Increased Resting-State Functional Connectivity in Obese Adolescents; A Magnetoencephalographic Pilot Study

BACKGROUND: Obesity is not only associated with metabolic abnormalities, but also with cognitive dysfunction and changes in the central nervous system. The present pilot study was carried out to investigate functional connectivity in obese and non-obese adolescents using magnetoencephalography (MEG). METHODOLOGY/PRINCIPAL FINDINGS: Magnetoencephalographic recordings were performed in 11 obese (mean BMI 38.8+/-4.6 kg/m(2)) and 8 lean (mean BMI 21.0+/-1.5 kg/m(2)) female adolescents (age 12-19 years) during an eyes-closed resting-state condition. From these recordings, the synchronization likelihood (SL), a common method that estimates both linear and non-linear interdependencies between MEG signals, was calculated within and between brain regions, and within standard frequency bands (delta, theta, alpha1, alpha2, beta and gamma). The obese adolescents had increased synchronization in delta (0.5-4 Hz) and beta (13-30 Hz) frequency bands compared to lean controls (P(delta total) = 0.001; P(beta total) = 0.002). CONCLUSIONS/SIGNIFICANCE: This study identified increased resting-state functional connectivity in severe obese adolescents. Considering the importance of functional coupling between brain areas for cognitive functioning, the present findings strengthen the hypothesis that obesity may have a major impact on human brain function. The cause of the observed excessive synchronization is unknown, but might be related to disturbed motivational pathways, the recently demonstrated increase in white matter volume in obese subjects or altered metabolic processes like hyperinsulinemia. The question arises whether the changes in brain structure and communication are a dynamic process due to weight gain and whether these effects are reversible or not

Increased cortico-cortical functional connectivity in early-stage Parkinson's disease: a MEG study

We set out to determine whether changes in resting-state cortico-cortical functional connectivity are a feature of early-stage Parkinson's disease (PD), explore how functional coupling might evolve over the course of the disease and establish its relationship with clinical deficits. Whole-head magnetoencephalography was performed in an eyes-closed resting-state condition in 70 PD patients with varying disease duration (including 18 recently diagnosed, drug-naive patients) in an "OFF" medication state and 21 controls. Neuropsychological testing was performed in all subjects. Data analysis involved calculation of three synchronization likelihood (SL, a general measure of linear and non-linear temporal correlations between time series) measures which reflect functional connectivity within (local) and between (intrahemispheric and interhemispheric) ten major cortical regions in five frequency bands. Recently diagnosed, drug-naive patients showed an overall increase in alpha1 SL relative to controls. Cross-sectional analysis in all patients revealed that disease duration was positively associated with alpha2 and beta SL measures, while severity of parkinsonism was positively associated with theta and beta SL measures. Moderately advanced patients had increases in theta, alpha1, alpha2 and beta SL, particularly with regard to local SL. In recently diagnosed patients, cognitive perseveration was associated with increased interhemispheric alpha1 SL. Increased resting-state cortico-cortical functional connectivity in the 8-10 Hz alpha range is a feature of PD from the earliest clinical stages onward. With disease progression, neighboring frequency bands become increasingly involved. These findings suggest that changes in functional coupling over the course of PD may be linked to the topographical progression of pathology over the brain. © 2008 Elsevier Inc. All rights reserved

Prediction of ADHD from a small dataset using an adaptive EEG Theta/Beta Ratio and PCA feature extraction

EEG Theta/beta ratio (TBR) is conventionally used as a biomarker in childhood Attention-Deficit/Hyperactivity Disorder (ADHD) prediction and treatment. Due to the heterogeneity of ADHD symptoms, several studies have applied machine learning algorithms for enhancing the recognition of ADHD. These methods, however, have limited performance in a small dataset. In this paper, we propose an adaptive EEG feature extraction approach using TBR and PCA. Repeated TBR-PCA feature extraction, SVM classification and statistical testing were applied on a small EEG sample with ADHD/typically developing (TD) labels. The steps were repeated with an update of the feature extraction technique until a high accuracy is achieved, allowing the small samples to be correctly identified (r = 0.833, one-sided, Bonferroni-corrected p < 0.0166). Within subjects EEG samples analyses performed better compared to between subject analyses, with accuracy getting worse with the increase of EEG segments. The contribution of this work is two-fold: the practical application allows for a reliable adoption of machine learning in non-invasive EEG screening of small ADHD dataset, while the theoretical contribution extends beyond the eyes closed resting state condition considered in this study and provides a methodological approach when working with limited sample

EEG Resting-State Brain Topological Reorganization as a Function of Age

Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated b y combining a variety of different imaging technologies (fMRI, EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signal srecordedatrestfrom71healthysubjects(age:20–63years). Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80

The Big Five Personality Traits and Brain Arousal in the Resting State

Based on Eysenck’s biopsychological trait theory, brain arousal has long been considered to explain individual differences in human personality. Yet, results from empirical studies remained inconclusive. However, most published results have been derived from small samples and, despite inherent limitations, EEG alpha power has usually served as an exclusive indicator for brain arousal. To overcome these problems, we here selected N = 468 individuals of the LIFE-Adult cohort and investigated the associations between the Big Five personality traits and brain arousal by using the validated EEG- and EOG-based analysis tool VIGALL. Our analyses revealed that participants who reported higher levels of extraversion and openness to experience, respectively, exhibited lower levels of brain arousal in the resting state. Bayesian and frequentist analysis results were especially convincing for openness to experience. Among the lower-order personality traits, we obtained the strongest evidence for neuroticism facet ‘impulsivity’ and reduced brain arousal. In line with this, both impulsivity and openness have previously been conceptualized as aspects of extraversion. We regard our findings as well in line with the postulations of Eysenck and consistent with the recently proposed ‘arousal regulation model’. Our results also agree with meta-analytically derived effect sizes in the field of individual differences research, highlighting the need for large (collaborative) studies.Peer Reviewe

Changes in MEG resting-state networks are related to cognitive decline in type 1 diabetes mellitus patients

OBJECTIVE: Integrity of resting-state functional brain networks (RSNs) is important for proper cognitive functioning. In type 1 diabetes mellitus (T1DM) cognitive decrements are commonly observed, possibly due to alterations in RSNs, which may vary according to microvascular complication status. Thus, we tested the hypothesis that functional connectivity in RSNs differs according to clinical status and correlates with cognition in T1DM patients, using an unbiased approach with high spatio-temporal resolution functional network.; METHODS: Resting-state magnetoencephalographic (MEG) data for T1DM patients with (n=42) and without (n=41) microvascular complications and 33 healthy participants were recorded. MEG time-series at source level were reconstructed using a recently developed atlas-based beamformer. Functional connectivity within classical frequency bands, estimated by the phase lag index (PLI), was calculated within eight commonly found RSNs. Neuropsychological tests were used to assess cognitive performance, and the relation with RSNs was evaluated.; RESULTS: Significant differences in terms of RSN functional connectivity between the three groups were observed in the lower alpha band, in the default-mode (DMN), executive control (ECN) and sensorimotor (SMN) RSNs. T1DM patients with microvascular complications showed the weakest functional connectivity in these networks relative to the other groups. For DMN, functional connectivity was higher in patients without microangiopathy relative to controls (all p<0.05). General cognitive performance for both patient groups was worse compared with healthy controls. Lower DMN alpha band functional connectivity correlated with poorer general cognitive ability in patients with microvascular complications.; DISCUSSION: Altered RSN functional connectivity was found in T1DM patients depending on clinical status. Lower DMN functional connectivity was related to poorer cognitive functioning. These results indicate that functional connectivity may play a key role in T1DM-related cognitive dysfunction

Frontal alpha asymmetry and negative mood: a cross-sectional study in older and younger adults

The data presented in this study are openly available in Open Science Framework and can be found here (accessed on 26 July 2022): https://osf.io/v7y62/.Frontal alpha asymmetry (FAA) has been associated with emotional processing and affective psychopathology. Negative and withdrawal stimuli and behaviors have been related to high levels of right cortical activity, while positive and approach stimuli and behaviors have been related to increased left cortical activity. Inconsistent results in terms of depressive and anxious symptoms and their relationship to FAA have been previously observed, especially at older ages. Additionally, no studies to date have evaluated whether loneliness, a negative feeling, is related to FAA. Therefore, this study aimed (i) to compare FAA between younger and older adults and (ii) to investigate the possible relationships between loneliness, depressive and anxious symptomatology with FAA in young and older adults. Resting electroencephalogram recordings of 39 older (Mage = 70.51, SD = 7.12) and 57 younger adults (Mage = 22.54, SD = 3.72) were analyzed. Results showed greater left than right cortical activity for both groups and higher FAA for older than younger adults. FAA was not predicted by loneliness, depressive or anxious symptomatology as shown by regression analyses. Findings bring clarity about FAA patterns at different ages and open the discussion about the relationship between negative emotional processing and frontal cortical imbalances, especially at older ages.This research was funded by Portuguese Foundation for Science and Technology (FCT) projects POCI-01-0145-FEDER-028682 (PTDC/PSI-GER/28682/2017) and NORTE-01-0145-FEDER-032152 (PTDC/PSI-GER/32152/2017) through national and European Regional Development (FEDER) funds. D.P. was supported by the FCT grant SFRH/BPD/120111/2016, and C.B. by FCT grant 2020.07157.BD. The Centre for Research in Psychology (CIPsi/UM-PSI/01662) is supported by FCT through the Portuguese State Budget (UIDB/01662/2020) and by the Portuguese Ministry of Education and Science through national and FEDER funds through COMPETE2020 under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007653)

Loss of brain inter-frequency hubs in Alzheimer's disease

Alzheimer's disease (AD) causes alterations of brain network structure and function. The latter consists of connectivity changes between oscillatory processes at different frequency channels. We proposed a multi-layer network approach to analyze multiple-frequency brain networks inferred from magnetoencephalographic recordings during resting-states in AD subjects and age-matched controls. Main results showed that brain networks tend to facilitate information propagation across different frequencies, as measured by the multi-participation coefficient (MPC). However, regional connectivity in AD subjects was abnormally distributed across frequency bands as compared to controls, causing significant decreases of MPC. This effect was mainly localized in association areas and in the cingulate cortex, which acted, in the healthy group, as a true inter-frequency hub. MPC values significantly correlated with memory impairment of AD subjects, as measured by the total recall score. Most predictive regions belonged to components of the default-mode network that are typically affected by atrophy, metabolism disruption and amyloid-beta deposition. We evaluated the diagnostic power of the MPC and we showed that it led to increased classification accuracy (78.39%) and sensitivity (91.11%). These findings shed new light on the brain functional alterations underlying AD and provide analytical tools for identifying multi-frequency neural mechanisms of brain diseases.Comment: 27 pages, 6 figures, 3 tables, 3 supplementary figure