Sleep neuroimaging: Review and future directions

Abstract

Sleep research has evolved considerably since the first sleep electroencephalography recordings in the 1930s and the discovery of well-distinguishable sleep stages in the 1950s. While electrophysiological recordings have been used to describe the sleeping brain in much detail, since the 1990s neuroimaging techniques have been applied to uncover the brain organization and functional connectivity of human sleep with greater spatial resolution. The combination of electroencephalography with different neuroimaging modalities such as positron emission tomography, structural magnetic resonance imaging and functional magnetic resonance imaging imposes several challenges for sleep studies, for instance, the need to combine polysomnographic recordings to assess sleep stages accurately, difficulties maintaining and consolidating sleep in an unfamiliar and restricted environment, scanner-induced distortions with physiological artefacts that may contaminate polysomnography recordings, and the necessity to account for all physiological changes throughout the sleep cycles to ensure better data interpretability. Here, we review the field of sleep neuroimaging in healthy non-sleep-deprived populations, from early findings to more recent developments. Additionally, we discuss the challenges of applying concurrent electroencephalography and imaging techniques to sleep, which consequently have impacted the sample size and generalizability of studies, and possible future directions for the field.

Keywords: functional connectivity; functional magnetic resonance imaging; neuroimaging; positron emission tomography; regional cerebral blood flow; sleep.

FIGURE 1

Functional connectivity patterns across different sleep stages. Light Sleep: the default mode network (DMN), which is characterized by brain activity without goal‐directed tasks, is preserved similarly to wakefulness, with increased connectivity in the dorsal attention network and heightened blood oxygenation level‐dependent (BOLD) signal fluctuations within the visual network. Deep non‐rapid eye movement (NREM) Sleep: DMN connectivity is significantly reduced, especially between the parietal cingulate cortex (PCC) and the medial prefrontal cortex, with the medial prefrontal cortex becoming decoupled from the rest of the DMN. Rapid eye movement (REM) Sleep: DMN activity is further reduced compared with deep NREM sleep, with decreased connectivity between the dorsomedial prefrontal cortex and the PCC. REMs‐locked DMN activity is reduced, while activity in the sensorimotor network is increased.

FIGURE 2

The most common challenges conducting sleep neuroimaging studies. Because polysomnography must be recorded to perform appropriate sleep scoring and identify electrophysiological microprocesses of interest such as sleep spindles or slow waves, hardware constraints might emerge, such as the limitation of adequate equipment, for instance, magnetic resonance imaging (MRI)‐compatible electroencephalography (EEG) caps and electrodes. Additionally, auxiliary electrodes and channels might be needed, which accounts for electrode placement, standardization and signal quality challenges. Sleep scoring online or offline becomes problematic, as data cleaning and artefact removal must be performed, particularly a concern for MRI studies. The lack of open‐source software does not facilitate individual‐based artefact removal algorithms, greatly benefiting sleep studies. Except for functional near‐infrared spectroscopy (fNIRS), any other scanner environment is restrictive, accounting for difficulties maintaining and consolidating sleep. Movement restrictions are a significant issue for MRI studies and can deteriorate the data due to movement artefacts. Acoustic noise in MRI could be reduced by developments and usability of silent MRI sequences combined with MRI‐compatible noise‐cancelling devices, such as headphones. Data quality and interpretability are crucial to advancing science. However, neuroimaging suffers from autonomic physiological confounds, especially during sleep. Current approaches usually model and regress physiological signals; however, it may account for signal loss. Developments in animal models and theoretical advances will help understand the complex relationship between metabolism, blood flow and neural activity.