Individual-specific precision neuroimaging of learning-related plasticity

TL;DR

This paper advocates for an individual-specific neuroimaging approach to study learning-related brain plasticity, emphasizing high-resolution, longitudinal data collection and multi-modal methods for personalized insights.

Contribution

It introduces a personalized, longitudinal neuroimaging framework combining fMRI and fNIRS to better track neural changes during skill learning within individuals.

Findings

High-quality, frequent neuroimaging captures detailed neural trajectories.

Multi-modal methods enable tracking of plasticity during naturalistic practice.

Personalized approaches improve understanding of individual learning processes.

Abstract

Studying learning-related plasticity is central to understanding the acquisition of complex skills, for example learning to master a musical instrument. Over the past three decades, conventional group-based functional magnetic resonance imaging (fMRI) studies have advanced our understanding of how humans' neural representations change during skill acquisition. However, group-based fMRI studies average across heterogeneous learners and often rely on coarse pre- versus post-training comparisons, limiting the spatial and temporal precision with which neural changes can be estimated. Here, we outline an individual-specific precision approach that tracks neural changes within individuals by collecting high-quality neuroimaging data frequently over the course of training, mapping brain function in each person's own anatomical space, and gathering detailed behavioral measures of learning, allowing neural trajectories to be directly linked to individual learning progress. Complementing fMRI with mobile neuroimaging methods, such as functional near-infrared spectroscopy (fNIRS), will enable researchers to track plasticity during naturalistic practice and across extended time scales. This multi-modal approach will enhance sensitivity to individual learning trajectories and will offer more nuanced insights into how neural representations change with training. We also discuss how findings can be generalized beyond individuals, including through statistical methods based on replication in additional individuals. Together, this approach allows researchers to design highly informative longitudinal training studies that advance a personalized account of skill learning in the human brain.