S²M: Single-Subject Morphometry
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S²M Introduction
S²M (Single-Subject Morphometry) is an open-source framework for individualized assessment of brain structural abnormalities using MRI. The software generates subject-specific maps of white and gray matter alterations and focal cortical dysplasia lesions based on normative models derived from healthy controls, enabling the detection and quantification of morphometric abnormalities at the voxel level. S²M supports the evaluation of atrophy, hypertrophy, and focal cortical dysplasia (FCD) recquiring only a high-quality T1-weighted MRI scan (FLAIR image optional). Demographic variables such as age and sex can be incorporated to improve model accuracy but are not required.
The framework features a fully integrated graphical user interface (GUI), providing an accessible workflow without the need for programming expertise. S²M supports both single-subject analyses and automated batch processing of large datasets. Brain tissue metrics are extracted using the CAT toolbox, while all harmonization and statistical mapping procedures are implemented within the S²M framework.
S²M Team
Coordinator and developer
Brunno M. de Campos, Ph.D.
Theoretical Collaborators
Fernando Cendes, MD, Ph.D.; Raphael Fernandes Casseb, Ph.D.; Luciana R. Pimentel-Silva
S²M third-party prerequisites:
Before running SSM, ensure you have the following dependencies installed and configured in your MATLAB environment.
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Matlab (The MathWorks Inc.): tested with versions from the 2019b to the 2026a
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Matlab Parallel Computing Toolbox (optional)
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Statistical Parametric Mapping 25 (SPM)
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Computational Anatomy Toolbox (CAT) Version 3347 (CAT26.0.rc4, from 2026-07-24)
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ComBat Multi-Site Harmonization Tool (Adapted version for SSM included with SSM code)
S²M Key Features:
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Advanced Site Harmonization: Embedded with S²M_combat to eliminate scanner and sequence biases (e.g., T1w vs. FLAIR) using single-subject projection algebra.
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Biological Confounder Control: Automatic regression for Age, Gender, and Total Intracranial Volume (TIV).
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Fast Non-Parametric Inference: Cluster-based permutation testing with an intelligent caching system for empirical thresholds.
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Outlier & Quality Control: Automated IQR-based outlier detection routines to protect the batch analysis from structural noise during harmonization.
Downloads
- The updates are cumulative.
- Please, report bugs!
- S²M is always updating due the continuous feedbacks, corrections, ideas or necessities. Thank you!
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To download the Toolbox and Manual: GitHub
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