Multivariate machine learning distinguishes cross-network dynamic functional connectivity patterns in state and trait neuropathic pain.

Abstract

Communication within the brain is dynamic. Chronic pain can also be dynamic, with varying intensities experienced over time. Little is known of how brain dynamics are disrupted in chronic pain, or relates to patients' pain assessed at various timescales (eg, short-term state vs long-term trait). Patients experience pain "traits" indicative of their general condition, but also pain "states" that vary day to day. Here, we used network-based multivariate machine learning to determine how patterns in dynamic and static brain communication are related to different characteristics and timescales of chronic pain. Our models were based on resting-state dynamic functional connectivity (dFC) and static functional connectivity in patients with chronic neuropathic pain (NP) or non-NP. The most prominent networks in the models were the default mode, salience, and executive control networks. We also found that cross-network measures of dFC rather than static functional connectivity were better associated with patients' pain, but only in those with NP features. These associations were also more highly and widely associated with measures of trait rather than state pain. Furthermore, greater dynamic connectivity with executive control networks was associated with milder NP, but greater dynamic connectivity with limbic networks was associated with greater NP. Compared with healthy individuals, the dFC features most highly related to trait NP were also more abnormal in patients with greater pain. Our findings indicate that dFC reflects patients' overall pain condition (ie, trait pain), not just their current state, and is impacted by complexities in pain features beyond intensity.