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REVIEW 4 major objections 6 minor 47 references

MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Voxel-wise MLP removes acquisition bias from brain connectivity measures

desk verdict A genuinely useful harmonization benchmark whose winning method has an unclarified test-time input—if it leaks acquisition B, the headline ranking doesn't hold. read the letter →

arxiv 2411.09618 v1 pith:QQ4AAV5H submitted 2024-11-14 physics.med-ph cs.LG

Nancy R. Newlin , Kurt Schilling , Serge Koudoro , Bramsh Qamar Chandio , Praitayini Kanakaraj , Daniel Moyer , Claire E. Kelly , Sila Genc
show 28 more authors
Jian Chen Joseph Yuan-Mou Yang Ye Wu Yifei He Jiawei Zhang Qingrun Zeng Fan Zhang Nagesh Adluru Vishwesh Nath Sudhir Pathak Walter Schneider Anurag Gade Yogesh Rathi Tom Hendriks Anna Vilanova Maxime Chamberland Tomasz Pieciak Dominika Ciupek Antonio Tristán Vega Santiago Aja-Fernández Maciej Malawski Gani Ouedraogo Julia Machnio Christian Ewert Paul M. Thompson Neda Jahanshad Eleftherios Garyfallidis Bennett A. Landman
This is my paper · ORCID
classification physics.med-phcs.LG PACS 87.61.-c
keywords DiffusionMRIharmonizationtractometryconnectomicstractographymultilayerperceptronmulti-siteimagingQuantConnchallenge
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 paper reports a challenge in which 103 people were scanned twice on the same scanner using two deliberately different diffusion-MRI protocols, and nine teams were asked to preprocess the raw data so that downstream measurements would no longer depend on which protocol was used. The central claim is that acquisition protocol biases tractometry and connectomics substantially, and that the bias can be largely removed by the right harmonization: the winning submission corrected distortions, registered the two acquisitions, and learned a voxel-wise signal mapping with a multilayer perceptron, reducing cross-acquisition differences to small effect sizes across all twelve complex network measures and across bundle microstructure and macrostructure. Rotation-invariant spherical-harmonic mapping and a neural implicit resampling method also performed well. If this holds, multi-site studies that pool diffusion-MRI data can trust their connectivity and white-matter measurements more, which matters because these measures are increasingly used as disease biomarkers.

What carries the argument

The central mechanism is a paired evaluation design with a fixed downstream pipeline. Each subject is scanned under both protocol A (anisotropic resolution, 27 directions) and protocol B (isotropic resolution, 94 directions); after each team's harmonization, all data pass through the same processing chain—tensor fitting, fiber orientation distribution estimation, whole-brain tractography, bundle segmentation, tractometry, and connectome construction with a standard cortical atlas—and the outputs are scored by intra-class correlation across acquisitions, Cohen's d effect sizes, and median comparisons. This makes reproducibility of the measurements researchers actually use the target quantity, rather than voxel-level signal similarity.

What would settle it

Re-run all nine harmonization methods through a different but equally standard downstream pipeline—for example, a different tractography algorithm or a different cortical parcellation—and check whether the winning MLP method still removes all significant acquisition effects and keeps the same ranking. If the ranking changes or significant biases reappear under a plausible alternative pipeline, the paper's conclusion is specific to its evaluation pipeline rather than general.

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Extended reading notes

Core claim

The paper's claim, stated in its own terms, is that harmonization should be evaluated on downstream bundle and connectome measures, not just on voxel-level signal, and that when evaluated this way, voxel-wise machine-learning correction is the most effective strategy. In the un-harmonized reference data, the two protocols produced large and statistically significant differences in bundle surface area, fractional anisotropy, and eight of the twelve complex network measures—assortativity, betweenness centrality, edge count, modularity, nodal strength, participation coefficient, and related measures—while AD, MD, RD, bundle length, density, efficiency, and path length were comparatively stable. The top submission combined distortion correction, boundary-based registration of one acquisition onto the other, and an MLP trained to predict one acquisition's voxel signal from the other; it removed all significant acquisition effects in the twelve network measures and in the bundle measures studied, and it did not collapse inter-subject variation. Notably, two submissions that simply mapped spherical-harmonic coefficients across protocols introduced significant biases where the baseline had none, which the paper takes as evidence that naive coefficient mapping is not a safe harmonization strategy.

Load-bearing premise

The conclusion rests on the assumption that the fixed measurement pipeline—tensor fitting, tractography, bundle segmentation, and the cortical atlas—is itself accurate and unbiased across the two acquisitions, so that any remaining cross-acquisition differences can be blamed on the harmonization method.

Editorial extensions

If this is right

  • With the winning MLP-based harmonization, connectome and tractometry measures from the two protocols become comparable, so data collected under different protocols on the same scanner can be pooled for analysis.
  • Bundle surface area, fractional anisotropy, assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient should be treated as acquisition-sensitive in multi-site diffusion-MRI studies unless harmonization is applied.
  • AD, MD, RD, bundle length, density, efficiency, and path length are relatively stable across the two protocols and can be compared with less correction.
  • Methods that only map spherical-harmonic coefficients from one protocol to another can worsen cross-acquisition bias, so harmonization methods need to be validated on downstream measurements rather than on signal statistics alone.

Reading between the lines

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

  • My inference: if the same paired-subject, downstream-scored evaluation were applied to multi-scanner and multi-site data, it could become a standard benchmark for harmonization methods, since the current experiment isolates protocol differences while holding scanner constant.
  • My inference: the winning method's requirement that the same subjects be scanned under both protocols and co-registered limits its use to retrospective traveling-subject cohorts; a natural extension is to test whether the learned voxel mapping transfers to new subjects scanned under only one protocol.
  • My inference: because the ranking averages intra-class correlation across many heterogeneous measures, a method could rank highly by succeeding on easy-to-harmonize measures while failing on a clinically important one; re-scoring with per-measure weights or pre-specified primary outcomes could change the winner.
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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 / 6 minor

Summary. The manuscript reports the design and outcome of the MICCAI-CDMRI 2023 QuantConn challenge, in which 103 subjects (25 held-out test subjects) were scanned with two different diffusion MRI acquisitions on the same scanner. Nine submitted harmonization pipelines were evaluated by feeding harmonized data through a fixed downstream pipeline consisting of tensor fitting, CSD-based tractography, RecoBundlesX bundle segmentation, and an 84-node Desikan-Killiany parcellation, and then measuring cross-acquisition agreement with ICC (rater=acquisition) for 12 connectomics measures, 6 bundles x 4 microstructure measures, and 6 bundles x 6 macrostructure measures. Bias is further characterized with Cohen's d, Wilcoxon rank-sum tests, and coefficient of variation. The paper's central finding is that the top-performing method, Submission 1 ('The Harmonizers 1'), which combines PreQual preprocessing, cross-acquisition registration, and a voxel-wise MLP mapping, best reduces acquisition bias while preserving biological variation, with RISH mapping (Submission 2) and NeSH resampling (Submission 3) also effective.

Significance. If the reported ranking is valid, this is a valuable community resource and a meaningful step forward for diffusion MRI harmonization evaluation. The challenge releases a relatively large traveling-subject dataset (206 scans, 103 pairs), publicly shares code and data, uses a properly held-out test split, and is, to my knowledge, the first harmonization challenge to evaluate tractometry and connectomics as downstream outcomes. The paper also makes a concrete, falsifiable claim: that a voxel-wise MLP trained on co-registered same-subject pairs is the most effective harmonization method among those tested. The main strength is the clarity of the evaluation protocol and the availability of the challenge infrastructure. However, the validity of the headline result depends on a test-time input ambiguity in the winning method and on adequate disclosure of the relationship between the organizers and the submissions.

major comments (4)
  1. [Section 3.3.2, Section 4.1] The description of Submission 1 is ambiguous about what data are used as input to the MLP at inference time. The text states that 'the subject's data from both sites were concatenated along the channel dimension per voxel for input' and that 'the output format was chosen based on acquisition B.' If this two-site concatenation is also performed for the 25 held-out test subjects, then the harmonized acquisition-A output for a subject is a function of that same subject's acquisition-B image. Because the primary evaluation computes ICC with 'rater=acquisition' to compare measurements from acquisitions A and B, a model that can access B during inference can trivially reproduce B and inflate the reported Conn ICC of 0.98. The CoV analysis in Section 4.4 would also be invalid under this reading, since the harmonized output would be partially derived from the target acquisition. The authors must state explicitly which inputs are used at test time. If both acquisitions are used, the evaluation must be rerun with an inference-time variant that uses only acquisition A, or the ranking must be revised to exclude Submission 1 as not a genuine cross-acquisition harmonization. The same ambiguity applies to Submission 7 in Section 3.3.8.
  2. [Section 3.4, Section 4.1] The significance testing used to support the claim that Submissions 1, 2, and 3 removed significant acquisition effects in all 12 connectomics measures relies on Wilcoxon rank-sum tests at p<0.05 without any correction for multiple comparisons. The paper reports many tests across 9 submissions, 12 network measures, 6 bundles, and multiple microstructure and macrostructure features, so dozens of false positives are expected at alpha=0.05. The authors should report the total number of tests performed, apply a false-discovery-rate or family-wise error control procedure, or explicitly justify why per-comparison inference is appropriate for the conclusions drawn. This issue is load-bearing because the claim that specific methods fully remove acquisition bias is stated categorically in the abstract and Section 6.
  3. [Section 3.3, Section 6] The manuscript does not disclose which submitted methods, if any, were developed by the challenge organizers. Submission 1, the winning method, is built from PreQual preprocessing and registration components closely associated with the organizing group, and the challenge organizers had full access to the training data and designed the downstream evaluation pipeline. This creates a material evaluation-design interdependence that should be transparently disclosed. The authors should state which submissions came from independent teams, whether any organizer-affiliated team competed, and provide a sensitivity analysis of the ranking when organizer-authored submissions are excluded. At present, the reader cannot assess whether the top ranking reflects a genuinely independent community result or an organizer-advantaged entry.
  4. [Section 3.2, Section 6] The central conclusion that a particular harmonization method is 'most effective' is conditioned on a single fixed downstream pipeline: WLS tensor fitting, constrained spherical deconvolution, 10-million-streamline tractography, RecoBundlesX segmentation, and the Desikan-Killiany parcellation. The paper does not test whether the rankings generalize to other tractography algorithms, bundle segmentation methods, or parcellation schemes, even though the stated goal is 'robust quantitative connectivity.' The conclusion in Section 6 should be explicitly qualified as holding for this pipeline, or the authors should validate the ranking with at least one alternative downstream pipeline before making the broader claim.
minor comments (6)
  1. [Section 3.3.10] The text for Submission 9 states that it 'used the same methods as Submission 1,' but Table 1 and Section 3.3.9 indicate that Submission 9 is a spherical-harmonic coefficient mapping method like Submission 8 with the target and reference reversed; this cross-reference is incorrect and should be fixed.
  2. [Section 3.1.3] The acquisition B description contains a typo: '23s cm FOV' should read '23 cm FOV.'
  3. [Section 3.2] The atlas name is spelled inconsistently as 'Desikan-Killany' and 'Deskian-Killany'; the correct spelling is 'Desikan-Killiany.' The reference for FreeSurfer also appears malformed.
  4. [Section 4.2] The sentence 'All but submission 1 failed to remove acquisition bias from all measures for bilateral arcuate fasciculus' is ambiguous: it is unclear whether Submission 1 removed bias for all measures in both arcuate fasciculi or only for the bilateral average. Please rephrase.
  5. [Section 3.4] The terms 'Wilcoxon ranksum test' and 'Mann-Whitney U-test' are used interchangeably; please use one consistent name throughout.
  6. [Table 1] Reporting ICC scores as mean +/- SD across measures conveys variability across measures rather than sampling uncertainty of the ranking. Please include bootstrap confidence intervals or explicitly point to the ChallengeR ranking-stability analysis in the main text.

Circularity Check

1 steps flagged · score 6.0 of 10

Submission 1's voxel-wise MLP concatenates both acquisitions as input, so its top cross-acquisition ICC is partly self-constructed from the target acquisition.

  1. fitted input called prediction [Section 3.3.2 (Submission 1) with evaluation defined in Table 1 / Section 3.4]
    "an MLP was trained to summarize the acquisition A and acquisition B in a voxel-wise manner. The subject's data from both sites were concatenated along the channel dimension per voxel for input. The output format was chosen based on acquisition B ... A machine learning approach that learned voxel-wise cross-acquisition relationships was the most effective at harmonizing connectomic, microstructure, and macrostructure features, but requires the same subject be scanned at each site co-registered."

    As written, the harmonization input for Submission 1 is the concatenation of both acquisitions and the output is in acquisition B's format; no A-only test-time procedure is stated, and the abstract says the method requires the same subject at each site. The harmonized acquisition-A signal is therefore a function of that subject's acquisition-B signal. The evaluation scores the submission by ICC with rater=acquisition, i.e., agreement between measures derived from the two acquisitions. Comparing B-derived measures with measures derived from f(A,B) can force high agreement because the MLP can in principle reproduce B from its input.

full rationale

Apart from Submission 1, the challenge evaluation is a standard held-out benchmark: 25 test subjects are separated from training, the downstream tractometry/connectomics pipeline is fixed, and the ICC, Cohen's D, and Wilcoxon comparisons are external to the harmonization methods. Self-citations in the paper, including Schilling et al., Newlin et al., Chandio et al., and Hendriks et al., provide background methods or prior data and are not the load-bearing justification for the rankings. Organizer participation in a submission is a conflict-of-interest concern but not by itself circularity. The circularity is localized to the top-ranked submission: if, as described, both acquisitions are concatenated as MLP input at inference, the reported cross-acquisition reproducibility of that submission is to some degree constructed from the very acquisition used as the comparison target. The other eight submissions, and the independent evidence that NIMG and NeSH reduce bias, do not share this reduction, so the paper is only partially circular.

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

This is an empirical evaluation, not a derivation, so there are no fitted free parameters or invented entities. The claims rest on domain assumptions about the validity of the imaging pipeline and the evaluation metric.

assumptions (3)
  • domain assumption The Desikan-Killany atlas and the six chosen bundles provide a valid representation of white matter connectivity.
    Used throughout Section 3.2 to derive all evaluation measures.
  • domain assumption ICC computed with acquisition as rater is an appropriate measure of cross-acquisition reproducibility.
    Section 3.4 defines the ranking based on ICC.
  • domain assumption The two acquisitions contain the same biological information; any difference is due to protocol.
    Challenge premise, Section 3.1.

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Cite this review

Pith. "Pith review of MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI." pith.science (2026). https://pith.science/paper/QQ4AAV5H

@misc{pith2026241109618,
  author       = {Pith},
  title        = {Pith review of: MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QQ4AAV5H}},
  note         = {Machine review of arXiv:2411.09618}
}
read the original abstract

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. There is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Submissions are evaluated on the reproducibility and comparability of cross-acquisition bundle-wise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences.

Figures

Figures reproduced from arXiv: 2411.09618 by the authors.

Figure 1
Figure 1. We released 206 scans across two acquisitions, “A” (blue) and “B” (orange). Acquisition A was acquired with anisotropic resolution and 27 gradient direc￾tions. Acquisition B was acquired with isotropic resolution and 94 gradient direc￾tions. Participants altered the DW-MRI with the harmonization of their choos￾ing. We then feed this harmonized data through a standard processing pipeline of tensor fitting, orientatio… view at source ↗
Figure 2
Figure 2. Using the harmonized data provided by participants, the full testing pipeline is as follows: tensor fitting, fODF estimation, whole brain tractography, bundle segmentation and tractometry, then connectomics, and finally complex network analysis. These processes result in three groups of analysis: complex network measures, bundle microstructure, and bundle macrostructure, which we evaluate the submissions on. 3.2 Dif… view at source ↗
Figure 3
Figure 3. Successful harmonization methods will reduce significant acquisition effects in these measures from the un-harmonized reference (“Ref”). Slashes indicate signif￾icant difference (p < 0.05) in median between measures derived from acquisitions A and B. We compute Cohen’s D effect-size differences between connectomics measures from acquisitions A and B. 1094 [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: We evaluate each submission on their ability to harmonize macrostructural fea￾tures of 6 bundles. Successful harmonization will reduce significant acquisition effects in these features from the reference (“Ref”). We report normalized effect￾size with Cohen’s D. Slashes…
Figure 5
Figure 5. Figure 5: We compare the cross-acquisition (Acquisition A is blue, Acquisition B is orange) shape agreement of reconstructed bundles for one subject in the un-harmonized reference dataset and top performing harmonization technique (The Harmonizers 1) with the BUAN shape similari…
Figure 6
Figure 6. Figure 6: We evaluate each submission on their ability to harmonize microstructural fea￾tures of 6 bundles. Successful harmonization will reduce significant acquisition effects in these features from the un-harmonized reference (“Ref”). We report normalized effect-size with Cohe…
Figure 7
Figure 7. Figure 7: We compute CoV from The Harmonizers 1 and the baseline (BL) connectomics, macrostructure, and microstructure measures for each acquisition. 6. Conclusion As a field, we are working toward quantitative analysis of DW-MRI. This necessitates con￾nectomic and tractometry m…

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Pith tools

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