Evidence map›Paper›PMID 38654844›Full record

ArticleMicrosystems & nanoengineering2024

Through-polymer, via technology-enabled, flexible, lightweight, and integrated devices for implantable neural probes.

Cunkai Zhou, Ye Tian, Gen Li, Yifei Ye, Lusha Gao, Jiazhi Li, Ziwei Liu, Haoyang Su, Yunxiao Lu, Meng Li and 5 more

Open access · goldAbstract read
In one paragraph

Article in Microsystems & nanoengineering, 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
2.7field-weighted citation impact, top 10% of its field
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, 10 citations in OpenAlex.

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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

15 authors at 4 institutions in 1 country.

Cunkai Zhou *College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China.
Ye Tian *2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Gen Li *2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Yifei Ye2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Lusha Gao2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Jiazhi Li2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Ziwei Liu2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.
Haoyang SuSchool of Graduate Study, University of Chinese Academy of Sciences, Beijing, China.
Yunxiao LuCollege of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China.
Meng LiSchool of Graduate Study, University of Chinese Academy of Sciences, Beijing, China.ORCID 0000-0002-3364-1969
Zhitao ZhouSchool of Graduate Study, University of Chinese Academy of Sciences, Beijing, China.
Xiaoling WeiSchool of Graduate Study, University of Chinese Academy of Sciences, Beijing, China.ORCID 0000-0002-0099-2260
Lunming QinCollege of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China.
Tiger H Tao2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China.ORCID 0000-0002-6583-5039
Liuyang SunCollege of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China.
Shanghai Institute of Microsystem and Information Technology · CNShanghai University of Electric Power · CNState Key Laboratory of Transducer Technology · CNChinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In implantable electrophysiological recording systems, the headstage typically comprises neural probes that interface with brain tissue and integrated circuit chips for signal processing. While advancements in MEMS and CMOS technology have significantly improved these components, their interconnection still relies on conventional printed circuit boards and sophisticated adapters. This conventional approach adds considerable weight and volume to the package, especially for high channel count systems. To address this issue, we developed a through-polymer via (TPV) method inspired by the through-silicon via (TSV) technique in advanced three-dimensional packaging. This innovation enables the vertical integration of flexible probes, amplifier chips, and PCBs, realizing a flexible, lightweight, and integrated device (FLID). The total weight of the FLIDis only 25% that of its conventional counterparts relying on adapters, which significantly increased the activity levels of animals wearing the FLIDs to nearly match the levels of control animals without implants. Furthermore, by incorporating a platinum-iridium alloy as the top layer material for electrical contact, the FLID realizes exceptional electrical performance, enabling in vivo measurements of both local field potentials and individual neuron action potentials. These findings showcase the potential of FLIDs in scaling up implantable neural recording systems and mark a significant advancement in the field of neurotechnology.

Indexed as

BiosensorsEngineering

Identifiers

PMID38654844
PMCPMC11035623
OpenAlexW4395010372

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.