Electrophysiological evidence of memory impairment in alcoholic patients

Default mode network aberrance in subjects of alcohol and opioid use disorders during working memory task: An exploratory EEG microstates study

Background

Aberrance in switching from default mode network (DMN) to fronto-parietal network (FPN) is proposed to underlie working memory deficits in subjects with substance use disorders, which can be studied using neuro-imaging techniques during cognitive tasks. The current study used EEG to investigate pre-stimulus microstates during the performance of Sternberg's working memory task in subjects with substance use disorders.

Methods

128-channel EEG was acquired and processed in ten age and gender-matched subjects, each with alcohol use disorder, opioid use disorder, and controls while they performed Sternberg's task. Behavioral parameters, pre-stimulus EEG microstate, and underlying sources were analyzed and compared between subjects with substance use disorders and controls.

Results

Both alcohol and opioid use disorder subjects had significantly lower accuracy (P < 0.01), while reaction times were significantly higher only in subjects of alcohol use disorder compared to controls (P < 0.01) and opioid use disorder (P < 0.01), reflecting working memory deficits of varying degrees in subjects with substance use disorders. Pre-stimulus EEG microstate revealed four topographic Maps 1-4: subjects of alcohol and opioid use disorder showing significantly lower mean duration of Map 3 (visual processing) and Map 2 (saliency and DMN switching), respectively, compared to controls (P < 0.05).

Conclusion

Reduced mean durations in Map 3 and 2 in subjects of alcohol and opioid use disorder can underlie their poorer performance in Sternberg's task. Furthermore, cortical sources revealed higher activity in both groups of substance use disorders in the parahippocampal gyrus- a hub of DMN; superior and middle temporal gyri associated with impulsivity; and insula that maintains balance between executive reflective system and impulsive system. EEG microstates can be used to envisage neural underpinnings implicated for working memory deficits in subjects of alcohol and opioid use disorders, reflected by aberrant switching between neural networks and information processing mechanisms.

An Eigenvalues-Based Covariance Matrix Bootstrap Model Integrated With Support Vector Machines for Multichannel EEG Signals Analysis

Identification of alcoholism is clinically important because of the way it affects the operation of the brain. Alcoholics are more vulnerable to health issues, such as immune disorders, high blood pressure, brain anomalies, and heart problems. These health issues are also a significant cost to national health systems. To help health professionals to diagnose the disease with a high rate of accuracy, there is an urgent need to create accurate and automated diagnosis systems capable of classifying human bio-signals. In this study, an automatic system, denoted as (CT-BS- Cov-Eig based FOA-F-SVM), has been proposed to detect the prevalence and health effects of alcoholism from multichannel electroencephalogram (EEG) signals. The EEG signals are segmented into small intervals, with each segment passed to a clustering technique-based bootstrap (CT-BS) for the selection of modeling samples. A covariance matrix method with its eigenvalues (Cov-Eig) is integrated with the CT-BS system and applied for useful feature extraction related to alcoholism. To select the most relevant features, a nonparametric approach is adopted, and to classify the extracted features, a radius-margin-based support vector machine (F-SVM) with a fruit fly optimization algorithm (FOA), (i.e., FOA-F-SVM) is utilized. To assess the performance of the proposed CT-BS model, different types of evaluation methods are employed, and the proposed model is compared with the state-of-the-art models to benchmark the overall effectiveness of the newly designed system for EEG signals. The results in this study show that the proposed CT-BS model is more effective than the other commonly used methods and yields a high accuracy rate of 99%. In comparison with the state-of-the-art algorithms tested on identical databases describing the capability of the newly proposed FOA-F-SVM method, the study ascertains the proposed model as a promising medical diagnostic tool with potential implementation in automated alcoholism detection systems used by clinicians and other health practitioners. The proposed model, adopted as an expert system where EEG data could be classified through advanced pattern recognition techniques, can assist neurologists and other health professionals in the accurate and reliable diagnosis and treatment decisions related to alcoholism.

The Effects of Individual Differences, Non-Stationarity, and the Importance of Data Partitioning Decisions for Training and Testing of EEG Cross-Participant Models

EEG-based deep learning models have trended toward models that are designed to perform classification on any individual (cross-participant models). However, because EEG varies across participants due to non-stationarity and individual differences, certain guidelines must be followed for partitioning data into training, validation, and testing sets, in order for cross-participant models to avoid overestimation of model accuracy. Despite this necessity, the majority of EEG-based cross-participant models have not adopted such guidelines. Furthermore, some data repositories may unwittingly contribute to the problem by providing partitioned test and non-test datasets for reasons such as competition support. In this study, we demonstrate how improper dataset partitioning and the resulting improper training, validation, and testing of a cross-participant model leads to overestimated model accuracy. We demonstrate this mathematically, and empirically, using five publicly available datasets. To build the cross-participant models for these datasets, we replicate published results and demonstrate how the model accuracies are significantly reduced when proper EEG cross-participant model guidelines are followed. Our empirical results show that by not following these guidelines, error rates of cross-participant models can be underestimated between 35% and 3900%. This misrepresentation of model performance for the general population potentially slows scientific progress toward truly high-performing classification models.

Comparison of Common Amplitude Metrics in Event-Related Potential Analysis

Waveform data resulting from time-intensive longitudinal designs require careful treatment. In particular, the statistical properties of summary metrics in this area are crucial. We draw on event-related potential (ERP) studies, a field with a relatively long history of collecting and analyzing such data, to illustrate our points. In particular, three summary measures for a component in the average ERP waveform feature prominently in the literature: the maximum (or peak amplitude), the average (or mean amplitude) and a combination (or adaptive mean). We discuss the methodological divide associated with these summary measures. Through both analytic work and simulation study, we explore the properties (e.g., Type I and Type II errors) of these competing metrics for assessing the amplitude of an ERP component across experimental conditions. The theoretical and simulation-based arguments in this article illustrate how design (e.g., number of trials per condition) and analytic (e.g., window location) choices affect the behavior of these amplitude summary measures in statistical tests and highlight the need for transparency in reporting the analytic steps taken. There is an increased need for analytic tools for waveform data. As new analytic methods are developed to address these time-intensive longitudinal data, careful treatment of the statistical properties of summary metrics used for null hypothesis testing is crucial.

Biometric identification of listener identity from frequency following responses to speech

OBJECTIVE

We investigate the biometric specificity of the frequency following response (FFR), an EEG marker of early auditory processing that reflects phase-locked activity from neural ensembles in the auditory cortex and subcortex (Chandrasekaran and Kraus 2010, Bidelman, 2015a, 2018, Coffey et al 2017b). Our objective is two-fold: demonstrate that the FFR contains information beyond stimulus properties and broad group-level markers, and to assess the practical viability of the FFR as a biometric across different sounds, auditory experiences, and recording days.

APPROACH

We trained the hidden Markov model (HMM) to decode listener identity from FFR spectro-temporal patterns across multiple frequency bands. Our dataset included FFRs from twenty native speakers of English or Mandarin Chinese (10 per group) listening to Mandarin Chinese tones across three EEG sessions separated by days. We decoded subject identity within the same auditory context (same tone and session) and across different stimuli and recording sessions.

MAIN RESULTS

The HMM decoded listeners for averaging sizes as small as one single FFR. However, model performance improved for larger averaging sizes (e.g. 25 FFRs), similarity in auditory context (same tone and day), and lack of familiarity with the sounds (i.e. native English relative to native Chinese listeners). Our results also revealed important biometric contributions from frequency bands in the cortical and subcortical EEG.

SIGNIFICANCE

Our study provides the first deep and systematic biometric characterization of the FFR and provides the basis for biometric identification systems incorporating this neural signal.

Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease

Interest in research involving health-medical information analysis based on artificial intelligence, especially for deep learning techniques, has recently been increasing. Most of the research in this field has been focused on searching for new knowledge for predicting and diagnosing disease by revealing the relation between disease and various information features of data. These features are extracted by analyzing various clinical pathology data, such as EHR (electronic health records), and academic literature using the techniques of data analysis, natural language processing, etc. However, still needed are more research and interest in applying the latest advanced artificial intelligence-based data analysis technique to bio-signal data, which are continuous physiological records, such as EEG (electroencephalography) and ECG (electrocardiogram). Unlike the other types of data, applying deep learning to bio-signal data, which is in the form of time series of real numbers, has many issues that need to be resolved in preprocessing, learning, and analysis. Such issues include leaving feature selection, learning parts that are black boxes, difficulties in recognizing and identifying effective features, high computational complexities, etc. In this paper, to solve these issues, we provide an encoding-based Wave2vec time series classifier model, which combines signal-processing and deep learning-based natural language processing techniques. To demonstrate its advantages, we provide the results of three experiments conducted with EEG data of the University of California Irvine, which are a real-world benchmark bio-signal dataset. After converting the bio-signals (in the form of waves), which are a real number time series, into a sequence of symbols or a sequence of wavelet patterns that are converted into symbols, through encoding, the proposed model vectorizes the symbols by learning the sequence using deep learning-based natural language processing. The models of each class can be constructed through learning from the vectorized wavelet patterns and training data. The implemented models can be used for prediction and diagnosis of diseases by classifying the new data. The proposed method enhanced data readability and intuition of feature selection and learning processes by converting the time series of real number data into sequences of symbols. In addition, it facilitates intuitive and easy recognition, and identification of influential patterns. Furthermore, real-time large-capacity data analysis is facilitated, which is essential in the development of real-time analysis diagnosis systems, by drastically reducing the complexity of calculation without deterioration of analysis performance by data simplification through the encoding process.

Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm

In this paper, we have employed K-d tree algorithmic based multiscale entropy analysis (MSE) to distinguish alcoholic subjects from non-alcoholic ones. Traditional MSE techniques have been used in many applications to quantify the dynamics of physiological time series at multiple temporal scales. However, this algorithm requires O(N2), i.e. exponential time and space complexity which is inefficient for long-term correlations and online application purposes. In the current study, we have employed a recently developed K-d tree approach to compute the entropy at multiple temporal scales. The probability function in the entropy term was converted into an orthogonal range. This study aims to quantify the dynamics of the electroencephalogram (EEG) signals to distinguish the alcoholic subjects from control subjects, by inspecting various coarse grained sequences formed at different time scales, using traditional MSE and comparing the results with fast MSE (fMSE). The performance was also measured in terms of specificity, sensitivity, total accuracy and receiver operating characteristics (ROC). Our findings show that fMSE, with a K-d tree algorithmic approach, improves the reliability of the entropy estimation in comparison with the traditional MSE. Moreover, this new technique is more promising to characterize the physiological changes having an affect at multiple time scales.

A review on EEG-based methods for screening and diagnosing alcohol use disorder

The screening test for alcohol use disorder (AUD) patients has been of subjective nature and could be misleading in particular cases such as a misreporting the actual quantity of alcohol intake. Although the neuroimaging modality such as electroencephalography (EEG) has shown promising research results in achieving objectivity during the screening and diagnosis of AUD patients. However, the translation of these findings for clinical applications has been largely understudied and hence less clear. This study advocates the use of EEG as a diagnostic and screening tool for AUD patients that may help the clinicians during clinical decision making. In this context, a comprehensive review on EEG-based methods is provided including related electrophysiological techniques reported in the literature. More specifically, the EEG abnormalities associated with the conditions of AUD patients are summarized. The aim is to explore the potentials of objective techniques involving quantities/features derived from resting EEG, event-related potentials or event-related oscillations data.

Persistent negative effects of alcohol drinking on aspects of novelty-directed behavior in male rhesus macaques

Humans with histories of prolonged heavy alcohol use exhibit poorer performance on cognitive tasks associated with problem solving, short-term memory, and visuospatial reasoning, even following the cessation of drinking, when compared with healthy controls. It is unclear, however, whether the cognitive problems are a consequence of alcohol exposure or a contributing factor to alcohol-use disorders. Here, we examined the relationship between performance on a novel object recognition (NOR) task and total alcohol consumption (TAC) in adult male rhesus macaques (n = 12; ETH group; trained to self-administer alcohol). NOR performance in this group was assessed prior to induction of alcohol drinking ("pre") and, again, after a 1-year abstinence period ("post") and was compared to the performance of a second group (n = 6; Control group), which was alcohol-naïve. In the NOR task, difficulty was manipulated across three phases by varying specific object features and/or by varying duration of access to objects. For each monkey, we measured aspects of novelty-related behavior including novelty detection, novelty reactivity, and perseverative behavior. TAC during induction and a "free" access period in which the monkey could choose between water and a 4% w/v ethanol solution also was determined. We found that performance deficits in the NOR task were a consequence of high total alcohol intake instead of a predictor of subsequent high intake. Poor NOR performance in drinkers with the highest intakes was characterized by increased perseverative behavior rather than an inability to detect or react to novelty. Finally, the observed deficits are long-lasting - persisting even after a year of abstinence. Given the prevalent and persistent nature of alcohol-induced cognitive deficits in patients in treatment settings, understanding the nature of the deficit and its neural basis could ultimately offer novel treatment approaches based on the reversal of alcohol-induced impairment.

Evidence of a Task-Independent Neural Signature in the Spectral Shape of the Electroencephalogram

Genetic and neurophysiological studies of electroencephalogram (EEG) have shown that an individual's brain activity during a given cognitive task is, to some extent, determined by their genes. In fact, the field of biometrics has successfully used this property to build systems capable of identifying users from their neural activity. These studies have always been carried out in isolated conditions, such as relaxing with eyes closed, identifying visual targets or solving mathematical operations. Here we show for the first time that the neural signature extracted from the spectral shape of the EEG is to a large extent independent of the recorded cognitive task and experimental condition. In addition, we propose to use this task-independent neural signature for more precise biometric identity verification. We present two systems: one based on real cepstrums and one based on linear predictive coefficients. We obtained verification accuracies above 89% on 4 of the 6 databases used. We anticipate this finding will create a new set of experimental possibilities within many brain research fields, such as the study of neuroplasticity, neurodegenerative diseases and brain machine interfaces, as well as the mentioned genetic, neurophysiological and biometric studies. Furthermore, the proposed biometric approach represents an important advance towards real world deployments of this new technology.

An EEG-based machine learning method to screen alcohol use disorder

Screening alcohol use disorder (AUD) patients has been challenging due to the subjectivity involved in the process. Hence, robust and objective methods are needed to automate the screening of AUD patients. In this paper, a machine learning method is proposed that utilized resting-state electroencephalography (EEG)-derived features as input data to classify the AUD patients and healthy controls and to perform automatic screening of AUD patients. In this context, the EEG data were recorded during 5 min of eyes closed and 5 min of eyes open conditions. For this purpose, 30 AUD patients and 15 aged-matched healthy controls were recruited. After preprocessing the EEG data, EEG features such as inter-hemispheric coherences and spectral power for EEG delta, theta, alpha, beta and gamma bands were computed involving 19 scalp locations. The selection of most discriminant features was performed with a rank-based feature selection method assigning a weight value to each feature according to a criterion, i.e., receiver operating characteristics curve. For example, a feature with large weight was considered more relevant to the target labels than a feature with less weight. Therefore, a reduced set of most discriminant features was identified and further be utilized during classification of AUD patients and healthy controls. As results, the inter-hemispheric coherences between the brain regions were found significantly different between the study groups and provided high classification efficiency (Accuracy = 80.8, sensitivity = 82.5, and specificity = 80, F-Measure = 0.78). In addition, the power computed in different EEG bands were found significant and provided an overall classification efficiency as (Accuracy = 86.6, sensitivity = 95, specificity = 82.5, and F-Measure = 0.88). Further, the integration of these EEG feature resulted into even higher results (Accuracy = 89.3 %, sensitivity = 88.5 %, specificity = 91 %, and F-Measure = 0.90). Based on the results, it is concluded that the EEG data (integration of the theta, beta, and gamma power and inter-hemispheric coherence) could be utilized as objective markers to screen the AUD patients and healthy controls.

Statistical mechanics of neocortical interactions: Large-scale EEG influences on molecular processes

Calculations further support the premise that large-scale synchronous firings of neurons may affect molecular processes. The context is scalp electroencephalography (EEG) during short-term memory (STM) tasks. The mechanism considered is Π=p+qA (SI units) coupling, where p is the momenta of free Ca(2+) waves, q the charge of Ca(2+) in units of the electron charge, and A the magnetic vector potential of current I from neuronal minicolumnar firings considered as wires, giving rise to EEG. Data has processed using multiple graphs to identify sections of data to which spline-Laplacian transformations are applied, to fit the statistical mechanics of neocortical interactions (SMNI) model to EEG data, sensitive to synaptic interactions subject to modification by Ca(2+) waves.

Relative brain signature: a population-based feature extraction procedure to identify functional biomarkers in the brain of alcoholics

BACKGROUND

A novel feature extraction technique, Relative-Brain-Signature (RBS), which characterizes subjects' relationship to populations with distinctive neuronal activity, is presented. The proposed method transforms a set of Electroencephalography's (EEG) time series in high dimensional space to a space of fewer dimensions by projecting time series onto orthogonal subspaces.

METHODS

We apply our technique to an EEG data set of 77 abstinent alcoholics and 43 control subjects. To characterize subjects' relationship to the alcoholic and control populations, one RBS vector with respect to the alcoholic and one with respect to the control population is constructed. We used the extracted RBS vectors to identify functional biomarkers over the brain of alcoholics. To achieve this goal, the classification algorithm was used to categorize subjects into alcoholics and controls, which resulted in 78% accuracy.

RESULTS AND CONCLUSIONS

Using the results of the classification, regions with distinctive functionality in alcoholic subjects are detected. These affected regions, with respect to their spatial extent, are frontal, anterior frontal, centro-parietal, parieto-occiptal, and occipital lobes. The distribution of these regions over the scalp indicates that the impact of the alcohol in the cerebral cortex of the alcoholics is spatially diffuse. Our finding suggests that these regions engage more of the right hemisphere relative to the left hemisphere of the alcoholics' brain.

Analysis of alcoholic EEG signals based on horizontal visibility graph entropy

This paper proposes a novel horizontal visibility graph entropy (HVGE) approach to evaluate EEG signals from alcoholic subjects and controlled drinkers and compare with a sample entropy (SaE) method. Firstly, HVGEs and SaEs are extracted from 1,200 recordings of biomedical signals, respectively. A statistical analysis method is employed to choose the optimal channels to identify the abnormalities in alcoholics. Five group channels are selected and forwarded to a K-Nearest Neighbour (K-NN) and a support vector machine (SVM) to conduct classification, respectively. The experimental results show that the HVGEs associated with left hemisphere, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C$$\end{document}C1, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C$$\end{document}C3 and FC5 electrodes, of alcoholics are significantly abnormal. The accuracy of classification with 10-fold cross-validation is 87.5 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% with about three HVGE features. By using just optimal 13-dimension HVGE features, the accuracy is 95.8 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}%. In contrast, SaE features associated cannot identify the left hemisphere disorder for alcoholism and the maximum classification ratio based on SaE is just 95.2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% even using all channel signals. These results demonstrate that the HVGE method is a promising approach for alcoholism identification by EEG signals.

Electroencephalographic field influence on calcium momentum waves

Macroscopic electroencephalographic (EEG) fields can be an explicit top-down neocortical mechanism that directly drives bottom-up processes that describe memory, attention, and other neuronal processes. The top-down mechanism considered is macrocolumnar EEG firings in neocortex, as described by a statistical mechanics of neocortical interactions (SMNI), developed as a magnetic vector potential A. The bottom-up process considered is Ca(2+) waves prominent in synaptic and extracellular processes that are considered to greatly influence neuronal firings. Here, the complimentary effects are considered, i.e., the influence of A on Ca(2+) momentum, p. The canonical momentum of a charged particle in an electromagnetic field, Π=p+qA (SI units), is calculated, where the charge of Ca(2+) is q=-2e, e is the magnitude of the charge of an electron. Calculations demonstrate that macroscopic EEG A can be quite influential on the momentum p of Ca(2+) ions, in both classical and quantum mechanics. Molecular scales of Ca(2+) wave dynamics are coupled with A fields developed at macroscopic regional scales measured by coherent neuronal firing activity measured by scalp EEG. The project has three main aspects: fitting A models to EEG data as reported here, building tripartite models to develop A models, and studying long coherence times of Ca(2+) waves in the presence of A due to coherent neuronal firings measured by scalp EEG. The SMNI model supports a mechanism wherein the p+qA interaction at tripartite synapses, via a dynamic centering mechanism (DCM) to control background synaptic activity, acts to maintain short-term memory (STM) during states of selective attention.

Event-Related Oscillations in Alcoholism Research: A Review

Alcohol dependence is characterized as a multi-factorial disorder caused by a complex interaction between genetic and environmental liabilities across development. A variety of neurocognitive deficits/dysfunctions involving impairments in different brain regions and/or neural circuitries have been associated with chronic alcoholism, as well as with a predisposition to develop alcoholism. Several neurobiological and neurobehavioral approaches and methods of analyses have been used to understand the nature of these neurocognitive impairments/deficits in alcoholism. In the present review, we have examined relatively novel methods of analyses of the brain signals that are collectively referred to as event-related oscillations (EROs) and show promise to further our understanding of human brain dynamics while performing various tasks. These new measures of dynamic brain processes have exquisite temporal resolution and allow the study of neural networks underlying responses to sensory and cognitive events, thus providing a closer link to the physiology underlying them. Here, we have reviewed EROs in the study of alcoholism, their usefulness in understanding dynamical brain functions/dysfunctions associated with alcoholism as well as their utility as effective endophenotypes to identify and understand genes associated with both brain oscillations and alcoholism.

Evaluating Functional Connectivity in Alcoholics Based on Maximal Weight Matching

EEG-based applications have faced the challenge of multi-modal integrated analysis problems. In this paper, a greedy maximal weight matching approach is used to measure the functional connectivity in alcoholics datasets with EEG and EOG signals. The major discovery is that the processing of the repeated and unrepeated stimuli in the γ band in control drinkers is significantly more different than that in alcoholic subjects. However, the EOGs are always stable in the case of visual tasks, except for a weakly wave when subjects make an error response to the stimuli.

ON THE COMPLEXITY AND ENERGY ANALYSES IN EEG BETWEEN ALCOHOLIC AND CONTROL SUBJECTS DURING DELAYED MATCHING TO SAMPLE PARADIGM

In this study, we have investigated the electrophysiological differences between alcoholic and control subjects using two different approaches namely complexity and energy (power) analyses. The electroencephalogram data used in this study were recorded from 77 alcoholic and 44 control subjects while the subjects were performing delayed matching to sample object recognition task for three types of stimuli. These were a single stimulus and a second matching or nonmatching stimulus that followed the single stimulus after a delay. The experimental paradigm evokes object recognition, visual short-term memory, and decision-making abilities. The results indicated that all regions (i.e. frontal, central, temporal, parietal, and occipital) in the brain exhibit more complexity and less energy for alcoholic subjects as compared to controls. When different visual stimuli pairs were compared among alcoholic and control subjects, the results from energy analysis showed groupwise differences in occipital and parietal regions. These results provide a strong indication on the impairment in brain's electrophysiological activity for alcoholic subjects due to a history of long-term alcohol abuse.

The importance of glucocorticoids in alcohol dependence and neurotoxicity

Alterations in hypothalamo-pituitary adrenal (HPA) function have been described in alcoholics and in rodents after chronic alcohol consumption but the role of glucocorticoids in alcohol consumption, and the mechanisms involved, has received little attention until recently. Both alcohol consumption and withdrawal from chronic alcohol intake raise circulating glucocorticoid levels, and prolonged high concentrations of glucocorticoids are known to have detrimental effects on neuronal function and cognition. This minireview covers the ways in which glucocorticoids may be involved in drinking behavior, from social drinking to dependence, and the negative consequences of alcohol consumption seen during withdrawal which may have a detrimental effect on treatment outcome. Research shows prolonged increases in brain glucocorticoid concentrations and decreased brain glucocorticoid receptor availability (consistent with increased levels of endogenous ligand) after withdrawal from chronic alcohol treatment. Evidence suggests that increased glucocorticoid levels in the brain after chronic alcohol treatment are associated with the cognitive deficits seen during abstinence which impact on treatment efficacy and quality of life. Studies on organotypic cultures also demonstrate the importance of glucocorticoids in the neuropathological consequences of alcohol dependence.

Effects of the glucocorticoid antagonist, mifepristone, on the consequences of withdrawal from long term alcohol consumption

BACKGROUND

Studies were carried out to test the hypothesis that administration of a glucocorticoid Type II receptor antagonist, mifepristone (RU38486), just prior to withdrawal from chronic alcohol treatment, would prevent the consequences of the alcohol consumption and withdrawal in mice.

MATERIALS AND METHODS

The effects of administration of a single intraperitoneal dose of mifepristone were examined on alcohol withdrawal hyperexcitability. Memory deficits during the abstinence phase were measured using repeat exposure to the elevated plus maze, the object recognition test, and the odor habituation/discrimination test. Neurotoxicity in the hippocampus and prefrontal cortex was examined using NeuN staining.

RESULTS

Mifepristone reduced, though did not prevent, the behavioral hyperexcitability seen in TO strain mice during the acute phase of alcohol withdrawal (4 hours to 8 hours after cessation of alcohol consumption) following chronic alcohol treatment via liquid diet. There were no alterations in anxiety-related behavior in these mice at 1 week into withdrawal, as measured using the elevated plus maze. However, changes in behavior during a second exposure to the elevated plus maze 1 week later were significantly reduced by the administration of mifepristone prior to withdrawal, indicating a reduction in the memory deficits caused by the chronic alcohol treatment and withdrawal. The object recognition test and the odor habituation and discrimination test were then used to measure memory deficits in more detail, at between 1 and 2 weeks after alcohol withdrawal in C57/BL10 strain mice given alcohol chronically via the drinking fluid. A single dose of mifepristone given at the time of alcohol withdrawal significantly reduced the memory deficits in both tests. NeuN staining showed no evidence of neuronal loss in either prefrontal cortex or hippocampus after withdrawal from chronic alcohol treatment.

CONCLUSIONS

The results suggest mifepristone may be of value in the treatment of alcoholics to reduce their cognitive deficits.

Effects of chronic drinking on verb generation: an event related potential study

In alcohol dependent individuals, abnormalities in brain functioning have been revealed using event-related potential (ERP) methods. In the present study, we investigated whether in non-alcohol dependent drinkers functioning of the brain is also compromised as a function of recent and lifetime drinking history (LDH). An ERP verb generation task consisting of two conditions (generating verbs describing the use of visually presented nouns versus reading nouns aloud) was used; subtracting ERPs in the latter condition from those in the former should reveal the sequence of brain processes involved in verb generation. Four groups were included, consisting of individuals drinking either lightly, moderately, heavily, or excessively (overall mean age 46.6 years). Participants were sober at the time of testing. Although the excessive group had the highest per cent retrieval errors, there was no continuous relationship between this score and amount of alcohol consumption. However, number of glasses per week affected differential ERPs associated with verb generation both at short (120-220 ms, mid-frontal sites) and at longer latencies (from 700 ms on),left-temporal and right-frontal electrode sites (T7, F6). It is concluded that moderate, heavy, and excessive drinkers, compared to light drinkers, show abnormal brain potentials associated with verb generation over frontal and temporal areas. Moderate to excessive drinking alters some but not all brain processes involved in verb generation. In particular the frontal and temporal brain areas appear to be vulnerable for the effects of chronic lifetime drinking.

Selection of a Subset of EEG Channels using PCA to classify Alcoholics and Non-alcoholics

The Principal Component Analysis (PCA) is proposed as feature selection method in choosing a subset of channels for Visual Evoked Potentials (VEP). The selected channels are to preserve as much information present as compared to the full set of 61 channels as possible. The method is applied to classify two categories of subjects: alcoholics and non-alcoholics. The electroencephalogram (EEG) was recorded when the subjects were presented with single trial visual stimuli. The proposed method is successful in selecting the a subset of channels that contribute to high accuracy in the classification of alcoholics and non-alcoholics.

Relationship between alcohol use/abuse, HIV infection and neuropsychological performance in African American men

This study examines the impact of alcohol use and HIV infection on neuropsychological performance in a sample of 497 community-resident African American men. HIV serostatus and alcohol use (during the past 12 months) exerted an interactive effect on psychomotor speed, reaction time, and motor speed, and in general, HIV infected heavy drinkers evidenced significantly poorer performance than other HIV positive subjects. Main effects for HIV serostatus were noted for reaction time, with seronegative men performing better than seropositives. This study examines a sample of men who continue to show increases in HIV infection, however, sample specific issues such as comorbid substance use, past histories of head injury, and lack of data on alcohol abuse and dependence require caution in definitively attributing the findings solely to alcohol and HIV. However, these findings suggest that relatively recent heavy alcohol use may represent a potential risk factor for more rapid or pronounced cognitive decline in HIV positive individuals, and that these patterns may be even more pronounced in persons with comorbid substance use.

Suppression of early evoked gamma band response in male alcoholics during a visual oddball task

We investigated the early evoked gamma frequency band activity in alcoholics (n=122) and normal controls (n=72) during a visual oddball task. A time-frequency representation method was applied to EEG data in order to obtain phase-locked gamma band activity (29-45 Hz) and was analyzed within a 0-150 ms time window range. Significant reduction of the gamma band response in the frontal region during target stimulus processing was observed in alcoholic compared to control subjects. In contrast, significantly higher gamma band response for the non-target stimulus was observed in alcoholics compared to controls. It is suggested that the reduction in early evoked frontal gamma band response to targets may be associated with frontal lobe dysfunction commonly observed in alcoholics. This perhaps can be characterized by a deficient top-down processing mechanism.

Similarities in the disturbances in cortical information processing in alcoholism and aging: a pilot evoked potential study

OBJECTIVE

To examine the hypothesis that chronic alcohol use causes accelerated aging of the brain.

METHODS

The auditory evoked potentials (EPs) were compared in three groups of 10 subjects each: (a) middle-aged individuals meeting DSM-IV criteria for alcohol dependence, (b) age- and gender-matched group of healthy individuals, and (c) an older (>65 years) group of gender-matched healthy individuals. Multiple levels of cortical information processing were examined using EPs. Early stages of information processing, related to sensory gating and stimulus classification (P50, N100/P200), were studied using a paired-click paradigm. Later stages of information processing associated with memory upgrading and identification of novel stimuli (P300) were studied using an oddball paradigm.

RESULTS

The amplitude and latency of the P300 of the alcoholic patients and the older healthy subjects differed significantly from those of the younger healthy group. Both groups showed changes that have been reported in association with aging. A tendency towards decreased sensory gating in later stages of information processing was noted in the aged healthy individuals.

CONCLUSIONS

These data suggest that alcohol dependence may accelerate the aging process. The tendency towards a sensory gating deficit during the attentive phase of information processing in older healthy subjects requires further investigation because it may be a marker for an increased proneness to developing psychotic symptoms in that group.