Title: Local-Global Coupling Spiking Graph Transformer  for Brain Disorders Diagnosis from Two Perspectives
Venue: NeurIPS 2025
Accepted: yes
Keywords: Spiking neural networks

brain-inspired computational model
Abstract: Brain disorders have been consistently associated with abnormalities in specific brain regions or neural circuits. Identifying key brain regional activities and functional connectivity patterns is essential for discovering more precise neurobiological biomarkers. However, previous studies have primarily emphasized alterations in functional connectivity while overlooking abnormal neuronal population activity within brain regions. To bridge this gap, we propose a novel Local-Global Coupling Spiking Graph Transformer (LGC-SGT) that jointly models both inter-regional connectivity differences and deviations in neuronal population firing rates within brain regions, enabling a dual-perspective neuropathological analysis. The global pathway leverages spike-based computation in LGC-SGT to model biologically plausible aberrant neural firing dynamics, while the local pathway adaptively captures abnormal graph-based representations of brain connectivity learned by local plasticity in the liquid state machine module.  Furthermore, we design a shortcut-enhanced output strategy in LGC-SGT with the hybrid loss function to suppress outlier interference caused by inter-individual and inter-center variability, enabling a more robust decision boundary. Extensive experiments on three brain disorder datasets demonstrate that our model consistently outperforms state-of-the-art graph methods in brain disorder diagnosis. Moreover, it facilitates the extraction of interpretable neurobiological biomarkers by jointly analyzing regional neural activity and functional connectivity, offering a more comprehensive framework for brain disorder understanding and diagnosis.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposed a model called Local-Global Coupling Spiking Graph Transformer (LGC-SGT) to predict brain disorders or healthy controls from functional connections and neuron activity data. The proposed model consists of a biological plausible part (learned by reservoir computing) for capturing the local pathway, and a graph neural network part for capturing the global pathway. The experiments show that the LGC-SGT achieves superior results compared to its competitors.\n\nSTRENGTHS AND WEAKNESSES:\n### Strength:\n1. The paper is clearly written and expressed.\n2. The proposed method show good performance on the brain disorder prediction task.\n3. The ablation study make it convincing that each component of the proposed method is useful.\n\n### Weakness:\n1. Using spiking-based model for biological plausibility seems unnecessary. Why not use continuous reservoir computing and continuous transformer, which may has better performance?\n2. The theoretical analysis of the MEE is not convincing. For an outlier input, the gradient if the MEE loss is vanishing due to the Gaussian kernel, however, the gradient of the CE loss is still no vanishing.\n\nQUESTIONS:\n1. What is the neural population size of each regions in the LSM? Is the size of the neural population has great impact on the results?\n2. How to set the input to the LSM (P4 L142, the vector x)? The model takes fMRI data as its inputs. Does the LSM transform the input fMRI data into spikes?\n3.What is the benefits of using spiking-based network (LSM and spiking transformer)? Why not using continuous-value reservoir computing to handle the input fMRI data and using the continuous transformer. The utilization of spiking-based computing is for energy saving in general, but the proposed model is not large and seems no necessary for energy saving. Additionally, the fMRI data lack single-neuron resolution, so the spike trains in the model do not directly correspond to biological neurons.\n4. What is the implementation details of the STDP used in the paper for LSM?\n5. Can the weight connections learned by the model in the LSM be consistent with that in the brain?\n\nLIMITATIONS:\n1. The use of spiking-based computing seems unnecessary.\n2. All the components used in the paper are not original, limiting the contribution and originality of the paper.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-14"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a novel Local-Global Coupling Spiking Graph Transformer (LGC-SGT) model for diagnosing brain disorders from two perspectives: inter-regional connectivity and neuronal population firing rates within brain regions. The model integrates a spike-based transformer with a local-global coupling module and a shortcut-enhanced output strategy to capture both global topology shifts and local firing rate dynamics.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1.The idea of combining a spike-based transformer with local-global coupling to jointly model inter-regional connectivity and neuronal population firing rates is interesting.\n\n2.The ability to extract interpretable neurobiological biomarkers from both inter-regional connectivity and neuronal firing rate abnormalities could offer valuable insights for clinical research and therapeutic strategies.\n\nWeaknesses:\n\n1.Some technical details in the manuscript are not clearly explained. For instance, in Line 132 of the Local-Global Coupling Module, the authors mention that the local pathway is learned by unsupervised learning via STDP, but they fail to provide a detailed explanation of STDP and its specific implementation (including the descriptions in Lines 135-147), which may cause confusion for readers.\n\n2.The choice of hyperparameters, such as the number of spiking transformer blocks, time steps, lambda, and MEE kernel, is not justified. It is unclear how these hyperparameters were determined and whether they are optimal for different datasets.\n\n3.In Eq. (10), the exact definition of $SN_{final}$ remains unclear, as the authors do not provide sufficient explanation. Additionally, the benefits and potential drawbacks of mixing the features before the spiking layer with the binarized features for the final output are not thoroughly discussed. A more detailed analysis on this aspect should be clearly discussed.\n\n4.The proposed method lacks comprehensive comparisons with existing approaches. Since this work is an SNN-based method, it is insufficient to compare primarily with ANN-based methods (all 12 baselines in the paper). Although two SNN-based baselines are included (e.g., SpikeGCN in 2022), they are relatively outdated. The authors should compare with more recent SNN-based graph learning methods, such as those in [1]–[4].\n\n[1] Dynamic spiking graph neural networks[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2024.\n\n[2] Continuous spiking graph neural networks[J]. arXiv preprint arXiv:2404.01897, 2024.\n\n[3] SpikeGraphormer: A high-performance graph transformer with spiking graph attention[J]. arXiv preprint arXiv:2403.15480, 2024.\n\n[4] SiGNN: A spike-induced graph neural network for dynamic graph representation learning[J]. Pattern Recognition, 2025.\n\nQUESTIONS:\n1.\tThe author should provide more detailed explanations about STDP implementation (Lines 132-147) to clarify the unsupervised learning process in the Local-Global Coupling Module.\n2.\tHow were the key hyperparameters (number of spiking transformer blocks, time steps, lambda, and MEE kernel) determined? Was any ablation study conducted to verify their optimality across different datasets?\n3.\tIt is suggested to clarify the definition of $SN_{final}$ in Eq. (10) and discuss the advantages/disadvantages of mixing features before the spiking layer with binarized features.\n4.\tWhy weren't more recent SNN-based graph learning methods included in the comparisons, given that most baselines are ANN-based baselines?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-22"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a framework for brain disorder diagnosis by combining neural dynamics and connectivity patterns in a novel way. The dual-perspective biomarkers discovered through this approach provide a richer, more comprehensive understanding of brain disorders and could facilitate the development of targeted therapeutic strategies.\n\nSTRENGTHS AND WEAKNESSES:\nS1:LGC-SGT uniquely captures both inter-regional functional connectivity and intra-regional spiking activity, offering a comprehensive and biologically grounded representation of brain disorders.\nS2: Through its hybrid loss function and shortcut-enhanced output strategy, the model demonstrates strong generalization ability and resilience to inter-subject and inter-site variability.\n\nW1: Please add a computational cost analysis and discussion/comparison of the following related work：[1] Brain Network Transofrmer. NeurIPS 2022 [2] Biologically Plausible Brain Graph Transformer. ICLR 2025 [3] BrainOOD: Out-of-distribution Generalizable Brain Network Analysis. ICLR 2025\nW2: While the paper states using a \"spiking transformer\" and describes SSA, the details regarding how attention mechanisms are specifically adapted for the discrete spiking nature and how they overcome the inherent challenges (e.g., non-differentiability of spikes beyond the surrogate gradient mentioned for the output layer) could be more thoroughly elucidated.\nW3: The shortcut-enhanced output strategy is introduced primarily to mitigate information loss from binarization in SNNs. While the theoretical justification for MEE is provided, a more explicit discussion of how this strategy fundamentally resolves the conflict between real-valued intermediate features and binarized SNNs, and its broader applicability beyond this specific SNN architecture, could be beneficial.\n\nQUESTIONS:\nPlease refert to the W1,2,3;\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel framework, LGC-SGT (Local-Global Coupling Spiking Graph Transformer), for brain disorder diagnosis by jointly modeling individual temporal dynamics and population-level brain network topology. It integrates spiking neural networks to capture biologically plausible local time-series patterns and graph transformers to learn global inter-subject connectivity. Experiments on ABIDE, ADHD-200, and REST-meta-MDD datasets demonstrate that LGC-SGT achieves state-of-the-art performance, highlighting its effectiveness and generalizability in brain disorder classification.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1.\tThis paper proposes the local-global coupling spiking graph transformer framework for brain disorder diagnosis and achieve good performance.\n2.\tThe problem of brain disorder diagnosis is important and the solution of LGC-SGT is complete.\n\n\nWeakness:\n\n1.\tWhile the proposed LGC-SGT framework aims to jointly model intra-regional neuronal firing dynamics and inter-regional connectivity patterns, the rationale for integrating both local and global perspectives is not fully justified. If the core pathological mechanism lies in abnormal spiking behavior within specific brain regions, it is unclear why modeling the global topology of brain connectivity is necessary, especially when such connectivity changes may be secondary consequences of local neuronal dysfunction. The paper lacks a clear explanation of how the fusion of local spiking dynamics and global connectivity contributes synergistically to improved diagnosis, or under what circumstances the global patterns provide non-redundant or complementary information beyond what is captured at the local level. This leaves the motivation for combining both scales insufficiently supported from a neuroscientific perspective.\n2.\tThe LGC-SGT framework utilizes two distinct types of information to separately model local and global brain characteristics: the local pathway leverages fMRI time series within individual brain regions to capture neuronal population firing dynamics via spiking neural networks (LSM and STDP), while the global pathway uses functional connectivity matrices derived from inter-regional correlations to model macro-scale network topology via adaptive spiking graph convolution. However, the baseline methods used for comparison typically rely on only one type of information, without combining both. This creates an unfair experimental setup, as LGC-SGT benefits from access to a richer and more comprehensive feature space, which may partially account for its superior performance. The paper does not adequately address this discrepancy, raising concerns about the fairness and rigor of the experimental comparison.\n3.\tThe paper does not provide a thorough ablation study to evaluate the individual contributions of the local and global pathways. Specifically, it remains unclear how effective the model would be if it relied solely on local information (neuronal firing dynamics) or global information (inter-regional connectivity). Without such analysis, the importance of integrating both perspectives in LGC-SGT is not well justified. This lack of experimental evidence weakens the claim that the fusion of local and global features is essential for improved brain disorder diagnosis.\n\nQUESTIONS:\nsee weaknesses\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}