{"id":"fb20f7b3-c6cd-41fd-bb45-b967b384d956","arxiv_id":"2502.06552","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":3.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"A review of diffusion model applications in brain imaging, spanning generation, reconstruction, super-resolution, translation, segmentation, diagnosis, and decoding.","lead":"This paper surveys how diffusion models are being adapted for brain imaging tasks like scan reconstruction, super-resolution, disease diagnosis, and brain decoding. It organizes the field into eight application areas and explains the design choices behind each use.","discovery_kind":"review","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The survey's 'comprehensive' map is not reproducible: no search protocol, inclusion criteria, or cutoff is stated, so the eight-category coverage claim cannot be distinguished from a convenience sample.","rationale":"The reader's weakest assumption is the same one I found most load-bearing: the survey's central value is a trustworthy map, and the absence of a stated methodology for selecting the 26 works makes the map's completeness unverifiable. I checked the internal descriptions of the cited works; they appear broadly consistent with the original papers as I know them (e.g., Song et al. 2022 for inverse-problem MRI/CT, Pinaya et al. 2022b for latent brain generation, Takagi & Nishimoto 2023 for fMRI-to-image), so I do not see a factual misrepresentation that would sink the survey. The serious soft spot is evidentiary: 'comprehensive' and 'eight major fields' are asserted, not demonstrated. Table 1 is sparse in several categories (speech decoding has one entry), and the text gives no criteria for what counts as a 'major field' or how representative papers were chosen. The GitHub repository is a good resource and partially mitigates the static-table issue, but the paper itself does not describe the repository's collection protocol, so coverage remains an act of trust rather than a checkable claim. The internal tension in Section 5.2 (diffusion models 'without memorizing the exact training data' followed immediately by reconstruction/privacy risks) is a real but secondary flaw; it concerns a future-directions remark, not the central map. The proposed test—a systematic search with a defined query and comparison to Table 1—would settle the coverage concern directly. If the systematic set is largely contained in the table/repository, the conditional verdict can be upgraded; if not, the paper should either narrow its claim to 'representative works' or add the missing methodology and entries.","tokens_in":12175,"tokens_out":5323,"duration_ms":44453,"concrete_test":"Run a reproducible literature search (PubMed, Scopus, arXiv, IEEE Xplore, ACM DL) with a defined query such as (\"diffusion model*\" OR \"denoising diffusion\" OR \"score-based\") AND (\"neuroimaging\" OR \"MRI\" OR \"fMRI\" OR \"EEG\" OR \"PET\" OR \"DTI\"), restricted to 2022–2025, and screen titles/abstracts against the paper's inclusion scope. Compare the eligible set to the 26 rows in Table 1 and to the GitHub repository. Count relevant works absent from both and record whether any absent work falls in one of the eight categories. If the missing set contains more than a handful (e.g., ≥5) of peer-reviewed diffusion-for-neuroimaging papers, the 'comprehensive' claim is materially incomplete; if coverage is complete for the query period, the concern is resolved.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's central claim is descriptive: Section 1 says 'a comprehensive review ... remains lacking' and that the authors 'categorized the applications ... into eight major fields' (generation, reconstruction, super-resolution, cross-modality translation, brain tumor segmentation, neural disorder diagnosis, visual decoding, speech decoding). The load-bearing condition is that the selected works are representative and the taxonomy is complete enough to serve as a field map. That condition is not secured. Section 4 and Table 1 present 26 works with no systematic search protocol, no databases queried, no inclusion/exclusion criteria, no quality screen, and no date cutoff. The GitHub repository is offered as 'comprehensive' but the paper does not specify how repository entries were collected or updated. Consequently the map is unfalsifiable as stated: a reader cannot tell whether the single speech-decoding entry [Liu et al., 2024] reflects the true size of that subfield or merely the authors' recall, and a category with one representative cannot justify a 'major field' claim. This is not an internal contradiction—the cited summaries look broadly consistent—but a missing evidentiary basis for the central organizational assertion. It is also time-sensitive: without a cutoff, the survey is outdated at submission and the repository rather than the paper carries the coverage burden.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","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.","tokens_in":12452,"tokens_out":3210,"duration_ms":29702,"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":[{"comment":"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.","section":"§1 and §4, Table 1"},{"comment":"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.","section":"§4.8 and Table 1"},{"comment":"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.","section":"§5 and GitHub repository"}],"minor_comments":[{"comment":"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.","section":"§4.1"},{"comment":"In the table and caption, 'Corase Prediction' should be 'Coarse Prediction' and 'super-resolusion' should be 'super-resolution'.","section":"Table 1"},{"comment":"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.","section":"Table 1"},{"comment":"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.","section":"§3.3 and §4.5"},{"comment":"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.'","section":"§3.2"}],"recommendation":"major_revision","confidential_remarks":"The manuscript's coverage claim is the load-bearing element, and the missing search and inclusion methodology is a substantive issue that can be fixed within the scope of a revision. The self-citation of the authors' own work in the core taxonomy examples (Section 3.3 and Section 4.5) is not circular, but it should be disclosed. Given the survey's dependence on an external repository, I would also recommend that the editor ask for a dated repository snapshot or a versioned supplement before final acceptance."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Quick take: this survey is a genuinely useful orientation map for a young field, and the table is worth having, but the 'comprehensive' claim is not backed by a search protocol. If you need a quick entry point to diffusion models in neuroimaging, this paper does the job; if you need guaranteed complete coverage, verify against the repo.\n\nWhat's new: the organizing frame — denoising starting point, condition input, generation target — is a mild but real synthesis. It's not in the original papers; it's the survey's contribution. The eight-category taxonomy tracks the literature well, and the table summarizing 26 papers by formulation, conditioning, modality, starting point, inputs, and targets is the most useful part. The descriptions of cited works look consistent with my prior knowledge of those papers. The background on DDPM, score-SDE, DDIM, and LDM is accurate, though standard. The GitHub repo is a practical resource.\n\nSoft spots: the 'comprehensive' claim is the main one. There is no stated search protocol, no databases, no inclusion/exclusion criteria, no date cutoff. The stress-test note is right: a category with a single entry (speech decoding) cannot justify a 'major field' claim without some evidence of coverage. That said, this is a fixable omission, not a fatal flaw. The survey's value is as an organized overview, not as a systematic review; if the authors hedge the claim, the paper stands. Minor mechanical issues: Section 4.1 has a truncated sentence ('...could.'). Table 1 has typos ('Corase'). The paper also doesn't engage prior diffusion surveys in medical imaging, so the 'gap' claim is slightly overstated.\n\nWho's it for: a grad student or researcher new to the area who wants a map of what's been done. It saves time. It doesn't answer scientific questions, but it doesn't pretend to.\n\nRecommendation: send it to peer review. It's a survey, and this one is competent and useful enough to warrant referee time, but only with a request to add a methodology paragraph and fix the mechanical errors. I'd accept it after minor revision.","headline":"A useful, competent survey whose 'comprehensive' claim outruns its methodology; fixable with a search-protocol paragraph.","tokens_in":12905,"tokens_out":1604,"would_cite":true,"duration_ms":14688,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"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…","keywords":["diffusion models","neuroimaging","generative models","MRI","fMRI","brain decoding","medical image synthesis","survey"],"falsifier":"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.","tokens_in":12010,"feed_emoji":"🧠","tokens_out":4203,"duration_ms":34943,"temperature":0.7,"pith_summary":"This survey argues that the fast-growing field of diffusion models for neuroimaging can be organized into a coherent design space. It categorizes applications into eight fields—generation, reconstruction, super-resolution, cross-modality translation, brain tumor segmentation, neural disorder diagnosis, visual decoding, and speech decoding—and shows how each field selects a diffusion formulation, a conditioning mechanism, and task-specific variations in the denoising starting point, condition input, and generation target. A sympathetic reader would take the paper as establishing a useful map: rather than a scattered collection of ad hoc models, these works form a structured landscape that researchers can navigate to choose designs for new neurological tasks.","feed_headline":"Diffusion models for brain data: one map, eight uses","feed_subtitle":"A new taxonomy groups diffusion-model neuroimaging work by task, from MRI synthesis to speech decoding.","key_machinery":"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.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"supporting_citations":[{"why":"Defines DDPM, the foundational diffusion formulation on which many surveyed neuroimaging models build.","marker":"[Ho et al., 2020]"},{"why":"Provides the score-based SDE formulation used for inverse-problem reconstruction in MRI and CT.","marker":"[Song et al., 2021b]"},{"why":"Introduces DDIM, the deterministic formulation whose endpoint is reused as a starting point for cross-modality translation.","marker":"[Song et al., 2021a]"},{"why":"Introduces latent diffusion models, the most widely used formulation across the surveyed applications.","marker":"[Rombach et al., 2022]"},{"why":"Supplies classifier-free guidance, the conditional training mechanism used in generation, diagnosis, and decoding tasks.","marker":"[Ho and Salimans, 2022]"},{"why":"Supplies classifier guidance, the conditional inference mechanism used in translation and super-resolution.","marker":"[Dhariwal and Nichol, 2021]"},{"why":"A representative generation work using latent diffusion on 3D MRI, cited as evidence of the generation category.","marker":"[Pinaya et al., 2022b]"},{"why":"A representative visual decoding work combining fMRI with latent diffusion, cited as evidence of the decoding category.","marker":"[Takagi and Nishimoto, 2023]"},{"why":"A representative reconstruction work applying score-SDEs to medical imaging inverse problems.","marker":"[Song et al., 2022]"},{"why":"A representative diagnosis work using a noisy real sample as the denoising starting point, illustrating a key task-specific variation.","marker":"[Zhao et al., 2025]"}],"fun_headline_variants":["A map of diffusion models for every brain scan use","Diffusion in neuroimaging: one design space, eight tasks","Survey charts diffusion-model design choices for brain data","Eight ways diffusion models read brain signals and scans","Neuroimaging diffusion models: a unified design taxonomy"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"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.","fun_headline_variants_meta":{"raw":{"variants":["A map of diffusion models for every brain scan use","Diffusion in neuroimaging: one design space, eight tasks","Survey charts diffusion-model design choices for brain data","Eight ways diffusion models read brain signals and scans","Neuroimaging diffusion models: a unified design taxonomy"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000676,"raw_usage":{"total_tokens":3023,"prompt_tokens":844,"completion_tokens":2179,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":460,"completion_tokens_details":{"reasoning_tokens":2104}},"tokens_in":460,"tokens_out":2179,"duration_ms":13937,"temperature":1.0,"reasoning_tokens":2104,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-08T15:03:57.529579+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"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.","supporting_citations":[{"cited_title":"Solving inverse problems in medical imaging with score-based generative models","cited_arxiv_id":null,"evidence_quote":"A representative reconstruction work applying score-SDEs to medical imaging inverse problems."},{"cited_title":"Diffusion transformer-augmented fmri func- tional connectivity for enhanced autism spectrum disorder diagnosis","cited_arxiv_id":null,"evidence_quote":"A representative diagnosis work using a noisy real sample as the denoising starting point, illustrating a key task-specific variation."}],"review_version":1}