A Brain Graph Foundation Model: Pre-Training and Prompt-Tuning across Broad Atlases and Disorders
Authors: Xinxu Wei
kanhao zhao
Yong Jiao
Lifang He
Yu Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Brain Graph Foundation Model
Functional Magnetic Resonance Imaging (fMRI)
Neuroscience
Graph Pre-Training
Fine-Tuning
Prompt Learning
Primary area: applications to neuroscience & cognitive science

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 connectome 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 pre-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 established 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. The code is available at https://github.com/weixinxu666/BrainGFM.

Published as a conference paper at ICLR 2026
A BRAIN GRAPH FOUNDATION MODEL:PRE-TTRINNG AND PROMPT-TUNING ACROSS BROAD ATLASES AND DISORDERS
Xinxu Wei", Kanhao Zhao", Yong Jiao", Lifang H Yu Zhang
"Lehigh University, Bethlehem PA, USA
Stanford University, Palo Alto CA, USA x xiw523elehigh.eo 1ih31 90lehigh.ehhhceeeeee edu, yzhangsu@stanford.eee
ABSTRACT
As large language models (LLMs) continue 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 connectome features, we propose a novel graph-based pre- training paradigm for constructing a brain graph foundation model. this paper, we introduce the Brain Graph Foundation Model, termed BrainGFM, unified framework that leverages graph contrastive learning and graph masked autoen- coders for large-scale fMMI-based pre-training. BrainGFM is pre-trained diverse mixture of brain atlases with varying parcellations, significantly expanding the pre-training corpus and enhancing the model's ability generalize across het- erogeneous fMMIIddrrieed 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 meta-learming to optimize the graph prompts, facilitating strong generalization to previously unseen disorders under both few-shot and zero-shot learning conditions via inguage-ggiide prompting. BrainGFM is established on 27 neuroimaging datasets spanning 25 common ncurological and psychiatric disorders, encompass- ing 2 types of brain atlases (functional and anatomical) across 8 widely- parcellations, and covering over 25,000 subjects, 60,000 fMRI scans, and a total 400,000 graph samples aggregated across all atlases and parcellations. The code is available at htthhtp::/// / com/ wwwxinxu6see / BrainGFM
INTRODUCTION
With the rise of large language models (Achiam et al. 2023), large-scale pre-trained foundation models (FMs) have been proposed across various domains. including computer vision (Touvron al., (023), natural language processing (Achiam et al.) 2023), and data mining (Xia et al. 2024). Recently, the field of neuroscience has also begun to witness the emergence of brain foundation models. As a widely used data modality neuroscience, functional magnetic resonance imaging (fMRI) (Markiewicz et al. 2021 Bycroft et al.) 2018) plays crucial role in understanding brain function and dysfunction. Developing a fMRI-based brain foundation model is of great importance for advancing neuroscience and its translational research. Due to the high complexity and cost of fMRI data acquisition (Van Essen et al. 2012 Cui et al. 2022), coupled with strong heterogeneity and substantial inter-subject variability, most existing traditional deep learning- based fMRI models (Wei)
et al. 2025 Kan et al.] 2022 Jiao et al., 2025) are trained on relatively small datasets. Consequently, these models typically tailored to specific tasks, disorders, cohorts, resulting in limited general- izability, poor flexibility, and weak transferability to unseen tasks, datasets or disorder conditions. Furthermore, training on small-scale datasets often leads to under-fitting, ultimately compromising model performance and reliability. These issues have become common limitations of traditional deep
Corrrsponning authors.

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learning models for fMRL. However, they can be effectively addressed by building fMRI brain FMs. Since FMs are typically pre- pre-trained on large-scale datasets with rich diversity (Touvron et al. 2023] Xia et al. 2024), spanning various data types and knowledge representations within the neuroscience domain, the resulting models exhibit strong generalization and broad applicability.
Previous fMRI FMs have uniformly adopted Transformer-basee architectures and were exclusively pre-trained on either time-series data or ROI-level features, resulting in two main categories: time-
series-based (Caro al., 2023 Thomas et al., 2022) and Comnectome/FC-based (Yang al.) 2024 Hu et al. 2024] Dong el al., (024a) IMRI brain FMs. However, building brain FMs faces several critical challenges that many previous approaches have either overlooked or failed to effectively address. (1) Data Availability & Heterogeneity. fMRI data are difficult and costly to collect and pre-process (Poldrack & Gorgolewski 2017), yet pre-training FMs typically requires large- scale datasets. Existing fMRI datasets are not only limited in quantity but also exhibit substantial heterogeneity across sources. Effectively leveraging and integrating these heterogeneous datasets is thus a fundamental challenge. Many existing brain FMs have constructed relatively large-scale IMRI pre-training datasets, but these are typically based on a single brain parcellation or atlas (Thomas et al. 2022). This overlooks the fact that integrating multiple parcellation templates not only expand the scale of available fMRI data but also provide diverse and even complementary features across different brain parcellations (Hermosillo et al. 2024). (2) Pre-Training Computational Cost. The computational cost of pre-training brain FMs typically depends on the form of the fMRI data and the chosen pre-training strategy. Time-scries-bide based brain FMs are pre-trained directly on raw fMRI time series, resulting in high computational demands with masked modeling pre-training paradigm. While Comnectome/FC-basse regional connectivity, leading to suboptimal performance on various downstream tasks. Striking a balance between computational efficiency and modeling effectiveness remains pressing issue. (3) Adaptability and Generalization for Few/Zero-Shho Transfer. Pre-trained brain FMs need to be fine-tuned to various downstream tasks, datasets,, atlases and disorders. However, full-parameter fine-tuning is often inefficient, requires large amounts of labeled data, and typically previous brain FMs (Caro et al. 2023] Yang et al. (024) support only one disorder or atlas during the downstream inference. In addition, many real-world scenarios, downstream tasks may involve new atlases, datasets and disorders unseen during pre-training, with very limited (few-shot) or even no labeled data available (zero-shot). Adapting FMs to such few-shot zero-shot settings poses significant yet highly valuable challenge. Most existing brain FMs (Caro et al. [2023] Thomas et al. 2022 Dong al. 2024a) have not considered few-shot or zero-shot scenarios, which limits their generalizability and flexibility. These three challenges correspond to four essential aspects of pre-training brain FMs:
brain FMs are more efficient, they often neglect inter-
data collection, model pre-trainnn traininiig & fine-tuning, and downstream task adaptation.
Contributions. To address the key challenges outlined above and overcome the limitations of prior work, we propose the Brain Graph Foundation Model, named BrainGFM, specifically designed for heterogeneous fMRI data, with particular focus on graph-based modeling. We propose correspond- ing solutions within our model to enhance BrainGFM, enabling it to become a more powerful brain FM compared to previous approaches. (1) To enable effective pre-training of brain FMs, we construct a large-scale fMRI dataset comprising 27 widely used fMRI datasets. This collection includes over 25,000 subjects, 60,000 fMRI scans, and 25 common neurological and psychiatric disorders. Unlike previous brain foundation models, each fMRI sample in our dataset is processed using different brain functional and anatomical atlases, including 8 parcellations with various resolutions and par- titions, significantly increasing the scale and diversity of the data. This also allows the pre-trained model capture complementary feature representations across multiple parcellations. (2) Prior brain FMs have predominantly relied on IMRI time series or ROI-level features for both pre-training and fine-tuning. In this work, we creatively introduce a graph- based backbone for building brain graph FMs. This approach offers the advantage of maintaining computational efficiency comparable to Connectome/FC-baatt
FMs. while achieving performance on par with time-series-be series-based FMs. (3) To enhance the generalizability and adaptability of the model, we discard conventional fine-tuning and introduce a graph prompt-tuning. Under the multi-task and multi-dataset training paradigm of meta- learning, this approach improves the model's I's ability to perform few-shot adaptation across diverse tasks and datasets. In addition, we incorporate language prompt tokens, including allss/paccellation tokens and task/disorder tokens, guide the pre-trained BrainGFM in adapting entirely unseen downstream datasets, atlases, tasks, and disorders in zero-shot settings.
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2 RELATED WORKS
2.1 PRE-TRAINING APPROACHES FOR BRAIN FOUNDATION MODELS USING FMRI
The emergence of large-scale foundation models, such as LLMs (Achiam et al. 2023). has demon- strated strong potential across various domains. In neuroscience, recent eftorts have introduced brain FMs (e.g., using fMRI) which can be broadly classified into time-series- based (Dong et al. 2022a
Caro et al. 2023 Thomas et al., )022) and Connectome/FC-basee models (Yang et al.) 2024 Hu
et a 2024). both primarily relying on generative pre-training using masked modeling. In contrast to these approaches, our work introduces the first graph-based fMRT foundation model, which leverages the brain's 's topological structure through graph representations. We incorporate both graph generative pre-training (Hou et al., 2022) and graph contrastive pre-training (Qiu et al. 22020) Wei et al. 2024 unifying two major paradigms in graph representation learning.
2.2 GRAPH PRE-TRAINING AND PROMPT LEARNING
Pre-training is fundamental step in the development of foundation models, with most approaches categorized into contrastive-base and generative-based paradigms. While graph model pre-training differs from that in vision and language domains, it generally follows these two strategies. To facilitate zero-shot generalization, language prompts (Achiam et al., 2023) have been widely used in NLP and vision, providing semantic guidance that enables pre-trained models to adapt to unseen tasks without parameter updates. In contrast, graph prompts have been proposed to address few-shot adaptation in graph neural networks. Inspired by prefix-tuning (Li & Liang/ 2021), graph prompts (Sun et al.) 2023) introduce a small set task-specific parameters that can be optimized efficiently while keeping the backbone frozen. This approach improves sample efficiency and reduces computational cost in adapting to new graph-based tasks with limited data. Recent studies have also introduced brain prompt-tuning methods (Xu al. 2025
Dong al. 2024b) adapt pre-trained IMRI models.
2.3 META-LEARNING
Meta-learning (Finn et al. 2017: Hospedales et al. 2021) also known "learn to learn" aims train models that can quickly adapt to new tasks using only a small number of labeled examples. It typically involves learning a good initialization or adaptation strategy by optimizing over a distribution of related tasks. Meta- Meta-lcarning has been widely adopted in few-shot learning scenarios and has shown strong potential for improving generalization across tasks and domains (Sun al., 2223). In our study, meta-learning is employed to train the graph prompt under the few-shot setting, enabling the unification and generalization across diverse brain atlases and neurological disorders.
3 METHODOLOOGY
As illustrated in Figure [ we propose BrainGFM, a graph-based paradigm that distinguishes itself from previous time series-based and Comnectome/FC-basee brain FMs. Our framework consists of four main stages: large-scale fMRI graph data collection and pre- pre-processing, graph pre-training for building our brain graph foundation model, multi- meta-learning optimization for few-shot learning, and graph/language prompt-tuning for zero-shot adaptation.
3.1 CONSTRUCTION OF LARGE-SCALE FMRI PRE-TRAINING DATASET
Motivation. Brain parcellations with different resolutions and partitions offer complementary repre- sentations of brain structure and function, and different disorders may be best characterized under different parcellations.
As shown in Tigure[a) we curated a large-scale fMRI dataset by aggregating 27 widely used IMRI datasets from different sites and institutions, covering 25 common neurological and psychiatric disor- ders. Unlike existing brain FMs, our dataset incorporates fMRI data processed using 8 parcellations, including Schaefer Schacfer100/Schaefeee 2000Schhefee200/Scaeee::000 (Schaefer et al. 2018), AALI 16/AAL3v1 (Tzourio Mazoyer et al. 2002). SHEN268 (Shen et al.) 2013), Power264 (Power et al. 2011), and Gordon333 2016). For each subject, we extracted raw fMRI time series using these brain atlases
(Gordon et al.
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Graph
Graph Contostive Treiiig
Controst
Meta Learning
Graph Prompt
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(40 Graph/ALsngungee Prompt- Ture to Any Zero Zero-Shet Aaaaaa//aaacellciooo and Disorder
Figure 1: The pipeline of proposed BrainGFM. (a) A large-scale brain fMRI graph dataset is constructed for pre-training. (b) BrainGFM is pre-trained using graph contrastive and masked autoencoder strategies, with atlas/parcellation tokens [A/P] encode atlas-specific information. (c) We introduce graph prompts and use meta-learning to optimize them for few-shot adaptation, keeping the graph FM backbone frozen. (d) Finally, we freeze both the model and graph prompts, and use language prompts to enable zero-shot transfer to new tasks. Note that "Schf." means Schaefer atlas.
and constructed fMRI brain graphs by computing and binarizing the Pearson correlation between time series among brain ROIs. Integrating multiple atlases allows us to expand the dataset to eight times the size of using a single parcellation, enabling more diverse representations and facilitating the learning of atlas-invarian brain patterns. The inclusion of multiple atlases not only increases the diversity and volume of the training data but also enables the model to learn parcellation-spedll features, significantly enhancing the generalization and robustness of the pre-trained BrainGFM. Note that detailed information regarding the benchmark settings, including task types, dataset splits, neurological disorder categories, and atlas/parcellation choices, can be found in Appendix R and
3.2 GRAPH PRE-TRAINING FOR BUILDING BRAINGFM
Motivation. The graph foundation model approaches the effectiveness of serie models, while matching the efficiency of Connectome/FC-basee foundation models.
We adopt Graph Transformer Yuun et al. 2019) as the backbone of our BrainGFM. As illustrated in Figure [b), we first transform the input fMRI brain graphs and project them to obtain brain graph embeddings, where each token corresponds to brain ROI. We apply Positional Encoding Tokens, denoted as [PE], to each brain ROI, enabling the model to perceive and learn the topological and spatial characteristies of each ROL Unlike conventional positional encodings used in standard Transformer models (Vaswani et al.) 2017), graph-based positional encodings are inherently different, as they require encoding the relative positions between nodes in the graph structure. Compared to the commonly used Laplacian positional encoding (Dwivedi et al., 2023) and node degree positional encoding (You et al. 2019) in graph-based models, adopt a more efficient alternative Random Walk Structural Encoding (RWSE) (Dwivedi et al. (002)) as our positional encoding strategy. More details about PEs can be found in Appendix Furthermore, inspired by language models in NLP, we insert Atlas/Parcellation Tokens, denoted as [A/P] and Task/Disorder Tokens, denoted as [T/D], to the brain graph embeddings during pre-training, enabling the model to better distinguish and learn from different atlases and parcellations. Note that the construction of [A/P] and [T/D] tokens is described in detail in Section 3.4 Incorporating this token enables the model to capture sarcellation-speciie patterns, which is crucial as prior studies (Hermosillo al. 2024 Liu et al.) 2023 Wu et al. 2025) show that different brain disorders are better represented by specific parcellations. For instance, MDD benefits from Schaefer200 or Power264 and ASD is better captured by Shen268 Schaefer200. Embedding such parcellation-awe information helps improve model generalization across disorders and atlas.
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We follow the graph pre-training paradigm to pre-train our BrainGFM. To fully leverage the potential of graph pee-traiiinn traininnnng we adopt two widely used pretext tasks in graph domain: graph contrastive learning (GCL) (You et al. (020) pre-training and graph masked autoencoders (GMAE) (Hou et al.) )022) pre-training. For GCL pre-training, we apply graph augmentation to the fMRI brain graphs by randomly dropping nodes and edges to generate positive and negative pairs of queries and keys. The contrastive loss is then computed by contrasting the positive and negative graph pairs. For GMAE pre-training, we randomly mask nodes and edges in the input brain graphs to obtain masked brain graphs. These graphs are then passed through graph autoencoder with an encoder-decoder architecture to reconstruct the masked nodes and edges, optimized using mean squared error (MSE) loss. Note that both GCL and GMAE pre-training share the same encoder, which serves as the core of our BrainGFM, which enables the encoder of BrainGFM to benefit from both contrastive and generative paradigms, resulting in more robust and these two graph pre-tra pre-training methods can be found in Appendix N and O
backbone. More details about
3.3 FEW-SHOT GRAPH PROMPT-TUNIS VIA META LEARNING OPTIMIZATIOO
Motivation. The graph prompt, optimized via multi-task meta-learning, enables the fully frozen graph foundation model to be effectively adapted to new, unseen tasks under few-shot settings.
As illustrated in Figure Te). after completing the pre-training stage, we need to fine-tune the pre- trained FM various downstream tasks, including different atlases and disorders. However, for fMRI data, traditional full-parameter fine-tuning faces two major limitations. (1). The collection of fMRI data for neurological and psychiatric disorders is often consumins and labor-intensive and for some rare diseases, only very limited number of samples are available. When performing full-parameter fine-tum fine-tuning on a large-scald foundation model with limited data, the optimization of model parameters becomes insufficient, leading to significant performance degradation. (2). Full- parameter fine-tuning requires substantial training time and computational resources, making it less practical in 2023) to prompt-tune to our BrainGFM to different diseases and atlases. Following prior work on graph prompt learning, design brain graph prompts specifically for brain graphs, with structure consistent with the input brain graphs Each node in the graph prompt is a learnable parameter, and the collection of all nodes forms learnable vector set. For the edges, we initialize fully learnalee edge matrix, where each entry is also trainable. This design allows the graph prompt to flexibly adapt the FM to various downstream tasks without modifyyng the backbone parameters.
environments. Therefore, introduce graph prompts (Sun et al.)
To optimize the parameters of our brain graph prompts, we introduce meta-learning to train the graph prompts. Specificallyy, we construct multi-task dataset in which each task corresponds to different brain disorder and atlas pair. By adopting this meta-learning paradigm, the optimized graph prompts can be flexibly transferred to unseen diseases and atlases, enabling effective adaptation using only small number of samples from the few-shot downstream tasks. During the meta-learning optimization process, all parameters of the pre-trained model are kept frozen, and only the graph prompt parameters, which are relatively lightweight, adaptation. Moreover, the few-shot sample setting is particularly well-suited for optimizing the small number of graph prompt parameters; in contrast, using limited samples fine-tune a large pre-trained foundation model with numerous parameters would lead to insufficient training and severe overfitting. The task/disorder-specffe features related to each disorder, atlas, or parcellation stored entirely within the well-trained brain graph prompts. As a result, with the help of the learned task-specific graph prompts, the frozen BrainGFM remains consistently ready to be efficiently and rapidly adapted via prompt-tuning to unseen tasks, datasets, disorders and atlases, even when only a few samples (few-shot settings) are available. More details about the meta-lcarning datasets split and training procedure can be found in the Appendix and Table 12
updated. This design enables fast tuning and
thus captured and
3.4 ZERO-SHOT GRAPH/LANGUAG
Motivation. The language prompt guides the frozen pre-trained graph foundation model and meta- learned graph prompt to achieve effective zero-shot transfer across diverse disorders and atlases.
Building on the sho- capability, further introduce language prompts to enable more generalized zero-shot learning by jointly guiding both the graph prompt and the pre- trained foundation model. In zero-shot scenarios, the parameters of the graph prompt are also frozen, meaning the model cannot
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rely on prompt adaptation through learning. Instead, the language prompt provides semantic guidance, allowing the model to generalize and adapt to unseen downstream data, tasks, and disorder types without any gradient-basee updates. As shown in Figure [ in order to enable the model to recognize and distinguish between different tasks and disorders in zero-shot settings, we introduce Task/Disorder Tokens, denoted as [T/D]. To construct the [T/D] tokens, we first generate textual description for each disorder, including its full name, abbreviation, and a concise climical summary. For example, for Major Depressive Disorder, the corresponding text description is: "Major Depressive Disorder (MDD) is a common mental illness characterized by persistent and profound low mood, loss of interest, and cognitive impairment, significantly affecting daily life and social functioning." " (Otte et al. 2016)
We then encode these textual descriptions using a BERT model (Devlin et al., 2019) pre-trained on large-scale medical corpora, such as BioClinicalBERT (Huang et al. [2019] |lsentzer et al., 2019), to obtain semantic-rich text embeddings. These embeddings are subsequently projected and embedded as [T/D] tokens, which are incorporated into the model during downstream adaptation. Similarly, the construction of [A/P] tokens is also based on language text. For each atlas and parcellation. we provide a textual description of its name, such as "Schaefer 100", "Schaefer200",, or "AAL116" These text descriptions
then encoded using the BioClinicalBERT pre-trane preitrained model to extract language embeddings, which are subsequently transformed into [A/P] tokens. As shown in Figure dd), the [T/D] and [A/P] tokens are ultimately concatenated with the ROI tokens from the graph embeddings as language prompt tokens. This combined input is then fed into the foundation model to guide feature extraction specific to the given dataset, task, and disorder. By introducing disorder-specific semantic priors through the [T/D] and [A/P] tokens, the model is better equipped to capture characteristics from different tasks, disorders, atlas and parcellations, thereby improving its downstream adaptation ability in zero-shot settings without any training.
EXPERIMENTS
4.1 COMPARISON WITH OTHER METHODS
Datasets. To demonstrate the superiority of our BrainGFM. we conducted comparative experiments. Specifically, 10 common types of neurological and psychiatric disorders were selected from a total of 25 disorders, spanning datasets among the 27 datasets we collected. More details about benchmarks and datasets can be found in Appendix R Baselines. We compare our method against a series of baseline models. Based on the data representation type, these baselines are categorized into three groups: time-series-bate based methods, Connectome/FC-baase methods, and graph-based methods. Based on the training paradigm, they are divided into two groups: non-pre- non-pre-trained FMs and pre- trained FMs. All pre-trained models are retrained fair comparison. More details about baselines can be found in Appendix Q Metrics. We evaluate all methods using four metrics: AUC, accuracy (ACC), sensitivity (SEN), and specificity (SPH). More detailed information on disorders, datasets, and benchmarks can be found in the supplementary material. As shown in Table our method outperform all previous approaches and achieves state-of- the-art performance. The pre-trained FMs significantly outperforms models without pre-training Our method, built upon a graph transformer backbone, substantially surpasses Connectome/FC-Be brain FMs MBrainMass and BrainNPT), and also outperforms time-series brain FMs (BrainLM).
collected pre trainini dataset to ensure
4.2 ABLATION STUDY FOR FOLL/FEWw/ZER--SIOO ON GRAPH/LANGUAGE PROMPT AND META LEARNING
Figure 10 illustrates the classification accuracy under four different data regimes, Full-Shot (100%), Few-Shot (10%). Few-Shot (1%), and Zero-Shot (0%), across three representative downstream datasets: ABIDE II, ADHD 200, and ADNI 2. We observe a consistent performance degradation across all methods as the available training data decreases, with the largest performance gap occurring under the most data-scarce setting (Zero-Shot). Vanilla Models, which lack any form of pre-training perform the worst across all settings, highlighting their limited generalization ability. Introducing the FM (BrainGFM) without graph prompts leads to notable performance improvements, confirming the effectiveness of graph-based pre-training. The inclusion of graph prompts (FM G-Prompt) further enhances accuracy, particularly in Few-Shot and Zero-Shr Shot regimes, indicating their role in injecting structural prior knowledge. When combined with meta-learning (FM + G-Prompt + Meta I..), the model demonstrates increased adaptability and robustness under limited supervision. Finally,

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Table 1: Comparison among different methods on 10 brain disorders on Schaefer 100 atlas. indicates the best performance.
Pink
bi
ADHIZIIO (ADHD)
ARIDE (ASD)
ADNI (AD)
HRN (MDD)
HRN (ANX)
AUCC
ACC
SEN
SPE
AUC
ACC
SEN
SPE
AUC
ACC
SEN
SPE
AUC
ACC
SEN
SPB
AUC
ACC
SEN
SPE
Vanilla GCN
h2.3uz Aaa 441161,8,80 22218 A... Ras 67 5.. 62.66 9.16650
2. 69. Jazs 711.1666.222. 2. 73.3 72.0 73 Sal 69.0 73.6.s 76 Pas 8a. 80.3 73
BminiNNN
x
51.32s, 444 0.20 59.9 9..62. 7.2.6 24:
71. late 22.6000ss7
(27666622 75,., 69.0.. 74: 68.5 6255 78.77 77.0 22, 8.i
Vanilia TF
63.7.2 668 661.9 9as
66 (66 (1,55 (6.7:2 A. 72.9 75.8 7002, 75.4 75.5 75.5 72 79,3.g 79.5 82.9 43.4,1 3uso
Graph TP
X
64.7 66 683.4.267 8.2.66 San (68 6.1.6.. 14.82s77.9.1 71.11:1.. 71.64477 75.3u1 74.. 26.92.96
A2x 77 0las
BraioNerTF
x 2.2 6600 2,64,2
22,66 2gu 25 77.6 7888 72. 74222 76.3 730 79.4.; 12 22,5 76. 6.
BranNPT
85 dus 607 02. 65.9., 66. S.. 68.5. 6..0. 70000. 72.0.. 77.2. 060,,0 76.42 74.0 6e (922. 27,4. 79. 83.2 77. 2
BrainLM
67. (4..0. 71.2
6669 6.2 3 22.
5.. A.: 77. 8.. 27, 27.9... 82.6 B. 26.556.5 $6.5
BrainMass
67.0 67 (64, 64.7.2x 7-2x7 71. 11, (: 700 09 09.5., 66. Sata & M0, 2 726., 181, A81, 76.9 81. S5g 8006 78. 1. daey 84.0gy 8013, 881 9e2
Brain JEPA
89.8.11 71 1.. 66.2..
73.8. 70.1 69.3.t 79.1er 81.6. 76.8.0 83.6.a 79.3. 18.8. 12.3. 78.5., (5.4.1 86.7 85,3 83.2as
BrainGEM [ P......
30.5..66...00 A. 71.2 73.5,, 70.4, 66.8.. 80, 81.9 762 844, 79.9.t 82.0 83.2, 79..2..88..2 85. 2.1. 86.3 18777 82.0. HBN (OCD)
Method
(PTSD)
(CLL)
UCLA.CNP (SCHZ)
UCLA.CNP (BP)
1 AUC
ACC
SEN
SPE
AUC
ACC
SEN
SPE
AUC
ACC
SEN
SPE
AUC
ACC
SEN
SPE
AUC
ACC
SEN SPE
Vasilla GEN
F1 21.2gg 78.3 (22.5.50 Susa has 78778 78.7 20 83.3 23. 65.3 67.8,2 57.3 73.9.2 2,3 81 Ogy 43,4 66,66l0e 3,3 71 18g2 99.7 7ass
BminiNNN
X
11.42 7600 78.2.2207 16. 16.9 70.
63.92
12.61.26...... 22, 19. 79.7 78 78. 85.0m144.0.110 68.26.. 2au 9.89 200
Vanilla
73.9zz 20.0,, (65.8 Rus 79.77 72 7766 8177
70.6 0.20 87. 122 67.0 6802 8.18 72,9tu 78422 62. 781,2 72. 72
Graph TP
75.0ss K2.8a 0..079. 79.19.114 Le 18.4 88 La 775.5.55. 81.7. 68.N., (69 65 Rur 20.00. Mll. 82 76.8, (8.2. 68, 7. 72.3 06 6.s.
BrainNetTT
x 26.124 -0.92 7.x 28.888880 78.8,z 232221766 76.2.185..
68.3
23.00, 78 18 75.0gz 79,3 68.12 73.2 6.1 66.3....2
BrainNPT
27.5 74.7727744..11 74 79.6.. 67. 82 82, 71.5,, 88.8 68.9,, 68.6ng 66. 22 20.7.r 764 77.9 22 63.2 68.60 70.3g 00d 7e.
BrainLM
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9. 20 76.3 3 60.6 78.2.
ABIDE
ADHD 200
ADNI
035
72
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25
Veril Model
Verls
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FM- Grheing
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Figure 2: Performance comparison across different settings (Full-Shot, Few-Shot, Zero-Shot) on three datasets: ABIDE II, ADHD 200, and ADNI 2. The results demonstrate the progressive performance gains achieved by incorporating graph prompts (G-Prompt), meta-learning (Meta L.), and language prompts (Lan. Prompt) into the FM (BrainGFM), especially in few-shot and zero-shot settings.
Waits Kolel Busaprn
Schuutrtto
3.80
AALI16
Schacfe2200
AALIVI
0.35
Gondes313
Shaofe2200
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schacfers0o
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sten255
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031301333
(b)
Figure 3: The performance of models pre-trained different atlases varies across downstream atlases. The experiments are conducted on ABIDE II dataset for ASD classification.
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incorporating language prompts (FM+ G- G-Prompt Meta L. Lan. Prompt) consistently achieves the best performance across all datasets and data regimes, underscoring the benefit of semantic guidance in enabling zero-shot generalization. These results collectively validate the synergistic contribution of these techniques in building flexible and generalizable brain FMs.
4.3 EXPERIMENTS ON DIFFERENT ATLASES AND PARCELLATIONS
To systematically assess the effectiveness of different pre-trained models across variety of brain atlases and parcellations. we conducted comprehensive ablation studies. Specifically, we fine-tuned four models on fMRI datasets spanning 2 representative atlases and 8 parcellation schemes. The four models include: a vanilla graph transformer trained from scratch; BrainGFM (Functional), pre- trained on the functional Schaefer 100 atlas; BrainGFM (Anatomical), pre-trained on the anatomical AAL116 atlas; and BrainGFM (Mixed), pre-trained on a combination of Schaefer 100 and AAL116 data. The atlases used in our experiments comprise both functional (Schaefer, SHEN, Power, and Gordon) and anatomical (AAL) types. Among these, the Schaefer atlas provides three resolutions (100, 200, and 300 parcels), and the AAL atlas includes both AAL116 and AAL3v1 parcellations. As illustrated in Figure 3 a). BrainGFM (Functional) outperforms BrainGFM (Anatomical) when evaluated functional atlases, while the reverse true for anatomical atlases. In all cases, both types of pre-trained BrainGFM models significantly outperform the vanilla graph transformer trained from scratch, highlighting the benefits of graph pre-training. Notably, although atlas-specific pre-training offers substantial improvements, the BrainGFM (Mixed) model, pre-trained jointly on both functional and anatomical data, achieves the best performance across all downstream atlases. We hypothesize that this superior generalization stems from the complementary nature of anatomical structures and functional connectivity patterns, which jointly enable the model to capture a richer and more diverse set of neurobiological representations.
Overall, as summarizzd in Figure ()), the relative performance of the four models follows two consistent patterns depending on the type of downstream atlas. For functional atlases, BrainGFM (Mixed) performs best, followed by BrainGFM (Functional), BrainGFM (Anatomical), and finally the vanilla model. In contrast, when evaluated anatomical atlases, the best performance is again achieved by BrainGFM (Mixed), followed by BrainGFM (Anatomical), BrainGFM (Functional), and lastly the vanilla model. These findings underscore the value of incorporating both anatomical and functional information during pre-training to enhance the generalizability of brain graph models across diverse parcellation schemes.
4.4 COMPARISON AMONG SERIES, ROI AND GRAPH-BASED FOUNDATION MODELS
As shown in Figure we compare four types of brain FMs: time-series-bart based FM (e.g., BrainLM), Connectome/FC-ben FM (e.g.. BrainMass), vanilla graph-based FM, and our proposed graph-based model, BrainGFM. The comparison spans five key dimensions: model performance, pre-training and fine-tuning efficiency, memory usage, and model complexity.
In terms of performance, BrainLM achieves the best results AUC and ACC due to its direct modeling of raw fMRI time series, effectively capturing both tem- poral and spatial patterns. BrainMass, which relies on static ROI features without modeling inter-regional interactions, performs the worst. The vanilla graph- based model shows intermediate performance by ex- plicitly modeling ROI connectivity. BrainGFM, which incorporates IMRI-specific enhancements such as graph prompts and structural encodings, significantly outper- forms the vanilla graph model and matches or exceeds the performance of time-series-bane based models. For com- models are
Fine-Tuning Speol_
14 anatua
Complexity
Pro-T Timnning Speed
Memory
Perfomaance
putational efficiency, Connectome/FC-bast the fastest in both pre- veeitrainngg and fine-tuning, given their compact input and lack of spatiotemporal mod- eling. Time-series models are the slowest due to the cost of processing long, high- high-dimensional sequences. Graph-based models, including BrainGFM, lie in between. Notably, BrainGFM achieves
Figure 4: Comparison of performance and efficiency across different FMs.
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fast fine-tuning via prompt tuning while maintaining pre-training efficiency similar to the vanilla graph FM, surpassing even Connectome/FC-Be -abased models in fine-tuning speed. Regarding resource consumption, time-series models are the most memory- and compute-intensive. Connectome/FC- based models are the most lightweight. Graph-based models, while slightly more demanding than Sateecttooe/CCbbeeeede ones due to edge computations, remain significantly more efficient than time-series models, Overall, this evaluation highlights the trade-offs between different brain model- ing paradigms and how input representations, time series, ROI features, or graphs, affect both the effectiveness and efficiency of large-scale brain FMS.
4.5 ABLATION STUDY ON PRE-TRAIIE TRAINING WITH DIFFERENT ATLASES AND PARCELLATIONS
To investigate and demonstrate the impact of different atlases and parcellations on the perfor- mance of the pre-trained model, we conducted ablation experiments using pre-training datasets constructed from various types of atlases and parcellations. Specifically, we categorized the pre-training datasets into five representative groups: (1) a dataset based on a single func- tional atlas and a single parcellation (Schacfer100), (2) a dataset based on a single anatomi- cal atlas and a single parcellation (AAL116), (3) mixed dataset combining both functional and anatomical atlases (Schaefer100) + AAL116), (4) a dataset based on a single atlas but incorporating multiple resolutions of parcellations mixed dataset comprising various atlases and parcellations (All Atlases with 8 Parcellations). As shown in Table 2 pre-training datasets with single-resolutie parcellation reveals that functional at- lases, such Schaefer, outperform anatomical atlases, such as 1A. This highlights that functional-based atlases more effective in capturing disease-specifie features in the diagnosis of neurological and psychiatric disorders. Additionally, pre-training on datasets mix- ing different parcellation resolutions within a single atlas (e.g., Schaefer 100+200+300) achieves comparable per- formance to pre-training
and (5) a fully
Table 2: Effect of different atlases on pre-training (ABIDE II, ASD).
Corpus
Atlas
Parcel.|
Acc.
w/o Pre-train Schacfer100
65.2/67.1
Func.
Single
67.5/70.2
AAI.11
Anat. Single
66.6 / 69.2
Sch( 2ch(100+000+300 Sch100 + 11
Func. Mixed
/
Mixed
Single
68.8 71.6
All Atlases
Mixed
Mixed
70.5 73.3
datasets combining multiple atlases with one parcellation each (e.g.. Schaefer 100 + AAL116). Finally, pre-training on datasets that incorporate multiple atlases and parcellations achieves substantially better performance than all previous settings. This improvement can be attributed to the model's ability to comprchensively Iearn features captured by different atlases, thereby acquiring knowledge from diverse medical and biological perspectives. In addition, the model benefits from learning across parcellations with varying resolutions, which enables it to capture the brain's feature distributions at both global and local scales.
4.6 ABLATION STUDY ON DIFFERENT FOUNDATION MODEL PRE-TRAINING METHODS
As shown in Figure we compare the effectiveness of different graph pre-training strategies, including graph contrastive learning, graph-masked autoencoders and their sequential combination. The results demonstrate that GCL slightly outperforms GMAE, and that combining GCL and GMAE yields further performance gains compared to using either method alone.
GCL pre-training primarily fo- cuses capturing global represen- tations of brain graphs by encour- aging the model to aggregate holis- tic graph-level features and distin- guish between different graph at- tributes and categories. In compar- ison, GMAE pre-training empha- sizes the learning of local repre- sentations, where the model recon- Figure 5: Performance of different graph pre-training methods. structs masked brain ROIs based on information from their local neighborhoods, thereby promoting specialization in ROI-level feature extraction. By sequentially combining GCL and GMAE during pre- pre-training, BrainGFM is able simultaneously acquire both global and local discriminative ca- pabilities. Notably, the integration of global and local information has been widely recognized as critical for understanding brain organization and pathology in neuroscience and neuroimaging studies.
ABIDE (ASD vs. NC) Schacfer
ABIDE (ASD vs.
0.75
AUC
0.75
3.70
0.70
2.65
0.60
0.55
3.50
pre-train
GCL
GMAE
GC, GGL+GMMAE
pre-trals
GCL
GMAE
GCL GGL+
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Consequently, our pre-trained model benefits from this multi-scale representation learning, leading to enhanced transferability and improved performance across various downstream tasks.
BROADER IMPACT
Our proposed Brain Graph Foundation Model (BrainGFM) designed to be unified and versatile architecture for graph-based modeling of brain data. While our current experiments focus on resting- state functional MRI (rs-fMRI), the model is modality-agnossee and readily extensible to other neuroimaging modalities, including task-based fMRI (task-fMRR), electroencephalograhhh (EEG), diffusion tensor imaging (DTI), and magnetoenncepaalogappyy (MEG). These diverse modalities can be represented as brain graphs, constructed from temporal correlations, structural connectivity, or mannulus-evokee activity patterns, making BrainGFM a generalizable framework for multi-modal neuroscience applications.
The ability to transfer knowledge across data types, brain atlases, and clinical conditions enables BrainGFM to benefit wide range of downstream tasks, including biomarker discovery, mental disorder diagnosis, and brain-computer interface (BCI) development. Its strong pre-training on large-scale brain graphs makes it particularly valuable for low-resource or small-sample settings.
LIMITATION AND FUTURE WORKS
While our work successfully constructed large-scale fMRI dataset for pre-training, certain limitations remain. Due to the significant manual effort involved, we were unable to include all datasets from the OpenNeuro platform (Markiewicz al. 2021), particularly the large number of task-based mnonrestting-statee fMRI datasets. addition, because of financial constraints, we were not able to incorporate fMRI data from the UK Biobank (Byeroft et al. 2018), including both resting-state and task-based scans, as access to this dataset requires paid licensing and ongoing maintenance costs.
In future work, dataset be further expanded by incorporating additional resources such the full OpenNeuro repository and the UK Biobank dataset. This would enable the construction of an even larger pre-training corpus for BrainGFMs. Moreover, combining task-based and resting-state IMRI data could lead to more comprehensive representation of brain dynamics. We believe that with the inclusion of more diverse datasets and task-based fMRI, the performance and generalization ability of BrainGFM can be further enhanced.
7 CONCLUSION
We propose BrainGFM, a graph-based brain foundation model pre-trained on heterogeneous fMRI brain graphs constructed from diverse atlases and parcellation schemes. To enhance its generalization and adaptability, we introduce a meta-learning framework to optimize graph prompts, enabling robust few-shot learning under limited data. In addition, we incorporate language prompt tokens to guide zero-shot generalization, allowing BrainGFM to transfer effectively across unseen datasets, tasks, atlases, and neurological disorders. Our large-scale, multi-atlas fMRI dataset provides a rich and diverse training corpus, and BrainGFM demonstrates superior performance in both effectiveness and efficiency compared prior time-series- based and Connectome/FC- foundation models.
ACKNOWLEDGEMEEN
This work supported in part by NIH grant nos. R21MH130956 R21AG080425,, R01MH12969, R01LM013519, RFI AG077820, NSF grant nos. IIS-2319451 and MRI-2215789, Alzheimer's Association Grant (AARG-22-972541) and Lehigh University CORE grant.
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A
SIMPLIFIED TRAINING PIPELINE OF BRAINGFM
Tosk oskk/bisarde
Datasets & Cohorts
GMAE
GCL
Meta Learning
Longuage Prompt Token
Graph Foundation
Graph Foundation
Graph Foundation Model
Atlases
Disorders
Model
Model
Attas/Parceliation Token
Atlas/Parcellation Token
Atlas/Parceliation Tohen
Graph
Brain Graph
Graph Prompt
Brain Graph Datasefs
Brain Groph Datosets
Graph
Prompt
(n) Groph Datosets Construction
(b) BrainGFM M Pre Training
(c) Few-Shoo Meta- Lato-LLerning
(4) Zero-Shot Shot Adaptation
Figure 6: The simplified training pipeline of BrainGFM, covering (a) IMRI graph construction for pre-training, (b) BrainGFM pre-training, (c) meta-learning for few-shot scenarios, and (d) zero-shot adaptation via language prompts.
B CONTRIBUTIONS OF BRAINGFM FOR UNIFYING COHORTS, ATLASES, AND DISORDERS
ABIDE
ASD
Schoef. 100
HCP
ADHD
200
268
200
BrainGFM
BrainGFM
BrainGFM
HBN
ADNI
MDD
ODD
Power
AAL
OASIS
CUD
AAL 3+1
(c) Unify Different Brain Cohorts
(b) Unify Different Brain Disorders
(c) Unify Different Brain Atlases
Figure 7: Our BrainGFM achieves unification the fMRI domain across three key dimensions: (a) diverse brain datasets and cohorts, (b) multiple neurological and psychiatric disorders, and (c) various brain atlases and parcellations.
C COMPARISON OF OUR VANILLA GRAPH FM AND BRAINGFM WITH PRIOR AND CONNECTOME/FC-BAS BRAIN FMS
Low Speed
High Spead
Speed
Speed
Time Series
Vanilla Graph FMs
FMs
BrainGFM
Accuracy
High Cost
courrcyy
Low
Low Cast
Normal Accuracy
Narmal Cost
High Accurocy
Low Gost
Time. Series
(b)
(c) Our Vonille FMs
(d) BrainGFMe
Figure 8: We compare different brain foundation models in terms performance, inference speed, and computational cost. The results show that Graph FM provides trade-off between performance and efficiency compared to Time-Series FM, while our BrainGFM achieves the best overall performance across all aspects.
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D ABLATION STUDY FOR FULL/FEW/ZERO-SHIE ON GRAPH/LANGUAGE PROMPT AND META LEARNING ON UNSEEN DISORDERS
HBN Speech Sound Disorder (SSD)
Adjustment Disorder (AJD)
75
70
Accuracy
75
Accuracy
65
70
60
Vanilla Modals
Vanilla Models
FM G.Prempt
G-ffompt
FM Prompt
55
FM G Prompt
G-Prompt
G-Prompt
FM Prompt + Meta L. + Lan. Prompt
50
FM G G-Prompt-+ + Mata L. Lan- Prompt
F00l-Shot(100%
Few Shot (30%)
Few Shot (1%)
Zero-Shot (0%)
(100%)
Few Shot (30%)
Few Shot (1%)
Zero- Eero-Shot (0%)
Figure 9: Performance comparison on two uncommon disorders from the HBN dataset under Full- Shot, Few-Shot, and Zero-Shot settings. The HBN dataset is excluded from the pre-training stage to ensure that the tested disorders are unseen during training. The results highlight the effectiveness of incorporating graph prompts (G-Prompt), meta-learning (Meta L.), and language prompts (Lan. Prompt) into the BrainGFM model, particularly in few-shot and zero-shot scenarios.
E ABLATION STUDY FOR DIFFERENT BRAIN GRAPH CONSTRUCTION METHODS
Table 3: Comparison of different brain graph construction methods on HBN dataset for MDD diagnosis. Our approach uses top-k sparsification of the Pearson correlation matrix to construct brain graphs. We also compare with KNN-based graph construction. The numbers in the table represent classification accuracy.
Corpus
Pearson Top-K
KNN
Partial Correlation
Mutual Information
Dynamic Connectivity
HBN MDD
85.5
86.7
84.8
83,9
85.7
F CROSS-KTLAA/PPRCELLLTTO EXPERIMENTS
Table 4: Ablation experiments on the ADNI2 dataset for Alzheimer's disease (AD) classification.
The reported improvements (%) indicate the performance gain over models without pre-trainnn training.
Type
Pre-training
Fine-tuning
Performance
Schaefer100
AAL116
3.3% +1.5%
Cross-Atla
Schaefer100
Power264
2.7%
+1.3%
Schaefer200 + AAALI16
Schaefer100
3.2% +1.6%
Schaefer100
Schaefer300
2.7% +1.4%
Cross-Parcellation
Schaefer100 + Schaefer300 AA
Schaefer200
3.5%
21.5%
AAL3v1
3.1%
+1.3%
G ABLATION STUDY ON DIFFERENT TUNING METHODS
As shown in Table 5, after completing model pre-training, explored three downstream adaptation strategies: full fine-tuning (Sun et al.] 2022), parameter-efficiem fine-tuning (PEFT) (Ding et al.) 2023), and graph prompt-tuning (Sun et al. 2023). Full fine-tuning updates all model parameters
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during downstream training, offering strong performance but at high computational cost. In contrast, PEFT methods reduce training overhead by modifying only a small subset of parameters or introducing lightweight modules. We specifically evaluate two popular PEFT variants: prefix-tuning and LoRA. Note that the details of all tuning methods are summarized in Table
Graph prompt-tuning further improves efficiency by freezing the entire pre-trained model and updating only a small set learnable prompt vectors injected into the input. This strategy allows the model to adapt without altering its core parameters, making it highly suitable for resource-constrained settings. In full-shot scenarios, fine-tuning delivers the best results, but PEFT methods achieve competitive performance with significantly lower computational demands. Graph prompt-tuning while slightly trade-off by minimizing trainable parameters. Given the structural nature of brain graphs, where node and edge features capture complex spatial and relational dependencies, we examine two prompt insertion mechanisms: addition ("+") and multiplication ("x"). Results show that multiplicative insertion consistently outperforms the additive version, likely because scaling features better preserves relational patterns. Moreover, to further leverage topological information, we extend the prompt-tuning framework by incorporating edge prompts in addition to node prompts. This design grants the model greater flexibilityy adjust local connectivity, leading to improved transfer performance across diverse downstream tasks.
less accurate, offers the best feficiency-adaattabel
Table 5: Comparison of Different Tuning Methods on ABIDE (ASD Classification).
Tuning Method
FT Flops
Full-Shot FT
w/o Pre- Training
Very High
65.2 67.1
Fine-Tuning
High
73.3
PEFT (Prefix)
Low
69.3
PEFT (LoRA)
Low
70.6
G Prompt-Tumme (+)
Very Low
67.4/68.7
G Frompt-Tuning (x)
Very Low
70.1
Srompt-Tuning Edge (x)
Very Low
71.2/73.5
Table 6: Overview of Tuning Methods Evaluated. Fine-tuning updates all model weights; PEFT strategies reduce trainable parameters by introducing lightweight modules; graph prompt-tuning updates only learnable prompts while freezing the backbone.
Tuning Method
Trainoble Parameters
Rackhone Frozen
Extra Module
Description
Tuning
No
Updates weights during downstream training.
PEFT (Pietix-Tuning)
Small prefix
Yes
Prefix
Injects trainable tokess input soguence.
PEFT (LoRA)
Low-rank mutrices
Yes
LoRA adapters
Adds trainable nank-deoomoooed projections attention layers.
Gruph Prompt- Tuning (+)
Small prompt vecturs
Yes
Prompt tekens
Adds prompts to nude feutures via element wise addition.
Graph Prompt- Tuning (x)
Small prompt vectors
Yes
Prompt tokens
Injects prompts via feature wise muliipiiaation.
Procept- Tuning w/ Edge
Node edge prompts
Yes
Node edge prompes
Extends prompt injection edge features better adepeation.
H BRAIN FMRI GRAPH CONSTRUCTION FROM FMRI TIME SERIES
To construct brain graphs from resting-state fMRI data, we follow correlation based approach that captures functional interactions between brain regions. Specifically, for each subject, we extract the regional mean time series (t, E RT) R from N brain regions of interest (ROIs), where T is the number of time points. We then compute the Pearson correlation coefficient between every pair of ROI time series:
Cov(ts, tz) Ai a(t)) ot,,
E [-1,1],
a(t.)
where Cov(-,.) denotes the covariance and ((-) the standard deviation. The resulting symmetric matrix A E NN serves as the weighted adjacency matrix of the brain graph, representing functional connectivity strengths.
To construct the node features, we reuse the correlation profile of each ROI its functional embedding. That is, for node i, we define its feature vector as:
A A E R.N.
X;
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which encodes the functional relationships between ROI and all other ROIs. The resulting graph = EV,E,A X) is fully connected and characterized by node features X [x] i...; x E NNN, and edge weights given by A.
I COMPARISON OF POSITIONAL ENCODING METHODS
We investigate four commonly used positional encoding (PE) strategies for graph neural networks: Laplacian Positional Encoding (L.PE), Node Degree Positional Encoding, Brain Gradient Positional Encoding (Dong et al.) 2024a), and Random Walk Structural Encoding (RWSE). Below, we define each method and evaluate their characteristics the context of IMRI-based brain graph modeling.
(1) Laplacian Positional Encoding. LPE leverages the eigenstructure of the graph Laplacian to capture global graph geometry. The symmetric normalized Laplacian defined as:
= D 1-2 AD-
11/2
where E N is the adjacency matrix, D the degree matrix with D;, D;= -, Aij. and I the identity matrix. The PE is obtained by taking the first l non-trivial eigenvectors of L:
PELPE = [u1, ug,.... Up
where each u, RN is the i-th eigenvector.
Analysis. LPE captures rich global topology, but computing eigenvectors expensive and unstable for large dynamic graphs. It also suffers from lack of cross- cross-graph consistency, which limits its effectiveness for pre-traing pre-training and transfer tasks.
(2) Node Degree Positional Encoding.
This method encodes each node en with its degree:
N
P Degree (2;) = deg(v;)
Aijo
j=1
and optionally normalized:
deg(v.) P Peeerree (v:) max, (eg((;))
Analysis. Degree encoding is fast and simple, requiring no matrix operations. However, it only captures local connectivity and lacks the expressiveness needed for distinguishing complex structural roles in brain graphs.
(3) Random Walk Structural Encoding (RWSE). RWSE encodes multi-scale structural roles by computing the probability that random walk returns to the same node at different steps. Define the one-step transition probability matrix:
P AA
then the 1-step return probability for node vi is:
PERWSE (v:) [(P') (P2)
PP )
Analysis. RWSE computationally efficient, avoids spectral decomposition, and encodes multi-hop recurrence statistics that are particularly well-suited for the hierarchical and modular structure of brain graphs. Empirically, provides the most favorable balance between accuracy and scalability.
As shown in Table we evaluate different positional encoding strategies on the ABIDE II dataset for ASD classification. The model without any positional encoding is the fastest but performs poorly. Applying graph pre-training substantially improves performance across all variants, validating its effectiveness. Laplacian PE yields competitive accuracy but suffers from high computational cost due to eigen-decomposition Node Degree Encoding is computationally efficient but underperforms compared to RWSE. Notably. RWSE achieves the highest performance while maintaining fast inference speed. These results indicate that RWSE offers the best trade-off between accuracy and efficiency, making it the most effective and scalable positional encoding method for our BrainGFM framework.
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Table Comparison of Positional Encoding Strategies on ABIDE II (ASD Classification)
[PE] Type
Pre-Trained
Efficiency
Performance (ACC/ AUC)
w/o [PE]
Very Fast
65.2 67.1
w/o [PE]
Very Fast
69.5/71.7
Laplacian [PE]
Slow
69.2/71.3
Node Degree [PE]
Fast
08.4/70.55
Brain Gradient [PE]
Fast
70.4/72.5
RWSE [PE]
Fast
66.1 68.0
RWSE [PE]
Fast
70.5
J UNIFIED TRAINING ON BRAIN GRAPHS WITH VARYING NUMBERS OF NODES
To handle brain graphs with varying numbers of nodes, introduce prompt-based unification mechanism that aligns all graphs to fixed size Nmax Given graph with N; nodes (N; S Naax). its node feature matrix X; R xxF first augmented with random walk structural encoding (RWSE), and then zero-padee paro-p to obtain (ad(X,) E R Nox
To inject inductive biases, we employ learnable node prompt matrix P E R. and perform element-wise fusion as:
X, Pad(X,)) ④ P.
where denotes the Hadamard product. To further guide the model, we prepend the task/dssorder token [T/D] XTD and the atlas/parcellation token [A/P] XAP, resulting in the full input matrix:
Z: = XTD: XAP; X;} E (N +2
Simultaneously, the original adjacency matrix A, NNN: is expanded to A, RN 2 Nan+2) by fully connecting the two prompt tokens to all other nodes:
A,
12x2
Nnm Pad(A))
Naw
N
An attention mask M, E {0, 1Ma is also constructed, where M. = 1 indicates that position j corresponds to padded node. This mask is used to prevent the self-attention mechanism from attending to invalid positions, ensuring consistency across variable-sized graphs.
To apply the attention mask, modify the attention matrix computed by the self-attention mechanism:
QKK
S.
Vd
where Q; = Z, We and K, = Z, W. are the query and key projections of the input matrix Z.
The attention mask M, is broadcast across the attention heads and used mask out the scores corresponding to padded nodes by replacing them with -00:
(S.iik), k).
if M, [k] 0, M,
$ و k)
-00,
k = 1.
This masked score matrix S, is then passed through the softmax function to obtain the final attention weights:
Attention Q;, K,, V.) Softmax(S.) V;,
where V. Z, Wv is the value projection. This ensures that attention is only distributed among valid (unpadded) nodes and the two prompt tokens, making the model robust to input graphs with varying node counts.
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K GRAPH PROMPT CONSTRUCTION AND INSERTION
To unify diverse brain graphs with variable node counts and enable flexible adaptation, we construct learnable graph prompt denoted as P |R..xxXP XF where Numsx is the maximum number of nodes across all graphs and is the feature dimension after RWSE augmentation. The prompt serves as set of element-we deement-wiee multiplicative masks for node-wise feature modulation.
Given brain graph with input node features X NXF, where F' = Fraw + Fiwse- Then, the graph prompt is applied via element-wise multiplication:
X
where @ denotes Hadamard (clement- wise) product. This modulated feature tensor X is projected into the model hidden space via learnable projection layer:
Hu = (roj(X) E IR Naas x Findtl
L META-LEARNING FOR UNIFYING DIVERSE ATLASES AND DISORDERS
To support cross-disorder and cross-atlas generalization, we design meta-learning framework that optimizes only the graph prompt module while keeping the entire pre-trained backbone Fg frozen. The goal is learn a prompt initialization that can quickly adapt any new brain graph classification task defined by varying disease types and brain parcellations.
Each task T; corresponds Schae fer100, ADHD + AAL116). 6). Given task Ti, we split its data into support set Dirain and a query set Diest
unique combination of brain disorder and an atlas (e.g., MDD
We perform task-specific adaptation using the support set by updating only the prompt parameters Pas while keeping the encoder Fø fixed:
Inner Loop: Prompt Adaptation on Single Task.
of a Cupa (Fo(Pp. D!rin))
(1)
This update reflects how the prompt adapts to a particular disorder-atlas context, without altering the pre-trained backbone.
Outer Loop: Meta-Update Across Tasks. We update the prompt initialization Ps by minimizing the query set losses across a batch of tasks:
B
Φ - Φ - B V Flstt (Fo(Po;, Diat))
(2)
This outer-loop update encourages the learned prompt to generalize across diverse tasks, each characterized by different disorder and parcellation, while the encoder remains frozen throughout.
Algorithm 1: Meta Learning for Graph Prompt Tuning (Frozen Backbone)
Input: Frozen backbone Fø, task set (T.) learning rates o, B
Output: Meta-leamed graph prompt parameters Initialize prompt parameters Po; while not converged do Sample batch of tasks BT
/ * Each task T, (disorder, atlas) pair
/ * Inner Loop: Adapt prompt on single task
for each task do
Split into support Dirain and query Diest Compute task-specific adapted prompt:
& = ---oVo ( (Fo(Po. )imin)):
aV
/ Note: Fo is frozen, only & of Ps is updated
*/
Outer Loop: Update shared prompt using query losses - o- 3 Σ, Vo (FoPo,
return s
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Table 8: Conceptual Similarities between Meta-ngtching and Our Prompt-based Meta-Learning
Framework
Aspect
Shared Property
Goal Data Efficiency
Both aim to transfer predictive models from large-scale to onall--cclle neuroimaging datasets. Both methods improve learning low-resource settings without retraining entire backbone. Both adopt parameter-effiiien efffciifttttt adaptation by freezing the main model and triningg small modules. Both address distributional shifts across cohorts, sites, acquisition protocols. Both customize predictions for downstream tasks using lightweight tusk-specific components.
Parameter Efficiency
Domain Shift Adaptation
Task-specific Specialization
Table 9: Key Differences between Metchnng (He et al. 2022 and Our Prompt- Meta-
Learning Framework
Aspect
Meta-Matching
Meta- Mets-Learming Frameuork
Meta- louningg Paradigm
No explicit meta learning: pos: hoc linear adaptation
Pronge based meta learaing with inner/outer optimization
Adaptation Mechanism
Learss cuiput mapping (x() via linear regressor
Leams task specific prompt tokens injected
backbooe model
Leuenable Madales
Linear curput head trained per uaik
Prompe tokens trained task and meta updated across tasks
Gradicnt
Only sogresoor trinnd;; backbone remains fised
Pronpes are updated via iunz--lop gradioats: backbonn finzea
Tisk Detinition
Each small dataset separate task: no shared structure
Tasks detined by (disoeder, iclas) tuples sumpled from unitedd distribution
Update Soape
No madel componest is updated during adaptation Fou-sboo only: does not supoor zoro-soot transfer
Ouly prompe modules tee adayted; hase cscoda: remains fixed
Generalizarinn Scope
Supports both few-shot and zemoshot generaization via pronze inference
M COMPARISON OF OUR META-LEARNING WITH THE
Our method adopts prompt- based- meta-learning strategy to enable efficient transfer across neu- roimaging tasks defined by different disorders and parcellation schemes. Specifically, we introduce learnable prompt tokens that serve as task-specific parameters injected into the backbone model. These prompts are meta-trained through episodic learning with inner- and outer-loop optimiza- tion, allowing the model to adapt new tasks without updating the backbone. This design enables parameter-efficient adaptation and supports both few-shot and zero-shot generalization, as the prompts can be tuned or inferred for unseen tasks based on their task identity.
related concept appears in Metc-nn (He et al. 2022), which aims to transfer pre-trained models from large-scale datasets (e.g., UK Biobank) to small-scale neuroimaging datasets. Meta- Matching freezes the entire backbonn and learns a lightweight linear regressor on the model outputs to align with new target labels. Each target dataset is treated an independent task, without leveraging shared structure or task distributions. While both approaches seek efficient cross-dataset transfer, their adaptation mechanisms and learning paradigms are fundamentally different.
As summarized in Tables 8 and 9 both methods address domain shift and operate in low-resource settings by introducing parameter-efficient task modules. However, unlike Meta-Matching's 's post-hoc linear adaptation, our method performs task-level meta-learning by explicitly optimizing prompt tokens via backpropagation. This allows our model to generalize not only to few-shot settings but also to zero-shot tasks, making it more flexible and extensible across diverse brain graph domains.
N DETAILS OF GRAPH MASKED AUTOENCODER AND GRAPH CONTRASTIVE LEARNING
We propose a unified pre-training framework that combines Graph Masked Autoencoder and Graph Contrastive Learning to learn robust and generalizable representations for brain graphs. These two components complement each other: GMAE enables fine-grained feature-level recovery through generative reconstruction, while GCL encourages invariance under perturbations by contrasting different views of the same graph.
N.1 GRAPH MASKED AUTOENCODER PRE-TRAINING
Inspired by recent advances in masked autoencoding (Hou et al. 2022), we apply random masking to both nodes and edges of the input graph. Given graph with N nodes, adjacency matrix A E R NNN and node features X E N NxF, we randomly sample a subset of nodes VM C V to mask. For each masked node U/ VMs its input feature is replaced with learnable mask token XIM] R.P. The
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masked node feature matrix X is defined as:
X(M)
if VM
X
otherwise
To further increase the learning difficulty, we also apply structural masking by dropping edges in the adjacency matrix. Specifically, each edge is dropped independently with probability resulting in corrupted adjacency matrix:
À A M.
where M, {0, N is a symmetric binary mask sampled from Bernoulli distribution with parameter 1 and @ denotes glement-wise multiplication.
The GMAE encoder processes the corrupted graph (X, A) and produces latent embeddings, which are then passed lightweight decoder to reconstruct the original node features of the masked nodes. The reconstruction objective is defined as:
Liee
[ x;11
where &; is the predicted feature from the decoder.
N.2 GRAPH CONTRASTIVE LEARNING PRE- TRAINING
In parallel with the generative pathway, apply contrastive learning to enforce view-invariant representations. Specifically, we generate two augmented views of the same input graph by applying lightweight stochastic perturbations (i.e., random feature dropout and edge dropout). One view is
treated the query, while the other serves as the key. Let (X()), A (4)) and (X (x), A (k) denote the two views; these are passed through shared encoder obtain corresponding representations mg and 2g. We adopt the NT-Xent contrastive loss (Qiu al. (2200) to maximize the similarity between matching query-key pairs from the same graph while distinguishing them from others the batch:
B La = log
exp(sim(22) (b) (b) )/T)
Σ If [o exp(sim( =.44) 1(7)
where sim(-.-) denotes cosine similarity, temperature hyperparameter, and B is the batch size. Here, b and b' index different samples within the batch, where each sample corresponds to an augmented graph.
By encouraging the embeddings of different augmented views of the same graph to be aligned, the model learns representations that are invariant to small perturbations in node features and topology, thereby improving generalization across downstream tasks.
O COMPARISON BETWEEN TWO GRAPH PRE- TRAINING METHODS
Table T0 provides comprehensive comparison between Graph Contrastive Learning and Graph Masked Autoencoder as two prominent pre-training paradigms for graph neural networks. GCL emphasizes learning discriminative representations by contrasting positive and negative graph pairs, making it particularly effective for classification and retrieval tasks. In contrast, GMAE focuses on reconstructing masked parts of the graph, encouraging the model to capture fine- fineggriiedd structural de- tails and local contextual information. While GCL tends to produce compact and abstract embeddings that distinguish samples globally, GMAE yields richer and more structure-aware representations suitable for reconstruction and local reasoning. However, both approaches have limitations: GCL's effectiveness heavily depends on the design of graph augmentations, potentially neglecting subtle local cues; GMAE, on the other hand, is sensitive to the masking ratio and may underperform in tasks requiring global discrimination. These complementary characteristics suggest that combining both strategies may lead to robust and generalizable graph representations.
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Table 10: Comparison between Graph Contrastive Learning and Graph Masked Autoencoder Pre- training.
Graph Contrastive Learning GCL
Graph Masked Autoencoder - GMMAE
Main Objective
Learn to pull together positive pairs and push
Learn reconstruct masked node/edge fea- tures, focusing on structure-awant and fine- grained representations.
apart negative pairs, focusing on discrimina- tive representations.
Feature Focus
Emphasizes global discriminative features that distinguish between different samples.
Emphasizes local structure awareness and detailed pattern recovery.
Pre-training Strategy
Contrastive loss (c.g., InfoNCE) between augmented graph views.
Masking parts of the graph and reconstruct- ing them via decoder.
Best for
Classification, retrieval, tasks requiring strong discrimination.
Reconstruction, generation, local reasoning, and also benefiting classification.
Learning Tendency
Leams compact, abstract representations
Learns rich, detailed representations that capture local and global graph structures.
that excel at distinguishing samples.
Potential Drawbacks
Sensitive to augmentation design; may over- look fine-grained local details.
Sensitive to masking ratio; focus too much on local patterns without sufficient global discrimination.
P IMPLEMENT DETAILS
During pre-training, we set the batch size to 128 and used the Adam optimizer with learning rate of 0.0001. For downstream classification tasks, we the batch size 16 in the full-shot setting, and to I in both the few-shot and zero-shot settings. The Adam optimizer was also used for these tasks, with the same learning rate of 0.0002. In the meta-learning setup, we trained the model for 50 epochs. More settings about pre- pre-training and graph transformer backbone and meta learning can be found in Table
12
Table 11: Training and architectural hyperparameters used in the Graph Transformer Backbone.
Parameter
Value
Description
batch.size
128
Number of samples per batch during training.
learning rate
0.0001
Learning rate used by the optimizer.
Jayys
4
Number of layers in the decoder of the Graph Masked Autoen- coder (GMAF).
fE.hidden.size
256
Hidden dimension of the feed-forward layer in the Transformer.
num cum.classes
2
Number output classes (mainly used for downstream classifica- tion tasks).
num.self.att
Number of Transformer self-attention layers used the encoder.
dropout
0.3
Dropout rate used for regularization.
num num.CNN.11. layeers
Number of GNN layers stacked encoder.
nhead
Number of attention heads in each multi-head self-attention layer.
hidden.dim
128
Dimensionality of hidden representations in the encoder.
max teature dim
512
Maximum input node feature dimension after projection.
rwse steps
5
Number of steps in random walk positional encoding.
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Table 12: Training hyperparameters used in the MAML-style Meta-Learning Framework.
Parameter
Value
Description
meta.epochs
50
Number of meta-training epochs (outer loop iterations).
meta.batch.size
8
Number of tasks sampled per meta-update step.
inner_steps
Number of inner-loop gradient update steps on each task.
inner.lr
0.0002
Learning rate used in inner loop (lask-specific adaptation).
outer_lr
0.0001
Learning rate used in the outer loop (meta-model update).
k-folds
5
Number of folds used in task- K-Fold data splitting.
support set size
80%
Number of samples used for inner-loop training (support set), determined by fold split.
query set size
20%
Number of samples used for outer-loop meta-update (query set), determined by fold split.
task.sampling
Random Strategy used to sample tasks from the training pool per meta- iteration.
Table 13: Common support/query set splits used in MAML-style meta-learning.
Support:Query
Typical Use Case
Description
50% 50%
Balanced Learning
Equal emphasis on adaptation and generalization. Often used when data size is sufficient.
66% 34%
Stronger Adaptation
More data is allocated support the inner loop updates. Suitable for tasks with high variability.
33% %
Stronger Generalization
Emphasis on generalization performance, especially useful when measuring transfcrability.
Q BASELINES
The Table 13 provides a systematic comparison of various brain foundation models and baseline methods across multiple dimensions, including architectural type, data domain, pre-training strategy, and tuning method. These approaches can be broadly categorized into three groups: conventional models without pre-training (c.g., Vanilla GCN (Kipf Welling 2016). BrainGNN (Li al. 2021], Vanilla Connectome/FC-Dasee trained models (e.g., BrainNPT (Hu et al. 2024), BrainMass (Yang al. 2024). BrainLM (Caro
Tansformer (TF) (Yun et al. 2019)). ROI- or time-scries-bate based pre-
et al. )0 and our proposed grasd foundation model, BrainGFM.
The conventional models do not leverage any pre- pre-training and are limited to single parcellation and single-disorder settings, resulting in restricted generalization capabilities. Connectome/FC-hasee models such as BrainNPT and BrainMass employ generative pre-training on region-level features, enabling improved performance across limited number of disorders. BrainIM further enhances temporal modeling through time-series-ben based generative pre-training.
In contrast, BrainGFM adopts both generative and contrastive graph-based pre-training strategies and supports multiple adaptation paradigms, including full fine-tuning and graph prompt-tuning. is the only model capable of handling full-shot, fhw-shot, and zero-shot scenarios. Trained multiple parcellations and evaluated across 25 disorders, BrainGFM demonstrates superior generalizability and adaptability. Overall, it stands out the only comprehensive brain FMs that supports structural graph modeling, multi-task transfer, cross- poossparcelllttion generalization, and versatile tuning strategies.
R BENCHMARKS, DATASETS, DISORDERS AND DOWNSTREAM TASKS
Table [o]vvied a comprehensive summary of the datasets used our framework, categorized into four functional groups: Pre-train, Internal Test, Semi-External Test, and External Test. This
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Table 14: Settings and considcrations for the number of inner loop steps in MAML-style meta- learning.
Inner Steps
Typical Scenario
Description
Fast Adaptation
Common choice with low compute cost. Provides basic task adaptation and supports batched meta-updates.
3
Balanced Trade-off
Provides stronger task-specific learning while maintaining rea- sonable training cost. Often used in practice.
5
Enhanced Adaptability
Allows deeper inner adaptation. Useful for complex or highly diverse tasks, but increases overfitting risk.
5
Rarely Used
Risk of overfitting support set and high computational cost. Not commonly used unless thoroughly validated.
Table 15: Comparison of brain foundation models and baselines across different architectural types,
domains, pre-training strategies, and tuning methods.
Model
Foundation
Input Type
Domala
Pre- Training Method
Tuning Method
Tuning Shot
Parcellation
Disorder
Vanilla GCN
Griph
Spatial
Single Single
Singl:
BriiGNN
Graph
Spiiia
Single
Vanilla TF
Connectome/PC
Spinial
Single
Single
Gruph IF
Gruph
Sputial
Single
Singlo
BriinNelTF
Co
Sputial
Single
Single
BrainNPT
Connecteme/FC
Spatial
Connectome//PC Generative
Fine- Tuning
Full/Few shot
Single
Muttiple 5)
BninMass
ConnectemeFCC
Spuial
Connettom/FCC Generutive
Fine Tuning
Full/Few-shot
Single
Multiple (10)
BrainLM
Time Series
Temporal
Time Series Generative
Fine Tuning
Full -bot
Single
Multiple (<t 5)
BEain-EPA
Time
Spurial-Temprrl
Time Stries Generative
Fine-Tuning
Full-shor
Single
Muttiple 5)
BrinGEM
Gruph
Sputial
Graph (Gener. Contra.)
Fine/Prompe Tuning FullFew shox
Mulkippe
Multiple (25)
partitioning is designed to systematically evaluate the performance and generalization ability of our brain FMs across varying levels of domain similarity. The Pre-train group includes 19 datasets comprising over 50,000 samples from both healthy individuals and patients diagnosed with broad spectrum of neurological and psychiatric disorders, such as Alzheimer's disease (AD), mild cognitive impairment (MCI), ADHD, ASD, major depressive disorder (MDD), post-traumatic stress disorder (PTSD), and substance use disorder (CUD). These datasets provide rich and diverse training samples for learning a robust and generalizable representation. The Internal Test group is constructed from subset of the pre-training datasets and is used to evaluate in-distribution performance, where both the disorders and acquisition protocols are seen during pre- pre-training. This setting assesses how well the model fits to familiar domains. The Semi-Externa Test group includes datasets involving diseases that overlap with the pre-training stage but originate from different sites, scanners, or cohort distributions. This setting simulates moderate domain shifts and is used to measure the model's 's transfcrability to partially unseen distributions. Finally, the External Test group consists of datasets that entirely excluded from pre-training and validation stages, containing distinet population sources and clinical conditions. This group serves as a stringent benchmark for zero-shot generalization, testing the model's ability adapt entirely new domains, disorders, and acquisition protocols. Overall, this structured dataset split enables a rigorous and hierarchical evaluation of the model's robustness, transfer performance, and zero-shot generalization capabilities across wide range of real-world neuroimaging scenarios.
Table 17 and 18 summarizes the 25 brain disorder classification tasks selected from various public neuroimaging datasets. These tasks span remarkably broad range in terms of disease types, age groups, and data sources, reflecting the diversity and complexity of real-world clinical scenarios. The downstream evaluation covers a wide spectrum of brain conditions, including neurodevelopmennal disorders (c.g., ADHD, ASD, SLD), affective and emotional disorders (e.g., MDD, anxicty, bipolar disorder), psychotic disorders (e.g., schizophrenia), neurodegenerative diseases (e.g., AD, MCI, dementia), and substance use disorders (e.g.. CUD). Subject age ranges from as young as 5 years old (e.g., ABIDE II, HBN) to elderly adults nearing 90 years old (e.g., ADNI, OASIS3), capturing the full human lifespan from brain development to cognitive decline. To ensure scientific rigor and fairness, we carefully constructed sex-balanced subsets for all labeled downstream tasks, meaning
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Table 16: Overview of Neuroimaging Datasets Used for Pre-Training and Evaluation. We group datasets by their function in our pipeline: pre-training, internal test, semi-external test, and external test. The table lists dataset names, number of unique subjects, total samples, and associated disorders.
Function
Datasets
Source
Subjects
Samples
Disease
ABCD
Casey al.
2018
11,878
35,770
Multiple
ADHD 200
consortium
2012
973
1382
ADHD
ABIDE
Di Martino
2014
1.112
1.112
ASD
3DNI
Jack Jr et al
2008
1,071
1,410
AD, MCI
AOMIC
Snock ct al.
2021
210
210
Multiple
AURORA
McLean ct
2020
284
284
PTSD
CAM.CAN
Shafto
2014)
652
652
CATD
Niclson ct al.
20255
127
454
Multiple
GSP
(Holmes et al.
2015
1,569
2,706
Pre-Train
HCP-Aging
Bookhermer et al.
2019)
724
724
EMBARC
Trivedi et
2016
308
308
MDD
LEMON
Babayan el al.
2019
213
MDD
HABS
Dagley et al.
2017
284
1,371
PREVEND
Tremblay Mercier Merbley
2021
343
2,427
AD
SRPBS_Jage Japan
Yamashita ct al.
20199)
1,410
1,410
ASD
NYU CUD
(Kllyy et 201T)
29
56
CUD
OASIS3
LaMontagne et al. 2019)
1,172
4,090
Dementia
HBN
Alexxnder el al. 2017)
2,228
4,039
Multiple
SubMex
Angeles-Valdez Valdez et al
2024
150
150
CUD
ADHD 200
(consortium
2012
973
1382
ADHD
Internal Test
HBN
Alexander et al. 2017
2,282
4,039
Multiple
OASIS3
Monnagge
2019
1,172
4,090
Dementia
ABIDE II
Di Martino al.
2014)
1,044
1,044
ASD
Semi-External Test
ADNI 2
Weiner et 2013)
1,171
1,306
AD, MCT
SubMex CUD
Angeles- Valdez et al.
2022
135
135
CUD
UCLA,CNP REST-META-MDD
PPolrack al.
2016)
261
261
Multiple
External Test
Yan et al.
2019
2,379
2,379
MDD
that each task includes approximately equal numbers of male and female samples. This prevents potential sex biases from influencing model performance. For multi-diagnostic datasets like HBN, created multiple binary classification tasks (e.g., MDD vs. NC, ADHD vs. NC), each treated independently within unified evaluation framework. By incorporating such comprehensive and diverse benchmark. we are able to thoroughly assess the robustness, transferability, and clinical relevance of our proposed BrainGFM.
S DATA ACQUISITION AND PREPROCESSING
For all cohorts, resting-state fMRI data were collected with varying protocols and scanner parameters specific each study site. All available resting-state fMRI data preprocessed using the well- established fMRIPrep pipeline (Esteban et al. 2019). The T1-weighted image was corrected for intensity non-uniformity and then stripped skull. Spatial normalization was done through nonlinear registration, with the Tlw reference (Avants et al. 2008). Using FSL, brain features such as cerebrospinal fluid, white matter, and grey matter were segmented from the reference, ertincextracted TI weighted image (Zhang et al. 2000). The fieldmap information was used to correct distortion in low-frequency and high-frequency components of fieldmap. Then, corrected scho-planar imaging reference was obtained from a more accurate co-registration with the anatomical reference. The (BOLD) reference was then transformed to the Te-weighted image with boundary-basse registration method, configured with nine degrees of freedom to account for distortion remaining in the BOLD reference (Greve & Fischl 2009). Head-motion parameters (rotation and translation parameters of cellume-to-rffeeenee transform matrices) were estimated with MCFLIRT (FSL). BOLD signals were slice-time corrected and resampled onto the participant's original space with head-motion correction, susceptibility distortion's 's correction, and
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Table 17: Overview of 25 Brain Disorders Across Public Neuroimaging Datasets. We select balanced samples (e.g., HBN, ADNI) for downstream classification. Note that all downstream tasks have balanced numbers of male and female samples.
Dataset
Dicess//Doorde//isabiilt
Downstream Task
Age
Sample Size
ADHD200
Att-ention Deniiion-DDfiiiiftyeeeetttiii Disorder (ADHD)
ADHD vs. 8-26
402/580
ABIDE II
Autistic Spectrum Disorders (ASD)
ASD vs. NC.
5-64
581/733
Alzheimers Discase (AD)
AD NC
55-89
91/100
ADNI
Mild Cognitive Impairment (MCT)
MCI vs. NC
55-89
168/200
OASIS3
Dementia (DM)
DM vs. NC
45-88
290/300
Major Depression Disorder (MDD)
MDD NC
6-20
261/100
Anxicty (ANX)
ANX vs. NC
6-20
224/250
Oppositional Defiant Disorder (ODD)
ODD vs. NC 6-20
519/319
Obsessive Obsessiee-oomulsive Disorder (OCD)
OCD vs. NC 6-20
130/150
Language Disorder (I.D)
L.D VS. NC
6-20
464/319
Specifie Learning Disorder (SLD)
SLD VS. NC
6-20
335/319
Enurcsis (ENU)
ENU VS. NC
6-20
279/319
Intellectual Disability (ID)
ID VS. NC
6-20
129/150
Post- Post-Traumatic Stress Disorder (PTSD)
PISD VS. NC 6-20
39/40
Encopresis (ECP)
ECP VS. NC
6-20
68/70
HBN
Dysthymia (PDD)
PDD VS. NC
6-20
85/90
Tourette Sydnrome (TS)
PS VS. NC
6-20
68/70
Adjustment Disorder (AJD)
AJD vs. NC
6-20
96/100
Provisional Disorder (PID)
PTD vs. NC
6-20
68/70
Motor Disorder (MD)
NC 6-20
123/130
Speech Sound Disorder (SSD)
SSD vs. NC 6-20
98/100
Communication Disorder (CD)
CD NC 6-20
43/45
SubMcx.CUD
Cocaine Use Disorder (CUD)
CUD vs. NC
18-45
72/63
Schizophrenia (SCHZZ)
SCHZ vs. NC
21-50
50/55
UCLA.CNP
Bipolar Disorder (BP)
BP vs. NC
21-50
49/55
REST-META-
Major Depression Disorder (MDD)
MDD vs. NC
21-50
1,252/1,101
then resampled into standard space, generating a preprocessed BOLD run MNII52NLin2009cAA50 space. Automatic removal of motion artifacts using independent component analysis AICA-AROMA) (Pruim et al., 2015) was performed on the preprocessed BOLD time-series on MNI space after removal of non-steady- -taatd volumes and spatial smoothing with an isotropic Gaussian kernel of 6 mm FWHM (full-width half-maximum).
T DEFINITION AND DESCRIPTION OF USED ATLASES/PARCELLATIIOS
Table T9 provides a systematic comparison of eight widely used brain atlases and parcellations adopted in our study, including the number of parcels, construction type (functional or anatomical), year of release, and key design features. These atlases represent the most commonly applied frame- works in both functional and structural neuroimaging studies and are constructed based on diverse methodological principles, making them suitable for different modeling objectives in neuroscience research. Specifically, the Schaefer atlas series (Schaefer (00/200/300) is derived from resting-statt (MRI data using gradient-weighte wreghtet-weighted weighted clustering to generate spatially contiguous functional parcels. Each parcel is assigned to one of the Yeo 7 or 17 functional networks, preserving hierarchical orga- nization and functional homogeneity. This design makes Schaefer atlases particularly suitable for functional connectivity analysis and graph neural network modeling. The availability of multiple spatial resolutions enables systematic evaluation of model behavior under coarse- fine- fine-grained parcellations. The Shen268 atlas, constructed via group-level independent component analysis (ICA), offers spatially contiguous and inter-subject consistent functional parcels and has become a standard in GNN-based IMRI research. In contrast, the Power264 atlas identifies spherical regions centered on functional hubs without enforcing spatial continuity. Although less anatomically constrained, it is widely used in network neuroscience, particularly for studying nodal centrality and modular organization. The Gordon333 atlas integrates local gradient information and functional network assignment define high-resolution, functionally coherent brain regions, enabling precise modeling
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Table 18: Disorders in Different Categories and Their Datasets.
Category
Disease/Disorder
Dataset(s)
Atteation-Deficinee Hyfeeiiiiyyyyyeettiiii Disorder (ADHD)
ADHD200
Autism Spectrum Disorder (ASD)
ABIDE II
Language Disorder (I.D)
HBN
Neurodevelopmental Disorders
Specific Learning Disorder (SLD)
HBN
Intellectual Disability (ID)
HBN
Spesch Sound Disorder (SSD)
HBN
Communication Disorder (CD)
IBBN
Alzheimer's Disease (AD)
ADNI
Neurodegeneraative Disorders
Mild Cognitive Impairment (MCT)
ADNI
Dementia (DM)
OASIS3
Major Depression Disorder (MDD)
HBN, REST-META-MDD
Amxiety (ANX)
HBN
Post- Tramattuii Stress Disorder (PTSD)
HBN
Mood and Anxiety Disorders
Adjustment Disorder (AJD)
HBN
Mild Depression Disorder (PDD)
HBN
Bipolar Disorder (BP)
UCLA.CNP
Ohsessive Compulie- Disorder (OCD)
HBN
Oheesive-Compukive and Impulse Control Disorders
Oppositional Defiant Disorder (ODD)
HBN
Tourette Syndrome (TS)
Disorders
Motor Disorder (MD)
HBN
Provisional Disorder (PTD)
HBN
Substance Use Disurders
Cocaine Use Disorder (CUD)
SubMex CUD
Psychotic Disorders
Schizophaenia (SCHZ)
UCLA.CNP
Table 19: Comparison of Common Brain Atlases and Parcellations Used in Our Study.
Atlas/Parcellation Parcel Num
Type
Year
Key Features
Schacfer100
100
Functional
2018
Based on resting-state fMRI; each parcel belongs to Yeo 7/17 networks; spatially contiguous; gradient-weighted clustering.
Schaefer200
200
Functional
2018 Higher resolution; suitable for fine-grained functional connectivity graph modeling.
Schaefer300
300
Functional
2018
Even finer granularity; suitable for detailed graph analy- sis hut may increase noise.
Shen268
268
Functional
2013
Group-wist ICA-based; spatially contiguous; widely used in functional connectomics and GNNs.
Power264
264
Functional
2011
Functional hubs spheres; spatially contiguous; commonly used network neuroscience.
Gordon333
333
Functional
2016
Combines local gradient and network assignment; fine resolution.
AAL116
116
Anatomical 2002
Based anatomical landmarks; widely used in struc- tural/functional neuroimaging; standard in SPM.
AAL3v1
170+
Anatomical 2020
Updated AAL; includes more dctailed subcortical and cerebellar regions.
of functional boundaries. terms of anatomical atlases, the AAL116 atlas one of the earliest struc- tural templates, based on anatomical landmarks and extensively used in both structural and functional neuroimaging studies. remains the default parcellation tools such as SPM. The AAL3v1 atlas is an updated version of AAL116, providing finer subdivisions of subcortical and cerebellar regions for enhanced spatial coverage and granularity, supporting more detailed crructual-functiinn
integration.
By incorporating both functional and anatomical atlases, well as wide range of spatial granularities (from 100 to 333 parcels), our study is designed to comprehensively evaluate the adaptability, scalability, and generalization capacity of brain graph models across heterogeneous parcellation
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strategies. This diverse atlas configuration facilitates pre-training under varied topological priors and enables robust transfer to downstream tasks involving unseen atlases or disorders. Such design is critical for building generalizable BrainGFMs capable of adapting diverse neuroimaging datasets and real-world clinical scenarios.
U BRAIN GRAPH INTERPRETABILITY ANALYSIS
As shown in Figure we present ROI-level attention attribution maps derived from the graph transformer layers of BrainGFM to illustrate how the model utilizes functional brain graph struc- tures for prediction. We visualize attribution differences between normal control (NC) and patient groups across two datasets: ABIDE (Autism Spectrum Disorder, ASD) and HBN (Major Depressive Disorder, MDD). Each heatmap represents the average pooled attention attribution values across all subjects within the corresponding group. Clear differences the attribution patterns can be observed between ASD vs. normal controls in ABIDE and MDD vs. normal controls in HBN. Regions with consistently higher attribution values indicate stronger contributions classification decisions. Notably, several frontal and temporal cortical regions, frequently reported as clinically relevant in psychiatric ncuroimaging studies, exhibit salient involvement. The distinct attention distributions demonstrate that BrainGFM captures meaningful disorder-related functional subnetworks rather than relying on noise or -ataset-specific artifacts.
These results support the interpretability of the learned graph representations and provide prelimi- nary evidence that BrainGFM can highlight potential biomarkers associated with neuropsychiatric disorders, helping bridge model decisions with neuroscientific insights.
ABIDE (ASD)
HBN (MDD)
340
F71
2401
Normal Control
VS
Patient
Normal Control
VS
Patient
Figure 10: ROI-level attention attribution maps for ABIDE (ASD) and HBN (MDD), showing distinct attention patterns between normal controls and patients.
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Reviews:
[{"review_id": "I6yDqKHsJE", "reviewer": "Reviewer_PE76", "summary": "This paper introduces **BrainGFM**, a graph-based fMRI foundation model that pre-trains a Graph Transformer on multi-atlas brain graphs using a dual SSL-based objective (graph contrastive learning and graph masked autoencoding) and adapts via **graph prompt-tuning** optimized with **meta-learning**. For zero-shot transfer, the model augments graph tokens with **language prompts** that encode disorder/task and atlas/parcellation semantics. Extensive experiments across diverse datasets and atlases show that multi-atlas pre-training improves downstream accuracy and that prompt-tuning is parameter-efficient in few/zero-shot settings. Overall, the work pushes toward a unified, atlas-aware foundation model for functional connectomics.", "questions": "**Q1)** GraphMAE performs only node reconstruction in its original formulation. How are edges reconstructed in BrainGFM? Additionally, beyond combining existing SSL components (GCL, GMAE), what constitutes the unique methodological novelty of the proposed self-supervised framework?\n\n**Q2)** In Section 4.1, comparisons with baseline models are presented only for the Schaefer100 atlas. Have the authors conducted similar evaluations under other atlas or parcellation settings to verify whether the observed performance gains generalize beyond a single atlas configuration?\n\n**Q3)** In Section 4.4, on which dataset are the reported comparison results based? The table shows BrainLM as “High” and BrainMass as “Low,” yet in Section 4.1 their numerical performance gap appears minimal. Could the authors clarify which dataset or evaluation protocol was used for Section 4.4 and how these qualitative rankings relate to the quantitative results in Section 4.1?\n\n**Q4)** The appendix states that BrainGFM uses a unified set of hyperparameters for fine-tuning across all datasets. Were the baseline models also tuned under fair and dataset-appropriate hyperparameter configurations, or did they rely on default or fixed settings? In particular, for pre-trained models such as BrainLM—which are limited to single-atlas training—were they pre-trained using the same data subset as BrainGFM and only the Schaefer100 atlas setting? Additionally, were the same fine-tuning epochs applied uniformly across all datasets, and were the baseline models and BrainGFM trained for an equivalent number of epochs? \n\n**Q5)** During the prompt-tuning stage, the model is trained with specific Task/Disorder (T/D) and Atlas/Parcellation (A/P) configurations. Does this imply that BrainGFM cannot generalize to entirely unseen tasks or atlases/parcellations during zero-shot transfer? If zero-shot generalization is claimed, could the authors clarify whether it applies only to unseen disorders/tasks (via language prompts) or also to unseen atlases, and how this is achieved given that A/P tokens are learned during training?\n\n**Q6)** Was a leakage audit performed to ensure subject-level disjointness across all pre-train, in-domain, and external groups after multi-atlas expansion? This is a key missing detail in the paper, as each subject’s fMRI scan can appear under multiple atlases. Clarifying whether strict subject-level separation was enforced would strengthen the reliability of the reported generalization.\n\n**Q7)** Were control experiments conducted to verify that the zero-shot improvements from language prompts truly arise from semantic alignment rather than label priors? For example, have the authors tested shuffled [task/disorder]–label mappings, semantically neutral texts of matched length, or atlas-only identifiers without disorder text?\n\n**Q8)** Have the authors examined whether the learned representations depend heavily on [A/P] tokens?  \nInstead of removing these tokens (which may collapse the model), analyzing representation similarity or clustering across different atlases could help determine whether BrainGFM learns truly atlas-invariant features.\n\n**Q9)** Could the authors provide approximate compute cost (e.g., GPU-hours, FLOPs, or peak memory) to contextualize the claimed efficiency advantage? While BrainGFM emphasizes computational efficiency compared to time-series or ROI-based foundation models, quantitative evidence would clarify the strength of this claim.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- **Well-articulated motivation and problem setting.**  \n  The paper clearly identifies the data scarcity, heterogeneity, and task-specific limitations of existing fMRI models, effectively motivating the need for a scalable, generalizable foundation model. The discussion connects practical constraints in fMRI acquisition and dataset diversity to methodological design choices, making the problem definition both convincing and well grounded.\n\n- **Multi-atlas large-scale pre-training with cross-parcellation gains.**  \n  The model leverages multiple parcellations/atlases per scan and reports improved generalization over single-atlas training on several benchmarks.\n\n- **Parameter-efficient adaptation and zero-shot generalization.**  \n  Parameter-efficient adaptation via graph prompts supports few-shot learning, and language prompts enable zero-shot transfer to unseen disorder or task settings.", "weaknesses": "1. **Limited pretraining methodological novelty.**  \n   The proposed pretraining framework combines existing self-supervised techniques—Graph Contrastive Learning (GCL), Graph Masked Autoencoding (GMAE), and MAML-based prompt tuning—without introducing a clearly novel algorithmic component. While the large-scale integration across atlases and disorders is valuable, the study could have been strengthened by exploring a more original approach, such as multimodal or text-guided self-supervised objectives. In addition, the authors could consider referencing or benchmarking against brain-specific self-supervised learning approaches that have recently emerged in fMRI.\n\n```\n[1] Yu, et al., \"Causal Invariance-aware Augmentation for Brain Graph Contrastive Learning\", ICML 2025.\n[2] Dong, et al., \"Counterfactual Brain Graph Augmentation Guided Bi-Level Contrastive Learning for Disorder Analysis\", ICDM 2024.\n[3] Ma, et al., \"Self-Promoted Clustering-based Contrastive Learning for Brain Networks Pretraining\", IJCAI 2024.\n[4] Zhang, et al., \"A-GCL: Adversarial graph contrastive learning for fMRI analysis to diagnose neurodevelopmental disorders.\", Medical Image Analysis, Volume 90, 2023 December.\n```\n\n2. **Unclear validation protocol and potential leakage.**  \n   The paper does not clarify whether subjects rendered under multiple atlases are strictly separated across pre-train and evaluation groups, which could inflate the reported generalization. In addition, the evaluation protocol is not fully consistent—comparisons are limited to the Schaefer100 atlas, and it remains unclear whether baseline models were fairly tuned under comparable single-atlas and hyperparameter settings.\n\n3. **Limited empirical grounding of claimed generalization.**  \n   While the language-prompt and cross-atlas transfer results are interesting, the paper lacks semantic or ablation controls (e.g., shuffled or neutral prompts) to confirm that the observed zero-shot gains reflect meaningful generalization rather than reliance on dataset or label cues.\n\n4. **Ambiguous evaluation of computational efficiency.**  \n   Although the paper highlights computational efficiency, it only provides qualitative labels such as *Fast/Slow* for training speed and *High/Low* for memory usage, without quantitative metrics (e.g., FLOPs, GPU-hours) to support these claims. As a result, it is difficult to assess the actual scalability and efficiency of the proposed model relative to the baselines.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9bMtqrqpfA", "reviewer": "Reviewer_F6f2", "summary": "This paper proposes BrainGFM, a large-scale graph-based fMRI foundation model. BrainGFM is pre-trained across multiple datasets with various parcellations. The model combines contrastive and generative pre-training of graph structures, then introduces meta-learned graph prompts for few-shot adaptation and language prompts for zero-shot generalization. The experiments demonstrate the validity of BrainGFM by comparative and ablation results.", "questions": "### Major\n- Please consider an experiment on the interpretability of the input graph within the BrainGFM framework (or at least adding a discussion on the possible interpretability).\n- There are claims on the computational efficiency of BrainGFM, but formal quantitative evidence to support this is limited. Please provide numerical benchmarks to substantiate the efficiency claims.\n\n\n### Minor\n- Please improve the readability of the figures (e.g., tick labels of the radar charts are too small to read).\n- Some abbreviations are defined multiple times.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Good motivation with an effective solution: scaling data across different atlases/parcellations\n- Extensive experiments and good results", "weaknesses": "- Neuroscientific interpretability (explainability of the input brain graph) is not addressed within the BrainGFM framework.\n- Computational efficiency of BrainGFM is not quantitatively provided.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rHW0piJLpb", "reviewer": "Reviewer_Rh73", "summary": "This submission presents BrainGFM, a novel graph-based foundation model for fMRI brain data that leverages both graph contrastive learning and graph masked autoencoders for large-scale pre-training. The model is trained on a diverse dataset comprising over 25,000 subjects and 60,000 scans across 25 neurological and psychiatric disorders, using multiple brain atlases and parcellations. BrainGFM introduces graph prompts and language prompts to enable efficient few-shot and zero-shot transfer learning. The authors also employ meta-learning to optimize graph prompts, allowing adaptation to unseen disorders and atlases without fine-tuning the entire model.", "questions": "1/ The paper uses meta-learning to optimize graph prompts for few-shot adaptation across disorders and atlases. However, it remains unclear how sensitive the model is to the choice of meta-training tasks — for example, if certain disorders or atlases are underrepresented in the meta-training phase, could this lead to biased or unstable prompt initialization that generalizes poorly to rare or unseen conditions?\n\n2/ While the model integrates language prompts for zero-shot transfer, the textual descriptions of disorders are generic clinical summaries. How does the model handle semantic overlap or ambiguity between similar disorders?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1/ BrainGFM uses graph-based pre-training with both generative (GMAE) and contrastive (GCL) objectives. This approach effectively captures both local and global brain graph structures.\n\n2/ The model is pre-trained on a large-scale, heterogeneous dataset that includes multiple brain atlases (functional and anatomical) and a wide range of neurological/psychiatric disorders.\n\n3/ The integration of graph and language prompts, along with meta-learning, enables BrainGFM to adapt to new tasks, atlases, and disorders with minimal labeled data.", "weaknesses": "1/ Despite claims of modality-agnostic design, all experiments are conducted solely on resting-state fMRI data for a brain foundation model. There is no validation on other neuroimaging modalities such as task-fMRI, DTI, or EEG. This limits the generalizability of the proposed framework and leaves its cross-modal transferability unverified.\n\n2/ While language prompts are introduced to guide zero-shot transfer, the paper lacks a detailed ablation study to isolate their contribution. It is unclear how much performance gain is attributable to the semantic guidance of language prompts versus the graph prompts or the pre-trained backbone itself.\n\n3/ The brain graphs are constructed using Pearson correlation and top-k sparsification, but the paper does not thoroughly explore how different graph construction strategies (e.g., partial correlation, mutual information, or dynamic connectivity) affect model performance. This is critical since graph quality directly influences downstream task outcomes.\n\n4/ While the paper emphasizes efficiency during fine-tuning via prompt-tuning, it does not thoroughly discuss the computational cost of inference or scalability challenges when deploying BrainGFM across large clinical datasets or real-time applications. For example, how does the model perform on edge devices or in hospitals with limited GPU resources? Without such analysis, the practical deployability of BrainGFM remains unclear.\n\n5/ By converting fMRI time series into static brain graphs, the model discards rich temporal information that may be critical for certain disorders (e.g., schizophrenia or bipolar disorder, which exhibit dynamic connectivity patterns). The paper does not justify this design choice or compare with temporal modeling approaches (e.g., BrainLM or Brain-JEPA), leaving open the question of whether graph-based modeling is truly superior for all types of disorders.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UCExvcw4rQ", "reviewer": "Reviewer_4mj1", "summary": "This paper introduces BrainGFM, a unified brain graph foundation model that combines graph contrastive learning and graph masked autoencoder objectives for large-scale pre-training on fMRI-based brain network datasets. The framework aims to handle heterogeneous datasets and diverse atlases across multiple disease domains, and the authors demonstrate that BrainGFM achieves superior performance compared to existing graph pre-training methods.", "questions": "Please refer to weaknesses", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. Timely and significant topic. The paper tackles an important and emerging direction—developing a foundation model for brain networks—addressing heterogeneity across datasets and atlases.\n2. Large-scale pre-training. Conducting large-scale pre-training on multiple fMRI datasets is of high value to the neuroimaging and machine learning communities.\n3. Cross-atlas and cross-disease generalization. The framework is designed to support different brain parcellations and disease categories, which is an ambitious and meaningful goal for improving model transferability.\n4. Empirical performance. The proposed approach outperforms existing graph pre-training methods across multiple benchmarks, suggesting that the unified framework is effective in large-scale neuroimaging representation learning.", "weaknesses": "1. Limited consideration of brain-specific properties.\n\t\t○ The pre-training strategy primarily adapts existing MAE and GCL techniques from general graph learning without tailoring them to the unique characteristics of brain networks. In neuroimaging, lesions may appear in localized regions (specific ROIs), where neighboring nodes are normal and carry limited predictive signal. Generic GCL or MAE objectives may therefore be suboptimal for such sparse or localized perturbations.\n\t\t○ The design of disease and atlas prompts is relatively simplistic. It fails to capture deeper relationships between diseases and specific brain regions, or the structural hierarchy across multiple atlases. The model could benefit from biologically informed prompting or hierarchical alignment strategies.\n\t2. Underutilization of multi-atlas information.\n\t\t○ For the same subject, multiple atlases provide complementary representations of brain connectivity. However, BrainGFM treats these views separately. Several prior works (e.g., [1–3]) have explored multi-atlas fusion to enhance representational richness and robustness. Integrating cross-atlas consistency learning could substantially improve the model’s capability.\n\t3. Sequential pre-training strategy.\n\t\t○ The current training pipeline processes datasets sequentially, which raises concerns about order sensitivity and incomplete exploitation of large-scale data. Sequential training can lead to dataset-specific overfitting and unstable convergence.\n\t\t○ A preferable approach would be joint pre-training—mixing or shuffling all datasets—to ensure more uniform optimization and reduce order-dependent biases. This would also yield a more scalable training complexity, dependent primarily on total sample size rather than dataset count.\n\t4. Reproducibility and data availability.\n\t\t○ The contribution would be significantly strengthened if the datasets used for pre-training could be made publicly available or at least described in sufficient detail for replication. Given the emphasis on large-scale pre-training, the lack of accessible data may hinder reproducibility and follow-up work.\n\t5. Minor presentation issues.\n\t\t○ The legend in Figure 4 overlaps with surrounding text and should be repositioned.\n\t\t○ The atlas used in each experiment should be explicitly mentioned in figure/table captions to make the results self-contained.\n\n\n[1] Multiview Feature Learning With Multiatlas-Based Functional Connectivity Networks for MCI Diagnosis. TCYB 2022\n[2] Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification. MLCN 2023\n[3] Multi-Atlas Brain Network Classification through Consistency Distillation and Complementary Information Fusion. JBHI 2025", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)