AI Insight
Researchers analyzed data from 260 individuals with diverse mental health diagnoses to identify how different components of sleep disturbance relate to psychiatric symptoms and brain connectivity patterns. They found two distinct dimensions: one linking widespread sleep problems (especially daytime dysfunction) to general symptom severity and altered connectivity between the default mode network and other brain systems, and another linking sleep initiation difficulties to reduced self-assurance and heightened sensitivity with different connectivity patterns involving cerebellar and attention networks. These findings suggest sleep disturbance in mental disorders is multidimensional rather than a single comorbid symptom, with different sleep components mapping onto separable brain network patterns and psychological profiles.
Why it matters
This research could help clinicians move beyond treating sleep as a general symptom and instead target specific sleep-related dimensions in mental health treatment. The identification of distinct brain connectivity patterns associated with different sleep-psychopathology profiles may enable more personalized interventions and improve patient stratification in psychiatric care.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Abstract Sleep disturbance is common and clinically consequential across mental disorders. Yet its transdiagnostic role remains difficult to interpret because sleep is often reduced to a global score and psychiatric symptoms to diagnosis-specific outcomes. Here, we leveraged the YaleNeuroConnect cohort to test whether subjective sleep components align with separable dimensions of symptom-related features and connectome-wide organization. We applied canonical correlation analysis to seven Pittsburgh Sleep Quality Index components and 55 clinical, affective, cognitive and interpersonal measures in 260 diagnostically diverse subjects, and mapped the resulting dimensions onto resting-state functional connectivity using network-based statistics. We identified two sleep-psychopathology dimensions. The dominant and cross-validated dimension linked widespread subjective sleep disturbance, especially daytime dysfunction, to general symptom burden, including global severity, perceived stress and fatigue. This dimension was expressed in reduced coupling between the default mode network and frontoparietal and cerebellar systems, reduced cerebellar-subcortical coupling, and stronger coupling between the somatomotor network and higher-order systems. A second, more circumscribed dimension linked shorter sleep duration and prolonged sleep latency to lower self-assurance and positive affect, together with heightened affective and perceptual sensitivity. This dimension showed stronger coupling between cerebellar and dorsal attention systems, together with weaker coupling between the default mode network and attention or cerebellar systems. These findings suggest that sleep is not merely a comorbid symptom, but a multidimensional organizing feature of transdiagnostic psychopathology. Distinguishing broad sleep-related distress from sleep-initiation and sensitivity profiles may help refine brain-based stratification and identify sleep dimensions relevant to intervention. Keywords: sleep disturbance; transdiagnostic psychopathology; multidimensional sleep; canonical correlation analysis; resting-state functional connectivity; connectome-wide networks