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

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🧠 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.

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🔬 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

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🎯 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

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📊 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

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