Evaluating State-Transition Models in Short Resting-State fMRI with Continuous-Dynamics Controls

Abstract

Hidden Markov models (HMMs) are widely used to describe changes in resting-state functional MRI as transitions between a finite set of inferred brain states. However, continuously varying brain activity can produce similar apparent transitions, raising questions about their biological validity. We tested whether discrete and continuous brain dynamics could be distinguished by predicting subsequent fMRI measurements, and if temporal filtering affects this distinction. We compared hidden Markov models (HMMs) with vector autoregressive (VAR) models, which predict signals from preceding measurements without assuming discrete states. Each time point contained signals from the seven Yeo functional networks. Both models used the same preceding observations to predict the next, with accuracy measured by the log probability density assigned to the observed signals. Across 864 simulated conditions, HMMs performed better in 139/144 unfiltered discrete-state cases, whereas VAR performed better in 67/72 continuous cases. Changing only the filter during testing increased the number of continuous cases favoring HMMs from 2/72 with fixed filtering to 68/72, despite unchanged underlying dynamics. We also analyzed resting-state fMRI from 270 ADNI participants (90 cognitively normal, 90 with mild cognitive impairment, and 90 with dementia). VAR outperformed HMM in 160/184 datasets with at least 180 time points and 60/86 shorter recordings. In simulations, HMMs inferred directional state transitions even from smoothly evolving activity without discrete switching. Thus, apparent fMRI state transitions do not necessarily indicate discrete underlying dynamics. Their interpretation requires comparison with continuous models and attention to temporal filtering.