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task_potency

GENERAL_SCRITPS

plug-in scripts to apply the task potency pipeline on any data.

  • roscha_task_potency.py requires path to preprocessed nifti files in the same registration space. It uses Mixture Modelling functions developped by Alberto Llera.
  • roscha_MM_thresholding.py can be used for network estimation

DATA_NeuroIMAGE_fc

  • subject and acquisition parameters (age, aroma, gender, TR, rms jenkinson)
  • task performance Z partial correlation matrices of the data used for both papers and permutation testing of age effect will be available after publication.

SCRIPT_Potency_method_paper

Scripts of analysis and lists of subject corresponding to the preprint: And the best task is ...? Using Task potency to infer task specificity https://www.biorxiv.org/content/early/2017/11/29/111187 (under revision - updated script will be released soon)

SCRIPT_Potency_age_paper

Scripts of analysis corresponding to the article: Assessing age-dependent multi-task functional co-activation changes using measures of task-potency www.sciencedirect.com/science/article/pii/S1878929317300592 lists of subjects are the same as the one in script_potency_method_paper

Pipeline related to both articles

To build matrices, the following pipeline is applyed: NOTE : all raw data are not provided to rerun initial steps leading to the Z partial correlation. However Z partial correlation matrices are provided and scripts leading to these matrices are provided for transparency.

  • reg_subjects_GENERIC (data not available to run this step): time series extraction

  • matrice_subjects_GENERIC (data not available to run this step): from time series to Z partial correlation matrices

  • MMnormalizeALLmat: mixture modelling normalization of the Z partial correlation matrices

  • taskpotency_submission (for method paper analysis - will be updated according to the review)

  • taskpotency_age_submission (for age paper analysis)

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  • Jupyter Notebook 98.9%
  • Python 1.1%