Predicting individual traits from models of brain dynamics accurately and reliably using the Fisher kernel.

ABSTRACT: Predicting an individual's cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individual's brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with t

SUBMITTER: Ahrends C 

PROVIDER: S-EPMC11785372 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature