# Determining the circuits and signals of sleep dysfunction in Parkinson's disease through chronic intracranial recordings and closed-loop Deep Brain Stimulation

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2023 · $666,176

## Abstract

ABSTRACT
Sleep dysfunction is highly prevalent and disabling across a wide range of neurological and psychiatric
conditions. In neurodegenerative diseases, including Alzheimer’s and Parkinson’s disease (PD), disruption of
sleep architecture has been linked to worsening of daytime motor and neuropsychiatric symptoms, as well as
accelerated disease progression. It therefore also marks a major, untapped therapeutic opportunity. However, it
is currently not known which cortical and basal ganglia structures and signals are responsible for disrupting
physiological sleep rhythms in PD. The rationale of this proposal is that identification of the cortical-basal signals
which disrupt sleep architecture in PD is an essential next step for developing sleep-specific neuromodulation
therapies. To date, a critical barrier to progress has been a lack of chronic intracranial neural recordings during
sleep in PD. This urgent need can be addressed by leveraging recent developments in sensing-enabled Deep
Brain Stimulation (DBS), supporting longitudinal, high-resolution, multi-site, intracranial recordings in patients’
own homes. The overall objective for this proposal is to establish the pathological network dynamics that disrupt
healthy sleep in PD and how they are modulated by DBS. Our preliminary work demonstrates abnormal cortico-
basal beta (13 - 30 Hz) and gamma (60 - 90 Hz) oscillations across different sleep phases in PD. Our central
hypothesis is that these pathological overnight neural rhythms disrupt physiological sleep signals, including slow
wave activity (<4 Hz), and induce maladaptive network changes during sleep to impact daytime cortico-basal
neural activity and connectivity. We will use sensing-enabled, closed-loop, DBS devices that are chronically
implanted in a cohort of 16 PD patients, combined with interpretable machine learning techniques, to identify
cortico-basal signal and connectivity changes during sleep disruption in PD. We will then evaluate causal
mechanisms of cortico-basal oscillations by measuring waking connectivity using cortical evoked responses and
through applying sleep-stage dependent closed-loop DBS. Bridging this knowledge gap will characterize the
pathological network dynamics of sleep in PD and uncover key mechanistic understandings linking sleep rhythms
to waking network activity. This will provide a foundation for the development of closed-loop DBS approaches
that can restore normal sleep in people with PD. Following successful completion of the proposed research, we
expect our contribution to have determined the principal pathological oscillatory cortico-basal dynamics of sleep
disruption in PD. The proposed research is innovative, using new sensing-enabled DBS for longitudinal sleep
recordings plus closed-loop neuromodulation to evaluate cortico-basal network models of sleep dysfunction in
PD. This contribution is expected to be significant because understanding the fundamental neurophysiology of
sleep dysfunction in...

## Key facts

- **NIH application ID:** 10630021
- **Project number:** 1R01NS131405-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Simon Little
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $666,176
- **Award type:** 1
- **Project period:** 2023-04-01 → 2028-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10630021

## Citation

> US National Institutes of Health, RePORTER application 10630021, Determining the circuits and signals of sleep dysfunction in Parkinson's disease through chronic intracranial recordings and closed-loop Deep Brain Stimulation (1R01NS131405-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10630021. Licensed CC0.

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