Adding changes package

main.tex

@@ -13,7 +13,7 @@
 
 % %% Using a mix of:
 % %%   - Overleaf's AMA style (https://www.overleaf.com/latex/templates/ama-format-assignment-template/tyjmwydvdqng),
-% %%   - DLI-stats biblatex-ama (https://github.com/dli-stats/biblatex-ama),
+% %%   - DLI-stats biblatex-ama (https://github.com/dli-stats/biblatex-ama)
 
 \usepackage[style=ama, citestyle=ama, bibstyle=ama]{biblatex}
 \usepackage[margin=1in]{geometry}
@@ -97,6 +97,10 @@
 % \usepackage{lineno}
 % \linenumbers
 
+\usepackage[authormarkup=none,defaultcolor=orange]{changes}
+% \usepackage[final]{changes}
+
+
 \title{Proceedings of the OHBM Brainhack 2022}
 %\subtitle{subtitle}
 
@@ -312,7 +316,7 @@
 \section*{Introduction}
 
 The Organisation of Human Brain Mapping BrainHack (shortened to OHBM
-Brainhack herein) is a yearly satellite event of the main OHBM
+Brainhack \added{herein}) is a yearly satellite event of the main OHBM
 meeting, organised by the Open Science Special Interest Group following
 the model of Brainhack hackathons\supercite{Gau2021}.
 Where other hackathons set up a competitive environment based on
@@ -494,13 +498,12 @@ \section{Platforms, website, and IT}
 
 \section{Project Reports}
 
-The peculiar nature of a Brainhack\supercite{Gau2021} reflects in the nature of the projects developed during the event, that can span very different types of tasks.
+\added{The peculiar nature of a Brainhack\supercite{Gau2021} reflects in the nature of the projects developed during the event, that can span very different types of tasks.
 While most projects feature more `hackathon-style' software development, in the form of improving software integration (\Cref{sec:DLDI}), API refactoring (\Cref{sec:Neuroscout}), or creation of new toolboxes and platforms (\Cref{sec:NeuroCausal,sec:NARPS,sec:pymc}), the inclusion of newcomers and participants with less strong software development skills can foster projects oriented to user testing (\Cref{sec:DLC,sec:NARPS}) or documentation compilation (\Cref{sec:physiopy}).
 The scientific scopes of Brainhacks were reflected in projects revolving around data exploration (\Cref{sec:AHEAD,sec:HyppoMRIQC}) or model development (\Cref{sec:pymc}), or adding aspects of open science practices (namely, the Brain Imaging Data Structure) to toolboxes  (\Cref{sec:FLUX,sec:vasomosaic}).
-Finally, fostering a collaborative environment and avoiding pitching projects against each others not only opens up the possibility for participants to fluidly move between different groups, but also to have projects which sole aim is supporting other projects (\Cref{sec:BHC}), learning new skills by having fun (\Cref{sec:explodingbrains}), or fostering discussions and conversations among participants to improve the adoption of open science practices (\Cref{sec:metadata}).  
-
+Finally, fostering a collaborative environment and avoiding pitching projects against each others not only opens up the possibility for participants to fluidly move between different groups, but also to have projects which sole aim is supporting other projects (\Cref{sec:BHC}), learning new skills with entertaining tasks (\Cref{sec:explodingbrains}), or fostering discussions and conversations among participants to improve the adoption of open science practices (\Cref{sec:metadata}).  
 
-Following are the 14 submitted reports of the 23 projects presented at the OHBM Brainhack.
+Following are the 14 submitted reports of the 23 projects presented at project wrap-up during the OHBM Brainhack.}
 
 \subfile{summaries/ahead-project.tex}
 \subfile{summaries/brainhack-cloud.tex}

summaries/exploding_brains.tex

@@ -7,7 +7,7 @@ \subsection{Exploding brains in Julia}\label{sec:explodingbrains}
 \authors{\"Omer Faruk G\"ulban, %
 Leonardo Muller-Rodriguez}
 
-Particle simulations are used to generate visual effects (in movies, games, etc.). In this project, we explore how we can use magnetic resonance imaging (MRI) data to generate interesting visual effects by using (2D) particle simulations. Aside from providing an entertaining avenue to the interested participants, our project has further educational utility. For instance, anatomical MRI data analysis is done in two major frameworks: (1) manipulating fixed regularly spaced points in space (also known as Eulerian point of view), and (2) manipulating moving irregularly spaced points in space (Lagrangian point of view). For instance, bias field correction is commonly done from Eulerian point of view (e.g. computing a bias field is similar to computing a particle velocity field in each frame of the explosions), whereas cortical surface inflation is commonly done from Lagrangian point of view of the MRI data (e.g. computing the inflated brain surface is similar to computing the new positions of particles in each frame of the explosion). Therefore, our project provides an educational opportunity for those who would like to peek into the deep computational and data structure manipulation aspects of MRI image analysis. We note that we already made two hackathon projects in 2020 (see below) and were first inspired by a blog post (\texttt{\url{https://nialltl.neocities.org/articles/mpm_guide.html}}) on the material point method\supercite{Jiang1965, Love2006, Stomakhin2013a}. Our additional aim in Brainhack 2022 is to convert our previous progress in Python programming language to Julia. The reason why we have moved to Julia language is because we wanted to explore this new programming language's potential for developing MRI image analysis methods as it has convenient parallelization methods that speeds-up the particle simulations (and any other advanced image manipulation algorithms).
+Particle simulations are used to generate visual effects (in movies, games, etc.). In this project, we explore how we can use magnetic resonance imaging (MRI) data to generate interesting visual effects by using (2D) particle simulations. \added{Aside from providing an entertaining avenue to the interested participants, our project has further educational utility. For instance, anatomical MRI data analysis is done in two major frameworks: (1) manipulating fixed regularly spaced points in space (also known as Eulerian point of view), and (2) manipulating moving irregularly spaced points in space (Lagrangian point of view). For instance, bias field correction is commonly done from Eulerian point of view (e.g.\ computing a bias field is similar to computing a particle velocity field in each frame of the explosions), whereas cortical surface inflation is commonly done from Lagrangian point of view of the MRI data (e.g.\ computing the inflated brain surface is similar to computing the new positions of particles in each frame of the explosion). Therefore, our project provides an educational opportunity for those who would like to peek into the deep computational and data structure manipulation aspects of MRI image analysis. We note that we already made two hackathon projects in 2020 (see below) and were first inspired by a blog post (\texttt{\url{https://nialltl.neocities.org/articles/mpm_guide.html}}) on the material point method\supercite{Jiang1965, Love2006, Stomakhin2013a}. Our additional aim in Brainhack 2022 is to convert our previous progress in Python programming language to Julia. The reason why we have moved to Julia language is because we wanted to explore this new programming language's potential for developing MRI image analysis methods as it has convenient parallelization methods that speeds-up the particle simulations (and any other advanced image manipulation algorithms).}
 
 -----------------------------------
 
@@ -20,7 +20,7 @@ \subsection{Exploding brains in Julia}\label{sec:explodingbrains}
 
 -----------------------------------
 
-As a result of this hackathon project, we delivered a video compilation of our animations (\Cref{fig:exploding_brains}) which can be seen at \texttt{\url{https://youtu.be/_5ZDctWv5X4}}. We highlight that in addition to its educational value, our project provided stress relief by means of entertaining the participants after the pandemic. We believe that our project provides a blueprint for the future brainhacks where MRI science, computation, and education can be disseminated within an engaging and entertaining context. Our future efforts will involve sophisticating the particle simulations, the initial simulation parameters to generate further variations of the visual effects, and potentially synchronizing the simulation effects with musical beats.
+As a result of this hackathon project, \added{we delivered a video compilation of our animations (\Cref{fig:exploding_brains}) which can be seen at \texttt{\url{https://youtu.be/_5ZDctWv5X4}}. We highlight that in addition to its educational value, our project provided stress relief by means of entertaining the participants after the pandemic. We believe that our project provides a blueprint for the future brainhacks where MRI science, computation, and education can be disseminated within an engaging and entertaining context.} Our future efforts will involve sophisticating the particle simulations, the initial simulation parameters to generate further variations of the visual effects, and potentially synchronizing the simulation effects with musical beats.
 
 \begin{figure}
 	\centering

summaries/flux.tex

@@ -15,6 +15,6 @@ \subsection{FLUX: A pipeline for MEG analysis and beyond}\label{sec:FLUX}
 
 These goals can be achieved in mid-term objectives, such as making the FLUX pipeline fully BIDS compatible and more automated. Another mid-term goal is to containerize the FLUX pipeline and the associated dependencies making it easier to use. Moreover, expanding the applications of this pipeline to other systems like MEG CTF, Optically Pumped Magnetometer (OPM) and EEG will be another crucial step in making FLUX a more generalized neurophysiological data analysis pipeline.
 
-During the 2022 Brainhack, the team focused on incorporating the BIDS standard into the analysis pipeline using MNE_BIDS\supercite{Appelhoff2019}. Consequently, an updated version of FLUX was released after the Brainhack meeting.
+\added{During the 2022 Brainhack, the team focused on incorporating the BIDS standard into the analysis pipeline using MNE_BIDS\supercite{Appelhoff2019}. Consequently, an updated version of FLUX was released after the Brainhack meeting.}
 
 \end{document}

summaries/neurocausal.tex

@@ -22,7 +22,7 @@ \subsection{NeuroCausal: Development of an Open Source Platform for the Storage,
 The second stage of the study aims at creating an online platform that allows for the direct uploading of clinical brain maps and their corresponding metadata. The platform will provide a basic automated preprocessing and a data-quality check pipeline, ensuring that all the ethical norms regarding patient privacy are met. The platform will automatically extract and synthesize key data to ultimately create probabilistic maps synthesizing transdiagnostic information on symptom-structure mapping, which will be dynamically updated as more data are gathered.
 
 The nature of the project requires expertise in different fields (from clinical neuroscience to computer science) in order to overcome both technical and theoretical challenges. The OHBM Brainhack 2022 gave us the opportunity to set the first stones. In small subteams, we worked on developing three key building blocks: (1) the input filtering pipeline to ensure the downloaded papers are neuropsychological in nature and offer causal symptom-structure mapping; (2) the extraction of key terms occurrences in the text as to assess which neural space is reported (as they will have to be converted to a common one), (3) the curation of clinical ontology mapping specific neuropsychological batteries and tasks to the cognitive term(s) they touch upon.
-During the hackahton we worked on developing three key building blocks in small subteams. First, we prepared an input filtering pipeline to ensure that the downloaded papers are neuropsychological in nature (and thus offer causal symptom-structure mapping): we count the occurrences of clinically relevant terms, and papers are included only if they pass an arbitrary threshold. Second, we coded a script automatically returning for each paper information on the neural spaced used (e.g., which atlas? MNI coordinates?), a crucial step to enable future conversion to a common reference space. Finally, we curated a list of clinically relevant terms and constructs (a clinical ontology) that maps specific neuropsychological batteries and tasks to the cognitive term(s) they touch upon.
+\added{During the hackahton we worked on developing three key building blocks in small subteams. First, we prepared an input filtering pipeline to ensure that the downloaded papers are neuropsychological in nature (and thus offer causal symptom-structure mapping): we count the occurrences of clinically relevant terms, and papers are included only if they pass an arbitrary threshold. Second, we coded a script automatically returning for each paper information on the neural spaced used (e.g., which atlas? MNI coordinates?), a crucial step to enable future conversion to a common reference space. Finally, we curated a list of clinically relevant terms and constructs (a clinical ontology) that maps specific neuropsychological batteries and tasks to the cognitive term(s) they touch upon.}
 
 As we keep tackling our roadmap (Figure 1), we believe our efforts will help promote open science practices in clinical neuroscience to the benefit of both the neuroscientific and the clinical communities.