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DS Analysis Pipeline

This repository contains notebooks and scripts for analyzing brain imaging data through a multi-stage pipeline using self-organizing maps (SOM) and heatmap generation.

Pipeline Overview

Input Images
     │
     ▼
Preprocess + Register
     │
     ▼
[Neural Network]
     │
     ├─────⮞ score-norms (B, 20) ────⮞ SOM Analysis ────⮞ Prototype Identification 
     │                                                              │
     │                                                              ▼
     │                                                      Gather Behavior Scores  ────⮞ Correlate
     │                                                                                       ▲
     └─────⮞ score-images (B,20,H,W,D) ────⮞ Likelihood Model ────⮞ Heatmaps (1,H,W,D) ─────┘

Pipeline Components

1. Image Processing and Feature Extraction

  • Input: Raw brain imaging data
  • Neural network processes images to generate:
    • Score norms: Batch x 20 dimensional feature vectors
    • Score images: Batch x 20 x Height x Width x Depth tensors

2. SOM Analysis Branch

  • Takes score-norms as input
  • Trains Self-Organizing Map for pattern discovery
  • Identifies clusters (prototypes)
  • Each sample will be matched to a protoype
  • Enables visualization of data distribution

3. Heatmap Generation Branch

  • Takes score-images as input
  • Feeds data through likelihood estimation model
  • Generates 3D heatmaps (H x W x D) highlighting relevant regions
  • Provides visualization of model attention/focus areas

4. Correlation Analysis

  • Combines outputs from both branches
  • Correlates behavioral scores with heatmap patterns
  • Enables statistical analysis of relationships between:
    • Brain structural patterns
    • Behavioral metrics
    • Prototype membership

Usage

Requirements

  • braintypicality-scripts repository
    • Contains all necessary code for preparing and processing the data
  • sade package available at https://github.com/ahsanMah/sade
    • Please install and run the docker container
  • simpsom package avilable at https://github.com/ahsanMah/simpsom 
    cd /codespace/ && git clone https://github.com/ahsanMah/simpsom
cd /codespace/simpsom && python setup.py install --user
  • Additionally for plotting, holoviews and hvplot is used
  • Other requirements (seaborn, pandas, etc.) should be covered by the packages above

Running the Pipeline

Data Preparation

We need to prepare data to be ingested by sade. Namely, the images should be two-channels (i.e. 4D images) with T1 and T2 concatenated. Please refer to the sade documentation for more details. You may use the run_preprocessing.py script to process the data.

To run inference sade will need a file e.g. ibis-inlier.txt with filenames of the images, and a directory where this file is present as specified by --config.data.splits_dir.

Score-Norm Extraction and Heatmap Generation

The inference sript of sade can extract score-norms and create heatmaps. The outputs will be saved as numpy files: <workdir>/experiments/<experiment.id>/<subject-id>.npz. An example config is given below.

python main.py --mode inference \
  --config configs/flows/gmm_flow_config.py \
  --workdir remote_workdir/cuda_opt/learnable/ \
  --config.data.splits_dir /ASD/ahsan_projects/Developer/braintypicality-scripts/split-keys \
  --config.eval.checkpoint_num=150 \
  --config.eval.experiment.flow_checkpoint_path=remote_workdir/cuda_opt/learnable/flow/psz3-globalpsz17-nb20-lr0.0003-bs32-np1024-kimg300_smin1e-2_smax0.8 \
  --config.eval.experiment.train=abcd-train  \
  --config.eval.experiment.inlier=abcd-val \
  --config.eval.experiment.id=default-ckpt-150 \
  --config.flow.patch_batch_size=16384 # Increasing this can help speed up inference - keep it powers of 2

Note

These heatmaps are only rigidly registered to MNI. Any downstream voxel-wise analysis will need them to be deformably registered to the same space.

It is possible to use the sade_registration.py script in braintypicality-scripts to deformably register to MNI. If you choose to use sade_registration.py, here is an example:

# This will *compute* the registrations for the conte dataset
# and save them to the transforms directory specified in the code.
python sade_registration.py --mode compute \
--config /codespace/sade/sade/configs/ve/biggan_config.py \
--dataset conte

# This will *apply* the registrations from the transforms directory
python sade_registration.py --mode apply \
--config /codespace/sade/sade/configs/ve/biggan_config.py \
--dataset conte \
--load_dir /ASD/ahsan_projects/braintypicality/workdir/cuda_opt/learnable/experiments/reprod-correct/conte \
--save_dir /ASD/ahsan_projects/Developer/ds-analysis/ebds/registered-heatmaps/

SOM Analysis

The build-som-plots notebook can be run to produce the SOM clustering and the CSV of samples alongside their cluster IDs. This csv is used by the roi_correlation_analysis notebook

Heatmap Plotting

Heatmaps are stored as Numpy files. So they may be loaded and plotted using any preferred tools. An example is available in the voxel-heatmaps notebook. This will plot the average heatmap across the Down Syndrome samples that belong to the prototype. Recall, that computing the average only makes sense if the images are properly registered to the same space.

Reproducing the ds-analysis

The score-based diffusion model was trained with the following commands. This assumes the script is run from the sade folder inside the sade repository. All commands were run from inside the docker container produced by sade/docker.

python main.py --project architecture --mode train \
--config configs/ve/biggan_config.py
--workdir /workdir/cuda_opt/learnable \
--config.data.cache_rate=1.0 \
--config.model.learnable_embedding \
--config.training.batch_size=8 \ # switched to 16 at ~1 million iter
--cuda_opt

The flow model was run using

python main.py --project flows --mode flow-train \
--config configs/flows/gmm_flow_config.py \
--workdir /ASD/ahsan_projects/braintypicality/workdir/cuda_opt/learnable/ \
--config.data.cache_rate=1 \
--cuda_opt=0 \
--config.msma.min_timestep=1e-2 \
--config.msma.max_timestep=0.8 \
--config.flow.training_kimg=300

The flow model will be created in <workdir>/flow/psz3-globalpsz17-nb20-lr0.0003-bs32-np1024-kimg300_smin1e-2_smax0.8/. Then inference was run with

python main.py --mode inference \
--config configs/flows/gmm_flow_config.py \
--workdir remote_workdir/cuda_opt/learnable/ \
--config.eval.checkpoint_num=150 \
--config.eval.experiment.flow_checkpoint_path=remote_workdir/cuda_opt/learnable/flow/psz3-globalpsz17-nb20-lr0.0003-bs32-np1024-kimg300_smin1e-2_smax0.8 \
--config.eval.experiment.id=default-ckpt-150 \
--config.eval.experiment.train=abcd-test  \ # can select different cohorts
--config.eval.experiment.inlier=ibis-inlier \
--config.eval.experiment.ood=ibis-ds-sa \
--config.flow.patch_batch_size=16384

Data Formats

Inputs

  • Raw Images: NIfTI format (.nii.gz)
  • Behavioral Scores: CSV files with subject IDs and metrics

Intermediate Data

  • score-norms: NumPy arrays (N x 20)
  • score-images: NumPy arrays (N x 20 x H x W x D)

Outputs

  • SOM prototype assignments: CSV mapping samples to clusters
  • Heatmaps: 3D NIfTI files (.nii.gz)
  • Correlation matrices: CSV files

Visualization

The pipeline includes visualization tools for:

  • SOM topology and clustering
  • Heatmap overlays
  • Correlation matrices

See the notebooks/ directory for interactive visualization examples.

References

[Include relevant papers?]