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Convert issues to project pages, yay! (#90)
Co-authored-by: Remi-Gau <[email protected]>
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| { | ||
| "Title": "PhysioQA", | ||
| "link_to_issue": "https://github.com/brainhackorg/global2023/issues/87", | ||
| "issue_number": 87, | ||
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| "content": "### Title\n\nPhysioQA\n\n### Leaders\n\n@RickReddy - Rithwik Guntaka\n\n### Collaborators\n\n@rgbayrak - Roza Bayrak\n\n### Brainhack Global 2023 Event\n\nBrainHack Vanderbilt\n\n### Project Description\n\nWe are working on creating a model that can classify physiological data (respiratory + cardiac) that is associated with fMRI data, so that the end user can determine whether the data is usable, if it needs to be modified to be usable, or if it is simply not usable.\r\n\r\nWhen it comes to using peripheral physiological data in your fMRI data analysis, the quality of the recordings is super important, but let's face it, checking the quality of this data can be a real headache. It usually involves a lot of manual work and you need to know what is real data, what is an artifact. That's why we want to create a nifty deep-learning tool to automate quality assessment! This tool doesn't just check the quality of your data; it also points out any issues and gives you tips on how to fix them. It's like having a friendly expert on your team, making sure your research data is as good as it can be!\n\n### Link to project repository/sources\n\nhttps://github.com/brainhack-vandy/projects/physioQA\n\n### Goals for Brainhack Global\n\n- Brainstorm different techniques to increase the classification accuracy of the model\r\n- Modify the MATLAB GUI to be more usable for classifying data\r\n- Potentially, getting data labeled by an expert who is familiar with physiological data\n\n### Good first issues\n\nClassification tool (beginner machine learning friendly)\r\n- experimenting with different neural network architectures using keras\r\n- experimenting with feature engineering\r\n- experiment with different hyperparameters for the model\r\nManual annotation tool\r\n- adding more button functionality to the GUI tool, to allow for more detailed labeling of data\r\n- modify the GUI to select and label certain sections of data\n\n### Communication channels\n\n#physioqa channel on https://discord.gg/GyeeVbYC\n\n### Skills\n\nHaving any one of these skills would enable an individual to contribute. However, if they have none of these there are onboarding documents that would help them experiment, learn, and contribute regardless.\r\n- Familiarity with Python and Jupyter notebooks\r\n- Familiarity with MATLAB\r\n- Familiarity with physiological data in order to asses its quality\n\n### Onboarding documentation\n\n_No response_\n\n### What will participants learn?\n\nParticipants will:\r\n- Learn the significance and influence of physiological data in fMRI analysis\r\n- Become familiar with and explore different aspects of ML, and how it can be used for timeseries data\r\n- Learning how to create/modify a GUI to analyze timeseries data using MATLAB toolbox\n\n### Data to use\n\nPublic HCP dataset that has physiological data paired with fMRI data.\r\n\r\nhttps://www.humanconnectome.org/study/hcp-young-adult\n\n### Number of collaborators\n\n3\n\n### Credit to collaborators\n\nCollaborators will be credited on the GitHub site and credited in any paper that results from this project\n\n### Image\n\n<img width=\"842\" alt=\"MicrosoftTeams-image (1)\" src=\"https://github.com/brainhackorg/global2023/assets/40832092/29c99dbf-8d4c-4515-bed4-a4cf4b6c5d72\">\r\n\n\n### Type\n\nmethod_development, pipeline_development, visualization\n\n### Development status\n\n1_basic structure\n\n### Topic\n\ndata_visualisation, deep_learning, machine_learning, physiology\n\n### Tools\n\nJupyter\n\n### Programming language\n\nMatlab, Python\n\n### Modalities\n\nfMRI, other\n\n### Git skills\n\n0_no_git_skills, 1_commit_push, 2_branches_PRs\n\n### Anything else?\n\nother under modalities: physiological data (cardiac + respiration)\n\n### Things to do after the project is submitted and ready to review.\n\n- [ ] Add a comment below the main post of your issue saying: `Hi @brainhackorg/project-monitors my project is ready!`\n- [ ] Twitter-sized summary of your project pitch.", | ||
| "project_url": "https://github.com/brainhack-vandy/projects/physioQA", | ||
| "project_description": "\n\nWe are working on creating a model that can classify physiological data (respiratory + cardiac) that is associated with fMRI data, so that the end user can determine whether the data is usable, if it needs to be modified to be usable, or if it is simply not usable.\r\n\r\nWhen it comes to using peripheral physiological data in your fMRI data analysis, the quality of the recordings is super important, but let's face it, checking the quality of this data can be a real headache. It usually involves a lot of manual work and you need to know what is real data, what is an artifact. That's why we want to create a nifty deep-learning tool to automate quality assessment! This tool doesn't just check the quality of your data; it also points out any issues and gives you tips on how to fix them. It's like having a friendly expert on your team, making sure your research data is as good as it can be!\n\n" | ||
| } |
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| { | ||
| "Title": "Deriving Resting State Networks and Observing their Behavior Across Age", | ||
| "link_to_issue": "https://github.com/brainhackorg/global2023/issues/88", | ||
| "issue_number": 88, | ||
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| "content": "### Title\r\n\r\nDeriving Resting State Networks and Observing their Behavior Across Age\r\n\r\n### Leaders\r\n\r\nKimberly Rogge-Obando\r\n\r\n### Collaborators\r\n\r\nTerra Lee\r\n\r\n### Brainhack Global 2023 Event\r\n\r\nBrainHack Vanderbilt\r\n\r\n### Project Description\r\n\r\nSince their discovery, resting state networks have elucidated our understanding of cognitive function such as emotion processing, working memory, and daydreaming. Additionally, a collective of scientists believe resting state networks may be a possible biomarker of mental disorders. However, before we can confirm resting state networks point to a characteristic of mental disorders it is important to model how they change across age. Many studies have identified that age does influence the connectivity of resting state networks however which brain regions within resting state networks change specifically needs to be further understood. The goal of this project is to compare methods of how resting state network information are retrieved and potentially model how they change across age. Anyone is welcome to join and will have the opportunity to learn common practices to derive resting state networks. Individuals are asked to have FSL and Matlab on their computers, but this is not a requirement to join however it may limit their contribution. \r\n\r\n### Link to project repository/sources\r\n\r\nhttps://drive.google.com/drive/folders/1Gd0Ra4BYukWS39978vVewpwzNDBOE7km?usp=sharing\r\n\r\n### Goals for Brainhack Global\r\n\r\nGoal 1 : Determine if dual regression on matlab gives similar results of FSL dual regression. Level of difficulty 1-2\r\n\r\nTasks to complete goal 1\r\n- [ ] Run ICA Melodic on 1 subjects with 10 components and label the networks , may test component numbers\r\n- [ ] Use current ICA Melodic values and run dual regression on FSL, with one subject\r\n- [ ] Use current ICA Melodic output to run dual regression on MATLAB, save time series and spatial maps with one subject\r\n- [ ] Compare Dual Regression timeseries in FSL and MATLAB, generate figures of comparison and correlation\r\n\r\nGoal 2: Determine how resting state networks change across age. Level of difficulty 3\r\n\r\nTasks to complete goal 2\r\n- [ ] Run dual regression with fslrandomise option and fslrandomise separately with age and gender as a covariate and see how these options replicate. Write up the steps for this project.\r\n\r\n\r\n### Good first issues\r\n\r\n1. Download FSL onto your computer and look into FSL melodic and dual_regression\r\nhttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation\r\n\r\n2. Download Afni on your computer\r\nhttps://afni.nimh.nih.gov/pub/dist/doc/htmldoc/background_install/install_instructs/index.html\r\n\r\n\r\n\r\n### Communication channels\r\n\r\n#rage channel on https://discord.gg/5vy8fTWQ\r\n\r\n### Skills\r\n\r\nNeuroimaging-Beginner\r\nMATLAB-Begninner\r\nFSL-Begninner- Intermediate\r\n\r\nWillingness to contribute to science communication part of the project, creating power point slides etc.\r\n\r\n### Onboarding documentation\r\n\r\n(https://drive.google.com/drive/folders/1Gd0Ra4BYukWS39978vVewpwzNDBOE7km?usp=sharing)\r\n\r\n\r\n### What will participants learn?\r\n\r\nThis project is perfect for beginners in fMRI resting state network analysis. \r\n\r\nThings participants will learn.\r\n\r\n-How to conduct Melodic ICA and eyeball resting state networks\r\n-Derive subject specific resting state network spatial maps and time series\r\n-Use FSL randomise and dual_regression code \r\n-Will learn how to use fsl randomise with co-variates that may transfer over to them investigating resting state networks across age or other variables of interest\r\n-Gain critical skills in team collaboration\r\n\r\n\r\n### Data to use\r\n\r\nWe will use a subset of the NKI -Rockland Sample dataset .\r\n\r\nhttps://fcon_1000.projects.nitrc.org/indi/enhanced/\r\n\r\nNooner et al, (2012). [The NKI-Rockland Sample: A model for accelerating the pace of discovery science in psychiatry.](http://www.ncbi.nlm.nih.gov/pubmed/23087608) Frontiers in neuroscience 6, 152.\r\n\r\n\r\n### Number of collaborators\r\n\r\n4\r\n\r\n### Credit to collaborators\r\n\r\nMembers of this team names will be listed on the code we upload to GitHub. They will also have the opportunity to join our formal NKI-rockland team as were in the process of finishing up a side project that is partially relevant to this project. \r\n\r\n### Image\r\n\r\nLeave this text if you don't have an image yet.\r\n\r\n\r\n\r\n### Type\r\n\r\ncoding_methods\r\n\r\n### Development status\r\n\r\n0_concept_no_content\r\n\r\n### Topic\r\n\r\nMR_methodologies\r\n\r\n### Tools\r\n\r\nAFNI, FSL\r\n\r\n### Programming language\r\n\r\nMatlab, shell_scripting\r\n\r\n### Modalities\r\n\r\nfMRI\r\n\r\n### Git skills\r\n\r\n0_no_git_skills\r\n\r\n### Anything else?\r\n\r\nThis will be a perfect opportunity for beginners using fMRI data! Look forward to meeting you!\r\n\r\n### Things to do after the project is submitted and ready to review.\r\n\r\n- [ ] Add a comment below the main post of your issue saying: `Hi @brainhackorg/project-monitors my project is ready!`\r\n- [ ] Twitter-sized summary of your project pitch.", | ||
| "project_url": "https://drive.google.com/drive/folders/1Gd0Ra4BYukWS39978vVewpwzNDBOE7km?usp=sharing", | ||
| "project_description": "\r\n\r\nSince their discovery, resting state networks have elucidated our understanding of cognitive function such as emotion processing, working memory, and daydreaming. Additionally, a collective of scientists believe resting state networks may be a possible biomarker of mental disorders. However, before we can confirm resting state networks point to a characteristic of mental disorders it is important to model how they change across age. Many studies have identified that age does influence the connectivity of resting state networks however which brain regions within resting state networks change specifically needs to be further understood. The goal of this project is to compare methods of how resting state network information are retrieved and potentially model how they change across age. Anyone is welcome to join and will have the opportunity to learn common practices to derive resting state networks. Individuals are asked to have FSL and Matlab on their computers, but this is not a requirement to join however it may limit their contribution. \r\n\r\n" | ||
| } |