Remi-Gau
commented
3 years ago hey @WeiShaoD
couple of suggestions: add the good first issues in your repo (https://github.com/WeiShaoD/MCI-Classification) and expand a bit on there on how some completely new to your project should proceed (where to get the data, what packages to install...).
On an unrelated note: your book (https://neuroimage-book02.readthedocs.io/en/latest/) looks like a super useful resource in general !!!
pinging @likeajumprope for a cross check on this project review
WeiShaoD
likeajumprope
Title
Classification of Mild Cognitive Impairment(MCI) with machine learning models
Leaders
Wei Shao
Collaborators
To be determined
Brainhack Global 2021 Event
BrainHack Toronto
Project Description
- What are you doing, for whom, and why?
Recent research has discovered that the subregions of hippocampus and medial temporal lobe (MTL) are related to Montreal Cognitive Assessment (MoCA) performance under a manual segmentation protocol. A significant volume reduction of anterolateral entorhinal cortex (alERC) has been found in the At-Risk group. This study also observed a positive linear relationship between MTL volumes and MoCA scores. Therefore, the aim of the project is to use machine learning models to analyze the structural data of MRIs.
Starting from the regions of the hippocampus and medial temporal lobe, we will use automatic segmentation tools like FreeSurfer or ASHS to get the volume, thickness or curve of different brain regions from ADNI dataset as the input for different machine learning models to evaluate the model performance.
- What makes your project special and exciting?
According to previous studies, It is not easy to classify mild cognitive impairment from healthy people, given the fact that the change of brain structure is not very clear. The recent advance of statistical models, especially machine learning models, might provide an alternative solution for these issues.
- How to get started?
I have done some initial works. We can start from the introduction of data structure, models and the most interesting part, python!!!
- introduction with the data
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) unites researchers with study data as they work to define the progression of Alzheimer’s disease (AD). ADNI researchers collect, validate and utilize data, including MRI and PET images, genetics, cognitive tests, CSF and blood biomarkers as predictors of the disease. Study resources and data from the North American ADNI study are available through this website, including Alzheimer’s disease patients, mild cognitive impairment subjects, and elderly controls.
- Where to find key resources?
https://neuroimage-book02.readthedocs.io/en/latest/
Link to project repository/sources
https://github.com/WeiShaoD/MCI-Classification
Goals for Brainhack Global
Good first issues
Communication channels
https://join.slack.com/t/mciclassifica-agm9145/shared_invite/zt-zbufngkk-F_XklCCFXTSi4vVIzy4CjQ
Skills
Onboarding documentation
No response
What will participants learn?
Data to use
- introduction with the data
The Alzheimer’s Disease Neuroimaging Initiative (ADNI) unites researchers with study data as they work to define the progression of Alzheimer’s disease (AD). ADNI researchers collect, validate and utilize data, including MRI and PET images, genetics, cognitive tests, CSF and blood biomarkers as predictors of the disease. Study resources and data from the North American ADNI study are available through this website, including Alzheimer’s disease patients, mild cognitive impairment subjects, and elderly controls.
Number of collaborators
1
Credit to collaborators
segmentation quality, psychological assessment, interpretation of results, preprocessing of data.
Image
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Type
coding_methods, method_development, visualization
Development status
2_releases_existing
Topic
bayesian_approaches, data_visualisation, deep_learning, machine_learning, MR_methodologies, statistical_modelling
Tools
Freesurfer, Jupyter
Programming language
Python, R
Modalities
MRI
Git skills
2_branches_PRs
Anything else?
No response
Things to do after the project is submitted.
Hi @Brainhack-Global/project-monitors: my project is ready!