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An innovative process that assists users in non-quantitative problem solving is presented. The process, called Ramic, employs the idea of psychological projection in an innovative way to help users focus, express and think through problems. Its applicability ranges from assisting with simple non-analytic decision-making to developing and assessing strategies.

In the virtual realm, harnessing the power of psychological projection for problem solving has been attempted in the form of a process called Sand Tray. Attempts at virtualization have garnered little traction potentially due to encumbrance of the interface. Ramic, in contrast, is innately set up for digital use through a relatively simple interface.

A key question this paper explores is how to quantitatively measure the value of Ramic in relation to the well-established process of Sand Tray. Even though these processes operate on qualitative problems, a preference analysis tool called conjoint analysis is used to build an experiment and derive specific user utilities for each process.

To perform the study, both processes required testing in the physical domain. A 32-person study is presented and indicates the Ramic projective process to have a 23% higher user utility than Sand Tray in the area of problem solving. As such, it presents an opportunity to explore a new way in which individuals can approach non-analytical problem solving and how computers can assist them in the task.

Attention Deficit Hyperactivity Disorder (ADHD) is a frequent learning disorder affecting about 5%–8% of the student population globally. Currently, the traditional methods for ADHD diagnosis are not fully specified, due to difficulties in identifying the particular factors that cause this disorder. In this paper, we present a novel system for diagnosing ADHD, which does not need special equipment. Instead, it is based on the application of machine learning (ML), using data gathered from gameplay sessions of a serious game named “ADHD360”, developed for this purpose. Participants were recruited with particular criteria in order to generate data for the study. The benefits of our approach include less subjectivity in the decision process, cost-efficiency and easier accessibility than the typical procedure. To this end, special data preprocessing steps and ML techniques were applied. Our models achieved up to 85.7% F1-score performance metric in predicting correctly a user’s label (ADHD or not) from his/her gameplay session in ADHD360. Our method also proved to be efficient using only a small amount of data for the training procedure. The results of our systems are very promising, indicating notable ability of the tool to distinguish players that probably suffer from ADHD than those who do not.

Attention deficit hyperactivity disorder (ADHD) is a mental health disorder that is very common among children and may last into their adulthood. It is known that ADHD affects the attention of patients due to problems with short-term memory. Therefore, analysis of attention and memory of these patients should come into consideration. In this study, the complexity and memory of Electroencephalogram (EEG) signals are analyzed to investigate the reduction in attention and memory of patients with ADHD compared to normal subjects while playing a serious game. To achieve this, the fractal dimension and sample entropy of EEG signals are analyzed to evaluate the alterations in the complexity of EEG signals. Moreover, the Hurst exponent of EEG signals for ADHD and non-ADHD subjects is calculated to discuss the memory of EEG signals. The results showed a smaller fractal dimension and sample entropy of EEG signals for patients with ADHD that reflects their lower attention. Besides, the Hurst exponent of EEG signals for these patients reflects their lower memory than normal subjects. Therefore, it can be concluded that the reductions of attention and memory in ADHD subjects are mapped on the reduction of complexity and memory of their EEG signals.

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This study demonstrates that the EEG phenotypes as described by Johnstone, Gunkelman & Lunt are identifiable EEG patterns with good inter-rater reliability. Furthermore, it was also demonstrated that these EEG phenotypes occurred in both ADHD subjects as well as healthy control subjects. The Frontal Slow and Slowed Alpha Peak Frequency and the Low Voltage EEG phenotype discriminated ADHD subjects best from controls (however the difference was not significant). The Frontal Slow group responded to a stimulant with a clinically relevant decreased number of false negative errors on the CPT. The Frontal Slow and Slowed Alpha Peak Frequency phenotypes have different etiologies as evidenced by the treatment response to stimulants. In previous research Slowed Alpha Peak Frequency has most likely erroneously shown up as a frontal theta sub-group. This implies that future research employing EEG measures in ADHD should avoid using traditional frequency bands, but dissociate Slowed Alpha Peak Frequency from frontal theta by taking the individual alpha peak frequency into account. Furthermore, the divergence from normal of the frequency bands pertaining to the various phenotypes is greater in the clinical group than in the controls. Investigating EEG phenotypes provides a promising new way to approach EEG data, explaining much of the variance in EEGs and thereby potentially leading to more specific prospective treatment outcomes.

Neonatal handling has an impact on adult behavior of experimental animals and is associated with rapid and increased palatable food ingestion, impaired behavioral flexibility, and fearless behavior to novel environments. These symptoms are characteristic features of impulsive trait, being controlled by the medial prefrontal cortex (mPFC). Impulsive behavior is a key component of many psychiatric disorders such as attention deficit hyperactivity disorder (ADHD), manic behavior, and schizophrenia. Others have reported a methylphenidate (MPH)-induced enhancement of mPFC functioning and improvements in behavioral core symptoms of ADHD patients. The aims of the present study were: (i) to find in vivo evidence for an association between neonatal handling and the development of impulsive behavior in adult Wistar rats and (ii) to test whether neonatal handling could have an impact on monoamine levels in the mPFC and the pharmacological response to MPH in vivo. Therefore, experimental animals (litters) were classified as: “non-handled” and “handled” (10min/day, postnatal days 1–10). After puberty, they were exposed to either a larger and delayed or smaller and immediate reward (tolerance to delay of reward task). Acute MPH (3mg/Kg. i.p.) was used to suppress and/or regulate impulsive behavior. Our results show that only neonatally handled male adult Wistar rats exhibit impulsive behavior with no significant differences in monoamine levels in the medial prefrontal cortex, together with a decreased response to MPH. On this basis, we postulate that early life interventions may have long-term effects on inhibitory control mechanisms and affect the later response to pharmacological agents during adulthood.

Based on published research on the combined cognitive behavioral therapy (CBT) versus drug therapy alone in children with attention deficit/hyperactivity disorder (ADHD), we systematically reviewed and analyzed the efficacy of two treatment methods in children with ADHD. The literature as at the end of 10 July 2017 in multiple databases was systematically searched. Standardized mean differences (SMD) and 95% confidence intervals (CIs) were calculated. The results indicated that combined CBT therapy was efficacious in reducing symptoms of ADHD (SDM $-$0.48, 95% CI $-$0.80 to $-$0.17). The executive function scores were not improved more in the combined CBT (SMD $-$0.34; 95% CI $-$0.71 to 0.03). This study suggests that combined CBT seems more efficacious in some domains affecting children with ADHD, but further evaluation is needed.