# Establishing that sleep spindle and slow wave deficits are present, are associated with cognitive dysfunction, and can be acutely manipulated in early course schizophrenia

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $781,301

## Abstract

Project Summary. In Schizophrenia (SCZ), early interventions can make a difference, particularly for cognitive
dysfunction, a core feature of SCZ that predicts poorer clinical trajectories and functional outcomes. Cognitive
impairments in SCZ have been linked to abnormalities in sleep and sleep-specific oscillations—spindles and
slow waves— which have been established in SCZ, although mostly in chronic patients. As first steps towards
translating these observations into novel, timely interventions, we aim to establish: whether sleep oscillatory and
cognitive deficits are present in early course (EC-SCZ) patients vs. healthy controls (HC), and if these deficits
are related to each other; and 2) whether sleep manipulation with auditory stimulation can improve sleep
oscillation deficits in EC-SCZ, and if sleep oscillatory changes are related to cognitive changes in these patients.
Sleep spindles are waxing and waning, 12-16 Hz oscillations that represent the hallmark of NREM stage 2 sleep.
Slow waves are large amplitude, ~1 Hz brain oscillations that characterize NREM stage 3. We and others have
demonstrated marked deficits in sleep spindles, and to a lesser extent, slow waves in SCZ and reduced spindle
density is associated with worse memory consolidation (MC), a key cognitive function known to be altered in
SCZ. However, most of the evidence is in chronic SCZ, while the presence of altered sleep oscillations and MD
at illness onset would establish these features as robust markers of SCZ. Thus, the first goal of this proposal is
to establish whether spindle and slow wave deficits are present and are associated with reduced MC
performance in EC-SCZ patients relative to HC. We will also examine the association between sleep oscillatory
deficits and cognitive controls (CC) and working memory (WM), cognitive functions that have been linked to
spindles and slow waves in HC and are impaired in EC-SCZ patients.
To establish whether ameliorating sleep alterations may contribute to novel treatment interventions, we first need
to ascertain that sleep and sleep oscillatory deficits can be manipulated. An EEG closed-loop system, which
delivers auditory stimuli during sleep, consistently enhances slow wave and spindle activity in HC. This system,
which involves using a wireless device, can be employed with a simultaneous polysomnogram (PSG) in the
sleep laboratory to enhance feasibility and validity. However, no study has used this closed-loop approach in
SCZ. The second goal of this proposal is to demonstrate that sleep spindles and slow waves deficits can be
acutely modified with closed-loop auditory stimulation (CLAS) during sleep in EC-SCZ patients and to examine
whether CLAS-related spindle and slow wave changes are associated with cognitive changes in these patients.
We propose to perform wireless/PSG overnight sleep recordings and assess performance in the motor sequence
tapping (MST), a MC task, as the main cognitive measure and performance in the AX-CP...

## Key facts

- **NIH application ID:** 10848440
- **Project number:** 5R01MH130376-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Fabio Ferrarelli
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $781,301
- **Award type:** 5
- **Project period:** 2023-06-01 → 2028-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10848440

## Citation

> US National Institutes of Health, RePORTER application 10848440, Establishing that sleep spindle and slow wave deficits are present, are associated with cognitive dysfunction, and can be acutely manipulated in early course schizophrenia (5R01MH130376-02). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10848440. Licensed CC0.

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