SpaceNet Model Doc

Overview

SpaceNet is a classical non-deep-learning method for neuroimaging-based disease classification.

• Model family: non-deep-learning supervised classification method
• Typical objectives:
  • classify patient vs control groups from voxel-wise neuroimaging maps
  • build sparse discriminative models in aligned image space
  • export predictive scores, evaluation metrics, and interpretable weight maps
• Primary input: aligned subject images, labels, optional covariates, optional mask
• Primary output: class predictions, decision scores, cross-validation metrics, coefficient maps

In NeuroClaw, this document is model-level guidance for SpaceNet-based disease classification workflows rather than deep learning phenotype prediction.

Upstream preparation should usually be delegated to:

• fmri-skill for fMRI preprocessing and voxel-wise feature preparation
• smri-skill for structural feature extraction when disease classification uses sMRI
• nilearn-tool for concrete SpaceNet fitting and coefficient map export

Research use only.

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Quick Start

1) Prepare disease classification inputs

Expected inputs:

• subject-level labels such as patient / control
• aligned subject-level voxel maps
• optional covariates such as age, sex, site
• optional train / validation / test split definition

If features are not ready, delegate preprocessing to fmri-skill or smri-skill first.

2) SpaceNet route

Representative operations:

• prepare subject-level voxel maps in aligned space
• fit SpaceNet for sparse discriminative disease classification
• export predictions and coefficient maps
• visualize discriminative regions for interpretation

Example execution route:

# delegated through claw-shell after voxel maps are prepared
python skills/nilearn-tool/scripts/spacenet_classifier_reference.py \
  --input-list path/to/image_list.txt \
  --labels path/to/labels.csv \
  --target diagnosis \
  --mask path/to/group_mask.nii.gz \
  --output-dir run_models_output/spacenet

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Input / Output Contract

Required inputs

• subject-level labels for disease classification
• aligned neuroimaging image list

Optional inputs

• confounds or covariates table
• train / validation / test split file
• mask image for voxel-wise models
• hyperparameter settings such as C, l1 ratio, or number of CV folds

Produced outputs

• predicted labels and decision scores
• cross-validation metrics such as accuracy, AUC, sensitivity, specificity
• fitted model artifact or coefficient table
• coefficient map for interpretation

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Recommended Delegation

• imaging preprocessing and feature preparation -> fmri-skill and/or smri-skill
• concrete implementation of SpaceNet -> nilearn-tool
• shell execution and logging -> claw-shell

No execution before explicit plan confirmation.

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When to Use SpaceNet

• The user wants classical disease classification instead of a deep learning model.
• The dataset size is moderate and model interpretability matters.
• The user wants voxel-wise discriminative maps and sparse spatial regularization.
• The task is case-control prediction, diagnosis support, or cross-validated disease discrimination.

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Limitations and Notes

• SpaceNet requires well-aligned images in a common space and can be computationally heavier than ROI-based methods.
• Site effects and confounds can dominate disease classification if not controlled properly.
• Small sample sizes can lead to optimistic estimates unless split strategy is rigorously managed.

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Reference

• Varoquaux G, Gramfort A, Poline JB, Thirion B. Brain covariance selection: better individual functional connectivity models using population prior.
• Nilearn decoding documentation: https://nilearn.github.io/stable/decoding/index.html

Created At: 2026-04-14 00:34 HKT Last Updated At: 2026-04-14 00:45 HKT Author: chengwang96