# Modeling Higher-Order Brain Interactions via a Multi-View Information Bottleneck Framework for fMRI-based Psychiatric Diagnosis

arXiv: https://arxiv.org/abs/2604.17713 · HTML: https://mlsanity.com/paper/2604.17713/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 56 references that are in the map.
- First posted on arXiv 20 Apr 2026.

## Builds on (most cited first, 19 of 19)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Discovering Invariant Rationales for Graph Neural Networks](https://mlsanity.com/paper/2201.12872/) (2022)
- [Graph Information Bottleneck](https://mlsanity.com/paper/2010.12811/) (2020)
- [Networks beyond pairwise interactions: structure and dynamics](https://mlsanity.com/paper/2006.01764/) (2020)
- [The deterministic information bottleneck](https://mlsanity.com/paper/1604.00268/) (2016)
- [Quantifying High-order Interdependencies via Multivariate Extensions of the Mutual Information](https://mlsanity.com/paper/1902.11239/) (2019)
- [Recognizing Predictive Substructures with Subgraph Information Bottleneck](https://mlsanity.com/paper/2103.11155/) (2021)
- [BrainIB: Interpretable Brain Network-based Psychiatric Diagnosis with Graph Information Bottleneck](https://mlsanity.com/paper/2205.03612/) (2022)
- [Learning High-Order Relationships of Brain Regions](https://mlsanity.com/paper/2312.02203/) (2023)
- [USBD: Universal Structural Basis Distillation for Source-Free Graph Domain Adaptation](https://mlsanity.com/paper/2602.08431/) (2026)
- [MvHo-IB: Multi-View Higher-Order Information Bottleneck for Brain Disorder Diagnosis](https://mlsanity.com/paper/2507.02847/) (2025)
- [Computationally Efficient Approximations for Matrix-based Renyi's Entropy](https://mlsanity.com/paper/2112.13720/) (2021)
- [Aberrant High-Order Dependencies in Schizophrenia Resting-State Functional MRI Networks](https://mlsanity.com/paper/2310.17445/) (2023)
- [BrainIB++: Leveraging Graph Neural Networks and Information Bottleneck for Functional Brain Biomarkers in Schizophrenia](https://mlsanity.com/paper/2510.03004/) (2025)
- [PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis](https://mlsanity.com/paper/2602.21046/) (2026)

## Built on by (most cited first, 3 of 3)

- [Learning beyond Site Bias for OOD Generalization in Brain Networks](https://mlsanity.com/paper/2605.06050/) (2026)
- [BrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis](https://mlsanity.com/paper/2606.29200/) (2026)
- [Beyond Site Agreement: Re-estimation for Brain Network Generalization](https://mlsanity.com/paper/2609.34611/) (2026)

## Abstract (arXiv)

Resting-state functional magnetic resonance imaging (fMRI) has emerged as a cornerstone for psychiatric diagnosis, yet most approaches rely on pairwise brain cortical or sub-cortical connectivities that overlooks higher-order interactions (HOIs) central to complex brain dynamics. While hypergraph methods encode HOIs through predefined hyperedges, their construction typically relies on heuristic similarity metrics and does not explicitly characterize whether interactions are synergy- or redundancy-dominated. In this paper, we introduce O-information, a signed measure that characterizes the informational nature of HOIs, and integrate third- and fourth-order O-information into a unified multi-view information bottleneck framework for fMRI-based psychiatric diagnosis. To enable scalable O-information estimation, we further develop two independent acceleration strategies: a Gaussian analytical approximation and a randomized matrix-based R'enyi entropy estimator, achieving over a 30-fold computational speedup compared with conventional estimators. Our tri-view architecture systematically fuses pairwise, triadic, and tetradic brain interactions, capturing comprehensive brain connectivity while explicitly penalizing redundancy. Extensive evaluation across four benchmark datasets (REST-meta-MDD, ABIDE, UCLA, ADNI) demonstrates consistent improvements, outperforming 11 baseline methods including state-of-the-art graph neural network (GNN) and hypergraph based approaches. Moreover, our method reveals interpretable region-level synergy-redundancy patterns which are not explicitly characterized by conventional hypergraph formulations.