CERB_2020 202108_01

choea@www.nitrc.org (Ann Choe) — Wed, 01 Sep 2021 14:58:00 GMT

Thank you for your interest in our study.

** 2022.07.15
We have added the "raw" fMRI and EGG data acquired during the study to our 202108_01 release.[list][*]The only preprocessing that was done to the structural data set was refacing. (*)[*]The only preprocessing that was done to the fMRI data set was defacing. (*)[*]The only preprocessing that was done to the EGG data set is trimming (i.e., the timing of the EGG recording matches that of the fMRI data set)[/list]The name of the file is 202207_01.tar.gz. The file contains 19 folders. Each folder includes two fMRI data sets and two EGG data sets acquired during the corresponding sessions.
(*)Note: AFNI reface/deface function was used (https://afni.nimh.nih.gov/pub/dist/doc/program_help/@afni_refacer_run.html).

** 202108_01 release is a correction of the 202101_01 release. In the 202101_01 release,
the original files were erroneously compressed in a way that the file structures of the
original files were preserved in the compressed files. We've therefore recompressed and
uploaded as a new data release here. Please note that no other changes were made. We
apologize for any inconvenience.

The 202108_01 release of the CERB dataset package consists of two files.

1. [b]fMRI_prepro.tar.gz[/b]
 a. This file contains a folder named [b]data_allrun[/b]. The folder structure of the [b]data_allrun[/b]
 is as follows:
 i. data_allrun >> [dates of imaging session] >> [run#] >> data
 ii. For example, a file named pb04.20190703jp.r01.blur+tlrc.nii can be found under
 the folder structure [data_allrun >> 0703 >> run1 >>
 pb04.20190703jp.r01.blur+tlrc.nii], and one can surmise that the resting state
 data was acquired on 2019/07/03, and is one of the two runs of data acquired on
 that date.
 b. [b]The fMRI data was preprocessed using the Analysis of Functional NeuroImages[/b]
[b] (AFNI) software (version AFNI_20.1.06)[/b]. The preprocessing pipeline included:
 1) despiking, 2) slice timing correction, 3) motion correction, 4) co-registration,
 5) normalization, 6) segmentation, and 7) spatial smoothing using a 6 mm (i.e., twice
 the nominal acquisition voxel size) full-width at half-maximum Gaussian kernel.

2. [b]EGGGICA_prepro.tar.gz[/b]
 a. This file contains a MATLAB .mat file named EGGGICA_prepro_ds_all, in a structure
 array format. The structure contains the following fields:
[u][b] [/b][/u][b]EGGGICA_prepro_ds_all.RSNinfo:[/b]
[b] RSN component numbers:[/b] Our of the 42 ICA components, 18 were identified as
 resting state networks (RSNs). This array contains the component numbers of the
 18 RSNs
[b] Corresponding network names: [/b]Network names of the 18 RSNs
[b] Re-ordering information: [/b]You can use this to reorder the RSNs that is pleasing
 to eyes (e.g., visual networks are group together, etc.)
[u][b] [/b][/u][b]EGGGICA_prepro_ds_all.sess.run.[/b]
[b] EGG: [/b]preprocessed EGG. Size: [signalLength x 1]
[b] GICAtc[/b]: time courses of RSNs. Size: [#dynamics x #RSN]
[b] GICAic:[/b] spatial maps of RSNs. Size [#voxels x #RSN]
 b. For example, if you wanted to look up the preprocessed EGG data that was acquired
 on day 1, run 2, you would use EGGGICA_prepro_ds_all.sess(1).run(2).EGG
 c. [b]EGG data was preprocessed[/b] following the pipeline developed by Rebollo, et al.*,
 using the FieldTrip toolbox (http://www.fieldtriptoolbox.org/)**, Matlab (Natick, MA;
 version R2018a), and custom code provided by Rebollo, et al.
 (https://github.com/irebollo/stomach_brain_Scripts). Data were low-pass filtered
 Below 5 Hz to avoid aliasing of higher-frequency signals, e.g., cardiac, and downsampled to
 10 Hz. To identify the EGG peak frequency (0.033–0.066 Hz) for each run, we computed
 the spectral density estimate for each EGG channel over the 900 s of EGG signal acquired
 during each fMRI scan using Welch's method on 200 s time windows with 150 s overlap.
 For each run, the spectral peak was identified by looking for a sharp peak within the
 normogastric frequency range of 0.033–0.066 Hz. Data from the EGG channel with the
 highest spectral peak were then bandpass filtered to isolate the signal related to gastric
 basal rhythm (linear phase finite impulse response filter, FIR, designed with Matlab
 function FIR2, centered at EGG peaking frequency, filter width ±0.015 Hz, filter order of
 5). Data were filtered in the forward and backward directions to avoid phase distortions
 and then further downsampled to match the sampling rate of the BOLD acquisition
 (0.5 Hz).

 *Rebollo I, Devauchelle AD, B ́eranger B, Tallon-Baudry C. Stomach-
 brainsynchrony reveals a novel, delayed-connectivity resting-state network in
 humans.Elife. 2018;7:e33321.
 **Oostenveld R, Fries P, Maris E, Schoffelen JM. FieldTrip: open source software
 for advanced analysis of MEG, EEG, and invasive electrophysiological data.
 Computational intelligence and neuroscience. 2011;2011:1

Concurrent Electrogastrography / Resting Brain fMRI highly-sampled-individual data resource (CERB) Releases

CERB_2020 202108_01