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doi
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highspeed-bids
forked from lnnrtwttkhn/highspeed-bids
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10.12751/g-node.4ivuv8
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DataLad dataset containing BIDS-converted MRI data used in Wittkuhn & Schuck, 2020, Nature Communications. For details, see:

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highspeed-bids
Lennart Wittkuhn 33c76ade2a change gitignore to include behavioral data subset 3 years ago
.datalad a4652dfd2b Configure metadata type(s) 3 years ago
.heudiconv a2b905b439 add defaced BIDS-converted MRI data after running heudiconv and defacing 3 years ago
code 962f0de47d update script to add IntentedFor to fieldmap jsons and add scripts to documentation 3 years ago
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README

TODO: Provide description for the dataset -- basic details about the study, possibly pointing to pre-registration (if public or embargoed)
datacite.yml
Title Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis
Authors Wittkuhn,Lennart;Max Planck Institute for Human Development, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany;ORCID:0000-0001-2345-6789
Schuck,Nicolas W.;Max Planck Institute for Human Development, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany;ResearcherID:X-1234-5678
Description Neural computations are often anatomically localized and executed on sub-second time scales. Understanding the brain therefore requires methods that offer sufficient spatial and temporal resolution. This poses a particular challenge for the study of the human brain because non-invasive methods have either high temporal or spatial resolution, but not both. Here, we introduce a novel multivariate analysis method for conventional blood-oxygen-level dependent functional magnetic resonance imaging (BOLD fMRI) that allows to study sequentially activated neural patterns separated by less than 100 ms with anatomical precision. Human participants underwent fMRI and were presented with sequences of visual stimuli separated by 32 to 2048 ms. Probabilistic pattern classifiers were trained on fMRI data to detect the presence of image-specific activation patterns in early visual and ventral temporal cortex. The classifiers were then applied to data recorded during sequences of the same images presented at increasing speeds. Our results show that probabilistic classifier time courses allowed to detect neural representations and their order, even when images were separated by only 32 ms. Moreover, the frequency spectrum of the statistical sequentiality metric distinguished between sequence speeds on sub-second versus supra-second time scales. These results survived when data with high levels of noise and rare sequence events at unknown times were analyzed. Our method promises to lay the groundwork for novel investigations of fast neural computations in the human brain, such as hippocampal replay.
License Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/)
References Wittkuhn, L. and Schuck, N. W. (2020). Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis. bioRxiv. doi:10.1101/2020.02.15.950667 [doi:10.1101/2020.02.15.950667] (IsSupplementTo)
Funding Max Planck Society (M.TN.A.BILD0004)
Max Planck Institute for Human Development
Keywords Neuroscience
functional magnetic resonance imaging
hippocampal replay
Resource Type Dataset