Investigating sleep microarchitecture to better understand idiopathic hypersomnia pathophysiology and phenotype heterogeneity

We propose to characterize idiopathic hypersomnia (IH) sleep microarchitecture and identify clinically-relevant IH subtypes based on their microarchitecture through advanced EEG signal analyses . We will analyze 601 participants including 135 IH patients, 94 healthy controls, 223 subjects with narcolepsy and 149 patients with suspected IH but who don’t reach objective sleepiness criteria. Sleep microarchitecture variables will include slow-wave activity dissipation, slow wave morphological characteristics, sleep spindle characteristics and measures of sleep-stage stability (e.g. transitions and sleep bouts). We will determine how age, sex, body mass index, and depression impact sleep microarchitecture. We will use data-driven cluster analyses to identify subgroups of IH based on their sleep macro and microarchitecture. Finally, we will compare IH patients to other central hypersomnia disorders to understand its specific microarchitecture signature. By exploiting the richness of the polysomnographic signal, this project could improve our understanding of IH pathophysiology and reveal IH subtypes, which are necessary steps before developing more targeted interventions.

Faculty Supervisor:

Alex Desautels;Nadia Gosselin

Student:

Partner:

University of Pavia

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology

University:

Université de Montréal

Program: