readme tweaks and matlab script cleanup

README.md

@@ -2,8 +2,8 @@
 
 [![Download the dataset from DataverseNL](https://img.shields.io/badge/download-BIDS%20dataset-9cf.svg)](https://doi.org/10.34894/R1TNL8) 
 [![Explore the data interactively](https://img.shields.io/badge/explore-Interactive%20Web%20App-ff69b4.svg)](https://rt-me-fmri.herokuapp.com/)
-[![Cite this work](https://img.shields.io/badge/cite-Data%20paper-brightgreen.svg)](https://www.biorxiv.org/content/10.1101/2020.12.07.414490v1) 
-[![Cite this work](https://img.shields.io/badge/cite-Methods%20paper-green.svg)]() 
+[![Read this work](https://img.shields.io/badge/read-Data%20paper-brightgreen.svg)](https://www.biorxiv.org/content/10.1101/2020.12.07.414490v1) 
+[![Read this work](https://img.shields.io/badge/read-Methods%20paper-green.svg)]() 
 [![Reproduce figures](https://img.shields.io/badge/reproduce-Figures%20(data%20paper)-red.svg)](https://github.com/jsheunis/rt-me-fMRI/blob/master/rt-me-fmri_reproduce_dataFigures.ipynb) 
 [![Reproduce figures](https://img.shields.io/badge/reproduce-Figures%20(methods%20paper)-orange.svg)](https://github.com/jsheunis/rt-me-fMRI/blob/master/rt-me-fmri_reproduce_methodsFigures.ipynb) 
 [![Reproduce results](https://img.shields.io/badge/reproduce-Results%20(methods%20paper)-blueviolet.svg)](https://github.com/jsheunis/rt-me-fMRI/blob/master/rt-me-fmri_reproduce_resultsSummaries.ipynb) 
@@ -22,15 +22,17 @@ This repository contains descriptions, code and data related to the real-time mu
     [**The effects of multi-echo fMRI combination and rapid <i>T<sub>2</sub>\*</i>-mapping on offline and real-time BOLD sensitivity**]()
 
 Below we provide more information and instructions regarding:
-- The dataset summary
-- How to download the data
-- How to explore the data
-- How to reproduce results and figures from the publications
-- Relevant software tools
-- How to cite this work
-- How to contribute
+- [The dataset summary](#summary)
+- [How to download the data](#download)
+- [How to explore the data](#explore)
+- [How to reproduce results and figures from the publications](#reproduce_data)
+- [Relevant software tools](#software)
+- [How to cite this work](#cite)
+- [How to contribute](#contribute)
 
 
+<div id="summary"></div>
+
 ## Data summary
 
 The `rt-me-fMRI` dataset is a multi-echo fMRI dataset (N=28 healthy participants) with four task-based and two resting state runs that were collected, curated and made available to the research community. Its main purpose is to advance the development of methods for real-time multi-echo functional magnetic resonance imaging analysis with applications in real-time quality control, adaptive paradigms, and neurofeedback, although the variety of experimental task paradigms supports a multitude of use cases. Tasks include finger tapping, emotional face and shape matching, imagined finger tapping and imagined emotion processing. This figure summarises the collected data:
@@ -41,6 +43,8 @@ The full data description is available as a [data article](https://doi.org/10.11
 
 Several depictions of the data tree can be viewed [here](https://github.com/jsheunis/rt-me-fMRI/blob/master/data_tree.md)
 
+<div id="download"></div>
+
 ## Downloading the data
 
 The `rt-me-fMRI` dataset is available for reuse for the purpose of scientific research or education in the field of functional magnetic resonance imaging. If you wish to use the data, you have to agree to the terms of a [Data Use Agreement](https://github.com/jsheunis/rt-me-fMRI/blob/master/DUA.md) when downloading the data.
@@ -51,22 +55,31 @@ The dataset was collected, processed and shared in accordance with the European
 
 Much of the work that went into this administrative process has been documented as part of the output of the [Open Brain Consent](https://open-brain-consent.readthedocs.io/en/stable/gdpr/index.html) Working Group, accessible [here](10.31234/osf.io/f6mnp).
 
+
+<div id="explore"></div>
+
 ## Exploring the data
 
 To explore the dataset's derivative measures interactively, visit [this web application](https://rt-me-fmri.herokuapp.com). It was built with Python using the [Plotly Dash framework](https://plotly.com/dash/). The open source code base is available at [this repository](https://github.com/jsheunis/rt-me-fmri-dash).
 
+<div id="reproduce_data"></div>
+
 ## Reproducibility: data preparation
 
 The data preparation process is [documented here](https://github.com/jsheunis/rt-me-fMRI/tree/master/data_setup). This includes code to convert neuroimaging, physiological and other data to BIDS format.
 
+<div id="reproduce_results"></div>
+
 ## Reproducibility: results
 
 After preprocessing and quality checking of the data (see a full description in the data article) the data were processed and analysed as described in the methods article. Because of data storage limitations, these derivative data are not shared together with the `rt-me-fMRI` dataset. However, code and instructions are provided to allow these derivative data to be reproduced. Additionally, code and instructions are provided to subsequently generate the summary data from which the results of the methods paper as well as the data underlying the Dash application are derived:
 
-- [Code and instructions to generate derivative data](https://github.com/jsheunis/rt-me-fMRI/blob/master/rt-me-fmri_reproduce_derivativeData.ipynb)
+- [Code and instructions to generate derivative data](https://github.com/jsheunis/rt-me-fMRI/blob/master/rt-me-fmri_reproduce_matlabProcessing.m)
 - [Code and instcurtions to generate summary data for figures and Dash application](https://github.com/jsheunis/rt-me-fMRI/blob/master/rt-me-fmri_reproduce_resultsSummaries.ipynb)
 
 
+<div id="reproduce_figures"></div>
+
 ## Reproducibility: figures
 
 The following notebooks contain code and descriptions that allows figures for the data and methods articles to be reproduced:
@@ -75,6 +88,8 @@ The following notebooks contain code and descriptions that allows figures for th
 - [Methods figures](https://github.com/jsheunis/rt-me-fMRI/blob/master/rt-me-fmri_reproduce_methodsFigures.ipynb)
 
 
+<div id="software"></div>
+
 ## Software tools
 
 All (pre)processing and major data analysis steps for both the data article and methods article were done using the open source MATLAB-based `fMRwhy` toolbox (v0.0.1; https://github.com/jsheunis/fMRwhy), which was developed over the course of this project. `fMRwhy` has has conditional dependencies:
@@ -86,6 +101,9 @@ All (pre)processing and major data analysis steps for both the data article and
 - TAPAS PhysIO (v3.2.0; https://github.com/translationalneuromodeling/tapas/releases/tag/v3.2.0; Kasper et al., 2017)
 - Raincloud plots (v1.1 https://github.com/RainCloudPlots/RainCloudPlots/releases/tag/v1.1; Allen et al., 2019).
 
+
+<div id="cite"></div>
+
 ## Citing this work
 
 Papers, book chapters, books, posters, oral presentations, and all other presentations of results derived from the rt-me-fMRI dataset should acknowledge the origin of the data as follows:
@@ -102,6 +120,7 @@ And the following citation when referring to the methods article:
 >Heunis, S., Breeuwer, M., Caballero-Gaudes, C., Hellrung, L., Huijbers, W., Jansen, J.F.A., Lamerichs, R., Zinger, S., Aldenkamp, A.P., 2020. The effects of multi-echo fMRI combination and rapid T2*-mapping on offline and real-time BOLD sensitivity. bioRxiv [tbd...]
 
 
+<div id="contribute"></div>
 
 ## Contributions / feedback
 

matlab/fmrwhy_settings_rtmefMRI.m

@@ -1,6 +1,4 @@
-% fmrwhy_settings_template
-% Template settings file for the fmrwhy_bids_workflowQC pipeline
-
+% Copied and altered from fmrwhy_settings_template, the template settings file for the fmrwhy_bids_workflowQC pipeline
 
 % ----------
 % Section 01