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REVIEW 4 major objections 5 minor 46 references

Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Federated learning can train FLAIR-based BrainAGE models on real multicenter stroke data without centralizing images, with accuracy and clinical associations close to centralized training and better than single-site training.

desk verdict A real first: FedAvg on a 16-center clinical stroke cohort for FLAIR BrainAGE, with the expected caveat that the clinical associations are not yet clean of lesion confounds. read the letter →

arxiv 2506.15626 v2 pith:EOITPR2C submitted 2025-06-18 cs.LG cs.AIcs.DC

Vincent Roca , Marc Tommasi , Paul Andrey , Aurélien Bellet , Markus D. Schirmer , Hilde Henon , Laurent Puy , Julien Ramon
show 3 more authors
Grégory Kuchcinski Martin Bretzner Renaud Lopes
This is my paper · ORCID
classification cs.LGcs.AIcs.DC
keywords federatedlearningbrainagegapFLAIRMRIischemicstrokefunctionaloutcomemechanicalthrombectomymulticenterstudy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tests whether federated learning can train brain-predicted age difference (BrainAGE) models from FLAIR MRI scans of acute ischemic stroke patients when patient data cannot leave their hospitals. Across 1,674 patients from 16 stroke centers, models trained with the FedAvg algorithm consistently beat models trained on a single site, and approached the accuracy of centralized training on pooled data. The authors also show that BrainAGE derived under federated training keeps its clinical signal: it is higher in patients with diabetes and in patients with poor three-month functional outcomes, and it remains a significant predictor after adjusting for age, stroke severity, and treatment variables. If the results hold, federated learning offers a privacy-preserving route to build and deploy BrainAGE-based prognostic tools in routine stroke care without constructing a central imaging repository.

What carries the argument

The machinery is BrainAGE, the difference between a brain's predicted age and its chronological age, computed from FLAIR MR images after a bias-adjustment step that regresses the predicted-age difference on chronological age. The training side rests on the FedAvg federated algorithm: each hospital center trains locally on its own images for one epoch, sends weight updates to a server, and the server averages them, so raw images never leave the site. Four model families, ranging from a simple volume-based linear regressor to a 3D convolutional neural network, test the approach across computational budgets, and the comparison to single-site training isolates the value of multicenter data.

What would settle it

Compare federated BrainAGE associations before and after masking stroke lesions on the FLAIR images, or in a subgroup with recorded symptom-onset-to-MRI times; if the diabetes and functional-outcome associations disappear after masking or track time-since-onset, the biomarker is capturing acute lesion burden rather than brain age.

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Extended reading notes

Core claim

The central discovery is that federated learning can produce FLAIR-based BrainAGE estimates on real multicenter stroke data whose clinical associations match those of centralized training. In head-to-head comparisons, centralized learning achieved the lowest absolute age-prediction errors, but federated models were statistically indistinguishable from centralized models for the two simpler volume-based regressors and consistently outperformed single-site models across all four architectures. BrainAGE computed from the federated models was significantly higher in patients with diabetes and in patients with poor functional outcomes at three months, and logistic regressions adjusting for age, sex, vascular risk factors, stroke severity, imaging-to-puncture time, thrombolysis, and recanalization gave BrainAGE odds ratios below 1 in every configuration. The authors conclude that FL-derived BrainAGE may be as clinically informative as centralized BrainAGE.

Load-bearing premise

The clinical conclusions stand on the assumption that BrainAGE measures pre-existing brain aging rather than acute stroke lesion effects, since the FLAIR images were not lesion-masked and the time from symptom onset to imaging was often unknown.

Editorial extensions

If this is right

  • Federated learning lets stroke networks train BrainAGE models on data from many hospitals without a data-sharing agreement that pools images.
  • FLAIR-based BrainAGE trained under FL remains a significant predictor of three-month functional outcome after adjustment for age, stroke severity, and treatment variables.
  • Diabetes is the vascular risk factor most robustly linked to higher BrainAGE, since the association appears in all models and both training strategies.
  • For small or compute-limited centers, FL gives access to multicenter training; the simpler volume-based federated models reach centralized-level accuracy.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable next step would lesion-mask the FLAIR images or record symptom-onset-to-imaging time; if BrainAGE's association with functional outcome weakens, part of the signal is acute lesion burden rather than pre-existing brain aging.
  • The comparison suggests that FL's benefit grows with model complexity and input dimensionality, since radiomics and CNN models showed the largest single-site degradation, making high-dimensional biomarkers the most promising federated targets.
  • If confirmed on cohorts with more heterogeneous age distributions across centers, FL-based BrainAGE could double as a harmonization tool, reducing the need for explicit cross-site image normalization.
  • The odds-ratio pattern hints that age-prediction accuracy and clinical relevance are related but not identical, so model selection for prognosis should use clinical association rather than age error alone.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper trains FLAIR-based BrainAGE models under three data-management strategies—centralized learning, federated learning with FedAvg, and single-site learning—on 1674 ischemic stroke patients treated with mechanical thrombectomy across 16 centers. The test set is fixed to 1023 images from Centers 2–16; centralized and federated models are evaluated with five-fold cross-validation, while single-site models are trained only on Center 1. The authors compare mean absolute age-prediction errors with paired Wilcoxon tests, test BrainAGE differences across vascular risk factors and functional outcome groups with Mann–Whitney tests, and fit logistic regressions for good versus poor three-month functional outcome adjusted for demographic, vascular, and treatment variables. The main reported findings are that centralized models have the lowest prediction errors, federated models are close to centralized and significantly better than single-site models, diabetes and poor functional outcome are associated with higher BrainAGE across models, and BrainAGE has significant odds ratios below one in the outcome regressions. The paper concludes that FL is a viable privacy-preserving approach to BrainAGE and that FL-derived BrainAGE may be as clinically informative as centralized BrainAGE.

Significance. If the conclusions hold, the paper is a useful empirical demonstration of FL for BrainAGE on real multicenter clinical stroke data rather than on simulated partitions. The study has notable strengths: a genuinely multicenter cohort, a fixed test set outside the largest center, five-fold cross-validation for the centralized and federated arms, nonparametric paired tests, several model complexities, and public code and radiomics parameters. The main quantitative result—that FL substantially reduces error relative to a single-site model trained only on Center 1—is plausible and supported by Figure 3. However, the clinical-informativeness claim rests on an unaddressed lesion confound, and the FL-versus-single-site comparison is weakened by the fact that the single-site model is evaluated out-of-distribution and with less data. The secondary clinical analyses also lack multiple-comparison correction. These issues do not invalidate the algorithmic comparison, but they do limit the strength of the conclusions as currently written.

major comments (4)
  1. [§3.4, §3.5, §4.5] The clinical conclusion depends on BrainAGE reflecting pre-existing brain health, but the FLAIR images are not lesion-masked and the time from symptom onset to imaging is often unknown. Acute FLAIR hyperintensity and edema can inflate predicted age, and lesion burden is plausibly correlated with diabetes and with three-month functional outcome; adjusting for NIHSS does not capture lesion volume or location. The paper acknowledges this in Section 4.5 but provides no sensitivity analysis, such as adding lesion volume as a covariate, excluding patients with large lesions, or recomputing BrainAGE on lesion-masked images. Without such an analysis, the conclusion that FL-derived BrainAGE is 'as clinically informative' as centralized BrainAGE is unsupported. I request either a sensitivity analysis along these lines or a substantially weakened clinical claim that restricts the conclusions to associations observed in this cohort.
  2. [§2.3.1, §3.3] The 'single-site' baseline trains only on Center 1 and is evaluated exclusively on images from Centers 2–16, so the FL-versus-single-site comparison conflates larger training data with distribution shift across scanners and sites. The paper acknowledges this in Section 4.5, but the conclusion still emphasizes 'FL consistently outperformed single-site training.' A held-out evaluation within Center 1, or a single-site baseline trained on a matched amount of data, would better isolate the effect of multicenter data. As it stands, the comparison is best described as an ablation demonstrating the value of multicenter data rather than as a general superiority claim over locally trained models.
  3. [§2.3.2, §2.3.3] The comparison between centralized and federated models is confounded by training-configuration choices. For the volume and radiomics linear models, centralized and single-site models use inverse-scaling learning-rate decay with L2 weights tuned by cross-validation, while federated models use linear learning-rate decay and adopt L2 weights from single-site training. For VoxelsCNN, centralized and single-site models use Adam for 1000 epochs with BatchNorm, while the federated model uses SGD for 500 epochs with LayerNorm. These choices are motivated by empirical stability, but the observed MAE differences between centralized and federated models could partly reflect hyperparameters or architecture rather than the data-management strategy itself. Please provide a matched-setting ablation, or explicitly discuss the extent to which these differences can affect the rank-ordering in Figure 3.
  4. [§3.4, §3.5, Table 3, Table B.1] The clinical association arm performs many univariate tests—six phenotypes across four models and three training configurations, plus twelve good-versus-poor outcome comparisons—and twelve logistic regressions, all without multiple-comparison correction. Several reported significant effects, such as hypertension for some models and sex differences whose direction depends on the model, could be false positives under correction. I recommend reporting FDR-corrected p-values or pre-specifying a small number of primary analyses. The headline findings for diabetes and functional outcome have very small p-values and would likely survive correction, but the secondary phenotype results should be described as exploratory.
minor comments (5)
  1. [§2.4.2.3, Table B.1] The methods section describes logistic regression with adjusted odds ratios, but Table B.1 is labeled 'Standardized odds ratio'; please clarify whether predictors were standardized and define the standardization, because Table 3 reports unstandardized odds ratios for BrainAGE.
  2. [§4.5] The sentence 'it is appropriate to compare federated models with centralized models trained on smaller datasets' appears to refer to the single-site models rather than to centralized models; please rephrase to avoid ambiguity.
  3. [Figure 3 and Figure 2] There is inconsistent spelling between 'centralized' in the text and 'centralised' in the figure captions; please unify the spelling.
  4. [§3.3] For the Wilcoxon signed-rank tests, reporting effect sizes or median paired differences would help quantify the magnitude of the differences beyond p-values.
  5. [§2.2 and §4.5] The variable P2P is defined as the interval between MRI and arterial puncture, but Section 4.5 refers to 'the time from imaging completion to treatment initiation'; please ensure the terminology is consistent.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the BrainAGE results are empirical evaluations on held-out predictions, not derivations that reduce to their inputs.

full rationale

This paper is an empirical evaluation, not a derivation, so the circularity burden is low. Age-prediction models are trained only on chronological age and brain images, and test-set predictions are generated under five-fold or ten-fold cross-validation, so the reported BrainAGE values are not fitted to the clinical outcomes they are later compared with. The PAD correction in Section 2.4.2.1 uses ten-fold cross-validation on the test set, which is a standard debiasing procedure and does not leak outcome information into the age-prediction models. Self-citations such as Bretzner et al. [7] and Roca et al. [17] are used for context, radiomics extraction parameters, and prior clinical motivation, but the paper's central claims about FL performance and BrainAGE-outcome associations are supported by its own held-out experiments, not by those citations. The acknowledged unfairness of comparing multicenter FL/centralized training with single-site training (Section 4.5) is a study-design limitation, not circularity: it does not make the observed outcome equivalent to the input by definition. Similarly, the acknowledged concern that unmasked FLAIR lesions may confound clinical associations (Section 4.5) is a validity threat, not a circularity threat. No step in the paper reduces a prediction to a fitted parameter or imports a load-bearing result solely from the authors' own prior work.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The central claims rely on domain assumptions about FLAIR-based age prediction, cross-center comparability, and lesion effects, imported from prior work or acknowledged as limitations. No new entities are introduced; BrainAGE is a standard biomarker.

free parameters (5)
  • Centralized/single-site learning-rate schedules and epochs = LR start 0.5 to 0.001 depending on model; 1000 epochs
    Chosen by hand and described as empirically stable, not derived from theory (Section 2.3.3).
  • Federated learning schedules = LR 0.1 to 0.00005 across models; 500 rounds
    Different schedules and fewer rounds than centralized, described as empirically more stable but potentially affecting the FL accuracy gap (Sections 2.3.2 and 2.3.3).
  • L2 penalty weights = Not reported numerically
    Tuned by five-fold CV for centralized and single-site models; FL adopts the single-site weights (Section 2.3.3.1).
  • PAD correction coefficients = Slope and intercept fitted via 10-fold CV on the test set
    BrainAGE is obtained by subtracting a linear regression of PAD on chronological age fitted on the test population; standard bias correction but still fitted on data (Section 2.4.2.1).
  • CNN architecture modifications = LayerNorm instead of BatchNorm; no downsampling along head-foot axis
    Hand-made modifications to Cole et al.'s VoxelsCNN for FL and weak slice resolution, with no ablation study (Section 2.3.3.3).
assumptions (5)
  • domain assumption FLAIR images contain a usable age signal in stroke patients
    The entire BrainAGE pipeline presumes FLAIR-based age prediction is valid, following Bretzner et al. [7]; not re-derived here (Sections 2.3.3 and 4.2).
  • domain assumption Age distributions are similar across centers
    FedAvg assumes clients have comparable label distributions; supported only by a Kruskal-Wallis test for age (p = 0.6824), while scanner parameters vary strongly across centers (Section 3.1).
  • domain assumption Acute stroke lesions do not dominate BrainAGE
    Images are not lesion-masked and onset-to-imaging time is often unknown; the authors acknowledge this can affect BrainAGE consistency (Section 4.5).
  • domain assumption Automatic preprocessing tools work on FLAIR images with lesions
    SynthSeg, HD-BET, and Pyradiomics are used without lesion-aware adaptation; lesion effects on these tools are mentioned only as future work (Sections 2.3.3.1, 2.3.3.2, and 4.5).
  • domain assumption FedAvg converges to useful solutions on this non-IID clinical data
    No convergence guarantees are invoked; stability is checked only empirically (Section 2.3.2).

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Cite this review

Pith. "Pith review of Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction." pith.science (2026). https://pith.science/paper/EOITPR2C

@misc{pith2026250615626,
  author       = {Pith},
  title        = {Pith review of: Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EOITPR2C}},
  note         = {Machine review of arXiv:2506.15626}
}
abstract

$\textbf{Objective:}$ Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health. However, training robust BrainAGE models requires large datasets, often restricted by privacy concerns. This study evaluates the performance of federated learning (FL) for BrainAGE estimation in ischemic stroke patients treated with mechanical thrombectomy, and investigates its association with clinical phenotypes and functional outcomes. $\textbf{Methods:}$ We used FLAIR brain images from 1674 stroke patients across 16 hospital centers. We implemented standard machine learning and deep learning models for BrainAGE estimates under three data management strategies: centralized learning (pooled data), FL (local training at each site), and single-site learning. We reported prediction errors and examined associations between BrainAGE and vascular risk factors (e.g., diabetes mellitus, hypertension, smoking), as well as functional outcomes at three months post-stroke. Logistic regression evaluated BrainAGE's predictive value for these outcomes, adjusting for age, sex, vascular risk factors, stroke severity, time between MRI and arterial puncture, prior intravenous thrombolysis, and recanalisation outcome. $\textbf{Results:}$ While centralized learning yielded the most accurate predictions, FL consistently outperformed single-site models. BrainAGE was significantly higher in patients with diabetes mellitus across all models. Comparisons between patients with good and poor functional outcomes, and multivariate predictions of these outcomes showed the significance of the association between BrainAGE and post-stroke recovery. $\textbf{Conclusion:}$ FL enables accurate age predictions without data centralization. The strong association between BrainAGE, vascular risk factors, and post-stroke recovery highlights its potential for prognostic modeling in stroke care.

Figures

Figures reproduced from arXiv: 2506.15626 by the authors.

Figure 1
Figure 1. Flowchart of data and analysis. 2.5. Data and code availability The FLAIR images used in this study are not publicly available due to the sensitive nature of the data, which could compromise participant privacy. However, parameters for radiomics extraction and code for training and applying the ML models with the different training configurations are accessible in a public repository: https://github.com/RocaVincent/… view at source ↗
Figure 2
Figure 2. BrainAGE estimated on two FLAIR images of the test set. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Absolute age prediction errors in the test set. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Comparison of BrainAGE for each clinical phenotype in the test set. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: Comparison of BrainAGE between good and poor functional outcomes in the test [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.