Network analysis identifies consensus physiological measures of neurovascular coupling in humans.

Intimate communication between neural and vascular structures is required to match neuronal metabolism to blood flow, a process termed neurovascular coupling. The number of laboratories assessing neurovascular coupling in humans is increasing due to clinical interest in disease states, and basic science interest in a non-anesthetized, non-craniotomized, unrestrained, in vivo model. However, there is a lack of knowledge regarding how best to characterize the neurovascular response. To address this knowledge gap, we have amassed a highly powered human neurovascular coupling dataset, and deployed a network-based approach to reveal the most powerful and consistent metrics for quantifying neurovascular coupling. Using dimensionality reduction, community-based clustering, and majority-voting of traditional metrics (e.g. peak response, time to peak) and non-traditional metrics (e.g. varying time windows, pulsatility), we have identified which of the existing metrics predominantly characterize the neurovascular coupling response, are stable within and across participants, and explain the vast majority of the variance within our dataset of over 300 trials. We then harnessed our empirical approach to generate powerful novel metrics of neurovascular coupling, termed iAmplitude, iRate, and iPulsatility, which increase sensitivity when capturing population differences. These metrics may be useful to optimally understand neurovascular coupling in health and disease.