Locus coeruleus integrity predicts ease of attaining and maintaining neural states of high attentiveness

Abstract

The locus coeruleus (LC), a small subcortical structure in the brainstem, is the brain’s principal source of norepinephrine. It plays a primary role in regulating stress, the sleep-wake cycle, and attention, and its degradation is associated with aging and neurodegenerative diseases associated with cognitive deficits (e.g., Parkinson’s, Alzheimer’s). Yet precisely how norepinephrine drives brain networks to support healthy cognitive function remains poorly understood – partly because LC’s small size makes it difficult to study noninvasively in humans. Here, we characterized LC’s influence on brain dynamics using a hidden Markov model fitted to functional neuroimaging data from healthy young adults across four attention-related brain networks and LC. We modulated LC activity using a behavioral paradigm, and also measured individual differences in LC neuromelanin. The model revealed five hidden states, including a stable ‘arousal’ state that occurred when subjects actively engaged with the task. LC neuromelanin correlated with this state’s stability across experimental manipulations, and with subjects’ propensity to enter into and remain in this state. These results provide new insight into LC’s role in driving spatiotemporal neural patterns associated with attention and arousal, and demonstrate that neuromelanin variation can explain individual differences in these patterns even in healthy young adults.

A predictor-informed multi-subject bayesian approach for dynamic functional connectivity

“…Therefore, pupil dilation has been used as a proxy for a metric of a person’s willingness to exert more effort and involve a greater mental effort to complete a task. Recent methods for studying such association use a multi-step approach, first identifying switches in subjects’ state sequences and then calculating the difference between the normalized pupil size before and after the estimated switch [ 38 ]. In our application, we demonstrate how the model can recover expected change points in dynamic FC states, as those states align quite well with the experimental events regulated by the behavioral task.…”