Penn LEAD: Penn Longitudinal Executive functioning in Adolescent Development - XCP-D Derivatives

## Description of Dataset

Penn LEAD (Penn Longitudinal Executive functioning in Adolescent Development) is a data resource designed to investigate transdiagnostic executive function during development.

This repository contains data derivatives from running XCP-D.
We provide derivatives including post-processed clean BOLD time series that are denoised, filtered, and censored, and ALFF and ReHo derivative maps. Preprocessed data from running fMRIPrep, which was used as input for XCP-D, can be found [here.] (https://openneuro.org/datasets/ds007088)

The link to the accompanying raw dataset is here: https://openneuro.org/datasets/ds007116 [accession number ds007116].

The Docker Hub link for XCP-D is [here.](https://hub.docker.com/layers/pennlinc/xcp_d/0.10.7/images/sha256-fa10fe96b9ed1cb63322cfebc7dff8a21b7dcca84487b977c06be3d2c863f33a)

The quality control recommendations for fMRI can be found on [Github](https://github.com/PennLINC/transdiagnostic_executive_function/tree/main/QC/qc_csvs/final_QC_csvs). Both QC recommendations for fMRI and T1 structural data should be taken into account for fMRI data.

The command used to XCP-D was:

```
xcp_d \
/data/fmriprep \
/data/xcp_d \
participant \
--stop-on-first-crash \
--mode linc \
--input-type fmriprep \
--dummy-scans auto \
--atlases \
4S1056Parcels \
4S156Parcels \
4S256Parcels \
4S356Parcels \
4S456Parcels \
4S556Parcels \
4S656Parcels \
4S756Parcels \
4S856Parcels \
4S956Parcels \
Glasser \
Gordon \
HCP \
MIDB \
Tian
```