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Nebe S, Reutter M, Baker DH, Bölte J, Domes G, Gamer M, Gärtner A, Gießing C, Gurr C, Hilger K, Jawinski P, Kulke L, Lischke A, Markett S, Meier M, Merz CJ, Popov T, Puhlmann LMC, Quintana DS, Schäfer T, Schubert AL, Sperl MFJ, Vehlen A, Lonsdorf TB, Feld GB. Enhancing precision in human neuroscience. eLife 2023; 12:e85980. [PMID: 37555830 PMCID: PMC10411974 DOI: 10.7554/elife.85980] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 07/23/2023] [Indexed: 08/10/2023] Open
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.
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Affiliation(s)
- Stephan Nebe
- Zurich Center for Neuroeconomics, Department of Economics, University of ZurichZurichSwitzerland
| | - Mario Reutter
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Daniel H Baker
- Department of Psychology and York Biomedical Research Institute, University of YorkYorkUnited Kingdom
| | - Jens Bölte
- Institute for Psychology, University of Münster, Otto-Creuzfeldt Center for Cognitive and Behavioral NeuroscienceMünsterGermany
| | - Gregor Domes
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
- Institute for Cognitive and Affective NeuroscienceTrierGermany
| | - Matthias Gamer
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Anne Gärtner
- Faculty of Psychology, Technische Universität DresdenDresdenGermany
| | - Carsten Gießing
- Biological Psychology, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University of OldenburgOldenburgGermany
| | - Caroline Gurr
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | - Kirsten Hilger
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
- Department of Psychology, Psychological Diagnostics and Intervention, Catholic University of Eichstätt-IngolstadtEichstättGermany
| | - Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Louisa Kulke
- Department of Developmental with Educational Psychology, University of BremenBremenGermany
| | - Alexander Lischke
- Department of Psychology, Medical School HamburgHamburgGermany
- Institute of Clinical Psychology and Psychotherapy, Medical School HamburgHamburgGermany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Maria Meier
- Department of Psychology, University of KonstanzKonstanzGermany
- University Psychiatric Hospitals, Child and Adolescent Psychiatric Research Department (UPKKJ), University of BaselBaselSwitzerland
| | - Christian J Merz
- Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University BochumBochumGermany
| | - Tzvetan Popov
- Department of Psychology, Methods of Plasticity Research, University of ZurichZurichSwitzerland
| | - Lara MC Puhlmann
- Leibniz Institute for Resilience ResearchMainzGermany
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Daniel S Quintana
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- NevSom, Department of Rare Disorders & Disabilities, Oslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), University of OsloOsloNorway
| | - Tim Schäfer
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | | | - Matthias FJ Sperl
- Department of Clinical Psychology and Psychotherapy, University of GiessenGiessenGermany
- Center for Mind, Brain and Behavior, Universities of Marburg and GiessenGiessenGermany
| | - Antonia Vehlen
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
| | - Tina B Lonsdorf
- Department of Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
- Department of Psychology, Biological Psychology and Cognitive Neuroscience, University of BielefeldBielefeldGermany
| | - Gordon B Feld
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychology, Heidelberg UniversityHeidelbergGermany
- Department of Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
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2
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Blohm G, Cheyne DO, Crawford JD. Parietofrontal oscillations show hand-specific interactions with top-down movement plans. J Neurophysiol 2022; 128:1518-1533. [PMID: 36321728 DOI: 10.1152/jn.00240.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
To generate a hand-specific reach plan, the brain must integrate hand-specific signals with the desired movement strategy. Although various neurophysiology/imaging studies have investigated hand-target interactions in simple reach-to-target tasks, the whole brain timing and distribution of this process remain unclear, especially for more complex, instruction-dependent motor strategies. Previously, we showed that a pro/anti pointing instruction influences magnetoencephalographic (MEG) signals in frontal cortex that then propagate recurrently through parietal cortex (Blohm G, Alikhanian H, Gaetz W, Goltz HC, DeSouza JF, Cheyne DO, Crawford JD. NeuroImage 197: 306-319, 2019). Here, we contrasted left versus right hand pointing in the same task to investigate 1) which cortical regions of interest show hand specificity and 2) which of those areas interact with the instructed motor plan. Eight bilateral areas, the parietooccipital junction (POJ), superior parietooccipital cortex (SPOC), supramarginal gyrus (SMG), medial/anterior interparietal sulcus (mIPS/aIPS), primary somatosensory/motor cortex (S1/M1), and dorsal premotor cortex (PMd), showed hand-specific changes in beta band power, with four of these (M1, S1, SMG, aIPS) showing robust activation before movement onset. M1, SMG, SPOC, and aIPS showed significant interactions between contralateral hand specificity and the instructed motor plan but not with bottom-up target signals. Separate hand/motor signals emerged relatively early and lasted through execution, whereas hand-motor interactions only occurred close to movement onset. Taken together with our previous results, these findings show that instruction-dependent motor plans emerge in frontal cortex and interact recurrently with hand-specific parietofrontal signals before movement onset to produce hand-specific motor behaviors.NEW & NOTEWORTHY The brain must generate different motor signals depending on which hand is used. The distribution and timing of hand use/instructed motor plan integration are not understood at the whole brain level. Using MEG we show that different action planning subnetworks code for hand usage and integrating hand use into a hand-specific motor plan. The timing indicates that frontal cortex first creates a general motor plan and then integrates hand specificity to produce a hand-specific motor plan.
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Affiliation(s)
- Gunnar Blohm
- Centre of Neuroscience Studies, Departments of Biomedical & Molecular Sciences, Mathematics & Statistics, and Psychology and School of Computing, Queen's University, Kingston, Ontario, Canada.,Centre for Vision Research, York University, Toronto, Ontario, Canada.,Canadian Action and Perception Network (CAPnet), Montreal, Quebec, Canada.,Vision: Science to Applications (VISTA) program, Departments of Psychology, Biology, and Kinesiology and Health Sciences and Neuroscience Graduate Diploma Program, York University, Toronto, Ontario, Canada
| | - Douglas O Cheyne
- Program in Neurosciences and Mental Health, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada
| | - J Douglas Crawford
- Centre for Vision Research, York University, Toronto, Ontario, Canada.,Canadian Action and Perception Network (CAPnet), Montreal, Quebec, Canada.,Vision: Science to Applications (VISTA) program, Departments of Psychology, Biology, and Kinesiology and Health Sciences and Neuroscience Graduate Diploma Program, York University, Toronto, Ontario, Canada
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3
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Niso G, Krol LR, Combrisson E, Dubarry AS, Elliott MA, François C, Héjja-Brichard Y, Herbst SK, Jerbi K, Kovic V, Lehongre K, Luck SJ, Mercier M, Mosher JC, Pavlov YG, Puce A, Schettino A, Schön D, Sinnott-Armstrong W, Somon B, Šoškić A, Styles SJ, Tibon R, Vilas MG, van Vliet M, Chaumon M. Good scientific practice in EEG and MEG research: Progress and perspectives. Neuroimage 2022; 257:119056. [PMID: 35283287 PMCID: PMC11236277 DOI: 10.1016/j.neuroimage.2022.119056] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 02/25/2022] [Accepted: 03/01/2022] [Indexed: 11/22/2022] Open
Abstract
Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community for further scrutiny or utilization. For experimental research using magneto- and electroencephalography (MEEG), GSP includes specific standards and guidelines for technical competence, which are periodically updated and adapted to new findings. However, GSP also needs to be regularly revisited in a broader light. At the LiveMEEG 2020 conference, a reflection on GSP was fostered that included explicitly documented guidelines and technical advances, but also emphasized intangible GSP: a general awareness of personal, organizational, and societal realities and how they can influence MEEG research. This article provides an extensive report on most of the LiveMEEG contributions and new literature, with the additional aim to synthesize ongoing cultural changes in GSP. It first covers GSP with respect to cognitive biases and logical fallacies, pre-registration as a tool to avoid those and other early pitfalls, and a number of resources to enable collaborative and reproducible research as a general approach to minimize misconceptions. Second, it covers GSP with respect to data acquisition, analysis, reporting, and sharing, including new tools and frameworks to support collaborative work. Finally, GSP is considered in light of ethical implications of MEEG research and the resulting responsibility that scientists have to engage with societal challenges. Considering among other things the benefits of peer review and open access at all stages, the need to coordinate larger international projects, the complexity of MEEG subject matter, and today's prioritization of fairness, privacy, and the environment, we find that current GSP tends to favor collective and cooperative work, for both scientific and for societal reasons.
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Affiliation(s)
- Guiomar Niso
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA; Universidad Politecnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Laurens R Krol
- Neuroadaptive Human-Computer Interaction, Brandenburg University of Technology Cottbus-Senftenberg, Germany
| | - Etienne Combrisson
- Aix-Marseille University, Institut de Neurosciences de la Timone, France
| | | | | | | | - Yseult Héjja-Brichard
- Centre d'Ecologie Fonctionnelle et Evolutive, CNRS, EPHE, IRD, Université Montpellier, Montpellier, France
| | - Sophie K Herbst
- Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, NeuroSpin center, Université Paris-Saclay, Gif/Yvette, France
| | - Karim Jerbi
- Cognitive and Computational Neuroscience Laboratory, Department of Psychology, University of Montreal, Montreal, QC, Canada; Mila - Quebec Artificial Intelligence Institute, Canada
| | - Vanja Kovic
- Faculty of Philosophy, Laboratory for neurocognition and applied cognition, University of Belgrade, Serbia
| | - Katia Lehongre
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm U 1127, CNRS UMR 7225, APHP, Hôpital de la Pitié Salpêtrière, Sorbonne Université, Centre MEG-EEG, Centre de NeuroImagerie Recherche (CENIR), Paris, France
| | - Steven J Luck
- Center for Mind & Brain, University of California, Davis, CA, USA
| | - Manuel Mercier
- Aix Marseille Univ, Inserm, INS, Inst Neurosci Syst, Marseille, France
| | - John C Mosher
- McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yuri G Pavlov
- University of Tuebingen, Germany; Ural Federal University, Yekaterinburg, Russia
| | - Aina Puce
- Psychological & Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Antonio Schettino
- Erasmus University Rotterdam, Rotterdam, the Netherland; Institute for Globally Distributed Open Research and Education (IGDORE), Sweden
| | - Daniele Schön
- Aix Marseille Univ, Inserm, INS, Inst Neurosci Syst, Marseille, France
| | | | | | - Anđela Šoškić
- Faculty of Philosophy, Laboratory for neurocognition and applied cognition, University of Belgrade, Serbia; Teacher Education Faculty, University of Belgrade, Serbia
| | - Suzy J Styles
- Psychology, Nanyang Technological University, Singapore; Singapore Institute for Clinical Sciences, A*STAR, Singapore
| | - Roni Tibon
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK; School of Psychology, University of Nottingham, Nottingham, UK
| | - Martina G Vilas
- Ernst Strüngmann Institute for Neuroscience, Frankfurt am Main, Germany
| | | | - Maximilien Chaumon
- Institut du Cerveau - Paris Brain Institute - ICM, Inserm U 1127, CNRS UMR 7225, APHP, Hôpital de la Pitié Salpêtrière, Sorbonne Université, Centre MEG-EEG, Centre de NeuroImagerie Recherche (CENIR), Paris, France..
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4
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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