Effort-Related Decision-Making in ADHD

Neurobiological foundations and clinical relevance of effort-based decision-making

Applying effort-based decision-making tasks provides insights into specific variables influencing choice behaviors. The current review summarizes the structural and functional neuroanatomy of effort-based decision-making. Across 39 examined studies, the review highlights the ventromedial prefrontal cortex in forming reward-based predictions, the ventral striatum encoding expected subjective values driven by reward size, the dorsal anterior cingulate cortex for monitoring choices to maximize rewards, and specific motor areas preparing for effort expenditure. Neuromodulation techniques, along with shifting environmental and internal states, are promising novel treatment interventions for altering neural alterations underlying decision-making. Our review further articulates the translational promise of this construct into the development, maintenance and treatment of psychiatric conditions, particularly those characterized by reward-, effort- and valuation-related deficits.

An Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder: The ADHD360 Project (Preprint)

Background

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders during childhood; however, the diagnosis procedure remains challenging, as it is nonstandardized, multiparametric, and highly dependent on subjective evaluation of the perceived behavior.

Objective

To address the challenges of existing procedures for ADHD diagnosis, the ADHD360 project aims to develop a platform for (1) early detection of ADHD by assessing the user’s likelihood of having ADHD characteristics and (2) providing complementary training for ADHD management.

Methods

A 2-phase nonrandomized controlled pilot study was designed to evaluate the ADHD360 platform, including ADHD and non-ADHD participants aged 7 to 16 years. At the first stage, an initial neuropsychological evaluation along with an interaction with the serious game developed (“Pizza on Time”) for approximately 30-45 minutes is performed. Subsequently, a 2-week behavior monitoring of the participants through the mADHD360 app is planned after a telephone conversation between the participants’ parents and the psychologist, where the existence of any behaviors characteristic of ADHD that affect daily functioning is assessed. Once behavior monitoring is complete, the research team invites the participants to the second stage, where they play the game for a mean duration of 10 weeks (2 times per week). Once the serious game is finished, a second round of behavior monitoring is performed following the same procedures as the initial one. During the study, gameplay data were collected and preprocessed. The protocol of the pilot trials was initially designed for in-person participation, but after the COVID-19 outbreak, it was adjusted for remote participation. State-of-the-art machine learning (ML) algorithms were used to analyze labeled gameplay data aiming to detect discriminative gameplay patterns among the 2 groups (ADHD and non-ADHD) and estimate a player’s likelihood of having ADHD characteristics. A schema including a train-test splitting with a 75:25 split ratio, k-fold cross-validation with k=3, an ML pipeline, and data evaluation were designed.

Results

A total of 43 participants were recruited for this study, where 18 were diagnosed with ADHD and the remaining 25 were controls. Initial neuropsychological assessment confirmed that the participants in the ADHD group showed a deviation from the participants without ADHD characteristics. A preliminary analysis of collected data consisting of 30 gameplay sessions showed that the trained ML models achieve high performance (ie, accuracy up to 0.85) in correctly predicting the users’ labels (ADHD or non-ADHD) from their gameplay session on the ADHD360 platform.

Conclusions

ADHD360 is characterized by its notable capacity to discriminate player gameplay behavior as either ADHD or non-ADHD. Therefore, the ADHD360 platform could be a valuable complementary tool for early ADHD detection.

Trial Registration

ClinicalTrials.gov NCT04362982; https://clinicaltrials.gov/ct2/show/NCT04362982

International Registered Report Identifier (IRRID)

RR1-10.2196/40189