When Language Models Meet NeuroGraphs: Exploring Enhanced Agentic LLM Framework Towards Brain Network Analysis
BrainAgent enhances brain network analysis accuracy through iterative reasoning and reflection.
Key Findings
Methodology
BrainAgent reformulates brain network analysis into a knowledge-intensive agentic reasoning problem. It first converts raw brain networks into multi-level structural descriptions using brain-specific tools, retrieves relevant neuroscience knowledge and task-specific cases, and finally generates structured predictions with reflective verification. This method combines topology-aware graph understanding, external knowledge retrieval, case retrieval, and reflection.
Key Results
- On the ABIDE dataset, BrainAgent's accuracy improved from 47.15% with direct prompting to 58.54%, achieving 86.18% on pass@3.
- On the ADHD dataset, BrainAgent's accuracy improved from 37.97% to 61.50%, achieving 86.18% on pass@3.
- On the HCP dataset, BrainAgent's accuracy improved from 45.41% to 60.84%, achieving 80.67% on pass@3.
Significance
BrainAgent provides a practical route toward interpretable and knowledge-grounded brain network analysis. By combining the reasoning capabilities of large language models with neuroscience expertise, BrainAgent improves prediction accuracy across various datasets, reduces over-prediction bias, and generates more interpretable reasoning processes.
Technical Contribution
BrainAgent significantly enhances prediction accuracy and interpretability by reformulating brain network analysis as an agentic reasoning problem, combining topology-aware graph understanding, external knowledge retrieval, case retrieval, and reflection. Unlike existing methods, BrainAgent achieves higher performance without relying on supervised fine-tuning.
Novelty
BrainAgent is the first to use large language models as predictors and analysts for brain networks, integrating external tools and evidence for reasoning, providing more comprehensive and verifiable explanations.
Limitations
- BrainAgent's performance in specific neuroimaging tasks is still limited, especially when handling small-scale and heterogeneous datasets.
- Its reasoning capabilities for open-domain questions still require further validation.
Future Work
Future research directions include improving BrainAgent's performance on multimodal data and exploring its potential in other neuroscience applications.
AI Executive Summary
Brain network analysis is crucial for understanding human cognition and neurological disorders, but existing methods often treat it as a graph classification problem, lacking interpretability. BrainAgent reformulates brain network analysis as a knowledge-intensive agentic reasoning problem, combining topology-aware graph understanding, external knowledge retrieval, case retrieval, and reflection, significantly enhancing prediction accuracy and interpretability. Experimental results show that BrainAgent outperforms direct prompting and standard reasoning baselines across multiple public datasets, especially in pass@3 performance. BrainAgent provides a practical route toward interpretable and knowledge-grounded brain network analysis. Future research directions include improving BrainAgent's performance on multimodal data and exploring its potential in other neuroscience applications.
Deep Analysis
Background
Brain network analysis represents neuroimaging measurements as graphs, where nodes denote brain regions and edges encode structural or functional interactions. Resting-state functional MRI (rs-fMRI) enables the construction of functional connectomes and is widely used for disease-related classification and biomarker discovery. However, existing methods mainly treat it as a supervised graph classification task, lacking interpretability.
Core Problem
Existing methods focus primarily on prediction accuracy in brain network analysis, neglecting the explanation of why certain topological patterns are associated with disorder-related labels. This lack of interpretability is a major bottleneck in clinical and scientific applications.
Innovation
BrainAgent reformulates brain network analysis as a knowledge-intensive agentic reasoning problem, combining topology-aware graph understanding, external knowledge retrieval, case retrieval, and reflection, significantly enhancing prediction accuracy and interpretability.
Methodology
- �� Convert raw brain networks into multi-level structural descriptions
- �� Retrieve relevant neuroscience knowledge and task-specific cases
- �� Generate structured predictions with reflective verification
- �� Combine topology-aware graph understanding, external knowledge retrieval, case retrieval, and reflection
Experiments
Experiments were conducted on four public rs-fMRI datasets, including ABIDE, ADHD, HCP, and Rest-meta-MDD. Each dataset corresponds to a binary graph-level classification task, using 20% of samples as the test set, with the remainder for retrieval database construction and model-related preparation.
Results
BrainAgent outperforms direct prompting and standard reasoning baselines across multiple datasets, especially in pass@3 performance. Specifically, BrainAgent's accuracy on the ABIDE dataset improved from 47.15% with direct prompting to 58.54%, achieving 86.18% on pass@3.
Applications
BrainAgent can be used for interpretable analysis of brain networks, especially in scenarios requiring reasoning with external knowledge and cases.
Limitations & Outlook
BrainAgent's performance in specific neuroimaging tasks is still limited, especially when handling small-scale and heterogeneous datasets. Future research directions include improving BrainAgent's performance on multimodal data and exploring its potential in other neuroscience applications.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. A large language model is like a chef's assistant, helping you understand the structure of ingredients (brain networks) and find relevant cooking techniques from a recipe book (neuroscience knowledge). BrainAgent is like an advanced assistant that not only helps you find the right recipes but also provides specific cooking suggestions based on the characteristics of the ingredients and continuously reflects and adjusts during the cooking process.
ELI14 Explained like you're 14
Imagine you're playing a complex video game, and you need to analyze the map (brain network) to find the best route to win. BrainAgent is like a super assistant that not only helps you understand the map's structure but also finds relevant strategies from the game guide (neuroscience knowledge) and continuously provides suggestions and adjusts strategies based on your game progress.
Glossary
Connectome
A connectome is the comprehensive map of neural connections in the brain.
Used as the foundational data for brain network analysis in the paper.
rs-fMRI
Resting-state functional MRI is an imaging technique used to measure brain activity by detecting changes in blood oxygen levels.
Used to construct functional connectomes and perform disease-related classification.
Graph Neural Network
A neural network designed to process graph-structured data, capturing complex relationships between nodes and edges.
Used in brain network analysis to learn discriminative graph representations.
AAL Atlas
A standardized brain region parcellation atlas used in neuroimaging analysis, containing 90 brain regions.
All experiments' brain networks are constructed based on the AAL atlas.
Pass@k
An evaluation metric indicating the proportion of correct predictions within k attempts.
Used to evaluate BrainAgent's predictive performance across different datasets.
Open Questions Unanswered questions from this research
- 1 How can BrainAgent's performance on multimodal data be further improved?
- 2 What are the limitations of BrainAgent when handling heterogeneous datasets?
Applications
Immediate Applications
Clinical Diagnosis
BrainAgent can be used in clinical diagnosis by providing interpretable analysis through reasoning with external knowledge and cases.
Long-term Vision
Neuroscience Research
BrainAgent has broad potential applications in neuroscience research, especially in scenarios requiring comprehensive analysis with multiple data sources.
Abstract
Brain network analysis is crucial for understanding cognition and neurological disorders, yet existing deep learning methods mainly treat connectome analysis as a graph-to-logit classification problem, offering limited explanatory reasoning. Large language models (LLMs) provide a promising interface for knowledge-intensive scientific analysis, but directly applying general-purpose LLMs to brain networks remains challenging due to the structure-language gap, limited neuroscience grounding, and overconfident positive predictions. In this paper, we propose \textbf{BrainAgent}, an agentic LLM framework for knowledge-enhanced brain network analysis. BrainAgent reformulates connectome classification as an iterative process of topology-aware understanding, external retrieval, reasoning, and reflection. Specifically, it first converts raw brain networks into compact multi-level structural descriptions through brain-specific analysis tools, then retrieves relevant neuroscience knowledge and task-specific cases to ground the reasoning process, and finally generates structured predictions with reflective verification. Experiments on four public rs-fMRI datasets show that BrainAgent consistently improves different closed-source and open-source LLM backbones over direct prompting and standard reasoning baselines. Further ablation and interpretability analyses demonstrate the effectiveness of each component and show that BrainAgent produces more comprehensive, multi-level, and verifiable explanations.These results indicate that agentic LLMs provide a practical route toward interpretable and knowledge-grounded brain network analysis.