Evidence map›Paper›PMID 30121338›Full record

ArticleNeuroImage2018

Optimal referencing for stereo-electroencephalographic (SEEG) recordings.

Guangye Li, Shize Jiang, Sivylla E Paraskevopoulou, Meng Wang, Yang Xu, Zehan Wu, Liang Chen, Dingguo Zhang, Gerwin Schalk

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.

0numbers the graph read from it
0cells of the map it votes in
66citing papers in PubMed
8.0field-weighted citation impact, top 2% 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

66 citing papers in PubMed, 165 citations in OpenAlex.

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6 more citing papers are in PubMed but not listed here.

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

9 authors at 4 institutions in 2 countries.

Guangye LiState Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China; National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, NY, USA. Electronic address: liguangye.sjtu@sjtu.edu.cn.
Shize JiangDepartment of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China. Electronic address: sjiang13@fudan.edu.cn.
Sivylla E ParaskevopoulouNational Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, NY, USA. Electronic address: paraskevopoulou@neurotechcenter.org.
Meng WangState Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China. Electronic address: wangmeng1995@sjtu.edu.cn.
Yang XuState Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China. Electronic address: yangeksu@sjtu.edu.cn.
Zehan WuDepartment of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China. Electronic address: zhwu08@fudan.edu.cn.
Liang ChenDepartment of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China. Electronic address: hschenliang@fudan.edu.cn.
Dingguo ZhangState Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China. Electronic address: dgzhang@sjtu.edu.cn.
Gerwin SchalkDepartment of Biomedical Sciences, State University of New York, Albany, NY, USA; Department of Neurology, Albany Medical College, Albany, NY, USA; National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, NY, USA. Electronic address: gschalk@neurotechcenter.org.
Shanghai Jiao Tong University · CNFudan University · CNNew York State Department of Health · USHuashan Hospital · CN

Funding

Project 5: ComputationalP50MH109429 · NIMH · NATHAN S. KLINE INSTITUTE FOR PSYCH RES · PI STEPHANIE Ruggiano JONES · 2017 to 2026
$25.3M
Training and DisseminationP41EB018783 · NIBIB · WADSWORTH CENTER · PI Jonathan Saul Carp · 2014 to 2026
$15.4M
NIBIB NIH HHS P41 EB018783NIMH NIH HHS P50 MH109429
6 · The paper itself

Abstract

Stereo-electroencephalography (SEEG) is an intracranial recording technique in which depth electrodes are inserted in the brain as part of presurgical assessments for invasive brain surgery. SEEG recordings can tap into neural signals across the entire brain and thereby sample both cortical and subcortical sites. However, even though signal referencing is important for proper assessment of SEEG signals, no previous study has comprehensively evaluated the optimal referencing method for SEEG. In our study, we recorded SEEG data from 15 human subjects during a motor task, referencing them against the average of two white matter contacts (monopolar reference). We then subjected these signals to 5 different re-referencing approaches: common average reference (CAR), gray-white matter reference (GWR), electrode shaft reference (ESR), bipolar reference, and Laplacian reference. The results from three different signal quality metrics suggest the use of the Laplacian re-reference for study of local population-level activity and low-frequency oscillatory activity.

Indexed as

Signal Processing, Computer-AssistedStereotaxic TechniquesAdultBrainBrain WavesElectrocorticographyElectromyographyEpilepsyGray MatterHumansMotor ActivityWhite MatterNoise subtractionReferencing methodSEEGSignal qualityStereo-electroencephalography

Identifiers

PMID30121338
PMCPMC6495648
OpenAlexW2887978199

What OpenQuestion holds

Textmetadata
LicenceTDM
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.