REVIEW 3 major objections 5 minor 1 cited by
SPM 25: open source neuroimaging analysis software
T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash
Pith's one-line read The paper reports the release of SPM 25.01, the first openly developed version of the Statistical Parametric Mapping software in a decade, with new Bayesian and OPM-MEG methods and MATLAB-free access.
desk verdict SPM 25.01 is a real, verifiable release note that improves the field's standard neuroimaging software workflow, though the paper's validation evidence for newly added methods is thin. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The central object is SPM itself: an integrated MATLAB/C codebase that applies voxel-wise General Linear Models to neuroimaging data, haemodynamic response convolution models for fMRI, random field theory corrections, voxel-based morphometry, dynamic causal modelling, and M/EEG source localisation. The release wraps this long-tested core in a new open development and build pipeline with automated tests, containers, and calendar versioning, and extends it with named new tools such as Bayesian Spectral Decomposition (BSD), which treats the periodic and aperiodic components of neural spectra as parameters of a generative model with posterior estimates, and the OPM interference-cancellation algorithms that model environmental fields as homogeneous fields or adaptive multipole expansions.
What would settle it
Run SPM 25.01's Bayesian Spectral Decomposition on the spectra used in the BSD paper and its OPM interference-cancellation routines on the datasets used in the adaptive multipole paper, then compare the outputs with the reference implementations; a mismatch would falsify the claim that the new components are correctly implemented. Also run a standard fMRI GLM analysis on a public dataset and check that SPM 25.01 reproduces SPM 12 results to numerical precision.
Extended reading notes
Core claim
The central claim is that SPM 25.01 is a stable, major new version of an established open-source neuroimaging analysis package that incorporates ten years of methodological developments. The paper describes the release as integrating novel analysis methods, such as Bayesian Spectral Decomposition (BSD) for parameterising neural power spectra with formal statistics, OPM interference cancellation via homogeneous field correction and adaptive multipole models, SCOPE for susceptibility-distortion correction, and multi-brain spatial normalisation, into the existing SPM framework of generative models and parametric statistics. It further claims that the software is now developed openly on a public repository platform, with automated regression tests, a new documentation website, automatically built standalone and container versions, and an in-development spm-python wrapper that will make SPM accessible from Python without a MATLAB license.
Load-bearing premise
The release's promise depends on the newly added routines, such as BSD, OPM interference cancellation, SCOPE, multi-brain normalisation, and the spm-python wrapper, being correctly implemented, because the paper gives no test results, benchmarks, or example outputs for them.
Editorial extensions
If this is right
- A researcher without a MATLAB licence can run SPM 25.01 analyses from the command line via the automatically built standalone, or inside Docker or Singularity containers.
- When spm-python ships in the first quarter of 2025, Python code will be able to call SPM's MATLAB/C routines directly, without a MATLAB installation.
- OPM-MEG users gain multi-manufacturer file IO, array simulation, and two interference-cancellation methods, supporting studies that use wearable, moving-head MEG.
- Users of behavioural modelling gain the latest Active Inference tooling, including POMDP inversion schemes, continuous active filtering, and hierarchical compositions.
- MRI pipelines gain population-shaped normalisation through the Multi-Brain Toolbox and blip-up/blip-down distortion correction through SCOPE.
Reading between the lines
- Beyond the paper, the move to a public development repository invites a direct test of community engagement: whether outside contributors submit improvements and whether the new regression tests catch platform-specific bugs that the old private development workflow missed.
- Because the paper reports no test outputs or benchmarks for the new components, any independent replication of the cited BSD and OPM results using the released code would strengthen confidence beyond the paper's own assurances.
- If spm-python delivers on its promise, SPM's MATLAB/C core could outlive the commercial viability of MATLAB itself, but the wrapper's success will depend on whether the compiled standalone runs reliably across diverse high-performance computing and cloud environments.
- The BSD implementation may shift how spectral parameterisation is done in M/EEG studies, but that depends on whether researchers adopt the Bayesian formalism over the existing frequentist FOOOF approach; a head-to-head comparison on openly shared spectra would clarify the choice.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. This paper announces the release of SPM 25.01, a major new version of the Statistical Parametric Mapping neuroimaging software package. It describes the move to a public GitHub repository, the introduction of automated tests and builds, a new documentation website, and lists several new analysis features in MRI, M/EEG, optically pumped magnetometer (OPM) processing, Bayesian statistics, and behavioural modelling. The paper also outlines a strategy for making SPM accessible without MATLAB, including a planned Python wrapper (spm-python) and the existing standalone and container distribution routes.
Significance. SPM is one of the most widely used software packages in neuroimaging, so a new release after more than a decade is intrinsically significant. If the claims are accurate, SPM 25.01 provides a stable, publicly developed release with new methods (Bayesian Spectral Decomposition, OPM interference correction, FOOOF/spectral parameterisation), automated build and container support, and a new documentation hub. The release itself is verifiable via the public GitHub repository, which is a concrete strength and aligns with open-science principles. However, the paper provides no release-specific evidence that the newly advertised methods are correctly implemented in SPM 25.01; the validation burden rests on external papers and on the long history of the MATLAB/C core, which does not cover these recent additions. The overall significance therefore depends on information that is not present in the manuscript.
major comments (3)
- [Sections 2.3.2 and 2.3.3] The paper lists Bayesian Spectral Decomposition (BSD), FOOOF (Specparam), and the OPM interference cancellation algorithms (Homogeneous Field Correction and Adaptive Multipole Models) as major new features of SPM 25.01, but provides no test results, example outputs, benchmarks, or pointers to reproducible runs for the SPM implementations. The blanket statement in Section 2.3.6 that the code has been 'highly optimised and thoroughly tested over 30 years of development' applies to the long-standing MATLAB/C core and cannot validate these relatively new additions. Because the central claim is that SPM 25.01 'incorporates novel analysis methods', this validation gap is load-bearing. Please add a section with results from the automated tests or Continuous Integration (CI), or provide a documented example dataset and expected outputs for each new feature, so that the implementations can be checked against the cited methods.
- [Section 2.3.6] The paper describes 'SPM without MATLAB' as part of the release strategy, but the Python wrapper spm-python is explicitly stated to be 'in the final stages of development and will be released in the first quarter of 2025', which is after SPM 25.01. The abstract and summary should make clear that Python support is a planned near-term addition and not a feature of the currently announced release. As written, readers could reasonably attribute Python access to SPM 25.01, which would be misleading and overstates the release's capabilities.
- [Section 2.1] The paper states that the move to GitHub has introduced 'automated unit and regression tests across platforms', but it does not provide any evidence that these tests exist, pass, or cover the new features. For a software release note, this claim should be substantiated with a link to the CI workflow (e.g., GitHub Actions status badge), a list of tested platforms, and a summary of test coverage or at least a list of the test suites. Without this, the 'improved practices for open science and software development' part of the central claim is not verifiable from the manuscript.
minor comments (5)
- [Section 2.3.3] The reference for Adaptive Multipole Models (Tierney et al., 2024) is given only as 'Wiley Online Library'; please provide full bibliographic details, including volume, article number, pages, or DOI.
- [Section 3] In addition to the GitHub releases page, please provide a persistent identifier (e.g., a Zenodo DOI) for the exact release version described, so that the software can be cited unambiguously in future studies.
- [Section 2.3.6] The statement that 'approximately 90% of the SPM 25.01 source code is written in MATLAB and the remainder is written in C' would benefit from a brief description of how this ratio was measured (e.g., line counts or file sizes) or a pointer to the relevant repository statistics.
- [Section 2.1] Please specify which platforms the automated tests are run on (e.g., Linux, macOS, Windows) and where the Continuous Integration status is publicly visible, so that users can independently confirm the claim of cross-platform testing.
- [Throughout] The manuscript alternates between 'SPM 25' and 'SPM 25.01' when referring to the release; please standardise the terminology to avoid confusion between the version series and the specific release.
Circularity Check
No circular derivation: SPM 25.01 is a verifiable software release; citations to prior method papers, including same-group work, are not load-bearing reductions.
full rationale
SPM 25 is a software release note rather than a derivation or modeling paper: it contains no equations, no fitted parameters, and no quantities predicted from inputs. The central claim, that SPM 25.01 is released with the listed features, is externally checkable through the public GitHub repository, release artifacts, containers, and automated builds. The new capabilities (BSD, FOOOF integration, OPM interference methods) are delegated to cited method papers, including some by authors of this paper, but this is standard practice for a software announcement and not a circular reduction: the cited papers contain their own derivations and evaluations, and the implementations here are open source and independently runnable. The only forward-looking item (spm-python release in Q1 2025) is explicitly announced as not part of SPM 25.01, so it does not function as a claimed result dressed as a prediction. No step of the announcement reduces, by definition or by construction, to its own inputs. Score 0.
Assumptions & free parameters
assumptions (2)
- domain assumption The cited source publications describe correct and valid methods.
- domain assumption The MATLAB/C codebase is reliable because of its long development history.
Cite this review
Pith. "Pith review of SPM 25: open source neuroimaging analysis software." pith.science (2026). https://pith.science/paper/XOUO4ELF
@misc{pith2026250112081,
author = {Pith},
title = {Pith review of: SPM 25: open source neuroimaging analysis software},
year = {2026},
howpublished = {\url{https://pith.science/paper/XOUO4ELF}},
note = {Machine review of arXiv:2501.12081}
}
read the original abstract
Statistical Parametric Mapping (SPM) is an integrated set of methods for testing hypotheses about the brain's structure and function, using data from imaging devices. These methods are implemented in an open source software package, SPM, which has been in continuous development for more than 30 years by an international community of developers. This paper reports the release of SPM 25.01, a major new version of the software that incorporates novel analysis methods, optimisations of existing methods, as well as improved practices for open science and software development.
Forward citations
Cited by 1 Pith paper
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Reference graph
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Reviewed August 10, 2026 · model on record in the stance chip above.
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