Title: DCA: Graph-Guided Deep Embedding Clustering for Brain Atlases
Venue: NeurIPS 2025
Accepted: yes
Keywords: Neuroscience

Brain atlas

Parcellation

Pretraining

Graph

Deep embedding clustering

voxel-level
Abstract: Brain atlases are essential for reducing the dimensionality of neuroimaging data and enabling interpretable analysis. However, most existing atlases are predefined, group-level templates with limited flexibility and resolution. We present Deep Cluster Atlas (DCA), a graph-guided deep embedding clustering framework for generating individualized, voxel-wise brain parcellations. DCA combines a pretrained voxel-level fMRI autoencoder with spatially regularized deep clustering to produce functionally coherent and spatially contiguous regions. Our method supports flexible control over resolution and anatomical scope, and generalizes to arbitrary brain structures. We further introduce a standardized benchmarking platform for atlas evaluation, using multiple large-scale fMRI datasets. Across multiple datasets and scales, DCA outperforms state-of-the-art atlases, improving functional homogeneity by 98.8% and silhouette coefficient by 29%, and achieves superior performance in downstream tasks. Furthermore, atlases demonstrate heterogeneous performance across various tasks and an atlas derived from a fine-tuned model yields superior results for its specific application. Codes are available at https://github.com/ncclab-sustech/DCA.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper suggests a method to derive personalized atlases for fMRI by learning the clustering jointly with finetuning the pretrained SWIN encoder backbone. Inspired by deep embedding clustering, clustering is learn by matching the learnt cluster centers with the results of spectral clustering of a 26-nearest neighbour graph derived from SWIN embedding outputs, where the adjacency is constructed with the cosine similarity between output embeddings.\nTo evaluate the atlases the paper uses homogeneity (averaged within parcel voxels correlation averaged across parcels later), silhouette score, and downstream tasks (eg age classification) performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n* DCA shows an improvement on homogeneity and silhouette score compared to the common atlases\n* Adopting silhouette score for using correlations as a distance function is a neat idea as its not vulnerable for the arbitrary shapes of curse of dimensionality problems this way\n* Paper provides the code and the plots contain error bars\n\nWeaknesses\n* DEC implies a specified number of clusters $k$ as input for the model, however, a discussion on what happens if $k$ is defined incorrectly is missing. [1-2] could be helpful for this. (varying $k$ could be interesting, especially if *\"discover novel parcellations\"*, lines 102-103, is the goal). \n* The metric performance (homogeneity and silhouette score) seems to be poor for all of the methods, including the proposed DCA. \n* Some crucial design choices (eg number of neighbours for the kNN graph for spectral clustering) are not explained and their impact is not experimentally explored\n\n[1] Leiber, C., Strauß, N., Schubert, M., & Seidl, T. (2024). Dying Clusters Is All You Need--Deep Clustering With an Unknown Number of Clusters. arXiv preprint arXiv:2410.09491.   \n[2] Ronen, M., Finder, S. E., & Freifeld, O. (2022). Deepdpm: Deep clustering with an unknown number of clusters. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 9861-9870).\n\nQUESTIONS:\n* Is there a conceptual reason not to replace Swin Transformer with Hiera [1] backbone, which is also hierarchical and is supposed to be simpler and better performing?\n* In why in plot 3b there are no points for DCA below 100 to compare, for example to compare with Yeo 7 and Yeo 17 ?\n* Do you have an explanation why homogeneity and silhouette score values are so low in general? How do you interpret it?\n* Сould you provide the comparison of your predicted atlases with Schaefer et al [3] using intersection over union or similar metrics. It might be helpful to see how actually different are the atlases in terms of space and be more interpretable than homogeneity or silhouette score, especially for the people not familiar with the fMRI data. Are there any fully human labeled datasets for the atlases? Would [2] be helpful?\n* In [3] they used metric close to homogeneity but also with accounting for the parcel size (see equation (2) from [3]). Why have you decided not to account for the parcel size here? Could we add the comparison between atlases using metric from [3]?\n* In appendix Fig 4 and Table 5 - What is your explanation that DCA is worse with the DCBC metric than the other methods?\n\n\nMinor\n* In lines 129-130 you write *\"In parallel, we build a 26-nearest-neighbour graph over the ROI masked voxels, weighting edges by pairwise embedding correlations. \"*. Why 26? How sensitive is the network for the change of $k$ here (eg 20 instead of 26)? Does it come from some biological motivation or some previous research? \n* In equation 2 (lines 163-164) you truncate the cosine similarity at 0. One could just keep $k$ edges with biggest absolute values. If its because inside the single region of interest everything should be only positively correlated, it might be worth adding an explicit explanation.\n\n[1] Ryali, C., Hu, Y. T., Bolya, D., Wei, C., Fan, H., Huang, P. Y., ... & Feichtenhofer, C. (2023, July). Hiera: A hierarchical vision transformer without the bells-and-whistles. In International conference on machine learning (pp. 29441-29454). PMLR.  \n[2] Hermosillo, R. J., Moore, L. A., Feczko, E., Miranda-Domínguez, Ó., Pines, A., Dworetsky, A., ... & Fair, D. A. (2024). A precision functional atlas of personalized network topography and probabilities. Nature neuroscience, 27(5), 1000-1013.  \n[3] Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X. N., Holmes, A. J., ... & Yeo, B. T. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral cortex, 28(9), 3095-3114.\n\nLIMITATIONS:\nyes, though I would add a sentence that the number of parcels are hard fixed right now and the model does not have a capability to adjust it now", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Deep Cluster Atlas (DCA), a novel deep learning framework for generating individualized, voxel-wise brain atlases from resting-state fMRI data. Unlike traditional atlases that are predefined and group-level, DCA uses graph-guided deep embedding clustering to create functionally coherent and spatially contiguous brain parcellations with controllable resolution and anatomical coverage. The method supports both group-level and subject-specific atlas construction and generalizes across different brain regions.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper identifies critical limitations of existing brain atlases: fixed resolution, group-level averaging, and lack of flexibility and individual specificity, convincingly argues for the need for individualized, voxel-level, and flexible parcellation methods that better reflect both functional and anatomical information.\n2. The proposed DCA framework has good flexibility and generalizability; the standardized benchmarking platform provides quantitative evidence of DCA's superiority over several state-of-the-art atlases across a range of tasks and spatial scales.\n3. The author provided the source code and corresponding results.\n\nWeakness:\n1. The comparative methods included in the main experiments are relatively outdated, lacking any recent approaches proposed after 2020, which may weaken the relevance and rigor of the evaluation.\n2. The figures in the main text suffer from low resolution (the appendix is good), and enhancing their clarity would significantly improve the readability and presentation quality of the paper.\n3. The ablation analysis in the main paper lacks depth, focusing solely on coarse-grained, modular ablations without exploring finer-grained factors that could provide deeper insights into the contribution of each component.\n\nQUESTIONS:\n1. I noticed that the Related Work section includes several recent approaches on MRI-based brain atlas construction and brain segmentation published in the past two to three years. However, these methods are not included in the comparative experiments. Could the authors clarify whether this omission is due to the lack of publicly available implementations or other reasons?\n2. In addition to the KL divergence loss, it would be interesting to know whether the authors have experimented with other distribution-based loss functions, and how those choices might impact the model's performance.\n\nLIMITATIONS:\nPlease see the weakness.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-19"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Deep Cluster Atlas (DCA), a novel framework for generating individualized and group-level voxel-wise brain parcellations. DCA leverages a pretrained Swin-UNETR encoder combined with spatially regularized deep clustering to create high-resolution brain atlases that are both functionally coherent and anatomically contiguous. It overcomes limitations of traditional brain atlases, which are often fixed, group-level templates with limited flexibility. DCA allows flexible control over parcellation granularity and anatomical scope, and it outperforms existing methods in terms of homogeneity, silhouette coefficient, and performance on downstream tasks like autism diagnosis and cognitive decoding.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. Innovative Framework. DCA introduces an advanced deep learning model for brain atlas construction, utilizing graph-guided deep clustering. This method combines functionally coherent brain regions with spatial continuity, which has been a challenge in previous methods.\n2. High Flexibility. The approach supports flexible control over parcellation granularity, allowing users to generate atlases tailored to their specific needs.\n3. Extensive Experiments. The paper presents extensive experiments demonstrating that DCA consistently outperforms state-of-the-art atlases (e.g., Yeo, Brodmann, Schaefer) across various metrics, including homogeneity, silhouette score, and performance on downstream tasks such as autism diagnosis and cognitive decoding.\n4. Comprehensive Evaluation. The authors develop a benchmarking platform to evaluate the quality of atlases, using both internal metrics (homogeneity, silhouette score) and external metrics based on downstream tasks.\n5. Well-Presented. The paper is well-organized and easy to understand. The presentation of the methodology and experimental results is clear, and the use of figures enhances the paper's readability.\n\nWeaknesses\n1. Modest Technical Innovation. The main contribution of the paper lies in designing individualized and group-level brain atlases with a focus on maintaining functionally coherent and anatomically contiguous regions. The paper also provides a platform for evaluating these atlases and presents extensive experimental validation. From a technical perspective, the approach essentially relies on a deep clustering model, using a pre-trained encoder with two clustering objectives: one based on prototypes and the other based on a graph, which correspond to the two goals of functionally coherent and anatomically contiguous regions, and aligns these clustering objectives. Technically, this is a multi-objective clustering and alignment task, which is a fairly common approach in clustering methods, and thus, the innovation in terms of technical novelty is not particularly significant.\n2. Unclear Technical Details. (1) In Figure 2(C), regarding the Voxel-level atlas evaluation platform, how is functional connectivity derived? Is it different from the standard method, which computes the Pearson correlation coefficient between average time series of different brain regions? This should be clarified. (2) In line 154, the cluster centroids are initialized with orthogonal rows and L2-normalized. What are the advantages of this initialization method? The rationale behind this choice needs further explanation. (3) In line 160, for the KNN graph, why is the number of neighbors K fixed at 26? Is there a specific reason for choosing this value rather than others? A more detailed explanation of this choice is needed.\n3. Not Sufficiently Convincing Evaluation. The paper primarily evaluates the proposed atlas using a single classification method (SVC) across all downstream tasks. However, to more comprehensively assess the performance, it would be beneficial to compare the results with other existing brain atlases using a variety of established models and algorithms, including more advanced techniques such as graph-based deep learning models and other state-of-the-art machine learning methods. This would provide a more robust validation of whether the proposed atlas consistently outperforms other atlases across different models and tasks.\n4. Method Stability. In the supplementary material, on page 11, Table 6 shows that the proposed method performs poorly on FC stability compared to other methods. Since the proposed method is based on clustering, which is often sensitive to various factors, the results might be unstable. How can the stability of the generated atlas and FC connectivity be guaranteed? Furthermore, in Table 6, the proposed method underperforms on several metrics compared to other atlases. The reasons for these results should be explained, and how the method can be made more stable should be clarified.\n5. Choice of Number of Parcels K. The paper mentions that different values of K provide different resolutions, but it would be interesting to explore how K influences downstream task performance. Is there an optimal K that yields the best experimental results, or is the impact of K on downstream tasks unstable? This needs further investigation.\n6. Computational and Storage Demands. The high computational and storage requirements of the method are mentioned, but the actual demands are not explicitly detailed in the paper. It would be helpful for readers if the practical computational cost of voxel-based methods, in terms of memory and execution time, were provided.\n7. On open access. The code has been released. However, in terms of data, more commonly used datasets should be provided with the generated atlas outputs, so that the community can conveniently conduct follow-up research.\n\nQUESTIONS:\n1. Regarding the Voxel-level atlas evaluation platform, how is functional connectivity derived? Is it different from the standard method, which computes the Pearson correlation coefficient between average time series of different brain regions? \n2. In line 154, the cluster centroids are initialized with orthogonal rows and L2-normalized. What are the advantages of this initialization method? \n3. In line 160, for the KNN graph, why is the number of neighbors K fixed at 26? Is there a specific reason for choosing this value rather than others? \n4. Since the method relies on clustering, which can be sensitive to various factors, how can the stability of the generated atlas and FC connectivity be ensured?\n5. The impact of K on downstream task performance should be explored. Is there an optimal K for the best results, or does it vary across tasks?\n\nLIMITATIONS:\n1. Technically, this is a multi-objective clustering task, a common approach in clustering methods, so the technical novelty is not particularly significant.\n2. The proposed atlas is evaluated using only SVC. Comparing the results with other brain atlases using a variety of advanced models, especially graph-based deep learning methods, would provide a more robust validation of the atlas's performance.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents Deep Cluster Atlas (DCA), a graph-guided deep-embedding framework for generating individualized, voxel-level brain parcellations from resting-state fMRI. It begins by pre-training a Swin-UNETR auto-encoder on heavily masked 4-D fMRI blocks, producing rich spatiotemporal embeddings for every voxel. DCA then alternates among three steps---re-weighting graph edges via spectral clustering, updating K cluster centroids, and refining the embeddings---until it converges to generate parcels that are both spatially contiguous and functionally coherent. Tested on 1000 Human Connectome Project subjects and resolutions ranging from 100 to 800 parcels, DCA achieves about 74% higher homogeneity and 24% better silhouette scores than widely used atlases.\n\nSTRENGTHS AND WEAKNESSES:\n## Strengths\n- The proposed method is explained in a clear and intuitive way, which facilitates the understanding. \n- The proposed method achieves about 74% higher homogeneity and 24% better silhouette scores, and the ablation study further demonstrates the effectiveness of each model component. \n- The problem of more individualized brain atlas construction is an important scientific question.  \n\n## Weaknesses\nThe full evaluation results on all 12 downstream tasks (as shown in Appendix Table 6) do not show consistent advantage of DCA against baseline methods. DCA 100 only outperforms 5 out of 12 tasks against the baselines, and DCA 400 only outperforms baselines on 2 out of 12 tasks. \n- This raises the question about whether DCA's gain in homogeneity and silhouette scores can be translated to practical performance improvement of downstream tasks. \n- The authors should clarify the criteria of how they decide which tasks are reported in the main paper to avoid the perception of cherrypicking the results. More detailed discussions on why the DCA have more advantages on certain tasks than others should also be included. \n- It is unclear what metrics the authors are reporting for the task performance in Fig. 4 and Table 6. Are these accuracy, F1 scores, or are they different from tasks to tasks? More details about the metrics should be added in Section 5 in the main paper.\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nYes, the authors have adequately addressed the limitations and potential negative societal impact of their work.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-08"}