Title: Predicting Functional Brain Connectivity with Context-Aware Deep Neural Networks
Venue: NeurIPS 2025
Accepted: yes
Keywords: neuroscience

connectomics

transcriptomics

transformers

fMRI
Abstract: Spatial location and molecular interactions have long been linked to the connectivity
patterns of neural circuits. Yet, at the macroscale of human brain networks,
the interplay between spatial position, gene expression, and connectivity remains
incompletely understood. Recent efforts to map the human transcriptome and
connectome have yielded spatially resolved brain atlases, however modeling the
relationship between high-dimensional transcriptomic data and connectivity while
accounting for inherent spatial confounds presents a significant challenge. In this
paper, we present the first deep learning approaches for predicting whole-brain
functional connectivity from gene expression and regional spatial coordinates, including
our proposed Spatiomolecular Transformer (SMT). SMT explicitly models
biological context by tokenizing genes based on their transcription start site (TSS)
order to capture multi-scale genomic organization, and incorporating regional
3D spatial location via a dedicated context [CLS] token within its multi-head
self-attention mechanism. We rigorously benchmark context-aware neural networks,
including SMT and a single-gene resolution Multilayer-Perceptron (MLP),
to established rules-based and bilinear methods. Crucially, to ensure that learned
relationships in any model are not mere artifacts of spatial proximity, we introduce
novel spatiomolecular null maps preserving key transcriptomic autocorrelation
structure. Context-aware neural networks outperform linear methods, significantly
exceed our stringent null map estimates, and generalize across diverse connectomic
datasets and parcellation resolutions. Together, these findings demonstrate a
strong, predictable link between the spatial distributions of gene expression and
functional brain network architecture, and establish a rigorously validated deep
learning framework for decoding this relationship. Code to reproduce our results is
available at: github.com/neuroinfolab/GeneEx2Conn.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes using a transformer model SMT to predict brain connectivity from high-dimensional transcriptomic data, incorporating coordinate information to capture each brain region's 3D position. For evaluation, the authors leverage a Spatiomolecular Null Mapping technique to help rigorously control for spatial autocorrelation effects. Across within-dataset tasks on UK Biobank and MPI-LEMON, as well as cross-dataset and cross-resolution tasks, SMT consistently outperforms baseline methods. The interpretability analyses presented in the supplementary material further elucidate the mechanisms by which SMT makes its predictions.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n- The paper introduces a nonlinear model mapping gene expression to functional connectivity. Comprehensive evaluations on two UKB and MPI-LEMON datasets demonstrate that the proposed nonlinear models can better capture the robust relationship between gene expression and functional connectivity compared to baseline approaches.\n\n- The design of the baselines is great. The analysis based on rigorously defined spatiomolecular null mapping baselines clearly disentangles the respective roles of spatial location and gene expression in predicting functional connectivity. \n\n- The methodology is clearly described, and the overall manuscript is well-structured.\n\n**Weaknesses**\n\n- The major concern is that the proposed SMT does not appear to significantly outperform the MLP model. This raises questions about the necessity of adopting a more complex transformer architecture. The same types of analyses, such as UMAP and activated gene segments, could also potentially be performed using an MLP. The paper does not clearly articulate the specific advantages of SMT or the additional insights gained from using it.\n\n- Other concerns relates to clarity in implementation details:\n\n    1. The process of constructing the population-averaged paired dataset from the Allen Human Brain Atlas and UKB needs to be clearly described. Additionally, details on the amount of data remaining after population-averaging should be provided to improve understanding and reproducibility.\n\n    2. The setting and the impact of $\\alpha_0$ in the Target-Side Distributional Augmentation needs clarification.\n\n    3. In Table 6, how exactly were the Euclidean coordinates projected through fixed layers implemented?\n\n    4. The architecture details of SMT and MLP should be described. Furthermore, the hyperparameter search ranges used in the grid search should be reported to enhance reproducibility.\n\nQUESTIONS:\n1. Why is the UMAP analysis for the MLP not provided in Figure 8?\n\n2. In the supplementary material (line 565), Figure 4 does not seem to include results for SMT w/ [CLS].\n\nLIMITATIONS:\nThe author has adequately discussed the limitations.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe manuscript entitled \"Predicting Functional Brain Connectivity with a Context-Aware Spatiomolecular Transformer\" presents a transformer-based model (Spatiomolecular Transformer (SMT)) with biologically-grounded data augmentation techniques to map functional brain connectivity with spatial transcriptomic data.\n\nThe model tokenizes gene expression by transcription start site (TSS) to preserve genomic context and includes a spatially initialized [CLS] token to account for brain region location. The proposed SMT model is compared with several linear and non-linear baselines models using datasets from UK Biobank and MPI-LEMON at various parcellation scales. Results demonstrates superior performance of SMT, especially in predicting long-range connections, indicating its capability to capture real biological mechanisms rather than just local similarities.\n\nSTRENGTHS AND WEAKNESSES:\nThe main task of this paper: mapping fMRI-derived functional connectivity from spatial transcriptomics, is an important task to bridge microscope, molecular and macroscope, systems-level neuroscience. However, the authors did not seize this opportunity to investigate deeper neuroscientific insights from the learned mappings. Considering the space limitation of a NIPS submission, the lack of such investigation is understandable, but definitely shall be work on in its future iterations. For example, gene sets contributing to long-range vs. short-range connectivity, or molecular signatures of subnetworks (e.g., default mode vs. visual), are only briefly discussed or left in the appendix. Recent neuroscience works pointed out the utility of spatial transcriptomics for uncovering mechanisms of cortical specialization, which this paper could have explored in more depth.\n\nMethodologically, although the model structure is not that complicated, SMT can advance the field by providing more effective, multimodal integration of gene expression and brain spatial structure, which is much needed in system neuroscience. Additionally, SMT’s architecture allows for interpretable attention maps, which could potentially reveal gene pathways underlying connectivity phenotypes. The development of distributional augmentation sampling subject-level fMRI matrices during training is a creative solution to the low sample size of transcriptomic data. It brings in inter-individual variability from fMRI while maintaining a fixed transcriptome. While the augmentation benefits were modest in ablation studies, this technique is of technical merit and aligns with trends in data-rich domains like genomics. It could serve as a template for integrating population data in low-sample transcriptomic domains in a more general perspective.\n\nQUESTIONS:\nThe reviewer is curious why the author formulate the problem only as a \"prediction\", rather than a \"mapping\" task, which has been extensively emphasized but never explained. What will be the limit to do the two-way mapping between functional connectivity and transcriptomic data?\n\nLIMITATIONS:\nIn addition to the more general comments listed above in the \"Summary\", the model’s generalization from UK Biobank to MPI-LEMON (Pearson r = 0.55) is relatively weak given the high-dimensional nature of the input. While some variation is expected due to demographic and methodological differences, the paper doesn't fully account for this or examine other transfer learning strategies to improve robustness.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces the Spatiomolecular Transformer (SMT), an attention-based architecture designed to predict human brain functional connectivity from gene expression data and spatial coordinates. The work addresses a fundamental question in neuroscience: can we predict how brain regions connect based on single-cell spatial transcriptomics. This work does not focus on improving single-cell representation and/or imputation but rather aims at leveraging recent prior works on single-cells and to apply on a real-world application. \n\nThe Spatiomolecular Transformer (SMT) tokenizes genes based on their genomic position (transcription start sites) and uses multi-head self-attention with ALiBi biases to capture both local and global genetic interactions. The authors incorporates 3D brain coordinates through a dedicated [CLS] token that participates in the attention mechanism, allowing spatial information to modulate molecular features. One key experiment in this work is also the Spatiomolecular Null Mapping, to verify that the SMT architecture learn more than simply spatial correlations.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n- The problem is extremely relevant: the authors address a fundamental question linking molecular biology to brain function with clear neuroscientific relevance.\n\n- A biologically relevant preprocessing and modeling: \"sorting each region’s expression vector by the genomic position of genes based on their transcription start site\" this is a witty and smart way to rank genes that has biological meaning. It assumes that genes that are physically close on chromosomes often work together or are regulated together. Also, the ALiBi attention slopes incorporate meaningful biological priors about genomic organization. \n\n- Causality: The spatiomolecular null testing is particularly well-designed, addressing a critical confound (spatial autocorrelation) often overlooked in neurogenomics studies. This experiment clearly underlines what the SMT architecture learns apart from spatial correlation. \n\n- Comprehensive Evaluation: Tests across multiple datasets (UK Biobank, MPI-LEMON) and parcellation resolutions, with proper cross-validation and distance-stratified analyses.\n\n\n**Weaknesses**\n\n- Limited Novelty: While well-executed, the transformer application is relatively straightforward. The performance gains over simpler MLP baselines are modest (SMT: 0.79 vs MLP: 0.78 Pearson-r). The authors briefly state that \"the attention mechanism does not substantially outperform comparable non-linear baselines\", this raises questions about the added complexity's value. \"It does enable key design features for qualitative evaluation\": this qualitative study could have been extended, the UMAP projections do not illustrate a qualitatively superior embedding space for SMT. \n- I believe that the SMT architecture could have been studied slightly more, meaning a larger ablation study. What is the impact of the learned linear map ? Could it be replaced by 1D convolutions ? What is the impact of the Transformer block vs only 1D convolutions ?\n- Also, if there is \"a scarcity of human brain gene expression data\", there are hundreds of millions of single-cell data in open public datasets. It could have been interesting to measure the impact of a pre-training on a large non-brain dataset. Beside, there are already some existing pre-trained checkpoints available (scBERT, scGPT, cellPLM, sCT...). \n- It seems that the authors limit themselves to 7380 genes, why is that ? how is the choice done ? could we maybe increase this to reduce the assumptions done ?\n\nQUESTIONS:\n- Contributions: are the authors the first ones to apply a Transformer's architecture to predicting functional connectivity ? This could be clearly stated in the contribution as well. \n- TSS ranking: if you do not use the TSS ranking but rather a random split, what is the impact in terms of performance ? It would be interesting to add this in the ablation study.\n- Architecture wise: what is the impact of the learned linear map ? Could it be replaced by 1D convolutions ? What is the impact of the Transformer block vs only 1D convolutions ?\n- Pre-training: what would be the benefits of leveraging a checkpoint pre-trained on dozens of millions of single-cell RNA sequences ? \n- Number of genes: why did the authors limit themselves to 7380 genes ? \n- The loss is not discussed in the main paper nor in the Appendix. I believe could also yield to interesting discussions. I may have missed something but, similarly, I believe different sampling schemes could be discussed for example to ensure balanced representation of short/medium/long-range connections. Do you think this could be useful in your setting ?\n\nLIMITATIONS:\nOne of the two main limitations of this paper, in my opinion, are the following:\n- clearly stating whether it is the first application of Transformers for the task of Brain Connectivity (if not, adding some baselines would greatly improve the comparison).\n- as mentioned the questions above, extending the ablation study would help understand better what elements in this SMT architecture brings performance.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-06"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a method for predicting population functional connectivity from transcriptomic data. The method, a spatiomolecular transformer (SMT), predicts from gene expression data ordered by their transcription start site and brain regional spatial coordinates in a self-attention mechanism, the embeddings of which are then passed to an MLP for functional connectivity prediction. The method is compared against baselines and results using spatiomolecular null maps in experiments using the Allen brain atlas for the genetic data and UKBiobank and MPI-lemon for the functional connectivity data.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n1. The motivation of the paper is to understand the link between functional connectivity and gene expression data. Such understanding would make a large impact in neuroscience.\n2. The approach of tokenizing the gene expression data by TSS makes sense in terms of keeping physically contiguous data together. \n3. The experimental approach includes analysis of spatiomolecular null maps to give a sense of whether the correlations learned by the model are meaningful. \n4. The experiments include some generalization experiments of model performance across atlas resolutions and datasets. \n\nWeaknesses: \n1. Concern regarding generalizability of trained models:  \nThe Allen brain atlas includes data from 6 donors - I think it is difficult to accept that a model learned from such a small number of subjects would be generalizability to the greater population. Also, the UK biobank is generally older subjects, and the MPI dataset are young adults - I am not sure it makes sense to build a model to predict between these datasets. \n2. Limited technical novelty:  \nThe novelty in this work may lie in the use of established / other methods (transformer style model, ALiBI biases, use of [CLS] token, etc) for the specific task of predicting functional connectome from gene expression data.\n3. Questions/concerns regarding experimental setup:  \n- The experimental setup involves reserving 75% of functional connections for training, and using 25% for testing, in prediction of a population functional connectome from population gene expression data. However, one may imagine that the connections in nearby or homologous regions may have similar relationships to the genetics - therefore, there may be some inadvertent leaking of information, making the training and testing sets not independent, and thus the results may be inflated. \n- The authors perform 10 random 4-fold splits for validation - I am not sure if this means 10 x 4-fold CV, or just 10 random splits where 25% was reserved for testing. \n4. Questions/concerns regarding significance of results:  \n- The authors perform multiple runs for the experiments which is great, but then only report the means. Reporting some sense of the variance across runs would be informative in understanding of there is really a difference in results.\n- While the authors do report some statistical testing results, it is for one-sided t-test, which is not best practice, unless there is a strong reason to not consider two-sided (which I am not seeing here). \n- The authors run a nice experiment to compute the correlation for a null model. While most compared approaches had a null r of 0.3-0.4 range, the proposed method had a null r of 0.71, yet the results of the proposed method with the real data are at similar to slightly higher levels than competing approaches (around 0.7-0.8). Thus, to me this suggests that the proposed approach suffers from more overfitting as it was able to create a model from the perturbed input data with very high correlation compared to baselines, yet the proposed model on the real data has similar predictive strength as other models. \n- The ablation experiments show very small nominal differences in performance which are likely with in standard deviation (unsure if that is what the sigmas refer to in the figure).\n5. Proofreading needed:  \nThe paper would benefit from additional proofreading to polish the language (edit for grammar, redundant words, etc). Also please check reporting of results in lines 324-326 (seems to be repeat of experiment setup but then reported different performance).\n\nQUESTIONS:\n1. Please see the questions/concerns in the weaknesses described above. I expand upon a few below.\n\n2. I am not so sure of the validity of the conceptual idea of predicting the functional connectome from gene expression data for a small number of subjects, from a different dataset, at the population level - I welcome further discussion from the authors on this.\n\n3. For the experiments, I would like to better understand the significance of presented results - how the small difference between the proposed method's performance and null model compared to baseline approaches might mean, variation in the runs.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-09"}