Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging

Abdullah Shahid; Abd Ur Rehman; Aleum Lee; Azka Rehman; Imran Razzak; Muhammad Usman; Sung-Min Gho; Tariq M.Khan

REVIEW 3 major objections 5 minor 1 cited by

Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A multimodal adversarial variational autoencoder estimates biological brain age with a mean absolute error of 2.77 years by disentangling shared and modality-specific features.

desk verdict Solid incremental architecture, but the evaluation leaks target information and the reported advantage lacks statistical support. read the letter →

arxiv 2411.10100 v1 pith:V64POMNK submitted 2024-11-15 cs.CV cs.AI

Muhammad Usman , Azka Rehman , Abdullah Shahid , Abd Ur Rehman , Sung-Min Gho , Aleum Lee , Tariq M.Khan , Imran Razzak This is my paper · ORCID

classification cs.CVcs.AI

keywords brainageestimationmultimodalneuroimagingadversarialvariationalautoencoderdisentangledrepresentationlearningmultitasksexclassificationstructuralMRIfunctional

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 introduces M-AVAE, a multitask adversarial variational autoencoder that estimates biological brain age from paired structural and functional MRI. The central claim is that splitting each modality's latent representation into shared (generic) and modality-specific (unique) codes, then regularizing those codes adversarially and variationally, lets the model fuse multimodal data better than prior autoencoder and regression baselines. On a 381-subject subset of the OpenBHB dataset, the model reports a mean absolute error of 2.77 years, outperforming the compared methods. The authors argue this improves the accuracy and robustness of brain age estimation, with potential for personalized healthcare in virtual environments.

What carries the argument

The central object is the M-AVAE architecture, which pairs two encoder-decoder branches (one for sMRI, one for fMRI) and splits each latent vector into a generic code $\operatorname{Gen}(z_i)$ and a unique code $\operatorname{Unq}(z_i)$. Cross-reconstruction forces the generic code of one modality to reconstruct the other modality, the distance ratio loss $L_{\mathrm{Dist}} = L^{\mathrm{Gen}}_{\mathrm{Dist}} / L^{\mathrm{Unq}}_{\mathrm{Dist}}$ pushes shared codes together and unique codes apart, an adversarial discriminator regularizes the generic latent space toward a Gaussian prior, and a KL divergence regularizes the unique codes. A regressor and a classifier predict age and sex from the combined representation.

What would settle it

Run the same 10-fold cross-validation but perform Random Forest feature selection independently inside each training fold, then record the held-out MAE for M-AVAE and the M-AAE baseline; if the M-AVAE advantage disappears or its MAE rises substantially above 2.77 years, the central performance claim is not supported.

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Extended reading notes

Core claim

The paper claims that M-AVAE outperforms existing brain age estimation methods on multimodal sMRI+fMRI data by disentangling latent variables into generic and unique parts, using cross-reconstruction between modalities, and adding sex classification as an auxiliary task. With this architecture the model achieves a mean absolute error of 2.77 years and a root mean square error of 3.185 years on a merged two-dataset subset of OpenBHB, compared with 3.125 years for a multitask adversarial autoencoder without variational regularization. The authors attribute the gain to the hybrid adversarial-variational regularization, which imposes distinct priors on generic and unique codes and promotes a disentangled latent space.

Load-bearing premise

The load-bearing assumption is that feature selection performed once on all 381 subjects does not leak test information into the 10-fold cross-validation; if that leakage occurs, the reported 2.77-year error is inflated.

Editorial extensions

If this is right

If the 2.77-year MAE holds in independent validation, the model offers a practical way to combine structural and functional MRI for brain age estimation without the noise that often degrades multimodal fusion.
The disentangled generic/unique coding scheme should make the model robust to missing or incomplete modalities, since shared information can be reconstructed from the other modality.
Adding sex classification as an auxiliary task improves age prediction, supporting the view that sex-specific aging patterns carry useful signal for brain age models.
The framework could be adapted to other multimodal biomedical inputs where separating shared and modality-specific variation matters.

Reading between the lines

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

The reported performance may be optimistic because Random Forest feature selection is applied to the full 381-subject dataset before the 10-fold split, allowing test-fold information to influence training features; a nested selection procedure is likely to raise the error and narrow the gap to the baselines.
If the disentanglement is as effective as claimed, a testable prediction is that the generic codes alone should carry most of the age signal, so an age regressor trained on $\operatorname{Gen}(z_1), \operatorname{Gen}(z_2)$ should nearly match the full-model performance.
The comparison with CAE [22] suggests the performance gap may shrink or reverse on datasets with thousands of subjects, since CAE reports lower RMSE on the much larger UK Biobank.
The metaverse and digital-twin framing is speculative; the concrete contribution is the architecture and the evaluation protocol, not the virtual healthcare deployment.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Here's my take on 2411.10100. The architecture is a reasonable incremental combination of published ideas: it merges adversarial autoencoders, VAEs, multitask learning, and the generic/unique latent split from Hu et al., and adds a distance ratio loss plus a sex classifier. The writing is clear, and the authors ship code. If the reported 2.77-year MAE on the 381-subject sMRI+fMRI OpenBHB subset is genuine, it's a competitive result.

The trouble is the evaluation. The Random Forest feature selection is described once, before the data is split for 10-fold CV. There is no sign that the selector is refit inside each training fold. That means the selected features are influenced by the labels of all subjects, including the validation folds, so the test numbers have target leakage. The text gives no basis for assuming the leakage is negligible, and flexible models like M-AVAE are exactly the kind that can exploit such leakage more than the linear baselines. That undermines the claimed win over M-AAE.

Even if the protocol were clean, the 0.35-year MAE gap between M-AVAE and M-AAE is inside the reported standard deviations, and there is no paired significance test. So the headline difference isn't statistically established. There's also a factual slip in the comparison with CAE: the paper says M-AVAE surpassed CAE in MAE, but Table III shows CAE at 2.71 and M-AVAE at 2.773. The datasets differ, so it's not a controlled comparison, but the sentence is simply wrong.

The missing lambda values and the lack of ablations on the loss weights also make it hard to judge how much each component buys. The 'metaverse' framing can be dropped without loss.

All that said, this is a real attempt at a real problem. The architecture is clearly specified, the code is available, and multimodal brain age on both sMRI and fMRI is genuinely underexplored. With a corrected CV protocol, statistical testing, and some honest re-analysis, it could be a useful paper.

My recommendation: send it to review, but the referee should treat it as a major-revision case. The central numeric claim needs to be re-run with nested feature selection, and the authors need to show the leakage is not responsible for the result. I would not cite the numbers as they stand.

Referee Report

3 major / 5 minor

Summary. The manuscript proposes M-AVAE, a multitask adversarial variational autoencoder that integrates sMRI and fMRI for brain-age estimation and sex classification. The architecture separates latent variables into generic (shared) and unique (modality-specific) codes, uses adversarial and variational losses, and adds sex classification as an auxiliary task. The authors evaluate M-AVAE on a 381-subject subset of the OpenBHB dataset and report a mean absolute error of 2.77 years, comparing against model-agnostic, model-based, and AAE-based baselines. The paper claims that M-AVAE outperforms all compared methods and positions it as a tool for metaverse-based healthcare.

Significance. If the reported performance is validated under a leakage-free evaluation protocol, the disentangled multimodal architecture with a sex-classification auxiliary task would be a useful contribution to brain-age estimation. The manuscript has strengths: the source code is made public, the evaluation uses a public dataset, and the model combines several mechanisms (adversarial learning, variational regularization, cross-reconstruction, and multitask learning) in a principled way. However, the central empirical claim is currently undermined by a likely information-leakage problem in feature selection and by the absence of statistical significance testing. The claimed advantage over M-AAE, the closest baseline, is within one standard deviation, so the headline result is not yet established.

major comments (3)

[Feature Extraction Process; Section H (Robustness Analysis)] The manuscript describes Random Forest feature selection before cross-validation, but does not state that the selector is refit inside each training fold. In the Feature Extraction Process, the authors write that after Random Forest feature selection they 'obtain the m1 and m2 features'; Section H then describes a 10-fold cross-validation on the resulting features. Because Random Forest importance is computed using the target variables y (age and sex) in Eq. (1), a selector fit once on all 381 subjects can leak validation-fold label information into the training folds. This can inflate the reported MAE and can also exaggerate the difference between flexible deep models such as M-AVAE and simpler baselines. The authors must either confirm that feature selection was nested inside each CV fold or rerun all experiments with feature selection performed only on training folds, and then report the resulting metrics.
[Section IV-G, Table II] No statistical significance tests are reported for the comparison between M-AVAE and the baselines. The headline improvement over M-AAE is 2.773 +/- 1.567 vs. 3.125 +/- 1.976 years MAE; these standard deviations overlap substantially, and the manuscript does not report paired tests. Since the same subjects are evaluated under all models, a paired test (e.g., a paired bootstrap or Wilcoxon signed-rank test on per-subject absolute errors) is appropriate and should be added. Without such a test, the claim that M-AVAE 'outperformed all the methodologies compared' is not supported.
[Section IV-G, Table III] The comparison with prior published methods in Table III mixes datasets, preprocessing pipelines, and sample sizes, so it cannot support the claim that M-AVAE outperforms those methods. For example, CAE on UKB achieves MAE 2.71 vs. 2.773 for M-AVAE, with lower RMSE (3.68 vs. 3.185) and higher PCC (0.868 vs. 0.824). The authors acknowledge the larger dataset for CAE, but the conclusion 'our model surpassed in terms of MAE' is based on a 0.06-year difference that is not shown to be statistically meaningful. This table should be reframed as an indicative comparison, or the authors should perform a same-data comparison.

minor comments (5)

[Section E (Datasets)] The sentence 'we applied the preprocessing pipeline outlined in Section to both datasets' has a missing section number; please provide the correct reference.
[Equation (14)] The reconstruction loss in Eq. (14) has typographical problems: the norm expression 'xi−Deci' should likely be '||xi - Deci(...)||', and the notation Exi~Pd(xi) is unclear about the summation indices. Please rewrite this equation for readability.
[Feature Extraction Process] The paper calls Random Forest feature selection a 'filter method,' but Random Forest importance is typically classified as an embedded method. This terminology should be corrected or clarified.
[Feature Extraction Process] The selected feature counts m1 and m2 are never reported, even though they are central to the input dimensionality. Please state these values explicitly.
[Abstract and Conclusions] The claim that M-AVAE is 'a powerful tool for metaverse-based healthcare applications' is speculative, since no metaverse or deployment experiments are performed. Please soften this claim to match the scope of the study.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the central result is an empirical benchmark, not a derivation that reduces to its inputs.

full rationale

The paper's central claim is an empirical performance measurement: M-AVAE achieves a mean absolute error of 2.77 years on a multimodal brain-age prediction benchmark. The model's training objective includes an L2 regression loss on age, while the reported metric is MAE on cross-validated folds; these are not the same quantity, so the result is not forced by construction. The loss functions and architectural choices are design decisions, and no fitted parameter is renamed as a prediction. The paper contains self-citations in the related-work section, but none is load-bearing for the central claim: the cited prior works are contextual and do not supply a uniqueness theorem, an ansatz, or a derivation on which the reported performance depends. The possible concern that Random Forest feature selection is applied once before 10-fold cross-validation is an experimental-protocol and leakage concern, not a circularity of the derivation chain: the manuscript does not exhibit an equation or fitted quantity that makes the reported MAE equivalent to its own input. In the absence of a specific reduction of a predicted result to a fitted parameter or self-citational premise, the appropriate finding is no significant circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on several untested domain assumptions and a number of unreported hyperparameters. The most consequential is the feature selection choice, which may leak information if not nested within the cross-validation.

free parameters (3)

lambda_1_to_lambda_6 = not reported
Trade-off weights in the full objective (Eq. 16) tuned on validation data; values omitted.
latent_dimensions = 120 (50 generic, 70 unique)
Latent dimensionality and split chosen empirically (Section III-F).
feature_counts_m1_m2 = not reported
Number of selected features from each modality after Random Forest filtering is unspecified.

assumptions (4)

domain assumption Random Forest filter method selects features that retain age- and sex-related information from sMRI and fMRI.
Invoked in Feature Extraction Process; no validation of the chosen features.
domain assumption A Gaussian prior on the generic latent code and standard Gaussian N(0,I) on unique codes is appropriate for disentanglement.
Used in adversarial and variational losses (Section III-D).
domain assumption Sex classification as an auxiliary task improves age prediction accuracy.
Motivates multitask design; no ablation isolating its contribution.
domain assumption The merged dataset of 381 subjects from two studies [58][59] is homogeneous enough for 10-fold cross-validation.
Used for all experiments; site effects not modeled.

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Cite this review

Pith. "Pith review of Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging." pith.science (2026). https://pith.science/paper/V64POMNK

@misc{pith2026241110100,
  author       = {Pith},
  title        = {Pith review of: Multi-Task Adversarial Variational Autoencoder for Estimating Biological Brain Age with Multimodal Neuroimaging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/V64POMNK}},
  note         = {Machine review of arXiv:2411.10100}
}

read the original abstract

Despite advances in deep learning for estimating brain age from structural MRI data, incorporating functional MRI data is challenging due to its complex structure and the noisy nature of functional connectivity measurements. To address this, we present the Multitask Adversarial Variational Autoencoder, a custom deep learning framework designed to improve brain age predictions through multimodal MRI data integration. This model separates latent variables into generic and unique codes, isolating shared and modality-specific features. By integrating multitask learning with sex classification as an additional task, the model captures sex-specific aging patterns. Evaluated on the OpenBHB dataset, a large multisite brain MRI collection, the model achieves a mean absolute error of 2.77 years, outperforming traditional methods. This success positions M-AVAE as a powerful tool for metaverse-based healthcare applications in brain age estimation.

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Biological Brain Age Estimation using Sex-Aware Adversarial Variational Autoencoder with Multimodal Neuroimages
cs.CV 2024-12 reject novelty 4.0 of 10

A multimodal brain-age estimator with sex input is presented, but its reported advantage over prior methods rests on an invalid comparison across different test datasets.

Reference graph

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Mtss-aae: Multi-task semi- supervised adversarial autoencoding for covid-19 detection based on chest x-ray images,

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Densely attention mech- anism based network for covid-19 detection in chest x-rays,

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Using deep autoencoders for facial expression recognition,

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Cascade multiscale residual attention cnns with adaptive roi for automatic brain tumor segmentation,

Z. Ullah, M. Usman, M. Jeon, and J. Gwak, “Cascade multiscale residual attention cnns with adaptive roi for automatic brain tumor segmentation,” Information sciences, vol. 608, pp. 1541–1556, 2022

2022
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Retrospec- tive motion correction in multishot mri using generative adversarial network,

M. Usman, S. Latif, M. Asim, B.-D. Lee, and J. Qadir, “Retrospec- tive motion correction in multishot mri using generative adversarial network,” Scientific reports, vol. 10, no. 1, p. 4786, 2020

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V olumetric lung nodule segmentation using adaptive roi with multi- view residual learning,

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Cross lingual speech emotion recognition: Urdu vs. western languages,

S. Latif, A. Qayyum, M. Usman, and J. Qadir, “Cross lingual speech emotion recognition: Urdu vs. western languages,” in 2018 International conference on frontiers of information technology (FIT) . IEEE, 2018, pp. 88–93

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Phonocardiographic sensing using deep learning for abnormal heartbeat detection,

S. Latif, M. Usman, R. Rana, and J. Qadir, “Phonocardiographic sensing using deep learning for abnormal heartbeat detection,” IEEE Sensors Journal, vol. 18, no. 22, pp. 9393–9400, 2018

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Leveraging data science to combat covid-19: A comprehensive review,

S. Latif, M. Usman, S. Manzoor, W. Iqbal, J. Qadir, G. Tyson, I. Castro, A. Razi, M. N. K. Boulos, A. Weller et al. , “Leveraging data science to combat covid-19: A comprehensive review,” IEEE Transactions on Artificial Intelligence, vol. 1, no. 1, pp. 85–103, 2020

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Dc-aae: dual channel adversarial autoencoder with multitask learning for kl-grade classifica- tion in knee radiographs,

M. U. Farooq, Z. Ullah, A. Khan, and J. Gwak, “Dc-aae: dual channel adversarial autoencoder with multitask learning for kl-grade classifica- tion in knee radiographs,” Computers in Biology and Medicine, vol. 167, p. 107570, 2023

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A. Makhzani, J. Shlens, N. Jaitly, I. Goodfellow, and B. Frey, “Adver- sarial autoencoders,” arXiv preprint arXiv:1511.05644 , 2015

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D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114, 2013

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

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