# Offline memory processing in schizophrenia

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $839,733

## Abstract

Memory impairment is a core, disabling feature of schizophrenia, yet we lack effective treatments. A major
limitation of efforts to understand and treat memory impairment is that memory is generally assessed during a
single session. This approach misses the critical, and arguably most important, aspects of memory that happen
offline, outside of conscious awareness, during both wake and sleep. Over the past 20 years, a virtual explosion
of research has produced a wealth of evidence of an evolutionary conserved function of offline processes in the
consolidation of multiple forms of memory, with wakeful rest and sleep playing complementary roles. This basic
work has revealed the importance of off-line memory consolidation to cognition across the lifespan and its
mechanisms, most notably the central role of the hippocampus. Yet this mechanistic knowledge has not been
translated to the clinic: hippocampal physiology has largely been neglected as a treatment target. This is the
unmet medical need that the present proposal addresses. Our laboratory has established that people with
schizophrenia show a deficit in sleep-dependent memory consolidation. And our preliminary data show a
correlated deficit in offline learning during wake. Converging evidence from human and rodent studies show that
both types of offline learning depend on hippocampal sharp-wave ripples and associated memory replay.
Schizophrenia is characterized by structural and functional hippocampal abnormalities that would be expected
to disrupt ripples. The primary goals of the proposed research are test the hypotheses that disrupted
hippocampal ripples in schizophrenia impair offline learning and can be treated using closed-loop auditory
stimulation during sleep (CLASS). CLASS is a novel noninvasive neurostimulation technique that can be
implemented at home. First, using archival data, we will establish that schizophrenia patients have correlated
deficits in offline memory during wake and sleep, consistent with a common underlying mechanism. Next, in a
mechanistic trial of CLASS, we will determine whether by repeatedly synchronizing sleep oscillations over a
hippocampal-prefrontal cortical network, CLASS can strengthen hippocampal-prefrontal interactions and
improve offline memory in schizophrenia. Finally, by studying epilepsy patients with direct hippocampal
recordings, we will definitively link ripples to offline learning. During wake, we will show that ripples increase
during the rest breaks that follow learning and correlate with performance gains. During sleep, we will show that
CLASS increases the ripple coupling with slow oscillations and spindles that is critical for sleep-dependent
memory consolidation. By studying memory during both wake and sleep, this research program will reveal
parallel mechanisms of consolidation in these distinct states. It will both augment our basic understanding of the
role of the hippocampus in offline memory and translate this mechanistic knowledge t...

## Key facts

- **NIH application ID:** 10792956
- **Project number:** 5R01MH092638-10
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** DARA S MANOACH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $839,733
- **Award type:** 5
- **Project period:** 2012-07-01 → 2027-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10792956

## Citation

> US National Institutes of Health, RePORTER application 10792956, Offline memory processing in schizophrenia (5R01MH092638-10). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10792956. Licensed CC0.

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