Beyond sleepy: structural and functional changes of the default-mode network in idiopathic hypersomnia

Abstract

Idiopathic hypersomnia (IH) is characterized by excessive daytime sleepiness but, in contrast to narcolepsy, does not involve cataplexy, sleep-onset REM periods, or any consistent hypocretin-1 deficiency. The pathophysiological mechanisms of IH remain unclear. Because of the involvement of the default-mode network (DMN) in alertness and sleep, our aim was to investigate the structural and functional modifications of the DMN in IH. We conducted multimodal magnetic resonance imaging (MRI) in 12 participants with IH and 15 good sleeper controls (mean age ± SD: 32 ± 9.6 years, range 22–53 years, nine males). Self-reported as well as objective measures of daytime sleepiness were collected. Gray matter volume and cortical thickness were analyzed to investigate brain structural differences between good sleepers and IH. Structural covariance and resting-state functional connectivity were analyzed to investigate changes in the DMN. Participants with IH had greater volume and cortical thickness in the precuneus, a posterior hub of the DMN. Cortical thickness in the left medial prefrontal cortex was positively correlated with thickness of the precuneus, and the strength of this correlation was greater in IH. In contrast, functional connectivity at rest was lower within the anterior DMN (medial prefrontal cortex) in IH, and correlated with self-reported daytime sleepiness. The present results show that IH is associated with structural and functional differences in the DMN, in proportion to the severity of daytime sleepiness, suggesting that a disruption of the DMN contributes to the clinical features of IH. Larger volume and thickness in this network might reflect compensatory changes to lower functional connectivity in IH.

Statement of Significance.

Idiopathic hypersomnia is a disorder characterized by excessive daytime sleepiness, and its underlying mechanisms remain largely unknown. Here, we evaluated with neuroimaging whether a major brain network involved in alertness (default-mode network [DMN]) was affected in idiopathic hypersomniacs. We showed that not only the structure but also the functional connections within the regions belonging to this network were altered. These changes suggest the involvement of the DMN in the daytime sleepiness encountered by patients with idiopathic hypersomnia. More studies are needed to investigate whether these modifications are specific to idiopathic hypersomnia as compared to other conditions characterized by excessive sleepiness during the day.

Introduction

Idiopathic hypersomnia (IH) is a central disorder of hypersomnolence which is characterized by excessive daytime sleepiness and difficulties waking up (called “sleep drunkenness”) stemming from an unknown cause. In contrast to narcolepsy, people suffering from IH do not present cataplexy, rapid REM sleep onset (at the multiple sleep latency test [MSLT]), or any consistent hypocretin-1 deficiency. The prevalence of IH remains unclear, but one report suggests it to be around 0.3% of the population [1]. IH has been shown associated with poor quality of life, cognitive impairments, and emotional disturbances [2]. As the pathophysiological mechanisms of IH remain unclear and there is no objective biomarker specific to the condition, it is often only diagnosed after exclusion of other disorders.

The neuroimaging studies of IH remain scarce. No obvious structural brain differences were detected on single-subject magnetic resonance imaging (MRI) images [3], and only two neuroimaging studies have investigated functional changes in IH. One study, using fluorodeoxyglucose-positron emission tomography (FDG-PET), found that patients with IH showed hypermetabolism in the insula, anterior and middle cingulate cortex, and caudate nucleus compared to good sleepers [4]. In the other study, our group used single photon emission computed tomography (SPECT) to demonstrate that regional cerebral blood flow was lower in the medial prefrontal cortex as well as in the posterior cingulate cortex, putamen, and cerebellum of IH [5]. This hypometabolism in the medial prefrontal cortex was correlated with objective and self-reported measures of sleepiness, pointing to the involvement of the default-mode network (DMN) in the daytime sleepiness in IH. The DMN is composed of the medial prefrontal cortex, bilateral posterior cingulate cortex, precuneus, inferior parietal lobule, parts of the hippocampal formation, and the lateral temporal cortex [6]. Generally, the DMN is deactivated during tasks and activated at rest, and is involved in multiple cognitive processes such as higher cognition, emotion, and interoception [7]. Therefore, changes in its structure, activity, or connectivity have been found in a wide variety of neurological disorders [8]. The DMN is a key network involved in sleep [9, 10], during which its overall activity level decreases. In addition, while the activities between the posterior (namely the posterior cingulate cortex and inferior parietal lobe) and anterior (medial prefrontal cortex/anterior cingulate cortex) regions of the DMN are positively correlated at wake, they have been shown disconnected from each other during sleep [11]. This disconnection becomes more pronounced as sleep deepens: functional connectivity between posterior cingulate cortex and anterior DMN regions progressively decreases with the descent into sleep and reaches its lowest level during slow-wave sleep [9].

Given the importance of the DMN for sleep physiology and the previously reported changes in blood flow or glucose metabolism in regions belonging to the DMN with IH, we therefore aimed at investigating the structural and functional differences (including connectivity) in the DMN using multimodal MRI in a group of participants with IH compared to good sleepers. We hypothesized that participants with IH would display changes in volume, thickness, and connectivity between nodes of the DMN compared to healthy controls.

Methods

Participants

Eighteen participants with IH were recruited from several sleep clinics in the Montreal area, as well as through advertisements in local patients’ associations. Participants with IH underwent a MSLT to evaluate mean sleep latency during the day as well as the presence of sleep-onset REM periods (SOREMPs). Inclusion criteria for IH participants were the following: (1) excessive daytime sleepiness present for at least 3 months; (2) daytime mean sleep latency < 8 minutes based on MSLT OR self-reported 24-hour total sleep time was > 11 hours; (3) absence of cataplexy; (4) number of SOREMPs < 2; (5) absence of other causes of hypersomnia (e.g. other sleep or neurological disorders, use of drugs or medications). These criteria are for the most part in line with the ICSD-3 [12] diagnostic criteria of IH, except item 2) on 24-hour total sleep time > 11 hours, for which ICSD-3 requires confirmation by actigraphy or ad libitum polysomnography. This criterion only concerned two of our IH participants, as all the others had a mean sleep latency at the MSLT < 8 minutes. Psychotropic medications that could influence sleep and alertness (e.g. psychostimulants) were withdrawn 2 weeks prior to the start of the protocol and during the whole study procedure. Seventeen good sleepers were also recruited for the study as healthy control participants. Recruitment was conducted through local advertisements. The following exclusion criteria were applied for all the participants: (1) sleep disorders, other than IH, as assessed by a semi-structured interview and polysomnography (e.g. sleep apnea-hypopnea syndrome with apnea–hypopnea index > 5/hour); (2) systemic or neurological diseases such as diabetes, hypertension, dementia, stroke, and epilepsy; (3) shift or night work; (4) psychiatric disorders according to the DSM-5 [13]; (5) history of head injury, encephalopathy, or intracranial surgery; (6) history of alcoholism or drug abuse; and contraindication to the imaging. The study protocol was approved by ethics committees of the Hôpital du Sacré-Coeur de Montréal and the Regroupement Neuroimagerie Quebec, Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal (CRIUGM), and all participants provided written informed consent.

Clinical variables

Demographic variables (age, sex, and level of education) and the self-reported onset of symptoms were collected. Daytime mean sleep latency (as an objective measure of sleepiness) and the number of SOREMPs were extracted from the MSLT. Additionally, self-reported daytime sleepiness was measured with the Epworth sleepiness scale (ESS) [14]. Other clinical variables included self-reported measures of general sleep quality assessed with the Pittsburgh Sleep Quality Index (PSQI) [15], chronotype assessed with the Morningness-Evening Questionnaire (MEQ) [16], anxiety assessed with the Beck Anxiety Inventory (BAI) [17] and depression assessed with the Beck Depression Inventory (BDI) [18]. Clinical variables were compared between groups using independent sample t-tests and some of them were further used to inform the significance of imaging data in whole-brain regression analyses detailed in the following sections. Given that depression has been associated with hypersomnolence symptoms [19] with an increase in depression symptoms previously reported in hypersomnia disorders [20], and because the DMN has been shown affected with depression [21, 22], all the analyses were controlled for depression scores obtained with the BDI to disentangle the anatomical and functional effects related to depression symptoms from those of IH. The analyses uncontrolled for depression scores are reported in the Supplementary Material.

Imaging acquisition

Anatomical MRI was conducted on a 3 Tesla Siemens Trio scanner, with a 32-channel head coil. The scanning procedure took place at the Unité de Neuroimagerie Fonctionnelle located at the CRIUGM during the afternoon (between 1 pm and 5 pm), starting with the resting-state sequence followed by the anatomical sequence. The resting-state functional MRI (fMRI) sequence was acquired with multi-slice T2*-weighted images with the following parameters: repetition time (TR) 2.6 seconds, echo time (TE) 30 ms, flip angle 90°, field of view (FoV) 218 mm, matrix size 64 × 64, 150 volumes, 42 slices, resolution 3.4 mm isotropic, in order to compute connectivity in the DMN. During the resting-state fMRI sequence, which lasted 6 minutes, participants were asked to keep their eyes open, not fall asleep, and fixate a dot at the center of the screen. The maintenance of wakefulness was verified using a camera with a focus on participant’s eyes, under the constant monitoring of a research assistant (F.L.) who ensured that participants kept their eyes open during resting-state fMRI. If participants showed signs of sleepiness on the camera (e.g. eyes starting to close) during the resting-state session, the research assistant talked to them using the interphone to remind them to keep their eyes open. Such reminders were only required in a minority of participants, at one or two instances maximum per session. High-resolution anatomical T1-weighted imaging (multi-echo magnetization-prepared rapid acquisition with gradient echo [MP-RAGE], TR 2.53 seconds, TE 1.64 ms, flip angle 7°, FoV 256 mm, matrix size 256 × 256, 176 slices, resolution 1 mm isotropic) was used to perform voxel-based morphometry [23] as well as measure cortical thickness. Visual quality check of all the MRI data was conducted prior to any analysis.

Some of these participants also underwent a SPECT scan, and SPECT results were previously reported in Boucetta et al. [5].

Gray matter volume analysis

To investigate subcortical as well as cortical regional gray matter volume, voxel-based morphometry was performed using the Computational Anatomy Toolbox (CAT12) [24] in Statistical Parametric Mapping software (SPM12, Wellcome Trust Centre for Neuroimaging). The following steps were used for data processing: (1) images were normalized to standard space by linearly registering them to the International Consortium for Brain Mapping (ICBM152) template; (2) segmented into gray matter, white matter, and cerebrospinal fluid using the intensity distribution of the images; (3) gray matter probability maps were modulated, i.e. the value at each voxel was multiplied by the Jacobian determinant accounting for the amount of contraction or retraction led by the non-linear spatial normalization to obtain regional volumes; (4) the modulated normalized gray matter maps were spatially blurred with an 8 × 8 × 8 mm (full width at half maximum) Gaussian smoothing kernel.

Whole-brain voxel-wise group differences in gray matter volume were assessed using a general linear model (GLM), controlling for total intracranial volume (gray matter + white matter + CSF), age, sex and depression score from the BDI. Separate linear regression analyses at the whole group level were conducted with self-reported sleepiness based on ESS scores and objective sleepiness score from mean sleep latency at the MSLT, controlling for age, sex, and BDI.

Cortical thickness analysis

Cortical thickness, corresponding to the distance between the inner cortical surface and the outer/pial surface, was computed using the CIVET pipeline version 2.1.0 [25–31] running on CBRAIN [32, 33]. The following steps were used for data processing: (1) anatomical images were corrected for image intensity inhomogeneities with N3 correction [26], linearly and nonlinearly registered to the Montreal Neurological Institute (MNI) standard space using the ICBM152 nonlinear 6th generation template [34] with linear transformations; (2) images in standard space were segmented into gray matter, white matter and cerebrospinal fluid with INSECT (Intensity Normalized Stereotaxic Environment for the Classification of Tissue) [27]; (3) surface extraction was computed using the “marching cubes” algorithm to produce surfaces without surface bridges from the segmented images, and a 30-mm surface-based smoothing was applied [35]; (4) each cortical surface was regularized to 40 962 vertices by icosahedron resampling and a spherical registration to the template surface [36]. The resampled surface was transformed back into the participant’s native space, then used to calculate cortical thickness [35–37].

Statistical analysis of cortical thickness was performed with SurfStat (http://www.math.mcgill.ca/keith/surfstat/, accessed July 11, 2019). Whole-brain group differences in cortical thickness were assessed using a GLM, controlling for age, sex, and depression score from the BDI. Separate linear regression analyses at the whole group level were conducted with ESS and mean sleep latency at the MSLT, controlling for age, sex, and BDI.

Structural covariance analysis

Structural covariance analysis allows estimating morphological similarity between cortical regions, based on their cortical thickness. Structural connectivity explains a substantial portion of inter-regional structural covariance within the brain [38]. This assumes that regions with related/similar function show greater correlation with each other, i.e. greater structural covariance [39]. The correlation between cortical thicknesses at a given seed vertex (Thickness@seed) and cortical thickness measured at each of all other vertices was computed in SurfStat for both the IH and the good sleeper participants. The seed was located in the posterior cingulate cortex/precuneus (MNI coordinates x = −14, y = −58, z = 32) and in the medial prefrontal cortex (MNI coordinates x = −2, y = 44, z = 16), based on the SPECT results from ref. [5].

Statistical analysis of cortical thickness was performed with SurfStat. By fitting a linear model and assessing the interaction effect of group (IH or good sleeper) × Thickness@seed [40], we compared the difference in the structural covariance between groups controlling for age, sex, and BDI. Separate linear regression analyses at the whole group level were conducted with ESS and mean sleep latency at the MSLT, controlling for age, sex, and depression score from the BDI.

Resting-state functional connectivity analysis

Resting-state functional connectivity measures the degree of correlation between the time course of fMRI signals. For this analysis, fMRI data was preprocessed using the Neuroimaging Analysis Kit (NIAK) version 1.0.1 [41], http://niak.simexp-lab.org, accessed July 11, 2019. Each fMRI dataset was corrected for inter-slice difference in acquisition time and the parameters of a rigid-body motion were estimated for each time frame. Rigid-body motion was estimated within as well as between runs. The median volume of one selected fMRI run for each participant was coregistered with a T1 individual scan using Minctracc [42], which was itself nonlinearly transformed to the MNI template [43] using the CIVET pipeline. The following steps were used: (1) registration to the MNI space was conducted using the ICBM152 nonlinear 6th generation symmetric template; the rigid-body, fMRI-to-T1 transform and T1-to-MNI transform were all combined, and the functional volumes were resampled in the MNI space at a 4-mm isotropic resolution; (2) “scrubbing” method was used to remove the volumes with excessive motion (frame displacement greater than 0.5) [44]; (3) the following nuisance parameters were regressed out from the time series at each voxel: slow time drifts (basis of discrete cosines with a 0.01 Hz high-pass cut-off), average signals in conservative masks of the white matter and the lateral ventricles as well as the first principal components (95% energy) of the six rigid-body motion parameters and their squares [45, 46]; (4) fMRI volumes were finally spatially smoothed with an 8-mm isotropic Gaussian blurring kernel.

Based on the SPECT findings [5] the average timeseries used for the functional connectivity analysis were extracted separately (masks) for the anterior and posterior DMN. Timeseries from all the voxels within each DMN mask were averaged and the average timeseries for each mask was correlated with all the voxels in the brain (including voxels within the DMN), resulting in a voxel-wise functional connectivity map (representing the Pearson’s correlation corrected by the Fisher transform for each participant). The DMN masks were extracted from the 12 distributed networks using the Cambridge parcellation, the anterior DMN mask comprising the anterior cingulate cortex, medial prefrontal cortex and superior frontal cortex, and the posterior DMN mask comprising the posterior cingulate cortex and precuneus (Supplementary Figure 1). The Cambridge parcellation is a derivative from the Cambridge sample found in the “1000 functional connectome project” (http://fcon_1000.projects.nitrc.org/fcpClassic/FcpTable.html, accessed July 11, 2019) [48], in which brain parcellations were generated from 200 young healthy participants resting-state fMRI images, using a method called bootstrap analysis of stable clusters (BASC).

Statistical analysis of functional connectivity was performed with SPM12. Group differences in functional connectivity were assessed using a GLM, controlling for age, sex, and depression score from the BDI. Separate linear regression analyses at the whole group level were conducted with ESS and mean sleep latency at the MSLT, controlling for age, sex, and BDI.

Correction for multiple comparisons

Voxel-wise and vertex-wise statistical analyses were corrected for multiple comparisons using the random field theory for non-isotropic images [47]. A statistical threshold of p < 0.001 corresponding to a T > 3.5 was first applied, which is the minimum height threshold recommended [49]. An extent threshold of p < 0.05 corrected for multiple comparisons was then applied at the cluster level. Images were overlaid on the average anatomical MRI scan in MNI standard space from all the participants.

Results

Clinical variables

Six out of 18 participants with IH were not included in the study, because five had comorbid clinical depression and one did not fulfill the ICSD-3 criteria. In addition, two good sleeper participants were not included in the analysis: one participant because of contraindication to the MRI scan but did complete the SPECT scan; another participant because they were identified as an outlier in the VBM analysis (large and asymmetric ventricles). Therefore, the final sample comprised 12 participants with IH and 15 good sleepers. Ten out of the 12 participants with IH had a history of daily long sleep time (i.e., 24-hour total sleep time > 11 hours). Demographic and clinical variables from all participants are presented in Table 1. Participants with IH had significantly higher scores on ESS and PSQI, indicating higher self-reported daytime sleepiness and more complaint about their sleep—especially its impact on daytime function. The difference in PSQI scores was indeed driven by the daytime dysfunction subscale (mean score IH: 2.3, GS:0.3, t-value = 9.9, p < 0.001). Although the clinical threshold for depression and anxiety was not reached, participants with IH had significantly higher scores in both BDI and BAI. No group difference was detected on chronotype, polysomnography measures, and demographic variables.

Table 1.

Participants’ characteristics including demographics, polysomnography measures, questionnaire scores, and brain measures

Clinical variables
	Good sleepers	Idiopathic hypersomnia
	Mean (SD)	Mean (SD)	P-value
Demographics
N	15	12
Ratio male/female	6M/9F	3M/9F	0.083
Age	31.2 (9.8)	33.4 (10.1)	0.523
BMI (kg/m2)	23.52 (2.35)	23.77 (4.56)	0.862
Education (years)	16.53 (1.88)	15.75 (2.83)	0.397
Symptoms duration (years)		12.0 (8.7)
Multiple Sleep Latency Test
Latency at MSLT (minutes)		7.28 (3.34)
Number of SOREMPs at MSLT		0.25 (0.45)
Polysomnography
TST (minutes)	420.88 (59.11)	455.46 (40.49)	0.097
Mean sleep latency (minutes)	13.21 (7.65)	13.34 (8.79)	0.966
REM latency (minutes)	117.85 (47.02)	97.88 (33.55)	0.294
sleep efficiency (%)	91.01 (3.95)	91.52 (5.49)	0.784
WASO (minutes)	23.22 (17.04)	32.32 (20.59)	0.220
REM (minutes)	100.64 (38.61)	90.42 (38.26)	0.550
Questionnaire scores
ESS (/24)	4.9 (2.4)	17.5 (4.4)	<0.0001***
PSQI (/21)	3.1 (1.2)	4.9 (1.0)	<0.0001***
MEQ (/86)	54.7 (8.9)	49.2 (8.2)	0.111
BDI (/63)	2.7 (3.0)	10.4 (6.8)	0.001*
BAI (/63)	2.4 (3.3)	10.2 (9.9)	0.008*
Brain measures
TIV (mm3)	1442 (123)	1486 (145)	0.402
GM (mm3)	674 (44)	681 (61)	0.743
WM (mm3)	491 (47)	519 (62)	0.205
CSF (mm3)	276 (50)	286 (38)	0.559

BMI: body mass index, MSLT: multiple sleep latency test; TST: total sleep time; WASO: wake after sleep onset; ESS: Epworth Sleepiness Scale; PSQI: Pittsburgh Sleep Quality Index; MEQ: Morningness-Eveningness Questionnaire; BDI: Beck Depression Inventory; BAI: Beck Anxiety Inventory; TIV: total intracranial volume; GM: gray matter; WM: white matter; CSF: cerebrospinal fluid. P-values from Student’s t-test comparing groups are reported. ***p <0.001. *p <0.05.

Gray matter volume

There was no significant difference in overall gray matter, white matter or CSF volume between IH and good sleepers (Table 1).

Participants with IH had smaller inferior frontal gyrus compared to good sleepers (Figure 1, left panel; Table 2). Participants with IH had larger gray matter volume in the left middle occipital gyrus and right precuneus, both of which belong to the posterior DMN (Figure 1, right panel; Table 2).

Figure 1.

Group differences in gray matter volume. The left panel depicts regions with lower volume in idiopathic hypersomnia compared to good sleepers. The right panel depicts regions with greater volume in hypersomnia compared to good sleepers. The figures represent T-maps. T-map of group difference in gray matter volume controlling for age, sex and depression score. A statistical threshold of p < 0.001 corresponding to a T > 3.5 was first applied, followed by an extent threshold of p < 0.05 corrected for multiple comparisons at the cluster level. Abbreviations: L: left, R: right, A: anterior, P: posterior, TIV: total intracranial volume, X,Y,Z represent coordinates in MNI standard space.

Table 2.

Statistics table of whole-brain analyses

Region	Side	Cluster size	Cluster p-value	T-value	Z-value equivalent	Voxel p-value	MNI coordinates (x, y, z)
GM: Good Sleepers > Idiopathic Hypersomnia
Inferior frontal gyrus	Left	233	0.044	4.48	3.71	<0.001	−57	21	12
				4.40	3.66	<0.001	−48	22	10
GM: Idiopathic Hypersomnia > Good Sleepers
Middle occipital gyrus	Left	500	<0.001	6.74	4.86	<0.001	−6	−69	14
				4.19	3.53	<0.001	−12	−80	0
Precuneus	Right	264	0.025	5.09	4.06	<0.001	2	−54	52
GM: Regression with subjective daytime sleepiness score (positive)
Middle occipital gyrus	Left	343	0.006	6.37	4.70	0.000	−9	−68	14
CT: Idiopathic Hypersomnia > Good Sleepers
Precuneus	Left	2403	0.046	3.75		<0.001	−14	−64	33
SC from MPFC: Interaction
Precuneus	Left	7145	0.005	3.75		<0.001	−14	−64	33
FC: Good Sleepers > Idiopathic Hypersomnia
Orbitofrontal cortex	Right	62	0.079	6.41	4.77	<0.001	6	36	−28
FC: Regression with subjective daytime sleepiness score (negative)
Fusiform gyrus	Left	73	0.045	5.21	4.16	<0.001	−50	−24	−24
Medial prefrontal cortex	Left	74	0.043	4.86	3.97	<0.001	−10	52	4
				4.41	3.69	<0.001	6	56	4
Orbitofrontal cortex	Right	95	0.016	4.67	3.85	<0.001	6	28	−24
				4.65	3.84	<0.001	6	8	−12
				4.54	3.77	<0.001	6	40	−24

Statistics table of the group differences and regressions with self-reported daytime sleepiness measured with the ESS at the whole group level for gray matter volume (GM), cortical thickness (CT), structural covariance (SC) and functional connectivity (FC) controlling for age, sex and depression score. A statistical threshold of p < 0.001 corresponding to a T > 3.5 was first applied, followed by an extent threshold of p < 0.05 corrected for multiple comparisons at the cluster level.

The voxel-wise regression showed no significant correlation with the DMN, but there was a significant positive correlation between gray matter volume in the middle occipital gyrus and self-reported daytime sleepiness (Table 2). No significant voxel-wise association was found between gray matter volume and objective daytime sleepiness from the MSLT in IH.

When no correction for depression score was applied, no region showed smaller gray matter volume in IH compared to good sleepers. Participants with IH had larger gray matter volume in the precuneus compared to good sleepers, which corresponds to the posterior part of the DMN (Supplementary Figure 2, Supplementary Table 1).

The voxel-wise regression showed a significant positive correlation between gray matter volume in the precuneus and subjective daytime sleepiness (Supplementary Figure 3, Supplementary Table 1).

Cortical thickness

Participants with IH had thicker precuneus, which corresponds to the posterior DMN; no regions showing lower cortical thickness were observed (Figure 2, Table 2).

Figure 2.

Cortical thickness maps. The left panel depicts the T-map of cortical thickness differences between idiopathic hypersomnia and good sleepers. Cold colors represent thinner cortex and warm colors represent thicker cortex in idiopathic hypersomnia compared to good sleepers. The right panel depicts the p-map of regions with significantly thicker cortex in hypersomnia compared to good sleepers, controlling for age, sex, and depression score. A statistical threshold of p < 0.001 corresponding to a T > 3.5 was first applied at the vertex level, followed by an extent threshold of p < 0.05 corrected for multiple comparisons at the cluster level. Abbreviations: L: left, A: anterior, P: posterior.

The vertex-wise regression showed no significant association between cortical thickness and subjective or objective daytime sleepiness.

No significant cortical thickness difference between groups was observed without controlling for depression scores.

Structural covariance

Structural covariance results showed that, in both groups, thickness of the precuneus was correlated with thickness of the rest of the DMN, i.e. in the inferior parietal lobe, medial prefrontal cortex, inferior frontal gyrus, and superior temporal gyrus; thickness of the medial prefrontal cortex was correlated with thickness of the rest of the DMN, i.e. in the medial prefrontal cortex, postcentral gyrus, inferior frontal gyrus and inferior temporal gyrus (Figure 3).

Figure 3.

Structural covariance maps. The top left panel depicts the R-map of structural covariance from the precuneus in idiopathic hypersomnia. The top right panel depicts the R-map of structural covariance from the precuneus in good sleepers. The bottom left panel depicts the R-map of structural covariance from the medial prefrontal cortex (MPFC) in idiopathic hypersomnia. The bottom right panel depicts the R-map of structural covariance from the MPFC in good sleepers. Abbreviations: L: left, A: anterior, P: posterior.

In addition, there was a structural covariance by group interaction: structural covariance between the left medial prefrontal cortex (MNI coordinates x = −2, y = 44, z = 16) and left precuneus was greater in IH participants compared to good sleepers (Figure 4). There was no significant group difference in structural covariance from the precuneus, only structural covariance within the precuneus showed a subthreshold difference between groups (T > 2 uncorrected). No regions showed higher structural covariance in good sleepers compared to IH, nor any vertex-wise regression with subjective or objective daytime sleepiness.

Figure 4.

Group difference in structural covariance. Structural covariance interaction T-map from the medial prefrontal cortex (right panel). A statistical threshold of p < 0.001 corresponding to a T > 3.5 was first applied, followed by an extent threshold of p < 0.05 corrected for multiple comparisons at the cluster level.

No significant structural covariance difference between groups was observed without controlling for depression scores.

Functional connectivity at rest

During the resting-state fMRI acquisition, none of the participants fell asleep. There was no functional connectivity difference from the “posterior DMN” between groups.

The “anterior DMN” was functionally connected with itself and with the rest of the DMN in both groups (Figure 5). Functional connectivity at rest was lower between the anterior DMN and the orbitofrontal cortex network in IH participants compared to good sleepers (Figure 6, Table 2). The network has been visually identified as the orbitofrontal network from the Cambridge parcellation [48] and includes the orbitofrontal cortex bilaterally and the fusiform gyrus. No regions showed stronger functional connectivity with the DMN at rest in IH compared to good sleepers.

Figure 5.

Functional connectivity maps. The left panel depicts the R-map of functional connectivity from the anterior default-mode network (anterior DMN, i.e. medial prefrontal cortex) in idiopathic hypersomnia and in good sleepers. The right panel depicts the T-map of regions showing lower functional connectivity from the anterior DMN in idiopathic hypersomnia compared to good sleepers controlling for age, sex, and depression score. A statistical threshold of p < 0.001 corresponding to a T > 3.5 was first applied, followed by an extent threshold of p < 0.05 corrected for multiple comparisons at the cluster level. Abbreviations: L: left, R: right, A: anterior, P: posterior, X,Y,Z represent coordinates in MNI standard space.

Figure 6.

Voxel-wise regression between functional connectivity and self-reported daytime sleepiness. The left panel represents the T-map of the whole brain regression between functional connectivity and self-reported daytime sleepiness from the Epworth Sleepiness Scale (ESS), controlling for age, sex, and depression score. A statistical threshold of p < 0.001 corresponding to a T > 3.5 was first applied, followed by an extent threshold of p < 0.05 corrected for multiple comparisons at the cluster level. The right panel shows the functional connectivity strength between the default-mode network (DMN) and the orbitofrontal network (ORB)—which was visually identified as the orbitofrontal network from the Cambridge parcellation—plotted against Epworth sleepiness score from the voxel with the maximum T-value. Abbreviations: L: left, R: right, A: anterior, P: posterior, X,Y,Z represent coordinates in MNI standard space.

The voxel-wise regression showed no significant positive correlation between functional connectivity at rest from the anterior DMN and subjective daytime sleepiness in both groups. There was a significant negative correlation between functional connectivity at rest within the anterior DMN and self-reported daytime sleepiness in both groups (i.e. inferior temporal gyrus, medial prefrontal cortex, orbitofrontal cortex, Figure 6, Table 2). No significant regression was found between the functional connectivity at rest from the anterior DMN and objective daytime sleepiness in IH.

Without controlling for depression score, functional connectivity at rest was lower between the anterior DMN and the fronto-parietal network in IH participants compared to good sleepers (Supplementary Figure 4, left panel; Supplementary Table 1). Functional connectivity was lower between the anterior DMN and the rest of the DMN (namely the medial prefrontal cortex, the inferior temporal gyrus, and postcentral gyrus) in IH participants compared to good sleepers (Supplementary Figure 4, right panel; Supplementary Table 1). The voxel-wise regression showed no significant positive correlation between functional connectivity at rest from the anterior DMN and self-reported daytime sleepiness in both groups. There was a significant negative correlation between the anterior DMN-fusiform functional connectivity and self-reported daytime sleepiness in both groups (Supplementary Figure 5, Supplementary Table 1).

Discussion

Given the importance of the DMN in alertness, sleep physiology, and based on previous findings in IH, we investigated the structural and functional modifications in the DMN with multimodal MRI to better characterize the pathophysiology of IH. We showed that functional connectivity at rest within the anterior (but not the posterior) DMN, i.e. the medial prefrontal cortex was lower in participants with IH. Functional connectivity was negatively correlated with subjective daytime sleepiness. In contrast, structural analyses showed that participants with IH had larger posterior DMN structure compared to good sleepers, namely increased gray matter volume and thicker precuneus. In addition, cortical thickness of the medial prefrontal cortex was more strongly correlated with cortical thickness of the posterior DMN (namely the precuneus) in participants with IH compared to good sleepers. Overall, the DMN hubs—the precuneus and medial prefrontal cortex—demonstrated significant changes in IH, and functional connectivity in the DMN correlates with self-reported but not objective sleepiness severity (as measured by the MSLT). Functional connectivity can be seen as a surrogate measure of regional cerebral blood flow for short-range connections [50, 51]. Therefore, the present findings of altered anterior DMN functional connectivity in IH are consistent with previous SPECT findings from our group that showed lower regional cerebral blood flow in the DMN (i.e. in the medial prefrontal cortex, posterior cingulate cortex, and putamen [5]. In narcolepsy with cataplexy, lower regional cerebral blood flow and metabolism in the medial prefrontal cortex has been described specifically within the inferior parietal lobe and anterior cingulate cortex [52–57], along with regions outside the DMN [52, 58–61], see [62] for a review. However, only one study investigated functional connectivity in those patients and showed lower functional connectivity outside the DMN, within the executive network (left medial frontal gyrus) and the salience network (right caudate) [63]. On the other hand, structural differences in the DMN have also been found in narcolepsy with cataplexy where gray matter volume in the medial prefrontal cortex, cingulate cortex, orbitofrontal cortex, and inferior parietal lobule was lower [64–69]. In addition, several regions outside the DMN also showed a smaller gray matter volume, particularly the hypothalamus, in line with a loss of hypocretinergic neurons [52, 69–74], see ref. [62] for a review. Our findings show a partial overlap between regions altered in IH and in narcolepsy with cataplexy but with opposite volume differences that could be explained by the absence of a loss of hypocretinergic neurons in IH and a structural compensation of the functional changes in other brain regions.

In primary insomnia, reduced metabolism, measured with FDG-PET, was found in the medial prefrontal cortex at wake [75]. However, fMRI evidence demonstrated that this reduced activity was not solely restricted to the anterior DMN, but rather extended to the posterior DMN at wake [76]. Structural differences in the DMN have also been found, corresponding to lower gray matter volume in the orbitofrontal cortex and precuneus [41, 77, 78], although other studies have also found increased gray matter volume in the rostral anterior cingulate cortex [79]. Additionally, lower structural covariance between the frontal and parietal regions of the DMN has been found in primary insomnia, potentially reflecting a lower propensity to sleep [41]. The structural changes in primary insomnia contrast with our present findings in IH, in which we found greater structural covariance within medial regions (i.e. the medial prefrontal cortex and the precuneus), which might reflect a greater propensity to sleep. Therefore, the greater cortical thickness and structural covariance are a feature of IH that makes it distinct from other sleep disorders such as primary insomnia or narcolepsy with cataplexy.

Functional connectivity changes have also been studied in the context of sleepiness associated with sleep deprivation. Sleep deprivation has been shown to reduce functional connectivity within the DMN (between the anterior and posterior parts) at rest [80]. These connectivity changes are dependent on the level of sleep pressure, i.e. functional connectivity decreases as a function of the duration prior wakefulness [80–82]. Lower anterior to posterior DMN connectivity has been shown related to increased daytime sleepiness in young healthy adults [83] and to worse sleep quality in adolescents [84]. In our current study, we observed a more focused difference in functional connectivity, which was restricted to the anterior part of the DMN in association with greater daytime sleepiness in IH, in contrast to the anterior to posterior connectivity change found after sleep deprivation.

There are some limitations to this study. First, there was a group difference in BDI score, with IH showing higher depression score than controls. That is why we controlled for BDI score in all our analyses. For completeness, results without accounting for BDI are presented in the supplementary data and show gray matter volume and functional connectivity differences that were similar to the corrected results. However, there was no difference in cortical thickness and structural covariance between groups when BDI was not accounted for. Second, the present study has a limited sample size, which warrants replication in a larger sample. Given the limited number of participants with IH (N = 12), our study might have been underpowered to evidence correlations between DMN connectivity and objective daytime sleepiness. Finally, the lack of association between MSLT and connectivity could be explained by the fact that the MSLT was conducted as part of the clinical diagnostic procedure of participants and was not repeated immediately prior to the imaging session.

In conclusion, the DMN—a brain network key to alertness and sleep—is affected in IH. Differences in the DMN in IH compared to controls are strikingly distinct from those reported in other sleep disorders such as narcolepsy with cataplexy and chronic insomnia. The greater gray matter volume and cortical thickness in the posterior DMN in IH may be a compensatory mechanism to the lower regional cerebral blood flow and functional connectivity in the anterior DMN. These opposite changes between functional and structural modalities have also been reported in conditions such as in multiple sclerosis, where structural white matter alterations were accompanied by higher functional connectivity and less cognitive efficiency [85]. Future studies are needed to further characterize the structural and functional changes that are specific to IH. This could be achieved by comparing a larger sample of individuals with IH to samples of narcolepsy participants (with and without cataplexy), as well as participants with sleepiness due to sleep deprivation. Ultimately these studies will be important for identification of neural biomarkers that are specific to each central disorder of hypersomnolence.

Funding

This work was supported by the Sleep Research Society Foundation. T.T.D-V. was also supported by the Canadian Institutes of Health Research (MOP 142191, PJT 153115 and PJT 156125), the Natural Sciences and Engineering Research Council of Canada (RGPIN 436006-2013), the Fonds de Recherche du Québec – Santé, the Canada Foundation for Innovation and Concordia University. H.K. was supported by the National Institutes of Health (P41EB015922) and BrightFocus Foundation (A2019052S).

Conflict of interest statement. J.M. has received grants or support from Merck and GSK, was on the advisory board of Jazz Pharmaceuticals, Valeant Pharmaceuticals, and UCB Canada, and was a consultant for Valeant Pharmaceuticals. The other authors have no conflicts of interest to disclose.