Imaging of the Sleep-Disordered Brain

C H A P T E R

38 Imaging of the Sleep-Disordered Brain Nathan Cross*,†,‡,§, Thien Thanh Dang-Vu*,†,‡,§ *Centre de recherche de l’institut universitaire de geriatrie de Montreal (CRIUGM), CIUSSS du Centre-Sud-de-l’ıˆle-deMontreal, Montreal, QC, Canada †Department of Health, Kinesiology and Applied Physiology, Concordia University, Montreal, QC, Canada ‡PERFORM Center, Concordia University, Montreal, QC, Canada §Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada

I INTRODUCTION Medical conditions that specifically impact on the function and quality of sleep are classified as sleep disorders. These may affect the ability to initiate or maintain sleep or vigilance, or involve abnormalities in other bodily processes during sleep, such as movement or the control of respiration. Given the role of sleep in maintaining a healthy neuronal environment and intact behavioral function, sleep disorders may lead to cognitive deficits or impairments in brain function. These consequences can be studied in humans through available neuroimaging techniques, such as magnetic resonance imaging (MRI), positron-emission tomography (PET), or single photon emission computed tomography (SPECT). MRI can be used to measure brain structure, function, or chemistry. Structural neuroimaging techniques quantitatively measure anatomical differences or changes in either volume or thickness of certain brain regions, which can be completed by manual tracing or automated segmentation through a range of toolboxes. Voxel-based morphometry (VBM) is an automated technique that transforms a brain scan from an individual to a standardized template, to find volumetric differences in small areas (voxels). Other techniques exist that measure differences in thickness across the cortical gray matter. Diffusion tensor imaging is another form of MRI, specialized to detect characteristics of structural connectivity. This technique constrains water molecule diffusivity in an anisotropic (uneven) manner, the extent of which can be measured by a scalar value, fractional anisotropy, which is sensitive to the organization, integrity, and myelination of white matter axons.

Handbook of Sleep Research, Volume 30 ISSN: 1569-7339 https://doi.org/10.1016/B978-0-12-813743-7.00038-4

Magnetic resonance spectroscopy (MRS) is a specialized form of MRI that can quantify concentrations of molecules within particular voxels of the brain. The temporal and spatial resolution of MRS is quite poor; therefore, it lies somewhere between functional and structural neuroimaging. Molecules measured via proton MRS (1H-MRS) can include neurotransmitters, such as glutamate and gamma-aminobutyric acid (GABA), and other molecules that may be considered markers of underlying neuronal or glial integrity. Functional MRI (fMRI) measures changes in cerebral blood flow through a blood-oxygen-level-dependent contrast. This method can indirectly measure the activity of specific brain regions, as activation results in an increase of oxygen consumption and therefore blood flow. This activity not only is usually measured during a resting state (rs-fMRI) or when subjects perform a cognitive task but also can be obtained during sleep when coupled with electroencephalograph (EEG) to detect sleep stages. Positron-emission tomography (PET) imaging involves injecting a radioactive tracer into the bloodstream that is detectable within the brain. Depending on the tracer, it is possible to measure the density of neurotransmitter receptors or regional brain metabolic rate. For example, 18F-fluorodeoxyglucose positron-emission tomography (FDG-PET) provides a measurement of regional glucose metabolism within the brain over a short time span (e.g., 20–30 min). Single photon emission computed tomography (SPECT) is a technique that also utilizes the injection of a tracer into the bloodstream and can be used to quantify regional cerebral perfusion (blood flow) or neurotransmission in the brain.

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Imaging sleep disorders not only assists in uncovering the physiological underpinnings of these conditions and refining treatment options for improving patient outcomes but also can further the understanding of the brain systems involved in the control of sleep and cognition. While there are a wide range of sleep disorders, this chapter will focus on four major sleep disorders: insomnia, narcolepsy, REM sleep behavior disorder, and obstructive sleep apnea.

II INSOMNIA Insomnia is the inability to obtain adequate sleep despite sufficient opportunity (American Academy of Sleep Medicine, 2001; American Psychiatric Association, 2013). Symptoms may include one or more of difficulties falling asleep, maintaining sleep, or waking early in the morning with the inability to return to sleep and cause distress or impairment in functioning during the day (American Psychiatric Association, 2013). Chronic insomnia is defined when these symptoms are present for at least three nights per week for 3 months or longer. Secondary symptoms include daytime fatigue, mood disturbances, and decreased performance in cognitive function (Zammit, Weiner, Damato, Sillup, & McMillan, 1999). Studying the pathophysiology of insomnia can be complicated by the presence of comorbid diseases in many cases (e.g., major depression and chronic pain) (Morin & Benca, 2012), which may exhibit their own abnormal physiology within the brain. Additionally, while up to a third of adults may experience symptoms of insomnia (Soldatos, Allaert, Ohta, & Dikeos, 2005), only around 10% meet formal diagnostic criteria (Morin & Benca, 2012). The following section will therefore focus on evidence from subjects where insomnia is the primary condition, otherwise known as primary insomnia.

A Functional Neuroimaging of Primary Insomnia 1 Nuclear Imaging An initial FDG-PET study in insomnia investigated the change in brain metabolism from wakefulness to sleep in 7 insomnia patients and 20 normal sleepers (Nofzinger et al., 2004). Metabolic rate decreased from wake to sleep; however, within the insomnia group, the metabolic reductions were of lesser magnitude in wake-promoting structures (e.g., the thalamus, reticular activating system, and anterior cingulate cortex). This finding is consistent with a hyperarousal model of insomnia and suggests that sleep onset and maintenance difficulties may be attributable to an abnormality in cortical deactivation during the transition from waking to sleep. However, relative to healthy participants, insomnia patients exhibited

hypometabolism during wakefulness across regions of the frontal, temporal, parietal, and occipital cortices, as well as in the thalamus, hypothalamus, and brain stem reticular formation, which is inconsistent with a model of global hyperarousal. In a later study, the same group extended these findings by demonstrating that a greater amount of wake after sleep onset (WASO) time was related to increased metabolism in a range of overlapping regions during NREM sleep (Nofzinger et al., 2006). A more recent study found that patients with insomnia had smaller sleep-wake differences in glucose metabolism across the left frontoparietal, occipital, fusiform, and posterior cingulate cortices than normal sleepers (Kay et al., 2016). These regions have been associated with cognition, self-reference, and affect, suggesting either that insomnia may be related to a disability within the brain to inhibit these processes during sleep or a reduced activation during wakefulness. Indeed, such findings suggest that insomnia-related arousal might be of a physiological, cognitive, or affective nature and it is likely that these categories overlap. A SPECT study in five insomnia subjects and four normal sleepers assessed blood flow during NREM sleep (Smith et al., 2002). Compared with controls, insomnia subjects were found to express cerebral hypoperfusion across multiple a priori regions of interest, most prominently within the basal ganglia. In a follow-up study, four of the five same insomnia subjects were rescanned following behavioral therapy for insomnia. A 43% reduction in sleep onset latency after treatment was accompanied by a 24% restoration of regional cerebral blood flow, especially in the basal ganglia (Smith, Perlis, Chengazi, Soeffing, & McCann, 2005). These findings suggest a relationship between decreased blood flow in specific parts of the brain and insomnia severity, observations that appear counterintuitive to a general model of hyperarousal in insomnia and highlight some of the unresolved questions regarding the etiology and pathophysiology of this complex behavioral disorder (Fig. 38.1). 2 Functional MRI Network connectivity in insomnia has been studied in a small number of wakeful resting-state fMRI studies (Fig. 38.2). In a study of 10 adults with insomnia and 10 good sleepers, decreased connectivity was found between the amygdala and the insula cortex, striatum, and thalamus in the insomnia group (Huang et al., 2012). Alternatively, increased connectivity was observed between the amygdala and the premotor and sensorimotor cortices, which was correlated with participant ratings on the Pittsburgh Sleep Quality Index (PSQI), a questionnaire of sleep quality. Given that the amygdala is involved in fear and emotional processing, a greater connectivity between the amygdala and the premotor cortex in insomnia subjects may be reflective of a hyperreactivity

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FIG. 38.1 Summary of findings from PET and SPECT studies in insomnia. (1) Hypometabolism during wake in relation to healthy subjects across regions of the frontal, temporal, parietal, and occipital cortices, as well as in the thalamus, hypothalamus, and brain stem reticular formation (Nofzinger et al., 2004). (2) Wake after sleep onset is positively correlated with cerebral glucose metabolism in the pontine tegmentum and in frontal, anterior temporal, and anterior cingulate cortices during NREM sleep (Nofzinger et al., 2006). (3) Decreased activation during wakefulness within the right precuneus/posterior cingulate cortex, left middle frontal gyrus, left inferior/superior parietal lobules, and right lingual gyrus (Kay et al., 2016). (4) Decreased regional cerebral blood flow during NREM sleep in the frontal medial and frontal lateral cortex; occipital, temporal, and parietal cortices; thalamus; basal ganglia; and pons (Smith et al., 2002).

FIG. 38.2 Summary of findings from fMRI studies in insomnia. (1) Decreased activation in the amygdala, insula, striatum, and thalamus. Increased connectivity between the amygdala and premotor cortex (Huang et al., 2012). (2) Decreased activation in the medial prefrontal cortex, middle temporal gyrus, and left temporal and left inferior parietal cortex (Nie et al., 2015). (3) Decreased activation in the left medial and inferior frontal gyrus during a letter fluency task (Altena et al., 2008). (4) Decreased activation in the dorsolateral prefrontal cortex, the anterior and posterior cingulate cortex, and the orbitofrontal cortex during a verbal working memory task (Drummond et al., 2013). (5) Increased activation within the left temporal and occipital lobes and right frontal lobe during a spatial memory task (Li et al., 2016).

to perceived threat. Further evidence of abnormal connectivity has been found in the default mode network (DMN), a brain network that is active in the absence of any task or active behavior. Specifically, decreased connectivity between the medial prefrontal cortex and middle temporal lobe and between the left temporal lobe and left inferior parietal cortex was detected in insomniacs relative to controls (Nie et al., 2015). Since these regions are implicated in sleep physiology and cognitive processing, these findings suggest that decreased connectivity may underlie cognitive deficits associated with insomnia. However, no cognitive data were collected. A further study examining the amplitude of low-frequency fluctuations (ALFF)—a recently developed technique that integrates the square root of the power spectrum in a low-frequency range and can detect abnormal spontaneous brain activity— found that ALFF was increased in the temporal and occipital lobes of insomnia patients (Dai et al., 2016). Overall, the findings from resting-state functional connectivity studies in insomnia support a model of excessive hyperarousal that is hypothesized to be a major factor in the pathogenesis of primary insomnia (Perlis, Smith, & Pigeon, 2006). Task-based fMRI studies have sought to explore the effects of insomnia on cognitive processing. One study

found that insomnia subjects had reduced activation of the left medial and inferior frontal gyrus during the completion of an executive functioning task of verbal fluency, even though there were no differences in performance (Altena et al., 2008). These brain regions are typically recruited during the performance of cognitive tasks assessing executive function. Another recent task-based fMRI study scanned insomnia subjects and controls during the completion of a working memory task (Drummond et al., 2013). This insomnia group demonstrated lower activation in multiple clusters of the frontal gyrus, motor cortex, and cingulate cortex that were engaged during the performance of the task. Interestingly, insomniacs also failed to deactivate irrelevant regions within the DMN, such as the posterior cingulate cortex. Although task performance did not differ between insomniacs and controls, the authors interpreted these findings as compromised cognitive processing in insomnia. The findings suggest a fundamental change in brain physiology and cerebral blood flow during cognitive performance that might be secondary to the effects of hyperarousal (Chee, 2013; Drummond et al., 2013). Another task-based fMRI study demonstrated that insomnia subjects exhibited lower activity within and connectivity between multiple

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regions of the neural network pertaining to performance on a working memory task compared with 30 normal sleepers (Li et al., 2016). Furthermore, insomnia subjects showed higher activation of other specific brain regions (e.g., the left temporal and occipital lobes and right frontal lobe). In another study, however, greater brain activation was found in the right lateral inferior frontal cortex and right superior temporal pole during a working memory task compared with controls (Son et al., 2018). Overall, these findings demonstrate a characteristic pathophysiology underlying decreased connectivity of task-based neural networks in insomnia. Increased connectivity in task-irrelevant regions suggests that other comorbid features of insomnia, such as anxiety or depression, may also be associated with functional changes within the brain, given that insomnia patients often self-report increased levels of symptoms related to these comorbid conditions(Li et al., 2016). The relationship between emotion and hyperarousal was investigated in a study measuring the alterations in emotional reactivity of the amygdala in response to images with varying emotional valence in insomnia subjects (Baglioni et al., 2014). While there were no differences between insomniacs and normal sleepers in amygdala activation following emotionally arousing stimuli, only the normal sleepers showed habituation to images over successive trials. Furthermore, insomnia patients did exhibit greater amygdala reactivity to sleep-related stimuli, suggesting that the association between insomnia and heightened arousal may involve a context-specific cognitive bias.

B Spectroscopy Neuroimaging in Insomnia The first MRS study of insomnia found that levels of the main inhibitory neurotransmitter, gamma-aminobutyric acid (GABA), were reduced in the insomniacs across all regions measured, including the basal ganglia; thalamus; and temporal, parietal, and occipital gray and white matter (Winkelman et al., 2008). A later study by the same group found decreases in GABA/creatine ratios within the occipital and anterior cingulate cortices, but not within the thalamus of insomnia patients (Plante, Jensen, Schoerning, & Winkelman, 2012). In contrast, a third study observed an increase in GABA levels in the occipital cortex (Morgan et al., 2012), suggesting that changes in GABA levels may not be global and instead are specific to local sections of the brain that may underpin different clinical symptoms, as two of these studies reported opposite relationships between GABA levels and WASO, one positive (Winkelman et al., 2008) and one negative (Morgan et al., 2012). Furthermore, a recent study found no significant differences in GABA concentrations within the anterior cingulate and prefrontal cortex between insomnia subjects and normal sleepers (Spiegelhalder et al., 2016). However,

while the levels of glutamine (an essential molecule in neurotransmission) did not differ between groups, they increased over the course of the day. The authors suggest that this may reflect a buildup of arousal that would culminate just prior to desired sleep onset. However, another study found lower levels of glutamine levels in the left occipital cortex of insomnia patients with short sleep duration compared with those with normal sleep duration (Miller et al., 2017), again suggesting that these changes may be regionally specific.

C Structural Neuroimaging of Primary Insomnia The earliest structural neuroimaging study in insomnia used manual tracing to measure five regions of interest in eight insomniacs and eight normal sleepers. They found that, on average, the insomnia group had smaller bilateral hippocampal volumes, even though this difference did not survive statistical correction for multiple comparisons (Riemann et al., 2007). These results were not replicated in two subsequent studies that also employed manual tracing techniques (Noh et al., 2012; Winkelman et al., 2010). However, in the second study, left hippocampal volumes were related to the duration of insomnia and the number of arousals during sleep in insomnia patients (Noh et al., 2012). A further study employing manual tracing, in combination with an automated surfaced-based mapping approach to measure regional subfield differences of hippocampal volume, found that all subfields of the hippocampus exhibited reduced volume in insomnia subjects (Joo, Kim, Kim, & Hong, 2012). Multiple other studies utilizing various automated approaches for measuring regional volumes have all failed to find changes in the hippocampus of insomniacs compared with good sleepers (Altena, Vrenken, Van Der Werf, van den Heuvel, & Van Someren, 2010; Spiegelhalder et al., 2013; Winkelman et al., 2013). However, an increased volume of the anterior cingulate cortex (Winkelman et al., 2013) and reduced volume of the orbitofrontal and parietal cortices have been observed in insomniacs compared with good sleepers (Altena et al., 2010). Alternatively, cortical thinning was found in the anterior cingulate cortex, precentral cortex, and lateral prefrontal cortex of 57 patients with persistent insomnia symptoms compared with 40 good sleepers (Suh, Kim, Dang-Vu, Joo, & Shin, 2016). This study also reported a decreased structural covariance within the DMN in the insomnia group, which was further associated with worse subjective sleep quality. Disrupted structural covariance within this important network may reflect a disconnection during the wake-sleep transition and is consistent with functional neuroimaging studies of insomnia. The most frequently studied brain area in insomnia has been the hippocampus, likely due to the significant role it plays in learning and

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TABLE 38.1

Structural and Spectroscopic Findings in Insomnia Sample size (number of females)

Study

Neuroimaging technique

Mean age (years)

Insomnia

Control

Insomnia

Control

Main findings in insomnia compared with controls

Structural Riemann et al., 2007

Manual tracing (1.5T)

8 (5)

8 (5)

48.4 (16.3)

46.3 (14.3)

Reduction in hippocampal volume (uncorrected P-values)

Altena et al., 2010

VBM (1.5T)

24 (17)

13 (9)

60.3 (6.0)

60.2 (8.4)

Reduced GM volume in the left orbitofrontal cortex strongly correlated with symptoms. No difference in hippocampal volume

Winkelman et al., 2010

Manual tracing (3.0T)

20 (10)

15 (6)

39.3 (8.7)

36.8 (5.3)

No difference in hippocampal volume

Noh et al., 2012

Manual tracing (1.5T)

20 (18)

20 (18)

50.8 (10.8)

50.4 (11.7)

No difference in hippocampal volume. Hippocampal volume was associated with insomnia duration and arousal index

Joo et al., 2014

VBM (1.5T)

27 (25)

27 (23)

52.3 (7.8)

51.7 (5.4)

Bilateral reduction in volume across all hippocampal subfields

Spiegelhalder et al., 2013

VBM (3.0T), automated segmentation

28 (18)

38 (21)

4.7 (14.2)

39.6 (8.9)

No difference in hippocampal volume. No difference in cortical thickness within any region or overall GM volume

Winkelman et al., 2013

Automated segmentation (3.0T)

21 (14)

20 (12)

35.8 (9.5)

34.1 (9.9)

Increased GM volume within the right anterior cingulate cortex

Koo, Shin, Lim, Seong, & Joo, 2017

Automated segmentation (1.5T)

27 (25)

30 (28)

51.2 (9.6)

50.4 (7.1)

Decreased hippocampal volume. Hippocampal volume correlated with greater subjective sleep disturbance and higher arousal indexes. Volumes of subcortical structures (the hippocampus, amygdala, basal ganglia, and thalamus) correlated negatively with cognitive tests

Magnetic resonance spectroscopy Winkelman et al., 2008

1

H-MRS (4.0T)

16 (8)

16 (7)

37.3 (8.1)

37.6 (4.5)

Reduced whole brain GABA levels

Morgan et al., 2012

1

H-MRS (4.0T)

16 (10)

17 (9)

39.0 (9.0)

36.0 (9.0)

Elevated levels of GABA in the occipital cortex

Plante et al., 2012

1

H-MRS (4.0T)

20 (12)

20 (12)

34.3 (8.3)

34.1 (9.9)

Reduced GABA levels in the occipital and anterior cingulate cortex

Spiegelhalder et al., 2016

1

H-MRS (3.0T)

20 (12)

20 (12)

42.7 (13.4)

44.1 (10.6)

No significant differences in GABA or glutamine

Miller et al., 2017

1

H-MRS (3.0T)

31 (21)

16 (11)

37.5 (9.9)

37.3 (9.6)

Lower levels of occipital glutamine levels in insomnia subjects with short sleep duration

VBM, voxel-based morphometry; GM, gray matter; 1H-MRS, proton magnetic resonance imaging; DTI, diffusion tensor imaging; NAA, N-acetylaspartate; Cr, creatine; Cho, choline; Ins, myoinositol; GSH, glutathione.

memory. Indeed, a recent study demonstrated that reduced hippocampal volumes were positively correlated with deficits in a range of cognitive tasks in insomnia patients (Koo et al., 2017). However, overall, there appears to be no conclusive relationship between insomnia and structural brain changes, most likely due to the heterogeneity of insomnia and various comorbidities (Table 38.1).

D Summary Despite some inconsistent observations, neuroimaging studies of insomnia generally tend to support a hyperarousal theory in the etiology of this sleep disorder. Overall, functional neuroimaging studies in insomnia have provided evidence that is consistent with a hyperarousal model; however, they also suggest that a more complex

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model is required. The available evidence has shown that insomnia is a multifaceted disorder, exhibiting differences in brain function that are both regionally and state (i.e., wake vs. sleep)-specific. Specifically, ineffective modulation of cortical arousal across the sleep-wake cycle may underpin the neurological mechanisms of insomnia. Furthermore, evidence of structural changes in insomnia, such as reduced hippocampal volume and prefrontal gray matter, may distinguish individuals susceptible to cognitive and emotional dysfunctions experienced in insomnia.

III NARCOLEPSY AND IDIOPATHIC HYPERSOMNIA Narcolepsy is a sleep disorder affecting the wake- and sleep-promoting arousal system; it is characterized by excessive daytime sleepiness and abnormalities in REM sleep (for details, see chapter by Sun et al.). Narcolepsy is categorized into two types, based on the presence of cataplexy (abrupt muscle atony, often triggered by emotional stimulation). Type 1 narcolepsy, or narcolepsy with cataplexy, is more commonly investigated than type 2 narcolepsy (without cataplexy) (American Academy of Sleep Medicine, 2001). The onset of narcolepsy most commonly manifests during adolescence and young adulthood, and the prevalence of narcolepsy has consistently been estimated to lie between 20 and 50 per 100,000 people (Longstreth, Koepsell, Ton, Hendrickson, & van Belle, 2007). Narcolepsy with cataplexy is associated with a deficiency in the hypothalamic neuropeptide orexin-A (hypocretin-1). Given that hypocretin-1 neurons are involved in the arousal motor function systems (Baumann & Bassetti, 2005; Govindaiah & Cox, 2006; Peyron et al., 1998), this may explain the abnormal sleep-wake patterns and cataplexy characteristic of narcolepsy and distinguish narcolepsy from similar disorders such as idiopathic hypersomnia—a neurological

disorder characterized primarily by excessive daytime sleepiness, together with an often prolonged total sleep time and unrefreshing sleep periods (sleep drunkenness). Neuroimaging can assist in uncovering the neural correlates of narcolepsy and further inform the neurological underpinnings and consequences of this sleep disorder.

A Functional Neuroimaging of Narcolepsy 1 Nuclear Imaging of Narcolepsy and Idiopathic Hypersomnia A number of nuclear imaging studies have investigated the brain metabolic characteristics associated with narcolepsy (Fig. 38.3). One early SPECT study observed decreased regional cerebral blood flow (rCBF) in the brain stem during wakefulness in 13 narcoleptic patients (10 with cataplexy) compared with healthy controls (Meyer, Sakai, Karacan, Derman, & Yamamoto, 1980). In this same study, rCBF increased shortly after sleep onset, particularly in temporoparietal regions. Activity in these regions was associated with reported dreaming, which is in line with other evidence (Meyer, Ishikawa, Hata, & Karacan, 1987). Another SPECT study compared activity across wakefulness and REM sleep in six narcoleptic patients with cataplexy and found no significant differences in metabolic uptake between each state (Asenbaum et al., 1995). Of note was that the level of activation within parietal regions remained high during REM sleep, even though these regions are commonly deactivated (Maquet, 2000). A SPECT study conducted in patients with narcolepsy-cataplexy found decreased perfusion throughout various regions of the brain, including the bilateral hypothalami, caudate nuclei, thalamus, cingulate gyrus, and frontoparietal cortices ( Joo et al., 2005). These regions of decreased activity are important targets of hypocretinergic projections, which is consistent with the loss of hypothalamic neurons in narcolepsy (see Sun et al., this volume). These findings

FIG. 38.3 Summary of findings from PET and SPECT studies in narcolepsy. (1) Decreased regional cerebral blood flow (rCBF) in the brain stem and cerebellum during wakefulness and increased activity in temporoparietal regions of the cortex and cerebellum during sleep (Meyer et al., 1980). (2) Increased perfusion within parietal regions during REM sleep (Asenbaum et al., 1995). (3) Focal hypoperfusion in the caudate nucleus, significant hypoperfusion in the subcallosal gyrus, the cingulate gyrus extending along corpus callosum, the parahippocampal gyrus, and bilateral paracentral areas ( Joo et al., 2005). (4) A reduction of glucose metabolism in the bilateral hypothalami, thalamic nuclei, and frontal-parietal cortices ( Joo et al., 2004). (5) Hypermetabolism evident in the anterior and midcingulate cortex, the right cuneus, and the lingual gyrus (Dauvilliers et al., 2010).

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can be contrasted with a recent study in idiopathic hypersomnia, which measured SPECT during wakefulness in 13 subjects with idiopathic hypersomnia and 16 controls (Boucetta et al., 2017). Regional cerebral blood flow decreases in the hypersomnia group were observed within the medial prefrontal cortex, posterior cingulate cortex, and putamen, as well as increases in amygdala and temporo-occipital cortices, while no changes were observed in the hypothalamus. Lower regional cerebral blood flow in the medial prefrontal cortex was associated with higher daytime sleepiness. While these findings require replication, they suggest that idiopathic hypersomnia is characterized by functional alterations in brain areas involved in the modulation of vigilance states, rather than a loss of hypothalamic orexin neurons. Only two studies have provided FDG-PET evidence during wakefulness in narcolepsy and have reported inconsistent findings. The first study in 24 narcoleptic patients and matched controls found a reduction of glucose metabolism in the bilateral hypothalami, thalamic nuclei, and frontal-parietal cortices ( Joo, Tae, Kim, Kim, & Hong, 2004). The second study of 21 patients and controls found no evidence of hypometabolism in any brain region but did detect hypermetabolism of the cingulate and visual association cortices (Dauvilliers et al., 2010). Neuroimaging data collected during cataplectic episodes in narcoleptic subjects are rare, given the difficulty in capturing these unpredictable events. Hong, Tae, and Joo (2006) captured cataplectic episodes in two narcoleptic patients using Tc-ECD SPECT. Compared with baseline wakefulness and REM sleep, the cataplectic state was characterized by hyperperfusion in limbic areas (including the right amygdala), the thalami, basal ganglia, brain stem, and parietal cortices, and hypoperfusion in the prefrontal and occipital cortices. A different case study with SPECT also found hyperperfusion throughout orbitofrontal, temporal, and cingulate regions of the cortex (Chabas et al., 2007). A FDG-PET study of cataplectic episodes in two narcoleptic subjects observed metabolic increases in the precentral and primary somatosensory cortex and decreased hypothalamic metabolism (Dauvilliers et al., 2010). The latter observation was supported by an fMRI study that showed marked hypoactivation of the hypothalamus during a cataplectic attack (Reiss et al., 2008). While these data are limited by the small number of observations, they are somewhat consistent with structural findings in narcolepsy, described in the next section. 2 Functional MRI Most of the fMRI studies of narcolepsy have focused on brain responses to emotional stimuli. In particular, based upon clinical observations that cataplectic episodes in narcolepsy can be triggered by positive emotions

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(e.g., hearing a joke), two studies used fMRI while subjects viewed humorous images. The first, in 12 narcoleptic patients, found reduced hypothalamic activity and increased amygdala activity in response to image presentation compared with matched control subjects (Schwartz et al., 2008). In contrast, the second study found enhanced activity in the hypothalamus and other regions of emotional networks, such as the ventral striatum, in 10 patients compared with both controls while viewing humorous pictures (Reiss et al., 2008). These results suggest that a dysregulation of hypothalamus-amygdala activity affects the processing of emotional stimuli in narcolepsy. Other studies have investigated emotional processing in a slightly different manner. During a reward task, 12 narcoleptic patients exhibited abnormal activity within emotional circuitry compared with controls— specifically, reduced activity in the midbrain during high-reward expectancy and increased activity in the amygdala and dorsal striatum on winning outcomes (Ponz et al., 2010). Furthermore, activity in the nucleus accumbens and the ventral-medial prefrontal cortex correlated with disease duration, suggesting that alternative neural circuitry may adapt and develop to modulate affective responses to emotional challenges in narcolepsy. The same group also conducted a study examining response to fear conditioning by pairing visual stimuli with an electric shock. Unlike healthy, matched control subjects, narcoleptic subjects failed to exhibit any amygdala response to conditioned stimuli while also lacking any increase in functional coupling between the amygdala and medial prefrontal cortex. These findings suggest that narcolepsy may be characterized by abnormal emotional learning, due to changes in the amygdala and hypocretin system (Fig. 38.4). Recent work has attempted to study the brains of narcoleptics in more detail using simultaneous EEG and fMRI. In one study, four distinct brain “microstates” (a global stable electric field configuration of the brain) were detected through the EEG in both narcoleptics and controls (Drissi et al., 2016). Compared with controls, individuals with narcolepsy spent less time in the microstate that most closely resembled the default mode network activity, as assessed by fMRI. These results suggest a disease-specific disruption of this network that is related to waking brain activity. Another study in 17 narcoleptic subjects and 20 healthy controls measured activation levels in the default mode network and left middle frontal gyrus during a verbal working memory task to investigate whether narcolepsy is characterized by an imbalance in cognitive resources (Witt et al., 2017). Significantly increased deactivation within the default mode network during task performance was observed for the narcoleptic subjects during both the encoding and recognition phases of the task. No performance deficits or reduced activation was noted within the left middle frontal gyrus for the

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FIG. 38.4 Summary of findings from fMRI studies in narcolepsy. (1) Decreased activation of the right hypothalamus and medial prefrontal and cingulate cortex and increased activation of the amygdala in response to presentation of humorous images (Schwartz et al., 2008). (2) Increased activity in the nucleus accumbens and hypothalamus, as well as the right inferior frontal gyri in response to presentation of humorous images (Reiss et al., 2008). (3) Reduced activity in the bilateral nucleus accumbens and the ventromedial prefrontal cortex and increased activity in the putamen and in the inferior lateral frontal cortex during winning on a gambling task (Ponz et al., 2010). (4) Increased deactivation within the default mode network during an encoding and recognition task (Witt et al., 2017).

narcoleptic subjects. The results suggested that narcolepsy is not characterized by a deficit in working memory, but rather an imbalance of cognitive resources in favor of monitoring and maintaining attention over actual task performance, pointing toward a dysregulation within the sustained attention system in narcolepsy.

B Structural Neuroimaging of Narcolepsy Early structural studies of narcolepsy focused on the pontine tegmentum region in the brain stem, as this structure controls the transition between vigilance states and was an early candidate as the neurological source of impairments in narcolepsy. Even though one study in three participants found structural abnormalities within this region (Plazzi et al., 1996), two further studies failed to replicate these findings (Bassetti, Aldrich, & Quint, 1997; Frey & Heiserman, 1997). In the latter study, two individuals with long-term hypertension exhibited pontine lesions (Frey & Heiserman, 1997), which suggest that the observations in the first study may be explained by age-related cardiovascular changes. More detailed studies employing VBM techniques to study gray matter volumetric changes in narcolepsy have mainly focused on the hypothalamus, in line with evidence suggestive of a hypocretin deficiency in this region in narcolepsy (Table 38.2). Indeed, several studies observed volumetric reductions in the hypothalamus (Buskova, Vaneckova, Sonka, Seidl, & Nevsimalova, 2006; Draganski et al., 2002; Joo, Tae, Kim, & Hong, 2009; Kim et al., 2009), with one of these studies also reporting correlations between these changes and disease severity (Kim et al., 2009). However, several other studies have failed to find any structural differences in the hypothalamus of narcoleptics (Brenneis et al., 2005; Kaufmann, Schuld, Pollmacher, & Auer, 2002; Overeem et al., 2003; Scherfler et al., 2012). Some regional changes that have been observed in narcoleptic subjects include volumetric decreases in the nucleus accumbens (Draganski et al., 2002; Joo et al., 2009) and thalamus (Joo et al., 2009; Kim

et al., 2009), both of which receive projections from hypocretinergic neurons. Studies utilizing VBM have also found decreased gray matter volume in the frontotemporal cortex (Brenneis et al., 2005; Joo et al., 2009; Kaufmann et al., 2002; Kim et al., 2009; Scherfler et al., 2012), speculated to be associated with attentional deficits pertinent to narcolepsy. These widespread changes may be related to the diffuse connectivity of hypocretinergic neurons in the hypothalamus, even though it is noteworthy that these global structural changes are not associated with disease duration (Kaufmann et al., 2002), and one study failed to find any structural brain changes in narcolepsy (Overeem et al., 2003). Not only these inconsistencies may be due to patient characteristics, but also it is likely that differences in neuroimaging processing techniques may account for the divergent results across studies. Studies utilizing cortical thickness analysis in narcolepsy have observed decreased cortical thickness in cingulate, inferior parietal, and frontotemporal regions (Joo et al., 2011), as well as within the paracentral lobule and the orbitofrontal cortex (Schaer, Poryazova, Schwartz, Bassetti, & Baumann, 2012). Interestingly, in both studies, certain changes were inversely associated with the severity of the disease. Consistent frontotemporal changes observed across VBM and cortical thickness techniques (Brenneis et al., 2005; Draganski et al., 2002; Joo et al., 2009, 2011; Kaufmann et al., 2002; Kim et al., 2009; Scherfler et al., 2012) may be related to cognitive impairments observed in narcolepsy. In contrast, one study observed an increased dorsolateral prefrontal cortex thickness in narcoleptic subjects compared with controls (Schaer et al., 2012). The authors interpreted this result as a compensatory mechanism to counteract cognitive dysfunction, but this result remains to be replicated. Furthermore, compared with late-onset narcoleptics, early-onset patients had reduced cortical thickness in the precentral gyrus, inferior parietal cortex, and temporal regions (Schaer et al., 2012). It is unclear whether these results are related to the differential disease duration or if they reflect distinct pathological subtypes of narcolepsy.

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III NARCOLEPSY AND IDIOPATHIC HYPERSOMNIA

TABLE 38.2

577

Structural and Spectroscopic Findings in Narcolepsy Sample size (number of females)

Mean age (years)

Neuroimaging technique

Narcolepsy

Control

Narcolepsy

Control

Kaufmann et al., 2002

VBM (1.5T)

12 (6)

32 (16)

36.9 (15.8)

36.2 (14.7)

No change in hippocampal GM. Reduced volume in the frontotemporal cortex

Draganski et al., 2002

VBM (1.5T)

29 (17)

29 (17)

39.7 (11.3)

38.6 (9.3)

Reduced GM in the hypothalamus, nucleus accumbens, and frontotemporal cortex

Overeem et al., 2003

VBM (1.5T)

15 (8)

15 (8)

44.7 (14.3)

44.5 (14.2)

No change in hippocampal GM

Brenneis et al., 2005

VBM (1.5T)

12 (4)

12 (2)

35.8 (13.2)

35.0 (8.4)

No change in hippocampal GM. Reduced volume in the frontotemporal cortex

Buskova et al., 2006

VBM (1.5T)

19 (9)

16 (7)

43.4 (13.8)

40.3 (10.9)

Reduced GM in the hypothalamus

Joo et al., 2009

VBM (1.5T)

29(14)

29(14)

31.2

31.2

Reduced GM in the hypothalamus, nucleus accumbens, thalamus, and frontotemporal cortex

Kim et al., 2009

VBM (3.0T)

17 (4)

17 (4)

24.6 (4.9)

26.6 (5.2)

Reduced GM in the hypothalamus, thalamus, and frontotemporal cortex

Joo et al., 2011

Automated segmentation (1.5T)

28 (18)

33 (18)

26.9 (7.9)

30.1 (11.1)

Reduced GM in the frontotemporal cortex

Brabec et al., 2011

Manual tracing (1.5T)

11 (6)

11 (6)

41.7 (17.7)

Not reported

Reduced GM in the amygdala

Joo et al., 2012

Automated segmentation (1.5T)

36 (11)

36 (11)

29

29

Reduced cortical thickness in the cingulate, frontotemporal, and inferior parietal cortices

Schaer et al., 2012

Automated segmentation (3.0T)

12 (7)

12 (7)

28.8 (6.8)

31.5 (6.2)

Increased cortical thickness in the lateral prefrontal cortex and decreased thickness in the paracentral lobule

Scherfler et al., 2012

VBM, DTI (1.5T)

16(4)

12 (5)

56.8 (10.1)

59.8 (4.4)

No change in hippocampal GM. Reduced volume in the frontotemporal cortex. Impaired white matter tracts within the hypothalamus and frontotemporal and anterior cingulate cortices

Menzler et al., 2012

DTI (1.5T)

8 (7)

12 (9)

49.5 (12.7)

56.8 (10.6)

Impaired white matter tracts within the hypothalamus, brain stem, caudate, and frontotemporal and cingulate areas

Nakamura et al., 2013

DTI (1.5T)

24 (9)

12 (6)

27.7 (3.1)

29.8 (2.2)

Impaired white matter tracts within the amygdala and frontoparietal cortex

Study

Main findings in narcolepsy compared with controls

Structural

Magnetic resonance spectroscopy Ellis, Simmons, Lemmens, Williams, & Parkes, 1998

1

H-MRS

12 (6)

12 (6)

33 (13)

33 (11)

No change in NAA/Cr ratio

Lodi et al., 2004

1

H-MRS

23 (10)

10 (4)

38 (16)

37 (14)

Reduced NAA/Cr ratio in the hypothalamus

Tonon et al., 2009

1

H-MRS

16 (8)

10 (NR)

40 (18

40 (12)

Reduced NAA/Cr ratio in the hypothalamus

Kim et al., 2008

1

H-MRS

17 (3)

17 (5)

25.1 (4.6)

26.8 (4.8)

Increased GABA concentrations in the medial prefrontal cortex

Poryazova et al., 2009

1

H-MRS

14 (7)

14 (7)

30.6 (2.3)

31.4 (2.0)

Reduced Ins/Cr ratio in the amygdala

VBM, voxel-based morphometry; GM, gray matter; 1H-MRS, proton magnetic resonance imaging; DTI, diffusion tensor imaging; NAA, N-acetylaspartate; Cr, creatine; Cho, choline; Ins, myoinositol; GSH, glutathione

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Manual volumetric analysis of key subcortical structures has been used to assess possible relations to cognitive deficiencies in narcolepsy. One study found smaller hippocampal volumes in narcoleptics compared with controls, but no differences in memory performance (Joo et al., 2012), even though these neuroanatomical changes were associated with markers of disease severity. Another study detected volumetric decreases in the amygdala of narcoleptic subjects (Brabec et al., 2011). Thus, structural changes in frontotemporal cortical regions and the hippocampus may underpin some cognitive dysfunction, while changes in the amygdala may be related to the emotional dysregulation that is prominent in narcolepsy. Recent studies have used DTI to detect alterations in white matter fiber tract integrity within certain regions of the brain of narcoleptic subjects. Using two metrics of white matter integrity, two studies found that white matter tracts were disrupted in the hypothalamus, which is consistent with the model of a dysfunctional hypothalamic hypocretin system in narcolepsy (Menzler et al., 2012; Scherfler et al., 2012). Abnormalities in white matter tracts were also observed in frontotemporal and anterior cingulate cortical regions, which are consistent with other structural findings in gray matter (Menzler et al., 2012; Scherfler et al., 2012) and may explain impairments in cognitive domains such as memory and attention in narcoleptic patients. A recent DTI study examined structural differences between narcolepsy with cataplexy and narcolepsy without cataplexy (Nakamura et al., 2013). Narcolepsy with cataplexy showed compromised white matter within tracts of the inferior frontal gyrus and the amygdala with respect to healthy controls, while narcolepsy without cataplexy displayed no significant differences. These results suggest that narcolepsy with and without cataplexy may express distinct pathophysiology.

C Spectroscopy Neuroimaging in Narcolepsy Early spectroscopy studies of narcolepsy focused on N-acetylaspartate (NAA) to creatine-phosphocreatine (Cr-PCr) ratios (a sensitive index of neuronal cell density) within the ventral pons—as this region has been implicated in the control of vigilance and cerebral arousal (Ellis et al., 1998). No significant differences between narcoleptic subjects and controls were found. Further studies investigated this ratio in the hypothalamus and found significantly lower ratios in narcoleptic subjects (Lodi et al., 2004; Tonon et al., 2009). This result is suggestive of cell loss within the hypothalamus and is consistent with evidence that hypocretinergic neurons within the hypothalamus are selectively damaged in narcolepsy (Thannickal et al., 2000). The latter study tested whether

regions innervated by these hypocretinergic neurons are also similarly affected, however, did not reveal any significant differences between narcoleptics and controls in neither of the two regions that were studied (the thalamus and parieto-occipital cortex) (Tonon et al., 2009). A further study found no differences in NAA/Cr ratios within the hypothalamus but did observe significant associations between NAA/Cr and myoinositol (ml)/Cr ratios between the hypothalamus and amygdala (Poryazova et al., 2009). This finding suggests that abnormal cell signaling in the amygdala may underlie emotional dysregulation in narcolepsy. In another study, correlations between spectroscopy and fMRI data indicated that deactivation of the anterior aspect of the default mode in narcoleptic subjects exhibited significant correlations with increased concentrations of glutamate and decreased concentrations of GABA (Witt et al., 2017). Finally, absolute GABA concentrations were found to be higher in the medial prefrontal cortex (mPFC) in 17 narcoleptic patients compared with controls, and that these levels were positively associated with nocturnal sleep quality (Kim et al., 2008). In contrast, deactivation in the default mode was correlated with increased concentrations of GABA and decreased concentrations of glutamate in controls. These results suggest that elevated GABA levels in the mPFC may be part of a compensatory mechanism to counteract the sleep disturbances in narcolepsy.

D Summary Overall, there is substantial convergence across structural and functional neuroimaging findings in narcolepsy. Abnormalities are most consistently observed in the hypothalamus, which appears to exhibit abnormal activation and regional blood flow during both resting wakefulness and cognitive performance. Although there is slightly more inconsistency in the structural evidence, reduced volume and impaired white matter connectivity of the hypothalamus have been observed in several studies. These observations are consistent with prior evidence demonstrating a loss of hypocretin neurons in the hypothalamus that is characteristic to narcolepsy. Additionally, findings of abnormal activity within limbic regions, such as the amygdala, are also consistent with behavioral evidence of impaired emotional processing in narcolepsy and provide biological explanations for the clinical characteristics, such as cataplexy. Furthermore, a reduced volume of the hippocampus may explain the cognitive impairments suffered by some narcoleptic patients. Despite these findings, future research using more detailed and combined multimodal neuroimaging techniques is required to better understand the neurological mechanisms and functional organization in narcolepsy, particularly in the various subtypes (cataplectic and noncataplectic), and in idiopathic hypersomnia.

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IV REM SLEEP BEHAVIOUR DISORDER

IV REM SLEEP BEHAVIOUR DISORDER REM sleep behavior disorder (RBD) is a parasomnia class of sleep disorder that is associated with abnormal bodily movements during REM sleep. Usually associated with the acting out of dreams, subjects exhibit an absence of atony during REM sleep, which may impact on sleep continuity. The idiopathic form of this disorder has been widely studied, due to a significantly increased risk of developing α-synucleinopathy neurodegenerative diseases such as dementia with Lewy bodies (DLB), Parkinson’s disease (PD), and multiple system atrophy (MSA) (Fantini, Ferini-Strambi, & Montplaisir, 2005).

A Functional Neuroimaging of REM Behavior Disorder 1 Nuclear Imaging A study using SPECT imaging in eight patients with RBD demonstrated decreased perfusion in the frontal and temporoparietal cortical regions, while increased activity was observed in the pons, putamen, and hippocampus (Mazza et al., 2006). Similar findings were observed in two other studies with larger sample sizes (Dang-Vu et al., 2012; Vendette et al., 2011). Another SPECT study also found decreased cerebral blood flow across parietal and occipital regions of the cortex and in the cerebellum and limbic system (Hanyu et al., 2011). In a longitudinal SPECT study of 20 patients with RBD, hyperperfusion within the hippocampus predicted the development of DLB or PD 3 years later, which occurred in half of the patients (Dang-Vu et al., 2012). This hyperperfusion was also related to motor and color vision ability and highlights the prognostic power of brain perfusion within certain regions for predicting the onset of neurodegenerative diseases linked to RBD. Imaging during a motor episode of RBD has only been captured

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in one patient thus far and revealed increases in perfusion in the supplementary motor cortex compared with wakefulness (Dauvilliers et al., 2011). While this was not observed in two controls, there were no imaging data from REM sleep outside of the RBD episode; thus, these results are not conclusive evidence of neural activity specific to RBD events (Fig. 38.5). Due to the common occurrence of comorbid PD and DLB with RBD, a few neuroimaging studies have focused on dopaminergic neurotransmission within the nigrostriatal system. Using SPECT ligand tracers specific to the dopamine transporter, densities of presynaptic dopamine terminals have been studied in three studies (Eisensehr et al., 2000, 2003; Kim et al., 2010). Generally, healthy individuals exhibited the greatest dopamine transporter densities, which were decreased in RBD patients and the lowest in PD patients. This evidence is not conclusive, however, as three additional studies found decreased dopamine transporter densities in only a minority of the studied RBD patients (Kim et al., 2010; Stiasny-Kolster et al., 2005; Unger et al., 2008). It is possible that dysfunctional dopaminergic neurotransmission is part of the link between neurodegenerative processes and RBD. To examine this possibility in more detail, two longitudinal studies have monitored the levels of dopamine transporter density in RBD patients, together with the incidence of neurodegenerative disease (Iranzo et al., 2010, 2013). The first study followed 43 RBD patients over 2.5 years and found that six out of eight subjects who developed a neurodegenerative disorder at follow-up had exhibited decreased dopamine transporter levels at baseline (Iranzo et al., 2010). The second study followed 44 RBD patients over 7 years and found that, at the final follow-up, 82% had developed either DLB, PD, or MSA (Iranzo et al., 2013). Further, four of the patients who were not diagnosed with a neurodegenerative disease had developed reduced dopamine transporter densities, suggesting they were at increased risk

FIG. 38.5 Summary of findings from PET and SPECT studies in REM behavior disorder. (1) Decreased perfusion in the frontal and temporoparietal cortical regions and increased activity in the pons, putamen, and hippocampus during wakefulness (Mazza et al., 2006). (2) Decreased regional cerebral blood flow in the frontal cortex and in medial parietal areas and increased regional cerebral blood flow in subcortical regions including the bilateral pons, putamen, and hippocampus, at wake (Vendette et al., 2011). (3) Increased activation in the hippocampus at wake was associated with significant disease progression (Dang-Vu et al., 2012). (4) Decreased cerebral blood flow across parietal and occipital regions of the cortex and in the cerebellum and limbic system (Hanyu et al., 2011). (5) Increased perfusion within the supplementary motor cortex during episodes of REM behavior disorder (Dauvilliers et al., 2011).

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of developing a disease soon. These studies provide supportive evidence that RBD exemplifies an early stage of α-synucleinopathy neurodegenerative disease. However, more evidence is needed to understand whether altered dopaminergic transmission is responsible for the pathophysiology of this process or a consequence of the underlying neurodegeneration. 2 Functional MRI Only one study thus far has utilized fMRI to investigate functional changes in RBD, studying resting-state connectivity in 26 RBD subjects, 48 subjects with PD, and 23 controls (Rolinski et al., 2016). Both RBD and PD patients exhibited altered connectivity measures in the basal ganglia network compared with controls, suggesting that RBD may be related to early basal ganglia dysfunction. Interestingly, RBD was indistinguishable from PD on measures of global resting-state fMRI connectivity, despite obvious differences in SPECT imaging of dopamine transportation. These results suggest that activity within the basal ganglia network may be an early pathophysiological trait linking RBD to PD, with potential utility as another biomarker of disease progression, even though these findings need to be replicated in longitudinal studies.

B Structural Neuroimaging of REM Behavior disorder Neuroanatomical characteristics associated with RBD have been revealed by multiple studies. One study using VBM analysis demonstrated decreased volume of the bilateral putamen in RBD patients compared with healthy controls and patients with early-stage PD who exhibited subclinical RBD symptoms (Ellmore et al., 2010). This finding is suggestive of a compensatory structural increase in the putamen during the transition from RBD to PD that is produced by deficits in dopaminergic function, which has also been observed in cocaine dependence (Jacobsen, Giedd, Gottschalk, Kosten, & Krystal, 2001). A larger study involving PD subjects with probable RBD, without RBD, and healthy controls demonstrated that RBD correlated with smaller volumes in the pontomesencephalic tegmentum, medullary reticular formation, hypothalamus, thalamus, putamen, amygdala, and anterior cingulate cortex. The prominent loss of volume in the pontomesencephalic tegmentum is of importance, since it contains cholinergic, GABAergic, and glutamatergic neurons implicated in the promotion of REM sleep and muscle atony (Boucetta et al., 2016). A subsequent study of brain-wide DTI revealed white matter microstructural changes in the pons and other regions known to be involved in REM sleep regulation (Unger et al., 2010). Another study combining DTI and VBM modalities found decreased white matter diffusion within the pontine region, in addition

to a significant gray matter increase in the hippocampus of RBD patients (Scherfler et al., 2011). The structural alterations in the pons and hippocampus are in accord with findings from functional studies described above (DangVu et al., 2012; Mazza et al., 2006; Vendette et al., 2011).

C Spectroscopy Neuroimaging of REM Behavior Disorder A case study of H-MRS in one RBD patient revealed an increased Cho/Cr ratio in the brain stem, suggesting local neural abnormalities in cell membrane integrity (Miyamoto et al., 2000). However, two further studies failed to replicate these findings. One study did not reveal any differences in NAA/Cr, Cho/Cr, or mI/Cr ratios in the pontine tegmentum and the midbrain (Iranzo et al., 2002). Similarly, another study in 15 PD patients with RBD and 15 PD patients without RBD detected no group differences in these ratios within the pontine region (Hanoglu, Ozer, Meral, & Dincer, 2006). Therefore, it appears that RBD does not involve detectable mesopontine metabolic abnormalities.

D Summary Structural and functional neuroimaging evidence implicates the pons in the underlying pathophysiology of RBD and dopamine transmission in the basal ganglia. Dopamine transporter densities appear related to the progression of RBD to neurodegenerative disease, as advanced reductions are observed on nuclear imaging from subclinical RBD to synucleinopathy diseases such as PD and LBD. However, it is unclear whether such dopamine abnormalities are a causal factor or consequence of RBD. It remains important to investigate potential hippocampal involvement in the progression from RBD to neurodegenerative disease and observe functional activations during sleep, particularly during RBD episodes.

V OBSTRUCTIVE SLEEP APNEA The sleep disorder obstructive sleep apnea (OSA) is characterized by recurrent episodes of partial or complete occlusion of the upper airway during sleep, which impedes breathing despite continued inspiratory efforts. These obstructions occur from changes in the shape and size of the upper airway during inspiration, due to altered tone of the pharyngeal dilator muscles that normally keep the airways open (American Academy of Sleep Medicine, 2001). The condition is usually worse during REM sleep, due to a further decrease in the tone and activity of these muscles (Peregrim et al., 2013; Sullivan & Issa, 1980). While the pathophysiology of

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V OBSTRUCTIVE SLEEP APNEA

OSA is not yet completely understood, several studies suggest that OSA is due to a combination of both anatomical airway narrowing and abnormal upper airway neuromotor tone. Increased risk of OSA is associated independently with both obesity and older age. Early estimates from large population-based cohort studies reported average OSA prevalence rates of approximately 2% of women and 4% of men (Bixler et al., 2001; Ip et al., 2004; Young et al., 1993). However, as diagnostic techniques and scoring criteria have been updated, prevalence rates have been reported up to 50% in men and 23% in women (Adams, Appleton, Taylor, McEvoy, & Wittert, 2016; Heinzer et al., 2015; Tufik, Santos-Silva, Taddei, & Bittencourt, 2010), while some studies have estimated OSA prevalence rates in the elderly of up to 73% (Adams et al., 2016; Heinzer et al., 2015; Tufik et al., 2010). The repetitive obstructive events that occur during sleep in OSA result in variable changes in blood gas concentrations, which lead to hypoxemia (low blood oxygen) and hypercapnia (high blood carbon dioxide). Repetitive episodes of apnea also trigger marked fluctuations in both blood pressure and heart rate, with consequent effects on the estimates of cardiovascular variability (Gula et al., 2003). Due to the body’s attempts to regain airflow, the apneic events are strongly correlated with arousals from sleep, leading to significant sleep fragmentation and a reduction in deeper sleep stages. It is well established that OSA is associated with impairments in cognitive function, particularly in vigilance, attention, memory, and executive function domains (Beebe, Groesz, Wells, Nichols, & McGee, 2003; Bucks, Olaithe, & Eastwood, 2013; Engleman, Kingshott, Martin, & Douglas, 2000; Wallace & Bucks, 2013). Importantly, OSA can be reversed with treatments such as

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continuous positive airway pressure (CPAP), which serves to maintain airway patency and prevent obstructions.

A Functional Neuroimaging of Obstructive Sleep Apnea 1 Nuclear Imaging Functional changes in OSA have been investigated in studies with PET and SPECT. An early SPECT study in 14 subjects reported a frontal hyperperfusion in five subjects and a reduced regional perfusion in the left parietal region (Ficker et al., 1997); these changes were reversed following CPAP treatment. In another SPECT study, cerebral blood flow was significantly reduced in the bilateral parahippocampal gyri and in the right lingual gyrus (Joo, Tae, Han, Cho, & Hong, 2007). In the only study utilizing FDG-PET, a decrease in brain metabolism was rightlateralized and involved the precuneus, the middle and posterior cingulate gyrus, and the parieto-occipital cortex, as well as the prefrontal cortex (Yaouhi et al., 2009). These changes might be due to compensatory mechanisms in response to blood flow alterations or blood gas changes in OSA. 2 Functional MRI Most functional neuroimaging studies in OSA subjects have utilized fMRI, mainly to attempt to detect brain activity patterns associated with performance deficits on cognitive tasks (Fig. 38.6). An early study in 16 OSA patients found an absence of dorsolateral prefrontal activation during a working memory task, which was associated with a reduction in task performance (Thomas, Rosen, Stern, Weiss, & Kwong, 1985). Another study in nine patients observed a similar outcome, finding

FIG. 38.6 Summary of findings from fMRI studies in OSA. (1) Reduced dorsolateral prefrontal activation during a working memory task (Thomas et al., 1985). (2) Reduced activation in the ACC, middle frontal gyrus, and inferior frontal gyrus and increased activity in the right anterior prefrontal gyrus during a task of executive function (Zhang et al., 2011). (3) Decreased activation in cingulate and parietal regions during an attention-based task (Ayalon, Ancoli-Israel, Aka, et al., 2009) and a response inhibition task (Ayalon, Ancoli-Israel, & Drummond, 2009). (4) Increased activation in the bilateral inferior frontal and middle frontal gyri and cingulate gyrus and the junction of the inferior parietal and superior temporal lobes, thalamus, and cerebellum (Ayalon et al., 2006). (5) Increased activation in the parietal cortex and decreased activation in the rostral cerebellum during a working memory task (Archbold et al., 2009). (6) Increased activation in the left frontal cortex, medial precuneus, and hippocampus and decreased activation in the caudal pons during a working memory task (Castronovo et al., 2009). (7) Reduced activation in the bilateral parahippocampal regions, right insula, bilateral claustrum, left precentral gyrus, and right precuneus and increased activation in the left temporo-occipital area (Prilipko et al., 2011). (8) Decreased connectivity between the medial prefrontal cortex, left precentral gyrus, and dorsolateral prefrontal cortex (Zhang et al., 2013).

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reduced frontal activation in the ACC and other regions during another task of executive function. Importantly, this pattern of activity was also associated with the level of oxygen desaturation and number of arousals in OSA patients (Zhang, Ma, Li, Wang, & Wang, 2011). During a sustained attention task, a study of 14 subjects with OSA showed decreased brain activation in cingulate, frontal, and parietal regions, which typically show activation during tasks of attention (Ayalon, Ancoli-Israel, Aka, McKenna, & Drummond, 2009). A follow-up study in the same group also observed decreased activation in the same areas during a response inhibition task, suggesting that these areas may underpin compromised brain function that may be general to cognitive deficits in OSA (Ayalon, Ancoli-Israel, & Drummond, 2009). In the same OSA patients, increasing arousal index, but not oxygen desaturation or apnea-hypopnea index, was associated with worse performance, in addition to the decreased activation in these cortical areas. These results suggest that these brain functions may be more closely related to disturbed sleep than insults due to hypoxemia. Studies have also observed increases in cortical activation of OSA subjects during cognitive tasks (Fig. 38.6). During a verbal learning task, OSA patients showed increased activation in a range of cortical areas, including the bilateral inferior frontal and middle frontal gyri, cingulate gyrus, areas at the junction of the inferior parietal and superior temporal lobes, and within the thalamus and cerebellum (Ayalon, Ancoli-Israel, Klemfuss, Shalauta, & Drummond, 2006). These findings likely represent an adaptive compensatory recruitment response, similar to those observed in young adults following total sleep deprivation and in healthy older adults (Drummond et al., 2000; Mu et al., 2005). In another study, similar activation patterns were observed between OSA and controls during a working memory task; however, OSA severity was associated with increased activation in the parietal cortex and decreased activation in the rostral cerebellum (Archbold, Borghesani, Mahurin, Kapur, & Landis, 2009). Therefore, parietal activation may be a compensatory neuronal response for reduced cerebellar activation in more severe cases of OSA. Other studies have found mixed results. In a study involving a working memory task, 17 OSA patients presented increased activation in the left frontal cortex, medial precuneus, and hippocampus while exhibiting decreased activations in the caudal pons (Castronovo et al., 2009). Such inconsistent findings can be explored by studying connectivity within brain networks, as opposed to activity within distinct cortical regions. For example, in one study of 17 subjects during a working memory task, significantly worse performance by OSA subjects was related to a distinct pattern of recruitment and deactivation of DMN-related regions, as opposed to abnormal activation of task-related brain networks

(Prilipko et al., 2011). Another study using resting-state fMRI demonstrated decreased connectivity between the medial prefrontal cortex, left precentral gyrus, and dorsolateral prefrontal cortex (DLPFC) in 24 OSA subjects (Zhang et al., 2013). This demonstrates that OSA may specifically affect resting-state functional connectivity in cognitive and sensorimotor-related brain networks, which may underpin the impaired cognitive and motor functions observed in patients. Moreover, OSA may lead to a general rearrangement of connectivity balanced across the cortex, suggested by findings of patterns of abnormal cortical and subcortical local connectivity measured during resting-state fMRI (Santarnecchi et al., 2013). Specifically, this study found a decrease of local coherence in right temporal, parietal, and frontal lobe regions, while an increased coherence within the somatosensory and motor cortices and the thalamus was also observed. This finding may be explained by an aberrant adaptation to incomplete sleep-wake transitions during apneic episodes, induced by repetitive sensations of choking and physical attempts to restore breathing. Functional recovery following CPAP treatment has been investigated in a few studies, the first of which found decreases in abnormal baseline activation in the left inferior frontal gyrus and anterior cingulate cortex and bilaterally in the hippocampus following treatment (Castronovo et al., 2009). In the second study, patients that received CPAP treatment demonstrated a global increase of cerebral activation in task-related networks while concurrently exhibiting cerebral deactivation in the anterior midline and medial temporal regions of the DMN during a working memory task (Prilipko et al., 2012). In both studies, changes were associated with a significant improvement in working memory performance. These findings propose that abnormal brain activation related to OSA may be reversible with treatment, which may potentially underpin recovery of behavioral function in OSA subjects.

B Spectroscopy Neuroimaging of Obstructive Sleep Apnea Single-voxel 1H-MRS has been used in several studies to assess the level of axonal and glial dysfunction and metabolism in OSA. An early study in 23 OSA subjects assessed metabolite levels in voxels containing gray matter within medial aspects of the occipital and parietal lobes, in addition to the posterior half of the periventricular white matter (Kamba, Suto, Ohta, Inoue, & Matsuda, 1997). There was a significant decrease in the NAA/Cho ratio in the periventricular white matter in subjects with moderate to severe OSA compared with controls and subjects with mild OSA. Two further studies observed

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V OBSTRUCTIVE SLEEP APNEA

similar reductions of NAA/Cho ratios in frontal white matter (Algin et al., 2012; O’Donoghue et al., 2012). Other studies have also found frontal white matter reductions in NAA/Cr (Algin et al., 2012; Sarchielli et al., 2008) and Cho/Cr ratio (Alchanatis et al., 2004). Alternatively, Cho/Cr ratios were increased in the thalamus (Algin et al., 2012) and temporal cortical regions (Sarchielli et al., 2008), which might indicate reactive gliosis or maladaptive changes in membrane metabolism in response to hypoxic insults. The other, most consistent 1H-MRS finding in OSA studies are decreased absolute and relative levels of creatine in the hippocampus. One study found increased NAA/Cr ratio in the hippocampus compared with controls, which was deduced by the authors to be more likely due to a decrease in Cr than a relative increase in NAA. This increased ratio was correlated with both worse OSA severity and neurocognitive performance (Bartlett et al., 2004). Similarly, in another study of mild-severe OSA patients, NAA/Cr and Cho/Cr ratios in the hippocampus were increased in those with severe OSA (Alkan et al., 2013). A decrease in the creatine signal in the hippocampus indicates reduced hippocampal function in OSA patients. These changes have even been observed in children, with increased NAA/Cr and Cho/Cr ratios in the hippocampus detected in children with OSA compared with children without OSA (Halbower et al., 2006). However, another study failed to find any significant differences in either NAA/Cr or Cho/Cr ratio in the hippocampus compared with controls (Algin et al., 2012), while another found OSA subjects had lower ratios than controls (Kizilgoz et al., 2013). The mechanisms behind changes in metabolite levels may be distinct, depending on the region of the brain. One study found significant correlations between arousal index and frontal NAA/Cho ratio, while reduced hippocampal Cho/Cr ratios were related to oxygen desaturation (O’Donoghue et al., 2012). Hippocampal ratios were reversed after 6 months of CPAP treatment, while frontal ratios remained unchanged, suggesting that some abnormalities persist after the disorder is rectified. Persistent damage may be specific to cortical regions, as another study found reduced levels of NAA in the parieto-occipital cortex of OSA patients that persisted after CPAP therapy, despite improvements in clinical and neuropsychological outcomes (Tonon et al., 2007). The neuropsychological deficits in OSA may also be related to the metabolite changes in specific cerebral regions, given the relationship between abnormal levels and neuropsychological performance (Bartlett et al., 2004; Halbower et al., 2006) and complex realworld tasks such as driving (D’Rozario et al., 2018). This may have extended importance, highlighted by a recent study, which observed increased levels of glutathione,

583

a 1H-MRS marker of oxidative stress, in the ACC of older adults with cognitive impairment, suggesting that OSA-related damage in the cortex may be involved in neurodegenerative diseases such as dementia (Duffy et al., 2016). Therefore, OSA may cause significant irreversible damage to the neuronal environment that may lead to lasting neurobehavioral consequences.

C Structural Neuroimaging of Obstructive Sleep Apnea Most studies assessing structural changes have utilized VBM techniques to detect gray matter volume changes specific to OSA (Table 38.3). These studies have detected a wide range of cerebral gray matter changes associated with OSA, including reductions in volume across the prefrontal, temporal, and parietal cortices (Canessa et al., 2011; Macey et al., 2002; Torelli et al., 2011; Yaouhi et al., 2009) and in the anterior cingulate cortex (Macey et al., 2002; Yaouhi et al., 2009). Multiple studies have also shown reduced volumes in subcortical structures, including the hippocampus (Canessa et al., 2011; Macey et al., 2002; Morrell et al., 2003; Torelli et al., 2011; Yaouhi et al., 2009), the thalamus (Yaouhi et al., 2009), and the cerebellum (Macey et al., 2002; Yaouhi et al., 2009). However, it has been argued that these findings should be interpreted cautiously, due to methodological considerations of VBM that differ across studies, and that some of these results may prove to be nonreproducible (Celle et al., 2016). Nevertheless, given that a range of studies using VBM methodology have found similar changes associated with OSA, it appears plausible that OSA is related to vast neuroanatomical alterations. These anatomical changes have often been associated with cognitive deficits in OSA patients (Canessa et al., 2011; Torelli et al., 2011; Yaouhi et al., 2009). Also, reduced hippocampal volume has been correlated with excessive daytime sleepiness in OSA patients (Dusak et al., 2013). Most studies to date have compared an OSA group with a control group, limiting our understanding of how increasing severity of OSA may be associated with graded changes in brain morphology. However, one study demonstrated that the degree of gray matter volume loss was associated with greater disease severity, as measured by a higher apnea-hypopnea index (Macey et al., 2002). In another study, more severe hypoxemia and sleep fragmentation both were related to reductions in gray matter volume (Canessa et al., 2011). Other techniques assessing structural changes in the brain of OSA subjects may provide more subtle measures. A recent study assessed cortical thickness in 48 OSA patients, rather than volume through VBM techniques (Macey et al., 2018), with patients exhibiting significant thinning of the pre- and postcentral gyri, the superior

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584 TABLE 38.3

38. IMAGING OF THE SLEEP-DISORDERED BRAIN

Structural and Spectroscopic Findings in Obstructive Sleep Apnea (OSA) Sample size (number of females)

Mean age (years)

Neuroimaging technique

OSA

Control

OSA

Control

Main Findings in OSA Compared With Controls

Macey et al., 2002

VBM (1.5T)

21 (0)

21 (0)

49 (11)

47 (11)

Reduced GM volume in the frontal and parietal cortex, temporal lobe, anterior cingulate, hippocampus, and cerebellum

Morrell et al., 2003

VBM (1.5T)

22 (0)

17 (0)

51.8 (15.4)

53.1 (14.0)

Reduced GM volume in the left hippocampus

O’Donoghue et al., 2005

VBM (3.0T)

25 (0)

23 (0)

45.7 (10.1)

43.3 (9.4)

No difference between OSA and controls or between preCPAP and post-CPAP

Celle et al., 2009

VBM (1.0T)

76 (52)

76 (45)

66 (0.6)

66.1 (0.7)

Reduced GM volume in the reticular zone of the pontine area in the brain stem

Yaouhi et al., 2009

VBM (1.5T)

16 (1)

14 (1)

45.8 (7.1)

52.7 (7.0)

Reduced GM volume in the frontal, temporal, parietal, and occipital cortices; thalamus; hippocampus; basal ganglia; and cerebellum

Joo et al., 2010

VBM (1.5T)

36 (0)

31 (0)

44.7 (6.7)

44.8 (5.4)

Reduced GM concentration in the ventromedial frontal cortex, the bilateral inferior cingulate gyri, the right anterior insular gyrus, the bilateral caudate nuclei, the thalamus, the amygdalo-hippocampal gyri, and the inferior temporal gyri

Morrell et al., 2010

VBM (3.0T)

60 (0)

60 (0)

47.3

46.1

Reduced GM volume in the right middle temporal gyrus and in the cerebellum

Canessa et al., 2011

VBM (3.0T)

17 (0)

17 (0)

44.0 (7.6)

42.2 (6.6)

Reduced GM volume in the left hippocampus, left posterior parietal cortex, and right superior frontal gyrus. Increases in GM volume were observed following treatment

Torelli et al., 2011

VBM (3.0T), automated segmentation

16 (3)

14 (5)

55.8 (6.7)

57.6 (5.2)

Reduced GM volume in the bilateral hippocampus and caudate and lateral temporal areas

Baril et al., 2017

VBM (3.0T), automated segmentation

71 (15)

N/A

65.3 (5.6)

N/A

Higher levels of hypoxemia correlated with increased volume and thickness of the left prefrontal cortex, the right frontal pole, the right lateral parietal cortex, and the left posterior cingulate cortex. Respiratory disturbances positively correlated with right amygdala volume, while more severe sleep fragmentation was associated with increased thickness of the inferior frontal gyrus. No associations were found using VBM

Cross et al., 2018

Automated segmentation (3.0T)

83 (53)

N/A

67.4 (7.5)

N/A

Higher levels of hypoxemia correlated with decreased thickness in the bilateral temporal cortex. More severe sleep fragmentation was associated with increased thickness in the right postcentral gyrus and lateral frontal cortex

Study Structural

Magnetic resonance spectroscopy Kamba et al., 1997

1

H-MRS

23 (4)

15 (8)

48.5 (13)

45.7 (17.6)

Reduced NAA/Cho ratio in cerebral white matter

Alchanatis et al., 2004

1

H-MRS

22 (0)

10 (0)

49 (9.7)

42.9 (10.5)

Reduced NAA/Cr and Cho/Cr ratios in frontal white matter

Bartlett et al., 2004

1

H-MRS

8 (0)

5 (0)

48.7

50.6

Increased NAA/Cr ratio in the hippocampus. Likely due to decreased Cr-containing compounds

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V OBSTRUCTIVE SLEEP APNEA

TABLE 38.3

Structural and Spectroscopic Findings in Obstructive Sleep Apnea (OSA)—cont’d Sample size (number of females)

Study

585

Neuroimaging technique

Mean age (years)

OSA

Control

OSA

Control

Main Findings in OSA Compared With Controls

Halbower et al., 2006

1

H-MRS

19 (8)

12 (4)

10.0 (2.5)

9.8 (2.6)

Reduced NAA/Cho ratio in the left hippocampus and right frontal cortex

Tonon et al., 2007

1

H-MRS

14 (0)

10 (0)

48 (7)

n.r.

Reduced absolute NAA in the parieto-occipital cortex. Levels remained low following CPAP treatment

Sarchielli et al., 2008

1

H-MRS

20 (7)

20 (6)

52.7 (11.0)

51.4 (13.2)

Reduced NAA/Cr ratio in the frontal cortex. Increased Ins/Cr ratio in temporal and frontal regions and increased Cho/Cr ratio in the temporal cortex

Algin et al., 2012

1

H-MRS

24 (1)

9 (3)

52

48

Reduced NAA/Cr and NAA/Cho ratios in the frontal cortex. Increased Cho/Cr ratio in the thalamus

O’Donoghue et al., 2012

1

H-MRS

30 (0)

23 (0)

45.2 (9.6)

41.3 (9.3)

Reduced NAA/Cho ratio in the frontal cortex and reduced Cho/Cr ratio in the hippocampus. Hippocampal differences disappeared following CPAP treatment but resulted in a reduction in frontal NAA/Cr ratio

Alkan et al., 2013

1

H-MRS

14 (n.r.)

17 (n.r.)

47 (27)

47 (27)

Increased NAA/Cr and Cho/Cr ratios in the hippocampus. Decreased NAA/Cho ratios in the putamen

Duffy et al., 2016

1

H-MRS

24(17)

N/A

67.9 (6.5)

N/A

Higher hypoxemia and apnea-hypopnea index during REM sleep correlated with greater levels of GSH/Cr ratio

VBM, voxel-based morphometry; GM, gray matter; 1H-MRS, proton magnetic resonance imaging; NAA, N-acetylaspartate; Cr, creatine; Cho, choline; Ins, myoinositol; GSH, glutathione.

temporal gyrus, and the insula; furthermore, females had reduced thickness bilaterally in the superior frontal lobe. Given the increased prevalence of OSA in older adults, studies have focused on the association between brain structure and OSA in later life. One study in 152 community-dwelling older adults found that undiagnosed sleep-disordered breathing was related to gray matter reductions in the brain stem, but not in any other subcortical or cortical region (Celle et al., 2009). In contrast, another study reported that greater thickness of various cortical regions was associated with increasing OSA severity (Baril et al., 2017). The authors proposed that this may be reflective of edema and reactive cellular processes. This result was partly replicated in a study of older adults with cognitive impairments, showing that OSArelated sleep disturbance was associated with increased cortical thickness of frontal regions (Cross et al., 2018). Interestingly, this study also demonstrated that more severe oxygen desaturation was related to reduced cortical thickness in the bilateral temporal lobes. These findings highlight that the neuroanatomical changes associated with OSA may be different in the elderly compared with younger and middle-aged adults and may constitute changes, relevant to neurodegenerative processes, similar to those observed with spectroscopy neuroimaging. The association between OSA and changes in neural white matter is less clear. White matter is intricately

linked to cardiovascular health, and given the known effects of OSA on nocturnal blood pressure and its association with cardiovascular disease, it is plausible that OSA has negative influences on white matter integrity. Only a few, small-scale studies in middleaged OSA patients have investigated white matter using diffusion-weighted imaging methods, and all found that OSA is associated with extensive reductions in white matter diffusivity within the hippocampus (Macey et al., 2008), ACC (Castronovo et al., 2014), and frontotemporal cortices (Chen et al., 2015). Importantly, these changes may be reversible after treatment. Studies investigating the relationship between OSA and white matter changes in older adults have provided mixed findings (Ding et al., 2004; Kim et al., 2009; Robbins et al., 2005).

D Summary Altogether, structural neuroimaging studies of OSA patients have demonstrated selective gray matter atrophy in several brain regions, including the frontal and parietal cortex, temporal lobe, anterior cingulate, thalamus, hippocampus, and cerebellum. While these findings have not always been consistent, they may underpin various neuropsychological deficits in OSA and potentially be related to OSA severity. It is also noteworthy that these observations are likely different in

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38. IMAGING OF THE SLEEP-DISORDERED BRAIN

older adults compared with middle-aged adults, since the risk of neurodegenerative processes is greater in the elderly and may be catalyzed by OSA. Furthermore, while functional and structural changes may be reversible with CPAP treatment, cognitive impairments are more resistant to treatment-induced normalization. This apparent discrepancy remains an important line of investigation to more fully understand the pathophysiological mechanisms behind the cognitive impairments in OSA.

VI CONCLUSIONS Neuroimaging has generated valuable insights into the neurological bases and consequences of sleep disorders. In addition to probing performance on cognitive tasks in individuals with sleep disorders, functional neuroimaging provides exceptional opportunities to study brain activity during normal and pathological sleep. However, to date, very few studies have been conducted during sleep in subjects with sleep disorders. Structural changes may demonstrate more severe or longer-term consequences of sleep disorders and explain part of the cognitive deficits associated with disordered sleep. Imaging techniques allow researchers to identify subtle, underlying neuropathologic mechanisms of sleep disorders, information that may also aid the development of improved treatment approaches. Nevertheless, the field of neuroimaging in sleep disorders is still emerging, and there are numerous questions regarding the nature and consequences of disturbed sleep that remain unexplored. Future studies will benefit from advanced multimodal neuroimaging and improved experimental designs. Additionally, studies involving larger sample sizes and more heterogeneous patient populations are required, which may be achieved by advanced collaborations among multiple research groups investigating similar research questions.

References Adams, R., Appleton, S., Taylor, A., McEvoy, D., & Wittert, G. (2016). Are the ICSD-3 criteria for sleep apnoea syndrome too inclusive? The Lancet Respiratory Medicine, 4(5), e19–e20. https://doi.org/ 10.1016/s2213-2600(16)00109-0. Alchanatis, M., Deligiorgis, N., Zias, N., Amfilochiou, A., Gotsis, E., Karakatsani, A., et al. (2004). Frontal brain lobe impairment in obstructive sleep apnoea: a proton MR spectroscopy study. The European Respiratory Journal, 24(6), 980–986. https://doi.org/ 10.1183/09031936.04.00127603. Algin, O., Gokalp, G., Ocakoglu, G., Ursavas, A., Taskapilioglu, O., & Hakyemez, B. (2012). Neurochemical-structural changes evaluation of brain in patients with obstructive sleep apnea syndrome. European Journal of Radiology, 81(3), 491–495. https:// doi.org/10.1016/j.ejrad.2010.12.092. Alkan, A., Sharifov, R., Akkoyunlu, M. E., Kilicarslan, R., Toprak, H., Aralasmak, A., et al. (2013). MR spectroscopy features of brain in

patients with mild and severe obstructive sleep apnea syndrome. Clinical Imaging, 37(6), 989–992. https://doi.org/10.1016/ j.clinimag.2013.07.010. Altena, E., Van Der Werf, Y. D., Sanz-Arigita, E. J., Voorn, T. A., Rombouts, S. A., Kuijer, J. P., et al. (2008). Prefrontal hypoactivation and recovery in insomnia. Sleep, 31(9), 1271–1276. Altena, E., Vrenken, H., Van Der Werf, Y. D., van den Heuvel, O. A., & Van Someren, E. J. (2010). Reduced orbitofrontal and parietal gray matter in chronic insomnia: a voxel-based morphometric study. Biological Psychiatry, 67(2), 182–185. https://doi.org/10.1016/ j.biopsych.2009.08.003. American Academy of Sleep Medicine. (2001). International classification of sleep disorders, revised: Diagnostic and coding manual. Chicago, IL: American Academy of Sleep Medicine. American Psychiatric Association. (2013). Sleep wake disorders diagnostic and statistical manual of mental disorders: DSM-5 (5th ed.). Washington, DC: American Psychiatric Association. Archbold, K. H., Borghesani, P. R., Mahurin, R. K., Kapur, V. K., & Landis, C. A. (2009). Neural activation patterns during working memory tasks and OSA disease severity: preliminary findings. Journal of Clinical Sleep Medicine, 5(1), 21–27. Asenbaum, S., Zeithofer, J., Saletu, B., Frey, R., Brucke, T., Podreka, I., et al. (1995). Technetium-99m-HMPAO SPECT imaging of cerebral blood flow during REM sleep in narcoleptics. Journal of Nuclear Medicine, 36(7), 1150–1155. Ayalon, L., Ancoli-Israel, S., Aka, A. A., McKenna, B. S., & Drummond, S. P. (2009). Relationship between obstructive sleep apnea severity and brain activation during a sustained attention task. Sleep, 32(3), 373–381. Ayalon, L., Ancoli-Israel, S., & Drummond, S. P. (2009). Altered brain activation during response inhibition in obstructive sleep apnea. Journal of Sleep Research, 18(2), 204–208. https://doi.org/10.1111/ j.1365-2869.2008.00707.x. Ayalon, L., Ancoli-Israel, S., Klemfuss, Z., Shalauta, M. D., & Drummond, S. P. (2006). Increased brain activation during verbal learning in obstructive sleep apnea. NeuroImage, 31(4), 1817–1825. https://doi.org/10.1016/j.neuroimage.2006.02.042. Baglioni, C., Spiegelhalder, K., Regen, W., Feige, B., Nissen, C., Lombardo, C., et al. (2014). Insomnia disorder is associated with increased amygdala reactivity to insomnia-related stimuli. Sleep, 37(12), 1907–1917. https://doi.org/10.5665/sleep.4240. Baril, A. A., Gagnon, K., Brayet, P., Montplaisir, J., De Beaumont, L., Carrier, J., et al. (2017). Gray matter hypertrophy and thickening with obstructive sleep apnea in middle-aged and older adults. American Journal of Respiratory and Critical Care Medicine, 195(11), 1509–1518. https://doi.org/10.1164/rccm.201606-1271OC. Bartlett, D. J., Rae, C., Thompson, C. H., Byth, K., Joffe, D. A., Enright, T., et al. (2004). Hippocampal area metabolites relate to severity and cognitive function in obstructive sleep apnea. Sleep Medicine, 5(6), 593–596. https://doi.org/10.1016/j.sleep.2004.08.004. Bassetti, C., Aldrich, M. S., & Quint, D. J. (1997). MRI findings in narcolepsy. Sleep, 20(8), 630–631. Baumann, C. R., & Bassetti, C. L. (2005). Hypocretins (orexins): clinical impact of the discovery of a neurotransmitter. Sleep Medicine Reviews, 9(4), 253–268. https://doi.org/10.1016/j.smrv.2005.01.005. Beebe, D. W., Groesz, L., Wells, C., Nichols, A., & McGee, K. (2003). The neuropsychological effects of obstructive sleep apnea: a meta-analysis of norm-referenced and case-controlled data. Sleep, 26(3), 298–307. Bixler, E. O., Vgontzas, A. N., Lin, H. M., Ten Have, T., Rein, J., VelaBueno, A., et al. (2001). Prevalence of sleep-disordered breathing in women: effects of gender. American Journal of Respiratory and Critical Care Medicine, 163(3 Pt 1), 608–613. https://doi.org/ 10.1164/ajrccm.163.3.9911064. Boucetta, S., Montplaisir, J., Zadra, A., Lachapelle, F., Soucy, J. P., Gravel, P., et al. (2017). Altered regional cerebral blood flow in idiopathic hypersomnia. Sleep, 40(10). https://doi.org/10.1093/sleep/zsx140.

PART G. DISTURBED SLEEP

REFERENCES

Boucetta, S., Salimi, A., Dadar, M., Jones, B. E., Collins, D. L., & DangVu, T. T. (2016). Structural brain alterations associated with rapid eye movement sleep behavior disorder in Parkinson’s disease. Scientific Reports, 6, 26782. https://doi.org/10.1038/srep26782. Brabec, J., Rulseh, A., Horinek, D., Pala, A., Guerreiro, H., Buskova, J., et al. (2011). Volume of the amygdala is reduced in patients with narcolepsy—a structural MRI study. Neuro Endocrinology Letters, 32(5), 652–656. Brenneis, C., Brandauer, E., Frauscher, B., Schocke, M., Trieb, T., Poewe, W., et al. (2005). Voxel-based morphometry in narcolepsy. Sleep Medicine, 6(6), 531–536. https://doi.org/10.1016/j.sleep.2005.03.015. Bucks, R. S., Olaithe, M., & Eastwood, P. (2013). Neurocognitive function in obstructive sleep apnoea: a meta-review. Respirology, 18(1), 61–70. https://doi.org/10.1111/j.1440-1843.2012.02255.x. Buskova, J., Vaneckova, M., Sonka, K., Seidl, Z., & Nevsimalova, S. (2006). Reduced hypothalamic gray matter in narcolepsy with cataplexy. Neuro Endocrinology Letters, 27(6), 769–772. Canessa, N., Castronovo, V., Cappa, S. F., Aloia, M. S., Marelli, S., Falini, A., et al. (2011). Obstructive sleep apnea: brain structural changes and neurocognitive function before and after treatment. American Journal of Respiratory and Critical Care Medicine, 183(10), 1419–1426. https://doi.org/10.1164/rccm.201005-0693OC. Castronovo, V., Canessa, N., Strambi, L. F., Aloia, M. S., Consonni, M., Marelli, S., et al. (2009). Brain activation changes before and after PAP treatment in obstructive sleep apnea. Sleep, 32(9), 1161–1172. Castronovo, V., Scifo, P., Castellano, A., Aloia, M. S., Iadanza, A., Marelli, S., et al. (2014). White matter integrity in obstructive sleep apnea before and after treatment. Sleep, 37(9), 1465–1475. https:// doi.org/10.5665/sleep.3994. Celle, S., Delon-Martin, C., Roche, F., Barthelemy, J. C., Pepin, J. L., & Dojat, M. (2016). Desperately seeking grey matter volume changes in sleep apnea: a methodological review of magnetic resonance brain voxel-based morphometry studies. Sleep Medicine Reviews, 25, 112–120. https://doi.org/10.1016/j.smrv.2015.03.001. Celle, S., Peyron, R., Faillenot, I., Pichot, V., Alabdullah, M., Gaspoz, J. M., et al. (2009). Undiagnosed sleep-related breathing disorders are associated with focal brainstem atrophy in the elderly. Human Brain Mapping, 30(7), 2090–2097. https://doi.org/10.1002/hbm.20650. Chabas, D., Habert, M. O., Maksud, P., Tourbah, A., Minz, M., Willer, J. C., et al. (2007). Functional imaging of cataplexy during status cataplecticus. Sleep, 30(2), 153–156. Chee, M. W. (2013). Functional imaging of primary insomnia: new images and fresh opportunities. Sleep, 36(9), 1273–1274. https:// doi.org/10.5665/sleep.2940. Chen, H. L., Lu, C. H., Lin, H. C., Chen, P. C., Chou, K. H., Lin, W. M., et al. (2015). White matter damage and systemic inflammation in obstructive sleep apnea. Sleep, 38(3), 361–370. https://doi.org/ 10.5665/sleep.4490. Cross, N., Memarian, N., Duffy, S. L., Paquola, C., D’Rozario, A. L., LaMonica, H., et al. (2018). Structural brain correlates of obstructive sleep apnoea in older adults at-risk for dementia. European Respiratory Journal, 52(1). pii: 1800740. D’Rozario, A. L., Bartlett, D. J., Wong, K. K. H., Sach, T., Yang, Q., Grunstein, R. R., et al. (2018). Brain bioenergetics during resting wakefulness are related to neurobehavioural deficits in severe obstructive sleep apnea: a 31P magnetic resonance spectroscopy study. Sleep, zsy117. https://doi.org/10.1093/sleep/zsy117. Dai, X. J., Nie, X., Liu, X., Pei, L., Jiang, J., Peng, D. C., et al. (2016). Gender differences in regional brain activity in patients with chronic primary insomnia: evidence from a resting-state fMRI study. Journal of Clinical Sleep Medicine, 12(3), 363–374. https://doi. org/10.5664/jcsm.5586. Dang-Vu, T. T., Gagnon, J. F., Vendette, M., Soucy, J. P., Postuma, R. B., & Montplaisir, J. (2012). Hippocampal perfusion predicts impending neurodegeneration in REM sleep behavior disorder. Neurology, 79(24), 2302–2306. https://doi.org/10.1212/WNL. 0b013e318278b658.

587

Dauvilliers, Y., Boudousq, V., Lopez, R., Gabelle, A., De Cock, V. C., Bayard, S., et al. (2011). Increased perfusion in supplementary motor area during a REM sleep behaviour episode. Sleep Medicine, 12(5), 531–532. https://doi.org/10.1016/j.sleep.2011.02.003. Dauvilliers, Y., Comte, F., Bayard, S., Carlander, B., Zanca, M., & Touchon, J. (2010). A brain PET study in patients with narcolepsycataplexy. Journal of Neurology, Neurosurgery, and Psychiatry, 81(3), 344–348. https://doi.org/10.1136/jnnp.2009.175786. Ding, J., Nieto, F. J., Beauchamp, N. J., Jr., Harris, T. B., Robbins, J. A., Hetmanski, J. B., et al. (2004). Sleep-disordered breathing and white matter disease in the brainstem in older adults. Sleep, 27(3), 474–479. Draganski, B., Geisler, P., Hajak, G., Schuierer, G., Bogdahn, U., Winkler, J., et al. (2002). Hypothalamic gray matter changes in narcoleptic patients. Nature Medicine, 8(11), 1186–1188. https://doi. org/10.1038/nm1102-1186. Drissi, N. M., Szakacs, A., Witt, S. T., Wretman, A., Ulander, M., Stahlbrandt, H., et al. (2016). Altered brain microstate dynamics in adolescents with narcolepsy. Frontiers in Human Neuroscience, 10, 369. https://doi.org/10.3389/fnhum.2016.00369. Drummond, S. P., Walker, M., Almklov, E., Campos, M., Anderson, D. E., & Straus, L. D. (2013). Neural correlates of working memory performance in primary insomnia. Sleep, 36(9), 1307–1316. https:// doi.org/10.5665/sleep.2952. Drummond, S. P. A., Brown, G. G., Gillin, J. C., Stricker, J. L., Wong, E. C., & Buxton, R. B. (2000). Altered brain response to verbal learning following sleep deprivation. Nature, 403, 655. https://doi.org/ 10.1038/35001068. Duffy, S. L., Lagopoulos, J., Terpening, Z., Lewis, S. J., Grunstein, R., Mowszowski, L., et al. (2016). Association of anterior cingulate glutathione with sleep apnea in older adults at-risk for dementia. Sleep, 39(4), 899–906. https://doi.org/10.5665/sleep.5650. Dusak, A., Ursavas, A., Hakyemez, B., Gokalp, G., Taskapilioglu, O., & Parlak, M. (2013). Correlation between hippocampal volume and excessive daytime sleepiness in obstructive sleep apnea syndrome. European Review for Medical and Pharmacological Sciences, 17(9), 1198–1204. Eisensehr, I., Linke, R., Noachtar, S., Schwarz, J., Gildehaus, F. J., & Tatsch, K. (2000). Reduced striatal dopamine transporters in idiopathic rapid eye movement sleep behaviour disorder. Comparison with Parkinson’s disease and controls. Brain, 123(Pt 6), 1155–1160. Eisensehr, I., Linke, R., Tatsch, K., Kharraz, B., Gildehaus, J. F., Wetter, C. T., et al. (2003). Increased muscle activity during rapid eye movement sleep correlates with decrease of striatal presynaptic dopamine transporters. IPT and IBZM SPECT imaging in subclinical and clinically manifest idiopathic REM sleep behavior disorder, Parkinson’s disease, and controls. Sleep, 26(5), 507–512. Ellis, C. M., Simmons, A., Lemmens, G., Williams, S. C., & Parkes, J. D. (1998). Proton spectroscopy in the narcoleptic syndrome. Is there evidence of a brainstem lesion? Neurology, 50(2 Suppl 1), S23–S26. Ellmore, T. M., Hood, A. J., Castriotta, R. J., Stimming, E. F., Bick, R. J., & Schiess, M. C. (2010). Reduced volume of the putamen in REM sleep behavior disorder patients. Parkinsonism & Related Disorders, 16(10), 645–649. https://doi.org/10.1016/j.parkreldis. 2010.08.014. Engleman, H. M., Kingshott, R. N., Martin, S. E., & Douglas, N. J. (2000). Cognitive function in the sleep apnea/hypopnea syndrome (SAHS). Sleep, 23(Suppl 4), S102–S108. Fantini, M. L., Ferini-Strambi, L., & Montplaisir, J. (2005). Idiopathic REM sleep behavior disorder: toward a better nosologic definition. Neurology, 64(5), 780–786. https://doi.org/10.1212/01.wnl.0000152878. 79429.00. Ficker, J. H., Feistel, H., Moller, C., Merkl, M., Dertinger, S., Siegfried, W., et al. (1997). Changes in regional CNS perfusion in obstructive sleep apnea syndrome: initial SPECT studies with injected nocturnal 99mTc-HMPAO. Pneumologie, 51(9), 926–930.

PART G. DISTURBED SLEEP

588

38. IMAGING OF THE SLEEP-DISORDERED BRAIN

Frey, J. L., & Heiserman, J. E. (1997). Absence of pontine lesions in narcolepsy. Neurology, 48(4), 1097–1099. Govindaiah, G., & Cox, C. L. (2006). Modulation of thalamic neuron excitability by orexins. Neuropharmacology, 51(3), 414–425. https:// doi.org/10.1016/j.neuropharm.2006.03.030. Gula, L. J., Krahn, A. D., Skanes, A., Ferguson, K. A., George, C., Yee, R., et al. (2003). Heart rate variability in obstructive sleep apnea: a prospective study and frequency domain analysis. Annals of Noninvasive Electrocardiology, 8(2), 144–149. Halbower, A. C., Degaonkar, M., Barker, P. B., Earley, C. J., Marcus, C. L., Smith, P. L., et al. (2006). Childhood obstructive sleep apnea associates with neuropsychological deficits and neuronal brain injury. PLoS Medicine, 3(8), e301. https://doi.org/10.1371/journal. pmed.0030301. Hanoglu, L., Ozer, F., Meral, H., & Dincer, A. (2006). Brainstem 1H-MR spectroscopy in patients with Parkinson’s disease with REM sleep behavior disorder and IPD patients without dream enactment behavior. Clinical Neurology and Neurosurgery, 108(2), 129–134. https://doi.org/10.1016/j.clineuro.2005.03.011. Hanyu, H., Inoue, Y., Sakurai, H., Kanetaka, H., Nakamura, M., Miyamoto, T., et al. (2011). Regional cerebral blood flow changes in patients with idiopathic REM sleep behavior disorder. European Journal of Neurology, 18(5), 784–788. https://doi.org/10.1111/ j.1468-1331.2010.03283.x. Heinzer, R., Vat, S., Marques-Vidal, P., Marti-Soler, H., Andries, D., Tobback, N., et al. (2015). Prevalence of sleep-disordered breathing in the general population: the HypnoLaus study. The Lancet Respiratory Medicine, 3(4), 310–318. https://doi.org/10.1016/s22132600(15)00043-0. Hong, S. B., Tae, W. S., & Joo, E. Y. (2006). Cerebral perfusion changes during cataplexy in narcolepsy patients. Neurology, 66(11), 1747–1749. https://doi.org/10.1212/01.wnl.0000218205.72668.ab. Huang, Z., Liang, P., Jia, X., Zhan, S., Li, N., Ding, Y., et al. (2012). Abnormal amygdala connectivity in patients with primary insomnia: evidence from resting state fMRI. European Journal of Radiology, 81(6), 1288–1295. https://doi.org/10.1016/j.ejrad.2011. 03.029. Ip, M. S., Lam, B., Tang, L. C., Lauder, I. J., Ip, T. Y., & Lam, W. K. (2004). A community study of sleep-disordered breathing in middle-aged Chinese women in Hong Kong: prevalence and gender differences. Chest, 125(1), 127–134. Iranzo, A., Lomena, F., Stockner, H., Valldeoriola, F., Vilaseca, I., Salamero, M., et al. (2010). Decreased striatal dopamine transporter uptake and substantia nigra hyperechogenicity as risk markers of synucleinopathy in patients with idiopathic rapid-eyemovement sleep behaviour disorder: a prospective study [corrected]. Lancet Neurology, 9(11), 1070–1077. https://doi.org/ 10.1016/s1474-4422(10)70216-7. Iranzo, A., Santamaria, J., Pujol, J., Moreno, A., Deus, J., & Tolosa, E. (2002). Brainstem proton magnetic resonance spectroscopy in idopathic REM sleep behavior disorder. Sleep, 25(8), 867–870. Iranzo, A., Tolosa, E., Gelpi, E., Molinuevo, J. L., Valldeoriola, F., Serradell, M., et al. (2013). Neurodegenerative disease status and post-mortem pathology in idiopathic rapid-eye-movement sleep behaviour disorder: an observational cohort study. Lancet Neurology, 12(5), 443–453. https://doi.org/10.1016/s1474-4422(13)70056-5. Jacobsen, J. K., Giedd, J. N., Gottschalk, C., Kosten, T. R., & Krystal, J. H. (2001). Quantitative morphology of the caudate and putamen in patients with cocaine dependence. The American Journal of Psychiatry, 158(3), 486–489. Joo, E. Y., Hong, S. B., Tae, W. S., Kim, J. H., Han, S. J., Cho, Y. W., et al. (2005). Cerebral perfusion abnormality in narcolepsy with cataplexy. NeuroImage, 28(2), 410–416. https://doi.org/10.1016/j.neuroimage. 2005.06.019. Joo, E. Y., Jeon, S., Lee, M., Kim, S. T., Yoon, U., Koo, D. L., et al. (2011). Analysis of cortical thickness in narcolepsy patients with cataplexy. Sleep, 34(10), 1357–1364. https://doi.org/10.5665/sleep.1278.

Joo, E. Y., Kim, S. H., Kim, S. T., & Hong, S. B. (2012). Hippocampal volume and memory in narcoleptics with cataplexy. Sleep Medicine, 13(4), 396–401. https://doi.org/10.1016/j.sleep.2011.09.017. Joo, E. Y., Kim, H., Suh, S., & Hong, S. B. (2014). Hippocampal substructural vulnerability to sleep disturbance and cognitive impairment in patients with chronic primary insomnia: magnetic resonance imaging morphometry. Sleep, 37(7), 1189–1198. https:// doi.org/10.5665/sleep.3836. Joo, E. Y., Tae, W. S., Han, S. J., Cho, J. W., & Hong, S. B. (2007). Reduced cerebral blood flow during wakefulness in obstructive sleep apnea-hypopnea syndrome. Sleep, 30(11), 1515–1520. Joo, E. Y., Tae, W. S., Kim, J. H., Kim, B. T., & Hong, S. B. (2004). Glucose hypometabolism of hypothalamus and thalamus in narcolepsy. Annals of Neurology, 56(3), 437–440. https://doi.org/10.1002/ana.20212. Joo, E. Y., Tae, W. S., Lee, M. J., Kang, J. W., Park, H. S., Lee, J. Y., et al. (2010). Reduced brain gray matter concentration in patients with obstructive sleep apnea syndrome. Sleep, 33(2), 235–241. Joo, E. Y., Tae, W. S., Kim, S. T., & Hong, S. B. (2009). Gray matter concentration abnormality in brains of narcolepsy patients. Korean Journal of Radiology, 10(6), 552–558. https://doi.org/10.3348/ kjr.2009.10.6.552. Kamba, M., Suto, Y., Ohta, Y., Inoue, Y., & Matsuda, E. (1997). Cerebral metabolism in sleep apnea. Evaluation by magnetic resonance spectroscopy. American Journal of Respiratory and Critical Care Medicine, 156(1), 296–298. https://doi.org/10.1164/ajrccm.156.1.9611063. Kaufmann, C., Schuld, A., Pollmacher, T., & Auer, D. P. (2002). Reduced cortical gray matter in narcolepsy: preliminary findings with voxel-based morphometry. Neurology, 58(12), 1852–1855. Kay, D. B., Karim, H. T., Soehner, A. M., Hasler, B. P., Wilckens, K. A., James, J. A., et al. (2016). Sleep-wake differences in relative regional cerebral metabolic rate for glucose among patients with insomnia compared with good sleepers. Sleep, 39(10), 1779–1794. https://doi. org/10.5665/sleep.6154. Kim, S. J., Lyoo, I. K., Lee, Y. S., Lee, J. Y., Yoon, S. J., Kim, J. E., et al. (2009). Gray matter deficits in young adults with narcolepsy. Acta Neurologica Scandinavica, 119(1), 61–67. https://doi.org/10.1111/ j.1600-0404.2008.01063.x. Kim, S. J., Lyoo, I. K., Lee, Y. S., Sung, Y. H., Kim, H. J., Kim, J. H., et al. (2008). Increased GABA levels in medial prefrontal cortex of young adults with narcolepsy. Sleep, 31(3), 342–347. Kim, Y. K., Yoon, I. Y., Kim, J. M., Jeong, S. H., Kim, K. W., Shin, Y. K., et al. (2010). The implication of nigrostriatal dopaminergic degeneration in the pathogenesis of REM sleep behavior disorder. European Journal of Neurology, 17(3), 487–492. https://doi.org/ 10.1111/j.1468-1331.2009.02854.x. Kizilgoz, V., Aydin, H., Tatar, I. G., Hekimoglu, B., Ardic, S., Firat, H., et al. (2013). Proton magnetic resonance spectroscopy of periventricular white matter and hippocampus in obstructive sleep apnea patients. Polish Journal of Radiology, 78(4), 7–14. https://doi.org/10.12659/pjr.889923. Koo, D. L., Shin, J. H., Lim, J. S., Seong, J. K., & Joo, E. Y. (2017). Changes in subcortical shape and cognitive function in patients with chronic insomnia. Sleep Medicine, 35, 23–26. https://doi.org/10.1016/j. sleep.2017.04.002. Li, Y., Liu, L., Wang, E., Zhang, H., Dou, S., Tong, L., et al. (2016). Abnormal neural network of primary insomnia: evidence from spatial working memory task fMRI. European Neurology, 75(1-2), 48–57. https://doi.org/10.1159/000443372. Lodi, R., Tonon, C., Vignatelli, L., Iotti, S., Montagna, P., Barbiroli, B., et al. (2004). In vivo evidence of neuronal loss in the hypothalamus of narcoleptic patients. Neurology, 63(8), 1513–1515. Longstreth, W. T., Jr., Koepsell, T. D., Ton, T. G., Hendrickson, A. F., & van Belle, G. (2007). The epidemiology of narcolepsy. Sleep, 30(1), 13–26. Macey, P. M., Haris, N., Kumar, R., Thomas, M. A., Woo, M. A., & Harper, R. M. (2018). Obstructive sleep apnea and cortical thickness in females and males. PLoS One, 13(3), e0193854. https:// doi.org/10.1371/journal.pone.0193854.

PART G. DISTURBED SLEEP

REFERENCES

Macey, P. M., Henderson, L. A., Macey, K. E., Alger, J. R., Frysinger, R. C., Woo, M. A., et al. (2002). Brain morphology associated with obstructive sleep apnea. American Journal of Respiratory and Critical Care Medicine, 166(10), 1382–1387. https://doi.org/10.1164/ rccm.200201-050OC. Macey, P. M., Kumar, R., Woo, M. A., Valladares, E. M., Yan-Go, F. L., & Harper, R. M. (2008). Brain structural changes in obstructive sleep apnea. Sleep, 31(7), 967–977. Maquet, P. (2000). Functional neuroimaging of normal human sleep by positron emission tomography. Journal of Sleep Research, 9(3), 207–231. Mazza, S., Soucy, J. P., Gravel, P., Michaud, M., Postuma, R., MassicotteMarquez, J., et al. (2006). Assessing whole brain perfusion changes in patients with REM sleep behavior disorder. Neurology, 67(9), 1618–1622. https://doi.org/10.1212/01.wnl.0000242879. 39415.49. Menzler, K., Belke, M., Unger, M. M., Ohletz, T., Keil, B., Heverhagen, J. T., et al. (2012). DTI reveals hypothalamic and brainstem white matter lesions in patients with idiopathic narcolepsy. Sleep Medicine, 13(6), 736–742. https://doi.org/10.1016/ j.sleep.2012.02.013. Meyer, J. S., Ishikawa, Y., Hata, T., & Karacan, I. (1987). Cerebral blood flow in normal and abnormal sleep and dreaming. Brain and Cognition, 6(3), 266–294. Meyer, J. S., Sakai, F., Karacan, I., Derman, S., & Yamamoto, M. (1980). Sleep apnea, narcolepsy, and dreaming: regional cerebral hemodynamics. Annals of Neurology, 7(5), 479–485. https://doi. org/10.1002/ana.410070515. Miller, C. B., Rae, C. D., Green, M. A., Yee, B. J., Gordon, C. J., D’Rozario, A. L., et al. (2017). An objective short sleep insomnia disorder subtype is associated with reduced brain metabolite concentrations in vivo: a preliminary magnetic resonance spectroscopy assessment. Sleep, 40(11). https://doi.org/10.1093/sleep/zsx148. Miyamoto, M., Miyamoto, T., Kubo, J., Yokota, N., Hirata, K., & Sato, T. (2000). Brainstem function in rapid eye movement sleep behavior disorder: the evaluation of brainstem function by proton MR spectroscopy (1H-MRS). Psychiatry and Clinical Neurosciences, 54(3), 350–351. https://doi.org/10.1046/j.1440-1819.2000.00710.x. Morgan, P. T., Pace-Schott, E. F., Mason, G. F., Forselius, E., Fasula, M., Valentine, G. W., et al. (2012). Cortical GABA levels in primary insomnia. Sleep, 35(6), 807–814. https://doi.org/10.5665/sleep.1880. Morin, C. M., & Benca, R. (2012). Chronic insomnia. Lancet, 379(9821), 1129–1141. https://doi.org/10.1016/s0140-6736(11)60750-2. Morrell, M. J., Jackson, M. L., Twigg, G. L., Ghiassi, R., McRobbie, D. W., Quest, R. A., et al. (2010). Changes in brain morphology in patients with obstructive sleep apnoea. Thorax, 65(10), 908–914. Morrell, M. J., McRobbie, D. W., Quest, R. A., Cummin, A. R., Ghiassi, R., & Corfield, D. R. (2003). Changes in brain morphology associated with obstructive sleep apnea. Sleep Medicine, 4(5), 451–454. Mu, Q., Nahas, Z., Johnson, K. A., Yamanaka, K., Mishory, A., Koola, J., et al. (2005). Decreased cortical response to verbal working memory following sleep deprivation. Sleep, 28(1), 55–67. Nakamura, M., Nishida, S., Hayashida, K., Ueki, Y., Dauvilliers, Y., & Inoue, Y. (2013). Differences in brain morphological findings between narcolepsy with and without cataplexy. PLoS One, 8(11), e81059. https://doi.org/10.1371/journal.pone.0081059. Nie, X., Shao, Y., Liu, S. Y., Li, H. J., Wan, A. L., Nie, S., et al. (2015). Functional connectivity of paired default mode network subregions in primary insomnia. Neuropsychiatric Disease and Treatment, 11, 3085–3093. https://doi.org/10.2147/ndt.s95224. Nofzinger, E. A., Buysse, D. J., Germain, A., Price, J. C., Miewald, J. M., & Kupfer, D. J. (2004). Functional neuroimaging evidence for hyperarousal in insomnia. The American Journal of Psychiatry, 161(11), 2126–2128. https://doi.org/10.1176/appi.ajp.161.11.2126. Nofzinger, E. A., Nissen, C., Germain, A., Moul, D., Hall, M., Price, J. C., et al. (2006). Regional cerebral metabolic correlates of WASO during

589

NREM sleep in insomnia. Journal of Clinical Sleep Medicine, 2(3), 316–322. Noh, H. J., Joo, E. Y., Kim, S. T., Yoon, S. M., Koo, D. L., Kim, D., et al. (2012). The relationship between hippocampal volume and cognition in patients with chronic primary insomnia. Journal of Clinical Neurology, 8(2), 130–138. https://doi.org/10.3988/jcn.2012.8.2.130. O’Donoghue, F. J., Briellmann, R. S., Rochford, P. D., Abbott, D. F., Pell, G. S., Chan, C. H., et al. (2005). Cerebral structural changes in severe obstructive sleep apnea. American Journal of Respiratory and Critical Care Medicine, 171(10), 1185–1190. O’Donoghue, F. J., Wellard, R. M., Rochford, P. D., Dawson, A., Barnes, M., Ruehland, W. R., et al. (2012). Magnetic resonance spectroscopy and neurocognitive dysfunction in obstructive sleep apnea before and after CPAP treatment. Sleep, 35(1), 41–48. https://doi.org/10.5665/sleep.1582. Overeem, S., Steens, S. C., Good, C. D., Ferrari, M. D., Mignot, E., Frackowiak, R. S., et al. (2003). Voxel-based morphometry in hypocretin-deficient narcolepsy. Sleep, 26(1), 44–46. Peregrim, I., Gresova, S., Pallayova, M., Fulton, B. L., Stimmelova, J., Bacova, I., et al. (2013). Does obstructive sleep apnea worsen during REM sleep? Physiological Research, 62(5), 569–575. Perlis, M. L., Smith, M. T., & Pigeon, W. R. (2006). Etiology and pathophysiology of insomnia. In M. H. Kryger, T. Roth, & W. C. Dement (Eds.), Principles and Practice of Sleep Medicine (4th ed., pp. 714–725). Philadelphia, PA, USA: Elsevier Saunders. Peyron, C., Tighe, D. K., van den Pol, A. N., de Lecea, L., Heller, H. C., Sutcliffe, J. G., et al. (1998). Neurons containing hypocretin (orexin) project to multiple neuronal systems. The Journal of Neuroscience, 18(23), 9996–10015. Plante, D. T., Jensen, J. E., Schoerning, L., & Winkelman, J. W. (2012). Reduced gamma-aminobutyric acid in occipital and anterior cingulate cortices in primary insomnia: a link to major depressive disorder? Neuropsychopharmacology, 37(6), 1548–1557. https://doi. org/10.1038/npp.2012.4. Plazzi, G., Montagna, P., Provini, F., Bizzi, A., Cohen, M., & Lugaresi, E. (1996). Pontine lesions in idiopathic narcolepsy. Neurology, 46(5), 1250–1254. Ponz, A., Khatami, R., Poryazova, R., Werth, E., Boesiger, P., Bassetti, C. L., et al. (2010). Abnormal activity in reward brain circuits in human narcolepsy with cataplexy. Annals of Neurology, 67(2), 190–200. https://doi.org/10.1002/ana.21825. Poryazova, R., Schnepf, B., Werth, E., Khatami, R., Dydak, U., Meier, D., et al. (2009). Evidence for metabolic hypothalamo-amygdala dysfunction in narcolepsy. Sleep, 32(5), 607–613. Prilipko, O., Huynh, N., Schwartz, S., Tantrakul, V., Kim, J. H., Peralta, A. R., et al. (2011). Task positive and default mode networks during a parametric working memory task in obstructive sleep apnea patients and healthy controls. Sleep, 34(3), 293–301a. Prilipko, O., Huynh, N., Schwartz, S., Tantrakul, V., Kushida, C., Paiva, T., et al. (2012). The effects of CPAP treatment on task positive and default mode networks in obstructive sleep apnea patients: an fMRI study. PLoS One, 7(12), e47433. https://doi.org/ 10.1371/journal.pone.0047433. Reiss, A. L., Hoeft, F., Tenforde, A. S., Chen, W., Mobbs, D., & Mignot, E. J. (2008). Anomalous hypothalamic responses to humor in cataplexy. PLoS One, 3(5), e2225. https://doi.org/10.1371/journal.pone.0002225. Riemann, D., Voderholzer, U., Spiegelhalder, K., Hornyak, M., Buysse, D. J., Nissen, C., et al. (2007). Chronic insomnia and MRI-measured hippocampal volumes: a pilot study. Sleep, 30(8), 955–958. Robbins, J., Redline, S., Ervin, A., Walsleben, J. A., Ding, J., & Nieto, F. J. (2005). Associations of sleep-disordered breathing and cerebral changes on MRI. Journal of Clinical Sleep Medicine, 1(2), 159–165. Rolinski, M., Griffanti, L., Piccini, P., Roussakis, A. A., SzewczykKrolikowski, K., Menke, R. A., et al. (2016). Basal ganglia dysfunction in idiopathic REM sleep behaviour disorder parallels

PART G. DISTURBED SLEEP

590

38. IMAGING OF THE SLEEP-DISORDERED BRAIN

that in early Parkinson’s disease. Brain, 139(Pt 8), 2224–2234. https:// doi.org/10.1093/brain/aww124. Santarnecchi, E., Sicilia, I., Richiardi, J., Vatti, G., Polizzotto, N. R., Marino, D., et al. (2013). Altered cortical and subcortical local coherence in obstructive sleep apnea: a functional magnetic resonance imaging study. Journal of Sleep Research, 22(3), 337–347. https://doi.org/10.1111/jsr.12006. Sarchielli, P., Presciutti, O., Alberti, A., Tarducci, R., Gobbi, G., Galletti, F., et al. (2008). A 1H magnetic resonance spectroscopy study in patients with obstructive sleep apnea. European Journal of Neurology, 15(10), 1058–1064. https://doi.org/10.1111/j.14681331.2008.02244.x. Schaer, M., Poryazova, R., Schwartz, S., Bassetti, C. L., & Baumann, C. R. (2012). Cortical morphometry in narcolepsy with cataplexy. Journal of Sleep Research, 21(5), 487–494. https://doi.org/10.1111/j.13652869.2012.01000.x. Scherfler, C., Frauscher, B., Schocke, M., Iranzo, A., Gschliesser, V., Seppi, K., et al. (2011). White and gray matter abnormalities in idiopathic rapid eye movement sleep behavior disorder: a diffusiontensor imaging and voxel-based morphometry study. Annals of Neurology, 69(2), 400–407. https://doi.org/10.1002/ana.22245. Scherfler, C., Frauscher, B., Schocke, M., Nocker, M., Gschliesser, V., Ehrmann, L., et al. (2012). White and gray matter abnormalities in narcolepsy with cataplexy. Sleep, 35(3), 345–351. https://doi.org/ 10.5665/sleep.1692. Schwartz, S., Ponz, A., Poryazova, R., Werth, E., Boesiger, P., Khatami, R., et al. (2008). Abnormal activity in hypothalamus and amygdala during humour processing in human narcolepsy with cataplexy. Brain, 131(Pt 2), 514–522. https://doi.org/10.1093/ brain/awm292. Smith, M. T., Perlis, M. L., Chengazi, V. U., Pennington, J., Soeffing, J., Ryan, J. M., et al. (2002). Neuroimaging of NREM sleep in primary insomnia: a Tc-99-HMPAO single photon emission computed tomography study. Sleep, 25(3), 325–335. Smith, M. T., Perlis, M. L., Chengazi, V. U., Soeffing, J., & McCann, U. (2005). NREM sleep cerebral blood flow before and after behavior therapy for chronic primary insomnia: preliminary single photon emission computed tomography (SPECT) data. Sleep Medicine, 6(1), 93–94. https://doi.org/10.1016/j.sleep.2004. 10.003. Soldatos, C. R., Allaert, F. A., Ohta, T., & Dikeos, D. G. (2005). How do individuals sleep around the world? Results from a single-day survey in ten countries. Sleep Medicine, 6(1), 5–13. https://doi.org/ 10.1016/j.sleep.2004.10.006. Son, Y. D., Kang, J. M., Cho, S. J., Lee, J. S., Hwang, H. Y., & Kang, S. G. (2018). fMRI brain activation in patients with insomnia disorder during a working memory task. Sleep & Breathing, 22(2), 487–493. https://doi.org/10.1007/s11325-017-1575-5. Spiegelhalder, K., Regen, W., Baglioni, C., Kloppel, S., Abdulkadir, A., Hennig, J., et al. (2013). Insomnia does not appear to be associated with substantial structural brain changes. Sleep, 36(5), 731–737. https://doi.org/10.5665/sleep.2638. Spiegelhalder, K., Regen, W., Nissen, C., Feige, B., Baglioni, C., Riemann, D., et al. (2016). Magnetic resonance spectroscopy in patients with insomnia: a repeated measurement study. PLoS One, 11(6), e0156771. https://doi.org/10.1371/journal.pone. 0156771. Stiasny-Kolster, K., Doerr, Y., Moller, J. C., Hoffken, H., Behr, T. M., Oertel, W. H., et al. (2005). Combination of ’idiopathic’ REM sleep behaviour disorder and olfactory dysfunction as possible indicator for alpha-synucleinopathy demonstrated by dopamine transporter FP-CIT-SPECT. Brain, 128(Pt 1), 126–137. https://doi.org/10.1093/ brain/awh322. Suh, S., Kim, H., Dang-Vu, T. T., Joo, E., & Shin, C. (2016). Cortical thinning and altered cortico-cortical structural covariance of the default mode network in patients with

persistent insomnia symptoms. Sleep, 39(1), 161–171. https://doi. org/10.5665/sleep.5340. Sullivan, C. E., & Issa, F. G. (1980). Pathophysiological mechanisms in obstructive sleep apnea. Sleep, 3(3-4), 235–246. Thannickal, T. C., Moore, R. Y., Nienhuis, R., Ramanathan, L., Gulyani, S., Aldrich, M., et al. (2000). Reduced number of hypocretin neurons in human narcolepsy. Neuron, 27(3), 469–474. Thomas, R. J., Rosen, B. R., Stern, C. E., Weiss, J. W., & Kwong, K. K. (1985). Functional imaging of working memory in obstructive sleep-disordered breathing. Journal of Applied Physiology, 98(6), 2226–2234. https://doi.org/10.1152/japplphysiol. 01225.2004. Tonon, C., Franceschini, C., Testa, C., Manners, D. N., Poli, F., Mostacci, B., et al. (2009). Distribution of neurochemical abnormalities in patients with narcolepsy with cataplexy: an in vivo brain proton MR spectroscopy study. Brain Research Bulletin, 80(3), 147–150. https://doi.org/10.1016/j.brainresbull. 2009.05.010. Tonon, C., Vetrugno, R., Lodi, R., Gallassi, R., Provini, F., Iotti, S., et al. (2007). Proton magnetic resonance spectroscopy study of brain metabolism in obstructive sleep apnoea syndrome before and after continuous positive airway pressure treatment. Sleep, 30(3), 305–311. Torelli, F., Moscufo, N., Garreffa, G., Placidi, F., Romigi, A., Zannino, S., et al. (2011). Cognitive profile and brain morphological changes in obstructive sleep apnea. NeuroImage, 54(2), 787–793. https://doi. org/10.1016/j.neuroimage.2010.09.065. Tufik, S., Santos-Silva, R., Taddei, J. A., & Bittencourt, L. R. (2010). Obstructive sleep apnea syndrome in the Sao Paulo epidemiologic sleep study. Sleep Medicine, 11(5), 441–446. https://doi.org/ 10.1016/j.sleep.2009.10.005. Unger, M. M., Belke, M., Menzler, K., Heverhagen, J. T., Keil, B., StiasnyKolster, K., et al. (2010). Diffusion tensor imaging in idiopathic REM sleep behavior disorder reveals microstructural changes in the brainstem, substantia nigra, olfactory region, and other brain regions. Sleep, 33(6), 767–773. Unger, M. M., Moller, J. C., Stiasny-Kolster, K., Mankel, K., Berg, D., Walter, U., et al. (2008). Assessment of idiopathic rapid-eye-movement sleep behavior disorder by transcranial sonography, olfactory function test, and FP-CIT-SPECT. Movement Disorders, 23(4), 596–599. https://doi.org/10.1002/mds. 21908. Vendette, M., Gagnon, J. F., Soucy, J. P., Gosselin, N., Postuma, R. B., Tuineag, M., et al. (2011). Brain perfusion and markers of neurodegeneration in rapid eye movement sleep behavior disorder. Movement Disorders, 26(9), 1717–1724. https://doi.org/ 10.1002/mds.23721. Wallace, A., & Bucks, R. S. (2013). Memory and obstructive sleep apnea: a meta-analysis. Sleep, 36(2), 203–220. https://doi.org/10.5665/ sleep.2374. Winkelman, J. W., Benson, K. L., Buxton, O. M., Lyoo, I. K., Yoon, S., O’Connor, S., et al. (2010). Lack of hippocampal volume differences in primary insomnia and good sleeper controls: an MRI volumetric study at 3 Tesla. Sleep Medicine, 11(6), 576–582. https://doi.org/10.1016/j.sleep.2010.03.009. Winkelman, J. W., Buxton, O. M., Jensen, J. E., Benson, K. L., O’Connor, S. P., Wang, W., et al. (2008). Reduced brain GABA in primary insomnia: preliminary data from 4T proton magnetic resonance spectroscopy (1H-MRS). Sleep, 31(11), 1499–1506. Winkelman, J. W., Plante, D. T., Schoerning, L., Benson, K., Buxton, O. M., O’Connor, S. P., et al. (2013). Increased rostral anterior cingulate cortex volume in chronic primary insomnia. Sleep, 36(7), 991–998. https://doi.org/10.5665/sleep.2794. Witt, S. T., Drissi, N. M., Tapper, S., Wretman, A., Szakacs, A., Hallbook, T., et al. (2017). Evidence for cognitive resource

PART G. DISTURBED SLEEP

REFERENCES

imbalance in adolescents with narcolepsy. Brain Imaging and Behavior, 12(2), 411–424. https://doi.org/10.1007/s11682-017-9706-y. Yaouhi, K., Bertran, F., Clochon, P., Mezenge, F., Denise, P., Foret, J., et al. (2009). A combined neuropsychological and brain imaging study of obstructive sleep apnea. Journal of Sleep Research, 18(1), 36–48. https://doi.org/10.1111/j.1365-2869.2008.00705.x. Young, T., Palta, M., Dempsey, J., Skatrud, J., Weber, S., & Badr, S. (1993). The occurrence of sleep-disordered breathing among middle-aged adults. The New England Journal of Medicine, 328(17), 1230–1235. https://doi.org/10.1056/nejm199304293281704.

591

Zammit, G. K., Weiner, J., Damato, N., Sillup, G. P., & McMillan, C. A. (1999). Quality of life in people with insomnia. Sleep, 22(Suppl 2), S379–S385. Zhang, Q., Wang, D., Qin, W., Li, Q., Chen, B., Zhang, Y., et al. (2013). Altered resting-state brain activity in obstructive sleep apnea. Sleep, 36(5), 651–659. https://doi.org/10.5665/sleep.2620. Zhang, X., Ma, L., Li, S., Wang, Y., & Wang, L. (2011). A functional MRI evaluation of frontal dysfunction in patients with severe obstructive sleep apnea. Sleep Medicine, 12(4), 335–340. https://doi. org/10.1016/j.sleep.2010.08.015.

PART G. DISTURBED SLEEP