update README.md and datacite.yml

README.md

@@ -1,4 +1,4 @@
-# Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis - fMRIPrep data
+# Highspeed fMRIPrep
 
 ## Overview
 
@@ -14,6 +14,10 @@ Pre-processing was performed using fMRIPrep, version 1.2.2.
 
 ## Citation
 
+> Wittkuhn, L. and Schuck, N. W. (2020). Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. *Nature Communications*.
+
+A preprint (old version) is available at:
+
 > 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](http://dx.doi.org/10.1101/2020.02.15.950667)
 
 ## Contact

datacite.yml

@@ -15,29 +15,29 @@ authors:
     firstname: "Nicolas W."
     lastname: "Schuck"
     affiliation: "Max Planck Institute for Human Development, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany"
-    id: "ResearcherID:X-1234-5678"
+    id: "ORCID:0000-0002-0150-8776"
 
 # A title to describe the published resource.
-title: "Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis"
+title: "Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex - Preprocessed MRI data using fMRIPrep"
 
 # Additional information about the resource, e.g., a brief abstract.
 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.
+  Neural computations are often fast and anatomically localized.
+  Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both.
+  Of particular relevance, fast neural replay is known to occur throughout the brain in a coordinated fashion about which little is known.
+  We develop a multivariate analysis method for functional magnetic resonance imaging that makes it possible to study sequentially activated neural patterns separated by less than 100 ms with precise spatial resolution.
+  Human participants viewed images individually and sequentially with speeds up to 32 ms between items.
+  Probabilistic pattern classifiers were trained on activation patterns in visual and ventrotemporal cortex during individual image trials.
+  Applied to sequence trials, probabilistic classifier time courses allow the detection of neural representations and their order.
+  Order detection remains possible at speeds up to 32 ms between items.
+  The frequency spectrum of the sequentiality metric distinguishes between sub- versus supra-second sequences.
+  Importantly, applied to resting-state data our method reveals fast replay of task-related stimuli in visual cortex.
+  This indicates that non-hippocampal replay occurs even after tasks without memory requirements and shows that our method can be used to detect such spontaneously occurring replay.
 
 # Lit of keywords the resource should be associated with.
 # Give as many keywords as possible, to make the resource findable.
 keywords:
-  - Neuroscience
+  - cognitive neuroscience
   - functional magnetic resonance imaging
   - hippocampal replay
 
@@ -54,7 +54,8 @@ license:
 # Funding information for this resource.
 # Separate funder name and grant number by comma.
 funding:
-  - "Max Planck Society (M.TN.A.BILD0004)"
+  - "Max Planck Society, Independent Max Planck Research Group grant"
+  - "European Union, ERC Starting Grant ERC-2019-StG REPLAY-852669"
   - "Max Planck Institute for Human Development"
 
 
@@ -64,6 +65,9 @@ funding:
 # Supported sources are: DOI, arXiv, PMID
 # In the citation field, please provide the full reference, including title, authors, journal etc.
 references:
+  -
+    reftype: "IsSupplementTo"
+    citation: "Wittkuhn, L. and Schuck, N. W. (2020). Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nature Communications"
   -
     id: "doi:10.1101/2020.02.15.950667"
     reftype: "IsSupplementTo"
@@ -71,6 +75,8 @@ references:
 
 
 
+
+
 # Resource type. Default is Dataset, other possible values are Software, DataPaper, Image, Text.
 resourcetype: Dataset