M.E. Beemster (student ID: 11674563)
. Kocevska and E.J.W. van Someren
February 2020 – July 2020
Universiteit van Amsterdam
Connectivity within
default mode
network is not
altered between
insomnia patients
that are at high or
low risk to develop
depression and
good sleepers
Bachelorthesis Psychobiology
Abstract
Background. Insomnia is a very burdening disease. People that suffer from insomnia have increased
disease risk. Specifically, people that suffer from insomnia have a risk twice as high to develop depression than good sleepers. Recently, five subtypes of insomnia have been uncovered. These subtypes differ in their risk for developing depression. Furthermore, the default mode network (DMN) is a resting-state network very commonly reported in both insomnia and depression. This could point to an underlying neural mechanism. Also, rumination (repetitive, prolonged, and recurrent thought about one’s self, one’s concerns and one’s experiences) is a phenomenon that occurs in both insomnia and depression. The main objective of this study was to test if any resting-state connectivity differences in the DMN exist between insomnia patients and good sleepers and between insomnia patients at high risk or at low risk for developing depression and good sleepers. Additionally, it was studied if rumination had a modulatory role in the differential risk for depression among insomnia subtypes.
Methods. To extract the DMN from the resting-state data, a group ICA was performed. Then, to test
for differences in resting-state connectivity in the DMN between groups, dual regression was carried out. Also, exploratory analysis was done for the other resting-state network components of the group ICA. For the rumination score, two linear regression models were set up. RRS (rumination response scale) score was added as a covariate in the second model
Results. No significant differences in activity were found in the DMN. Neither between insomnia
patients and good sleepers, nor between insomnia patients at high or low risk to develop depression and good sleepers. In the exploratory analysis higher activity was found in a cluster in the left cerebellum (p=0.030, z=1.491) in insomnia patients at high risk for depression versus good sleepers. This finding indicates that resting-state connectivity in the low risk for depression group is higher than in the control group in this specific cluster. Further, RRS score did not act as a significant modulator for the risk for depression among the insomnia subtypes.
Conclusion. This study did not support the hypothesis that DMN resting-state connectivity is altered
between insomnia patients and good sleeps. Also, resting-state connectivity in the DMN was not altered between the subtypes of insomnia that are at high or low risk for developing depression. Thus, the neurobiological basis for the link between insomnia and depression remains unclear. Also, rumination does not modulate the differential risk for depression among the subtypes of insomnia.
Introduction
Humans spend approximately one third of their lives asleep. This is for a good cause, because sleep is essential to maintain good health, physically as well as mentally. Poor sleep increases the risk for certain heart diseases, type II diabetes and hypertension (Khan & Aouad, 2017) and is a predictor of depression, anxiety and psychosis (Hertenstein et al., 2019). Unfortunately, many people meet the criteria for insomnia disease (ID) as classified in the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Cao et al. (2017) found that 15% of the population in China suffers from chronic insomnia and even numbers ranging up until 37.2% were reported in France and Italy in representative cohorts of over 1000 people. (Leger & Poursain, 2005). Besides the negative effects insomnia has on the lives of those who suffer from it, these sleep problems also pose a huge burden on society (Léger & Bayon, 2010). These burdens are mainly caused by absenteeism at work and the large amount of healthcare costs that it comes with (Daley et al., 2009).
Insomnia is a neurobiological disorder and the brain mechanisms underlying it have been studied by many researchers using different MRI techniques. Although there is no consensus yet about what aberrant brain morphology or altered activity might lead to or result from insomnia, some interesting findings have been reported. In a review of (Riemann, Kloepfer and Berger (2009) is stated that hippocampal volume is decreased in people with insomnia. Another review conducted by (Jiang et al. (2019) meta-analysed 28 studies including 951 ID patients and 884 healthy controls. The researchers found that ID patients showed increased activity during resting-state fMRI in the right
parahippocampal gyrus and the left median (para)cingulate gyri. On the other hand, the same meta-analysis showed that insomnia patients showed decreased activity in the right cerebellum and the left superior frontal gyrus (Jiang et al., 2019). In another resting-state study enhanced functional connectivity (FC) between the bilateral hippocampus and the left middle frontal gyrus was found (Leerssen et al., 2019).
As is apparent from the previous paragraph, the hippocampus is commonly reported in insomnia. The hippocampus is part of the default mode network (DMN) (Gebhart & Schmidt, 2013), a network also frequently reported in insomnia (Li et al., 2018; Marques et al., 2017; Regen et al., 2016). Research conducted in 2016 looked specifically into the interconnectivity within the DMN in relation to insomnia (Regen et al., 2016). What the researchers found was a negative association between DMN interconnectivity and sleep efficiency (Regen et al., 2016). This association was specifically found for connectivity of the hippocampus with other areas of the DMN (Regen et al., 2016). In another study of Marques et al. (2017) it was found that in insomnia patients the DMN did not
deactivate when words were shown related to past and present ruminations and future worries. In a study investigating functional connectivity between networks, it was reported that some areas connecting the DMN to other resting state networks showed decreased functional connectivity (Li et al., 2018). These results were also summarized in a review, that again emphasized the role of the DMN in insomnia (Khazaie et al., 2017).
The DMN is a resting state network that comprises of the following brain areas: ventral medial prefrontal cortex (vmPFC), dorsal medial prefrontal cortex (dmPFC), medial temporal lobe (MTL), inferior parietal lobe (IPL), the precuneus (PCN), posterior cingulate cortex (PCC)/retrosplenial cortex (Rsp) and the hippocampal formation (HF+) (Marques et al., 2017). Andrews-Hanna et al. (2010) came up with a subdivision of the DMN. Three parts of the network were discerned; a midline core and two subsystems. The midline core activates when making self-relevant, affective decisions. The dmPFC subsystem is mainly engaged in considering current mental states and the MTL subsystem is for making episodic decisions about the future (Andrews-Hanna et al., 2010). Overall, it can be stated that the DMdefaultN is mainly engaged when someone is not focusing on the outside world, but is turned inwards, e.g. during mind-wandering (Christoff et al., 2009).
As noted earlier, people that suffer from insomnia have a risk twice as high for developing depression, as compared to healthy controls (Baglioni et al., 2011). This could point to a shared underlying (neural) mechanism of insomnia and depression. A considerable amount of literature has been published studying the neural mechanisms of both these diseases (Bagherzadeh-Azbari et al., 2019; Liu et al., 2018; Zhu et al., 2020). Some striking evidence was found for similarities in those brain circuits. For example, Liu et al. (2018) found increased Amplitude of Low Frequency
Fluctuations (ALLF, a method of assessing resting-state connectivity) in the Inferior Frontal Gyrus (IFG) and Anterior Insula (AI) in Major Depressive Disorder (MDD) patients with high insomnia compared to low insomnia. Even a model was proposed in which the resting-state Functional Connectivity (rsFC) between DMN and visual networks acted as a mediator between low sleep efficiency and height of the Hamilton Rating Scale for Depression (HAMD) score. (Zhu et al., 2020). Another meta-analysis was performed which brought forward that the DMN (among other networks) was the most consistently altered in both MDD and insomnia (Bagherzadeh-Azbari et al., 2019).
In summary, it is well established that the DMN plays a significant role in insomnia and in depression. But insomnia is not a homogeneous disorder. As recently reported by Blanken et al. (2019), insomnia disorder can be subdivided into five subtypes. These subtypes are characterized by different distress
levels, reward-sensitivity and reactivity (Blanken et al., 2019). In the light of this study it is interesting that these subtypes also differ in their risk for developing depression. Subtype one, two and three have a high risk for developing depression and will therefore be referred to as the high risk for depression group in the remainder of this thesis. Subtype one shows the highest levels of distress and is mainly characterised by increased negative affect and pre-sleep arousal and reduced
subjective happiness. Subtype two and three are both characterised by moderate distress levels and are respectively reward-sensitive and reward-insensitive. These two subtypes show the same characteristics as subtype one, but subtype two shows no reduced subjective happiness but does exhibit an insomnia response to stress. Subtype four and five have a low risk for developing
depression as a consequence of their insomnia, these subtypes will be addressed as the low risk for depression group in the rest of this thesis. Both subtype four and five exhibit low levels of distress and are primarily characterised by an increased duration of insomnia response to life events, but less fatigue and rumination as compared to the first three subtypes.
Thus, a factor which is different between the high risk for depression and the low risk for depression group is rumination. Rumination is defined as ‘repetitive, prolonged, and recurrent thought about one’s self, one’s concerns and one’s experiences’ (Watkins, 2008). It is a phenomenon that presents itself often in both depression and insomnia (Blanken et al., 2019; Watkins, 2008). Furthermore, it was found in a meta-analysis that the DMN is a very dominant circuit involved in rumination. Especially the core subsystem of the DMN plays a significant role as almost half of the voxels of this subsystem activated during rumination induction (Zhou et al., 2020).
The main aim of this study is to investigate whether differences exist in resting-state connectivity within the DMN. First, between insomnia patients and good sleepers and then between insomnia patients in the high risk for depression and in the low risk for depression group versus the good sleepers. Another question that will be addressed is whether rumination modulates the association between insomnia subtype (high vs. low risk for depression) and depression symptoms. It is
hypothesized that the DMN resting-state connectivity will be higher in the high risk group than in the control group. The same is hypothesized for the low risk group versus the control group, but the difference with the controls is hypothesized less apparent than when comparing the high risk group with the controls. The hypothesis will be tested by analysing resting-state fMRI (rs-fMRI) data and questionnaire data regarding rumination.
The primary goal of this study is to gain more insights into neurobiological basis of insomnia and the neurobiological link between insomnia and depression and how rumination might modulate this link. Additionally, evidence for the insomnia subtypes proposed by Blanken et al. (2019) will be expanded by investigating if differences exist in neurological basis between insomniacs at high and low risk for depression versus good sleepers. This study might also be useful when considering the goal of the Research Domain Criteria (RDoC) efforts. The RDoC committee advocate to not only focus on psychiatric diagnoses (using the DSM), but to also take observable behavioural and neurobiological measures into consideration when diagnosing people with a psychopathology (Regen et al., 2016). Also, few studies have used as many subjects as will be used in this study, therefore it will now be possible to detect less robust changes in resting-state connectivity with statistical significance.
Methods
Participants
The study sample consisted of 199 participants. Of these, 163 are insomnia patients divided in 5 sub-groups and 36 are controls. Due to technical issues, only 192 participants are included in the rs-fMRI analysis. The participants for this study are recruited via sleepregistry.nl or through online flyers and advertisements. Only after informed consent was given, the data of the participants was used. Inclusion criteria for the insomnia group were to meet the criteria of Insomnia Disorder (ID) as described in the DSM-5 and in the 3rd edition of the International Classification of Sleep Disorders (ICSD-3), an Insomnia Severity Index (ISI) score of at least 10 (cut-off score) and age must be between 18 and 70 years old. Exclusion criteria were: a current diagnosis of sleep apnoea, restless leg
syndrome, narcolepsy or any other neurological, psychiatric or somatic disorders; the use of sleep medication in the 2 months prior to the study; shift-work; or any MRI contraindication such as non-MR compatible metal implants, claustrophobia or pregnancy (Leerssen et al., 2019). The control subjects are healthy sleepers in the same age range and the same exclusion criteria apply to them (Leerssen et al., 2020). The insomnia patients were described to a certain subtype (see introduction for further elaboration on the subtypes) based on the criteria described in the paper of Blanken et al. (2019).
Questionnaires
In this study, three questionnaires were used. The Insomnia Severity Index (ISI) was used to assess severity of insomnia symptoms. This questionnaire is a reliable self-report measure (Bastien, Vallières & Morin, 2000). An exemplary question is: ‘how satisfied are you with your current sleep pattern?’. Every question is assessed using a scale ranging from 0 to 4. The meaning of the scale is indicated in every question. The outcome score of the questionnaire is the sum of the scores for all the
questions. The Inventory of Depressive Symptoms (IDS) is used to determine severity of depressive symptoms. It is recommended by (Buysse et al., 2006)to use this questionnaire in insomnia research. In this study, the self-report version (IDS-SR) was used. The questionnaire consists of 30 questions, whereof 28 need to be filled out. The questions address topics such as mood and self-image and are rated by the participant on a scale of 0 to 3. Lastly, the Rumination Response Scale (RRS) is used to assess the degree to which the participants tend to ruminate. The scale used in this study consists of 26 items, which are all being rated by the participants on a scale from 1 (almost never) to 4 (almost always) (Treynor, Gonzalez & Nolen-Hoeksema 2003). Participants need to indicate for every statement if they generally do what is specified in the statement when they feel down, sad or depressed. The statements measure reflection, brooding and depression-relatedness. An example of a statement is ‘think about how sad you feel’.
fMRI data-acquisition specifics
All scans were made using a 3-Tesla MRI scanner in combination with a 32-channel head coil (Philips Achieva, Best, The Netherlands). A total of 4 scans were obtained. The first scan was a T1 weighted structural scan. This scan was made following the ADNI protocol (Clifford et al., 2008). Echo time (TE) and repetition time (TR) were respectively 6.5 ms and 2.9 ms. The voxel size was 1 mm3, 211 slices were obtained and the field of view (FOV) was 256x256x11mm. The flip angle was 9°. The second and third scans were functional scans; a task based and a resting state scan (gradient-echo planar imaging (EPI) was employed with a multi-band factor of 4. TR/TE = 700/30 ms, voxel size = 2.7mm3, 44 slices with no interslice gap, FOV = 216x216x118, flip angle = 55°). The fourth and last scan was a diffusion weighted scan (multiband factor 2, TR/TE = 4683/95 ms, voxel size = 2 mm3, 66 slices with no interslice gap, FOV = 224x224x132mm, flip angle = 90°, b = 1000, 29 directions, b = 2000, 59 directions). Before the functional scans and the DTI a B0 fieldmap scan was performed (TR/TE both shortest, voxel size 2mm3, 128 slices with no interslice gap, FOV = 256 x 208 x 256, flip angle = 8°).Total scan time was approximately 45 minutes.
During the resting-state scan a fixation cross was displayed for 12 minutes. While looking at the fixation cross, participants were instructed to keep their eyes open and to let their minds wander. Also, they were told to try to not fall asleep, this was being monitored during the scan by video recording the participants’ eyes. (Leerssen et al., 2020).
Pre-processing data steps
Several steps were performed to pre-process the structural (T1-weighted) and functional (resting-state) images (Leerssen et al., 2019). Pre-processing was performed using FSL (version 5.0.10,
Jenkinson et al., 2012). Firstly, brain extraction was performed on the structural images to eliminate any non-brain matter (e.g. neck, skull, eye nerve). This was done using the BET tool (Smith, 2002). To correct for signal instabilities, the first three volumes were disposed of and by using the FNIRT tool (Andersson, Jenkinson & Smith, 2010) the images were corrected for motion in a non-linear way. The FEAT (FMRI Expert Analysis Tool, Version 6.0) is part of FSL (version 5.0.10, Jenkinson et al., 2012) and was used to pre-process the resting-state images. Then, for the resting-state scans B0 fieldmap correction was carried out (
https://osf.io/hks7x
) to correct for any field inhomogeneities that were caused by the scanner. Then the image was spatially smoothed using a Gaussian kernel with a full width at half maximum (FWHM) of 5mm, to enhance the signal to noise ratio. Subsequently, the ICA-AROMA (Independent Component Analysis – Automatic Removal of Motion Artifacts) algorithm was applied (Pruim et al., 2015). This tool uses ICA to decompose the data into a set of spatiallyindependent maps and time-courses. Using the ICA, activity was classified into artefacts (e.g. motion (Pruim et al., 2015) and cardiac and respiratory activity) or signal (resting-state networks). Then, regression out of nuisance variables was carried out. Examples of nuisance variables are white matter or CSF (cerebrospinal fluid). This step is important because fluctuations over time in these types of tissue are unlikely to be modulated by neuronal activity (Noorbaloochi, Nelson & Asgharian, 2010). As second last, high pass filtering was done with a cut-off of 100s (0.01 Hz). This was done to filter out any effects of slow changes in magnetic field strength (Smith et al., 1999). Lastly, non-linear registration was done to register the rs-fMRI image onto the 2mm3 Montreal Neurological Institute
(MNI) standard brain template (Regen et al., 2016). This took place in two steps. First, the rs-fMRI image was mapped onto the participants structural image (from the BET preprocessing), and second, this image was then mapped onto the standard brain. Mapping the rs-fMRI images onto the standard brain facilitates comparing resting state networks between participants and groups.
Statistical analysis
Demographics. The demographic characteristics of the insomnia and the control group were
compared using R studio (version 3.5.2). The continuous variables (Age, ISI score, RRS score, IDS score) were compared using Mann-Whitney U tests, since the assumptions of normal distribution was not met in any of the variables (tested with Shapiro-Wilk tests). Also, none of the continuous variables met the assumption of equal variances between groups (assessed using Levene’s tests). Sex, a categorical variable, was compared between groups using a Chi-square test.
Group ICA. In the first step of the analysis, a group ICA was carried out (Erhardt et al., 2011). The
was separated into 20 components, each of which represent large scale patterns of functional connectivity. These components were visually inspected and compared to earlier detected resting state networks by Beckmann et al. (2005) and Nickerson (2018) to determine in which component the DMN was best represented (Nickerson, 2018). The reason 20 components were used was to assure that the DMN would be represented in one component instead of being split up in two or more components.
DMN analysis. Then, all the group IC components were fed into a dual regression, together with the
events and contrasts of the group level design-matrix and the input files from the group-ICA. Two dual regressions were carried out; one comparing the insomnia patients with the controls and one comparing the high risk for depression group and the low risk for depression group with the controls. In this analysis step the group IC maps are used to identify corresponding resting connectivity maps in the separate subjects by doing voxel-wise comparisons (Filippini et al., 2009; Nickerson, 2018). A dual regression involves two steps. The first step is a spatial regression in which matrices are created that describe temporal dynamics for each ICA component (resting-state network) and subject. These matrices are created by making a linear model fit with the full set of group-ICA spatial maps. In a second step, with these matrices a temporal regression is carried out. Thus, for this study, two temporal regressions were carried out. One for every group comparisons. Lastly, six unpaired one-sided two-sample t-tests were carried out using the randomise command (1000 permutations). For the first comparison (insomnia patients versus controls) two t-tests were done to compare resting-state connectivity in the DMN of the insomnia group with resting-resting-state connectivity in the DMN in the controls in both ways. For the second comparison (high risk for depression and low risk for depression group versus controls) four t tests were performed to test whether any significant differences in resting-state connectivity in the DMN exist between the high risk for depression group and the controls (in both ways) and between the low risk for depression group and the controls (in both ways). In this step the different component maps are being transformed into 4D files (the dimensions represent voxels, time, number of components and individual subjects) and these are then tested voxel-wise for significant differences between groups (Filippini et al., 2009). Finally, the output was corrected for multiple-comparisons (within group-ICA components) (Han & Glenn, 2018). This main analysis was performed was into the DMN. By performing two dual regressions, it was assessed whether any differences in resting state connectivity in the DMN exist between insomnia patients and controls and/or between the high risk for depression group and the low risk for depression group compared to the control group.
Exploratory analysis. Also, an exploratory analysis was done. In the exploratory analysis the other
resting-state networks identified with the group ICA were assessed for any differences in resting state connectivity between insomnia patients and controls and/or between the high risk for depression group and the low risk for depression group as compared to controls. Clusters were considered as significant when p<0.05 and if they consisted of 10 or more voxels. Besides this, results were considered indicative for an association using a trend significance threshold of p<0.1.
Rumination analysis. Apart from the imaging analysis, the rumination scores were also evaluated.
Two linear regression models (Bruin, 2011) were set up in R studio (version 3.5.2). To test whether rumination modulates the differential risk for depression among the subtypes of insomnia, first the association was tested between insomnia group (high risk for depression, low risk for depression) versus controls and IDS score as an outcome variable. This model was also adjusted for age and sex. In the second model, RRS score was added as a covariate.
Results
Demographics
In total, 199 participants were included. Ages ranged from 19 to 69 [mean ± SD: 49.1 ± 12.6], and 71% were female (Table 1). Age and sex did not differ significantly between the insomnia and the
control group. However, ISI-score, RRS-score and IDS-score were all significantly higher in the high risk for depression group than in the control group. For further statistics, see Table 1.
Characteristics Total (N = 199) Insomnia (N = 163) Controls (N = 36) p (U/χ2) (Insomnia vs. Controls) ID patients at high risk for depression (N = 129) ID patients at low risk for depression (N
= 34) Age 49.1 (12.6) 49.2 (12.5) 48.3 (13.1) .69 (2807) 48.3 (12.8) 53.0 (10.6)
Sex (female) 142 116 26 .90 (.016) 95 21
ISI score 13.3 (6.1) 15.6 (3.7) 3.0 (3.7) 2.2e-16*** (116)
15.9 (3.7) 14.2 (3.6)
RRS score 8.1 (5.7) 8.8 (5.6) 5.1 (5.0) .00046 ***(1844)
8.9 (5.6) 8.1 (5.7)
IDS score 15.1 (8.4) 17.2 (7.4) 5.2 (4.9) 1.88e-15*** (449.5)
18.8 (7.1) 10.9 (5.3)
Table 1. Demographics and clinical characteristics of all participants.
Note: Every datapoint is mean (sd) unless indicated otherwise.
Abbreviations: ISI, Insomnia Severity Index; RRS, Rumination Response Scale; IDS, Inventory of Depressive Symptomatology; ID, Insomnia Disorder.
Significance codes: <0.05*, <0.01**, <0,001*** Group ICA analysis
The group ICA composed of 20 components. All of the components were visually assessed and it was determined that the network shown in Figure 1 was the DMN. When assessing which component comprised of the DMN, mainly the prefrontal regions and the PCC were considered.
Figure 1. One of the 20 components of the group-ICA identified as the DMN network, shown in
red-yellow onto a standard space MNI brain template. The threshold for the network was 2-5.
z = 28
DMN analysis
In the DMN no significant clusters were found that could indicate differences in resting connectivity between insomnia patients and healthy controls. Also, between the high risk and low risk for depression group compared to the controls, no significant differences in the DMN were found. Resting state connectivity in the DMN was thus not different among the insomnia patients at high and low risk for depression as compared to controls.
Exploratory analysis
Although no significant cluster was found in the DMN, differences between the groups in other resting state networks were identified. In a cluster in the cerebellar network the low risk for depression group showed higher activity as compared to the control group (p=0.030, z=1.491) (see figure 2). The coordinates of the cluster were -18/-82/-30 in standard MNI space and the cluster consisted of 129 voxels and mainly occupied the Crus Ia region of the cerebellum. The cluster of increased activity indicates that resting-state connectivity in the low risk for depression group is higher in this specific cluster than in the control group.
Figure 2. The red-yellow parts indicate the cerebellar network. The blue parts show the cluster (X/Y/Z
z = -30 y = -82
= -18/-82/-30) in which activity was higher in the low risk for depression group than in the control group. Both the network and the cluster are shown onto a standard space MNI template. The min-max threshold is 2-5 for the network and .95-1.0 for the cluster.
Trend level analysis
On trend level (p<0.1), two other significant clusters were found. The first cluster showed higher activity in the low risk group than in the control group (p=0.067, z=1.499) and was also located in the cerebellar network (X/Y/Z = -22/-60/-64, 72 voxels). The second cluster was located in the
frontoparietal network (X/Y/Z = 34/38/16, 65 voxels) and showed increased activity in the insomnia patients at high risk for depression as compared to the control group (p = 0.068, z =1.491). However, this cluster was located in the right cerebral white matter for 76%. Thus, this finding needs to be interpreted with caution since it could also be noise.
Rumination analysis
Both the high risk for depression group (beta=13.66, p<2e-16) and the low risk for depression group (beta=5.81, p=0.0003) had higher rumination scores compared to the control group. As expected, the differences were more pronounced in the high risk for depression group. When adding the RRS score (beta=-0.080, p=0.040) to the model, these associations did not change. This indicates that
rumination does not significantly modulate the risk for depression among the insomnia subtypes.
Variable
Model 1
β t-value p-value
Model 2
β t-value p-value
High risk for depression group
13.658 11.239 <2e-16*** 13.967 11.107 <2e-16***
Low risk for depression group
5.813 3.710 0.000271*** 6.060 3.815 0.00018
RRS score - - - -0.080 -0.957 0.340
Note: Outcome in both models is depression severity, IDS score. Model 1 is adjusted for age and sex. Model 2 is additionally adjusted for RRS-score.
Abbreviations: RRS, Rumination Response Scale. Significance codes: <0.05*, <0.01**, <0,001***
Discussion
This study set out to investigate if there are any differences present in resting state cortical
connectivity in the DMN between people with insomnia and good sleepers, and more specifically if resting state connectivity differences are present between insomniacs that are at high and low risk for developing depression versus good sleepers. The second aim of this research was to assess whether rumination influences the association between insomnia and depression. Specifically, we sought to find whether depression scores of insomnia patients at high risk or low risk for depression are influenced by their rumination score. The overall goal was to gain more knowledge about the underlying neural mechanisms of both insomnia and depression and how the DMN is involved in this. A secondary goal was to examine how rumination is involved in in the link between insomnia and depression. Also, this study could help in reaching the goals of the RDoC, but this however is a more far-fetching goal.
The dual regression that was performed on the resting-state fMRI data revealed that insomnia patients overall did not show any significant differences in DMN activity from the control group. Also, the high risk for depression group and the low risk for depression group did not differ significantly from the controls in activity in the DMN. However, a significant cluster was found in the cerebellar network. In this cluster, located in the left cerebellum, activity was higher in the low risk for
depression group than in the controls. On trend level, differences in two other clusters were found. The first cluster was also located in the cerebellar network and also showed higher activity in the low risk for depression group as compared to the controls. The second cluster where differences were significant on a trend level was located in the right frontoparietal network in the cerebral cortex and showed higher activity for the high risk for depression group than in the controls. This cluster must be viewed with some caution since it was predominantly located in the white matter, and only 24% in the cerebral cortex, which could indicate that it contains some remining noise. The linear
regression analysis revealed that rumination did not act as a significant modulator for risk for depression among the insomnia subtypes. However, it turned out that both the low risk and high risk for depression group had significantly higher rumination scores than the good sleepers.
Contrary to expectations, no significant effects were found when comparing insomnia patients with the control group (in none of the networks). This result is not in accordance with several other studies, that did find differences between insomnia patients and controls (Khazaie et al., 2017). Since so many studies did find altered DMN activity between insomnia patients and controls, the
discrepancy could likely be attributed to methodological differences. Also, the control group of this study was relatively small in comparison to the whole insomnia group. This could lead to less power in the analysis and therefore no significant results. Also, in this study no covariates were included in the dual regression. Although age and gender did not differ significantly between the control and the insomnia group, that does not mean these variables could have no effect on the regression. Further, some of the participants fell asleep or closed their eyes during part of the resting state scan. The volumes where they had their eyes closed were not excluded although that would improve the test-retest reliability of resting state scanning (Wang et al., 2017). Lastly, the insomnia group was not a homogeneous group. The insomnia group consists of five subtypes that each have diverse patterns of distress, reward sensitivity and reactivity.
Although higher connectivity in the DMN in the high risk for depression group than in the controls was hypothesized, no significant difference in activity was found (in neither risk group) in comparison
to the control group. By all means, this could be due to the same methodological issues as
mentioned above, but it could also stem from other methodological artefacts. For instance, the high risk and low risk group are also not homogeneous groups. Both groups consist of respectively three and two subtypes that all have distinct patterns of reward sensitivity or reactivity (Blanken et al., 2019). Additionally, depression is also not a homogeneous disorder. Unipolar depression, bipolar depression, dysthymia and seasonal affective disorder (SAD) are four forms of depression that all differ in their disease course and severity (Bowden, 2005). It is already stated that depression is comorbid with insomnia (Staner, 2010), but it is also comorbid with several other mental disorders such as anxiety (Cummings, Caporino & Kendall, 2014) or substance use disorder (Swendsen & Merikangas, 2000). Both the different types of depression and the comorbidity could influence the results, since they are not considered in the analysis.
Although no significant results were found in the initial DMN analysis, one cluster was found in the exploratory analysis that showed significant higher activity in the low risk for depression insomnia group than in the control group. However, a note of caution is due here when interpreting and comparing these results with other studies, because in the analysis 1.000 permutations were used (due to computational constraints), whereas in other studies 5.000-10.000 permutations were used, since this is the norm for publishing. The cluster was located in the left cerebellum, more precisely in the Crus Ia. (Guo et al., 2013). The Crus is one of the most prominent, regions of the cerebellum and is located laterally (Stoodley & Schmahmann, 2009). There are several possible explanations for this result. Firstly, several studies have been performed to study the role of the cerebellum in insomnia. A study by Canto et al. (2017) stated that malfunction of the cerebellum can lead to disruption of the sleep-wake cycle, which can eventually lead to sleep disorders. Also, the cerebellum and the cerebral cortex interact during both sleep and wake state, which aids memory consolidation. Another study by DelRosso & Hoque (2014) found that people suffering from insomnia have significant decreases in grey matter in the cerebellum compared to controls. Although it remains unclear if this is a
contributing cause or a consequence of insomnia. Furthermore, patients with Fatal Familial Insomnia (FFI) develop moderate atrophy of the cerebellum (Cortelli et al., 2014). Lastly, in a recent review it was reported that deviant connectivity of a part of the cerebellum was found with parts of the DMN (PCC and vmPFC) in patients suffering from depression (Depping et al., 2018). All these findings taken together show that aberrant activity or structure in the cerebellum is linked to insomnia and/or depression. When considering this link of the cerebellum with depression, it is notable that the low risk for depression group differed from the control group in this cluster instead of the high risk for depression group. This could mean that an increased activity in the cerebellum is more closely linked
to insomnia than to depression, since in the low risk group depression is a smaller contributing factor than in the high risk group. A finding that contradicts this explanation is made by Phillips and
colleagues (2015), where they found increased cerebellar activity in depressed patients compared to healthy controls (Phillips et al., 2015). Nonetheless, this increased activity was found in the vermis (a part of the cerebellum) (Guo et al., 2013), which could show altered activity patterns compared to the area that was found to be altered in this study. Another interesting thing to note is that the Crus Ia of the cerebellum, in which the cluster was mainly located, is linked to the dorsal part of the executive control network (ECN) (Habas et al., 2009), which is a resting-state network located in frontoparietal areas of the brain involved in executive control (Zhao, Sang, Metmer, Swati & Lu, 2019). A contradictory finding to our result was made by Jiang et al. (2019), the researchers found decreased activity in the right cerebellum in people with insomnia compared to good sleepers. This could be explained by the fact that the specific cluster which showed differences was located more medially in the cerebellum. This more medial area is possibly altered differently as a consequence of insomnia, since this area is connected to other resting-state networks than the area we found to be altered in insomnia patients (Habas et al., 2009).
The last part of the imaging analysis that considered differences at a more inclusive significance threshold; results significant at trend level. Here, two clusters that differed significantly between groups were found. One cluster also showed increased activity in the low risk for depression group versus the controls and was also located in the cerebellum. The other cluster was located in the fronto-parietal network (FPN), a network involved in cognitive control (Marek & Dosenbach, 2018), and showed increased activity in the high risk for depression group compared to the controls. Part of this cluster was located in the cerebral cortex, but it was mainly situated in the white matter. This cluster could thus be interpreted as mostly noise, as only 24% of the cluster included cortical activity. An explanation for this cluster could be that regressing out the nuisance variables, and then
especially the white matter, did not perform adequately to clean the data. A study by Nalci, Luo & Lio (2019) showed that the regression of nuisance variables, among which white matter, is not always fully effective (Nalci, Luo & Lio, 2019). An explanation that goes beyond methodological concerns is one posed by a study of Peer et al. (2017) which states that white matter function could contribute to the understanding of how grey matter brain networks are connected. However, the methods that were used in this study are not fit for identifying white matter networks. As mentioned earlier, the FPN is mainly involved in cognitive control (Marek & Dosenbach, 2018), a brain function patients suffering from depression show deficits in (Lam et al., 2014; McIntyre et al., 2013). The part of the
cluster in the cerebral cortex could be explained by a possible compensatory mechanism for the cognitive dysfunction as a consequence of the high depression (risk).
Last, contrary to expectations, the linear regression did not reveal a modulating role of rumination in the relationship between people suffering from depression that are at high risk for depression and at low risk for depression group membership and depression score. This might be due to the fact that the RRS scale can be separated into two factors according to Crane, Barnhofer & Williams (2007). The first factor being brooding and the second factor being emotionally neutral pondering. The first factor is linked more strongly to depression than the second factor. Therefore, it could be the case that the questions in the RRS scale addressing the second factor introduces noise that attenuated the results. Another explanation could be that rumination is not an important mediating factor in the developing risk for depression among people suffering from insomnia. Even though, both people suffering from insomnia and people suffering from depression have increased rumination (Watkins & Roberts, 2020).
The strengths of this study comprise the large sample size as compared to other resting state fMRI studies. Additionally, not only neuroimaging data was analysed but also behavioural data,
contributing to the versatile set up of this research. Limitations include the relatively small sample size of the control group, therefore the results of this study have lower power than when the control group would have been greater. Also, in this study a 3T scanner was used. Whereas when using a 7T scanner, scans can be made with more level of detail, also on vessel-level, so this would improve the exactness of the BOLD-signal.
In future research looking into the DMN connectivity in insomnia, a seed-based approach could be taken on (Jiang et al., 2019), instead of the ICA-based approach used in this study. Also, it would be better to focus on differences between the five insomnia subtypes instead of grouping those into a high risk and low risk for depression group. This will increase the homogeneity of the (sub)samples. Additional covariates should be added in the analysis, that could potentially decrease any residual noise in the results that were reported in this study. An example of an extra covariate could be educational level, since this variable is a predictor for health and wellbeing (Easterbrook, Kuppens & Manstead, 2016), which could in turn influence proneness to (mental) disease. Other further research might explore the role of other resting state brain networks that could explain the neurobiological underpinnings of the high comorbidity between insomnia and depression. For instance, the salience network (SN) could be considered. Rumination could also be taken into
account. An example of how, could be to study the role of rumination in other, more specific factors that are related to insomnia, such as negative affect and reduced subjective happiness, two
characteristics that stand out in the high risk for depression group. Namely, it was found in a study of Chen et al. (2014) that the insula (a key hub of the SN) is connected to rumination and insomnia. Also, the ECN could be considered in future research, because the area of the cerebellum that showed increased activity in the low risk for depression group versus the controls is linked to the dorsal part of this network (Habas et al., 2009). Lastly, the FPN could be considered, since this network differed among the insomniacs at low risk for developing depression compared to the good sleepers in our exploratory analysis. A natural progression of the findings in the cerebellum would be to study (inter)connectivity of the cerebellum in more depth and to also take into consideration different parts of the cerebellum (Stoodley & Schmahmann, 2009).
Overall, this study did not succeed in providing more support for the role of the DMN in insomnia and/or depression. These findings could be indicative of the DMN not being involved in these diseases. However, the role of the DMN in insomnia and depression can certainly not be discarded solely based on this study, therefore this study needs to be replicated and the suggestions for future research need to be taken into consideration. For example, the insight that was gained that it is better to study the five insomnia subtypes separately, instead of grouping them together in two groups, such as the high and low risk for depression groups used in this study.
In conclusion, no altered DMN connectivity was found, neither between insomnia patients and good sleepers, nor between insomnia patients at either high or low risk for depression and controls. Our study also showed that rumination does not modulate the differential risk for developing depression among insomnia sufferers. Furthermore, this study did not find any support for the role of the DMN in insomnia and/or depression and based on this study, rumination is not involved in the differential risk subgroups of insomnia carry for developing depression. Thus, the underlying neurobiological mechanisms for these insomnia subgroups (at differential risks for depression) remain unrevealed. Nevertheless, this research provides new interesting insight into the role of possible other resting-state networks in insomnia and set out to further investigating the neurobiological link of insomnia and depression. As insomnia and depression are both burdening diseases, their neurobiological bases and link is important to investigate further.
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