Evidence map›Paper›PMID 39366673›Full record

ArticleAccounts of chemical research2024

Magnetoelectrics for Implantable Bioelectronics: Progress to Date.

Fatima Alrashdan, Kaiyuan Yang, Jacob T Robinson

Abstract read
In one paragraph

Article in Accounts of chemical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Smart Nanotechnologies for Multimodal Neuromodulation and Brain Interfacing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  3. Review
  4. Article
  5. 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

3 authors.

Fatima AlrashdanDepartment of Electrical and Computer Engineering, Rice University, 6100 Main St, Houston, Texas 77005, United States.
Kaiyuan YangDepartment of Electrical and Computer Engineering, Rice University, 6100 Main St, Houston, Texas 77005, United States.
Jacob T RobinsonDepartment of Electrical and Computer Engineering, Rice University, 6100 Main St, Houston, Texas 77005, United States.ORCID 0000-0002-3509-3054

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coupling of magnetic and electric properties manifested in magnetoelectric (ME) materials has unlocked numerous possibilities for advancing technologies like energy harvesting, memory devices, and medical technologies. Due to this unique coupling, the magnetic properties of these materials can be tuned by an electric field; conversely, their electric polarization can be manipulated through a magnetic field. Over the past seven years, our lab work has focused on leveraging these materials to engineer implantable bioelectronics for various neuromodulation applications. One of the main challenges for bioelectronics is to design miniaturized solutions that can be delivered with minimally invasive procedures and yet can receive sufficient power to directly stimulate tissue or power electronics to perform functions like communication and sensing. Magnetoelectric coupling in ME materials is strongest when the driving field matches a mechanical resonant mode. However, miniaturized ME transducers typically have resonance frequencies >100 kHz, which is too high for direct neuromodulation as neurons only respond to low frequencies (typically <1 kHz). We discuss two approaches that have been proposed to overcome this frequency mismatch: operating off-resonance and rectification. The off-resonance approach is most common for magnetoelectric nanoparticles (MENPs) that typically have resonance frequencies in the gigahertz range. In vivo experiments on rat models have shown that MENPs could induce changes in neural activity upon excitation with <200 Hz magnetic fields. However, the neural response has latencies of several seconds due to the weak coupling in the off-resonance regime. To stimulate neural responses with millisecond precision, we developed methods to rectify the ME response so that we could drive the materials at their resonant frequency but still produce the slowly varying voltages needed for direct neural stimulation. The first version of the stimulator combined a ME transducer and analog electronics for rectification. To create even smaller solutions, we introduced the first magnetoelectric metamaterial (MNM) that exhibits self-rectification. Both designs have effectively induced neural modulation in rat models with less than 5 ms latency. Based on our experience with in vivo testing of the rectified ME stimulators, we found it challenging to deliver the precisely controlled therapy required for clinical applications, given the ME transducer's sensitivity to the external transmitter alignment. To overcome this challenge, we developed the ME-BIT (MagnetoElectric BioImplanT), a digitally programmable stimulator that receives wireless power and data through the ME link. We further expanded the utility of this technology to neuromodulation applications that require high stimulation thresholds by introducing the DOT (Digitally programmable Overbrain Therapeutic). The DOT has voltage compliance up to 14.5 V. We have demonstrated the efficacy of these designs through various in vivo studies for applications like peripheral nerve stimulation and epidural cortical stimulation. To further improve these systems to be adaptive and enable a network of coordinated devices, we developed a bidirectional communication system to transmit data to and from the implant. To enable even greater miniaturization, we developed a way to use the same ME transducer for wireless power and data communication by developing the first ME backscatter communication protocol.

Indexed as

Prostheses and ImplantsAnimalsElectronicsHumansRats

Identifiers

PMID39366673
PMCPMC11483720

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.