Characterization of Neurocognitive Profiles in Neurodevelopmental Disorders and Epilepsy: a Transdiagnostic Approach
Characterization of Neurocognitive Profiles in Neurodevelopmental Disorders and Epilepsy: a Transdiagnostic Approach
Neurodevelopmental disorders (NDDs) are early-onset conditions affecting personal, social, and academic functioning, with high comorbidity rates and shared neurobiological traits. Traditional diagnostic categories struggle to explain symptom overlap, supporting a transdiagnostic approach that views NDDs as dimensions rather than distinct entities.
This project aims to identify common neuropsychological and neurophysiological profiles in ADHD, autism, intellectual disability, and learning disorders using high-density EEG and cognitive tasks assessing cognitive control. By integrating neuropsychological assessments, EEG responses, and behavioral data, the study seeks to identify shared functional profiles rather than disorder-specific biomarkers.
Additionally, it will analyze the relationship between NDDs and epilepsy to validate behavioral and neurophysiological markers. Conducted in collaboration with the University of Padua, the study employs advanced data analysis to enhance personalized interventions.
Neurodevelopmental disorders (NDDs) are a heterogeneous group of clinically significant conditions that emerge in early childhood, leading to mild to severe impairments in personal, social, and academic functioning, negatively impacting quality of life. Up to 10% of children are diagnosed with one or more NDDs. Epilepsy is one of the most common neurological comorbidities associated with NDDs.
Traditional categorical diagnoses have shaped scientific research under the assumption that each NDD category is characterized by specific neurocognitive and biological markers. However, current classification systems struggle to explain the symptomatic overlap observed among disorders traditionally considered distinct in their etiology. In fact, comorbidity among different NDDs is more common than the exception. For instance, learning disorders are present in 44% of children with ADHD and 65-85% of autistic children, while ADHD and autism have comorbidity rates ranging from 30% to 70%. Recent neuroimaging studies have also demonstrated that structural and functional neural patterns are shared across multiple NDDs, concluding that no disorder-specific biomarkers could be identified in large patient cohorts.
These clinical findings suggest adopting an alternative transdiagnostic approach, considering NDDs in terms of dimensions rather than categories. A deeper understanding of the shared neural, behavioral, and biological mechanisms among NDDs could scientifically validate the shift from a category-based to a dimensional approach, emphasizing individual neurocognitive variability and improving therapeutic efficacy.
Aligned with the transdiagnostic paradigm, this project aims to characterize the neuropsychological and neurophysiological profiles of certain NDDs (i.e., attention-deficit/hyperactivity disorder (ADHD), autism, intellectual disability, language and learning disorders). The presence of common neurocognitive profiles across diagnostic categories will be investigated using high-density EEG in both resting-state conditions and during specific computerized cognitive tasks. Specifically, these tasks will assess the ability to flexibly adapt cognitive control (CC) to environmental demands. Cognitive control refers to the set of mental abilities required to regulate thoughts, emotions, and behavior based on external demands and internal motivational states. It includes processes such as inhibition, working memory, cognitive flexibility, and planning-executive functions that are essential for self-regulation and often impaired in many NDDs. Thus, investigating CC across different diagnostic groups is crucial for developing intervention protocols tailored to individual needs rather than categorical diagnoses.
A battery of tests and questionnaires, part of the routine neuropsychological assessment administered to all patients at the IRCCS E. Medea "La Nostra Famiglia" center, will be given to both patients and their families. Additionally, participants will complete a computerized behavioral task (Mario task) and a second computerized task while undergoing electroencephalogram (EEG) recording (Flanker task). Administering both tasks allows for understanding how predictive context modulates the ability to inhibit inappropriate actions (Mario task) and manage cognitive conflict in the presence of distractor stimuli (Flanker task). The details of the questionnaires and computerized tasks will be described in the "Experimental Protocol" section.
The integration of various computerized cognitive tasks with neurophysiological measures will enable the creation of a multidimensional profile for each participant, encompassing the evaluation of brain network flexibility and organization at rest, EEG responses to cognitive tasks, and neuropsychological assessment outcomes. A dimensionality reduction approach will be applied to the data to identify separable clusters of neurocognitive functioning, characterized not only by neural organization and neuropsychological performance but also by environmental factors (e.g., socioeconomic indicators). In this framework, the goal is not to identify population-specific neurocognitive markers but rather shared (transdiagnostic) functional profiles that can define an individual's characteristics independently of their diagnostic category.
Additionally, analyzing shared and specific profiles among individuals with NDDs with or without epilepsy and, conversely, among individuals with epilepsy with or without an NDD diagnosis, represents a powerful validation tool for behavioral and neurophysiological markers.
This project, in line with the Framework Agreement (IRCCS-University Platform) approved by the Ministry of Health on May 3, 2023 (Id MIUR 4525), involves collaboration with the Department of General Psychology at the University of Padova. This collaboration aims to apply advanced data analysis techniques to both EEG signals and behavioral data (neuropsychological assessment and tasks)
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