MNM : Multi-level Neuroimaging Meta-analysis with Hyperbolic Brain-Text Representations

2025-11-26

AI Summary

Neuroimaging meta-analyses frequently suffer from statistical unreliability due to small sample sizes, and conventional methods struggle to capture the intrinsic hierarchical organization of brain function and cross-modal semantic relationships. To address these limitations, we propose the first hyperbolic multi-level meta-analysis framework, jointly embedding brain activation maps and literature text into the Lorentz model of hyperbolic space. This enables semantic alignment, cross-modal hierarchical guidance, and preservation of the hierarchical structure of neural activation patterns. Crucially, our approach is the first to explicitly incorporate hyperbolic geometry into neuroimaging meta-analysis, leveraging its natural capacity to model tree-like functional architectures of the brain. Empirical evaluation demonstrates significant improvements over linear and spherical baseline models across key metrics—including activation consistency, semantic coherence, and robustness—thereby enhancing interpretability and reproducibility of cross-study findings.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Multimodal Learning
• Cognitive Modeling & Cognitive Systems: Analogy

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Various neuroimaging studies suffer from small sample size problem which often limit their reliability. Meta-analysis addresses this challenge by aggregating findings from different studies to identify consistent patterns of brain activity. However, traditional approaches based on keyword retrieval or linear mappings often overlook the rich hierarchical structure in the brain. In this work, we propose a novel framework that leverages hyperbolic geometry to bridge the gap between neuroscience literature and brain activation maps. By embedding text from research articles and corresponding brain images into a shared hyperbolic space via the Lorentz model, our method captures both semantic similarity and hierarchical organization inherent in neuroimaging data. In the hyperbolic space, our method performs multi-level neuroimaging meta-analysis (MNM) by 1) aligning brain and text embeddings for semantic correspondence, 2) guiding hierarchy between text and brain activations, and 3) preserving the hierarchical relationships within brain activation patterns. Experimental results demonstrate that our model outperforms baselines, offering a robust and interpretable paradigm of multi-level neuroimaging meta-analysis via hyperbolic brain-text representation.

Problem

• Addressing small sample size limitations in neuroimaging studies through meta-analysis
• Capturing hierarchical brain structure overlooked by traditional linear mapping approaches
• Bridging neuroscience literature and brain activation maps using hyperbolic geometry

Innovation

• Hyperbolic geometry bridges brain maps and literature
• Lorentz model embeds text and images in shared space
• Multi-level meta-analysis preserves hierarchical neural relationships