Personality Traits and Cortical Activity Affect Gambling Behavior in Parkinson's Disease

Exploring the impact of smartphone addiction on decision-making behavior in college students: an fNIRS study based on the Iowa Gambling Task

The pervasive use of smartphones, while enhancing accessibility to information and communication, has raised concerns about its potential negative effects on physical and mental health, including the impairment of decision-making abilities. This study investigates the influence of smartphone addiction on decision-making in college students. A sample of 80 individuals aged 17 to 26 was selected and divided into two groups based on their Smartphone Addiction Scale-Short Version (SAS-SV) scores. Participants underwent the Iowa Gambling Task (IGT) to evaluate their decision-making in risky and uncertain conditions, while fNIRS recorded their prefrontal cortex activity. The study found that individuals prone to smartphone addiction tend to make riskier choices in risky situations. However, when faced with decisions based on ambiguity, the smartphone addiction group showed increased brain activity in the dlPFC (specifically in channels 4, 9, and 11) compared to when making risky decisions. Despite this increased brain activation, there was no observable difference in behavior between the addiction-prone and control groups in ambiguous scenarios. Notably, the left dlPFC (e.g., channel 4) exhibited significantly higher activation in the addiction group compared to the control group. Findings suggest that smartphone addiction can detrimentally influence decision-making, behaviorally and neurologically, particularly in uncertain contexts. This study supports the classification of smartphone addiction as a genuine addiction and underscores its significance in psychiatric research. In essence, our research underscores the adverse effects of excessive smartphone use on decision-making processes, reinforcing the necessity to treat smartphone addiction as a pressing public health issue.

Impulsivity Traits in Parkinson's Disease: A Systematic Review and Meta-Analysis

Background

In Parkinson's disease (PD), impulsivity as a personality trait may be linked to the risk of developing impulse control disorders (ICDs) during dopaminergic therapy. However, studies evaluating differences in trait impulsivity between patients with PD and healthy controls or between patients with PD with and without ICDs reported partly inconsistent findings.

Objectives

We conducted a systematic review and meta-analysis (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) of studies comparing Barratt Impulsiveness Scale (BIS-11) scores between patients with PD and healthy controls and between patients with PD with and without ICDs.

Methods

Eligible studies were identified through a systematic search in 3 databases. Mean differences with 95% confidence intervals (CIs) for BIS-11 total and subscale scores were separately calculated for studies comparing patients with PD and healthy controls and patients with PD with and without ICDs. Meta-regressions were performed to explore sources of heterogeneity (percentage of men, age, disease duration, and levodopa equivalent daily dose).

Results

A total of 40 studies were included in the quantitative analyses. BIS-11 total scores were significantly higher in patients with PD compared with healthy controls (mean difference 2.43; 95% CI, 1.03, 3.83), and in patients with PD with active ICDs compared with patients without ICDs (6.62; 95% CI, 5.01, 8.23). No significant moderators emerged by meta-regression analyses.

Conclusions

The present meta-analysis supports that impulsivity, as a personality trait, may characterize patients with PD, even in the absence of ICDs. Moreover, these data corroborate findings of clinical studies reporting higher levels of trait impulsivity in PD patients with ICDs compared with patients without ICDs.

Gaming behavior and brain activation using functional near-infrared spectroscopy, Iowa gambling task, and machine learning techniques

INTRODUCTION

The current study investigates the utilization and performance of machine learning (ML) algorithms in the cognitive task of finding the correlation between numerical parameters of the human brain activation during gaming. We hypothesize that our integrated feature extraction platform is able to distinguish between different psychosomatic conditions in the gaming process as measured by the functional near-infrared brain imaging technique.

METHODS

For demonstration, the decision-making process was constructed in the experiment environment that combined gaming simulator, such as the Iowa Gaming Task (IGT), with functional near-infrared spectroscopy (fNIRS) as the neuroimaging technique. Features of fNIRS levels were extracted, averaged, and synchronized by time with the IGT dataset to predict the task score inside ML algorithms, such as multiple regression, classification and regression trees, support vector machine, artificial neural network, and random forest. For findings validation, the experiment data were resampled by training and testing sets. Further, a training dataset was used to train the ML algorithms, and prediction accuracy was estimated by repeated cross-validation methods and compared by R squared and root mean square error (RMSE). The model with the best accuracy was used with the testing dataset and finalized the experiment.

RESULTS

During the experiment, the highest correlation was identified in the fourth block between the oxy-hemoglobin signal and IGT score in average value (0.24) and signal feature (0.57). Such relationship is due to block 4 characterization as "conceptual" period when participants task experience reaches the maximum, and rewards raise accordingly. Simultaneously, ML algorithms, constructed based on training data set, demonstrate acceptable performance, and RMSE as the primary performance metric dynamically increases from block 1 to block 5, from the state of uncertainty and unknown to the certainty and risky. In contrast, R squared decreases during the same transition. In most IGT blocks, the best fitted model was determined as support vector machine with radial bases function kernel, and predictions were made with the highest accuracy (lowest RMSE) than in training models.

CONCLUSION

Obtained findings showed the applicability and capability of ML models as a powerful technique to evaluate the cognitive neuroimaging task result. Moreover, in terms of features it was identified that the hemodynamic response reacts to the acceleration decision-making process and raises more significance than it was observed before.

Pathological Gambling in Parkinson's disease: Autonomic measures supporting impaired decision-making

According to the somatic marker hypothesis, autonomic measures and arousal modulation can reveal a difference in subgroups of patients developing impaired decision-making because of addictions. Previously, pathological gambling (PG) and Parkinson's disease (PD) have been associated with differential arousal levels during gambling behavior. However, no research considered the specific autonomic responses of Parkinson's disease patients with pathological gambling and with a previous history of gambling. Thus, this study investigated skin conductance responses (SCRs), skin conductance level (SCL) and heart rate (HR) during the Iowa Gambling Task (IGT) in two groups of PD patients with gambling disorder, active (PD Gamblers; n = 14) or remitted (PD Non-Gamblers; n = 13) and a control group of patients with Parkinson's disease only (n = 13). Anticipatory autonomic responses to disadvantageous decks and advantageous decks during the Iowa Gambling Task were measured for each participant. The PD Gamblers group performed worse than the PD Non-Gamblers and the control groups at the IGT task and exhibited lower SCRs, SCL, and HR during the decision-making processing of cards belonging to disadvantageous decks. The role of autonomic and behavioral measures was considered.