Conference on Cognitive Computational Neuroscience

Quantifying how brain functional architecture differs from person to person is a key challenge in human neuroscience. Current individualized models of brain functional organization are based on brain regions and networks, limiting their use to study fine-grained vertex- or voxel-level differences. In this work, we present the Individualized Neural Tuning (INT) model, a fine-grained individualized model of brain functional organization. The INT model re-represents each individual’s brain responses as a linearly transformed functional template, and it factorizes the modeled responses into temporal information capturing how the stimulus changes over time (shared across individuals) and stimulus-general neural tuning (specific to each individual and each vertex). The INT model is designed to have vertex-level granularity, to capture both representational and topographic differences, and to model stimulus-general neural tuning. Through a series of analyses, we demonstrate that (a) our INT model provides a reliable individualized measure of fine-grained brain functional organization, (b) it accurately predicts individualized brain response patterns to new stimuli, and (c) it only requires 10–20 minutes of data for good performance. The high reliability, specificity, precision, and generalizability of our INT model affords new opportunities to build brain-based biomarkers based on naturalistic movie viewing.