Time-varying brain state dynamics in trait impulsivity and anxiety: An HSMM analysis of resting-state fMRI

Identifying the neural characteristics of impulsivity and anxiety is important, as both traits confer risk for mental health conditions. In this study, we applied a Hidden Semi-Markov Model (HSMM) to capture the temporal patterns of brain activity to identify brain states associated with impulsivity and anxiety. Using the Leipzig Study for Mind-Body Emotion Interactions (LEMON) resting-state fMRI dataset of healthy individuals (N = 56), we analyzed three groups: High Impulsivity (HI), High Anxiety (HA), and High Impulsivity & High Anxiety (HIHA), assessed with the STAI-T and UPPS scales. HSMM identified three distinct functional brain states characterized by mean activation, functional connectivity, and topological properties of the frontoparietal, default mode, salience/ventral attention, and limbic networks. Notably, the HI group spent more time in a state with an inverse pattern between the default mode network and the salience/ventral attention network, with anticorrelated connectivity and opposing activation, compared with the HA and HIHA groups. Furthermore, the HI group showed a stronger tendency to persist in this state, which may reflect the neural characteristic distinguishing impulsivity from anxiety. However, no distinctive features were observed in the HIHA group. Nevertheless, these findings provide initial insights into the time-varying characteristics of impulsivity and anxiety.