REVIEW 3 major objections 5 minor 35 references
Diffusion Models for Computational Neuroimaging: A Survey
T0 review · 3 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash
Pith's one-line read This survey argues that the apparently scattered applications of diffusion models in neuroimaging form a coherent design space, organized into eight application fields defined by how each model chooses its denoising starting point…
desk verdict A useful, competent survey whose 'comprehensive' claim outruns its methodology; fixable with a search-protocol paragraph. 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 organizing device is the taxonomy of task-related variations: denoising starting point, condition input, and generation target. These three adjustable components, applied on top of the four foundational diffusion formulations (DDPM, score-SDE, DDIM, latent diffusion) and the two conditioning mechanisms (conditional training and conditional inference), constitute the descriptive engine that maps every surveyed application onto a common grid.
What would settle it
A systematic literature search for diffusion models applied to neuroimaging, with stated inclusion criteria and a cutoff date, that checks whether every qualifying paper fits one of the eight categories; any substantial cluster of unclassifiable work would refute the taxonomy's completeness.
Extended reading notes
Core claim
On its own terms, the survey's central discovery is that the varied applications of diffusion models in neuroimaging share a common design space. Every surveyed work can be placed according to its diffusion formulation (DDPM, score-SDE, DDIM, or latent diffusion), its conditioning mechanism (conditional training via classifier-free guidance or conditional inference via classifier guidance), and three task-driven variations: denoising starting point, condition input, and generation target. Organizing roughly representative works into eight application fields, the survey claims that tailoring these three variations is what enhances specific neurological tasks—for example, using a noisy real sample as the starting point to keep generated functional connectivity biologically plausible, or using the deterministic endpoint of a DDIM process as a bridge for unpaired MRI-to-CT translation. The result is a taxonomy that frames future neuroimaging diffusion work as choices within a structured design space rather than isolated inventions.
Load-bearing premise
The survey's map is only as good as its selection of representative papers, and the authors do not state a systematic search protocol, inclusion criteria, or cutoff date, so the taxonomy could silently omit relevant work.
Editorial extensions
If this is right
- A newcomer to neuroimaging can select a diffusion formulation and conditioning mechanism by locating their task in one of the eight categories and reading off the design choices used there.
- The survey predicts that task performance gains come less from the base diffusion formulation than from matching the denoising starting point, condition input, and generation target to the task at hand.
- Across reconstruction, super-resolution, and translation, the dominant pattern is conditioning the generative model on the observed signal itself rather than on a classifier, offering a practical guideline for future methods.
- For brain decoding, the recurring pattern is to align fMRI representations with latent diffusion models pretrained on natural images or speech, making pretrained generative models a reusable component for neural decoding.
- The taxonomy indicates that data augmentation, counterfactual generation, and anomaly detection all arise from the same core mechanism of learning the healthy brain's data distribution, so methodological advances in one task may transfer to the others.
Reading between the lines
- The same three-axis design space could be applied to diffusion-model surveys in other volumetric medical domains, such as cardiac or abdominal imaging, a transfer the paper does not claim but its structure invites.
- The taxonomy predicts that unexplored combinations—for example, score-SDE formulations with noisy-real-sample starting points for EEG reconstruction—are likely to be viable research niches; this is a testable benchmark prediction.
- The authors' listed future directions (representation learning, federated learning, foundation models, and causal inference) apply to generative medical imaging generally, suggesting that if the taxonomy is right, the field will consolidate around those directions.
- Because the survey groups works by task rather than by data modality, it implicitly suggests that design choices transfer across MRI, fMRI, EEG, DTI, CT, and PET, which is a stronger claim than the paper explicitly defends.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript is a survey of diffusion-model applications in computational neuroimaging. It first introduces common neuroimaging modalities and the basics of diffusion formulations and conditioning mechanisms, then organizes applications into eight categories: generation, reconstruction, super-resolution, cross-modality translation, neural disorder diagnosis, brain tumor segmentation, visual decoding, and speech decoding. For each category, the paper describes representative works and summarizes them in Table 1, and it closes with future directions and a pointer to a GitHub repository. The central claim is that the survey fills a gap by providing a comprehensive, application-oriented map of this emerging area.
Significance. If the coverage and taxonomy are reliable, this survey would be a useful entry point for researchers working at the intersection of generative modeling and neuroimaging, especially because it connects task-specific design choices such as denoising starting point, conditioning input, and generation target to concrete applications. The taxonomy is coherent, and the descriptions of individual cited works are broadly consistent with their known contributions. The publicly available repository is a practical strength, and the future-directions discussion is sensible. However, the central 'comprehensive' claim is not backed by a reproducible selection methodology, so the survey's value as a definitive field map is currently limited and its coverage claims are difficult to verify.
major comments (3)
- [§1 and §4, Table 1] The paper's central claim of providing a 'comprehensive review' and categorizing 'the applications of diffusion models in neuroimaging into eight major fields' is not supported by a stated search protocol. The manuscript does not report which databases were queried, what search terms were used, what inclusion and exclusion criteria were applied, what quality screen was performed, or the date cutoff for the literature search. Without these elements, the eight-category map cannot be distinguished from a convenience sample, and the claim of comprehensiveness is not testable. Please either add a methodology section describing the search and selection process or soften the claim to 'selected representative works' if a systematic search was not conducted.
- [§4.8 and Table 1] Speech decoding is presented as one of the eight major fields, yet Table 1 lists only a single representative work for this category ([Liu et al., 2024]). In the absence of a search protocol, the reader cannot tell whether one entry reflects the actual size of the subfield or the authors' recall. The paper should either provide evidence that this single work is representative of a genuinely established application area or explicitly acknowledge that speech decoding is a nascent direction with very few published diffusion-based studies.
- [§5 and GitHub repository] The survey is time-sensitive: it was posted in February 2025 with no stated literature cutoff, and the GitHub repository is offered as the vehicle for a 'comprehensive overview of the ongoing research.' However, the manuscript does not specify how repository entries are collected, curated, or updated, nor does it identify the repository snapshot on which the paper's claims are based. This makes the central map hard to reproduce or update and shifts the evidentiary burden to an unversioned external resource. Please define a curation protocol and cite a dated snapshot of the repository used for this version of the survey.
minor comments (5)
- [§4.1] The sentence 'It applies an extra spectral loss to ensure the realistic neural oscillation and could.' is incomplete and appears to be a copyediting error; it should either be completed or rewritten.
- [Table 1] In the table and caption, 'Corase Prediction' should be 'Coarse Prediction' and 'super-resolusion' should be 'super-resolution'.
- [Table 1] The table headers 'Form.', 'Train Cond.', and 'Inf. Cond.' are abbreviated without a legend explaining that they refer to formulation, conditional training, and conditional inference; please expand or define these abbreviations in the caption.
- [§3.3 and §4.5] The paper's own prior work [Zhao et al., 2025] is used as the primary example of the noisy-real-sample denoising starting point; this self-citation is not flagged in the text, and a brief disclosure would be appropriate.
- [§3.2] The phrase 'which we refer to "conditional training" and "conditional inference" in this survey' is ungrammatical; it should be 'which we refer to as "conditional training" and "conditional inference" in this survey.'
Circularity Check
No circularity: the survey's taxonomy is descriptive and its self-citations are not load-bearing.
full rationale
This is a literature survey, not a derivation chain. It makes no quantitative predictions and fits no parameters, so there is no fitted-input-called-prediction or self-definitional reduction. The central claim is taxonomic: the paper organizes existing works into eight application fields. That organization is descriptive; the categories are not derived from the cited works by equations, nor does any cited result function as a premise that forces a conclusion. The self-citations (e.g., Zhao et al. 2025 in Sections 3.3 and 4.5, Peng et al. 2023 in Section 4.1, and Zhou et al. 2023a in Section 5.4) are used only as representative examples or future-direction pointers, and removing them would not change the taxonomy or any statement about the field. The absence of an explicit search protocol or inclusion criteria is a legitimate scope and reproducibility limitation, but it is not circular reasoning: the survey's map could be incomplete or biased without any claim reducing to its own inputs. No uniqueness theorem, ansatz-by-citation, or renamed-known-result pattern is present. Accordingly, no specific circular step can be exhibited, and the appropriate finding is no significant circularity.
Assumptions & free parameters
assumptions (3)
- domain assumption The cited papers are accurately summarized and representative of the field.
- standard math Diffusion model formulations (DDPM, Score-SDE, DDIM, LDM) are described correctly enough for the taxonomy to be meaningful.
- ad hoc to paper The three-variation framework (denoising starting point, condition input, generation target) is a valid organizing principle.
Cite this review
Pith. "Pith review of Diffusion Models for Computational Neuroimaging: A Survey." pith.science (2026). https://pith.science/paper/BCQDX4VE
@misc{pith2026250206552,
author = {Pith},
title = {Pith review of: Diffusion Models for Computational Neuroimaging: A Survey},
year = {2026},
howpublished = {\url{https://pith.science/paper/BCQDX4VE}},
note = {Machine review of arXiv:2502.06552}
}
read the original abstract
Computational neuroimaging involves analyzing brain images or signals to provide mechanistic insights and predictive tools for human cognition and behavior. While diffusion models have shown stability and high-quality generation in natural images, there is increasing interest in adapting them to analyze brain data for various neurological tasks such as data enhancement, disease diagnosis and brain decoding. This survey provides an overview of recent efforts to integrate diffusion models into computational neuroimaging. We begin by introducing the common neuroimaging data modalities, follow with the diffusion formulations and conditioning mechanisms. Then we discuss how the variations of the denoising starting point, condition input and generation target of diffusion models are developed and enhance specific neuroimaging tasks. For a comprehensive overview of the ongoing research, we provide a publicly available repository at https://github.com/JoeZhao527/dm4neuro.
Figures
Reference graph
Works this paper leans on
-
[1]
Federated learning for pri- vacy preservation in smart healthcare systems: A compre- hensive survey
[Ali et al., 2022] Mansoor Ali, Faisal Naeem, Muhammad Tariq, and Georges Kaddoum. Federated learning for pri- vacy preservation in smart healthcare systems: A compre- hensive survey. IEEE journal of biomedical and health informatics, 27(2):778–789,
work page 2022
-
[4]
Deep learning in neuroimaging data analysis: applications, challenges, and solutions
[Avberˇsek and Repovˇs, 2022] Lev Kiar Avberˇsek and Grega Repovˇs. Deep learning in neuroimaging data analysis: applications, challenges, and solutions. Frontiers in neu- roimaging, 1:981642,
work page 2022
-
[5]
Seeing beyond the brain: Conditional diffusion model with sparse masked modeling for vision decoding
[Chen et al., 2023] Zijiao Chen, Jiaxin Qing, Tiange Xiang, Wan Lin Yue, and Juan Helen Zhou. Seeing beyond the brain: Conditional diffusion model with sparse masked modeling for vision decoding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22710–22720,
2023
-
[6]
Deconstructing denoising diffusion models for self-supervised learning
[Chen et al., 2024] Xinlei Chen, Zhuang Liu, Saining Xie, and Kaiming He. Deconstructing denoising diffusion models for self-supervised learning. arXiv preprint arXiv:2401.14404,
arXiv 2024
-
[9]
Contrastive diffusion model with auxiliary guidance for coarse-to-fine pet reconstruction
[Han et al., 2023] Zeyu Han, Yuhan Wang, Luping Zhou, Peng Wang, Binyu Yan, Jiliu Zhou, Yan Wang, and Ding- gang Shen. Contrastive diffusion model with auxiliary guidance for coarse-to-fine pet reconstruction. In Inter- national Conference on Medical Image Computing and Computer-Assisted Intervention, pages 239–249. Springer,
work page 2023
-
[10]
Classifier-free diffusion guidance
[Ho and Salimans, 2022] Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598,
arXiv 2022
-
[11]
Denoising diffusion probabilistic models
[Ho et al., 2020] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems , 33:6840–6851,
2020
-
[13]
[Kim and Park, 2024] Jonghun Kim and Hyunjin Park. Adaptive latent diffusion model for 3d medical image to image translation: Multi-modal magnetic resonance imag- ing study. In Proceedings of the IEEE/CVF Winter Con- ference on Applications of Computer Vision , pages 7604– 7613,
work page 2024
Show all 35 references
-
[14]
Reverse the auditory processing path- way: Coarse-to-fine audio reconstruction from fmri
[Liu et al., 2024] Che Liu, Changde Du, Xiaoyu Chen, and Huiguang He. Reverse the auditory processing path- way: Coarse-to-fine audio reconstruction from fmri. arXiv preprint arXiv:2405.18726,
2024 arXiv
-
[15]
Segment anything in medi- cal images
[Ma et al., 2024] Jun Ma, Yuting He, Feifei Li, Lin Han, Chenyu You, and Bo Wang. Segment anything in medi- cal images. Nature Communications, 15(1):654,
2024
-
[16]
Disc-diff: Disentangled conditional diffusion model for multi-contrast mri super-resolution
[Mao et al., 2023] Ye Mao, Lan Jiang, Xi Chen, and Chao Li. Disc-diff: Disentangled conditional diffusion model for multi-contrast mri super-resolution. In International Conference on Medical Image Computing and Computer- Assisted Intervention, pages 387–397. Springer,
2023
-
[17]
Multi-modal modality- masked diffusion network for brain mri synthesis with ran- dom modality missing
[Meng et al., 2024] Xiangxi Meng, Kaicong Sun, Jun Xu, Xuming He, and Dinggang Shen. Multi-modal modality- masked diffusion network for brain mri synthesis with ran- dom modality missing. IEEE Transactions on Medical Imaging,
2024
-
[18]
Scalable diffusion models with transformers
[Peebles and Xie, 2023] William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceed- ings of the IEEE/CVF International Conference on Com- puter Vision, pages 4195–4205,
2023
-
[19]
Generating realistic brain mris via a conditional diffusion probabilistic model
[Peng et al., 2023] Wei Peng, Ehsan Adeli, Tomas Bosschi- eter, Sang Hyun Park, Qingyu Zhao, and Kilian M Pohl. Generating realistic brain mris via a conditional diffusion probabilistic model. In International Conference on Med- ical Image Computing and Computer-Assisted Inter...
2023
-
[20]
High-resolution image synthesis with latent diffusion models
[Rombach et al., 2022] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 10684–10695,
2022
-
[21]
Generative modeling by estimating gradients of the data distribution
[Song and Ermon, 2019] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Advances in neural information processing systems, 32,
2019
-
[22]
Solving inverse problems in medical imaging with score-based generative models
[Song et al., 2022] Yang Song, Liyue Shen, Lei Xing, and Stefano Ermon. Solving inverse problems in medical imaging with score-based generative models. In Interna- tional Conference on Learning Representations ,
2022
-
[23]
Dual diffusion implicit bridges for image- to-image translation
[Su et al., 2022] Xuan Su, Jiaming Song, Chenlin Meng, and Stefano Ermon. Dual diffusion implicit bridges for image- to-image translation. arXiv preprint arXiv:2203.08382 ,
2022 arXiv
-
[24]
High-resolution image reconstruction with latent diffusion models from human brain activity
[Takagi and Nishimoto, 2023] Yu Takagi and Shinji Nishi- moto. High-resolution image reconstruction with latent diffusion models from human brain activity. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14453–14463,
2023
-
[25]
Self-supervised learning of brain dy- namics from broad neuroimaging data.Advances in neural information processing systems, 35:21255–21269,
[Thomas et al., 2022] Armin Thomas, Christopher R ´e, and Russell Poldrack. Self-supervised learning of brain dy- namics from broad neuroimaging data.Advances in neural information processing systems, 35:21255–21269,
2022
-
[26]
Wkgm: weighted k-space generative model for parallel imaging reconstruc- tion
[Tu et al., 2023] Zongjiang Tu, Die Liu, Xiaoqing Wang, Chen Jiang, Pengwen Zhu, Minghui Zhang, Shanshan Wang, Dong Liang, and Qiegen Liu. Wkgm: weighted k-space generative model for parallel imaging reconstruc- tion. NMR in Biomedicine, 36(11):e5005,
2023
-
[27]
Inversesr: 3d brain mri super-resolution using a latent diffusion model
[Wang et al., 2023] Jueqi Wang, Jacob Levman, Walter Hugo Lopez Pinaya, Petru-Daniel Tudosiu, M Jorge Car- doso, and Razvan Marinescu. Inversesr: 3d brain mri super-resolution using a latent diffusion model. In In- ternational Conference on Medical Image Computing and Computer...
2023
-
[28]
Medsegdiff: Medical image segmentation with diffusion probabilistic model
[Wu et al., 2024] Junde Wu, Rao Fu, Huihui Fang, Yu Zhang, Yehui Yang, Haoyi Xiong, Huiying Liu, and Yanwu Xu. Medsegdiff: Medical image segmentation with diffusion probabilistic model. In Medical Imaging with Deep Learning, pages 1623–1639. PMLR,
2024
-
[29]
Diffusion model as representation learner
[Yang and Wang, 2023] Xingyi Yang and Xinchao Wang. Diffusion model as representation learner. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 18938–18949,
2023
-
[30]
Diffusion-ts: Interpretable diffusion for general time series generation
[Yuan and Qiao, 2024] Xinyu Yuan and Yan Qiao. Diffusion-ts: Interpretable diffusion for general time series generation. In The Twelfth International Confer- ence on Learning Representations,
2024
-
[31]
Diffusion transformer-augmented fmri func- tional connectivity for enhanced autism spectrum disorder diagnosis
[Zhao et al., 2025] Haokai Zhao, Haowei Lou, Lina Yao, and Yu Zhang. Diffusion transformer-augmented fmri func- tional connectivity for enhanced autism spectrum disorder diagnosis. Journal of Neural Engineering,
2025
-
[33]
On the op- portunity of causal deep generative models: A survey and future directions
[Zhou et al., 2023a] Guanglin Zhou, Lina Yao, Xiwei Xu, Chen Wang, Liming Zhu, and Kun Zhang. On the op- portunity of causal deep generative models: A survey and future directions. arXiv preprint arXiv:2301.12351,
-
[34]
Brainnetdiff: Gen- erative ai empowers brain network construction via multi- modal diffusion
[Zong et al., 2024a] Yongcheng Zong, Changhong Jing, Jonathan H Chan, and Shuqiang Wang. Brainnetdiff: Gen- erative ai empowers brain network construction via multi- modal diffusion. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI) , pages 1–5. IEEE,
2024
-
[35]
A new brain network construction paradigm for brain disorder via diffusion-based graph contrastive learning
[Zong et al., 2024b] Yongcheng Zong, Qiankun Zuo, Michael Kwok-Po Ng, Baiying Lei, and Shuqiang Wang. A new brain network construction paradigm for brain disorder via diffusion-based graph contrastive learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024
2024
-
[2020]
Cola-diff: Conditional latent diffu- sion model for multi-modal mri synthesis
[Jiang et al., 2023] Lan Jiang, Ye Mao, Xiangfeng Wang, Xi Chen, and Chao Li. Cola-diff: Conditional latent diffu- sion model for multi-modal mri synthesis. InInternational Conference on Medical Image Computing and Computer- Assisted Intervention, pages 398–408. Springer,
2023
-
[2021]
Federated learning vul- nerabilities: Privacy attacks with denoising diffusion prob- abilistic models
[Gu et al., 2024] Hongyan Gu, Xinyi Zhang, Jiang Li, Hui Wei, Baiqi Li, and Xinli Huang. Federated learning vul- nerabilities: Privacy attacks with denoising diffusion prob- abilistic models. In Proceedings of the ACM on Web Con- ference 2024, pages 1149–1157,
2024
-
[2022]
Synthetic sleep eeg signal generation using latent diffusion models
[Aristimunha et al., 2023] Bruno Aristimunha, Raphael Yokoingawa de Camargo, Sylvain Cheval- lier, Oeslle Lucena, Adam G Thomas, M Jorge Cardoso, Walter Hugo Lopez Pinaya, and Jessica Dafflon. Synthetic sleep eeg signal generation using latent diffusion models. In Deep Generat...
2023
-
[2023]
Diffusion vi- sual counterfactual explanations
[Augustin et al., 2022] Maximilian Augustin, Valentyn Bor- eiko, Francesco Croce, and Matthias Hein. Diffusion vi- sual counterfactual explanations. Advances in Neural In- formation Processing Systems, 35:364–377,
2022
-
[2024]
Diffusion models beat gans on image synthe- sis
[Dhariwal and Nichol, 2021] Prafulla Dhariwal and Alexan- der Nichol. Diffusion models beat gans on image synthe- sis. Advances in neural information processing systems , 34:8780–8794,
2021
-
[2025]
Spatio-temporal adaptive diffusion models for eeg super- resolution in epilepsy diagnosis
[Zhou and Wang, 2024] Tong Zhou and Shuqiang Wang. Spatio-temporal adaptive diffusion models for eeg super- resolution in epilepsy diagnosis. arXiv preprint arXiv:2407.03089,
2024 arXiv
Reviewed August 8, 2026 · model on record in the stance chip above.
Discussion (0). Continue with ORCID to comment.