upload a page for my project (#386)

* upload a page for my project

* Update index.md

* upload a page for my project

* Update content/en/project/CVAE_ADHD/index.md

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Co-authored-by: Lune Bellec <lune.bellec@umontreal.ca>

Author: christina20001206

content/en/project/CVAE_ADHD/cover.png

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+---
+type: "project" # DON'T TOUCH THIS ! :)
+date: "2025-06-13" # Date you first upload your project.
+title: "CVAE-based ADHD neuroimaging analysis"
+names: [Cian-Ya Lan, Jia-Ling Sun]
+github_repo: https://github.com/Cleo-Lan-school/BHS_2025-project
+website:
+tags: [adhd, mri, cvae, iq]
+summary: "This project applies a contrastive variational autoencoder (CVAE) to Burner-preprocessed MRI data from the ADHD-200 dataset to disentangle ADHD-specific brain features from shared anatomical variation. We explore latent representations using RSA and clustering to better understand neuroanatomical heterogeneity in ADHD."
+image: "cover.png"
+---
+
+## Project definition
+
+### Background
+
+- Reconstruct 3D brain MRIs using CVAEs.
+- Disentangle "salient" ADHD-related features from "background" features common to both ADHD and typically developing children.
+- Assess how well the learned latent spaces reflect behavioral and clinical variation using:
+  - **Silhouette Analysis**
+  - **Representational Similarity Analysis (RSA)**
+
+### Tools
+
+This project used:
+- Python (NumPy, Pandas, SciPy, Scikit-learn, Matplotlib, Seaborn)
+- Keras (TensorFlow backend) for deep learning
+- UMAP for latent space visualization
+- Representational Similarity Analysis (RSA) using Kendall’s tau
+- Silhouette analysis for latent space separability
+- GitHub for version control
+
+### Data
+
+This project used the publicly available [ADHD-200 dataset](http://fcon_1000.projects.nitrc.org/indi/adhd200/), specifically the **Burner-preprocessed version**:
+- Structural MRI data processed via voxel-based morphometry (VBM)
+- Normalized 3D gray matter volumes (64×64×64)
+- Accompanied by phenotypic variables: age, sex, diagnosis, subtype, medication, IQ, etc.
+
+### Deliverables
+
+At the end of the project, we produced:
+- A working CVAE framework for modeling neuroanatomical variation
+- Visualizations of latent features and synthetic brain reconstructions
+- RSA and clustering results relating brain features to clinical data
+- Well-documented code and reproducible analysis notebooks
+
+## Results
+
+### Progress overview
+
+We trained a CVAE model with two latent components:
+- `s` (salient features): ADHD-specific anatomical variation
+- `z` (background features): common/shared variation
+
+We evaluated the model using:
+- **Silhouette scores** for latent clustering
+- **RSA** to correlate latent dimensions with phenotypic variables
+- **GMM clustering and BIC** to test for discrete vs. continuous subtype structure
+
+### Tools I learned during this project
+
+- Contrastive representation learning in generative models
+- Implementation of CVAEs in Keras
+- Preprocessing and working with VBM MRI data
+- Representational Similarity Analysis
+- Model interpretability techniques for brain data
+
+### Results
+
+#### Deliverable 1: CVAE analysis
+
+- Salient features (`s`) were significantly correlated with:
+  - **ADHD Index**
+  - **Inattentive and Hyperactive/Impulsive scores**
+  - **Medication status** and **age**
+
+- Shared features (`z`) were more related to:
+  - **IQ**, **gender**, and **scan site**
+
+#### Deliverable 2: Clustering analysis
+
+- Gaussian Mixture Models (GMM) with Bayesian Information Criterion (BIC) showed:
+  - **Lowest BIC at 1 cluster**, suggesting **continuous heterogeneity**
+  - Results align with dimensional models of psychiatric disorders
+
+#### Deliverable 3: Code and notebook
+
+- Full training pipeline in `Train-CVAE-ADHD200.ipynb`
+- Visualization and RSA in helper scripts
+- Readme documentation and reproducibility checklist included
+
+## Conclusion and acknowledgement
+
+This project demonstrates the potential of contrastive deep generative models like CVAEs to disentangle disorder-specific neuroanatomical features from shared variation. Our findings suggest ADHD may be better described along a spectrum rather than discrete subtypes. We thank the Brainhack School instructors and the open neuroimaging community for providing tools and data that made this project possible.