Your brain's next 5 seconds, predicted by AI
Transformer predicts brain activity patterns 5 seconds into future using just 21 seconds of fMRI data
Achieves 0.997 correlation using modified time-seri
Your brain's next 5 seconds, predicted by AI
Transformer predicts brain activity patterns 5 seconds into future using just 21 seconds of fMRI data
Achieves 0.997 correlation using modified time-series Transformer architecture
-----
🧠 Original Problem:
Predicting future brain states from fMRI data remains challenging, especially for patients who can't undergo long scanning sessions. Current methods require extensive scan times and lack accuracy in short-term predictions.
-----
🔬 Solution in this Paper:
→ The paper introduces a modified time series Transformer with 4 encoder and 4 decoder layers, each containing 8 attention heads
→ The model takes a 30-timepoint window covering 379 brain regions as input and predicts the next brain state
→ Training uses Human Connectome Project data from 1003 healthy adults, with preprocessing including spatial smoothing and bandpass filtering
→ Unlike traditional approaches, this model omits look-ahead masking, simplifying prediction for single future timepoints
-----
🎯 Key Insights:
→ Temporal dependencies in brain states can be effectively captured using self-attention mechanisms
→ Short input sequences (21.6s) suffice for accurate predictions
→ Error accumulation follows a Markov chain pattern in longer predictions
→ The model preserves functional connectivity patterns matching known brain organization
-----
📊 Results:
→ Single timepoint prediction achieves MSE of 0.0013
→ Accurate predictions up to 5.04 seconds with correlation >0.85
→ First 7 predicted timepoints maintain high accuracy
→ Outperforms BrainLM with 20-timepoint MSE of 0.26 vs 0.568
The Figen
Rohan Paul
勃勃OC
