Openneuro based demos#

Download with datalad#

All those data can be installed with datalad.

Datalad datasets can be accessed via their siblings on: https://github.com/OpenNeuroDatasets

Check the content of the Makefile to see the code snippets you need to run to install those datasets.

Otherwise you can also get them by using the Datalad superdataset.

For example:

datalad install ///
cd datasets.datalad.org/
datalad install openneuro
datalad install openneuro/dsXXXXXX
cd openneuro/dsXXXXXX
# get rest data first subject
datalad get /openneuro/dsXXXXXX/sub-0001/func/sub-0001*

ds000001: Balloon analog risk#

Features#

  • one task

  • one session

  • several runs

  • parametric analysis

Scripts#

ds000001_run#

Runs: - preprocessing - stats at the suject level - stats at the group level

ds000001_smooth_run#

Runs: - smoothing of fmriprep data - stats at the suject level

ds000001_aroma_run#

Runs: - select only the ‘AROMA’ files from the fMRIprep output - stats at the suject level - stats at the group level

ds000114: test-retest#

Features#

  • several tasks

  • several sessions

  • one or several runs depending on task

  • fmriprep data

Scripts#

ds000114_run#

Demo to compare activations across sessions.

ds000224#

Features#

  • fmriprep data

Scripts#

ds000224_run#

show ROI based analysis.

ds001168#

Features#

  • resting state

  • several sessions

  • several acquisition

  • fieldmaps

  • physio data

Scripts#

ds001168_run#

Show how to denoise resting state data using the GLM by removing confounds from the time series

ds001734: NARPS#

Features#

  • one task

  • one session

  • several runs

  • several groups

  • >100 participants

  • fmriprep data

Details#

More details here.

% compute euclidean distance to the indifference line defined by
% gain twice as big as losses
% https://en.wikipedia.org/wiki/Distance_from_a_point_to_a_line
a = 0.5;
b = -1;
c = 0;
x = onsets{iRun}.gain;
y = onsets{iRun}.loss;
dist = abs(a * x + b * y + c) / (a^2 + b^2)^.5;
onsets{iRun}.EV = dist; % create an "expected value" regressor

{
    "Description": "Time points with a framewise displacement (as calculated by fMRIprep) > 0.5 mm were censored (no interpolation) at the subject level GLM..",
    "Name": "Threshold",
    "Input": [
        "framewise_displacement"
    ],
    "Threshold": 0.5,
    "Binarize": true,
    "Output": [
        "thres_framewise_displacement"
    ]
},

Scripts#

ds001734_run#

Analysis of the NARPS dataset

ds002799#

Features#

  • resting state and task

  • several sessions

  • fmriprep data

ds003379#

Features#

  • checkerboard localizer

  • 3 groups

  • fmriprep data

Scripts#

ds003397_run#

Run a one-way ANOVA across group

Only a few subjects are run because of the large size of each run.