New Data Analysis Pipeline - Based on Delayed Correlation Directed Analysis

[mitemijalkov]

Describe the solution you'd like Implementation of pipeline related to the paper (https://academic.oup.com/cercor/article/32/3/593/6330940?login=true). This pipeline will implement delayed correlation method that produces directed functional connectivity networks. It uses fMRI data (time series).

Additional context This method computes weighted directed matrices. Do we need 2 separate pipelines if we are going to analyze both weighted and binary directed matrices (based on density)?

Solution

• [ ] Add folder to pipelines. Functional Delayed Correlation (braph2genesis/pipelines/functional delayed_correlation/)
• [ ] Use SubjectFUN, importers, exporters and sample data from the pipeline functional (there's no need to copy into folder)
• [ ] Create AnalysisEnsemble_FUN_D_WD
• [ ] Create AnalysisEnsemble_FUN_D_BDD
• [ ] Create AnalysisEnsemble_FUN_D_BDT
• [ ] Create pipeline script pipeline_functional_delayed_analysis_wd.braph2
• [ ] Create pipeline script pipeline_functional_delayed_analysis_bdd.braph2
• [ ] Create pipeline script pipeline_functional_delayed_analysis_bdt.braph2
• [ ] Create pipeline script pipeline_functional_delayed_comparison_wd.braph2
• [ ] Create pipeline script pipeline_functional_delayed_comparison_bdd.braph2
• [ ] Create pipeline script pipeline_functional_delayed_comparison_bdt.braph2

[egolol]

We will add 1 pipeline folder that manages all different directed graphs analyses. Which type of data are you planning to use?

[mitemijalkov]

We have 2 groups of Parkinson's disease patients. The input data is time series obtained by functional MRI. Age, sex and motion parameters as covariates.