with L1 regularization applied to the source estimation layer. Performance of using scaled labels derived from MEEG, magnetoencephalography (MEG), or electroencephalography (EEG) were compared, as were results from volumetric source space with free dipole orientation and surface source space with fixed dipole orientation. Exact low-resolution electromagnetic tomography (eLORETA) and mixed-norm L1/L2 (MxNE) source estimation methods were also applied to these data for comparison with the RNN method.The RNN approach outperformed other methods in terms of output signal-to-noise ratio, correlation and mean-squared error metrics evaluated against reference event-related field (ERF) and event-related potential (ERP) waveforms. Using MEEG labels with fixed-orientation surface sources produced the most consistent estimates.To estimate sources of ERF and ERP waveforms, the RNN generates temporal dynamics within its internal computational units, driven by sequential structure in neural data used as training labels. It thus provides a data-driven model of computational transformations from psychophysiological events into corresponding event-related neural signals, which is unique among MEEG source reconstruction solutions.

Is parental overcontrol a specific form of child maltreatment? Insights from a resting state EEG connectivity study

IntroductionRecent studies suggest that parental overcontrol could be considered a specific form of childhood trauma (CT). Although previous research has shown that CT alters the functional and structural architecture of large-scale networks in the brain, the neural basis associated with parental overcontrol has not been sufficiently explored. Therefore, the main aim of the current study was to investigate the relationship between parental overcontrol and electroencephalography (EEG) triple network (TN) functional connectivity during the resting state (RS) condition in a non-clinical sample (N = 71; 39 females, mean age 23.94 ± 5.89 SD). MethodsEEG was recorded during 5 min of RS with eyes closed. All participants were asked to self-report maternal and paternal overcontrol, CT and general psychopathology. All EEG analyses were performed using the exact low-resolution electromagnetic tomography software (eLORETA). ResultsOur results showed a significant positive correlation between maternal overcontrol and theta connectivity between the salience network and the central executive network. This connectivity pattern was independently associated with maternal overcontrol even when controlling for relevant confounding variables, including the severity of CT and the general level of psychopathology. This neurophysiological pattern may reflect a predisposition to detect and respond to potentially threatening stimuli in the environment, which is typically associated with excessive overcontrol. ConclusionsOur findings support the hypothesis that parental overcontrol should be considered a form of CT in all respects independent of the forms traditionally studied in the literature (i.e., emotional abuse, physical abuse, sexual abuse, and physical and emotional neglect).

Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers

BackgroundMood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing treatment. Diagnostic biomarkers could significantly improve clinical care by reducing dependence on symptomatic presentation. MethodsWe used deep learning analysis (DLA) of resting electroencephalograph (EEG) to differentiate healthy control (HC) subjects (N = 239), from those with major depressive disorder (MDD) (N = 105), MDD-atypical (MDD-A) (N = 27), MDD-psychotic (MDD-P) (N = 35), bipolar disorder-depressed episode (BD-DE) (N = 71), BD-manic episode (BD-ME) (N = 49), and schizophrenia (SCZ) (N = 122) and also differentiate subjects with mental disorders on a pair-wise basis. DSM-III-R diagnoses were determined and supplemented by computerized Quick Diagnostic Interview Schedule. After EEG preprocessing, robust exact low-resolution electromagnetic tomography (ReLORETA) computed EEG sources for 82 brain regions. 20 % of all subjects were then set aside for independent testing. Feature selection methods were then used for the remaining subjects to identify brain source regions that are discriminating between diagnostic categories. ResultsPair-wise classification accuracies between 90 % and 100 % were obtained using independent test subjects whose data were not used for training purposes. The most frequently selected features across various pairs are in the postcentral, supramarginal, and fusiform gyri, the hypothalamus, and the left cuneus. Brain sites discriminating SCZ from HC were mainly in the left hemisphere while those separating BD-ME from HC were on the right. LimitationsThe use of superseded DSM-III-R diagnostic system and relatively small sample size in some disorder categories that may increase the risk of overestimation. ConclusionsDLA of EEG could be trained to autonomously classify psychiatric disorders with over 90 % accuracy compared to an expert clinical team using standardized operational methods.

Social Buffering Effects during Craft Activities in Parallel Group Session Revealed by EEG Analysis and Parasympathetic Activity

Introduction: The therapeutic structure of occupational therapy (OT) includes groups. Although the presence of others is expected to be relaxing due to the social buffering effect and the tend and befriend theory, it has not been sufficiently validated in accordance with the therapeutic structure of OT. The aim of this study was to investigate the electrophysiological evidence for the effectiveness of parallel groups and states of concentration on craft activities used in OT. Methods: Thirty healthy young adults were used as controls to measure EEG and autonomic activity during craft activities in three conditions: alone, parallel, and nonparallel. EEG was analyzed using exact low-resolution electromagnetic tomography, and autonomic activity was analyzed using Lorenz plot analysis. Results: Parasympathetic activity was significantly higher in the parallel condition than in the alone condition. A significant negative correlation was found between current source density and parasympathetic activity in the region centered on the right insular cortex in the α1 band, and functional connectivity in regions including the anterior cingulate cortex and insular cortex was associated with autonomic activity. Conclusion: Craft activities that occurred during frontal midline theta rhythm also increased parasympathetic activity. The results suggest that the parallel groups used in OT and the intensive state of craft activities induce a social buffering effect that increases parasympathetic activity despite the absence of physical contact or social support. This provides evidence for the effectiveness of the therapeutic structure of occupational activities and groups in OT.

Pupil-linked arousal modulates network-level EEG signatures of attention reorienting during immersive multitasking

Objective. When multitasking, we must dynamically reorient our attention between different tasks. Attention reorienting is thought to arise through interactions of physiological arousal and brain-wide network dynamics. In this study, we investigated the relationship between pupil-linked arousal and electroencephalography (EEG) brain dynamics in a multitask driving paradigm conducted in virtual reality. We hypothesized that there would be an interaction between arousal and EEG dynamics and that this interaction would correlate with multitasking performance. Approach. We collected EEG and eye tracking data while subjects drove a motorcycle through a simulated city environment, with the instructions to count the number of target images they observed while avoiding crashing into a lead vehicle. The paradigm required the subjects to continuously reorient their attention between the two tasks. Subjects performed the paradigm under two conditions, one more difficult than the other. Main results. We found that task difficulty did not strongly correlate with pupil-linked arousal, and overall task performance increased as arousal level increased. A single-trial analysis revealed several interesting relationships between pupil-linked arousal and task-relevant EEG dynamics. Employing exact low-resolution electromagnetic tomography, we found that higher pupil-linked arousal led to greater EEG oscillatory activity, especially in regions associated with the dorsal attention network and ventral attention network (VAN). Consistent with our hypothesis, we found a relationship between EEG functional connectivity and pupil-linked arousal as a function of multitasking performance. Specifically, we found decreased functional connectivity between regions in the salience network (SN) and the VAN as pupil-linked arousal increased, suggesting that improved multitasking performance at high arousal levels may be due to a down-regulation in coupling between the VAN and the SN. Our results suggest that when multitasking, our brain rebalances arousal-based reorienting so that individual task demands can be met without prematurely reorienting to competing tasks.

EEG Source Localization during an Arm Isometric Force Exertion Task at Different Levels of Perceived Exertion.

Neuroergonomics is an emerging science that focuses on the human brain's performance during physical work. The advent of portable neurophysiological methods, including electroencephalography (EEG), has enabled measurements of real-time brain activity during physical tasks without restricting body movements. However, the EEG signatures of different levels of physical exertion activity involving the musculoskeletal system remain poorly understood. This study investigated the EEG source localization activity induced by predefined force exertion levels during an isometric arm force exertion task in healthy female participants for the alpha and beta frequency bands. Exact low-resolution electromagnetic tomography (eLORETA) was used to localize the current source densities (CSDs) in 84 anatomical brain regions of interest. The maximum CSDs for extremely hard force exertion levels for the alpha frequency were localized in Brodmann area (BA) 6, whereas CSDs associated with other exertion levels were localized in BA 8. The maximum CSDs for extremely hard force exertion levels for beta were localized in BA 5, whereas CSDs associated with other exertion levels were localized in BA 7. These findings extend the current understanding of the neurophysiological basis of physical exertion with various force levels and suggest that specific brain regions are involved in generating the sensation of force exertion. To our knowledge, this is the first study localizing EEG activity among various predefined force exertion levels during an isometric arm exertion task in healthy female participants.

Exposure to nature is associated with decreased functional connectivity within the distress network: A resting state EEG study.

Despite the well-established evidence supporting the restorative potential of nature exposure, the neurophysiological underpinnings of the restorative cognitive/emotional effect of nature are not yet fully understood. The main purpose of the current study was to investigate the association between exposure to nature and electroencephalography (EEG) functional connectivity in the distress network. Fifty-three individuals (11 men and 42 women; mean age 21.38 ± 1.54 years) were randomly assigned to two groups: (i) a green group and (ii) a gray group. A slideshow consisting of images depicting natural and urban scenarios were, respectively, presented to the green and the gray group. Before and after the slideshow, 5 min resting state (RS) EEG recordings were performed. The exact low-resolution electromagnetic tomography (eLORETA) software was used to execute all EEG analyses. Compared to the gray group, the green group showed a significant increase in positive emotions (F 1; 50 = 9.50 p = 0.003) and in the subjective experience of being full of energy and alive (F 1; 50 = 4.72 p = 0.035). Furthermore, as compared to urban pictures, the exposure to natural images was associated with a decrease of delta functional connectivity in the distress network, specifically between the left insula and left subgenual anterior cingulate cortex (T = -3.70, p = 0.023). Our results would seem to be in accordance with previous neurophysiological studies suggesting that experiencing natural environments is associated with brain functional dynamics linked to emotional restorative processes.

A Robust eLORETA Technique for Localization of Brain Sources in the Presence of Forward Model Uncertainties.

In this paper, we present a robust version of the well-known exact low-resolution electromagnetic tomography (eLORETA) technique, named ReLORETA, to localize brain sources in the presence of different forward model uncertainties. We first assume that the true lead field matrix is a transformation of the existing lead field matrix distorted by uncertainties and propose an iterative approach to estimate this transformation accurately. Major sources of the forward model uncertainties, including differences in geometry, conductivity, and source space resolution between the real and simulated head models, and misaligned electrode positions, are then simulated to test the proposed method. ReLORETA and eLORETA are applied to simulated focal sources in different regions of the brain and the presence of various noise levels as well as real data from a patient with focal epilepsy. The results show that ReLORETA is considerably more robust and accurate than eLORETA in all cases. Having successfully dealt with the forward model uncertainties, ReLORETA proved to be a promising method for real-world clinical applications. eLORETA is one of the localization techniques that could be used to study brain activity for medical applications such as determining the epileptogenic zone in patients with medically refractory epilepsy. However, the major limitation of eLORETA is sensitivity to the uncertainties in the forward model. Since this problem can substantially undermine its performance in real-world applications where the exact lead field matrix is unknown, developing a more robust method capable of dealing with these uncertainties is of significant interest.

Online Left-Hemispheric In-Phase Frontoparietal Theta tACS Modulates Theta-Band EEG Source-Based Large-Scale Functional Network Connectivity in Patients with Schizophrenia: A Randomized, Double-Blind, Sham-Controlled Clinical Trial.

EEG studies indicated that schizophrenia patients had increased resting-state theta-band functional connectivity, which was associated with negative symptoms. We recently published the first study showing that theta (6 Hz) transcranial alternating current stimulation (tACS) over left prefrontal and parietal cortices during a working memory task for accentuating frontoparietal theta-band synchronization (in-phase theta-tACS) reduced negative symptoms in schizophrenia patients. Here, we hypothesized that in-phase theta-tACS can modulate theta-band large-scale networks connectivity in schizophrenia patients. In this randomized, double-blind, sham-controlled trial, patients received twice-daily, 2 mA, 20-min sessions of in-phase theta-tACS for 5 consecutive weekdays (n = 18) or a sham stimulation (n = 18). Resting-state electroencephalography data were collected at baseline, end of stimulation, and at one-week follow-up. Exact low resolution electromagnetic tomography (eLORETA) was used to compute intra-cortical activity. Lagged phase synchronization (LPS) was used to measure whole-brain source-based functional connectivity across 84 cortical regions at theta frequency (5-7 Hz). EEG data from 35 patients were analyzed. We found that in-phase theta-tACS significantly reduced the LPS between the posterior cingulate (PC) and the parahippocampal gyrus (PHG) in the right hemisphere only at the end of stimulation relative to sham (p = 0.0009, corrected). The reduction in right hemispheric PC-PHG LPS was significantly correlated with negative symptom improvement at the end of the stimulation (r = 0.503, p = 0.039). Our findings suggest that in-phase theta-tACS can modulate theta-band large-scale functional connectivity pertaining to negative symptoms. Considering the failure of right hemispheric PC-PHG functional connectivity to predict improvement in negative symptoms at one-week follow-up, future studies should investigate whether it can serve as a surrogate of treatment response to theta-tACS.

Neuronal networks underlying ictal and subclinical discharges in childhood absence epilepsy

Childhood absence epilepsy (CAE), involves 3 Hz generalized spikes and waves discharges (GSWDs) on the electroencephalogram (EEG), associated with ictal discharges (seizures) with clinical symptoms and impairment of consciousness and subclinical discharges without any objective clinical symptoms or impairment of consciousness. This study aims to comparatively characterize neuronal networks underlying absence seizures and subclinical discharges, using source localization and functional connectivity (FC), to better understand the pathophysiological mechanism of these discharges. Routine EEG data from 12 CAE patients, consisting of 45 ictal and 42 subclinical discharges were selected. Source localization was performed using the exact low-resolution electromagnetic tomography (eLORETA) algorithm, followed by FC based on the imaginary part of coherency. FC based on the thalamus as the seed of interest showed significant differences between ictal and subclinical GSWDs (p < 0.05). For delta (1–3 Hz) and alpha bands (8–12 Hz), the thalamus displayed stronger connectivity towards other brain regions for ictal GSWDs as compared to subclinical GSWDs. For delta band, the thalamus was strongly connected to the posterior cingulate cortex (PCC), precuneus, angular gyrus, supramarginal gyrus, parietal superior, and occipital mid-region for ictal GSWDs. The strong connections of the thalamus with other brain regions that are important for consciousness, and with components of the default mode network (DMN) suggest the severe impairment of consciousness in ictal GSWDs. However, for subclinical discharges, weaker connectivity between the thalamus and these brain regions may suggest the prevention of impairment of consciousness. This may benefit future therapeutic targets and improve the management of CAE patients.

Correlation between brain functional connectivity and neurocognitive function in patients with left frontal glioma

The association between neurocognitive function (NCF) impairment and brain cortical functional connectivity in glioma patients remains unclear. The correlations between brain oscillatory activity or functional connectivity and NCF measured by the Wechsler Adult Intelligence Scale full-scale intelligence quotient scores (WAIS FSIQ), the Wechsler Memory Scale-revised general memory scores (WMS-R GM), and the Western aphasia battery aphasia quotient scores (WAB AQ) were evaluated in 18 patients with left frontal glioma using resting-state electroencephalography (EEG). Current source density (CSD) and lagged phase synchronization (LPS) were analyzed using exact low-resolution electromagnetic tomography (eLORETA). Although 2 and 2 patients scored in the borderline range of WAIS FSIQ and WMS-R GM, respectively, the mean WAIS FSIQ, WMS-R GM, and WAB AQ values of all patients were within normal limits, and none had aphasia. In the correlation analysis, lower WMS-R GM was associated with a higher LPS value between the right anterior prefrontal cortex and the left superior parietal lobule in the beta1 band (13–20 Hz, R = − 0.802, P = 0.012). These findings suggest that LPS evaluated by scalp EEG is associated with memory function in patients with left frontal glioma and mild NCF disorders.

High-Frequency Transcranial Random Noise Stimulation Modulates Gamma-Band EEG Source-Based Large-Scale Functional Network Connectivity in Patients with Schizophrenia: A Randomized, Double-Blind, Sham-Controlled Clinical Trial.

Schizophrenia is associated with increased resting-state large-scale functional network connectivity in the gamma frequency. High-frequency transcranial random noise stimulation (hf-tRNS) modulates gamma-band endogenous neural oscillations in healthy individuals through the application of low-amplitude electrical noises. Yet, it is unclear if hf-tRNS can modulate gamma-band functional connectivity in patients with schizophrenia. We performed a randomized, double-blind, sham-controlled clinical trial to contrast hf-tRNS (N = 17) and sham stimulation (N = 18) for treating negative symptoms in 35 schizophrenia patients. Short continuous currents without neuromodulatory effects were applied in the sham group to mimic real-stimulation sensations. We used electroencephalography to investigate if a five-day, twice-daily hf-tRNS protocol modulates gamma-band (33–45 Hz) functional network connectivity in schizophrenia. Exact low resolution electromagnetic tomography (eLORETA) was used to compute intra-cortical activity from regions within the default mode network (DMN) and fronto-parietal network (FPN), and functional connectivity was computed using lagged phase synchronization. We found that hf-tRNS reduced gamma-band within-DMN and within-FPN connectivity at the end of stimulation relative to sham stimulation. A trend was obtained between the change in within-FPN functional connectivity from baseline to the end of stimulation and the improvement of negative symptoms at the one-month follow-up (r = −0.49, p = 0.055). Together, our findings suggest that hf-tRNS has potential as a network-level approach to modulate large-scale functional network connectivity pertaining to negative symptoms of schizophrenia.

Mapping and understanding of correlated electroencephalogram (EEG) responses to the newsvendor problem

Decision-making is one of the most critical activities of human beings. To better understand the underlying neurocognitive mechanism while making decisions under an economic context, we designed a decision-making paradigm based on the newsvendor problem (NP) with two scenarios: low-profit margins as the more challenging scenario and high-profit margins as the less difficult one. The EEG signals were acquired from healthy humans while subjects were performing the task. We adopted the Correlated Component Analysis (CorrCA) method to identify linear combinations of EEG channels that maximize the correlation across subjects (n=23) or trials (n=40). The inter-subject or inter-trial correlation values (ISC or ITC) of the first three components were estimated to investigate the modulation of the task difficulty on subjects’ EEG signals and respective correlations. We also calculated the alpha- and beta-band power of the projection components obtained by the CorrCA to assess the brain responses across multiple task periods. Finally, the CorrCA forward models, which represent the scalp projections of the brain activities by the maximally correlated components, were further translated into source distributions of underlying cortical activity using the exact Low Resolution Electromagnetic Tomography Algorithm (eLORETA). Our results revealed strong and significant correlations in EEG signals among multiple subjects and trials during the more difficult decision-making task than the easier one. We also observed that the NP decision-making and feedback tasks desynchronized the normalized alpha and beta powers of the CorrCA components, reflecting the engagement state of subjects. Source localization results furthermore suggested several sources of neural activities during the NP decision-making process, including the dorsolateral prefrontal cortex, anterior PFC, orbitofrontal cortex, posterior cingulate cortex, and somatosensory association cortex.

Decreased brain network global efficiency after attachment memories retrieval in individuals with unresolved/disorganized attachment-related state of mind

The main aim of the study was to examine how brain network metrics change after retrieval of attachment memories in individuals with unresolved/disorganized (U/D) attachment-related state of mind and those with organized/resolved (O/R) state of mind. We focused on three main network metrics associated with integration and segregation: global (Eglob) efficiency for the first function, local (Eloc) efficiency and modularity for the second. We also examined assortativity and centrality metrics. Electroencephalography (EEG) recordings were performed before and after the Adult Attachment Interview (AAI) in a sample of 50 individuals previously assessed for parenting quality. Functional connectivity matrices were constructed by means of the exact Low-Resolution Electromagnetic Tomography (eLORETA) software and then imported into MATLAB to compute brain network metrics. Compared to individuals with O/R attachment-related state of mind, those with U/D show a significant decrease in beta Eglob after AAI. No statistically significant difference among groups emerged in Eloc and modularity metrics after AAI, neither in assortativity nor in betweenness centrality. These results may help to better understand the neurophysiological patterns underlying the disintegrative effects of retrieving traumatic attachment memories in individuals with disorganized state of mind in relation to attachment.

Need for cognitive closure is associated with different intra-network functional connectivity patterns: A resting state EEG study

ABSTRACT Need for Cognitive Closure (NCC) is a construct referring to the desire for predictability, unambiguity and firm answers to issues. Neuroscientific literature about NCC processes has mainly focused on task-related brain activity. According to the Triple Network model (TN), the main aim of the current study was to investigate resting state (RS) electroencephalographic (EEG) intra-network dynamics associated with NCC. Fifty-two young adults (39 females) were enrolled and underwent EEG recordings during RS. Functional connectivity analysis was computed through exact Low-Resolution Electromagnetic Tomography (eLORETA) software. Our results showed that higher levels of NCC were associated with both i) decreased alpha EEG connectivity within the Central Executive Network (CEN), and ii) increased delta connectivity within the Default Mode Network (DMN). No significant correlations were observed between NCC and functional connectivity in the Salience Network (SN). Our data would seem to suggest that high levels of NCC are characterized by a specific communication pattern within the CEN and the DMN during RS. These neurophysiological patterns might reflect several typical NCC-related cognitive characteristics (e.g., lower flexibility and preference for habitual and rigid response schemas).

Decreased Resting State Inter- and Intra-Network Functional Connectivity Is Associated with Perceived Stress in a Sample of University Students: An eLORETA Study

Introduction: Although the study of the Triple Network (TN) model has gained attention in the exploration of stress-related processes, the neurophysiological mechanisms of TN in relation to perceived stress have been relatively understudied in nonclinical samples so far. The main objective of the present study was to investigate, in a sample of university students, the association of perceived stress with resting state electroencephalography (EEG) functional connectivity in the TN. Methods: Ninety university students (40 males and 50 females; mean age 22.30 ± 2.43 years; mean educational level 16.60 ± 1.62 years) were enrolled. EEG data were analyzed through the exact low-resolution electromagnetic tomography (eLORETA). Results: Higher levels of perceived stress were associated with decreased delta EEG connectivity within the central executive network (CEN) and between the CEN and the salience network (SN). Higher levels of perceived stress were also associated with decreased theta EEG connectivity between the CEN and the SN. The associations between perceived stress and EEG connectivity data were significant even when relevant confounding factors (i.e., sex, age, educational level, and psychopathological symptoms) were controlled for. Discussion: Taken together, our results suggest that higher levels of perceived stress are associated with a dysfunctional synchronization within the CEN and between the SN and the CEN. This functional pattern might in part reflect the negative impact of high levels of perceived stress on cognitive functioning.

Human Brain Networks in Physiological and Pathological Aging: Reproducibility of Electroencephalogram Graph Theoretical Analysis in Cortical Connectivity.

Background: Physiological and pathological brain aging plays a central role in brain network modulation. The aim of the present article was to assess the stability of a proposed method for evaluation of small-world (SW) characteristics for the study of the human connectome. Subjects and Methods: Eighty subjects were recruited: 36 young healthy controls, 32 elderly healthy controls, and 12 patients affected by Alzheimer's disease (AD). Electroencephalograms (EEGs) were recorded during six separate sessions (480 recordings) at an average intersession interval of 3.8 ± 0.2 days. We applied graph theory functions to the weighted and undirected networks obtained by the lagged linear coherence estimated by exact low-resolution electromagnetic tomography (eLORETA). We explored the following frequency bands: delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz). Results: The proposed method for evaluation of SW characteristics exhibited good reproducibility and stability. Furthermore, results showed the pattern, Young>Elderly>AD, in low-frequency delta and theta bands and vice versa in the higher alpha band. Finally, a correlation with age was confirmed in healthy subjects, showing that the older the age, the higher the SW values for alpha 2. Discussion: Evidences from the present study confirm the stability of the SW index and suggest that the analysis of connectivity patterns evaluated from EEGs can be supported by the graph theory. The proposed method for evaluation of SW characteristics has shown good reproducibility and stability. This technique, applied to patient data, could provide more information on the pathophysiological processes underlying age-related brain disconnection, as well as on administration of rehabilitation treatments at the right time, which could allow patients to avoid unnecessary interventions. Impact statement The graph analysis tools described in this study represent an interesting approach to study the distinctive characteristics of physiological aging by focusing on functional connectivity networks. The proposed method for evaluation of small-world characteristics has shown good reproducibility and stability. This technique, applied to patient data, could provide more information on the pathophysiological processes underlying age-related brain disconnection, as well as on delivery of rehabilitation treatments at the right time, which could allow patients to avoid unnecessary interventions.

Fast and Robust Capacitive Imaging of Cylindrical Non-Metallic Media

In this paper, a unique approach to the imaging of non-metallic media using capacitive sensing is presented. By using customized sensor plates in single-ended and differential configurations, responses to hidden objects can be captured over a cylindrical aperture surrounding the inspected medium. Then, by processing the acquired data using a novel imaging technique based on the convolution theory, Fourier and inverse Fourier transforms, and exact low resolution electromagnetic tomography (eLORETA), images are reconstructed over multiple radial depths using the acquired sensor data. Imaging hidden objects over multiple depths has wide range of applications, from biomedical imaging to nondestructive testing of the materials. Performance of the proposed imaging technique is demonstrated via experimental results.

Dissociative-Traumatic Dimension and Triple Network: An EEG Functional Connectivity Study in a Sample of University Students

Aims: We investigated the association among triple network electroencephalographic (EEG) functional connectivity, dissociative symptoms, and childhood trauma (CT) in a sample of university students. Sampling and Methods: Seventy-six participants (30 males and 46 females; mean age 22.12 ± 2.35) completed self-report measures investigating dissociative symptoms, CT, and depressive symptoms. Participants also performed an eyes-closed resting-state EEG recording. EEG analyses were conducted through the exact low-resolution electromagnetic tomography (eLORETA) software. Results: A 2-step cluster analysis revealed 2 groups: participants (N = 23) with high dissociative-traumatic dimension symptoms (DTD+) and participants (N = 53) with low DTD symptoms (DTD−). Compared to DTD− subjects, DTD+ participants showed decreased theta connectivity between the salience network (SN) and central executive network (CEN), specifically between the right anterior insula and the left posterior parietal cortex. No significant correlation was detected between EEG data and clinical variables. Conclusion: Our results raise the possibility of a dysfunctional connectivity pattern occurring between the SN and CEN in individuals with high DTD symptoms. Such connectivity pattern might reflect the neuropsychophysiological disintegration related to pathological dissociation.

Source space reduction for eLORETA

Objective. We introduce Sparse exact low resolution electromagnetic tomography (eLORETA), a novel method for estimating a nonparametric solution to the source localization problem. Its goal is to generate a sparser solution compared to other source localization methods including eLORETA while benefitting from the latter’s superior source localization accuracy. Approach. Sparse eLORETA starts by reducing the source space of the Lead Field Matrix using structured sparse Bayesian learning from which a Reduced Lead Field Matrix is constructed, which is used as input to eLORETA. Main results. With Sparse eLORETA, source sparsity can be traded against signal fidelity; the proposed optimum is shown to yield a much sparser solution than eLORETA’s with only a slight loss in signal fidelity. Significance. When pursuing a data-driven approach, for cases where it is difficult to choose specific regions of interest, or when subsequently a connectivity analysis is performed, source space reduction could prove beneficial.