Evidence map›Paper›PMID 36403326›Full record

ArticleBiomaterials2022

Longitudinal neural and vascular recovery following ultraflexible neural electrode implantation in aged mice.

Fei He, Yingchu Sun, Yifu Jin, Rongkang Yin, Hanlin Zhu, Haad Rathore, Chong Xie, Lan Luan

Abstract read
In one paragraph

Article in Biomaterials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Chronic alteration of CabioRxiv : the preprint server for biology · 2025
    Article
  3. Article
  4. Article
  5. In vivo microelectrode arrays for neuroscience.Nature reviews. Methods primers · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Fei HeDepartment of Electrical and Computer Engineering, Rice University, Houston, USA; Rice Neuroengineering Initiative, Rice University, Houston, USA.
Yingchu SunDepartment of Electrical and Computer Engineering, Rice University, Houston, USA; Rice Neuroengineering Initiative, Rice University, Houston, USA.
Yifu JinDepartment of Electrical and Computer Engineering, Rice University, Houston, USA; Rice Neuroengineering Initiative, Rice University, Houston, USA.
Rongkang YinDepartment of Electrical and Computer Engineering, Rice University, Houston, USA; Rice Neuroengineering Initiative, Rice University, Houston, USA.
Hanlin ZhuDepartment of Electrical and Computer Engineering, Rice University, Houston, USA; Rice Neuroengineering Initiative, Rice University, Houston, USA.
Haad RathoreRice Neuroengineering Initiative, Rice University, Houston, USA; Applied Physics Graduate Program, Rice University, Houston, USA.
Chong XieDepartment of Electrical and Computer Engineering, Rice University, Houston, USA; Rice Neuroengineering Initiative, Rice University, Houston, USA; Department of Bioengineering, Rice University, Houston, USA.
Lan LuanDepartment of Electrical and Computer Engineering, Rice University, Houston, USA; Rice Neuroengineering Initiative, Rice University, Houston, USA; Department of Bioengineering, Rice University, Houston, USA. Electronic address: lan.luan@rice.edu.

Funding

Maximizing flexibility: Optimized neural probes and electronics for long term, high bandwidth recordingsU01NS115588 · NINDS · RICE UNIVERSITY · PI FRANK, LOREN M, XIE, CHONG · 2020 to 2023
$4.6M
A Nanoelectronic Strategy for Reliable, Large-scale Chronic Neural RecordingR01NS102917 · NINDS · UNIVERSITY OF TEXAS AT AUSTIN · PI Chong Xie · 2017 to 2026
$3.5M
Longitudinal multimodal mapping to decipher the neurovascular impact of microinfarctsR01NS109361 · NINDS · UNIVERSITY OF TEXAS AT AUSTIN · PI LUAN, LAN · 2019 to 2023
$2.9M
NINDS NIH HHS R01 NS102917NINDS NIH HHS R01 NS109361NINDS NIH HHS U01 NS115588
6 · The paper itself

Abstract

Flexible neural electrodes improve the recording longevity and quality of individual neurons by promoting tissue-electrode integration. However, the intracortical implantation of flexible electrodes inevitably induces tissue damage. Understanding the longitudinal neural and vascular recovery following the intracortical implantation is critical for the ever-growing applications of flexible electrodes in both healthy and disordered brains. Aged animals are of particular interest because they play a key role in modeling neurological disorders, but their tissue-electrode interface remains mostly unstudied. Here we integrate in-vivo two-photon imaging and electrophysiological recording to determine the time-dependent neural and vascular dynamics after the implantation of ultraflexible neural electrodes in aged mice. We find heightened angiogenesis and vascular remodeling in the first two weeks after implantation, which coincides with the rapid increase in local field potentials and unit activities detected by electrophysiological recordings. Vascular remodeling in shallow cortical layers preceded that in deeper layers, which often lasted longer than the recovery of neural signals. By six weeks post-implantation vascular abnormalities had subsided, resulting in normal vasculature and microcirculation. Putative cell classification based on firing pattern and waveform shows similar recovery time courses in fast-spiking interneurons and pyramidal neurons. These results elucidate how structural damages and remodeling near implants affecting recording efficacy, and support the application of ultraflexible electrodes in aged animals at minimal perturbations to endogenous neurophysiology.

Indexed as

NeuronsVascular RemodelingAnimalsBrainElectrodesInterneuronsMiceChronicElectrophysiologyIn vivo two-photon imagingTissue-electrode interfaceUltraflexible electrodeVasculature

Identifiers

PMID36403326
PMCPMC9701172

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.