# Non-isomorphic Inter-modality Graph Alignment and Synthesis for Holistic Brain Mapping

arXiv: https://arxiv.org/abs/2107.06281 · HTML: https://mlsanity.com/paper/2107.06281/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://mlsanity.com/paper/1704.02901/) (2017)
- [Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis](https://mlsanity.com/paper/2002.05000/) (2020)
- [Deep Representation Learning For Multimodal Brain Networks](https://mlsanity.com/paper/2007.09777/) (2020)
- [Deep EvoGraphNet Architecture For Time-Dependent Brain Graph Data Synthesis From a Single Timepoint](https://mlsanity.com/paper/2009.13217/) (2020)
- [GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes](https://mlsanity.com/paper/2009.11080/) (2020)
- [Topology-Aware Generative Adversarial Network for Joint Prediction of Multiple Brain Graphs from a Single Brain Graph](https://mlsanity.com/paper/2009.11058/) (2020)

## Built on by (most cited first, 8 of 8)

- [Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications](https://mlsanity.com/paper/2204.00323/) (2022)
- [Comparative Survey of Multigraph Integration Methods for Holistic Brain Connectivity Mapping](https://mlsanity.com/paper/2204.05110/) (2022)
- [Predicting Brain Multigraph Population From a Single Graph Template for Boosting One-Shot Classification](https://mlsanity.com/paper/2209.06005/) (2022)
- [Strongly Topology-preserving GNNs for Brain Graph Super-resolution](https://mlsanity.com/paper/2411.02525/) (2024)
- [CogGNN: Cognitive Graph Neural Networks in Generative Connectomics](https://mlsanity.com/paper/2509.10864/) (2025)
- [GORDA: Graph-based ORientation Distribution Analysis of SLI scatterometry Patterns of Nerve Fibres](https://mlsanity.com/paper/2204.05776/) (2022)
- [GraphTreeGen: Subtree-Centric Approach to Efficient and Supervised Graph Generation](https://mlsanity.com/paper/2508.09710/) (2025)
- [Rethinking Graph Super-resolution: Dual Frameworks for Topological Fidelity](https://mlsanity.com/paper/2511.08853/) (2025)

## Abstract (arXiv)

Brain graph synthesis marked a new era for predicting a target brain graph from a source one without incurring the high acquisition cost and processing time of neuroimaging data. However, existing multi-modal graph synthesis frameworks have several limitations. First, they mainly focus on generating graphs from the same domain (intra-modality), overlooking the rich multimodal representations of brain connectivity (inter-modality). Second, they can only handle isomorphic graph generation tasks, limiting their generalizability to synthesizing target graphs with a different node size and topological structure from those of the source one. More importantly, both target and source domains might have different distributions, which causes a domain fracture between them (i.e., distribution misalignment). To address such challenges, we propose an inter-modality aligner of non-isomorphic graphs (IMANGraphNet) framework to infer a target graph modality based on a given modality. Our three core contributions lie in (i) predicting a target graph (e.g., functional) from a source graph (e.g., morphological) based on a novel graph generative adversarial network (gGAN); (ii) using non-isomorphic graphs for both source and target domains with a different number of nodes, edges and structure; and (iii) enforcing the predicted target distribution to match that of the ground truth graphs using a graph autoencoder to relax the designed loss oprimization. To handle the unstable behavior of gGAN, we design a new Ground Truth-Preserving (GT-P) loss function to guide the generator in learning the topological structure of ground truth brain graphs. Our comprehensive experiments on predicting functional from morphological graphs demonstrate the outperformance of IMANGraphNet in comparison with its variants. This can be further leveraged for integrative and holistic brain mapping in health and disease.