Interactive flow fields for resting-state brain activity
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About Interactive flow fields for resting-state brain activity
MindVisualizer is an open-source Python tool for exploring how information may move through the resting brain using continuous flow fields and raw effective connectivity graphs. It features three interactive modes: a general information flow model that combines rDCIM with anatomical geometry to produce a spatial, dynamic picture of information propagation; a raw rDCIM connectivity mode that visualizes the brain as a 3D graph of ROI connections; and an ROI flow mode that shows particle flow through a learned resting-state manifold alongside ROI activation patterns, with LLM-based interpretation of flow trajectories via a RAG database. Users can place probes in the flow field, perturb ROI states to observe connectivity propagation, and receive natural-language explanations of what neural pathways may be activated.
Key Features
Three visualization modes: information flow model, raw rDCIM connectivity graph, ROI flow with manifold embedding
Interactive probe placement (G key) that follows flow field trajectories
LLM-based interpretation of flow trajectories using a RAG database (Shift + G)
State perturbation in rDCIM mode: assign initial states to ROIs and propagate changes in real time
Dual-window display: particle flow through resting-state manifold alongside matching ROI activation patterns
kNN interpolation maps positions in low-dimensional space to brain-region activation vectors
Pros & Cons
Pros
- Provides a unique spatial, dynamic visualization of resting-state brain activity
- LLM integration offers context-aware interpretation of flow trajectories
- Open source and free to use with all code available on GitHub
- Supports both global flow fields and detailed ROI-level connectivity graphs
Cons
- Requires Python environment and potentially complex setup (e.g., RAG database)
- Limited documentation beyond the README and code comments
- Dependent on external RAG database and LLM for trajectory interpretation
- May require technical expertise in neuroscience and computational modeling
Best For
Neuroscience research on resting-state brain dynamics and effective connectivityEducational demonstrations of information flow in the brainExploratory analysis of rDCIM-derived connectivity patternsHypothesis generation about functional roles of neural pathways during rest
FAQ
What are the three modes in MindVisualizer?
MindVisualizer has three modes: 1) a general information flow model combining rDCIM with anatomical geometry to produce a continuous flow field; 2) a raw rDCIM connectivity mode showing a 3D graph of ROI connections; 3) an ROI flow mode with a dual-window visualization of particle flow through a resting-state manifold and corresponding ROI activation patterns.
How does the flow mode work?
In flow mode, users press G to place a probe in the flow field. The probe is carried by the flow, tracing a path through the brain. Then pressing Shift + G triggers the LLM to explain the trajectory by identifying anatomical regions passed through and interpreting the sequence based on neuroscience knowledge from a RAG database.
What is the raw rDCIM mode used for?
In raw rDCIM mode, users initialize each ROI in the connectivity graph with a state (e.g., 'processing a human face'), then select any ROI and perturb its state. The system propagates the perturbation through the rDCIM connectivity graph in real time, showing how changes spread according to effective connectivity.
What does the ROI flow mode show?
The ROI flow mode displays a dual-window visualization: the left panel shows particle flow through a learned resting-state manifold (2SDM-style low-dimensional embedding of resting-state fMRI), and the right panel shows the corresponding ROI activation pattern. The system maps each position to a brain-region activation vector using kNN interpolation, and the LLM interprets which networks become more or less involved.
Is MindVisualizer free to use?
Yes, MindVisualizer is open source and free to use. The source code is publicly available on GitHub under the Pixedar/MindVisualizer repository.