Title: A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning for Any Atlas and Disorder
Venue: NeurIPS 2025
Accepted: no
Keywords: Brain Graph Foundation Model

Functional Magnetic Resonance Imaging (fMRI)

Neuroscience

Graph Pre-Training

Fine-Tuning

Prompt Learning
Abstract: As large language models (LLMs) continue to revolutionize AI research, there is a growing interest in building large-scale brain foundation models to advance neuroscience. While most existing brain foundation models are pre-trained on time-series signals or region-of-interest (ROI) features, we propose a novel graph-based pre-training paradigm for constructing a brain graph foundation model. In this paper, we introduce the Brain Graph Foundation Model, termed BrainGFM, a unified framework that leverages graph contrastive learning and graph masked autoencoders for large-scale fMRI-based pre-training. BrainGFM is trained on a diverse mixture of brain atlases with varying parcellations, significantly expanding the pre-training corpus and enhancing the model's ability to generalize across heterogeneous fMRI-derived brain representations. To support efficient and versatile downstream transfer, we integrate both graph prompts and language prompts into the model design, enabling BrainGFM to flexibly adapt to a wide range of atlases, neurological and psychiatric disorders, and task settings. Furthermore, we employ meta-learning to optimize the graph prompts, facilitating strong generalization to previously unseen disorders under both few-shot and zero-shot learning conditions via language-guided prompting.BrainGFM is pre-trained on 27 neuroimaging datasets spanning 25 common neurological and psychiatric disorders, encompassing 2 types of brain atlases (functional and anatomical) across 8 widely-used parcellations, and covering over 25,000 subjects, 60,000 fMRI scans, and a total of 400,000 graph samples aggregated across all atlases and parcellations.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces the Brain Graph Foundation Model (BrainGFM), a novel graph-based framework for analyzing fMRI data, pretrained and tested on extensive and diverse datasets parcellated by different atlas. BrainGFM leverages a pre-training paradigm that combines graph contrastive learning with graph masked autoencoders to effectively model brain connectivity. The model incorporates graph prompts optimized via meta-learning for few-shot generalization to unseen disorders, and language prompts that provide semantic guidance for zero-shot transfer to new tasks, atlases, and disorders. BrainGFM has achieved superior performance and efficiency compared to previous brain foundation models.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\nIncorprating heterogeneous atlas in one single model is a novel setting, which is also quite useful in fMRI domain.\n\nThe datasets and downstream tasks span quite diverse brain disorders.\n\nThe model has demonstrated zero-shot capability, which should be the first time for brain foundation models.\n\nThe model works better than previous SOTA models on brain graph like BrainMass.\n\nWeakness:\n\nWhat is the difference between \"time-series-based\" models and \"ROI-based\" models mentioned in intro? This is confusing, for example, BrainLM [1] and Brain-JEPA [2] both have the parcellated time series as input.\n\n“While ROI-based brain FMs are more efficient, they often neglect inter-regional connectivity” What does this mean? The whole point about functional connectivity is to capture inter-regional connectivity?\n\nThe authors may want to say time series based and connectome based? The ROI based is confusing as those time series were parcellated as well.\n\nThe discussion on those previous works was really confusing. In addition, since many of the previous brain foundation models were based on time series,  it would be beneficial to more clearly articulate the benefits of FC as input in this domain from the neuroscience perspective. \n\nThe lack of formatting distinction between main content and citations (e.g., xxx et al.) impairs readability.\n\nThere are previous works on prompt tuning of brain models including [3] and [4] which were missed in the related work. The authors should also discuss Meta-Matching [5] which represents a widely used meta learning framework in FC.\n\nWhy 100-300 ROIs were chosen in Schaefer atlas? How about the 400 and more? How is the age distribution like in pretraining datasets? It seems that the majority of the pretraining data is childhood and young adult, with the middle age to aging population under-represented. \n\nBrain gradient positioning [2] has been proved to work well for fMRI data, which should be compared in positional encoding part as well.\n\nline 211-213, using limited samples to fine tune large models, it leads to overfitting instead of \"underfitting\"?\n\nFor the meta learning, does it mean that data from multiple diseases is required during this stage? which may be a limitation in real-world application?\n\nBrain-JEPA was missed in comparison, which should be a more advanced model than BrainLM.\n\nThere are significant concerns about the reliability of their checklist responses and the reproducibility of this work. In checklist Question 5, the authors state that \"anonymized code and benchmark instructions will be released in the supplementary material,\" yet no supplementary material is provided. On the other hand, while the authors claim to include error bars for their results, Table 1 shows no error bars for the main experimental findings.\n\n[1] BrainLM: A foundation model for brain activity recordings. ICLR 2024.\n\n[2] Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking. NeurIPS 2024.\n\n[3] BrainPrompt: Multi-Level Brain Prompt Enhancement for Neurological Condition Identification. MICCAI 2025.\n\n[4] Prompt Your Brain: Scaffold Prompt Tuning for Efficient Adaptation of fMRI Pre-trained Model. MICCAI 2024.\n\n[5] Meta-matching as a simple framework to translate phenotypic predictive models from big to small data. Nature Neuroscience 2022.\n\nQUESTIONS:\nPlease refer to the weakness section.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-14"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe manuscript is well written and presents an in-depth exploration of integrating multiple brain parcellation schemes within a unified framework for foundation models in Graph Theory. This study addresses the challenges of few-shot and zero-shot learning in neuroimaging. The authors introduce BrainGFM, a novel model that is pre-trained to use a combination of graph-specific strategies, incorporating multiple parcellations to boost performances.\n\nThis work uses data from 27 widely used fMRI datasets from diverse sites, and 25 common neurological and psychiatric disorders. This scale of data enhances the model’s robustness and generalizability.\n\nSTRENGTHS AND WEAKNESSES:\nThe manuscript is very clear, I particularly enjoy the Methodology section where each contribution is justified with a clear and direct motivation. The manuscript is very well written and well organized. The experiment is adequate for the presented hypotheses.\nThe 6 subsections to the experiments (comparison with references, ablation Full/Few/Zero-Shot, parcellations, time series/ROI/Graph FM, ablation parcellation, ablation on FM pre-training) are well designed to justify and quantify the author's contributions.\n\nOne weakness (which is not that problematic) is that the authors are pushing multiple high complexity 'story' into a somewhat short manuscript with a lot of very important details in supplementary materials. The contributions are such a high level, with so many layers of expected knowledge that the clarity of the main text is decreasing and the impact potentially obscure by the complexity of the multidimensional experimentations. There is no clear solution, I just wanted to share this.\n\nQUESTIONS:\n1) While the manuscript makes a strong case for leveraging large-scale data to address several challenges, it appears to underemphasize critical aspects of data harmonization prior to processing, as well as class imbalances, both within and across datasets. It remains unclear whether variables such as age and sex distributions are uniform across cohorts, or whether control subjects are consistently included in each dataset. These factors can significantly influence model performance and generalizability. I also wonder whether there is any correlation between the model’s overall accuracy and the balance of these demographic and clinical variables. While I appreciate that the primary focus is on advancing the technical aspects of the foundation model, the curation and construction of the input data are equally vital and warrant more thorough discussion.\n\n2) In Table, I would be curious to have more context about the conditions and the datasets, for these conditions I would like to know more about the clinical significance. In some case sensitivity is a useless metric and specificity is much more important (AD for example, a clinician knows something is wrong, but being certain about a diagnostic is much harder), and what would be the target of clinical significance ? In my experience specificity is by far the most important one. And overall for this condition what is the overall target for the field, is it already achieved with a Vanilla GCN (probably not), but are we getting closer with the proposed method? This information would be very important context, in a clinical setting what is the sensitivity/specificity expected for a visit with a neurologist? Or is the goal to screen people before they see a neurologist (and in that case what is the expected values for sensitivity/specificity?)\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-16"}

comments: SUMMARY:
This paper introduces BrainGFM, a novel brain graph foundation model leveraging a graph-based pre-training paradigm for fMRI data. Key strengths include its ability to unify heterogeneous fMRI datasets, its robust performance across various brain atlases and disorders through innovative graph and language prompt-tuning, and its demonstrated superior efficiency and effectiveness compared to existing foundation models.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The paper proposes a unique graph-based pre-training paradigm using graph contrastive learning (GCL) and graph masked autoencoders (GMAE), which effectively captures the topological structure of brain graphs, addressing limitations of prior time-series or ROI-based models.
2. A significant contribution is the aggregation of over 25,000 subjects and 60,000 fMRI scans from 27 diverse datasets, processed with multiple atlases and parcellations. 
3. The integration of graph prompts and language prompts, optimized through meta-learning, enables BrainGFM to flexibly adapt to new, unseen tasks, atlases, and disorders in both few-shot and zero-shot settings, showcasing strong transferability.

Weaknesses:
The paper does not provide a detailed ablation study or analysis on the individual contributions of GMAE and GCL components within the pre-training framework. Understanding their isolated and combined impact would further clarify their effectiveness and the design choices.

QUESTIONS:
Please refer to the weaknesses.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nBrainGFM combines graph-based modeling, meta-learning, and language-guided prompting to create a versatile model that adapts to various brain disorder classifications and datasets.\n\nSTRENGTHS AND WEAKNESSES:\nS1.  BrainGFM demonstrates strong adaptability by leveraging a large and diverse pre-training dataset, allowing it to perform consistently well across various neurological and psychiatric conditions.\nS2. The model is well-suited for clinical scenarios with limited labeled data, showing solid performance in few-shot and even zero-shot classification tasks.\n\nHere are the three points translated to English:\n\nW1. The brain graph construction method described in Section 3.1 and Appendix E has fundamental flaws. Using simple Pearson correlation coefficients between ROIs followed by binarization discards critical information. In Appendix E, the correlation matrix is directly used as node features, meaning each node's features are its connection strengths to all other nodes. Does this approach of using connectivity patterns directly as node features cause the model to over-rely on specific connection configurations rather than learning abstract brain network representations?\n\nW2. The meta-learning design in Section 3.3 contains conceptual confusion. The authors define each \"task Ti\" as a \"disease+atlas\" combination (line 738), using support set Dtrain for graph prompt adaptation in the inner loop and query set Dtest for meta-parameter updates in the outer loop. However, the paper fails to clarify what constitutes a \"task\" - if it's disease classification, different atlases should be viewed as different perspectives of the same task; if it's atlas adaptation, disease labels become irrelevant. In Table 2's ablation study, using the same test set (ABIDE II) to evaluate different pretraining strategies violates meta-learning principles by using target domain data to guide pretraining strategy selection.\n\nW3. Regarding zero-shot capabilities, Table 12's External Test group includes UCLA_CNP and REST-META-MDD, but are the disease types in these datasets actually included in HBN? Figure 2's zero-shot performance evaluation is conducted on known disease types rather than truly unseen diseases. Does this contradict the paper's claimed \"zero-shot generalization to unseen diseases\"?\n\nQUESTIONS:\nplease refer to weakness\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}