A standardised open science framework for sharing and re-analysing neural data acquired to continuous stimuli.

Autor:	Di Liberto GM; School of Computer Science and Statistics, University of Dublin, Trinity College, Ireland; ADAPT Centre, Trinity College Institute of Neuroscience., Nidiffer A; Dept Biomedical Engineering, Dept Neuroscience, Del Monte Institute for Neuroscience, Center for Visual Science, University of Rochester, NY, USA., Crosse MJ; Segotia, Galway, Ireland.; Department of Mechanical, Manufacturing and Biomedical Engineering, TCBE, Trinity College Dublin, Ireland., Zuk NJ; Department of Psychology, Nottingham Trent University, Nottingham, UK., Haro S; Human Health and Performance Systems, MIT Lincoln Laboratory, Lexington, Massachusetts, USA.; Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, Massachusetts, USA., Cantisani G; Laboratoire des systémes perceptifs, Département d'études cognitives, ENS, PSL University, CNRS, 75005 Paris, France.; School of Computer Science and Statistics, University of Dublin, Trinity College, Ireland; ADAPT Centre, Trinity College Institute of Neuroscience., Winchester MM; School of Computer Science and Statistics, University of Dublin, Trinity College, Ireland; ADAPT Centre, Trinity College Institute of Neuroscience., Igoe A; School of Computer Science and Statistics, University of Dublin, Trinity College, Ireland; ADAPT Centre, Trinity College Institute of Neuroscience., McCrann R; School of Computer Science and Statistics, University of Dublin, Trinity College, Ireland; ADAPT Centre, Trinity College Institute of Neuroscience., Chandra S; School of Computer Science and Statistics, University of Dublin, Trinity College, Ireland; ADAPT Centre, Trinity College Institute of Neuroscience., Lalor EC; Dept Biomedical Engineering, Dept Neuroscience, Del Monte Institute for Neuroscience, Center for Visual Science, University of Rochester, NY, USA.; Dept Biomedical Engineering, Center for Visual Science, University of Rochester, NY, USA., Baruzzo G; Department of Information Engineering, University of Padova, Padova, Italy.
Jazyk:	angličtina
Zdroj:	ArXiv [ArXiv] 2024 Sep 16. Date of Electronic Publication: 2024 Sep 16.
Abstrakt:	Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in scenarios involving continuous sensory streams, such as speech and music. Over the past 10 years or so, novel analytic frameworks combined with the growing participation in data sharing has led to a surge of publicly available datasets involving continuous sensory experiments. However, open science efforts in this domain of research remain scattered, lacking a cohesive set of guidelines. This paper presents an end-to-end open science framework for the storage, analysis, sharing, and re-analysis of neural data recorded during continuous sensory experiments. We propose a data structure that builds on existing custom structures (Continuous-event Neural Data or CND), providing precise naming conventions and data types, as well as a workflow for storing and loading data in the general-purpose BIDS structure. The framework has been designed to interface with existing EEG/MEG analysis toolboxes, such as Eelbrain, NAPLib, MNE, and mTRF-Toolbox. We present guidelines by taking both the user view (rapidly re-analyse existing data) and the experimenter view (store, analyse, and share), making the process straightforward and accessible. Additionally, we introduce a web-based data browser that enables the effortless replication of published results and data re-analysis. Competing Interests: Competing Interests The authors do not have competing interests to report.
Databáze:	MEDLINE
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