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22 changes: 22 additions & 0 deletions _projects/federated-learning.md
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---
title: Benchmarking federated learning approaches against siloed and mega-analysis regimes
# subtitle:
status: active
image: img/projects/fl_overview.png
layout: project
people:
- Michelle
- Nikhil
---

Although neuroimaging is seeing a growing number of datasets, the international adoption of strong data privacy frameworks ([Marelli & Testa, 2018](https://doi.org/10.1126/science.aar5419)) has led to many of these datasets remaining in so-called “silos”. When data cannot readily be shared, it becomes imperative to develop distributed data processing tools and federated analysis methods to enable large-scale multi-site studies.

In this project, we compare a simple federated analysis setup (i.e. sharing only fitted model parameters) with two traditional experimental setups:
- Siloed analysis (no sharing of data or model parameters)
- Mega-analysis (sharing data)

We evaluate the performance of machine learning (ML) models on several neuroimaging datasets of Parkinson’s (PD) and Alzheimer’s disease (AD) on two common prediction tasks in neurodegenerative diseases: 1) brain age and 2) cognitive decline. We hypothesize that model performance improves as we go from siloed to federated to mega-analysis setups.

<p align="center">
<img src="/img/projects/fl_setups.jpg" alt="Machine learning setups" width="400">
</p>
35 changes: 20 additions & 15 deletions _projects/nipoppy.md
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---
title: "Nipoppy: A framework for the reproducible organization and processing neuroimaging-clinical datasets"
subtitle: A Neurobagel complement project
image: img/other/nipoppy_overview.png
image: img/projects/nipoppy_protocol.png
status: active
layout: project
repository: https://github.com/neurodatascience/nipoppy/tree/main
documentation: https://www.neurobagel.org/documentation/Nipoppy/overview/
repository: https://github.com/nipoppy/nipoppy/tree/main
documentation: https://nipoppy.readthedocs.io/en/latest
people:
- Michelle
- Nikhil
- Mathieu
- Brent
- Mohammad
- Jacob
- Julia
- Remi
- Vincent
- Ines
Expand All @@ -21,24 +25,25 @@ Nipoppy is a lightweight framework for standardized organization and processing

The framework consists of the following:

1. A **data organization specification** for both imaging and non-imaging data

- Whenever possible, we follow the [Brain Imaging Data Structure (BIDS) standard](https://bids.neuroimaging.io/)
- We provide additional specifications for cases not (yet) covered by BIDS, such as phenotypic data and imaging derivatives

<p align="center">
<img src="/img/other/nipoppy_layout.jpg" alt="Nipoppy dataset layout" width="400">
</p>

2. A **standardized workflow process (i.e., protocol)** covering the following:
1. A **standardized workflow process (i.e., protocol)** covering the following:
- Curation and organization of MRI and tabular data
- E.g., conversion of DICOM data to BIDS
- Standardized processing of imaging data using existing or custom pipelines
- E.g., fMRIPrep, MRIQC
- E.g., [fMRIPrep](https://fmriprep.org/en/stable/), [MRIQC](https://mriqc.readthedocs.io/en/stable/)
- The [Boutiques framework](https://boutiques.github.io/) is used to flexibly execute and add new pipelines
- Tracking of availability status for raw and processed data
- We also develop an interactive [dashboard](https://digest.neurobagel.org/) for easy visualization of tracker results
- Extraction of imaging-derived phenotypes (IDPs) into files ready for downstream analysis
3. A [software package](https://github.com/neurodatascience/nipoppy/tree/main) with tools to help work within the framework

2. A **data organization specification** for both imaging and non-imaging data

- Whenever possible, we follow the [Brain Imaging Data Structure (BIDS) standard](https://bids.neuroimaging.io/)
- We provide additional specifications for cases not (yet) covered by BIDS, such as phenotypic data and imaging derivatives

<p align="center">
<img src="/img/projects/nipoppy_specification.jpg" alt="Nipoppy dataset layout" width="400">
</p>

3. A [software package](https://github.com/nipoppy/nipoppy/tree/main) with tools to help work within the framework

Nipoppy can also provide metadata to [Neurobagel](https://www.neurobagel.org/documentation/) tools to allow for data harmonization and federated search of participants across multiple studies.
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