# A Nanoelectronic Strategy for Reliable, Large-scale Chronic Neural Recording

> **NIH NIH R01** · RICE UNIVERSITY · 2024 · $596,541

## Abstract

Abstract
 The brain functions that give rise to perception, cognition and behavior are generated by complex,
distributed neural circuits whose activity patterns change on the timescale of milliseconds. The dynamics of
these activity patterns reflect complex interactions among many neurons, evolve with experience and change in
disease. Technologies that have the potential to help us understand that evolving complexity must therefore be
able to record and stimulate at cellular-spatial and millisecond-temporal resolutions, flexibly distribute large
numbers of recording sites to target both local and distributed circuits, and importantly, minimally disrupt the
integrity of neural tissue and maintain functionality stably for long time periods. Neural electrodes have been a
primary tool for these purposes and contributed tremendously to fundamental and clinical neuroscience.
However, conventional neural electrodes are limited by the inability to consistently record high quality neural
signals over both the short and long terms. In time scales as short as hours substantial recording condition
changes often occur due to the micro-movements of the implanted electrodes relative to the brain tissue. Over
weeks to months, deterioration in recording efficacy and fidelity are caused by sustained foreign body reactions
at the tissue-probe interface including neural degradation, reoccurring leakage in capillaries and glial scar tissue
formation. The PI’s laboratory has previously created the ultraflexible neural electrodes. These devices are as
thin as only 1 µm (up to 5 – 8 for large animal brains) and are therefore extremely flexible, allowing for seamless
tissue integration with no observable neuronal degeneration and glial scaring. The overall objective of this project
is to develop an integrated, fully implanted and untethered system to democratize large-scale, chronic stable
neural recording. Empowered by this system, we will perform a comprehensive characterization on chronic
neural recordings tracking the same neuron populations, and delineate stable and drifting features. Our aims
are: AIM-1. To develop high-density NETs and their integration with implantable electronics. AIM-2.
Develop integrated circuits (IC) and electronics for distributed and untethered neural recording. AIM-3.
To establish a standard data set that characterizes endogenous changes in neural recordings.
Importantly, the approach is innovative, because the technology we will develop is expected to be the most
biocompatible large-scale neural recording and stimulation tool in neuroscience, and can enable new, very high-
density recording studies and lead to fundamental discoveries.

## Key facts

- **NIH application ID:** 10883380
- **Project number:** 2R01NS102917-06
- **Recipient organization:** RICE UNIVERSITY
- **Principal Investigator:** Chong Xie
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $596,541
- **Award type:** 2
- **Project period:** 2017-07-01 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10883380

## Citation

> US National Institutes of Health, RePORTER application 10883380, A Nanoelectronic Strategy for Reliable, Large-scale Chronic Neural Recording (2R01NS102917-06). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10883380. Licensed CC0.

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