SVM Pipeline for article ‘Machine learning-based prediction of future dementia using routine clinical MRI brain scans and healthcare data’

Parminder Reel 2025-10-29

SVM Pipeline

This is a comprehensive README for the SVM code used in the article:

Machine learning-based prediction of future dementia using routine clinical MRI brain scans and healthcare data

Authors: Parminder Singh Reel, Salim Al-Wasity, Craig Edwards, Smarti Reel, Esma Mansouri-Benssassi, Szabolcs Suveges, Muthu Rama Krishnan Mookiah, Susan Krueger, Emanuele Trucco, Emily Jefferson, Alexander Doney and J. Douglas Steele.

This SVM Pipeline uses SPM and MATLAB. For demonstration purpose, it uses publicly available T1 Brain scans from IXI database for the entire workflow and describes configuration, outputs, and how to run the pipeline. This SVM pipeline was developed under the PICTURES Exemplar-2 Project.

For any queries regarding the code contact the corresponding author at [email protected]

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Table of Contents

1. Prerequisites
2. Pipeline Execution (Step-by-step)
3. Performance Metrics
4. Key Parameters
5. Output Summary
6. Funding
7. Licence

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Prerequisites

Software Requirements

• MATLAB R2021b or later
• SPM12 (Statistical Parametric Mapping toolbox)
• Parallel Computing Toolbox (for distributed jobs)
• IXI Dataset (T1-weighted MRI scans)

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Pipeline Execution (Step-by-step)

Below is a detailed, practical breakdown of each pipeline stage. Each step includes the purpose, rationale, and commands to run the step.

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Step 0 — Download and Preparation of T1 Brain Scans

Purpose & Rationale
The data related to the results presented in this article can only be accessed within the HIC TRE subject to ethical and governance approvals. Therefore, publicly accessible T1 Brain Scans from IXI data is used to demonstrate the functional pipeline. Also, create a single master table that links IXI metadata to MRI file paths and produces the binary classification labels using patient age. This is critical because downstream cross-validation and stratification depend on accurate mapping between subjects and images.

Download T1 Brain Scans from (https://brain-development.org/ixi-dataset/)

Command

srun -n1 -N1 /usr/local/MATLAB/R2021b/bin/matlab -nosplash -r "Rseed= 1; setup_IXI_data"

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Step 1 — Nested K-Fold Split Generation

Purpose & Rationale
Implement nested cross-validation to provide an unbiased estimate of generalization and to separate model selection (inner loop) from performance estimation (outer loop).

Command

srun -n1 -N1 /usr/local/MATLAB/R2021b/bin/matlab -nosplash -r "Rseed= 1; Step1"

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Step 2 — Job Driver Generation

Purpose & Rationale
Produce job driver tables that enumerate all independent combinations of outer/inner folds, p-thresholds, and hyperparameters. These tables enable parallel dispatch to cluster nodes or local workers and act as reproducible experiment manifests.

Command

srun -n1 -N1 /usr/local/MATLAB/R2021b/bin/matlab -nosplash -r "Rseed= 1; Step2_alt"

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Step 3 — MRI Preprocessing and Voxel-wise t-Test Feature Selection (SPM12)

Purpose & Rationale
Standardize all images to a common anatomical space (MNI) and segment tissue classes. SPM’s unified segmentation + normalization or shoot/DARTEL produce images that are comparable across subjects and suitable for voxelwise statistics. Perform voxel-wise statistical tests to detect voxels showing significant group differences. Masks derived from these p-values reduce dimensionality and help focus the classifier on biologically plausible regions.

Command

srun -n25 -N25 /usr/local/MATLAB/R2021b/bin/matlab -nosplash -r "Rseed= 1; Step3_alt"

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Step 4 — Inner Loop SVM Training & Validation

Purpose & Rationale
Train SVM classifiers on masked voxel features and evaluate on held-out inner folds to estimate performance of each hyperparameter combination.

Command

srun -n128 -N64 /usr/local/MATLAB/R2021b/bin/matlab -nosplash -r "Rseed= 1; Step4_alt"
srun -n1 -N1 /usr/local/MATLAB/R2021b/bin/matlab -nosplash -r "Rseed= 1; Step4a"

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Step 5 — Hyperparameter Optimisation

Purpose & Rationale
Aggregate inner-fold results to select the best hyperparameter configuration for each outer fold without tapping into outer test data.

Command

srun -n5 -N5 /usr/local/MATLAB/R2021b/bin/matlab -nosplash -r "Rseed= 1; Step5"

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Step 6 — Outer Loop Final Training & Testing

Purpose & Rationale
Use the chosen hyperparameters to train a final model on the full outer training set and evaluate on the outer test set to estimate generalisation.

Command

srun -n5 -N5 /usr/local/MATLAB/R2021b/bin/matlab -nosplash -r "Rseed= 1; Step6_alt"

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Step 7 — Validation, Aggregation & Reporting

Purpose & Rationale
Validate that all jobs completed correctly, aggregate fold-level results, compute summary statistics, and prepare a report for publication or further analysis.

Command

srun -n1 -N1 /usr/local/MATLAB/R2021b/bin/matlab -nosplash -r "Rseed= 1; Step7_alt"

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Performance Metrics

The pipeline records a comprehensive set of performance metrics for each evaluation. Each metric is saved per job and aggregated across folds.

Per-model / per-fold metrics

• ACC — Accuracy (correct predictions / total observations)
• AUC — Area Under ROC Curve (discrimination performance)
• SENS — Sensitivity / True Positive Rate
• SPEC — Specificity / True Negative Rate
• PPV — Positive Predictive Value
• NPV — Negative Predictive Value
• NUM_OBS — Number of observations in test set
• Confusion matrix entries — CM11, CM12, CM21, CM22

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Key Parameters

Below are the most important configuration variables controlling the experiment. Keep track of them in config_experiment.m.

• seed — random seed for reproducibility (e.g., 42)
• kFolds_outer — number of outer folds (e.g., 5)
• kFolds_inner — number of inner folds (e.g., 3)
• p_Thres_range — p-value thresholds for mask creation (e.g., [0.01 0.001 0.0001])
• C_value_range — SVM regularization constants (e.g., [0.01 0.1 1 10])
• K_value — kernel scale parameter when using Gaussian/RBF
• spmpath — path to SPM12 installation
• imagespath — path to preprocessed images
• ttestoutpath, driverpath, outertraintestpath, innertraintestpath — output directories
• min_p, max_p, min_C, max_C — ranges for final summary bookkeeping

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Output Summary

A consistent directory & filename convention is used so downstream analysis is straightforward.

Top-level output directories

• ttestout/ — p-matrix and binary masks ({outer}_{inner}_ttest2_result.mat, {p}_binarymask.mat)
• innertraintest/ — inner fold training/validation CSVs and per-worker logs
• outertraintest/ — outer fold evaluation CSVs and predictions
• splits/ — outer/inner split CSVs
• driver/ — driver CSVs used to distribute work
• runtime_log/ — step-wise runtime logs (Step1_runtime.txt, etc.)

Important file formats

• .mat — MATLAB data objects (p-matrix, masked volumes, saved SVM models)
• .csv — human-readable driver & results tables
• .nii — neuroimaging volumes (preprocessed tissue maps)
• .txt — runtime or completion flags

Example final artifacts

• outer_fold_summary.csv — final performance summary across outer folds
• Inner_loop_validated.txt — indicates all inner jobs completed
• final_masks/ — best masks for each outer fold (for interpretation)
• predictions/ — subject-level predicted labels & decision scores

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Funding

This work was funded by the Medical Research Council (MRC) MICA Programme Grant: InterdisciPlInary Collaboration for efficienT and effective Use of clinical images in big data health care RESearch: PICTURES (MR/S010351/1).

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Licence

This code is licenced under the MIT License.

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