Dynamic Functional Network Connectivity Patterns Distinguish Neurobiological Substrates of Narcolepsy Type 1 and Idiopathic Hypersomnia: Potential Biomarkers From Resting-State fMRI.

This study aimed to explore dynamic functional network connectivity (dFNC) differences between narcolepsy type 1 (NT1), idiopathic hypersomnia (IH), and healthy controls (HCs), and evaluate the potential of dFNC as a neurobiological marker for differentiating these hypersomnolent disorders. We recruited 50 drug-naive NT1 patients, 31 IH patients, and 50 HCs. Resting-state fMRI data were acquired, and intrinsic connectivity networks (ICNs) were identified using group independent component analysis (ICA), yielding 10 networks (e.g., visual network [VIN], auditory network [AUN], sensorimotor network [SMN], default mode network [DMN]). dFNC was analysed via sliding-window and k-means clustering to identify recurring functional connectivity states, and temporal properties (fractional windows, mean dwell time [MDT]) were compared across groups. Machine learning models (support vector machine, random forest [RF], logistic regression) were constructed using state-specific functional connectivity (FC) features to distinguish NT1 and IH. Five distinct FNC states were identified. State II (39% of windows, sparse connectivity with strengthened DMN/SMN/VIN coupling) was more prevalent in NT1 (47.68% ± 34.5%) than in IH (37.07% ± 28.73%) or HCs (31.32% ± 23.67%). Conversely, State I (33% of windows, sparse ICN connectivity) was less frequent in NT1 (13.24% ± 22.04%) versus IH (39.14% ± 35.92%) and HCs (49.28% ± 30.42%). NT1 also showed longer MDT in State II and shorter MDT in State I compared to IH and HCs (p < 0.05, ANOVA with post hoc tests FDR corrected). FC features in State I and II (notably AUN-VIN and SMN-VIN) effectively distinguished NT1 and IH, with the RF model achieving an AUC of 0.9 in State II. These findings reveal distinct dFNC patterns in NT1 and IH, reflecting divergent perturbations in sleep-wake regulatory circuits, particularly involving VIN, which may underpin their neurobiological heterogeneity. dFNC holds promise as a biomarker for differentiating these disorders, with VIN-centered connectivity emerging as a key discriminative feature.