Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.
Who will be affected?
Readers: No access to all 28 journals. We recommend accessing our articles via PubMed Central
Authors: No access to the submission form or your user account.
Reviewers: No access to your user account. Please download manuscripts you are reviewing for offline reading before Wednesday, July 01, 2020 at 7:00 PM.
Editors: No access to your user account to assign reviewers or make decisions.
Copyeditors: No access to user account. Please download manuscripts you are copyediting before Wednesday, July 01, 2020 at 7:00 PM.
Blasco-Fontecilla H, Sánchez-Cerezo J, Gómez I, Abreu-Fernández G, Ortiz S, Villoria JF, Blanco M, García A, Ballesteros J, Martínez R, Gálvez G, Maestú F, López-Medrano
Measuring Accuracy (Classification Probabilities, Positive, and Negative Predictive Values) of Executive Function Electroencephalogram Metrics in Attention-Deficit/Hyperactivity Disorder Diagnosis: Protocol for and Perspectives From the SINCRONIA Study
Measuring Accuracy (classification probabilities, positive and negative predictive values) of Executive Function EEG Metrics in ADHD Diagnosis: Study Protocol and Perspective
Hilario Blasco-Fontecilla;
Javier Sánchez-Cerezo;
Irene Gómez;
Georgelina Abreu-Fernández;
Sandra Ortiz;
Jesús F Villoria;
Miguel Blanco;
Ana García;
Julia Ballesteros;
Roldán Martínez;
Gerardo Gálvez;
Fernando Maestú;
Álvaro López-Medrano
ABSTRACT
Background:
Attention deficit/hyperactivity disorder (ADHD) is the most prevalent neurodevelopmental disorder worldwide, affecting approximately 5-7% of school-aged children and 2-5% of adults worldwide. However, there is still no reliable diagnostic tool for it. The lack of specific biomarkers further complicates the accurate diagnosis of ADHD. The SINCRONIA study seeks to develop and optimize an electroencephalogram (EEG)-based ADHD diagnostic classification algorithm by identifying biomarkers that provide optimal diagnostic performance.
Objective:
To demonstrate that EEG-derived brain connectivity metrics during an executive control task combined with machine learning algorithms achieve minimally acceptable classification probabilities (i.e., sensitivity and specificity), that are at least not inferior to the best clinical diagnosis of ADHD currently achievable for the pediatric population.
Methods:
This is a single-center, case-control study involving 162 participants, aged between 7 and 12 years that is being conducted at the Puerta de Hierro University Hospital in Madrid, Spain. Participants will be allocated to three groups: ADHD predominantly inattentive, ADHD predominantly combined or hyperactive/impulsive, and control group according to the best estimated diagnosis based on clinical interviews and a neuropsychological assessment that includes the Conners’ Continuous Performance Test 3rd Edition. In addition, an EEG recording will be conducted separately, and functional connectivity metrics will be used to characterize brain networks associated with inhibitory control.
Results:
A total of 162 participants were recruited until December 2024. Data collection started on July 2023 and ended on December 2024. Data analysis started on December 2024 and is expected to finish on September 2025. Results are expected to be published in 2026.
Conclusions:
The index test is expected to match or improve the clinical diagnosis of ADHD in children between 7 and 12 years of age and provide a set of eventual biomarkers that maximize diagnostic performance and provide pathophysiological clues. Clinical Trial: ISRCTN12110752. Date: 20th February 2025
Citation
Please cite as:
Blasco-Fontecilla H, Sánchez-Cerezo J, Gómez I, Abreu-Fernández G, Ortiz S, Villoria JF, Blanco M, García A, Ballesteros J, Martínez R, Gálvez G, Maestú F, López-Medrano
Measuring Accuracy (Classification Probabilities, Positive, and Negative Predictive Values) of Executive Function Electroencephalogram Metrics in Attention-Deficit/Hyperactivity Disorder Diagnosis: Protocol for and Perspectives From the SINCRONIA Study