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REVIEW 5 major objections 4 minor 23 references

Enhancing Brain Age Estimation with a Multimodal 3D CNN Approach Combining Structural MRI and AI-Synthesized Cerebral Blood Volume Measures

T0 review · 5 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Adding AI-synthesized blood-volume maps to structural MRI cuts brain-age error to 3.96 years.

desk verdict The empirical gain is small and real, but the paper overclaims clinical results missing from the body, and the 'functional' addition may be just ensembling. read the letter →

arxiv 2412.01865 v4 pith:OAZ6ZIQE submitted 2024-12-01 eess.IV cs.LG

Jordan Jomsky , Kay C. Igwe , Zongyu Li , Yiren Zhang , Max Lashley , Tal Nuriel , Andrew Laine , Jia Guo (for the Frontotemporal Lobar Degeneration Neuroimaging Initiative
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for the Alzheimer's Disease Neuroimaging Initiative)
This is my paper · ORCID
classification eess.IVcs.LG
keywords brainagegapestimationAICBVcerebralbloodvolumesynthesis3DconvolutionalneuralnetworkmultimodalMRIT1-weightedmildcognitiveimpairment
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 is trying to establish that brain-age estimation—usually built from structural T1-weighted MRI alone—becomes both more accurate and more clinically informative when AI-synthesized cerebral blood volume (AICBV) maps, generated from the same T1w scan, are added as a second modality. It reports that a late fusion of two 3D VGG-based age predictors reaches a mean absolute error of 3.96 years and $R^2$ = 0.943 on held-out scans, beating T1w-only (4.10) and AICBV-only (4.49) models. It also reports that the AICBV branch highlights vascular-rich and periventricular regions, especially in older adults, and that AICBV-based BrainAGE separates stable from progressive mild cognitive impairment. If the claim holds, routine non-contrast MRI could produce a functional-vascular brain-aging biomarker without extra scanning or contrast agents.

What carries the argument

The carrying mechanism is a two-stream late-fusion pipeline. A 3D patch-based CNN-Mamba generator, the AICBV model from reference [12], synthesizes a cerebral blood volume map from each non-contrast T1w scan; two separate 3D VGG8 networks then predict age, one from T1w and one from AICBV; and a linear regression combines the two predicted ages, optionally with sex, into the final brain age estimate. The load-bearing element is the AICBV map itself, since the claimed improvement depends on it carrying functional blood-volume information that is not already visible in the T1w input. Grad-CAM heatmaps from the final convolutional layer of each encoder show which regions drive each modality's prediction.

What would settle it

On held-out subjects with both T1w MRI and true contrast-enhanced steady-state CBV maps (gadolinium or ferumoxytol), compute voxel-wise correlation between AICBV and true CBV in periventricular and dentate-gyrus regions; if that correlation is no higher than the correlation between true CBV and a nonlinear intensity transform of T1w, the AICBV maps carry no independent vascular signal and the multimodal interpretation fails.

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Extended reading notes

Core claim

The authors' central discovery claim is that a multimodal BrainAGE model formed by linearly regressing the age predictions of a T1w-trained 3D VGG8 network and an AICBV-trained 3D VGG8 network outperforms either modality alone: MAE 3.96 years and $R^2$ = 0.943 on the 288-subject test set, versus MAE 4.10 for T1w-only and 4.49 for AICBV-only, with sex as a covariate yielding 3.95 years. They interpret the gain as evidence that AICBV supplies independent functional vascular information absent from T1w, supporting this with Grad-CAM saliency maps that show the T1w encoder emphasizing white-matter and cortical atrophy while the AICBV encoder emphasizes central vascular-rich and periventricular regions, shifting from medial prefrontal cortex in younger adults to the dentate gyrus in older adults. The abstract further claims that BrainAGE values increase across cognitively normal, MCI, and Alzheimer's strata and that AICBV-based BrainAGE separates stable from progressive MCI at $p = 1.47 \times 10^{-8}$.

Load-bearing premise

The load-bearing premise is that AICBV maps synthesized from T1w MRI carry functional vascular information that is not already contained in the T1w structural scan; if AICBV is only a learned nonlinear re-expression of T1w tissue contrast, the multimodal gain is an ensemble effect of two views of the same structural image, not structural-plus-functional fusion.

Editorial extensions

If this is right

  • Brain-age estimation can extract functional-vascular information from existing non-contrast MRI scans, eliminating the need for contrast agents or additional perfusion scans.
  • The reported MCI-separation result implies that AICBV-based BrainAGE could serve as an early, non-invasive marker of prodromal cognitive decline, complementing atrophy-based biomarkers.
  • The age-stratified results imply that structural and vascular modalities contribute differently across the lifespan, with AICBV carrying more weight in older age groups and T1w in younger groups.
  • The small but significant sex coefficient (MAE 3.95, $p$ = 0.02) is consistent with the model encoding known sex differences in brain aging.
  • Grad-CAM's focus on periventricular regions and the dentate gyrus suggests the model tracks known aging-vulnerable vascular territories, which could make predictions easier for clinicians to interpret.

Reading between the lines

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

  • A natural null control would feed the same late-fusion design with two views of the T1w scan, e.g., an autoencoder's reconstruction as the 'second modality'; if accuracy matches the TA-model, the AICBV branch may be capturing structure rather than independent function.
  • If AICBV carries true vascular signal, the method should transfer to other structural contrasts or external cohorts not among the 13 training sets, with a gain proportional to how much functional information the structural input lacks.
  • The abstract's stable-vs-progressive MCI result is stated without a corresponding detailed analysis in the supplied body; reproducing that analysis on ADNI would be the natural check before relying on it.
  • Because AICBV is generated deterministically from T1w, the two branches are not independent measurements; comparing AICBV against true contrast-enhanced CBV would settle how much functional signal this approach actually adds.
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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

5 major / 4 minor

Summary. The paper proposes a brain-age estimation pipeline in which two separate 3D VGG-based regressors are trained, one on T1-weighted MRI and one on AICBV maps synthesized from those same T1w scans by a CNN-Mamba generator, and the predicted ages are then combined by linear regression, optionally with sex as an additional covariate. On a single split of 2,851 scans from 13 public datasets, the combined model achieves MAE 3.96 and R² 0.943, compared with MAE 4.10 for the T1w-only model. The abstract additionally reports clinical concordance results (stable vs. progressive MCI separation, correlations with CDRSB and MMSE) that do not appear in the body of the manuscript.

Significance. If the reported improvement is robust and the AICBV maps truly carried independent vascular information, the approach would be a clinically attractive, contrast-free way to augment brain-age biomarkers with functional information. The paper has concrete strengths: it uses a large multi-site sample of 13 public datasets, a full 3D architecture, an interpretability analysis with Grad-CAM, and it directly compares with a prior T1w-only model. However, the central premise is not established: AICBV is a deterministic function of the same T1w input, so the observed gain may reflect ensembling of two views of one structural scan rather than structural-plus-functional fusion. The abstract also makes clinical claims that cannot be verified from the body. The significance of the work therefore depends on additional validation and a major reframing of the claims.

major comments (5)
  1. [§2.2, §4.1] The core claim that AICBV provides an 'additional independent covariate' is not supported. As stated in §2.2, AICBV is generated from each T1w MRI scan by a 3D patch-based CNN-Mamba model, so at inference AICBV = g(T1w) is a deterministic function of the same structural input. Any information in AICBV is therefore a transformation of the T1w scan, not an independently measured vascular modality. The improvement of the TA-model over the T-model (MAE 3.96 vs. 4.10) could be a variance-reduction/ensembling effect from combining two networks trained on different transforms of the same image. To support the multimodal claim, the authors should provide a control experiment using a synthetic non-vascular modality derived from T1w (e.g., a smoothed or noise-transformed T1w) and test whether the TA gain is specific to AICBV; they should also report residual correlations and prediction-error correlations between the T-model and A-model, and, if possible, validate AICBV against actual contrast-enhanced CBV on at least a subset of the 13 datasets. The AICBV generator from reference [12] is not released, which further prevents readers from assessing what the AICBV maps contain.
  2. [Abstract vs. Body] The abstract reports clinical concordance results that are absent from the Methods, Results, and Discussion sections: the separation between stable and progressive MCI with p = 1.47×10⁻⁸, the CDRSB correlation r = 0.403, and the MMSE correlation r = -0.310. None of these analyses appears in the body of the manuscript, and no table or figure reports them. This is a load-bearing omission because these results are used to argue clinical utility. The authors must either add the full analysis (including subject selection, statistical procedure, and appropriate multiple-comparison handling) or remove these claims from the abstract.
  3. [§3.1, Table 1] The ANOVA procedure is not described, and the reported p-values are not interpretable without details. The text says 'we performed ANOVA statistical tests' but does not state what the response variable was, how the model comparison was structured, whether the test was on per-subject absolute errors, whether it was repeated over multiple data splits, or how the p-values were adjusted. In addition, all results are based on a single 8:1:1 split with no confidence intervals for MAE, MSE, or R², and no measurement of split-to-split variability. Given that the absolute improvement of the TA-model over the T-model is only 0.14 years, the authors should demonstrate that this difference is stable across repeated splits and report effect sizes and confidence intervals.
  4. [§3.4] The Grad-CAM analysis does not establish that AICBV contributes independent functional information. The observation that the T1w and AICBV encoders focus on different regions is expected when the two encoders are trained on different transformations of the same underlying scan; different learned features of the same input do not demonstrate that AICBV captures variance in brain age beyond T1w. The authors should either connect the saliency maps to an external validation of AICBV, or temper the interpretation that the maps reveal 'functional features that purely anatomical information from T1w MRI cannot provide.'
  5. [§4.2] The interpretation of the sex coefficient appears inconsistent with the coding described. The text states that sex was coded as 0 for female and 1 for male and that the coefficient was -0.168. The authors then write that this 'suggests that the model adjusts male age predictions downward, indicating that male brains are perceived as appearing older than female brains.' A negative coefficient for male would lower predicted age for males, implying that male brains are predicted as younger, not older. This should be corrected or clarified, because the directional claim is part of the validation narrative.
minor comments (4)
  1. [§3.3, Tables 2 and 3] Table 3 uses 'DLBS' while the text uses 'dlbs'; Tables 2 and 3 label the combined model with sex as 'TAG-model' whereas the text and Table 1 use 'TAS-model.' Please unify all abbreviations.
  2. [§4.1] The text refers to the AICBV generation method as 'the DeepC algorithm,' but §2.2 and reference [12] describe a 3D patch-based CNN-Mamba model. The name 'DeepC' is not introduced or defined, which is confusing.
  3. [§5, Future Work] The future-work paragraph promises 'unsupervised learning' and 'large language models' without explaining how these would integrate with the current regression framework; consider either trimming or making these directions concrete.
  4. [General] There are minor typographical errors (e.g., 'occuring' in the Introduction, 'andR2' in §3.1) and a few sentences with missing spaces. A careful proofreading pass is recommended.

Circularity Check

2 steps flagged · score 6.0 of 10

The central 'functional information' claim is circular: AICBV maps are generated from the same T1w scans and then called an 'additional independent covariate,' so the multimodal gain is an ensemble of two views of one structural input, with vascular validity resting on a self-cited, unreleased prior model.

  1. self definitional [Section 2.2 (AICBV Model) and Section 4.1 (Performance Improvement with AICBV)]
    "we utilized a 3D patch-based hybrid CNN-Mamba model to generate the AICBV from each MRI scan... AICBV provides an additional independent covariate that a T1w-trained model alone could not replicate due to a lack of prior knowledge of the importance of functional information."

    By the paper's own construction, AICBV = g(T1w): Section 2.2 states the CNN-Mamba model generates AICBV from each MRI scan. The TA model is then a linear regression of yhat_T1w and yhat_AICBV = h(g(T1w)), so no independent functional measurement enters the fusion. Calling AICBV an 'additional independent covariate' is therefore false by definition. The observed MAE improvement can be explained by ensembling two regressors trained on the same underlying scan, not by adding vascular information. The Grad-CAM differences show different learned features of one input, not independent modalities.

  2. self citation load bearing [Section 1 and Section 4.1; Reference [12]]
    "Originally generated by our group, AICBV builds upon the idea that non-contrast MRI scans inherently contain subtle blood-tissue contrasts that, while challenging to detect with traditional methods, can be revealed using deep learning techniques [12]... By leveraging the DeepC algorithm to generate functional information without requiring additional data collection..."

    The paper's premise that AICBV maps carry true cerebral blood volume / functional information is imported from [12], a same-group paper, and is not revalidated on the 13 datasets used here; the AICBV generator is not released. This self-citation is load-bearing because the entire 'structural + functional' multimodal claim collapses if AICBV is merely a nonlinear re-expression of T1w tissue contrast. The prior work is invoked as established fact, but the present study does not independently verify that AICBV adds information beyond a learned transformation of T1w.

full rationale

The empirical MAE comparisons are not themselves circular: the T-model, A-model, and TA-model are trained and evaluated on held-out test data, and the linear-regression fusion is a standard stacking procedure. The circularity lies in the interpretation. The paper defines AICBV as the output of a model that takes the T1w MRI scan as input, then claims that combining it with T1w introduces 'functional information' and an 'additional independent covariate.' Since AICBV is a deterministic function of the same structural scan, the multimodal claim reduces by construction to ensembling two networks on one input; any improvement can be attributed to variance reduction or feature re-representation rather than to an independent vascular measurement. Additionally, the validity of AICBV as a CBV proxy is inherited from a self-cited prior paper ([12]) that shares authors and is not reproduced or released here. This is partial circularity, not a complete one: the reported MAE values are genuine experimental results, but the central scientific claim that the gain reflects structural-plus-functional fusion is forced by the paper's own definitions and self-citation chain.

Assumptions & free parameters 3 free parameters · 3 assumptions · 1 invented entities

All contributions rest on the validity of the AICBV synthesis model, which is cited but not shipped, and on the assumption that the synthetic maps are independent of T1w. The late-fusion coefficients are fitted on training predictions. These are the main unpaid inputs beyond the public MRI datasets.

free parameters (3)
  • VGG8 network weights (T1w and AICBV models) = not disclosed
    Learned on the 80% training split; all brain-age predictions depend on these weights.
  • Linear regression fusion coefficients (intercept, T1w-age, AICBV-age, sex) = not reported except sex coefficient -0.168
    TA and TAS models are linear fits on training and validation predictions; test MAE is evaluated after this fit.
  • AICBV synthesis model weights (CNN-Mamba from [12]) = pre-trained, not disclosed
    AICBV maps are generated by this external model; its parameters control the synthetic input and are not provided in this paper.
assumptions (3)
  • domain assumption AICBV maps contain functional/vascular information not present in T1w MRI.
    Invoked throughout Sections 1, 2.2, and 4.1; the multimodal fusion claim collapses if AICBV is just a nonlinear transform of T1w.
  • domain assumption The pre-trained AICBV model generalizes from its training distribution to all 13 datasets and to ADNI test subjects.
    AICBV is generated for every scan without per-dataset validation or distribution-shift analysis (Section 2.2, Table 3).
  • domain assumption The age-stratified 8:1:1 split with KL-divergence selection produces a leakage-free, representative test set.
    Section 2.4; the test set is a single split, and no repeated splits or external validation cohort are used.
invented entities (1)
  • AICBV (AI-generated cerebral blood volume maps) independent evidence
    purpose: Synthetic vascular/perfusion-like input derived from non-contrast T1w MRI; claimed to provide functional signal for brain-age estimation and disease concordance.
    The synthesis model was trained against contrast-enhanced CBV in the authors' prior work [12], giving an external benchmark. However, this paper does not revalidate the maps on its 13 datasets, and the generator is not released, so the independent evidence is inherited, not demonstrated here.

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Cite this review

Pith. "Pith review of Enhancing Brain Age Estimation with a Multimodal 3D CNN Approach Combining Structural MRI and AI-Synthesized Cerebral Blood Volume Measures." pith.science (2026). https://pith.science/paper/OAZ6ZIQE

@misc{pith2026241201865,
  author       = {Pith},
  title        = {Pith review of: Enhancing Brain Age Estimation with a Multimodal 3D CNN Approach Combining Structural MRI and AI-Synthesized Cerebral Blood Volume Measures},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/OAZ6ZIQE}},
  note         = {Machine review of arXiv:2412.01865}
}
abstract

Brain age gap estimation (BrainAGE) is a promising imaging-derived biomarker of neurobiological aging and disease risk, yet current approaches rely predominantly on T1-weighted structural MRI (T1w), overlooking functional vascular changes that may precede tissue damage and cognitive decline. Artificial intelligence-generated cerebral blood volume (AICBV) maps, synthesized from non-contrast MRI, offer an alternative to contrast-enhanced perfusion imaging by capturing vascular information relevant to early neurodegeneration. We developed a multimodal BrainAGE framework that integrates brain age predictions using linear regression from two separate 3D VGG-based networks, one model trained on only structural T1w scans and one trained on only AICBV maps generated from a pre-trained 3D patch-based deep learning model. Each model was trained and validated on 2,851 scans from 13 open-source datasets and was evaluated for concordance with mild cognitive impairment (MCI) and Alzheimer's disease (AD) using ADNI subjects (n=1,233). The combined model achieved the most accurate brain age gap for cognitively normal (CN) controls, with a mean absolute error (MAE) of 3.95 years ($R^2$=0.943), outperforming models trained on T1w (MAE=4.10) or AICBV alone (MAE=4.49). Saliency maps revealed complementary modality contributions: T1w emphasized white matter and cortical atrophy, while AICBV highlighted vascular-rich and periventricular regions implicated in hypoperfusion and early cerebrovascular dysfunction, consistent with normal aging. Next, we observed that BrainAGE increased stepwise across diagnostic strata (CN < MCI < AD) and correlated with cognitive impairment (CDRSB r=0.403; MMSE r=-0.310). AICBV-based BrainAGE showed particularly strong separation between stable vs. progressive MCI (p=$1.47 \times 10^{-8}$), suggesting sensitivity to prodromal vascular changes that precede overt atrophy.

Figures

Figures reproduced from arXiv: 2412.01865 by the authors.

Figure 1
Figure 1. (a) The count of subject age per 10-year interval within the dataset. (b) The distribution of [PITH_FULL_IMAGE:figures/full_fig_p010_1.png] view at source ↗
Figure 2
Figure 2. General pipeline for estimating brain age from T1w MRI and AICBV with deep learning [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Scatter and residual plots for each model on the test data set. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Average top 20% gradient values from T1w and AICBV encoder are overlaid on average [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]

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