From 2301808205e9335744c42f2bbcbef6cbf72cf470 Mon Sep 17 00:00:00 2001 From: crnolan Date: Tue, 29 Oct 2024 03:16:42 +0000 Subject: [PATCH] deploy: 8ad2f5fe7af58e4291e7dd4fe9bcbdbe5ade6686 --- index.html | 2 +- index.json | 2 +- project/project_1/index.html | 2 +- project/project_2/index.html | 2 +- project/project_5/index.html | 2 +- projects/index.html | 14 ++++++++------ sitemap.xml | 2 +- 7 files changed, 14 insertions(+), 12 deletions(-) diff --git a/index.html b/index.html index 5f2f5f8..bc15354 100644 --- a/index.html +++ b/index.html @@ -46,7 +46,7 @@ Brainhack Global 2024!

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The Brainhack Global team built up a set of resources for you to help you organize your local Brainhack including guidelines, checklists, template forms, press release materials and more.

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Kelly Garner

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a/project/project_1/index.html b/project/project_1/index.html index f675ee0..d1f3cdc 100644 --- a/project/project_1/index.html +++ b/project/project_1/index.html @@ -1,5 +1,5 @@ -BIDS: IP-Freely | Brainhack Global 2024

BIDS: IP-Freely

Last updated on Oct 29, 2024

Title

Free manipulation of BIDS datasets according to the Inheritance Principle

Leaders

Robert E. Smith; https://x.com/Lestropie

Collaborators

No response

Brainhack Global 2024 Event

Brainhack Aus

Project Description

The Inheritance Principle (“IP”) has been a feature of the Brain Imaging Data Structure (BIDS) since its inception. It describes how the metadata relevant to one specific data file may in fact originate from multiple metadata files. Encoding some common metadata field just once, and having it be deemed applicable to multiple metadata files, may both reduce redundancy and communicate the intrinsic hierarchical structure of the dataset. This however comes at considerable complexity to the specification itself and the software responsible for interfacing with such data. Indeed nearly a decade after its creation, many software APIs for parsing BIDS data still do not support the Inheritance Principle.

The future of the IP is at a crossroads, particularly as work progresses toward BIDS 2.0:

  • On one hand, some would like for the IP to disappear entirely: all metadata relevant to any given data file would need to be stored in a sidecar metadata file alongside that data file, regardless of duplication.
  • On the other, not just preserving the IP but augmenting it could better communicate the complex hierarchical dependencies of complex datasets, particularly derivative data.

The goals of this project are twofold.

  1. There is a dearth of existing software tools for managing the IP. If a dataset that exploits the IP were provided as input to a BIDS App that is naive to the IP, that App could perform improper association of metadata. Existing datasets may contain substantial metadata redundancy, due to not currently having an easy way through which to exploit the IP. A tool to automate such data manipulations could therefore be useful in the BIDS ecosystem.

  2. Consequential decisions regarding the IP and related issues in BIDS 2.0 would be better informed if stakeholders could see the effects that proposed specifications would have on how compliant datasets appear.

https://github.com/Lestropie/IP-freely

Goals for Brainhack Global

Good first issues

For anyone not accustomed to open source projects, perhaps the most accessible entrypoint will be the construction of one or more exemplar datasets: https://github.com/Lestropie/IP-freely/issues/5

Communication channels

TBA

Skills

  • Python: Moderate

    The code logic for manipulating metadata will necessitate some competence with Python dictionaries; nothing super complex, but maybe not accessible for someone who has never touched them. diff --git a/project/project_2/index.html b/project/project_2/index.html index f8cf3c4..5d18ede 100644 --- a/project/project_2/index.html +++ b/project/project_2/index.html @@ -1,5 +1,5 @@ -MRI denoising using MP-PCA | Brainhack Global 2024

    MRI denoising using MP-PCA

    Last updated on Oct 29, 2024

    Title

    Improving MP-PCA denoising of diffusion and functional MRI data using MP-PCA

    Leaders

    Robert E. Smith; http://x.com/Lestropie

    Collaborators

    No response

    Brainhack Global 2024 Event

    Brainhack Aus

    Project Description

    Denoising of EPI MRI data has become a common component of MRI pre-processing pipelines. It is most prevalent in the pre-processing of diffusion MRI data given its low signal magnitude, but it is also becoming increasingly prevalent for functional MRI pre-processing.

    The MRtrix3 software provides a command dwidenoise for this purpose. This is a sliding-window approach, where processing for each voxel is performed based on data in a local spatial neighbourhood, exploiting the density of EPI data across multiple volumes. After performing a Principal Component Analysis (PCA) decomposition for the data across DWI volumes within that window, it determines the appropriate cutoff for which components to retain vs. remove based on the Marchenko-Pastur distribution, which predicts the distribution of eigenvalues for random matrices. By doing so it intends to retain the useful signal present in that location, but remove those components that manifest from the thermal noise of the imaging process.

    While currently in wide use, there are many prospective enhancements that could be made to this command to improve the quality of denoising. The goal of this project is to address features with strong prospects for improving performance that are achievable within the short time frame of a Hackathon.

    dwidenoise enhancements meta-issue: https://github.com/MRtrix3/mrtrix3/issues/3023

    Goals for Brainhack Global

    • Primary goal: Spherical kernel

      Give the command the ability to use a spherically-shaped sliding window kernel as an alternative to the current cubic kernel.

    • Primary goal: Noise level as input/output

      Currently, the command receives as input a DWI series, produces as output a denoised DWI series, and may optionally export the estimated noise level. Would like to change this in two ways. Firstly, it should be possible to just export the estimated noise level, withholding the output DWI series. Secondly, it should be possible to take as input an estimated noise level, and denoise the input DWI series based on that pre-estimated noise level. Doing this would allow for the filtering of that noise level estimate, potentially addressing errors in the otherwise independent-per-voxel noise level estimation.

    • Secondary goals: Any other items in https://github.com/MRtrix3/mrtrix3/issues/3023 that may be of interest to a participant.

    Good first issues

    This Project is only intended for potential contributors who already possess strong software engineering skills.

    Communication channels

    TBA

    Skills

    • C++: requisite diff --git a/project/project_5/index.html b/project/project_5/index.html index b775188..4e6a6d7 100644 --- a/project/project_5/index.html +++ b/project/project_5/index.html @@ -1,5 +1,5 @@ -behapy: A behavioural neuroscience analysis package for Python | Brainhack Global 2024

      behapy: A behavioural neuroscience analysis package for Python

      Last updated on Oct 29, 2024

      Title

      behapy: A behavioural neuroscience analysis package for Python

      Leaders

      Chris Nolan

      Collaborators

      No response

      Brainhack Global 2024 Event

      Brainhack Aus

      Project Description

      Studies using optic fibres to record real-time fluorescent biosensors in-vivo are now commonplace, yet despite an increasing literature on best practices for analysing such data, there is a surprising lack of fit-for-purpose API-level tooling. This project is a continuing effort to fill this gap by providing flexible Python-based implementations of common normalisation and artefact correction procedures for fluorescent biosensors, along with useful event-based analyses.

      The goals of this project will extend beyond Brainhack Global 2024, but all are in an effort to create an open-source API and workbench for analysing fibre photometry data in a behavioural neuroscience context. Since Brainhack Global 2022, we have created a basic artefact-rejection workbench, a preprocessing stage and implemented simple linear regression for event-level analysis. This year the goal is to create a method to benchmark normalisation methods by creating data simulation functionality under different assumptions about the sources of recording noise. We are also aiming to generalise second-level linear mixed model testing for events of interest.

      https://github.com/crnolan/behapy

      Goals for Brainhack Global

      • Create a utility for simulating raw fibre photometry data under a variety of assumptions (for testing the toolkit).
      • Implement second-level linear mixed model tests for events of interest.
      • Develop both API-level and tutorial documentation. Docstrings are currently sparse and light on detail, and walkthroughs are virtually non-existent.
      • Add import scripts for MedPC datasets, and put together a skeleton framework for event summary and plotting functions.

      Good first issues

      1. Check and update installation instructions for behapy package.
      2. Document end-user experience for running existing fibre preprocessing workbench on sample data.
      3. Establish a configuration format / tool for importing structured MedPC files.
      4. Add the capability in the pre-processing interface to interactively compare / select different but reasonable normalisation methods.

      Communication channels

      https://mattermost.brainhack.org/brainhack/channels/behapy

      Skills

      Primarily, some knowledge of fluorescent biosensor normalisation and analysis procedures will be useful. We’ll be predominantly working in Python, but there will be tasks for all levels of Python competency.

      Bonus useful skills:

      • Signal processing (we’ll be filtering and fitting timeseries data)
      • Python interactive visualisation (bokeh / holoviews / panel / seaborn / matplotlib)
      • Working knowledge of linear regression and mixed effects models
      • BIDS experience - while we won’t be attempting to add an official BIDS extension for fibre photometry in this project, we are trying to stay approximately in line with BIDS format

      Onboarding documentation

      No response

      What will participants learn?

      • Data manipulation in Python (numpy / pandas)
      • Signal filtering in Python
      • GitHub collaboration techniques

      Data to use

      BYO fibre & behavioural data - we’ll create a repository of useful examples.

      Number of collaborators

      3

      Credit to collaborators

      Project contributors will be listed on the project README (hosted on github).

      Image

      Leave this text if you don’t have an image yet.

      Type

      data_management, pipeline_development, visualization

      Development status

      0_concept_no_content

      Topic

      data_visualisation, systems_neuroscience

      Tools

      BIDS, other

      Programming language

      Python

      Modalities

      behavioral, other

      Git skills

      1_commit_push

      Anything else?

      No response

      Things to do after the project is submitted and ready to review.

      • Add a comment below the main post of your issue saying: Hi @brainhackorg/project-monitors my project is ready!
      • Twitter-sized summary of your project pitch.

      Date
      Jan 1, 0001 12:00 AM
      diff --git a/projects/index.html b/projects/index.html index 71830a5..0f10c34 100644 --- a/projects/index.html +++ b/projects/index.html @@ -5,9 +5,9 @@ Git level: 1 commit push Git level: 2 branches PRs Git level: 3 continuous integration -Hub australasia aus Hub donostia esp Hub marseille fra +Hub melbourne aus Hub montreal can Hub toronto can Hub vanderbilt usa @@ -76,11 +76,11 @@ single neuron models statistical modelling systems neuroscience -tractography

behapy: A behavioural neuroscience analysis package for Python

Studies using optic fibres to record real-time fluorescent biosensors in-vivo are now commonplace, yet despite an increasing …

 Project URL +tractography

behapy: A behavioural neuroscience analysis package for Python

Studies using optic fibres to record real-time fluorescent biosensors in-vivo are now commonplace, yet despite an increasing …

 Project URL Git level: 1 commit push -Hub australasia aus +Hub melbourne aus behavioral @@ -100,9 +100,11 @@ data visualisation -systems neuroscience

BIDS: IP-Freely

The Inheritance Principle (“IP”) has been a feature of the Brain Imaging Data Structure (BIDS) since its inception. It …

 Project URL +systems neuroscience

BIDS: IP-Freely

The Inheritance Principle (“IP”) has been a feature of the Brain Imaging Data Structure (BIDS) since its inception. It …

 Project URL Git level: 0 none + +Hub melbourne aus behavioral @@ -146,11 +148,11 @@ TDCS -TMS

MRI denoising using MP-PCA

Denoising of EPI MRI data has become a common component of MRI pre-processing pipelines. It is most prevalent in the …

 Project URL +TMS

MRI denoising using MP-PCA

Denoising of EPI MRI data has become a common component of MRI pre-processing pipelines. It is most prevalent in the …

 Project URL Git level: 1 commit push -Hub australasia aus +Hub melbourne aus DWI diff --git a/sitemap.xml b/sitemap.xml index f692ba4..7e07652 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -1 +1 @@ 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