Self-control problems in Intermittent Explosive Disorder: Presentation of an explanatory approach.

BACKGROUND AND OBJECTIVES: Intermittent Explosive Disorder (IED) is an impulsive aggression disorder with self-control problems. However, the mechanisms underpinning the self-control problems in IED have not been clearly investigated. Therefore, this study examined the nature of self-control problems and their types, including cognitive inhibition, behavioral inhibition, and emotional interference in IED. METHODS: Participants included three groups: IED (n = 54), psychiatric control (n = 59), and healthy control (n = 62). They were first screened with SCL-90-R, and then they were clinically interviewed. They all did computerized neurocognitive tasks, including Color-Word Stroop Task, Emotional Stroop Task, Go-NoGo Task, and Stop-Signal Task. RESULTS: MANOVA analyses showed that the IED group had poorer performance in cognitive inhibition, response inhibition, and increased emotional interference than the two psychiatric and healthy control groups. They performed much worse than the other two groups, particularly in action cancellation (Stop-Signal Task), and showed increasingly emotional interference. LIMITATIONS: The brain reaction of individuals while doing the tasks was not examined, and some variables were not measured. Also, it is unclear how the emotional eruption interferes with cognitive content and behavioral inhibition. CONCLUSIONS: These findings indicate that self-control problems in IED can be due to deficient cognitive, emotional, and behavioral inhibitions, each appearing sequentially during a step-by-step process and facilitating the onset of IED signs and symptoms. Such a distinguished understanding of the role of neurocognitive mechanisms can lead to the development of accurate explanatory approaches and increase the effectiveness of treatment.

Probabilistic elderly person's mood analysis based on its activities of daily living using smart facilities.

The world's population is aging, and eldercare services that use smart facilities such as smart homes are widely common in societies now. With the aid of smart facilities, the present study aimed at understanding an elder's moods based on the person's activities of daily living (ADLs). With this end in view, an explainable probabilistic graphical modeling approach, applying the Bayesian network (BN), was proposed. The proposed BN-based model was capable of defining the relationship between the elder's ADLs and moods in three different levels: Activity-based Feature (AbF), Category of Activity (CoA), and the mood state. The model also allowed us to explain the transformations among the different levels/nodes on the defined BNs. A framework featured with smart facilities, including a smart home, a smartphone, and a wristband, was utilized to assess the model. The smart home was an elderly woman's house, equipped with a set of binary-based sensors. For about five months, the ADLs' data have been recorded through daily behavioral-based information, registered by experts using a defined questionnaire. The obtained results proved that the proposed BN-based model of the current study could promisingly estimate the elder's moods and CoA states. Moreover, in contrast to the machine learning techniques that behave like a black box, the effect of each feature from the lower levels to the higher levels of information of the BNs can be traced. Implications of the findings for future diagnosis and treatment of the elderly are considered.