Auditory event-related potentials in methadone substituted opiate users

Effects of Radiofrequency Electromagnetic Radiation on Neurotransmitters in the Brain

With the rapid development of electronic information in the past 30 years, technical achievements based on electromagnetism have been widely used in various fields pertaining to human production and life. Consequently, electromagnetic radiation (EMR) has become a substantial new pollution source in modern civilization. The biological effects of EMR have attracted considerable attention worldwide. The possible interaction of EMR with human organs, especially the brain, is currently where the most attention is focused. Many studies have shown that the nervous system is an important target organ system sensitive to EMR. In recent years, an increasing number of studies have focused on the neurobiological effects of EMR, including the metabolism and transport of neurotransmitters. As messengers of synaptic transmission, neurotransmitters play critical roles in cognitive and emotional behavior. Here, the effects of EMR on the metabolism and receptors of neurotransmitters in the brain are summarized.

Biomarkers and neuromodulation techniques in substance use disorders

Addictive disorders are a severe health concern. Conventional therapies have just moderate success and the probability of relapse after treatment remains high. Brain stimulation techniques, such as transcranial Direct Current Stimulation (tDCS) and Deep Brain Stimulation (DBS), have been shown to be effective in reducing subjectively rated substance craving. However, there are few objective and measurable parameters that reflect neural mechanisms of addictive disorders and relapse. Key electrophysiological features that characterize substance related changes in neural processing are Event-Related Potentials (ERP). These high temporal resolution measurements of brain activity are able to identify neurocognitive correlates of addictive behaviours. Moreover, ERP have shown utility as biomarkers to predict treatment outcome and relapse probability. A future direction for the treatment of addiction might include neural interfaces able to detect addiction-related neurophysiological parameters and deploy neuromodulation adapted to the identified pathological features in a closed-loop fashion. Such systems may go beyond electrical recording and stimulation to employ sensing and neuromodulation in the pharmacological domain as well as advanced signal analysis and machine learning algorithms. In this review, we describe the state-of-the-art in the treatment of addictive disorders with electrical brain stimulation and its effect on addiction-related neurophysiological markers. We discuss advanced signal processing approaches and multi-modal neural interfaces as building blocks in future bioelectronics systems for treatment of addictive disorders.

Bouncing back: Brain rehabilitation amid opioid and stimulant epidemics

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Frontoparietal event related potentials predict/track recovery.

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Frontostriatal functional magnetic resonance imaging signals predict/track recovery.

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Transcranial magnetic left prefrontal stimulation reduces craving and drug use.

Recent methamphetamine and opioid use epidemics are a major public health concern. Chronic stimulant and opioid use are characterized by significant psychosocial, physical and mental health costs, repeated relapse, and heightened risk of early death. Neuroimaging research highlights deficits in brain processes and circuitry that are linked to responsivity to drug cues over natural rewards as well as suboptimal goal-directed decision-making. Despite the need for interventions, little is known about (1) how the brain changes with prolonged abstinence or as a function of various treatments; and (2) how symptoms change as a result of neuromodulation. This review focuses on the question: What do we know about changes in brain function during recovery from opioids and stimulants such as methamphetamine and cocaine? We provide a detailed overview and critique of published research employing a wide array of neuroimaging methods – functional and structural magnetic resonance imaging, electroencephalography, event-related potentials, diffusion tensor imaging, and multiple brain stimulation technologies along with neurofeedback – to track or induce changes in drug craving, abstinence, and treatment success in stimulant and opioid users. Despite the surge of methamphetamine and opioid use in recent years, most of the research on neuroimaging techniques for recovery focuses on cocaine use. This review highlights two main findings: (1) interventions can lead to improvements in brain function, particularly in frontal regions implicated in goal-directed behavior and cognitive control, paired with reduced drug urges/craving; and (2) the targeting of striatal mechanisms implicated in drug reward may not be as cost-effective as prefrontal mechanisms, given that deep brain stimulation methods require surgery and months of intervention to produce effects. Overall, more studies are needed to replicate and confirm findings, particularly for individuals with opioid and methamphetamine use disorders.

Personalised modelling with spiking neural networks integrating temporal and static information

This paper proposes a new personalised prognostic/diagnostic system that supports classification, prediction and pattern recognition when both static and dynamic/spatiotemporal features are presented in a dataset. The system is based on a proposed clustering method (named d2WKNN) for optimal selection of neighbouring samples to an individual with respect to the integration of both static (vector-based) and temporal individual data. The most relevant samples to an individual are selected to train a Personalised Spiking Neural Network (PSNN) that learns from sets of streaming data to capture the space and time association patterns. The generated time-dependant patterns resulted in a higher accuracy of classification/prediction (80% to 93%) when compared with global modelling and conventional methods. In addition, the PSNN models can support interpretability by creating personalised profiling of an individual. This contributes to a better understanding of the interactions between features. Therefore, an end-user can comprehend what interactions in the model have led to a certain decision (outcome). The proposed PSNN model is an analytical tool, applicable to several real-life health applications, where different data domains describe a person's health condition. The system was applied to two case studies: (1) classification of spatiotemporal neuroimaging data for the investigation of individual response to treatment and (2) prediction of risk of stroke with respect to temporal environmental data. For both datasets, besides the temporal data, static health data were also available. The hyper-parameters of the proposed system, including the PSNN models and the d2WKNN clustering parameters, are optimised for each individual.

Using Mismatch Negativity to Investigate the Pathophysiology of Substance Use Disorders and Comorbid Psychosis

Substance use disorders (SUDs) have a devastating impact on society and place a heavy burden on health care systems. Given that alcohol, tobacco, and cannabis use have the highest prevalence, further understanding of the underlying pathophysiology of these SUDs is crucial. Electroencephalography is an inexpensive, temporally superior, and translatable technique which enables investigation of the pathobiology of SUDs through the evaluation of various event-related potential components, including mismatch negativity (MMN). The goals of this review were to investigate the effects of acute and chronic alcohol, tobacco, and cannabis use on MMN among nonpsychiatric populations and patients with comorbid psychosis. A literature search was performed using the database PubMed, and 36 articles met our inclusion and exclusion criteria. We found a pattern of attenuation of MMN amplitude among patients with alcoholism across acute and chronic alcohol use, and this dysregulation was not heritable. Reports were limited, and results were mixed on the effects of acute and chronic tobacco and cannabis use on MMN. Reports on comorbid SUDs and psychosis were even fewer, and also presented mixed findings. These preliminary results suggest that MMN deficits may be associated with SUDs, specifically alcohol use disorder, and serve as a possible biomarker for treating these common disorders.

Reduction in N2 amplitude in response to deviant drug-related stimuli during a two-choice oddball task in long-term heroin abstainers

RATIONALE

Chronic heroin use can cause deficits in response inhibition, leading to a loss of control over drug use, particularly in the context of drug-related cues. Unfortunately, heightened incentive salience and motivational bias in response to drug-related cues may exist following abstinence from heroin use.

OBJECTIVES

The present study aimed to examine the effect of drug-related cues on response inhibition in long-term heroin abstainers.

METHODS

Sixteen long-term (8-24 months) male heroin abstainers and 16 male healthy controls completed a modified two-choice oddball paradigm, in which a neutral "chair" picture served as frequent standard stimuli; the neutral and drug-related pictures served as infrequent deviant stimuli of different conditions respectively. Event-related potentials were compared across groups and conditions.

RESULTS

Our results showed that heroin abstainers exhibited smaller N2d amplitude (deviant minus standard) in the drug cue condition compared to the neutral condition, due to smaller drug-cue deviant-N2 amplitude compared to neutral deviant-N2. Moreover, heroin abstainers had smaller N2d amplitude compared with the healthy controls in the drug cue condition, due to the heroin abstainers having reduced deviant-N2 amplitude compared to standard-N2 in the drug cue condition, which reversed in the healthy controls.

CONCLUSIONS

Our findings suggested that heroin addicts still show response inhibition deficits specifically for drug-related cues after longer-term abstinence. The inhibition-related N2 modulation for drug-related could be used as a novel electrophysiological index with clinical implications for assessing the risk of relapse and treatment outcome for heroin users.

Acute effects of radiofrequency electromagnetic field emitted by mobile phone on brain function

Due to its attributes, characteristics, and technological resources, the mobile phone (MP) has become one of the most commonly used communication devices. Historically, ample evidence has ruled out the substantial short-term impact of radiofrequency electromagnetic field (RF-EMF) emitted by MP on human cognitive performance. However, more recent evidence suggests potential harmful effects associated with MP EMF exposure. The aim of this review is to readdress the question of whether the effect of MP EMF exposure on brain function should be reopened. We strengthen our argument focusing on recent neuroimaging and electroencephalography studies, in order to present a more specific analysis of effects of MP EMF exposure on neurocognitive function. Several studies indicate an increase in cortical excitability and/or efficiency with EMF exposure, which appears to be more prominent in fronto-temporal regions and has been associated with faster reaction time. Cortical excitability might also underpin disruption to sleep. However, several inconsistent findings exist, and conclusions regarding adverse effects of EMF exposure are currently limited. It also should be noted that the crucial scientific question of the effect of longer-term MP EMF exposure on brain function remains unanswered and essentially unaddressed. Bioelectromagnetics. 38:329-338, 2017. © 2017 Wiley Periodicals, Inc.