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Author: ihintza

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                     <h1 class="section-heading">BHD 2024 | Projects</h1>
-			<p class="text-muted"> Take a look at last year's projects before you propose your own at BHD2024 by clicking the button below! </p>
+			<p class="text-muted"> Take a look at BHD2024's projects and propose your own by clicking the button below! </p>
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-		    <h3 class="section-heading">Clustintime: a toolbox for spatio-temporal clustering of fMRI data <br/> <a href="https://github.com/Cristina-Tobias"> (@ctobias)</a> </h3>
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-		    <p class="text-muted"> This project is a toolbox that allows researchers in neuroimage to apply clustering methods to fMRI data on the spatiotemporal domain. Conventional methods of clustering in fMRI allow to see spatial patterns but cannot describe the temporal dynamics of functional activity.<br /> 
-		    Providing a tool for researchers interested in analyzing brain patterns in uncontrolled fmri experiments or clinical settings is a necessity that has not been covered yet and that could be done through clustintime. <br />
-		    The project already has its repository on GitHub (<a href="https://github.com/Cristina-Tobias/clustintime">see repo</a>) and a public fMRI data will be used for testing. </p>
-		    <h3 class="section-subsubheading text-muted">
-			    Goals</h3>
-		    <p class="text-muted"> Create integration testing suite/framework<br />
-Create CI/CD pipeline<br />
-Populate CI/CD pipeline with checks<br /> </p>
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-		    <h3 class="section-heading">Tedana: TE-dependent analysis<br/> <a href="https://github.com/eurunuela"> (@eurunuela)</a> </h3>
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-		    <p class="text-muted"> 
-		    TE-dependent analysis (tedana) is a Python library for denoising multi-echo functional magnetic resonance imaging (fMRI) data. tedana originally came about as a part of the ME-ICA pipeline, although it has since diverged. An important distinction is that while the ME-ICA pipeline originally performed both pre-processing and TE-dependent analysis of multi-echo fMRI data <br />
-		    Check the project release page  at  <a href="https://tedana.readthedocs.io/en/stable/index.html">see documentation page</a>  </p>
-		    <h3 class="section-subsubheading text-muted">
-			    Goals</h3>
-		    <p class="text-muted"> Follow the BHD2023 goals discussion at: <a href="https://github.com/ME-ICA/tedana/discussions/987">github discussion page</a><br /> </p>
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 		    <h3 class="section-heading">Spyking AI: building a cortical inspired network <br/> <a href="https://github.com/marco7877/"> (@marco7877)</a> </h3>
 
 		    <p class="text-muted"> This project aims to build a Tensorflow wrapper to build and train spiking neuron arrays connected though hebbian conections. This tool is proposed to study neural mechanisms during cognitive events, i.e., language production/comprehension </p>
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 Build a hebbian learning python class<br />
 Test module with one hot vectors labels<br /> </p>
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-		    <h3 class="section-heading">phys2CVR: a BIDS-compliant python toolbox to compute cerebrovascular reactivity mapping <br/> <a href="https://github.com/smoia"> (@smoia)</a> </h3>
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-		    <p class="text-muted"> phys2cvr is a python toolbox that aims at offering various approaches to compute cerebrovascular mapping, starting from at least a functional MRI hypervolume.
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-		    While not the first toolbox to compute CVR out there, I'm aiming at making something easy to adopt, a swiss knife to compute all sorts of maps to image cerebral physiology (not to denoise fMRI timeseries, for that you can check <a href= "https://github.com/physiopy/phys2denoise">phys2denoise </a>). phys2cvr should become one of the most complete tools for CVR mapping available, easy to adopt through a CLI (and, if possible, a GUI), with nice reports and plots, and allowing the highest automation through BIDS compliance. No repetition allowed thoough: for all python-based approaches out there, phys2cvr should only act as wrap around.
-All contributions are welcome - and all contributions are recognised via all-contributors guidelines (and authorship on publications). </p>
-		    <h3 class="section-subsubheading text-muted">
-			    Goals</h3>
-		    <p class="text-muted"> Go through this<a href="https://docs.google.com/document/d/1MFgwIjM5IaT7RlHtc5vzplPXZi8xaW76Fa-dLzknckA/edit?usp=sharing"> list</a> as much as possible: </p>
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-		    <h3 class="section-heading">Launchcontainers: A Python tool for launching containerized analysis on HPC<br/> <a href="https://github.com/yongninglei"> (@yongninglei)</a> </h3>
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-		    <p class="text-muted"> Launchcontainers is a Python-based program built to automatically launch containerlized MRI processing pipelines. This program takes one config.ymal file, one container.json file, and one subject-session-list.txt file as inputs. Using 1 line of bash command, it will automatically send jobs to HPC clusters regarding your computing demands.<br />
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-		    This program is well-suited for multi-subject, multi-scan datasets. And it will save a lot of time if you need analysis your entire dataset with different parameters multiple times.<br />
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-		    In future versions, we are trying to add more functionality to this repository so that you can launch all the MRI data analysis pipelines such as Heudiconv, fMRIprep, pRF pipelines and etc. Please keep track of this repo and if you have any questions or suggestions, don't hesitate to contact Gari: <a href="[email protected]">[email protected]</a> and Tiger: <a href="[email protected]">[email protected]</a> </p>
-		    <h3 class="section-subsubheading text-muted">
-			    Goals</h3>
-		    <p class="text-muted"> Let more people get to know the advantages of using containers to process MRI data to improve the data availability and reproducibility<br />
-		Let beginner level users get familiar with the UNIX operation and git operation<br />
-		Add new features to launchcontainer so that it supports more MRI processing pipeline (ie. HeuDiconv, fMRIprep, PRF-analyze)<br /> </p>
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