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REVIEW 4 major objections 5 minor 1 cited by

A Review of Latent Representation Models in Neuroimaging

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Latent generative models are essential tools for decoding brain images.

desk verdict Useful narrative survey of latent generative models in neuroimaging, but the 'essential tools' conclusion is stronger than the evidence base and the model equations have outright errors. read the letter →

arxiv 2412.19844 v1 pith:OJTTWXLQ submitted 2024-12-24 cs.CV cs.AIcs.LG

C. Vázquez-García , F. J. Martínez-Murcia , F. Segovia Román , Juan M. Górriz This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords latentrepresentationneuroimagingvariationalautoencodergenerativeadversarialnetworkdiffusionmodelmanifoldhypothesisharmonizationfMRIreconstruction
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

This review argues that latent generative models, especially variational autoencoders, generative adversarial networks, and latent diffusion models, have become essential for making sense of high-dimensional neuroimaging data. Its central claim is that these models compress MRI, PET, and fMRI scans into lower-dimensional latent spaces in which meaningful biological patterns, such as disease progression, brain aging, and sensory encoding, become accessible. The review surveys evidence that these representations drive clinical applications including diagnosis, harmonization of multi-site data, and image reconstruction, and that they connect to a Bayesian view of the brain as an active inference machine.

What carries the argument

The central object is the latent space itself, a low-dimensional manifold assumed to underlie high-dimensional neuroimages under the manifold hypothesis. Three mechanisms construct it: the variational autoencoder's explicit probabilistic encoder, which approximates an intractable posterior and balances reconstruction against regularization through the ELBO; the generative adversarial network's implicit generator-discriminator game, which produces realistic images but leaves the latent space unstructured and inaccessible; and the latent diffusion model, which adds and removes noise in a learned latent space to generate high-quality samples. The review's analytical payoff is the contrast between explicit and implicit latents: explicit representations can be inspected statistically and tied to biomarkers, while implicit ones trade that access for image fidelity.

What would settle it

A head-to-head benchmark that trains the surveyed VAE, GAN, and LDM methods on matched data and shows their gains over simple linear baselines like PCA plus a classifier vanish would falsify the review's central claim about the utility of latent generative models.

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Extended reading notes

Core claim

The review's central claim is that generative models, particularly VAEs, GANs, and LDMs, have proven to be essential tools for uncovering meaningful latent structures in neuroimaging data. On the clinical side, the review holds that latent spaces allow early prediction of Alzheimer's disease progression, disentangle anatomical from contrast information to harmonize images across sites and scanners, model brain-aging trajectories, and synthesize realistic images for augmentation. On the fundamental side, it claims the same models provide a formal language for active inference and predictive coding, with explicit VAE-style representations matching the brain's Bayesian belief updating and implicit GAN-style representations modeling perception as a discriminator judging generated content. The review further contends that explicit latent models, unlike implicit GANs and LDMs, allow statistical analysis of latent variables, which is why most interpretable insights come from VAE-based work.

Load-bearing premise

The review's conclusions inherit the reported numbers of the studies it surveys; if those accuracies and similarity scores are optimistic, the review's case for latent models is optimistic too.

Editorial extensions

If this is right

  • If the review's picture is right, VAE-based classifiers can be used to predict future Alzheimer's status from a single MRI, not just to label current symptoms.
  • Harmonization models that disentangle anatomy from scanner contrast should allow multi-center and cross-modal studies to be pooled without site-specific artifacts.
  • Latent diffusion models conditioned on both visual and semantic features should keep improving fMRI-based reconstruction, moving from blurry outlines toward recognizable scenes.
  • Explicit VAE latent representations can serve as a statistical substrate for finding brain regions and genetic markers tied to dementia risk.
  • The alignment between generative-model inference and predictive-coding theories suggests latent generative models can be used as testable computational hypotheses about how the brain perceives and predicts.

Reading between the lines

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

  • Implicit in the review is a practical division of labor: clinical decision support should favor explicit VAE-style latents for interpretability, while synthesis and harmonization should use GANs and LDMs for fidelity.
  • Because the surveyed studies rarely use common benchmarks, a standardized re-evaluation protocol would be needed to confirm the claimed superiority of one model family over another.
  • If latent spaces truly capture disease-related variation, then statistical analyses of latent variables against clinical and genetic data could become a routine biomarker-discovery pipeline in neuroimaging.
  • The generative-adversarial-brain analogy suggests a testable prediction: patients with delusions or hallucinations should show impaired discriminator-like reality monitoring in prefrontal regions, as the cited work already hints.
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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 manuscript is a narrative review of latent generative models (VAEs, GANs, and LDMs) in neuroimaging. It introduces the manifold hypothesis, derives or states the basic objective functions of the three model families, and then surveys applications across image harmonization, visual reconstruction from fMRI, brain aging, disease classification, functional brain networks, multimodal integration, and image synthesis. The paper concludes in Section 5 that these models "have proven to be essential tools for uncovering meaningful latent structures in neuroimaging data" and discusses open issues such as interpretability of implicit latent spaces.

Significance. If the technical foundations are corrected and the central conclusion is appropriately qualified, the review would be a timely and useful map of a fast-moving field. Its strength is breadth: it organizes a diverse literature into a clear application taxonomy, includes a summary table (Table 1), and identifies real open problems, especially the limited statistical use of latent variables in most VAE-based studies. The paper's value is as a synthesis rather than a source of new experimental evidence, so the reliability of the synthesis depends directly on the accuracy of its technical exposition and on how carefully it represents the strength of the surveyed evidence.

major comments (4)
  1. [Section 3.1, Eq. (1) and following paragraph] The ELBO trade-off is stated backwards. The text says "If the KL divergence is very low the reconstruction will be very accurate but the variability of the latent space will be very small". In a VAE, a very low KL term means the inferred posterior is close to the prior, which restricts latent capacity and typically degrades reconstruction quality; conversely, a large KL term allows the posterior to overfit the data, improving reconstruction at the expense of a regularized latent space. This misstatement also weakens the subsequent explanation of why VAE reconstructions are blurry, which is more accurately attributed to the Gaussian likelihood and decoder limitations than to this trade-off. Please correct the sentence and the surrounding discussion.
  2. [Section 3.2, Eq. (3)] The diffusion loss is incorrectly notated. The objective is written as L(θ) = E_{z0,t}[||ϵθ(zt,t) − ϵ(zt)||²], which implies the target noise is a deterministic function of zt. In the standard LDM/DDPM formulation, the target is the sampled noise ε used in the forward process, and the expectation is over z0, t, and ε. As written, the equation is misleading about what the denoising network actually predicts and cannot be used to reproduce the method.
  3. [Section 3.3, Eq. (4)] The GAN objective has a typo in the second expectation: it reads E_{z∼pz(x)} but the latent vector z is drawn from the prior distribution p_z(z), not from p_z(x). This should be E_{z∼p_z(z)}[log(1 − D(G(z)))]. Since this is a foundational equation for one of the three model families, the typo should be fixed.
  4. [Section 5, together with Sections 4.5–4.7] The central conclusion that VAEs, GANs, and LDMs "have proven to be essential tools" is stronger than the evidence presented. The review reports quantitative results as reported in the source papers—e.g., the 74.40±0.01 accuracy in Section 4.5, the 81.9% accuracy in Section 4.6, and the R² of 0.86 in Section 4.7—without discussing validation protocols, dataset leakage, comparator baselines, or statistical comparability across studies. It provides no inclusion criteria or search protocol and does not discuss negative or null results. The conclusion should be softened to reflect that the current literature supports these models as promising tools, or the authors should explicitly frame the review as a narrative synthesis of selected positive results and add a limitations paragraph describing the risks of publication bias and metric heterogeneity.
minor comments (5)
  1. [Section 4.9] The text after the description of [42] ends with "[REVISAR]", an apparent editorial note that must be removed before publication.
  2. [Section 4.7] The term "Maximum Mean Discrepancy (MDD)" should read "Maximum Mean Discrepancy (MMD)".
  3. [Section 4.2] There is a typo at the beginning of a paragraph: "The. authors in [44]" should be "The authors in [44]".
  4. [Section 4.1] The phrase "the paper of feedback connections" should likely be "the role of feedback connections" or similar.
  5. [Section 3.1] The factorization p(x,z) = p(x)p(z|x) is mathematically true but does not convey the generative direction. It would be clearer to write p(x,z) = p(z)p(x|z) when introducing the generative model, with p(z|x) introduced as the posterior to be approximated.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the review's conclusions are literature-level summaries supported by independent cited studies; the single self-citation is descriptive, not load-bearing.

full rationale

This is a review paper, so there is no original derivation chain whose outputs could reduce to its inputs. The nearest formal content—the VAE ELBO (Eq. 1), the LDM forward process (Eq. 2) and denoising loss (Eq. 3), and the GAN objective (Eq. 4)—is standard background exposition, not a prediction derived from the paper's own conclusions. The central claim in Section 5 that VAEs, GANs, and LDMs 'have proven to be essential tools' is a literature-level inference; its support is a body of surveyed studies with external datasets and metrics, not a quantity fitted by the review authors. The only self-citation is [47] (Martinez-Murcia et al., 2024), described in Section 4.7 and cited again in the Discussion among independent references [12, 4, 48, 47]; it is an application example and one of several corroborating sources, not a load-bearing axiom or uniqueness theorem. No equation is defined in terms of the review's target claim, and no reported accuracy or R² is produced from the review's own fitted parameters. The review's uncritical acceptance of cited metrics is an evidence-quality limitation, not circularity. The stray editorial marker '[REVISAR]' after [42] indicates unfinished editing but does not affect the argument. Under the standard that self-citation is circular only when the load-bearing argument reduces to it, no circular step is present.

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

The review introduces no new free parameters or invented entities. Its conclusions rely on the four domain assumptions listed, all drawn from the cited literature or from the absence of independent validation.

assumptions (4)
  • domain assumption High-dimensional neuroimaging data lie on or near a lower-dimensional manifold (manifold hypothesis).
    Invoked in Section 2 to motivate the use of latent models.
  • domain assumption The brain performs Bayesian inference or predictive coding using generative internal models.
    Adopted in Section 4.1 to map VAEs and GANs onto brain mechanisms (Friston [24], Gershman [26]).
  • domain assumption The performance metrics and conclusions reported in the cited studies are accurate and comparable.
    The review does not re-analyze data; all summary numbers (accuracy, SSIM, etc.) come from the cited papers.
  • domain assumption The selected papers are representative of the field.
    No systematic search or inclusion criteria are described in the paper.

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Cite this review

Pith. "Pith review of A Review of Latent Representation Models in Neuroimaging." pith.science (2026). https://pith.science/paper/OJTTWXLQ

@misc{pith2026241219844,
  author       = {Pith},
  title        = {Pith review of: A Review of Latent Representation Models in Neuroimaging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OJTTWXLQ}},
  note         = {Machine review of arXiv:2412.19844}
}
read the original abstract

Neuroimaging data, particularly from techniques like MRI or PET, offer rich but complex information about brain structure and activity. To manage this complexity, latent representation models - such as Autoencoders, Generative Adversarial Networks (GANs), and Latent Diffusion Models (LDMs) - are increasingly applied. These models are designed to reduce high-dimensional neuroimaging data to lower-dimensional latent spaces, where key patterns and variations related to brain function can be identified. By modeling these latent spaces, researchers hope to gain insights into the biology and function of the brain, including how its structure changes with age or disease, or how it encodes sensory information, predicts and adapts to new inputs. This review discusses how these models are used for clinical applications, like disease diagnosis and progression monitoring, but also for exploring fundamental brain mechanisms such as active inference and predictive coding. These approaches provide a powerful tool for both understanding and simulating the brain's complex computational tasks, potentially advancing our knowledge of cognition, perception, and neural disorders.

Figures

Figures reproduced from arXiv: 2412.19844 by the authors.

Figure 1
Figure 1. Simplified scheme of the latent representation. High-dimensional neuroimaging scans are [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Simplified scheme of the VAE. This explicit model captures the priori distribution that encodes [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Simplified scheme of the LDM. This latent generative model takes a latent space and generate a [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Simplified scheme of the GAN model. A latent space is fed to a generator that produces [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

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Forward citations

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.