neurovista-multimodal-brain

NeuroVista: Multimodal Brain-Response Prediction System

A production-grade, compact multimodal brain-response prediction model inspired by Meta's TRIBE v2 and related neural encoding research. NeuroVista predicts human brain (fMRI BOLD-like) responses to text, images, audio, and video stimuli while providing interpretable explanations, region-level summaries, cautious Q&A, and 3D brain visualizations.

Architecture Overview

┌─────────────────────────────────────────────────────────────────┐
│                    NeuroVista Architecture                        │
├─────────────────────────────────────────────────────────────────┤
│  Modality Encoders (frozen pretrained + LoRA adapters)          │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐        │
│  │  Text    │  │  Image   │  │  Audio   │  │  Video   │        │
│  │ OPT-1.3B │  │CLIP ViT-B│  │Whisper   │  │CLIP+Temp │        │
│  │ ~2.6GB   │  │ ~570MB   │  │~280MB   │  │ ~100MB   │        │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘  └────┬─────┘        │
│       └──────────────┴──────────────┘             │               │
│                       │                          │               │
│                       ▼                          ▼               │
│              ┌─────────────────────────────────┐                  │
│              │  Cross-Modal Fusion (RoPE)     │                  │
│              │  2 layers, 8 heads, 512 dim     │                  │
│              │  ~20MB                          │                  │
│              └─────────────┬───────────────────┘                  │
│                            ▼                                     │
│              ┌─────────────────────────────────┐                  │
│              │  Brain Decoder (temporal trans)   │                  │
│              │  4 layers, 8 heads, 512 dim       │                  │
│              │  Subject-conditioned + population │                  │
│              │  ~100MB                         │                  │
│              └─────────────┬───────────────────┘                  │
│                            ▼                                     │
│              ┌─────────────────────────────────┐                  │
│              │  Interpretation Heads           │                  │
│              │  ROI, Network, Modality, Unc.   │                  │
│              │  ~50MB                          │                  │
│              └─────────────────────────────────┘                  │
└─────────────────────────────────────────────────────────────────┘

Total Deployable Size: ~3.5-5 GB (fits 6-10 GB target with headroom)

Features

Prediction Outputs

• Brain Activity Map: 1000-parcel whole-brain predictions
• Temporal Dynamics: Per-TR predictions over stimulus window
• Subject Conditioning: Subject-specific or population-average modes
• Uncertainty Estimates: Epistemic + aleatoric uncertainty via MC dropout

Interpretation Outputs

• ROI Activation Ranking: Top positive/negative regions
• Network Attribution: 7 functional network engagement scores
• Modality Contributions: Which input stream drove the prediction
• Confidence Scoring: Low/moderate/high confidence indicators

Cautious Q&A System

Answers questions using only predicted activation maps and atlas knowledge:

• "Which regions are most activated?"
• "Are these regions associated with language, vision, or emotion?"
• "How certain is this prediction?"
• "What would a population-level response look like?"

All answers include calibrated uncertainty language and explicit caveats. Never claims to read minds or diagnose.

3D Visualization

• Cortical surface heatmap overlays (nilearn-based)
• Hemisphere comparison views (lateral, medial, dorsal, ventral)
• Parcel-level activation bars with network coloring
• Uncertainty overlay plots
• Modality contribution pie charts
• Comprehensive interpretation reports

Installation

pip install -r requirements.txt

Quick Start

Training

python -m neurovista.scripts.train --config configs/base_config.yaml --data_dir ./data --dry_run

Inference

python -m neurovista.scripts.infer --model_dir ./model_export --text "A natural scene" --image scene.jpg --output_dir ./output

Demo

python neurovista/demo.py

Scientific Framing & Safety

What This Model Estimates

• Likely cortical response patterns from stimulus features
• Probable functional systems engagement
• Sensory, language, attention, memory, and affect-related correlates
• Uncertainty-aware predictions with explicit confidence levels

What This Model Does NOT Claim

• Exact thoughts or mental content
• Exact emotions (only correlates with uncertainty labels)
• Clinical diagnoses
• Guaranteed behavior of "normal" individuals

References

• TRIBE (Meta/Facebook): arxiv:2507.22229 — Algonauts 2025 brain encoding
• VIBE: arxiv:2507.17958 — Video-input brain encoder
• BraInCoRL: arxiv:2505.15813 — In-context brain prediction

License

MIT License — Research use only. Not for clinical diagnosis.