Intitulé du sujet: Identification of ASD Subtypes Using Subtype and Stage Inference Algorithm and Exploration of the Relationship Between ASD Subtypes and Sleep Disorders
Sujet
Codirection: FEFEU Myl?ne
Nombre de mois: 48 mois
Ecole Doctorale: ED 158 - Cerveau, Cognition, Comportement
Unité de recherche et équipe:
INSERM
UMR 1141 NeuroDiderot
Equipe NeoPhen/SleepCMD
Hôpital Robert Debré
Coordonnées de l’équipe:
Chef d’équipe
Christophe Delclaux, Service de Physiologie - Explorations fonctionnelles, Hôpital Robert Debré, 48 boulevard Sérurier, 75019 Paris
Boris Matrot, Inserm UMR1141, Hôpital Robert Debré, 48 boulevard Sérurier, 75019 Paris
La prise en charge et le traitement des troubles respiratoires liés au sommeil est un enjeu important de santé publique, notamment pendant la période néonatale. Récemment, des découvertes importantes ont été effectuées pour identifier les structures neurologiques de la génération du rythme respiratoire chez la souris, ouvrant la voie au développement de nouveaux outils de diagnostic et de traitement. De plus, au cours des dernières années au niveau clinique, de nouveaux outils de traitement avancé des signaux physiologiques ont été développés. L’équipe a été rejointe en 2020 et 2021 par un enseignant chercheur psychiatre (P Geoffroy) et une chercheuse INSERM (K Spruyt) permettant d’étendre le champ de nos recherches aux troubles de l’humeur et rythmes biologiques ainsi qu’aux conséquences des troubles du sommeil (notamment liés aux apnées du sommeil et aux troubles de l’humeur) sur le neuro-développement de l’enfant.
http://neurodiderot.org/index.php/neophen/
Secteur: Sciences de la vie / Life Sciences
Langue attendue: Anglais
Niveau de langue attendu: C2
Description
Description du sujet:
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by social impairments, restricted interests, and repetitive behaviors, affecting up to 1% of the global population (Zeidan, J., et al., 2022). Manifesting before age three, ASD significantly impacts both mental and physical health, contributing to substantial economic and psychological burdens due to its high disability rate and the need for long-term care (Jiang, X., et al., 2024). Sleep disorders, particularly insomnia, are highly prevalent in individuals with ASD, with up to 86% experiencing sleep disturbances. However, the underlying mechanisms of this association remain poorly understood (Wintler, T., et al., 2020). Traditional ASD classifications, which rely on symptom severity or regression, do not capture the biological complexity of the disorder. Recent advances in machine learning, such as the Subtype and Stage Inference (SuStaIn) model (Young, A.L., et al., 2018), provide a data-driven approach to identifying distinct disease subtypes and progression patterns. Initially applied in neurodegenerative diseases, schizophrenia, and epilepsy, the SuStaIn model holds promise for subtyping ASD and elucidating its pathophysiological mechanisms, thereby facilitating personalized treatment strategies.
The relationship between ASD subtypes and sleep disorders remains underexplored. Given that sleep plays a critical role in brain development, particularly during periods of rapid growth and synaptic plasticity, abnormal sleep patterns in early life may contribute to neurodevelopmental anomalies. Therefore, it is crucial to examine how ASD subtypes are associated with sleep disturbances. This research aims to clarify these connections and lay the foundation for future interventional studies. We hypothesize that ASD subtypes, identified through the SuStaIn algorithm, exhibit distinct neurodevelopmental progression patterns and are differentially associated with specific sleep disorders, reflecting shared pathophysiological mechanisms contributing to both ASD and sleep dysfunction.
Our objectives are to: 1) Apply the SuStaIn algorithm to identify and classify novel ASD subtypes, thereby enhancing the understanding of their underlying pathophysiological mechanisms; and 2) Investigate the associations between these novel ASD subtypes and sleep disorders, shedding light on their interrelationships. This study will develop a data-driven framework for ASD subtyping and staging, utilizing head MRI data and clinical parameters, and validated through sleep disorder assessments and polysomnography. The results will clarify the connection between ASD subtypes and sleep disturbances, identifying potential biomarkers and progression patterns that could inform targeted interventions. This research will advance our understanding of ASD heterogeneity by introducing a novel, imaging-based classification system and establishing links between ASD subtypes and sleep disorders. Ultimately, it will contribute to the growing body of evidence on the neurodevelopmental and neurophysiological interplay between ASD and sleep, informing the development of personalized diagnostic tools and therapeutic strategies that enhance clinical care and long-term outcomes for individuals with ASD.
Compétences requises:
- Ability to work in a team and/or in an interdisciplinary context
- Analytical, synthesis, and written expression skills
- Intellectual curiosity and appropriate initiative
- Skills in statistical software, e.g., SPSS, R, SAS, or others
- Skills in Microsoft Office programs, e.g., excel
Références bibliographiques:
Zeidan, J., et al., Global prevalence of autism: A systematic review update. Autism Res, 2022. 15(5): p. 778-790.
Jiang, X., et al., Prevalence of autism spectrum disorder in mainland china over the past 6 years: a systematic review and meta-analysis. BMC Psychiatry, 2024. 24(1): p. 404.
Wintler, T., et al., Sleep, brain development, and autism spectrum disorders: Insights from animal models. J Neurosci Res, 2020. 98(6): p. 1137-1149.
Young, A.L., et al., Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat Commun, 2018. 9(1): p. 4273.
