Drug polyconsumption is associated with increased synchronization of brain electrical-activity at rest and in a counting task

Brain optimization with additional study time: potential brain differences between high- and low-performance college students

This study investigates potential differences in brain function among high-, average-, and low-performance college students using electroencephalography (EEG). We hypothesize that the increased academic engagement of high-performance students will lead to discernible EEG variations due to the brain's structural plasticity. 61 third-year college students from identical majors were divided into high-performance (n = 20), average-performance (n = 21), and low-performance (n = 20) groups based on their academic achievements. We conducted three EEG experiments: resting state, Sternberg working memory task, and Raven progressive matrix task. Comprehensive analyses of the EEG data from the three experiments focused on power spectral density (PSD) and functional connectivity, with coherence (COH) employed as our primary metric for the latter. The results showed that in all experiments, there were no differences in working memory ability and IQ scores among the groups, and there were no significant differences in the power spectral densities of the delta, theta, alpha1, alpha2, beta, and gamma bands among the groups. Notably, on the Raven test, compared to their high-performing peers, low-performing students showed enhanced functional connectivity in the alpha 1 (8-9 Hz) band that connects the frontal and occipital lobes. We explored three potential explanations for this phenomenon: fatigue, anxiety, and greater cognitive effort required for problem-solving due to inefficient self-regulation and increased susceptibility to distraction. In essence, these insights not only deepen our understanding of the neural basis that anchors academic ability, but also hold promise in guiding interventions that address students' diverse academic needs.

Improved HHT-microstate analysis of EEG in nicotine addicts

Background

Substance addiction is a chronic disease which causes great harm to modern society and individuals. At present, many studies have applied EEG analysis methods to the substance addiction detection and treatment. As a tool to describe the spatio-temporal dynamic characteristics of large-scale electrophysiological data, EEG microstate analysis has been widely used, which is an effective method to study the relationship between EEG electrodynamics and cognition or disease.

Methods

To study the difference of EEG microstate parameters of nicotine addicts at each frequency band, we combine an improved Hilbert Huang Transformation (HHT) decomposition with microstate analysis, which is applied to the EEG of nicotine addicts.

Results

After using improved HHT-Microstate method, we notice that there is significant difference in EEG microstates of nicotine addicts between viewing smoke pictures group (smoke) and viewing neutral pictures group (neutral). Firstly, there is a significant difference in EEG microstates at full-frequency band between smoke and neutral group. Compared with the FIR-Microstate method, the similarity index of microstate topographic maps at alpha and beta bands had significant differences between smoke and neutral group. Secondly, we find significant class × group interactions for microstate parameters at delta, alpha and beta bands. Finally, the microstate parameters at delta, alpha and beta bands obtained by the improved HHT-microstate analysis method are selected as features for classification and detection under the Gaussian kernel support vector machine. The highest accuracy is 92% sensitivity is 94% and specificity is 91%, which can more effectively detect and identify addiction diseases than FIR-Microstate and FIR-Riemann methods.

Conclusion

Thus, the improved HHT-Microstate analysis method can effectively identify substance addiction diseases and provide new ideas and insights for the brain research of nicotine addiction.

An automated drug dependence detection system based on EEG

OBJECTIVE

Substance abuse causes damage to the brain structure and function. This research aim is to design an automated drug dependence detection system based on EEG signals in a Multidrug (MD) abuser.

METHODS

EEG signals were recorded from participants categorized into MD-dependents (n = 10) and Healthy Control (HC) (n = 12). The Recurrence Plot investigates the dynamic characteristics of the EEG signal. The entropy index (ENTR) measured from the Recurrence Quantification Analysis was considered the complexity index of the delta, theta, alpha, beta, gamma, and all-band EEG signals. Statistical analysis was performed by t-test. The support vector machine technique was used for the data classification.

RESULTS

The results show decreased ENTR indices in the delta, alpha, beta, gamma, and all-band EEG signal and increased theta band in MD abusers compared to the HC group. That indicated the reduction of complexity in the delta, alpha, beta, gamma, and all-band EEG signals in the MD group. Additionally, the SVM classifier distinguished the MD group from the HC group with 90% accuracy, 89.36% sensitivity, 90.7% specificity, and 89.8% F1 score.

CONCLUSIONS AND SIGNIFICANCE

The nonlinear analysis of brain data was used to build an automatic diagnostic aid system that could identify HC people apart from those who abuse MD.

Brain oscillatory functioning after long-term alcohol abstinence

The heterogeneity of the population with alcohol use disorder (AUD) sometimes makes the knowledge generated in areas such as neuroscience appear to be contradictory. One aspect that may help elucidate this apparent contradiction is controlling for certain variables that are not usually controlled, such as the abstinence time in people with AUD. This research aims to study the neuroelectrical oscillations in people with AUD with longer and shorter abstinence time in comparison with healthy individuals. We recruited twenty-nine individuals with AUD with abstinence time longer than fifteen days and shorter than six months (STA), twenty-six individuals with AUD with abstinence time longer than six months and shorter than thirteen months (LTA), and sixteen healthy individuals (HC). All participants underwent electroencephalographic recording in resting-state with eyes closed. The oscillatory activity obtained was analyzed to obtain the spectral power and phase synchronization level. Regarding the obtained spectral power results, these revealed that the STA group showed higher theta band power and lower alpha band power than the LTA and HC groups. The obtained results at the phase synchronization level also show two main results. On the one hand, the STA group showed lower alpha band phase synchronization than the LTA and HC groups. On the other hand, the HC group showed higher beta band phase synchronization than the STA and LTA groups. In conclusion, the obtained results reflect that abstinence maintenance for six or more months appears to produce an important oscillatory brain functioning normalization in people with AUD.

EEG Spectral Power Analysis: A Comparison Between Heroin Dependent and Control Groups

Previous studies indicated that heroin abuse would result in abnormal functional organization of the brain. However, studies of heroin abuse- related brain dysfunction are scarce. The purpose of the present study was to investigate heroin effects on brain function by studying relationships between Electroencephalograph (EEG) spectral power and heroin abuse. The resting EEG signals were acquired from 15 male heroin dependent group and 15 male control group. The differences in the EEG components of each group were evaluated using the statistical Mann-Whitney examination and Davis Bouldin Index. The results show that heroin dependent group has an attenuated relative beta-2 power compared with other EEG frequency sub bands. Nevertheless, the results indicate heroin dependent group have an increase of power spectrum density for theta at all locations, as well as delta in the temporal, frontal and central areas compared with control group. Compared to control group, the heroin dependent group decreased its spectral power more than the control group in all three alpha bands. The present findings using the Davis Bouldin Index provide evidence that alpha-3 band in the FZ channel is more affected by heroin abuse than other frequency sub bands.

Electrophysiological and behavioral correlates of cannabis use disorder

Current research indicates deficits in cognitive function together with widespread changes in brain activity following long-term cannabis use. In particular, cannabis use has been associated with excessive spectral power of the alpha rhythm (8-12 Hz), which is also known to be modulated during attentional states. Recent neuroimaging studies have linked heavy cannabis use with structural and metabolic changes in the brain; however, the functional consequences of these changes are still not fully characterized. This study investigated the electrophysiological and behavioral correlates of cannabis dependence by comparing patients with a cannabis use disorder (CUD; N = 24) with cannabis nonuser controls (N = 24), using resting state electroencephalogram (EEG) source-imaging. In addition to evaluating mean differences between groups, we also explored whether particular EEG patterns were associated with individual cognitive-behavioral measures. First, we replicated historical findings of elevated levels of (relative) alpha rhythm in CUD patients compared with controls and located these abnormalities to mainly prefrontal cortical regions. Importantly, we observed a significant negative correlation between alpha spectral power in several cortical regions and individual attentional performance in the Go/NoGo task. Because such relationship was absent in the nonuser control group, our results suggest that reduced prefrontal cortical activation (indexed by increased relative alpha power) could be partly responsible for the reported cognitive impairments in CUD. Our findings support the use of electroencephalography as a noninvasive and cost-effective tool for biomarker discovery in substance abuse and have the potential of directly informing future intervention strategies.

Study of Human Tacit Knowledge Based on Electroencephalogram Signal Characteristics

Tacit knowledge is the kind of knowledge that is difficult to transfer to another person by means of writing it down or verbalizing it. In the mineral grinding process, the proficiency of the operators depends on the tacit knowledge gained from their experience and training rather than on knowledge learned from a handbook. This article proposed a method combining the electroencephalogram (EEG) signals and the industrial process to detect the proficiency of the operators in the mineral grinding process to reveal the effect of tacit knowledge on the functional cortical connection. The functional brain networks of operators were established based on partial direct coherence and directed transfer function of EEG, and the multi-classifiers were used with the graph-theoretic indexes of the FBNs as input to distinguish the trained operators (Hps) from the non-trained operators (Lps). The results showed that the brain networks of Hps had a better connectivity than those of Lps (p < 0.01), and the accuracy of classification was up to 94.2%. Our studies confirm that based on the performance of EEG features and the combination of industrial operational operation and cognitive processes, the proficiency of the operators can be detected.

Chronic pain as a brain imbalance between pain input and pain suppression

Chronic pain is pain that persists beyond the expected period of healing. The subjective experience of chronic pain results from pathological brain network interactions, rather than from persisting physiological sensory input of nociceptors. We hypothesize that pain is an imbalance between pain evoking dorsal anterior cingulate cortex and somatosensory cortex and pain suppression (i.e. pregenual anterior cingulate cortex). This imbalance can be measured objectively by current density ratios between pain input and pain inhibition. A balance between areas involved in pain input and pain suppression requires communication, which can be objectively identified by connectivity measures, both functional and effective connectivity. In patients with chronic neuropathic pain, electroencephalography is performed with source localization demonstrating that pain is reflected by an abnormal ratio between the dorsal anterior cingulate cortex, somatosensory cortex and pregenual anterior cingulate cortex. Functional connectivity demonstrates decreased communication between these areas, and effective connectivity puts the culprit at the dorsal anterior cingulate cortex, suggesting that the problem is related to abnormal behavioral relevance attached to the pain. In conclusion, chronic pain can be considered as an imbalance between pain input and pain suppression.

Alterations of Functional Connectivity During the Resting State and Their Associations With Visual Memory in College Students Who Binge Drink

This study investigated the characteristics of neural oscillation and functional connectivity (FC) in college students engaging in binge drinking (BD) using resting-state electroencephalography (EEG). Also, the associations of visual memory, evaluated by the Rey-Osterrieth Complex Figure Test (RCFT), and neural oscillation with FC during the resting state were investigated. The BD (n = 35) and non-BD (n = 35) groups were selected based on scores of the Korean version of the Alcohol use disorders (AUDs) Identification Test and the Alcohol Use Questionnaire. EEG was performed for 6 min while the participants rested with eyes closed. The theta, lower-alpha, and upper alpha powers did not differ between the BD and non-BD groups. Concerning FC, the BD group exhibited stronger theta coherence than that of the non-BD group, and in the lower and upper alpha bands, the BD group showed stronger coherence in some areas but weaker coherence in others compared with the non-BD group. However, these significant results were not observed after Bonferroni correction. The BD group showed significantly lower delayed recall scores on the RCFT than did the non-BD group. A positive correlation between the left prefrontal-parietal-occipital midline connection and performance on the delayed recall of the RCFT was observed in the BD group. The present results could suggest that binge drinkers have alterations in brain FC, which may be related to their visual memory deficits.

Using machine-learning approach to distinguish patients with methamphetamine dependence from healthy subjects in a virtual reality environment

BACKGROUND

The aim of this study was to evaluate whether machine learning (ML) can be used to distinguish patients with methamphetamine dependence from healthy controls by using their surface electroencephalography (EEG) and galvanic skin response (GSR) in a drug-simulated virtual reality (VR) environment.

METHODS

A total of 333 participants with methamphetamine (METH) dependence and 332 healthy control subjects were recruited between January 2018 and January 2019. EEG (five electrodes) and GSR signals were collected under four VR environments: one neutral scenario and three METH-simulated scenarios. Three ML classification techniques were evaluated: random forest (RF), support vector machine (SVM), and logistic regression (LR).

RESULTS

The MANOVA showed no interaction effects among the two subject groups and the 4 VR scenarios. Taking patient groups as the main effect, the METH user group had significantly lower GSR, lower EEG power in delta (p < .001), and alpha bands (p < .001) than healthy subjects. The EEG power of beta band (p < .001) and gamma band (p < .001) was significantly higher in METH group than the control group. Taking the VR scenarios (Neutral versus METH-VR) as the main effects, the GSR, EEG power in delta, theta, and alpha bands in neutral scenario were significantly higher than in the METH-VR scenario (p < .001). The LR algorithm showed the highest specificity and sensitivity in distinguishing methamphetamine-dependent patients from healthy controls.

CONCLUSION

The study shows the potential of using machine learning to distinguish methamphetamine-dependent patients from healthy subjects by using EEG and GSR data. The LR algorithm shows the best performance comparing with SVM and RF algorithm.

Transcranial Magnetic Stimulation Meets Virtual Reality: The Potential of Integrating Brain Stimulation With a Simulative Technology for Food Addiction

The aim of this perspective is to propose and discuss the integration of transcranial magnetic stimulation (TMS) over the dorsolateral prefrontal cortex with virtual reality (VR) food exposure for therapeutic interventions for food addiction. "Food addiction" is a dysfunctional eating pattern which is typically observed in eating disorders (ED) such as bulimia nervosa and binge eating disorder. Food addiction has been compared to substance use disorder due to the necessity of consuming a substance (food) and the presence of a dependence behavior. In recent years, VR has been applied in the treatment of ED because it triggers psychological and physiological responses through food exposure in place of real stimuli. Virtual reality-Cue exposure therapy has been proven as a valid technique for regulating anxiety and food craving in ED. More, TMS has been proven to modulate circuits and networks implicated in neuropsychiatric disorders and is effective in treating addiction such as nicotine craving and consumption and cocaine use disorder. The combination of a simulative technology and a neurostimulation would presumably provide better improvement compared to a single intervention because it implies the presence of both cognitive and neuropsychological techniques. The possible advantage of this approach will be discussed in the perspective.

Neuropsychophysiological Measures of Alcohol Dependence: Can We Use EEG in the Clinical Assessment?

Addiction management is complex, and it requires a bio-psycho-social perspective, that ought to consider the multiple etiological and developmental factors. Because of this, a large amount of resources has been allocated to assess the vulnerability to dependence, i.e., to identify the processes underlying the transition from substance use to dependence, as well as its course, in order to determine the key points in its prevention, treatment, and recovery. Consequently, knowledge \from neuroscience must be taken into account, which is why different initiatives have emerged with this objective, such as the "Research Domain Criteria" (RDoC), and the "Addiction Neuroclinical Assessment" (ANA). Particularly, neuropsychophysiological measures could be used as markers of cognitive and behavioral attributes or traits in alcohol dependence, and even trace clinical change. In this way, the aim of this narrative review is to provide an overview following ANA clinical framework, to the most robust findings in neuropsychophysiological changes in alcohol dependence, that underlie the main cognitive domains implicated in addiction: incentive salience, negative emotionality, and executive functioning. The most consistent results have been found in event-related potential (ERP) analysis, especially in the P3 component, that could show a wide clinical utility, mainly for the executive functions. The review also shows the usefulness of other components, implicated in affective and substance-related processing (P1, N1, or the late positive potential LPP), as well as event-related oscillations, such as theta power, with a possible use as vulnerability or clinical marker in alcohol dependence. Finally, new tools emerging from psychophysiology research, based on functional connectivity or brain graph analysis could help toward a better understanding of altered circuits in alcohol dependence, as well as communication efficiency and effort during mental operations. This review concludes with an examination of these tools as possible markers in the clinical field and discusses methodological differences, the need for more replicability studies and incipient lines of work. It also uses consistent findings in psychophysiology to draw possible treatment targets and cognitive profiles in alcohol dependence.

Brain functional connectivity after cognitive-bias modification and behavioral changes in abstinent alcohol-use disorder patients

The use of the cognitive-bias modification (CBM) method has emerged as a therapeutic complement in the treatment of alcoholism, producing changes at behavioral and brain level. Nevertheless, the impact of the CBM procedure could be improved by the memory retrieval-extinction process (REP). Different studies have demonstrated that the retrieval of drug memories before extinction training later reduced the reinstatement of drug-seeking behavior. The main aim of this work was to study the effect of the CBM procedure itself, as well as in combination with the activation of alcohol-related memories, on the brain oscillatory activity of abstinent patients with alcohol-use disorder. The study sample comprised 33 patients divided into three groups: A-CBM (alcohol-related memory activation + CBM), N-CBM (neutral memory activation + CBM) and N-INT (no-intervention) groups. A resting-state EEG was obtained before and after each protocol, along with the assessment of the automatic action tendencies. A-CBM group showed a general alpha synchronization increase after the protocol, while the other groups did not show any significant change. Besides, A-CBM group showed significant intra and inter-group differences in the automatic action tendencies after the protocol, reflected in higher avoidance bias toward appetitive, aversive and without context alcohol-related stimuli. The alpha phase synchronization increase could be the neural manifestation of the conditioning produced between the alcohol-related stimuli and the automatic avoidance response. Moreover, the activation of the alcohol-related memories favors this conditioning with those alcohol-related stimuli associated with the activated memories, because it increases their threat level for the abstinence maintenance.

Altered topological connectivity of internet addiction in resting-state EEG through network analysis

The results of some neuroimaging studies have revealed that people with internet addiction (IA) exhibit structural and functional changes in specific brain areas and connections. However, the understanding about global topological organization of IA may also require a more integrative and holistic view of brain function. In the present study, we used synchronization likelihood combined with graph theory analysis to investigate the functional connectivity (FC) and topological differences between 25 participants with IA and 27 healthy controls (HCs) based on their spontaneous EEG activities in the eye-closed resting state. There were no significant differences in FC (total network or sub-networks) between groups (p > .05 for all). Graph analysis showed significantly lower characteristic path length and clustering coefficient in the IA group than in the HC group in the beta and gamma bands, respectively. Altered nodal centralities of the frontal (FP₁, FPz) and parietal (CP₁, CP₅, PO₃, PO₇, P₅, P_6, TP₈) lobes in the IA group were also observed. Correlation analysis demonstrated that the observed regional alterations were significantly correlated with the severity of IA. Collectively, our findings showed that IA group demonstrated altered topological organization, shifting towards a more random state. Moreover, this study revealed the important role of altered brain areas in the neuropathological mechanism of IA and provided further supportive evidence for the diagnosis of IA.

A Novel Method of Building Functional Brain Network Using Deep Learning Algorithm with Application in Proficiency Detection

Functional brain network (FBN) has become very popular to analyze the interaction between cortical regions in the last decade. But researchers always spend a long time to search the best way to compute FBN for their specific studies. The purpose of this study is to detect the proficiency of operators during their mineral grinding process controlling based on FBN. To save the search time, a novel semi-data-driven method of computing functional brain connection based on stacked autoencoder (BCSAE) is proposed in this paper. This method uses stacked autoencoder (SAE) to encode the multi-channel EEG data into codes and then computes the dissimilarity between the codes from every pair of electrodes to build FBN. The highlight of this method is that the SAE has a multi-layered structure and is semi-supervised, which means it can dig deeper information and generate better features. Then an experiment was performed, the EEG of the operators were collected while they were operating and analyzed to detect their proficiency. The results show that the BCSAE method generated more number of separable features with less redundancy, and the average accuracy of classification (96.18%) is higher than that of the control methods: PLV (92.19%) and PLI (78.39%).

Assessment of driver drowsiness using electroencephalogram signals based on multiple functional brain networks

This paper proposes a comprehensive approach to explore whether functional brain network (FBN) changes from the alert state to the drowsy state and to find out ideal neurophysiology indicators able to detect driver drowsiness in terms of FBN. A driving simulation experiment consisting of two driving tasks is designed and conducted using fifteen participant drivers. Collected EEG signals are then decomposed into multiple frequency bands by wavelet packet transform (WPT). Based on this, two novel FBN approaches, synchronization likelihood (SL) and minimum spanning tree (MST) are combined and applied to feature recognition and classification system. Unlike other methods, our approaches focus on the interaction and correlation between different brain regions. Statistical analysis of network features indicates that the difference between alert state and drowsy state are significant and further confirmed that brain network configuration should be related to drowsiness. For classification, these brain network features are selected and then fed into four classifiers considered namely Support Vector Machines (SVM), K Nearest Neighbors classifier (KNN), Logistic Regression (LR) and Decision Trees (DT). It is found that combining MST method and SL method is actually increasing the classification accuracy with all classifiers considered in this work especially the KNN classifier from 95.4% to 98.6%. Moreover, KNN classifier also gives the highest precision of 98.3%, sensitivity of 98.8% and specificity of 98.9%. Thus this kind of methodology might be a useful tool for further understanding the neurophysiology mechanisms of driver drowsiness, and as a reference work for future studies or future 'systems'.

Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

OBJECTIVE

Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools.

APPROACH

The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1-45 Hz frequency domain.

MAIN RESULTS

This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways.

SIGNIFICANCE

These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

The neural correlates of the unified percept of alcohol-related craving: a fMRI and EEG study

Alcohol addiction is accompanied by aberrant neural activity. Previously, task-based fMRI and resting-state EEG studies have revealed that craving, a critical component of addiction, is linked to abnormal activity in cortical regions including the dorsal anterior cingulate cortex (dACC), nucleus accumbens (NAcc), posterior cingulate cortex (PCC) and pregenual anterior cingulate cortex (pgACC), etc. In this study, we combine these two imaging techniques to investigate a group of alcohol-addicted patients and provide convergent evidence for the neural correlates of craving not only in alcohol but substance abuse in general. We observe abnormal BOLD signal levels in the dACC, NAcc, pgACC, PCC, amygdala, and parahippocampus (PHC) in a cue-reactivity fMRI experiment. These findings are consistent with increased beta-band activity in the dACC and pgACC in resting-state EEG. We further observe desynchronization characterized by decreased functional connectivity in cue-based fMRI and hypersynchronization characterized by increased functional connectivity between these regions in the theta frequency band. The results of our study show a consistent pattern of alcohol craving elicited by external cues and internal desires. Given the advantage of superior spatial and temporal resolution, we hypothesize a "central craving network" that integrates the different aspects of alcohol addiction into a unified percept.

Network Disruption in the Preclinical Stages of Alzheimer’s Disease: From Subjective Cognitive Decline to Mild Cognitive Impairment

Introduction: Subjective Cognitive Decline (SCD) is a largely unknown state thought to represent a preclinical stage of Alzheimer’s Disease (AD) previous to mild cognitive impairment (MCI). However, the course of network disruption in these stages is scarcely characterized. Methods: We employed resting state magnetoencephalography in the source space to calculate network smallworldness, clustering, modularity and transitivity. Nodal measures (clustering and node degree) as well as modular partitions were compared between groups. Results: The MCI group exhibited decreased smallworldness, clustering and transitivity and increased modularity in theta and beta bands. SCD showed similar but smaller changes in clustering and transitivity, while exhibiting alterations in the alpha band in opposite direction to those showed by MCI for modularity and transitivity. At the node level, MCI disrupted both clustering and nodal degree while SCD showed minor changes in the latter. Additionally, we observed an increase in modular partition variability in both SCD and MCI in theta and beta bands. Conclusion: SCD elders exhibit a significant network disruption, showing intermediate values between HC and MCI groups in multiple parameters. These results highlight the relevance of cognitive concerns in the clinical setting and suggest that network disorganization in AD could start in the preclinical stages before the onset of cognitive symptoms.

Modification of EEG functional connectivity and EEG power spectra in overweight and obese patients with food addiction: An eLORETA study

We evaluated the modifications of electroencephalographic (EEG) power spectra and EEG connectivity in overweight and obese patients with elevated food addiction (FA) symptoms. Fourteen overweight and obese patients (3 men and 11 women) with three or more FA symptoms and fourteen overweight and obese patients (3 men and 11 women) with two or less FA symptoms were included in the study. EEG was recorded during three different conditions: 1) five minutes resting state (RS), 2) five minutes resting state after a single taste of a chocolate milkshake (ML-RS), and 3) five minutes resting state after a single taste of control neutral solution (N-RS). EEG analyses were conducted by means of the exact Low Resolution Electric Tomography software (eLORETA). Significant modification was observed only in the ML-RS condition. Compared to controls, patients with three or more FA symptoms showed an increase of delta power in the right middle frontal gyrus (Brodmann Area [BA] 8) and in the right precentral gyrus (BA 9), and theta power in the right insula (BA 13) and in the right inferior frontal gyrus (BA 47). Furthermore, compared to controls, patients with three or more FA symptoms showed an increase of functional connectivity in fronto-parietal areas in both the theta and alpha band. The increase of functional connectivity was also positively associated with the number of FA symptoms. Taken together, our results show that FA has similar neurophysiological correlates of other forms of substance-related and addictive disorders suggesting similar psychopathological mechanisms.

Superlinear Summation of Information in Premotor Neuron Pairs

Whether premotor/motor neurons encode information in terms of spiking frequency or by their relative time of firing, which may display synchronization, is still undetermined. To address this issue, we used an information theory approach to analyze neuronal responses recorded in the premotor (area F5) and primary motor (area F1) cortices of macaque monkeys under four different conditions of visual feedback during hand grasping. To evaluate the sensitivity of spike timing correlation between single neurons, we investigated the stimulus dependent synchronization in our population of pairs. We first investigated the degree of correlation of trial-to-trial fluctuations in response strength between neighboring neurons for each condition, and second estimated the stimulus dependent synchronization by means of an information theoretical approach. We compared the information conveyed by pairs of simultaneously recorded neurons with the sum of information provided by the respective individual cells. The information transmission across pairs of cells in the primary motor cortex seems largely independent, whereas information transmission across pairs of premotor neurons is summed superlinearly. The brain could take advantage of both the accuracy provided by the independency of F1 and the synergy allowed by the superlinear information population coding in F5, distinguishing thus the generalizing role of F5.

Functional Connectivity and Quantitative EEG in Women with Alcohol Use Disorders: A Resting-State Study

This study was aimed at exploring the electroencephalographic features associated with alcohol use disorders (AUD) during a resting-state condition, by using quantitative EEG and Functional Connectivity analyses. In addition, we explored whether EEG functional connectivity is associated with trait impulsivity. Absolute and relative powers and Synchronization Likelihood (SL) as a measure of functional connectivity were analyzed in 15 AUD women and fifteen controls matched in age, gender and education. Correlation analysis between self-report impulsivity as measured by the Barratt impulsiveness Scale (BIS-11) and SL values of AUD patients were performed. Our results showed increased absolute and relative beta power in AUD patients compared to matched controls, and reduced functional connectivity in AUD patients predominantly in the beta and alpha bands. Impaired connectivity was distributed at fronto-central and occipito-parietal regions in the alpha band, and over the entire scalp in the beta band. We also found that impaired functional connectivity particularly in alpha band at fronto-central areas was negative correlated with non-planning dimension of impulsivity. These findings suggest that functional brain abnormalities are present in AUD patients and a disruption of resting-state EEG functional connectivity is associated with psychopathological traits of addictive behavior.

Effect of cocaine dependence on brain connections: clinical implications

Cocaine dependence (CD) is associated with several cognitive deficits. Accumulating evidence, based on human and animal studies, has led to models for interpreting the neural basis of cognitive functions as interactions between functionally related brain regions. In this review, we focus on magnetic resonance imaging (MRI) studies using brain connectivity techniques as related to CD. The majority of these brain connectivity studies indicated that cocaine use is associated with altered brain connectivity between different structures, including cortical-striatal regions and default mode network. In cocaine users some of the altered brain connectivity measures are associated with behavioral performance, history of drug use, and treatment outcome. The implications of these brain connectivity findings to the treatment of CD and the pros and cons of the major brain connectivity techniques are discussed. Finally potential future directions in cocaine use disorder research using brain connectivity techniques are briefly described.

Exploratory Analysis of Power Spectrum and Functional Connectivity During Resting State in Young Binge Drinkers: A MEG Study

Binge Drinking (BD) is a pattern of intermittent intensive alcohol intake which has spread among young adults over the last decades. Adolescence constitutes a critical neuromaturation period in which the brain is particularly sensitive to the effects of alcohol. However, little is known about how BD affects the brain activity. This study aimed to characterize the brain's functional organization in BD and non-BD young population by means of analyzing functional connectivity (FC) and relative power spectra (PS) profiles measured with magnetoencephalography (MEG) during eyes-closed resting state. Our sample composed 73 first-year university students (35 BDs and 38 controls). Results showed that the BD subjects displayed a decreased alpha FC in frontal-parietal regions, and conversely, an enhanced FC in the delta, theta and beta bands in fronto-temporal networks. Besides the FC differences, the BD group showed a decreased PS within alpha range and an increased PS within theta range in the brain's occipital region. These differences in FC and PS measurements provide new evidence of the neurophysiological alterations related to the alcohol neurotoxicity and could represent an initial sign of an anomalous neural activity caused by a BD pattern of alcohol consumption during youth.

Mechanisms underlying sleep-wake disturbances in alcoholism: focus on the cholinergic pedunculopontine tegmentum

Sleep-wake (S-W) disturbances are frequently associated with alcohol use disorders (AUD), occurring during periods of active drinking, withdrawal, and abstinence. These S-W disturbances can persist after months or even years of abstinence, suggesting that chronic alcohol consumption may have enduring negative effects on both homeostatic and circadian sleep processes. It is now generally accepted that S-W disturbances in alcohol-dependent individuals are a significant cause of relapse in drinking. Although significant progress has been made in identifying the socio-economic burden and health risks of alcohol addiction, the underlying neurobiological mechanisms that lead to S-W disorders in AUD are poorly understood. Marked progress has been made in understanding the basic neurobiological mechanisms of how different sleep stages are normally regulated. This review article in seeking to explain the neurobiological mechanisms underlying S-W disturbances associated with AUD, describes an evidence-based, easily testable, novel hypothesis that chronic alcohol consumption induces neuroadaptive changes in the cholinergic cell compartment of the pedunculopontine tegmentum (CCC-PPT). These changes include increases in N-methyl-d-aspartate (NMDA) and kainate receptor sensitivity and a decrease in gamma-aminobutyric acid (GABAB)-receptor sensitivity in the CCC-PPT. Together these changes are the primary pathophysiological mechanisms that underlie S-W disturbances in AUD. This review is targeted for both basic neuroscientists in alcohol addiction research and clinicians who are in search of new and more effective therapeutic interventions to treat and/or eliminate sleep disorders associated with AUD.

Synchrony can destabilize reward-sensitive networks

When exposed to rewarding stimuli, only some animals develop persistent craving. Others are resilient and do not. How the activity of neural populations relates to the development of persistent craving behavior is not fully understood. Previous computational studies suggest that synchrony helps a network embed certain patterns of activity, although the role of synchrony in reward-dependent learning has been less studied. Increased synchrony has been reported as a marker for both susceptibility and resilience to developing persistent craving. Here we use computational simulations to study the effect of reward salience on the ability of synchronous input to embed a new pattern of activity into a neural population. Our main finding is that weak stimulus-reward correlations can facilitate the short-term repetition of a pattern of neural activity, while blocking long-term embedding of that pattern. Interestingly, synchrony did not have this dual effect on all patterns, which suggests that synchrony is more effective at embedding some patterns of activity than others. Our results demonstrate that synchrony can have opposing effects in networks sensitive to the correlation structure of their inputs, in this case the correlation between stimulus and reward. This work contributes to an understanding of the interplay between synchrony and reward-dependent plasticity.