Evidence map›Paper›PMID 30687038›Full record

ArticleFrontiers in human neuroscience2018

Current Status and Issues Regarding Pre-processing of fNIRS Neuroimaging Data: An Investigation of Diverse Signal Filtering Methods Within a General Linear Model Framework.

Paola Pinti, Felix Scholkmann, Antonia Hamilton, Paul Burgess, Ilias Tachtsidis

Abstract read
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In one paragraph

Article in Frontiers in human neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 219 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
219citing papers in PubMed, 1 pooled it
–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

219 citing papers in PubMed, 1 synthesis or guideline pooled it.

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

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Paola PintiDepartment of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.
Felix ScholkmannDepartment of Neonatology, Biomedical Optics Research Laboratory, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Antonia HamiltonInstitute of Cognitive Neuroscience, University College London, London, United Kingdom.
Paul BurgessInstitute of Cognitive Neuroscience, University College London, London, United Kingdom.
Ilias TachtsidisDepartment of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.

Funding

Wellcome Trust
6 · The paper itself

Abstract

Functional near-infrared spectroscopy (fNIRS) research articles show a large heterogeneity in the analysis approaches and pre-processing procedures. Additionally, there is often a lack of a complete description of the methods applied, necessary for study replication or for results comparison. The aims of this paper were (i) to review and investigate which information is generally included in published fNIRS papers, and (ii) to define a signal pre-processing procedure to set a common ground for standardization guidelines. To this goal, we have reviewed 110 fNIRS articles published in 2016 in the field of cognitive neuroscience, and performed a simulation analysis with synthetic fNIRS data to optimize the signal filtering step before applying the GLM method for statistical inference. Our results highlight the fact that many papers lack important information, and there is a large variability in the filtering methods used. Our simulations demonstrated that the optimal approach to remove noise and recover the hemodynamic response from fNIRS data in a GLM framework is to use a 1000th order band-pass Finite Impulse Response filter. Based on these results, we give preliminary recommendations as to the first step toward improving the analysis of fNIRS data and dissemination of the results.

Indexed as

digital filterfunctional data analysisfunctional near infrared spectroscopygeneral linear modelpre-processing guidelinespre-processing standardization

Identifiers

PMID30687038
PMCPMC6336925

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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.