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Published on: January 13, 2022
Endovascular neural interfaces: current platforms and clinical readiness
Hisayuki Hosoo1, Kota Araki2, Yosuke Masuda2
1Division of Stroke Prevention and Treatment, Department of Neurosurgery, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan.
Neurointerventional techniques offer novel neural interfaces via endovascular approaches, enhancing brain activity recording and stimulation. These methods provide a safer alternative to conventional implants, improving signal quality for epilepsy and brain-computer interface applications.
Area of Science:
- Neurointerventional techniques
- Biomedical engineering
- Neurosurgery
Background:
- Conventional electroencephalography (EEG) is limited by skull interference.
- Intracranial implants (electrocorticography, stereoelectroencephalography) carry procedural risks.
- Endovascular approaches offer a middle ground for neural interfaces.
Purpose of the Study:
- Review the evolution and device classes of endovascular neural interfaces.
- Synthesize evidence on implantation, signal characteristics, and clinical applications.
- Provide recommendations for neurointerventional adoption and future development.
Main Methods:
- Narrative review of historical and current endovascular neural interface technologies.
- Emphasis on clinical platforms like Stentrode and EP-01.
- Synthesis of data on deliverability, signal quality, stimulation, and clinical translation.
Main Results:
- Endovascular techniques provide enhanced signal quality over scalp EEG with reduced procedural risks.
- Platforms discussed include stent-electrode arrays and catheter-based electrodes.
- Evidence reviewed covers epilepsy evaluation, brain-computer interfaces, and translational constraints.
Conclusions:
- Endovascular neural interfaces represent a promising advancement in brain activity monitoring and stimulation.
- Further research, registries, and trials are needed for widespread clinical adoption.
- Next-generation device development should address identified translational constraints.
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