Enhancing precision in human neuroscience

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
  • Stephan Nebe
  • Mario Reutter
  • Daniel H Baker
  • Jens Boelte
  • Gregor Domes
  • Matthias Gamer
  • Anne Gaertner
  • Carsten Giessing
  • Caroline Gurr
  • Kirsten Hilger
  • Philippe Jawinski
  • Louisa Kulke
  • Alexander Lischke
  • Sebastian Markett
  • Maria Meier
  • Christian J Merz
  • Tzvetan Popov
  • Lara MC Puhlmann
  • Daniel S Quintana
  • Tim Schaefer
  • Anna-Lena Schubert
  • Matthias FJ Sperl
  • Antonia Vehlen
  • Tina B Lonsdorf
  • Gordon B Feld
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:001045892500001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.7554/eLife.85980
Externe Identifier
  • Clarivate Analytics Document Solution ID: O7XG6
  • PubMed Identifier: 37555830
ISSN
2050-084X
Zeitschrift
ELIFE
Schlüsselwörter
  • human neuroscience
  • precision
  • experimental methods
  • sample size
  • reliability
  • generalizability
Artikelnummer
ARTN e85980
Datum der Veröffentlichung
2023
Status
Published
Titel
Enhancing precision in human neuroscience
Sub types
  • Review
Ausgabe der Zeitschrift
12

Datenquelle: Web of Science (Lite)

Andere Metadatenquellen:
Abstract
<jats:p>Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability – in science in general, but also specifically in human neuroscience – have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.</jats:p>
Date of acceptance
2023
Autoren
  • Stephan Nebe
  • Mario Reutter
  • Daniel H Baker
  • Jens Bölte
  • Gregor Domes
  • Matthias Gamer
  • Anne Gärtner
  • Carsten Gießing
  • Caroline Gurr
  • Kirsten Hilger
  • Philippe Jawinski
  • Louisa Kulke
  • Alexander Lischke
  • Sebastian Markett
  • Maria Meier
  • Christian J Merz
  • Tzvetan Popov
  • Lara MC Puhlmann
  • Daniel S Quintana
  • Tim Schäfer
  • Anna-Lena Schubert
  • Matthias FJ Sperl
  • Antonia Vehlen
  • Tina B Lonsdorf
  • Gordon B Feld
DOI
10.7554/elife.85980
eISSN
2050-084X
Zeitschrift
eLife
Sprache
en
Online publication date
2023
Status
Published online
Herausgeber
eLife Sciences Publications, Ltd
Herausgeber URL
http://dx.doi.org/10.7554/elife.85980
Datum der Datenerfassung
2023
Titel
Enhancing precision in human neuroscience
Ausgabe der Zeitschrift
12

Datenquelle: Crossref

Abstract
Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.
Addresses
  • Zurich Center for Neuroeconomics, Department of Economics, University of Zurich, Zurich, Switzerland.
Autoren
  • Stephan Nebe
  • Mario Reutter
  • Daniel H Baker
  • Jens Bölte
  • Gregor Domes
  • Matthias Gamer
  • Anne Gärtner
  • Carsten Gießing
  • Caroline Gurr
  • Kirsten Hilger
  • Philippe Jawinski
  • Louisa Kulke
  • Alexander Lischke
  • Sebastian Markett
  • Maria Meier
  • Christian J Merz
  • Tzvetan Popov
  • Lara MC Puhlmann
  • Daniel S Quintana
  • Tim Schäfer
  • Anna-Lena Schubert
  • Matthias FJ Sperl
  • Antonia Vehlen
  • Tina B Lonsdorf
  • Gordon B Feld
DOI
10.7554/elife.85980
eISSN
2050-084X
Externe Identifier
  • PubMed Identifier: 37555830
  • PubMed Central ID: PMC10411974
Funding acknowledgements
  • Deutsche Forschungsgemeinschaft: FE1617 2-1
  • Deutsche Forschungsgemeinschaft: LO1980 4-1
  • Deutsche Forschungsgemeinschaft: LO1980/7-1
Open access
true
ISSN
2050-084X
Zeitschrift
eLife
Schlüsselwörter
  • Humans
  • Magnetic Resonance Imaging
  • Reproducibility of Results
  • Sample Size
  • Neurosciences
Sprache
eng
Medium
Electronic
Online publication date
2023
Open access status
Open Access
Paginierung
e85980
Datum der Veröffentlichung
2023
Status
Published
Publisher licence
CC BY
Datum der Datenerfassung
2023
Titel
Enhancing precision in human neuroscience.
Sub types
  • Research Support, Non-U.S. Gov't
  • review-article
  • Review
  • Journal Article
Ausgabe der Zeitschrift
12

Files

https://europepmc.org/articles/PMC10411974?pdf=render

Datenquelle: Europe PubMed Central

Abstract
Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.
Date of acceptance
2023
Autoren
  • Stephan Nebe
  • Mario Reutter
  • Daniel H Baker
  • Jens Bölte
  • Gregor Domes
  • Matthias Gamer
  • Anne Gärtner
  • Carsten Gießing
  • Caroline Gurr
  • Kirsten Hilger
  • Philippe Jawinski
  • Louisa Kulke
  • Alexander Lischke
  • Sebastian Markett
  • Maria Meier
  • Christian J Merz
  • Tzvetan Popov
  • Lara MC Puhlmann
  • Daniel S Quintana
  • Tim Schäfer
  • Anna-Lena Schubert
  • Matthias FJ Sperl
  • Antonia Vehlen
  • Tina B Lonsdorf
  • Gordon B Feld
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/37555830
DOI
10.7554/eLife.85980
eISSN
2050-084X
Externe Identifier
  • PubMed Central ID: PMC10411974
Zeitschrift
Elife
Schlüsselwörter
  • experimental methods
  • generalizability
  • human neuroscience
  • neuroscience
  • precision
  • reliability
  • sample size
  • Humans
  • Reproducibility of Results
  • Neurosciences
  • Sample Size
  • Magnetic Resonance Imaging
Sprache
eng
Country
England
PII
85980
Datum der Veröffentlichung
2023
Status
Published online
Datum, an dem der Datensatz öffentlich gemacht wurde
2023
Titel
Enhancing precision in human neuroscience.
Sub types
  • Review
  • Journal Article
  • Research Support, Non-U.S. Gov't
Ausgabe der Zeitschrift
12

Datenquelle: PubMed

Author's licence
CC-BY
Autoren
  • Stephan Nebe
  • Mario Reutter
  • Daniel H Baker
  • Jens Bölte
  • Gregor Domes
  • Matthias Gamer
  • Anne Gärtner
  • Carsten Gießing
  • Caroline Gurr
  • Kirsten Hilger
  • Philippe Jawinski
  • Louisa Kulke
  • Alexander Lischke
  • Sebastian Markett
  • Maria Meier
  • Christian J Merz
  • Tzvetan Popov
  • Lara MC Puhlmann
  • Daniel S Quintana
  • Tim Schäfer
  • Anna-Lena Schubert
  • Matthias FJ Sperl
  • Antonia Vehlen
  • Tina B Lonsdorf
  • Gordon B Feld
Hosting institution
Universitätsbibliothek Mainz
Sammlungen
  • JGU-Publikationen
Resource version
Published version
DOI
10.7554/eLife.85980
File(s) embargoed
false
Open access
true
ISSN
2050-084X
Zeitschrift
eLife
Schlüsselwörter
  • 150 Psychologie
  • 150 Psychology
  • 610 Medizin
  • 610 Medical sciences
Sprache
eng
Open access status
Open Access
Paginierung
e85980
Datum der Veröffentlichung
2023
Public URL
https://openscience.ub.uni-mainz.de/handle/20.500.12030/9692
Herausgeber
eLife Sciences Publications
Datum der Datenerfassung
2023
Datum, an dem der Datensatz öffentlich gemacht wurde
2023
Zugang
Public
Titel
Enhancing precision in human neuroscience
Ausgabe der Zeitschrift
12

Files

enhancing_precision_in_human_-20231110095354528.pdf

Datenquelle: OPENSCIENCE.UB

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