Neural consequences of 5-Hz transcranial alternating current stimulation over right hemisphere: An eLORETA EEG study.

INTRODUCTION: Transcranial alternating current stimulation (tACS) at 5-Hz to the right hemisphere can effectively alleviate anxiety symptoms. This study aimed to explore the neural mechanisms that drive the therapeutic benefits. METHODS: We collected electroencephalography (EEG) data from 24 participants with anxiety disorders before and after a tACS treatment session. tACS was applied over the right hemisphere, with 1.0 mA at F4, 1.0 mA at P4, and 2.0 mA at T8 (10-10 EEG convention). With eLORETA, we transformed the scalp signals into the current source density in the cortex. We then assessed the differences between post- and pre-treatment brain maps across multiple spectra (delta to low gamma) with non-parametric statistics. RESULTS: We observed a trend of heightened power in alpha and reduced power in mid-to-high beta and low gamma, in accord with the EEG markers of anxiolytic effects reported in previous studies. Additionally, we observed a consistent trend of de-synchronization at the stimulating sites across spectra. CONCLUSION: tACS 5-Hz over the right hemisphere demonstrated EEG markers of anxiety reduction. The after-effects of tACS on the brain are intricate and cannot be explained solely by the widely circulated entrainment theory. Rather, our results support the involvement of plasticity mechanisms in the offline effects of tACS.

Resting-state EEG rhythms are abnormal in post COVID-19 patients with brain fog without cognitive and affective disorders.

OBJECTIVES: Several persons experiencing post-covid-19 (post-COVID) with "brain fog" (e.g., fatigue, cognitive and psychiatric disorders, etc.) show abnormal resting-state electroencephalographic (rsEEG) rhythms reflecting a vigilance dysfunction. Here, we tested the hypothesis that in those post-COVID persons, abnormal rsEEG rhythms may occur even when cognitive and psychiatric disorders are absent. METHODS: The experiments were performed on post-COVID participants about one year after hospitalization for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Inclusion criteria included a "brain fog" claim, no pre-infection, and actual organic chronic disease. Matched controls (no COVID) were also enrolled. All participants underwent clinical/neuropsychological assessment (including fatigue assessment) and rsEEG recordings. The eLORETA freeware estimated regional rsEEG cortical sources at individual delta (<4 Hz), theta (4-7 Hz), and alpha (8-13 Hz) bands. Beta (14-30 Hz) and gamma (30-40 Hz) bands were pre-fixed. RESULTS: More than 90% of all post-COVID participants showed no cognitive or psychiatric disorders, and 75% showed ≥ 2 fatigue symptoms. The post-COVID group globally presented lower posterior rsEEG alpha source activities than the Control group. This effect was more significant in the long COVID-19 patients with ≥ 2 fatigue symptoms. CONCLUSIONS: In post-COVID patients with no chronic diseases and cognitive/psychiatric disorders, "brain fog" can be associated with abnormal posterior rsEEG alpha rhythms and subjective fatigue. SIGNIFICANCE: These abnormalities may be related to vigilance and allostatic dysfunctions.

A posterior-alpha ageing network is differentially associated with antidepressant effects of venlafaxine and rTMS.

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder, but chances for remission largely decrease with each failed treatment attempt. It is therefore desirable to assign a given patient to the most promising individual treatment option as early as possible. We used a polygenic score (PGS) informed electroencephalography (EEG) data-driven approach to identify potential predictors for MDD treatment outcome. Post-hoc we conducted exploratory analyses in order to understand the results in depth. First, an EEG independent component analysis produced 54 functional brain networks in a large heterogeneous cohort of psychiatric patients (n = 4,045; 5-84 yrs.). Next, the network that was associated to PGS for antidepressant-response (PRS-AR) in an independent sample (n = 722) was selected: an age-related posterior alpha network that explained >60 % of EEG variance, and was highly stable over recording time. Translational analyses were performed in two other independent datasets to examine if the network was predictive of psychopharmacotherapy (n = 535) and/or repetitive transcranial magnetic stimulation (rTMS) and concomitant psychotherapy (PT; n = 186) outcome. The network predicted remission to venlafaxine (p = 0.015), resulting in a normalized positive predicted value (nPPV) of 138 %, and rTMS + PT - but in opposite direction for women (p = 0.002) relative to men (p = 0.018) - yielding a nPPV of 131 %. Blinded out-of-sample validations for venlafaxine (n = 29) and rTMS + PT (n = 36) confirmed the findings for venlafaxine, while results for rTMS + PT could not be replicated. These data suggest the existence of a relatively stable EEG posterior alpha aging network related to PGS-AR that has potential as MDD treatment predictor.

Default mode function in patients with generalised epilepsy syndromes: from generalised to focal findings.

INTRODUCTION: Many recent studies have suggested that generalised epilepsy is associated with cortical epileptogenic focus, and therefore distinguishing between focal and generalised often becomes difficult. AIM OF STUDY: We aimed to detect differences between default mode function in patients with idiopathic generalised epilepsy who have discharges on EEG, and healthy persons. MATERIAL AND METHODS: This was a case-control study; we performed EEG analysis with LORETA in 17 patients with a type of generalised epilepsy and a control group represented by 17 healthy age-matched persons. We performed statistical non- -parametric tests for current density electrical distribution for our two groups ('t-statistic on Log transformed data') and we defined regions of interest (ROIs) from the default mode network. In the second part, we compared the average activation for each ROI for each timeframe in the epoch for the group with epilepsy, and for controls (we performed a Wilcoxon rank-sum test for two means). RESULTS: In the first part, we obtained that in the medial frontal gyrus (BA 9) delta oscillations significantly differed in patients with epilepsy who had electrical discharges on EEG in resting state conditions compared to healthy controls (medial frontal gyrus in this group had a greater number of synchronously oscillating neurons than did the controls). In the second part, we ran statistics on our localised activity from the default mode network (defined ROIs) and we obtained statistically significant differences in the left medial frontal gyrus (the values were higher for the group with epilepsy, p-value = 0.0066). CONCLUSIONS AND CLINICAL IMPLICATIONS: It may be possible to move from a 'generalised theory' about epilepsy to a 'focused theory' by understanding how various areas of interest are activated within default mode networks. Insights into the pathophysiology of generalised epilepsy may lead to new treatment options.

Parietal resting-state EEG alpha source connectivity is associated with subcortical white matter lesions in HIV-positive people.

OBJECTIVE: Parietal resting-state electroencephalographic (rsEEG) alpha (8-10 Hz) source connectivity is abnormal in HIV-positive persons. Here we tested whether this abnormality may be associated with subcortical white matter vascular lesions in the cerebral hemispheres. METHODS: Clinical, rsEEG, and magnetic resonance imaging (MRI) datasets in 38 HIV-positive persons and clinical and rsEEG datasets in 13 healthy controls were analyzed. Radiologists visually evaluated the subcortical white matter hyperintensities from T2-weighted FLAIR MRIs (i.e., Fazekas scale). In parallel, neurophysiologists estimated the eLORETA rsEEG source lagged linear connectivity from parietal cortical regions of interest. RESULTS: Compared to the HIV participants with no/negligible subcortical white matter hyperintensities, the HIV participants with mild/moderate subcortical white matter hyperintensities showed lower parietal interhemispheric rsEEG alpha lagged linear connectivity. This effect was also observed in HIV-positive persons with unimpaired cognition. This rsEEG marker allowed good discrimination (area under the receiver operating characteristic curve > 0.80) between the HIV-positive individuals with different amounts of subcortical white matter hyperintensities. CONCLUSIONS: The parietal rsEEG alpha source connectivity is associated with subcortical white matter vascular lesions in HIV-positive persons, even without neurocognitive disorders. SIGNIFICANCE: Those MRI-rsEEG markers may be used to screen HIV-positive persons at risk of neurocognitive disorders.

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

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

Frontotemporal phase lag index correlates with seizure severity in patients with temporal lobe epilepsy.

Objectives: To find the brain network indicators correlated with the seizure severity in temporal lobe epilepsy (TLE) by graph theory analysis. Methods: We enrolled 151 patients with TLE and 36 age- and sex-matched controls with video-EEG monitoring. The 90-s interictal EEG data were acquired. We adopted a network analyzing pipeline based on graph theory to quantify and localize their functional networks, including weighted classical network, minimum spanning tree, community structure, and LORETA. The seizure severities were evaluated using the seizure frequency, drug-resistant epilepsy (DRE), and VA-2 scores. Results: Our network analysis pipeline showed ipsilateral frontotemporal activation in patients with TLE. The frontotemporal phase lag index (PLI) values increased in the theta band (4-7 Hz), which were elevated in patients with higher seizure severities (P < 0.05). Multivariate linear regression analysis showed that the VA-2 scores were independently correlated with frontotemporal PLI values in the theta band (ß = 0.259, P = 0.001) and age of onset (ß = -0.215, P = 0.007). Significance: This study illustrated that the frontotemporal PLI in the theta band independently correlated with seizure severity in patients with TLE. Our network analysis provided an accessible approach to guide the treatment strategy in routine clinical practice.

Neural substrates of tinnitus severity.

Subjective chronic tinnitus is a prevalent auditory perception characterized by an absence of a corresponding acoustic source. It is often accompanied by hearing deficits and may lead to various psychological problems including sleep disorder, depression and anxiety. To investigate the differential neuronal profile of patients with severe and less severe chronic tinnitus, 34 tinnitus patients were distributed in two groups and their EEG resting state activity was compared. Using standardized Low Resolution Electromagnetic Tomography (sLORETA) a significant and substantial frontal increase in theta wave activity was found in the group with severe tinnitus (p = .013). The correlated severity of depression and anxiety had no influence on the electrophysiological metric. These results support a tinnitus-related global network change in which prefrontal areas are part of a network which exerts a top-down influence on the auditory cortices. The demonstrated slowing of oscillations in the responsible network may constitute a neuronal marker for the prefrontal brain network lacking the capacity to inhibit overexcitation. The magnitude of this influence is linked to the subjective strength of the tinnitus distress.

Influence of the number of trials on evoked motor cortical activity in EEG recordings.

Objective. Improvements in electroencephalography enable the study of the localization of active brain regions during motor tasks. Movement-related cortical potentials (MRCPs), and event-related desynchronization (ERD) and synchronization are the main motor-related cortical phenomena/neural correlates observed when a movement is elicited. When assessing neurological diseases, averaging techniques are commonly applied to characterize motor related processes better. In this case, a large number of trials is required to obtain a motor potential that is representative enough of the subject's condition. This study aimed to assess the effect of a limited number of trials on motor-related activity corresponding to different upper limb movements (elbow flexion/extension, pronation/supination and hand open/close).Approach. An open dataset consisting on 15 healthy subjects was used for the analysis. A Monte Carlo simulation approach was applied to analyse, in a robust way, different typical time- and frequency-domain features, topography, and low-resolution electromagnetic tomography.Main results. Grand average potentials, and topographic and tomographic maps showed few differences when using fewer trials, but shifts in the localization of motor-related activity were found for several individuals. MRCP and beta ERD features were more robust to a limited number of trials, yielding differences lower than 20% for cases with 50 trials or more. Strong correlations between features were obtained for subsets above 50 trials. However, the inter-subject variability increased as the number of trials decreased. The elbow flexion/extension movement showed a more robust performance for a limited number of trials, both in population and in individual-based analysis.Significance. Our findings suggested that 50 trials can be an appropriate number to obtain stable motor-related features in terms of differences in the averaged motor features, correlation, and changes in topography and tomography.

A polygenic-informed approach to a predictive EEG signature empowers antidepressant treatment prediction: A proof-of-concept study.

The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in each treatment step, which is partly due to a lack of firmly established outcome-predictive biomarkers. Here, we hypothesize that polygenic-informed EEG signatures may help predict antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a brain network in a large cohort (N=1,123), and discover it is sex-specifically (male patients, N=617) associated with polygenic risk score (PRS) of antidepressant response. Subsequently, we demonstrate in three independent datasets the utility of the network in predicting response to antidepressant medication (male, N=232) as well as repetitive transcranial magnetic stimulation (rTMS) and concurrent psychotherapy (male, N=95). This network significantly improves a treatment response prediction model with age and baseline severity data (area under the curve, AUC=0.623 for medicaton; AUC=0.719 for rTMS). A predictive model for MDD patients, aimed at increasing the likelihood of being a responder to antidepressants or rTMS and concurrent psychotherapy based on only this network, yields a positive predictive value (PPV) of 69% for medication and 77% for rTMS. Finally, blinded out-of-sample validation of the network as predictor for psychotherapy response in another independent dataset (male, N=50) results in a within-subsample response rate of 50% (improvement of 56%). Overall, the findings provide a first proof-of-concept of a combined genetic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders, and should encourage researchers to incorporate genetic information, such as PRS, in their search for clinically relevant neuroimaging biomarkers.

Performance prediction in a visuo-motor task: the contribution of EEG analysis.

Brain state in the time preceding the task affects motor performance at single trial level. Aim of the study was to investigate, through a single trial analysis of the Power Spectral Density (PSD) of the cortical sources of EEG rhythms, whether there are EEG markers, which can predict trial-by-trial the subject's performance as measured by the reaction time (RT). 20 healthy adult volunteers performed a specific visuomotor task while continuously recorded with a 64 electrodes EEG. For each single trial, the PSD of the cortical sources of EEG rhythms was obtained from EEG data to cortical current density time series in 12 regions of interest at Brodmann areas level. Results showed a statistically significant increase of posterior and limbic alpha 1 and of frontal beta 2 power, and a reduction of frontal and limbic delta and of temporal alpha 1 power, during triggering stimulus presentation for better performance, namely faster responses. At single trial level, correlation analyses between RTs and significant PSD, revealed positive correlations in frontal delta, temporal alpha 1, and limbic delta bands, and negative ones in frontal beta 2, parietal alpha 1, and occipital alpha 1 bands. Furthermore, the subject's faster responses have been found as correlated with the similarity between the PSD values in parietal and occipital alpha 1. Predicting individual's performance at single trial level, might be extremely useful in the clinical context, since it could allow to launch rehabilitative therapies in the most efficient brain state, avoiding useless interventions.

Erratum: Neuronal Effects of Listening to Entrainment Music Versus Preferred Music in Patients With Chronic Cancer Pain as Measured via EEG and LORETA Imaging.

[This corrects the article DOI: 10.3389/fpsyg.2021.588788.].

Neuronavigated Magnetic Stimulation combined with cognitive training for Alzheimer's patients: an EEG graph study.

Alzheimer's disease (AD) is the most common neurodegenerative disorder in elderly subjects. Recent studies verified the effects of cognitive training combined with repetitive transcranial magnetic stimulation (rTMS-COG) in AD patients. Here, we analyzed neuropsychological and neurophysiological data, derived from electroencephalography (EEG), to evaluate the effects of a 6-week protocol of rTMS-COG in 72 AD. We designed a randomized, double-blind, sham-controlled trial to evaluate efficacy of rTMS on 6 brain regions obtained by an individual MRI combined with COG related to brain areas to stimulate (i.e., syntax and grammar tasks, comprehension of lexical meaning and categorization tasks, action naming, object naming, spatial memory, spatial attention). Patients underwent neuropsychological and EEG examination before (T0), after treatment (T1), and after 40 weeks (T2), to evaluate the effects of rehabilitation therapy. "Small World" (SW) graph approach was introduced allowing us to model the architecture of brain connectivity in order to correlate it with cognitive improvements. We found that following 6 weeks of intensive daily treatment the immediate results showed an improvement in cognitive scales among AD patients. SW present no differences before and after the treatment, whereas a crucial SW modulation emerges at 40-week follow-up, emphasizing the importance of rTMS-COG rehabilitation treatment for AD. Additional results demonstrated that the delta and alpha1 SW seem to be diagnostic biomarkers of AD, whereas alpha2 SW might represent a prognostic biomarker of cognitive recovery. Derived EEG parameters can be awarded the role of diagnostic and predictive biomarkers of AD progression, and rTMS-COG can be regarded as a potentially useful treatment for AD.

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.

Linear distributed inverse solutions for interictal EEG source localisation.

OBJECTIVE: To compare the spatial accuracy of 6 linear distributed inverse solutions for EEG source localisation of interictal epileptic discharges: Minimum Norm, Weighted Minimum Norm, Low-Resolution Electromagnetic Tomography (LORETA), Local Autoregressive Average (LAURA), Standardised LORETA, and Exact LORETA. METHODS: Spatial accuracy was assessed clinically by retrospectively comparing the maximum source of averaged interictal discharges to the resected brain area in 30 patients with successful epilepsy surgery, based on 204-channel EEG. Additionally, localisation errors of the inverse solutions were assessed in computer simulations, with different levels of noise added to the signal in both sensor space and source space. RESULTS: In the clinical evaluations, the source maximum was located inside the resected brain area in 50-57% of patients when using LORETA or LAURA, while all other inverse solutions performed significantly worse (17-30%; corrected p < 0.01). In the simulation studies, when noise levels exceeded 10%, LORETA and LAURA had substantially smaller localisation errors than the other inverse solutions. CONCLUSIONS: LORETA and LAURA provided the highest spatial accuracy both in clinical and simulated data, alongside with a comparably high robustness towards noise. SIGNIFICANCE: Among the different linear inverse solution algorithms tested, LORETA and LAURA might be preferred for interictal EEG source localisation.

Comparing LORETA Z Score Neurofeedback and Cognitive Rehabilitation Regarding Their Effectiveness in Reducing Craving in Opioid Addicts.

Introduction: Previous studies have shown that conventional neurofeedback and cognitive modification treatments have numerous psychological benefits for patients with substance use disorders. However, the effectiveness of LORETA (Low-Resolution Brain Electromagnetic Tomography) Z Score Neurofeedback (LZNFB) and cognitive rehabilitation therapy in reducing opioid craving has not been investigated. Thus, the present study aimed to compare the effectiveness of LZNFB and cognitive rehabilitation therapy with Methadone Maintenance Treatment (MMT) in reducing craving in patients with opioid use disorder. Methods: Thirty patients with opioid use disorder undergoing MMT were randomly assigned into three groups: LZNFB with MMT, cognitive rehabilitation with MMT (as experimental groups), and MMT alone control group. The LZNFB and cognitive rehabilitation groups received 20 and 15 sessions of treatment, respectively. The three groups were assessed using several questionnaires and dot-probe task at pretest, posttest, and one-month follow-up. Results: The results showed that both experimental groups accomplished a significantly greater reduction in opioid craving than MMT alone group at posttest and follow-up (P<0.05). The LZNFB plus MMT group showed a greater decrease in opioid craving than the cognitive rehabilitation plus MMT group. In addition, the cognitive rehabilitation plus MMT group experienced greater improvement in attentional bias towards craving cues than the LZNFB with MMT group at posttest and follow-up. Finally, the LZNFB plus MMT group and cognitive rehabilitation plus MMT group got higher scores on the recovery assessment scale than MMT alone group at posttest and follow-up. According to study results, LZNFB training is more effective than cognitive rehabilitation in decreasing cravings and improving the quality of life in addiction to opioids. Conclusion: The current study's findings provided preliminary support for the effectiveness of LZNFB and cognitive rehabilitation in reducing opioid craving, improving attentional bias towards craving cues, and the quality of life among Iranian opioid use patients. Highlights: LZNFB training showed higher decrease in opioid craving than the Cognitive rehabilitation in opioid addicts.Cognitive rehabilitation group experienced greater improvement on attentional bias towards craving cues than LZNFB.LZNFB and Cognitive rehabilitation with MMT group got higher scores on the recovery assessment scale than MMT alone group.LZNFB training is more effective than Cognitive Rehabilitation in decreasing of craving in addiction. opioids. Plain Language Summary: Addiction is a chronic relapsing disease that makes many problems for human society. Routine medical treatments are not completely effective and they have relapse. New forms of non-medical treatments such as neurofeedback and cognitive rehabilitation are effective and safe without impressive side effects . This article shows the efficacy of above mentioned interventions for decrease craving and control of this problem.

Electroencephalographic and morphometric abnormalities in psychopath offenders.

The main goals of the present study were to replicate and extend current knowledge related to paralimbic dysfunctions associated with psychopathy. The research evaluated the quantitative electroencephalography, current density (CD) source and synchronization likelihood analysis during the rest condition and structural magnetic resonance imaging images to compare volumetric and cortical thickness, in inmates recruited from two prisons located in Havana City. The Psychopathy Checklist-Revised (PCL-R) was used as a quantitative measure of psychopathy. This study showed most beta energy and less alpha activity in male psychopath offenders. Low-resolution electromagnetic tomography signified an increase of beta activity in psychopath offender groups within paralimbic regions. The superior temporal gyrus volume was associated with the F1 factor while the fusiform, anterior cingulate and associative occipital areas were primarily associated with the F2 factor of PCL-R scale. Cortical thickness in the left dorsal anterior cingulate cortex and the temporal pole was negatively associated with PCL-R total score.

Classification of Patients with Alzheimer's Disease and Dementia with Lewy Bodies using Resting EEG Selected Features at Sensor and Source Levels: A Proof-of-Concept Study.

BACKGROUND: Early differentiation between Alzheimer's disease (AD) and Dementia with Lewy Bodies (DLB) is important for accurate prognosis, as DLB patients typically show faster disease progression. Cortical neural networks, necessary for human cognitive function, may be disrupted differently in DLB and AD patients, allowing diagnostic differentiation between AD and DLB. OBJECTIVE: This proof-of-concept study assessed whether the application of machine learning techniques to data derived from resting-state electroencephalographic (rsEEG) rhythms (discriminant sensor power, 19 electrodes) and source connectivity (between five cortical regions of interest) allowed differentiation between DLB and AD. METHODS: Clinical, demographic, and rsEEG datasets from DLB patients (N=30), AD patients (N=30), and control seniors (NOld, N=30), matched for age, sex, and education, were taken from our international database. Individual (delta, theta, alpha) and fixed (beta) rsEEG frequency bands were included. The rsEEG features for the classification task were computed at both sensor and source levels. The source level was based on eLORETA freeware toolboxes for estimating cortical source activity and linear lagged connectivity. Fluctuations of rsEEG recordings (band-pass waveform envelopes of each EEG rhythm) were also computed at both sensor and source levels. After blind feature reduction, rsEEG features served as input to support vector machine (SVM) classifiers. Discrimination of individuals from the three groups was measured with standard performance metrics (accuracy, sensitivity, and specificity). RESULTS: The trained SVM two-class classifiers showed classification accuracies of 97.6% for NOld vs. AD, 99.7% for NOld vs. DLB, and 97.8% for AD vs. DLB. Three-class classifiers (AD vs. DLB vs. NOld) showed classification accuracy of 94.79%. CONCLUSION: These promising preliminary results should encourage future prospective and longitudinal cross-validation studies using higher resolution EEG techniques and harmonized clinical procedures to enable the clinical application of these machine learning techniques.

What to Believe? Impact of Knowledge and Message Length on Neural Activity in Message Credibility Evaluation.

Understanding how humans evaluate credibility is an important scientific question in the era of fake news. Message credibility is among crucial aspects of credibility evaluations. One of the most direct ways to understand message credibility is to use measurements of brain activity of humans performing credibility evaluations. Nevertheless, message credibility has never been investigated using such a method before. This article reports the results of an experiment during which we have measured brain activity during message credibility evaluation, using EEG. The experiment allowed for identification of brain areas that were active when participant made positive or negative message credibility evaluations. Based on experimental data, we modeled and predicted human message credibility evaluations using EEG brain activity measurements with F1 score exceeding 0.7.

Brain network modulation in transradial amputee with finger perception restored through biomimetic intraneural stimulation.

INTRODUCTION/AIMS: The hand is a sophisticated tool which allows humans to interact with the external world mainly via the sense of touch. Previous evidences demonstrated that electrical stimulations of the nerve trunks governing the hand are able to restore touch perception in transradial amputees. The aim of this study was to evaluate the neurological correlates of restored perception by the evaluation of network characteristics of the brain connectome via EEG recordings in amputees utilizing a fully sensorized bionic hand. METHODS: A 48-year-old female with a left wrist traumatic disarticulation incurred 23 years prior to the study was enrolled for 6 months, during which experimental sessions were randomized. The control group included 12 healthy subjects performing a similar protocol. RESULTS: Results showed that in both amputee and control group, a trend of SW in delta and beta 1 is recognizable (Delta and Beta 1 opposite spread) selectively in the hemisphere contralateral to the stimulus. DISCUSSION: Delta increases (less orderly network) while Beta 1 decreases (more ordered network). It could be seen as a sign of higher attention and concentration of subjects to understand the perception and this monolateral modulation is similar to the bilateral attention given to the mathematical task difficulty's increment.