doi/highspeed-bids: DataLad dataset containing BIDS-converted MRI data used in Wittkuhn & Schuck, 2020, Nature Communications. For details, see: @ f89333c27a83a0e8678b0fca89771e411dc2faf3

DataLad dataset containing BIDS-converted MRI data used in Wittkuhn & Schuck, 2020, Nature Communications. For details, see:

23 提交历史

4 代码分支

1 版本发布

Lennart Wittkuhn f89333c27a add requirements.txt file listing required packages		4 年之前
.datalad	a4652dfd2b Configure metadata type(s)	4 年之前
.heudiconv	a2b905b439 add defaced BIDS-converted MRI data after running heudiconv and defacing	4 年之前
code	86f7d1150b Merge remote-tracking branch 'origin/master'	4 年之前
input	b2e0c94005 add empty input directory using .gitkeep	4 年之前
logs	4223e4692d add empty logs directory	4 年之前
sub-01	a2b905b439 add defaced BIDS-converted MRI data after running heudiconv and defacing	4 年之前
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sub-40	2f8992b781 re-run heudiconv and defacing for sub-40 after increasing memory demand in heudiconv code	4 年之前
tools @ 4dac41c4fc	1455f7af90 add LICENSE and DOI	4 年之前
.gitattributes	f89333c27a add requirements.txt file listing required packages	4 年之前
.gitignore	d10ec6f900 add html rendering of docs page to .gitignore	4 年之前
.gitmodules	29783c8c87 [DATALAD] Recorded changes	4 年之前
CHANGES	a2b905b439 add defaced BIDS-converted MRI data after running heudiconv and defacing	4 年之前
LICENSE	1455f7af90 add LICENSE and DOI	4 年之前
README	a2b905b439 add defaced BIDS-converted MRI data after running heudiconv and defacing	4 年之前
datacite.yml	1455f7af90 add LICENSE and DOI	4 年之前
dataset_description.json	a2b905b439 add defaced BIDS-converted MRI data after running heudiconv and defacing	4 年之前
highspeed-bids.Rproj	ea54b9049a add R project to git	4 年之前
participants.json	a2b905b439 add defaced BIDS-converted MRI data after running heudiconv and defacing	4 年之前
participants.tsv	a2b905b439 add defaced BIDS-converted MRI data after running heudiconv and defacing	4 年之前
requirements.txt	f89333c27a add requirements.txt file listing required packages	4 年之前

		
				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