The future of education may depend on how early we can understand the brain.

Long before a child enters a classroom, neural systems are already forming the foundations for learning. These early patterns are not random—they are structured, measurable, and, increasingly, predictable.

My work has focused on identifying these early neural signals, particularly those associated with reading development. By analyzing brain connectivity in young children, we can begin to forecast how literacy skills will evolve.

This predictive capacity has transformative potential. It allows educators to move from reactive models—where intervention follows failure—to proactive systems that anticipate and prevent challenges.

For conditions like dyslexia, this shift is critical. Early identification can significantly improve outcomes, reducing the long-term impact on academic and personal development.

The recognition of this work reflects a broader shift in neuroscience: from description to prediction, and from prediction to application.