Novel Objective Measures of Hypersomnolence

Abstract

Purpose of review:

To provide a brief overview of current objective measures of hypersomnolence, discuss proposed measure modifications, and review emerging measures.

Recent findings:

There is potential to optimize current tools using novel metrics. High-density and quantitative EEG-based measures may provide discriminative informative. Cognitive testing may quantify cognitive dysfunction common to hypersomnia disorders, particularly in attention, and objectively measure pathologic sleep inertia. Structural and functional neuroimaging studies in narcolepsy type 1 have shown considerable variability but so far implicate both hypothalamic and extra-hypothalamic regions; fewer studies of other CDH have been performed. There is recent renewed interest in pupillometry as a measure of alertness in the evaluation of hypersomnolence.

Summary:

No single test captures the full spectrum of disorders and use of multiple measures will likely improve diagnostic precision. Research is needed to identify novel measures and disease-specific biomarkers, and to define combinations of measures optimal for CDH diagnosis.

INTRODUCTION

The multiple sleep latency test (MSLT) is currently considered the gold standard test for measuring physiological sleep propensity, as reflected by sleep latency (1), and as such is part of the diagnostic criteria of many of the central disorders of hypersomnolence (2). In people with narcolepsy type 1 (NT1), MSLT results have good sensitivity, specificity, and test-retest reliability (1, 3). However, for other central disorders of hypersomnolence, there may be gaps in the MSLT’s diagnostic abilities, because of difficulty distinguishing sleep deprived from sleep disordered populations, high rates of abnormal MSLT results in the general population, lack of sensitivity in some sleepiness disorders, poor test-retest reliability, and confounding effects of medications and substances. Hence, there is a strong need for innovative methods of assessing sleepiness and characterizing disorders of hypersomnolence. In this review, we will first briefly review the current gold-standard, then discuss novel and emerging measures for identifying and characterizing hypersomnolence. Such measures include modifications to current gold standard MSLT, actigraphy, and polysomnography (PSG), high-density and qualitative electroencephalography (EEG), cognitive testing, neuroimaging, and pupillometry.

CURRENT DIAGNOSTIC TOOLS: THE MULTIPLE SLEEP LATENCY TEST

The MSLT is designed to measure physiological sleep propensity by providing multiple opportunities to fall asleep in controlled conditions with minimal external alerting stimuli (1). Current American Academy of Sleep Medicine (AASM) protocols for the MSLT, updated in 2021 (4), recommend 5 nap trials. Each nap trial takes place at 2-hour intervals, beginning 1.5 hours after awakening from overnight PSG. The preceding night polysomnogram (PSG) should allow for a minimum of 6 hours of sleep in at least 7 hours of recording time; in the weeks leading up to PSG/MSLT, at least 7 hours of sleep per night is recommended.

The MSLT is a core component of NT1 diagnosis, which in addition to excessive daytime sleepiness requires either typical MSLT findings plus cataplexy or demonstration of low cerebrospinal fluid hypocretin. MSLT findings of either type of narcolepsy (NT1 or narcolepsy type 2, NT2) are a mean sleep latency (MSL) of less than or equal to 8 minutes and at least two sleep-onset rapid eye movement periods (SOREMs), between all MSLT nap opportunities and the initial sleep onset from the preceding night’s PSG (see Table 1). The MSLT demonstrates high sensitivity, specificity, and test-retest reliability for the diagnosis of NT1 (1, 5, 6).

Table 1:

Current objective tools and possible extensions of these tools

Tool	Narcolepsy type 1 (NT1)	Narcolepsy type 2 (NT2)	Idiopathic hypersomnia (IH)
Actigraphy: current use	Not part of diagnostic criteria; helpful in assessing sleep durations in the week(s) prior to PSG/MSLT	Not part of diagnostic criteria; helpful in assessing sleep durations in the week(s) prior to PSG/MSLT	Can be used to objectively confirm IH diagnosis if demonstrates at least 11 hours sleep per 24 hours, averaged over at least a week; helpful in assessing sleep durations in the week(s) prior to PSG/MSLT
Actigraphy: future potential	Capturing sleep-wake fragmentation typical of NT1; combination of nocturnal sleep motor activity, overnight awakenings, and daytime nap duration differentiates NT1	--	Further validation of different devices, different device settings (e.g., optimal sensitivity threshold & time of immobility), and diagnostic cutoffs to maximize sensitivity/specificity
PSG: current use	Nocturnal SOREM used toward the 2+ total SOREMs needed for narcolepsy diagnosis; helpful in assessing for other sleep disorders and ensuring sufficient sleep duration prior to MSLT	Nocturnal SOREM used toward the 2+ total SOREMs needed for narcolepsy diagnosis; helpful in assessing for other sleep disorders and ensuring sufficient sleep duration prior to MSLT	Can be used to confirm IH diagnosis if demonstrates at least 11 hours of PSG-measured sleep in up to 24 hours of recording; helpful in assessing for other sleep disorders and ensuring sufficient sleep duration prior to MSLT
PSG: future potential	Assessment of sleep fragmentation and state transitions; people with NT1 more likely to have increased wake time after sleep onset (WASO), arousal/awakening indices, N1%, and reduced SE, SOL, and N2%; wake to REM and N1 to REM transitions	Spindle density/distribution through the night distinct in NT2 vs NT1	Spindle index in N2 higher in IH compared NT1/NT2 combined
MSLT: current use	Can be used to confirm diagnosis if mean sleep latency ≤8 minutes with 2+ SOREMs (alternatively, measurement of CSF hypocretin)	Can be used to confirm diagnosis if mean sleep latency ≤8 minutes with 2+ SOREMs (no current alternative for objective confirmation)	Can be used to confirm diagnosis if mean sleep latency ≤8 minutes with 0–1 sleep onset REM periods (alternatively, measurement of long sleep duration)
MSLT: future potential	W/N1 to REM transitions		Prolonged latency to sustained sleep may help differentiate from NT1 & NT2

For references, see text. Abbreviations PSG: polysomnography; MSLT: multiple sleep latency test; SOMEM: sleep onset rapid eye movement (REM) period; WASO: wake after sleep onset; SE; sleep efficiency, SOL: sleep onset latency;

The MSLT is also a component of the diagnostic criteria for NT2 and idiopathic hypersomnia (IH), with number of SOREMs being the distinguishing feature between these two diagnoses. However, there is growing evidence that the MSLT may be less reliable in these two disorders than in NT1. First, in the general population, a pathologic MSL of less than or equal to 8 minutes is relatively common, observed in 22% of adults (7). Due to wide standard deviations and non-Gaussian distribution of MSL values, it is difficult to derive a cutoff that reliably distinguishes between clinical and non-clinical populations (1). Multiple SOREMs, while less commonly observed in the general population, are still seen in up to 7%, with a male predominance (7). On the other hand, the MSLT may fail to capture sleepiness associated with IH and psychiatric conditions (8–10). For example, MSL may be normal in up to 71% of people with IH with objectively confirmed long sleep durations (10). The presence of pathologic mean sleep latency ≤8 minutes in psychiatric hypersomnolence is also similar to that of the general population (i.e., approximately 25%)(7, 8), and subjective and objective measures of sleepiness show opposite relationship with risk of depression in the general population (11). Finally, in clinical populations of people with non-cataplectic hypersomnias, the MSLT demonstrates poor test-retest reliability, resulting in a change in diagnosis in >50% of cases, much poorer than the repeatability observed in NT1 (3, 5, 6, 12–14).

MSLT measures may be affected by many physiological, psychological, and operational variables. Since early in its development, the MSLT has been shown to be strongly influenced by experimental and ecological variables (15, 16), and sleep deprivation among patients undergoing hypersomnolence work-up can result in false positives. Many medications can impact mean sleep latency and/or rapid eye movement (REM) sleep propensity, including commonly prescribed antidepressants but also a wide variety of other psychiatric, neurological, cardiac, and pain medications (4). Urine toxicology plays an important role in MSLT interpretation, as illicit substances can also impact MSLT results (4). Studies of clinical populations, for example, have shown high rates of illicit or unreported substance use in patients undergoing MSLT, and a potential association between marijuana use and both shortened sleep latency and sleep onset REM periods on MSLT (17, 18).

CURRENT DIAGNOSTIC TESTS: POLYSOMNOGRAPHY AND ACTIGRAPHY FOR EXTENDED SLEEP DURATIONS

Under current nosology, IH is the only one of the CDHs for which measurement of extended sleep durations can be used to satisfy the objective diagnostic testing requirement (2). At least eleven hours of sleep measured in up to 24 hours of polysomnography, or an average of at least eleven hours of sleep per 24 hours across one or more weeks of actigraphy, can confirm an IH diagnosis in the proper clinical context. Ruling out sleep deprivation or sleep debt prior to either PSG or actigraphy is important to avoid inadvertent false positives. Twenty-four-hour protocols are time- and labor-intensive. Several different protocols have been developed, varying on factors such as whether time in bed is ad lib or strict bed-rest and total duration of monitoring, without consensus about a single protocol (10, 19–21). Use of actigraphy to assess for long sleep durations awaits full validation (2). Such validation work to date has highlighted that, even within a single device, variable settings such as sleep-wake activity threshold and time of immobility required for sleep onset and offset determination can have a large impact on agreement between PSG and actigraphy in hypersomnolent patients (22). In addition, other aspects of validation, including device-related variability in accuracy and optimal rest duration to distinguish IH patients from controls, are still needed.

OPTIMIZING CURRENT TOOLS: THE MULTIPLE SLEEP LATENCY TEST

Sleep latency (SL) during an MSLT is defined as the first epoch of any stage of sleep after lights out (4), although older studies have used a variety of sleep onset criteria (23). In particular, latency to sustained sleep (SusSL), i.e., three consecutive epochs of N1 or one epoch of any other stage) has been employed, showing a mean sleep latency of 0.7 minutes longer in a non-clinical population of adolescents with sustained sleep versus first epoch of sleep criteria (16, 23). The difference between these two definitions of sleep latency on MSLT might provide a marker that differentiates among CDHs. Patients with IH tend to have a greater difference between SL and SusSL, attributed to more transitions between wake and N1 prior to sustained sleep onset, while patients with either type of narcolepsy tend to have more similar SL and SusSL because they are more likely to transition from wake directly into sustained sleep; a difference between SusSL and SL of ≥27 seconds provided optimal differentiation between IH and narcolepsy in one study (24), suggesting this or other modifications to MSLT scoring might have additional utility in hypersomnolence diagnostics.

OPTIMIZING CURRENT TOOLS: POLYSOMNOGRAPHY

Currently, polysomnography is used in the differentiation between CDH diagnoses only based on the presence or absence of a short (≤15 minute) nocturnal REM latency, which contributes to the total number of SOREMs needed to distinguish narcolepsy from IH, and the measurement of long sleep durations for IH; the presence of a high sleep efficiency (SE) on PSG is currently considered supportive of an IH diagnosis (2) but may not distinguish IH patients from controls as cleanly as previously believed (25).

Differences in sleep macro-architecture between the CDHs may be evident on PSG and might in future contribute to diagnostic algorithms. In meta-analysis, compared to patients with either IH or NT2, patients with NT1 demonstrated significantly increased wake time after sleep onset (WASO), arousal index or number of awakenings, and percentage of N1, and significantly reduced SE, SL, and N2 percentage (26, 27), i.e., more fragmented sleep in people with NT1. In contrast, nocturnal PSG findings were generally similar between people with NT2 and IH, with the exception of significantly reduced N2 sleep in NT2 (albeit to a lesser degree than in NT1) (27).

Given this fragmentation of sleep unique to NT1, considering patterns of sleep-stage transitions and sequencing may further help segregate the CDHs. For example, Maski and colleagues examined the stability of nocturnal wake and sleep stages in NT1, NT2, IH and subjectively sleepy controls. Compared to controls, NT1 was the only one of the CDH to display sleep-wake instability manifesting as more frequent periods of each sleep stage (except for N3) and wake as well as longer stage N1 periods and shorter REM periods in the first 8 hours of sleep (28).

Patients with either type of narcolepsy have evidence for REM-sleep dyscontrol (e.g., SOREMs, sleep paralysis) and so assessment for REM state changes on PSG may also have diagnostic utility. Specifically, transition into the first nocturnal REM period directly from wake or N1 sleep is seen in both NT1 and NT2 but not in IH with long sleep durations nor sleep deprivation (29). This tendency for people with narcolepsy to enter REM from stages other than N2/N3 appears more common in NT1 than NT2, more common during MSLT than PSG, stable over time, and associated with HLA DQB1*0602 positivity (30).

Sleep spindles might also be useful in the identification and classification of CDHs. Sleep spindles arise from the thalamus and play a role in increasing arousal threshold (31). Early work demonstrated an increase in sleep spindle density (SSD) in CDH patients compared to controls, particularly patients with IH, potentially implicating a deteriorated arousal system (32). More recent work has confirmed disease-specific SSD alterations among the CDHs. Spindle index (number of spindles per N2 epoch) in the first and last 50 epochs of N2 sleep was significantly higher in IH compared to a combined group of NT1 and NT2 (33). Spindle density and distribution through the night is similar between people with NT1 and controls, but shows a distinct pattern in people with NT2, although this study did not include participants with IH (34).

OPTIMIZING CURRENT TOOLS: ACTIGRAPHY

Separate from measurement of long sleep duration to confirm IH and measurement of short sleep duration to identify occult sleep restriction, actigraphy might be useful for the assessment of hypersomnolence in other ways. In an actigraphy study comparing people with NT1, IH, and healthy controls, a combination of traditional and non-traditional actigraphy measures (consisting of nocturnal sleep motor activity, overnight awakenings, and daytime nap duration) provided good diagnostic discrimination for identifying participants with NT1 (35), again likely reflecting the state-instability known to be present in this disorder. Such combinations of actigraphic measures, incorporating daytime as well as nocturnal activity, might theoretically also have utility in other CDHs, for example a set of measures capturing long sleep durations, long daytime naps, and tendency for phase delay seen in IH, but this awaits further study.

NOVEL MEASURES: QUANTITATIVE EEG (QEEG) AND HIGH-DENSITY EEG (HD-EEG)

qEEG decomposes complex electrographic waveforms into their discrete frequency bands to allow more detailed evaluation than with visual scoring alone (36). Of particular interest is slow-wave activity (SWA), defined by frequency bands of 0.5-4.5 Hertz, during non-rapid eye movement (NREM) sleep, which is considered to be an objective surrogate of homeostatic sleep requirement and depth (37). Homeostatic sleep drive is a function of preceding wakefulness, such that in an intact system, SWA is highest at sleep onset and expectedly dissipates with each successive NREM period (38–40). Enhanced theta activity during wake is considered to be the “wake analog” of SWA in sleep (41–43) and correlates with subjective daytime sleepiness (37, 44). These neurophysiological correlates of sleep need are local and experience-related (37).

NT1 may involve impairment in homeostatic sleep regulation, as identified by spectral EEG studies (Table 2). While SWA indeed decays over subsequent NREM periods in people with NT1, the rate of decrement is steeper than in controls (45). Reduction in NREM sleep intensity in NT1 is related to increased frequency and duration of wake episodes, potentially interfering with the mounting of SWA in the second NREM cycle. However, when enhancing NREM intensity in recovery sleep following 40 hours of sleep deprivation, the differences in SWA dynamics between NT1 and controls normalized (46). This was associated with reduced frequency of awakenings in first and second NREM periods, strengthening the evidence that sleep fragmentation and altered NREM sleep intensity are interrelated in NT1, in an otherwise intact homeostatic system. More recently, others have demonstrated that declines in delta activity and increases in theta activity halted after the third NREM period in narcolepsy, but after the fourth NREM episode in controls, further suggesting dysfunction in NREM mechanisms in narcolepsy (47).

Table 2:

Novel and Emerging Measures

	Narcolepsy Type 1 (NT1)	Narcolepsy Type 2 (NT2)	Idiopathic hypersomnia (IH)	Notes
qEEG	Altered decay pattern of slow wave activity overnight compared to controls		Altered decay pattern of low frequency activity compared to NT2 and controls
HD-EEG			People with DSM5 hypersomnolence disorder (similar but not identical to IH) have regional (centroparietal) reductions in slow wave activity
Cognitive testing	Attentional impairments, altered decision-making and emotion processing	Attentional impairments	Attentional impairments. Psychomotor vigilance testing before and after sleep (night sleep or naps) can capture sleep inertia	Abnormal sustained attention might support an “attention” subtype of excessive daytime sleepiness
Neuroimaging	Structural (DTI, VBM) and functional (SPECT, MR-spectroscopy, FDG-PET) imaging studies have shown inconsistent results compared to controls, within and outside of hypothalamus		Many fewer studies than in NT1 but potential involvement of salience and default mode network; overlapping but different metabolic patterns in IH and NT1 patients on FDG-PET
Pupillometry	Spontaneous oscillations, decreased diameter		May increase diagnostic yield as part of multimodal IH assessment, reduced post-illumination pupil response compared to controls

For references, see text. Abbreviations: qEEG: quantitative EEG; HD-EEG: high density EEG; DSM5: diagnostic and statistical manual, 5^th edition; DTI: diffusion tensor imaging; VBM: voxel based morphology; SPECT: single photon emission computed tomography; MR-spect: magnetic resonance imaging with spectroscopy; FDG-PET: positron emission tomography with 18-fluorodeoxyglucose

qEEG studies have compared people with NT1 to those with other CDHs. Low frequency activity (LF; 0.5–2 Hz, typical of N3 sleep) shows alterations in both NT1 and IH participants, with rapid decrement over the night in comparison to NT2 and controls, pointing toward impaired efficiency of homeostatic sleep drive in these two disorders (48). Further, patients with NT1 and NT2 demonstrated increasing mixed frequency high-energy bands (MF1; 3–7 Hz, typical of REM sleep and wake) over the course of the night with discrete oscillations occurring every few hours, likely reflecting the expected circadian evolution of REM sleep. There was also advanced timing of MF1 bands compared to controls by approximately 30 minutes, which the authors concluded may be indicative of enhanced REM pressure or instability. In patients with IH, MF1 organization was mixed with the following findings: advanced first oscillation similar to that of NT1 and NT2, a second oscillation overlapping with controls as well as a burst prior to wakefulness which was higher than NT1, NT2 and controls, suggesting heterogeneity in this group of patients (i.e, indicating that a subset of IH patients may have features characteristic of narcolepsy) (48).

HD-EEG improves spatial precision by using a minimum of 64 electrodes (compared to 3+ in typical PSG and 19–25 in low-density EEG) (49, 50), with or without subsequent qEEG analyses. Recently, a polysomnographic study utilizing 256-electrode HD-EEG followed by quantitative analysis assessed ad libitum sleep in patients with hypersomnolence disorder (51), a disorder classified by the Diagnostic and Statistical Manual (DSM5) and similar to IH; objective confirmation with MSLT or measured sleep duration is not mandatory, but excessive daytime sleepiness along with recurrent daytime sleep episodes, habitual sleep duration >9 hours, or pronounced sleep inertia are required, along with ruling out other causes that better explain the symptoms (52). Compared to controls, people with hypersomnolence disorder showed localized reduction in SWA in bilateral centroparietal regions, particularly in the first NREM sleep cycle. A negative correlation between subjective sleepiness and localized reduction in SWA was observed. Furthermore, source localization mapped deficits in SWA to the supramarginal gyrus, somatosensory and transverse temporal cortices (51). Between-group differences in stage N3 sleep were insignificant, underscoring the value of HD-EEG in detecting topographic differences in SWA. Advanced EEG analysis has also been utilized in the assessment of hypersomnolence in the context of major depressive disorder (MDD). A pilot HD-EEG study compared electrographic characteristics in MDD with and without hypersomnia (53). MDD comorbid with hypersomnia demonstrated regional reduction in parieto-occipital SWA in relation to MDD subjects without hypersomnia. Furthermore, Plante and colleagues showed that the localized reduction in SWA in subjects with hypersomnolence disorder did not occur in those with MDD without hypersomnolence, suggesting that such changes may be unique to the symptom of hypersomnolence (51).

NOVEL MEASURES: COGNITIVE TESTING

Cognitive symptoms are commonly experienced by people with CDHs and there has been recent interest in objective cognitive testing to assess these patients. Cognitive performance has been best studied in people with NT1, but attention impairments have emerged across available studies of patients with NT1, NT2 and IH. In NT1 patients specifically, impairments have also been documented in higher-order cognition, with poorer decision-making and impaired emotional processing. Whether these are seen in other CDHs requires further study.

The psychomotor vigilance task (PVT) is a simple reaction time task that required respondents to press a button in response to every presented stimulus. It provides a variety of attention measures, including reaction time and number of lapses in attention (54). The PVT has been used extensively in sleep deprivation paradigms and to a lesser extent in the CDHs. Multiple groups have demonstrated that participants with CDHs have poorer PVT performance than non-sleepy controls (55–57). More recent work has evaluated the PVT as a measure of sleep inertia within the CDHs. In one such study, the PVT was performed before and after nap 2 and 4 during the MSLT. Performance worsened with napping, particularly in nap 2, in CDH patients but was stable in non-sleepy controls (57). Similarly, among CDH patients, PVT performance worsens upon awakening from PSG compared to pre-sleep baseline, with worsening performance correlating with increasing self-reported sleep inertia severity (58). Both approaches suggest that the PVT may be a useful way to objectively quantify sleep inertia experienced by CDH patients.

The sustained attention to response task (SART) is similar to PVT but participants only press a button in response to a subset of the presented stimuli (59). While different hypersomnolent groups showed similar impairments in performance, performance was generally worse than that of historical controls (59). The SART has recently been incorporated into a European consensus panel’s proposed diagnostic framework for an “attention” subtype of probable idiopathic excessive daytime sleepiness (i.e., EDS not meeting proposed criteria for narcolepsy or idiopathic hypersomnia) (60). Using a combination of vigilance and sustained attention measures may instead highlight disease-specific effects, with one study showing impaired performance among hypersomnolent patients but somewhat distinct patterns of impairment in people with NT1 versus those with IH (61).

STRUCTURAL AND FUNCTIONAL NEUROIMAGING

Multiple studies have assessed both structural and functional neuroimaging in NT1. It is well-known that loss of hypocretin (orexin)-secreting neurons in the lateral hypothalamus is the fundamental pathological finding in NT1 (62). Given this, it is perhaps not surprising that a number of studies have demonstrated that people with NT1 demonstrate changes in hypothalamic white matter (WM) on diffusion-tensor imaging (DTI) magnetic resonance imaging (MRI) and reduction in bilateral hypothalamic gray matter via voxel-based morphometric (VBM) MRI analyses (63–65), although the latter finding has been called into question based on coordinate-based meta-analysis (66).

The hypocretin system projects beyond the hypothalamus to numerous brainstem, subcortical and cortical loci, and DTI imaging in NT1 patients has also highlighted changes in mean diffusivity (a marker of tissue integrity) or fractional anisotropy (a marker of fiber integrity) in these areas compared to controls, including in brainstem WM tracts, anterior and medial thalamic WM tracts, and orbitofrontal, frontotemporal, anterior cingulate WM bundles (63, 65, 67–69). Involvement of frontotemporal gray matter in narcolepsy has also been reported based on meta-analysis, but not confirmed on independent re-analysis of the same dataset (64, 66), leaving unresolved questions about the presence and extent of grey matter involvement in NT1.

Functional neuroimaging studies have largely supported the idea of hypothalamic dysfunction in NT1. A single photon emission computed tomography (SPECT) imaging study demonstrated attenuated cerebral perfusion in the bilateral anterior hypothalami, caudate nuclei and thalamic pulvinar nuclei during the wake state in patients with narcolepsy with cataplexy (70). Furthermore, MRI with spectroscopy demonstrated reduced N-acetyl aspartate (NAA)/creatine-phosphocreatine ratio in the hypothalamus (71), consistent with neuronal loss. However, positron emission tomography (PET) with [18F]fluorodeoxyglucose (FDG) studies have produced discordant results. Joo and colleagues demonstrated hypometabolic activity involving the bilateral hypothalamic (and thalamic) regions in narcoleptic patients whereas Dauvilliers and colleagues only found hypometabolism during cataplectic attacks (72, 73). FDG-PET studies comparing narcolepsy with healthy controls have also demonstrated heterogeneous findings with regard to involvement of other brain regions. Several studies have demonstrated solely reduced metabolic activity involving the bilateral thalamic and hypothalamic regions (72, 74) whereas others demonstrated extra-hypothalamic increased metabolic activity involving the anterior and middle cingulate, temporal lobe, cuneus and precuneus, insula and fusiform gyrus, and pre- and post-central gyri (73, 75, 76). Others have shown a combination of hyper- and hypometabolic activity (albeit in largely different anatomical distributions previously noted) (77, 78). These differences may have been accounted for by a combination of population features, under-powering, protocol and analysis differences, failure to control for sleep-wake state, and lack of differentiation between NT1 and NT2, depending on the study. A recent meta-analysis of structural and functional neuroimaging studies of narcolepsy failed to identify common areas of dysfunction across studies (79), suggesting the need for considerable additional work in this area, ideally via collaborations that allow larger patient populations from multiple sites using common protocols.

Substantially fewer studies have evaluated structural or functional neuroimaging in NT2 or IH. Recent attention in IH has focused on the default-mode network (DMN), a network of neural structures including the medial prefrontal cortex (mPFC), bilateral posterior cingulate cortices, precuneus, inferior parietal lobe, portions of the hippocampus and lateral temporal cortex (80) that is implicated in the resting (non-task) wake state (80, 81), and the salience network, a network involved the top-down processing of large amounts of sensory data to determine “relevance” of information (82). Participants with IH demonstrate increased cortical thickness and volume in parts of the precuneus and occipital gyrus and show reduced resting state MRI functional connectivity in the anterior DMN, with occipital gyrus and anterior DMN findings correlating with the degree of self-reported sleepiness (83). People with IH also demonstrate altered regional cerebral blood flow (rCBF) during resting wakefulness within the DMN, including mPFC and posterior cingulate, with mPFC changes correlating with subjective and objective sleepiness (84). Interestingly, similar cerebral perfusion changes have been seen during NREM sleep in “good sleepers” (85), suggesting that localized NREM-like brain activity may be occurring during the resting wake state of IH patients (84).

Alternatively, Dauvilliers and colleagues performed an [18F]FDG-PET imaging study of awake IH participants that instead implicated involvement of the salience network versus controls (75), although no statistically significant group differences between IH and NT1 participants were observed. More recently, another [18F]FDG-PET imaging study comparing patients with NT1, IH and non-sleepy controls used simultaneous EEG to control for sleep-wake state during the procedure (76). While there was overlap between the two CDHs, with hypermetabolism in precuneus and inferior parietal lobule observed in both NT1 and IH, distinct hypermetabolic profiles were observed, with IH participants demonstrating hypermetabolism in the posterior cingulate, broadly consistent with earlier reports of DMN involvement in IH (76).

PUPILLOMETRY

Although classically considered as part of the circadian system, melanopsin-rich intrinsically photosensitive retinal ganglion cells (IpRGCs) also influence sleep and alertness in a circadian-independent manner (86, 87). IpRGCs also play a role in mediating the pupillary light reflex (88), making pupillary response to light a potential avenue to non-invasively assess the activity of IpRGCs and, by extension, their sleep-wake influencing role. Pupillometry has long been studied as a metric to differentiate people with narcolepsy from non-sleepy controls, particularly with respect to spontaneous oscillations and baseline pupillary diameter (89–91). However, more recent interest has focused on the potential role of pupillometry as a method of assessing IH. As part of a multi-modal assessment, pupillometry has been shown to increase diagnostic yield in patients undergoing evaluation for IH, beyond that of conventional testing (92). The Post-Illumination Pupil Response (PIPR) has also been evaluated in people with IH. IpRGCs are maximally sensitive to blue light; following withdrawal of blue light, pupillary constriction is sustained, known as the PIPR (93). Compared to controls, patients with IH with long sleep times had significantly reduced PIPR, suggesting that PIPR may have promise as a unique phenotypic marker for IH (94), although evaluation of PIPR differences across CDHs is still needed.

CONCLUSION

Excessive daytime sleepiness is a multifaceted physiological state and, as such, there is no single objective or subjective test that currently assesses and encompasses this entire construct. The identification of hypocretin deficiency as a biomarker for NT1 served as a critical development in diagnostic certainty within this disease; the identification of pathologic biomarkers within other CDHs remains to be realized, but should someday inform diagnostic classifications. In the meantime, current diagnostic tools might be refined or supplemented with emerging objective measures, to help identify and classify across and between these disorders.