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

Christine Ahrends, Diego Vidaurre Henche

Research output: Working paper/Preprint Preprint


How specifically brain activity unfolds across time, namely the nature of brain dynamics, can sometimes be more predictive of behavioural and cognitive subject traits than both brain structure and summary measures of brain activity that average across time. Brain dynamics can be described by models of varying complexity, but what is the best way to use these models of brain dynamics for characterising subject differences and predicting individual traits is unclear. While most studies aiming at predicting subjects’ traits focus on having accurate predictions, for many practical applications, it is critical for the predictions not just to be accurate but also reliable. Kernel methods are a robust and computationally efficient way of expressing differences in brain dynamics between subjects for the sake of predicting individual traits, such as clinical or psychological variables. Using the Hidden Markov model (HMM) as a model of brain dynamics, we here propose the use of the Fisher kernel, a mathematically principled approach to predict phenotypes from subject-specific HMMs. The Fisher kernel is computed in a way that preserves the mathematical structure of the HMM. This results in benefits in terms of both accuracy and reliability when compared with kernels that ignore the structure of the underlying model of brain dynamics.
Original languageEnglish
Publication statusPublished - 2 Mar 2023


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