Evidence for a differential visual M300 brain response in gamblers

Separating desire from prediction of outcome value

Individuals typically want what they expect to like, often based on memories of previous positive experiences. However, in some situations desire can decouple completely from memories and from learned predictions of outcome value. The potential for desire to separate from prediction arises from independent operating rules that control motivational incentive salience. Incentive salience, or 'wanting', is a type of mesolimbic desire that evolved for adaptive goals, but can also generate maladaptive addictions. Two proof-of-principle examples are presented here to show how motivational 'wanting' can soar above memory-based predictions of outcome value: (i) 'wanting what is remembered to be disgusting', and (ii) 'wanting what is predicted to hurt'. Consequently, even outcomes remembered and predicted to be negatively aversive can become positively 'wanted'. Similarly, in human addictions, people may experience powerful cue-triggered cravings for outcomes that are not predicted to be enjoyable.

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.

High-gamma oscillations as neurocorrelates of ADHD: A MEG crossover placebo-controlled study

Attention Deficit Hyperactive Disorder (ADHD) is a common neurobehavioral disorder with a significant and pervasive impact on patients' lives. Identifying neurophysiological correlates of ADHD is important for understanding its underlying mechanisms, as well as for improving clinical accuracy beyond cognitive and emotional factors. The present study focuses on finding a diagnostic stable neural correlate based on evaluating MEG resting state frequency bands. Twenty-two ADHD patients and 23 controls adults were blindly randomized to two methylphenidate/placebo evaluation days. On each evaluation day state anxiety was assessed, a 2N-back executive function task was performed, and resting state MEG brain activity was recorded at three timepoints. A frequency-based cluster analysis yielded higher high-gamma power for ADHD over posterior sensors and lower high-gamma power for ADHD over frontal-central sensors. These results were shown to be stable over three measurements, unaffected by methylphenidate treatment, and linked to cognitive accuracy and state anxiety. Furthermore, the differential high-gamma activity evidenced substantial ADHD diagnostic efficacy, comparable to the cognitive and emotional factors. These results indicate that resting state high-gamma activity is a promising, stable, valid and diagnostically-relevant neurocorrelate of ADHD. Due to the evolving understanding both in the cellular and network level of high-gamma oscillations, focusing future studies on this frequency band bears the potential for a better understanding of ADHD, thus advancing the specificity of the evaluation of the disorder and developing new tools for therapy.

Negative and positive life events and their relation to substance and behavioral addictions

BACKGROUND

Research has shown that negative life events (LEs) may be connected to the development and maintenance of addictions. However, few studies have examined the potential relationship between positive events and addictive disorders, and even fewer studies evaluated the subjective perception of LEs that may underlie these relationships. Importantly, addictive disorders include both substance-related and behavioral addictions, but the relative relationship of each type of addiction with LEs remains unclear.

METHODS

The present study compared 212 participants suffering from an addiction (drugs, alcohol, gambling, and sex) and 79 controls on self-report measures of negative and positive LEs.

RESULTS

Compared with controls, individuals with an addiction reported experiencing a larger number of both negative and positive LEs and also tended to be more influenced by negative LEs. Findings also demonstrated differential patterns across addiction types, such that participants with compulsive sexual behavior (CSB) reported experiencing less negative events than those with drug use disorders (DUD) and were less influenced by these events than participants with alcohol use disorder (AUD). Finally, analyses within each group further revealed differences in the way each group experienced negative compared to positive events. Controls and participants with CSB reported experiencing a similar number of positive and negative events, whereas participants with DUD, AUD, and gambling disorder reported more negative events in their lives.

CONCLUSIONS

These findings suggest a unique profile among different types of addictions, which should be taken into account when planning personalized prevention and intervention approaches.